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Improving Traffic Safety in Jakarta Through Video Analysis

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Improving Traffic Safety in Jakarta Through Video Analysis

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62xxxxxx Nichnan Kittiphattanabawon

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UPDATE: We are pleased to announce that this project team won a Highlighted Paper Award at the AI For Social Good NIPS2018 Workshop! Congratulations to the Jakarta Fellows!

Improving Traffic Safety in Jakarta Through Video Analysis

The World Health Organization (WHO) estimates that over 1.25 million people die each year in traffic accidents. Nearly 2000 such fatalities occur annually in Jakarta, Indonesia alone, making it one of the most dangerous cities in the world for traffic safety. These deaths are tragic, but many of them are preventable through effective city planning. This summer, our team at the Data Science for Social Good Fellowship (DSSG) at the University of Chicago set out to help the city of Jakarta bring this figure down and protect its residents from traffic-related injury and death.

Jakarta is a city in transition, having experienced explosive population growth over the last few decades. In 1970, just over 2 million people inhabited Jakarta. Today, the city's population eclipses 10 million, and continues to grow. With this growth comes a rise in vehicle ownership and congestion. These factors inevitably lead to traffic incidents. Our goal was to enable the city to reduce the number of these incidents through city planning.

The most important resource that any city needs when planning is good information. From a data science perspective though, one of the core challenges in addressing traffic safety problems is that it is difficult to collect high quality traffic data. This problem is especially intractable in megacities with bustling commuting patterns and millions of inhabitants. Like other megacities, Jakarta was confronted with the same issues surrounding effective data collection. However, the city also has a promising resource: thousands of traffic cameras posted throughout the city, with many more to come.

Machine learning tools provide the means to use traffic video to their maximum potential primarily by permitting the processing of video at scale. One could imagine hiring a technician to continually watch a video feed of some particular intersection, and count the number and types of vehicles they see, as well as interesting events such as a car driving down the wrong side of the road. This approach would be cumbersome even for one video feed, but becomes unfathomable when scaling to hundreds or thousands of cameras. It simply would not be worth the human resources involved to attempt to glean information from raw videos in a manual fashion.

We saw this problem as an opportunity to showcase the potential for data science by building a system where computers do the work of processing video, freeing humans to do analysis and planning. We sought to design a pipeline that could take in raw video and output a structured database. This database would contain information about activity on the roadways, and thus facilitate policymakers' work by providing them with rich information about vehicle behaviors throughout the city.

However, building this pipeline was not simply a technical challenge. Successful development relied on a deep understanding of the social context in Jakarta. We could not begin making decisions about pipeline design, computer vision techniques, and validation without understanding our partners' priorities, and their definition of "traffic safety." Behaviors such as having more than two people on a motorcycle, carrying a food cart through a street, and weaving through traffic were surprising to some members of our team, and we had to learn the cultural context that was specific to Jakarta. This was a critical step because importing our own preconceptions and biases about traffic patterns in the U.S. and other parts of the world would have ultimately not served Jakarta well. Through several conversations with our partners at Jakarta Smart City and Pulse Lab Jakarta, we gradually defined the scope of the problem that we were facing.

Because traffic safety is such a broad topic, much of our work was driven by the eventual interventions that our partners hoped to inform. From our perspective, several technical decisions could ultimately only be made after we understood the policy interventions that particular techniques were meant to facilitate. We therefore sought to understand the medium- and long-term interventions that our partners had in mind, so that we could focus our work on collecting the data that would help direct those interventions most effectively.

Together with our partners, we honed in on several interesting and useful priorities. In the medium term, we learned that Jakarta was interested in better management of its traffic signals and deployment of "traffic stewards." More efficient interventions in these two areas would help the city manage overbearing congestion, and possibly prevent accidents arising from poor traffic flow management. In the long term, the city was interested in understanding what types of infrastructure changes could improve safety. For instance, installing a median might prevent vehicles from crossing into oncoming traffic, or building a bus lane might improve congestion along a popular route.

Once we understood the scope of interventions, we were then able to develop a plan for the data science techniques that could best address the general problem of improving traffic safety. In particular, we identified three central goals:

1. Detect and classify objects in a frame

2. Estimate the direction of movement for objects across frames

3. Understand the "context" of a scene (i.e. what is a road, a sidewalk etc.)

We settled on these goals because achieving these would enable the interventions that Jakarta was ultimately interested in deploying. Consider, for example, that one of the main problems that our partners were concerned with was vehicles driving on the wrong side of the road, and therefore creating an immediate safety hazard. For a computer to successfully record such an event, it needs to understand that there is a vehicle, understand that it is moving in a particular direction, and know what the "wrong" direction is on a roadway. Alternatively, another issue might be motorcycles and scooters driving on sidewalks and endangering pedestrians. Again, the computer would need to understand what a motorcycle is, know that it is moving and not parked, and recognize that it is traveling on a sidewalk and not a road.

Figure 5: Our task was to take raw video footage (pictured top), and extract information like what type of vehicle is being detected, what direction is it moving in, and what surface is it traveling on.

Once we defined goals, we then turned to pulling together the best techniques for each goal into a cohesive pipeline. Combining this information allowed us to create a powerful tool that we hope will aid Jakarta in its efforts to improve traffic safety throughout the city. By the end of the summer, our pipeline was able to successfully determine when a vehicle was moving on the wrong side of the road, and flag this event in the database. We expect that in the future, Jakarta will be able to identify a range of events of interest, and build a database that contains rich information about traffic behaviors throughout the city.

We want our work this summer to provide a blueprint for how cities around the world might approach the task of developing effective smart city initiatives. Traffic cameras are fantastic examples of the sensors that several cities have experimented with in the past few years. More than any time in history, cities have an unprecedented capacity to gather high-quality information about how people move in an urban landscape. Our code and technical approaches should illustrate the potential for using such sensors in 21 st -century urban planning.

Beyond our technical approach, we hope our work teaches policymakers, academics, and other stakeholders about best practices in applying data science to social problems. Our biggest challenges were not necessarily technical in nature. Instead, we spent much of our time understanding the parameters of the social problem. Once we understood this aspect, we were able to map technical solutions to policy interventions. This exercise was indispensable, and we cannot emphasize enough the importance of proper scoping of social context in successfully designing a technical system.

Over the summer, we gained a strong understanding of why traffic safety is a difficult problem to solve, what the core challenges are in addressing it, and how data science might help. Going forward, we hope these insights will pave the way for cities to eliminate traffic safety hazards altogether. We hope that our work moves us closer to a world where traffic accidents are not considered an inevitability, and we give people the security of knowing that they can safely move about their cities for work and play.

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Improving Traffic Safety in Jakarta Through Video Analysis | Data Science for Social Good Felllowship

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João Caldeira, Alex Fout, Aniket Kesari, Raesetje Sefala UPDATE: We are pleased to announce that this project team won a Highlighted Paper Award at the AI For Social Good NIPS2018 Workshop! Congratulations to the Jakarta Fellows! Improving Traffic Safety in Jakarta Through Video Analysis The World Health Organization (WHO) estimates that over 1.25 million people [...]

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A New Era of Automation in Manufacturing

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A New Era of Automation in Manufacturing

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62101001 Kewalee Sittisomboon

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A New Era of Automation in Manufacturing - RapidMiner

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Automation is disrupting and displacing old models of production and permanently changing manufacturing. Let’s uncover how we got here and what this new technology means for manufacturers.

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Alternative Feature Selection Methods in Machine Learning

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Alternative Feature Selection Methods in Machine Learning

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62105309 Burutsakorn Saeton

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Image by
Gerd Altmann from Pixabay

You've probably done your online searches on "Feature Selection", and you've probably found tons of articles describing the three umbrella terms that group selection methodologies, i.e., "Filter Methods", "Wrapper Methods" and "Embedded Methods".

Under the "Filter Methods", we find statistical tests that select features based on their distributions. These methods are computationally very fast, but in practice they do not render good features for our models. In addition, when we have big datasets, p-values for statistical tests tend to be very small, highlighting as significant tiny differences in distributions, that may not be really important.

The "Wrapper Methods" category includes greedy algorithms that will try every possible feature combination based on a step forward, step backward, or exhaustive search. For each feature combination, these methods will train a machine learning model, usually with cross-validation, and determine its performance. Thus, wrapper methods are very computationally expensive, and often, impossible to carry out.

The "Embedded Methods," on the other hand, train a single machine learning model and select features based on the feature importance returned by that model. They tend to work very well in practice and are faster to compute. On the downside, we can't derive feature importance values from all machine learning models. For example, we can't derive importance values from nearest neighbours. In addition, co-linearity will affect the coefficient values returned by linear models, or the importance values returned by decision tree based algorithms, which may mask their real importance. Finally, decision tree based algorithms may not perform well in very big feature spaces, and thus, the importance values might be unreliable.

Filter Methods are hard to interpret and are not commonly used in practice; Wrapper Methods are computationally expensive and often impossible to carry out; and Embedded Methods are not suitable in every scenario or for every machine learning model. What do we do then? How else can we select predictive features?

Fortunately, there are more ways to select features for supervised learning. And I will cover three of them in detail throughout this blog post. For more feature selection methods, check out the online course Feature Selection for Machine Learning.

Alternative feature selection methods

In this article, I will describe three algorithms that select features based on their impact on model performance. They are often referred to as "Hybrid Methods" because they share characteristics of Wrapper and Embedded methods. Some of these methods rely on training more than one machine learning model, a bit like wrapper methods. Some selection procedures rely on feature importance like Embedded Methods.

But nomenclature aside, these methods have been successfully used in the industry or in data science competitions, and provide additional ways of finding the most predictive features for a certain machine learning model.

Throughout the article, I will lay out the logic and procedure of some of these feature selection methods and show how we can implement them in Python using the open source library Feature-engine. Let's get started.

We will discuss selection by:

Feature shuffling

Feature performance

Target mean performance

Feature shuffling

Feature shuffling, or permutation feature importance consists of assigning importance to a feature based on the decrease in a model performance score when the values of a single feature are randomly shuffled. Shuffling the order of the feature values (across the rows of the dataset) alters the original relationship between the feature and the target, so the drop in the model performance score is indicative of how much the model depends on that feature.

The procedure works as follows:

1. It trains a machine learning model and determines its performance.

2. It shuffles the order of the values of 1 feature.

3. It makes predictions with the model trained in step 1, and determines the performance.

4. If the performance drops below a threshold, it keeps the feature, otherwise it removes it.

5. It repeats from step 2 until all features are examined.

Selection by shuffling features has several advantages. First, we need to train only one machine learning model. The importance is subsequently assigned by shuffling the feature values and making predictions with that model. Second, we can select features for any supervised machine learning model of our choice. Third, we can implement this selection procedure utilizing open source, and we will see how to do this in the coming paragraphs.

It only trains one machine learning model, so it is quick.

It is suitable for any supervised machine learning model.

It is available in Feature-engine, a Python open source library.

On the downside, if two features are correlated, when one of the features is shuffled, the model will still have access to the information through its correlated variable. This may result in a lower importance value for both features, even though they might actually be important. In addition, to select features, we need to define an arbitrary importance threshold below which features will be removed. With higher threshold values, fewer features will be selected. Finally, shuffling features introduces an element of randomness, so for features with borderline importance, that is, importance values close to the threshold, different runs of the algorithm may return different subsets of features.

Correlations may affect the interpretation of the feature's importance.

The user needs to define an arbitrary threshold.

The element of randomness makes the selection procedure non-deterministic.

With this in mind, selecting features by feature shuffling is a good feature selection method that focuses on highlighting those variables that directly affect the model performance. We can manually derive the permutation importance with Scikit-learn, and then select those variables that show an importance above a certain threshold. Or we can automate the entire procedure with Feature-engine.

Python Implementation

Let's see how to carry out selection by feature shuffling with Feature-engine. We will use the diabetes dataset that comes with Scikit-learn. First, we load the data:

import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from feature_engine.selection import SelectByShuffling

# load dataset
diabetes_X, diabetes_y = load_diabetes(return_X_y=True)
X = pd.DataFrame(diabetes_X)
y = pd.DataFrame(diabetes_y)

We set up the machine learning model we are interested in:

# initialize linear regression estimator
linear_model = LinearRegression()

We will select features based on the drop in the r ² using 3 fold cross-validation:

# initialize the feature selector
tr = SelectByShuffling(estimator=linear_model, scoring="r2", cv=3)

With the method fit() the transformer finds the important variables -those that cause a drop in r ² when shuffled. By default, features will be selected if the performance drop is bigger than the mean drop caused by all features.

# fit transformer
tr.fit(X, y)

With the method transform() we drop the unselected features from the dataset:

We can inspect the individual feature's importance through one of the transformer's attributes:

tr.performance_drifts_

{0: -0.02368121940502793,
 1: 0.017909161264480666,
 2: 0.18565460365508413,
 3: 0.07655405817715671,
 4: 0.4327180164470878,
 5: 0.16394693824418372,
 6: -0.012876023845921625,
 7: 0.01048781540981647,
 8: 0.3921465005640224,
 9: -0.01427065640301245}

We can access to the names of the features that will be removed in another attribute:

tr.features_to_drop_

[0, 1, 3, 6, 7, 9]

That's it, simple. We have a reduced dataframe in Xt.

Feature performance

A direct way of determining the importance of a feature is to train a machine learning model using solely that feature. In this case, the "importance" of the feature is given by the performance score of the model. In other words, how well a model trained on a single feature predicts the target. Poor performance metrics speak of weak or non-predictive features.

The procedure works as follows:

1. It trains a machine learning model for each feature.

2. For each model, it makes predictions and determines model performance.

3. It selects features with performance metrics above a threshold.

In this selection procedure, we train one machine learning model per feature. The model uses an individual feature to predict the target variable. Then, we determine the model performance, usually with cross-validation, and select features whose performance falls above a certain threshold.

On one hand, this method is more computationally costly because we would train as many models as features we have in our data set. On the other hand, models trained on a single feature tend to train fairly quickly.

With this method, we can select features for any model that we want, because the importance is given by the performance metric. On the downside, we need to provide an arbitrary threshold for the feature selection. With higher threshold values, we select smaller feature groups. Some threshold values can be fairly intuitive. For example, if the performance metric is the roc-auc, we can select features whose performance is above 0.5. For other metrics, like accuracy, what determines a good value is not so clear.

It is suitable for any supervised machine learning model.

It explores features individually, thus avoiding correlation issues.

It is available in Feature-engine, a Python open source project.

Training one model per feature can be computationally costly.

The user needs to define an arbitrary threshold.

It does not pick up feature interactions.

We can implement selection by single feature performance utilizing Feature-engine.

Python Implementation

Let's load the diabetes dataset from Scikit-learn:

import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from feature_engine.selection import SelectBySingleFeaturePerformance

# load dataset
diabetes_X, diabetes_y = load_diabetes(return_X_y=True)
X = pd.DataFrame(diabetes_X)
y = pd.DataFrame(diabetes_y)

We want to select features whose r ² > 0.01, utilizing a linear regression and using 3 fold cross-validation.

# initialize the feature selector
sel = SelectBySingleFeaturePerformance(
        estimator=LinearRegression(), scoring="r2", cv=3, threshold=0.01)

The transformer uses the method fit() to fit 1 model per feature, determine performance, and select the important features.

# fit transformer
sel.fit(X, y)

We can explore the features that will be dropped:

sel.features_to_drop_

[1]

We can also examine each individual feature's performance:

sel.feature_performance_

{0: 0.029231969375784466,
 1: -0.003738551760264386,
 2: 0.336620809987693,
 3: 0.19219056680145055,
 4: 0.037115559827549806,
 5: 0.017854228256932614,
 6: 0.15153886177526896,
 7: 0.17721609966501747,
 8: 0.3149462084418813,
 9: 0.13876602125792703}

With the method transform() we remove the features from the dataset:

# drop variables
Xt = sel.transform(X)

And that's it. Now we have a reduced dataset.

Target mean performance

The selection procedure that I will discuss now was introduced in the KDD 2009 data science competition by Miller and co-workers. The authors do not attribute any name to the technique, but since it uses the mean target value per group of observations as a proxy for predictions, I like to call this technique "Selection by Target Mean Performance."

This selection methodology also assigns an "importance" value to each feature. This importance value is derived from a performance metric. Interestingly, the model does not train any machine learning models. Instead, it uses a much simpler proxy as a prediction.

In a nutshell, the procedure uses the mean target value per category or per interval (if the variable is continuous) as a proxy for prediction. With this prediction, it derives a performance metric, like r ², accuracy, or any other metric that assesses a prediction against the truth.

How does this procedure exactly work?

For categorical variables:

1. It splits the dataframe into a training and a testing set.

2. For every categorical feature, it determines the mean target value per category (using the train set).

3. It replaces categories with corresponding target mean values in the test.

4. It determines a performance metric using the encoded features and the target (on the test set).

5. It selects features whose performance is above a threshold.

For categorical values, the mean value of the target is determined for each category based on the training set. Then, the categories are replaced by the learned values in the test set, and these values are used to determine the performance metric.

For continuous variables, the procedure is fairly similar:

1. It splits the dataframe into a training and a testing set.

2. For every continuous feature, it sorts the values into discrete intervals finding the limits using the train set.

3. It determines the mean target value per interval (using a training set).

4. It sorts variables in the test set into the intervals identified in 2.

5. It replaces intervals with corresponding target mean values (using the test set).

6. It determines a performance metric between the encoded feature and the target (on the test set).

7. It selects features whose performance is above a threshold.

For continuous variables, the authors first separated the observations into bins, a process otherwise called discretization. They used 1% quantiles. Then they determined the mean value of the target in each bin using the training set and evaluated the performance after replacing the bin values with the target mean in the test set.

This feature selection technique is very simple; it involves taking the mean of the responses for each level (category or interval), and comparing these values to the target values to obtain a performance metric. Despite its simplicity, it has a number of advantages.

First, it does not involve training a machine learning model, so it is incredibly fast to compute. Second, it captures non-linear relationships with the target. Third, it is suitable for categorical variables, unlike the great majority of the existing selection algorithms. It is robust to outliers as these values will be allocated to one of the extreme bins. According to the authors, it offers comparable performance between categorical and numerical variables. And, it is model-agnostic. The features selected by this procedure should, in theory, be suitable for any machine learning model.

It is fast because no machine learning model is trained.

It is suitable for categorical and numerical variables alike.

It is robust to outliers.

It captures non-linear relationships between features and the target.

It is model-agnostic.

This selection method also presents some limitations. First, for continuous variables, the user needs to define an arbitrary number of intervals in which the values will be sorted. This poses a problem for skewed variables, where most of the values may fall into just one bin. Second, categorical variables with infrequent labels may lead to unreliable results as there are few observations for those categories. Therefore, the mean target value per category will be unreliable. In extreme cases, if a category was not present in the training set, we would not have a mean target value to use as a proxy to determine performance.

It needs tuning of interval numbers for skewed variables.

Rare categories will offer unreliable performance proxies or make the method impossible to compute.

With these considerations in mind, we can select variables based on the target mean performance with Feature-engine.

Python implementation

We will use this method to select variables from the Titanic dataset, which has a mix of numerical and categorical variables. When loading the data, I will do some preprocessing to facilitate the demonstration and then separate it into train and test.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from feature_engine.selection import SelectByTargetMeanPerformance

# load data
data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')


# extract cabin letter
data['cabin'] = data['cabin'].str[0]

# replace infrequent cabins by N
data['cabin'] = np.where(data['cabin'].isin(['T', 'G']), 'N', data['cabin'])

# cap maximum values
data['parch'] = np.where(data['parch']>3,3,data['parch'])
data['sibsp'] = np.where(data['sibsp']>3,3,data['sibsp'])

# cast variables as object to treat as categorical
data[['pclass','sibsp','parch']] = data[['pclass','sibsp','parch']].astype('O')

# separate train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    data.drop(['survived'], axis=1),
    data['survived'],
    test_size=0.3,
    random_state=0)

We will select features based on the roc-auc using 2 fold cross-validation. The first thing to note is that Feature-engine allows us to use cross-validation, which is an improvement with respect to the original method described by the authors.

Feature-engine also allows us to decide how we will determine the intervals for numerical variables. We can choose equal frequency or equal width intervals. The authors used 1% quantiles, which is suitable for continuous variables with a fair spread of values, but not often suitable for skewed variables. In this demo, we will separate numerical variables into equal frequency intervals.

Finally, we want to select features for which the roc-auc is greater than 0.6.

# Feature-engine automates the selection of
# categorical and numerical variables

sel = SelectByTargetMeanPerformance(
    variables=None,
    scoring="roc_auc_score",
    threshold=0.6,
    bins=3,
    strategy="equal_frequency",
    cv=2,# cross validation
    random_state=1, # seed for reproducibility
)

With the method fit() the transformer:

replaces categories by the target mean

sorts numerical variables into equal frequency bins

replaces bins by the target mean

using the target mean encoded variables returns the roc-auc

selects features whose roc-auc > 0.6

# find important features
sel.fit(X_train, y_train)

We can explore the ROC-AUC for each feature:

sel.feature_performance_

{'pclass': 0.6802934787230475,
 'sex': 0.7491365252482871,
 'age': 0.5345141148737766,
 'sibsp': 0.5720480307315783,
 'parch': 0.5243557188989476,
 'fare': 0.6600883312700917,
 'cabin': 0.6379782658154696,
 'embarked': 0.5672382248783936}

We can find the features that will be dropped from the data:

sel.features_to_drop_

['age', 'sibsp', 'parch', 'embarked']

With the method transform() we drop the features from the data sets:

# remove features
X_train = sel.transform(X_train)
X_test = sel.transform(X_test)

Simple. Now we have reduced versions of the train and test sets.

Wrapping up

We've reached the end of the article. If you made it this far, well done and thank you for reading. If you want to know more about feature selection, including Filter, Wrapper, Embedded and a number of Hybrid methods, check out the online course Feature Selection for Machine Learning.

For additional courses on machine learning, including Feature Engineering, Hyperparameter Optimization and Model Deployment, visit our website.

To implement Filter, Wrapper, Embedded and Hybrid selection methods in Python, check out the selection modules in Scikit-learn, MLXtend and Feature-engine. The libraries come with extensive documentation that will help you understand the underlying methodology.

Bio: Soledad Galli, PhD is the Lead Data Scientist and machine learning instructor at Train in Data. Sole teaches intermediate and advanced courses in data science and machine learning. She worked in finance and insurance, received a Data Science Leaders Award in 2018 and was selected as " LinkedIn's voice" in data science and analytics in 2019. She is also the creator and maintainer of the Python open source library Feature-engine. Sole is passionate about sharing knowledge and helping others succeed in data science.

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Alternative Feature Selection Methods in Machine Learning - KDnuggets

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Image by Gerd Altmann from Pixabay You've probably done your online searches on "Feature Selection", and you've probably found tons of articles describing the three umbrella terms that group selection methodologies, i.e., "Filter Methods", "Wrapper Methods" and "Embedded Methods". Under the "Filter Methods", we find statistical tests that select features based on their distributions.

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5 Powerful Use Cases of AI in Manufacturing

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5 Powerful Use Cases of AI in Manufacturing

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62116397 Pakanan Nudam

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We often talk about how AI is being used in manufacturing, and it's no mystery why. Manufacturers operate in a highly competitive environment, and there are many proven use cases for AI solutions. Given the early success and wealth of data that's found on modern shop floors, manufacturing is an industry that's primed for continued data-driven innovation.

Today, manufacturing is no longer just about volume and efficiency. Amidst the Industry 4.0 revolution, modern manufacturers are dealing with many more angles, complexities, and multistage processes. At the heart of it all are a few very simple questions that lack simple solutions:

What's the best way for us to forecast demand and allocate resources most profitably?

How can we optimize production, keeping costs low and product quality high?

How do we best manage production risks to ensure, smooth, consistent product delivery?

AI solutions can not only help address these these issues, but also unlock and operationalize rich insights about your production process that would have otherwise gone undiscovered.

For example, by building a model to help predict machine maintenance needs, you can often identify the most fragile components. Similarly, modeling for optimal product delivery times can surface insights about where your supply chain is most likely to break.

There's clearly an abundance of opportunity for manufacturers to leverage AI and, fortunately, most are already embracing cyber-physical systems and using available IoT data. However, those that are slow to get started with enterprise-wide AI adoption are at a big risk of being outperformed by global competitors.

For this reason, we're discussing a way forward amidst Industry 4.0-specifically, how AI can be adopted to optimize and innovate nearly every aspect of the manufacturing business.

Key Benefits of AI in Manufacturing

Simply put, artificial intelligence helps all kinds of manufacturers work quicker and smarter. Before getting into specific use cases, it's worth exploring the three high-level business benefits of leveraging AI in manufacturing.

Drive revenue: Manufacturers can use AI to drive revenue in a number of ways-everything from predicting and forecasting demand to creating intelligent, connected products that generate new and innovative business models.

Cut costs: In addition to driving revenue, AI can also help to drastically cut costs. Manufacturers can leverage it to predict maintenance needs before they arise, optimize production and supply chain efficiency, and analyze service patterns to improve product design and minimize warranty costs.

Avoid risks: Risk avoidance is another benefit that shouldn't be overlooked, as the impact is significant. Manufacturers can utilize AI to detect product issues early and improve quality assurance processes to reduce liability, manage production risks, address customer service issues before they have a widespread impact, and even minimize safety hazards by predicting the likelihood of harm from certain activities.

5 Powerful Use Cases of AI in Manufacturing

Now that we have a clear understanding around the major benefits of AI for manufacturers, let's explore some of the most impactful use cases in more depth.

1. Demand forecasting

Accurately forecasting demand is crucial for manufacturers to best plan production and, in turn, reduce inventory costs and maximize profit. Demand forecasting isn't a new concept, but many manufacturers still utilize traditional forecasting methods (like Excel) to manage their supply chain.

These methods are semi-manual, inflexible, and worst of all, often inaccurate. However, AI brings a new level of sophistication and precision to this age-old task.

Machine learning models can aggregate historical data with new data from a variety of sources, like enterprise resource planning systems, point-of-sale systems, and social media marketing campaigns. When paired with other important variables like raw material prices, supplier issues, and changing consumer preferences, this data provides a comprehensive understanding of how popular a product is - allowing manufacturers to plan production accordingly.

And unlike traditional forecasting solutions, machine learning models are, by definition, constantly taking in new information and adapting. This allows manufacturers to quickly react to changing circumstances. The result? More dynamically accurate forecasts and, ultimately, reduced costs.

2. Product quality assurance

It's still common today to see humans performing visual inspections to detect product flaws on the assembly line. However, human senses, attention spans, and energy levels have limitations, which can lead to costly errors. People can make more efficient use of their time elsewhere, and AI allows them to do just that.

Manufacturers can leverage AI with human-in-the-loop inspectors to improve quality assurance, and drastically reduce the likelihood of product flaws and imperfections. On top of that, this solution frees up workers' time for more strategic tasks.

Smart cameras and other AI-enabled technology can be trained to analyze images and identify anomalies. Such image recognition programs review product features like curves, colors, and corners, then evaluate the products against a set of standards to determine if they pass or fail the inspection process.

While the process differs based on the product, image recognition programs are incredibly flexible and can be applied to almost any product with a high degree of accuracy. One McKinsey study found that image recognition programs may increase defect detection rates by up to 90% compared to human inspection.

3. Operations optimization

Operations optimization refers to methods that increase throughput and uncover hidden efficiencies in the manufacturing process. There are hundreds of different value creation opportunities baked into this one particular use case.

For instance, one of the largest inputs for any manufacturer is electricity. Though most factories operate 24-hours a day, machine learning makes it possible to schedule more energy-intensive activities for different times, ensuring they occur when power is cheapest.

Because these algorithms can process great amounts of data, they can consider other important factors alongside energy prices, like equipment maintenance, inventory, and labor costs to ensure maximum savings.

Another example is supply chain optimization, which is crucial for every manufacturer due to inventory and cash flow implications. If you increase your supply chain's efficiency by 10%, you can produce 10% more product, while also reducing volatility in the production process.

AI can analyze a millions of variables that impact the supply chain-this includes production-specific variables like throughput quantity, production defect rates, and machine downtime, as well as global factors like fuel prices and tariff rates. Machine learning models can assess all this information in real-time, and meanwhile, a supply chain analyst with an AI-based decision-making app can adjust operations on the fly.

4. Health & safety risk avoidance

Fostering a safe work environment is crucial for a handful of reasons like protecting employees and boosting morale. This issue is especially pertinent to chemical manufacturing, which is strictly regulated through a mix of national and international protocols for operational procedures, as well as health, safety, and environmental factors.

Traditional preventive methods like annual training, posters, and protective equipment leave too much room for unsafe worker activity and hazards. But AI offers a better way forward.

Using industrial IoT data, manufacturers can monitor worker activity, machinery compliance, and causes of safety shutdowns in real-time. Employees can wear smart devices to track biometrics like body temperature, which notify supervisors when health concerns arise.

Additionally, machine learning can help to enable video surveillance systems to detect when workers are not wearing proper protective equipment or are engaged in unsafe practices. Paired with sensors on the shop floor, machine learning can also provide insight into the performance of safety systems to identify root causes of shutdowns, as well as identify leading indicators of machine issues so facility managers can address them before an accident occurs.

5. Predictive maintenance

Though there are an incredible array of AI use cases in manufacturing, the one that often dominates the conversation is predictive maintenance, and for good reason.

Ensuring maximum availability of critical manufacturing systems while simultaneously minimizing the cost of maintenance and repairs is essential. However, reactive (fixing something after it breaks) and preventative (periodic examinations) maintenance models are not flexible or cost-effective.

Using machine learning for predictive maintenance allows manufacturers to foresee when equipment failure is likely to happen so they can proactively replace parts or schedule repairs. Data from IoT sensors can continuously be fed into machine learning models that will compare this live operating data with historical data to provide extremely accurate predictions.

Paired with cloud computing, IoT also empowers you to leverage information from multiple machines, making predictions even more reliable. The overall result is maximized efficiency, minimal downtime, drastically lower maintenance costs.

Wrapping Up

As you can see, there's limitless potential when it comes to leveraging AI in the manufacturing industry. While the technology has already had a tremendous impact, there's still so much untapped potential that can help manufacturers optimize every facet of their business.
Figuring out how your business can benefit from AI and machine learning is overwhelming. Let us help - request a free AI assessment where RapidMiner will create and analyze a portfolio of use cases for your organization.

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5 Powerful Use Cases of AI in Manufacturing | RapidMiner

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We often talk about how AI is being used in manufacturing, and it's no mystery why. Manufacturers operate in a highly competitive environment, and there are many proven use cases for AI solutions. Given the early success and wealth of data that's found on modern shop floors, manufacturing is an industry that's primed for continued data-driven innovation.

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Tips for Learning Hadoop in a Month of Lunches

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Tips for Learning Hadoop in a Month of Lunches

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62104500 Noppamart Manyu

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Delicious lunch served on table with laptop and Hadoop.

Learning new technologies is a never-ending process. Even the most experienced developer will be familiar with this experience of trying to keep up with the ever-changing technology landscape and it's shifting trends, buzzwords, and languages. Big Data doesn't seem to be an exception to this rule. There seems to be a steady stream of new tools and frameworks popping up every few months in the Big Data world. So what's a developer to do? How can we get our hands dirty, roll up our sleeves, and start digging into this exciting world without burying ourselves under all these new technologies?
This article offers one approach for approaching Hadoop that was based on my experiences over the past few years at work and as an instructor.

The first step is to find a sandbox or development environment where you can play around with the technology without too much overhead and risk. The best way for me has been to use my own laptop as a tested, but I also know some developers who like to use Amazon EC2 instances (e.g., on AWS). This article assumes that we're using our laptops and that we want to follow along and learn by example (i.e., we're not going to go through all the details of how everything works under the hood).

A local sandbox also implies that we don't necessarily need access to Hadoop infrastructure such as Zookeeper, Name Nodes, secondary Name Node, Job History Server, Task Trackers, Data Nodes, etc. But we do need to be able to set up a Hadoop cluster! There are several ways of achieving this goal. A few years ago I came across Cloudera's Vagrant project and have been happily using it ever since. Now that Cloudera supports both Vagrant and Docker-based deployments for CDH5, it has become even more awesome. We can create a sandbox in just a couple of minutes that is compatible with the Horton works Sandbox.

We'll start by installing Virtual Box (the virtual machine software) and Vagrant on our laptops. If you don't want to use Vagrant, you might consider these other options: An Amazon EC2 cloud instance with Hadoop pre-installed. Although there is a number of good AMIs out there, I didn't find a Vagrant-compatible one that allowed me to set up a Hadoop cluster quickly. Sorry! EC2 also doesn't let you change the amount of memory for an instance without going through some hoops and launching a new instance type or using startup scripts. A bare-metal installation on your laptop (be aware of the 32-bit vs 64-bit requirements). In my case, this was an HP Envy M6 laptop with 16GB RAM running Windows 8 x64, but make sure it's got sufficient disk space available as well as CPU power.

Although there is no fixed standard for deploying Hadoop clusters on Vagrant, most of the time you'll need at least 3 machines. This Vagrantfile I'm using is based on one originally by Karthik Srinivas - thanks for sharing your work! So let's fetch the source code and create our sandbox:

Note that this will download an Ubuntu Precise image with Virtual Box Guest Additions installed by default from the Oracle public repo. When everything has been deployed successfully, we should see a bunch of running VMs in our Virtual Box UI:

That's it! We're now ready to start working with Hadoop and a local sandbox. The only thing that we still need is a good book or tutorial on Hadoop. Although I know there are many excellent resources out there, the following one has been my favorite over the past couple of years: Sam R. Alapati's Learning Apache Hadoop - this is also where the screenshots of directory contents come from.

If you want to be more adventurous (and install some additional goodies), take a look at these Vagrantfiles for Horton works Sandbox and Cloudera Quick Start VM (based on CDH4). You can use them as-is or as a starting point to create your own sandbox. I've also created one for Horton works Data Platform (HDP) on Windows Server 2012 x64, which you can find here.

I'd like to thank my colleagues Colin Cameron and Joe Crobak who helped me with getting this article just right. If you have questions or comments about Hadoop on Vagrant, I encourage you to ping us.

Conclusion:

Vagrant gives you the ability to define your infrastructure in code. This makes it easy for developers to get started quickly on their laptops with an exact replica of production environments. It also allows admins to have repeatable processes, e.g., by building reusable VM templates that can be spun up/down quickly across different projects or teams. Moreover, Vagrant is extremely popular for automating test cases both locally and in the cloud (e.g., run them on Open Stack bare metal). All these are reasons why I've been using Vagrant extensively over the past few years! And if you don't yet know about Vagrant, what are you waiting for?

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Learning new technologies is a never-ending process. Even the most experienced developer will be familiar with this experience of trying to keep up with the ever-changing technology landscape and it's shifting trends, buzzwords, and languages. Big Data doesn't seem to be an exception to this rule.

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Big Data Analytics in Cybersecurity: Role and Applications

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Big Data Analytics in Cybersecurity: Role and Applications

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62104153 Tanyalax Khodhreem

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Big data analytics in cybersecurity

When we talk about big data analytics in cyber security, it reflects the capacity of collecting enormous amounts of digital information. It works by extracting, visualizing, and analyzing futuristic insights so that disastrous cyber threats and attacks can be predicted well in advance.

A stronger and robust cyber defense posture enables organizations to get a more firm idea of all the activities and actions that can potentially lead to cyber-attacks.

As the world's information is getting doubled every year, big data analytics is bringing more and more data into play. Connected devices and the Internet of Things have made available an unprecedentedly large surface that always remains under the threat of attack by cyber attackers.

Whenever a new technology or system develops, so do new threats and vulnerabilities along with it. Security has undoubtedly become a moving target as unprecedented amounts of data are generated in various forms at unpredictable speeds.

According to Ernst & Young - "Global companies are in a hurry to adopt new technologies and media, leaving threats to security as an afterthought"

In such a scenario as described above, fast and accurate detection of vulnerabilities and cyber threats is needed in this era of the internet of things. Big data analytics tools are accompanied by a multitude of advantages that help in making cyber threat analysis systems more robust and infallible.

Big data gathered from networks, computers, sensors, and cloud systems, enables system admins and analysts to get to know the details of vulnerabilities and cyber threats accurately. They can then plan a better framework for developing security solutions to cope with the threats. For any cyber security solution to be effective, big data analytics is gradually becoming a prerequisite.

As explained by the research paper, the major reason behind this development is the fact that large volumes of valuable data are generated in various forms at surprisingly high speeds.

In order to put a limit on the vulnerability of the systems and boost their resilience, big data analytics is bound to act as the key. Big data analytics tools have been widely used for the past few years.

New approaches in the field of cybersecurity or cyber defense are being developed keeping numerous aspects in consideration. The aspects that need to be kept in mind include:

unified data representation,

zero-day attack detection,

data sharing across threat detection systems,

real-time analysis,

sampling and dimensionality reduction,

resource-constrained data processing, and

time-series analysis for anomaly detection.

How big data analytics is making cyber security robust?

Business leaders are prioritizing fast and accurate detection of modern cyber security threats, leading to the exponential rise of big data analytics in cyber security networks.

Due to the incorporation of big data analytics in cyber defense, enormously large volumes of data can be easily processed within no time. This, in turn, makes possible the quick discovery of vulnerabilities and anomalies so that overall resilience can be boosted to a significant extent.

Elements that big data analytics offers such as statistics, machine learning algorithms, predictive models, and computing systems help to raise the standard of business intelligence and cyber security networks.

Cyber security analysts and defense engineers can figure out statistical models or artificial intelligence-based algorithms based on the analysis of historical data isolated from a large variety of sources.

Creating a baseline for normal activity enables experts to quickly detect vulnerabilities. Therefore, it can be said that harnessing the power of big data analytics in cyber security networks is making cyber analysts able to predict cyber attacks.

Combining technologies and new-age solutions like artificial learning, data mining, machine learning, natural language processing, and statistics is enabling big data analysts to detect variations from the norm to predict imminent attacks.

Big data analytics provides automated monitoring and threat detection systems that facilitate real-time detection and continuous monitoring of the environment.

As many organizations bear witness to cyber security challenges emerging from insider threats and negligence, it becomes pertinent to assign credentials to authorized users.

Automated monitoring resolves this concern, by collecting user behavior data and analyzing it. In case any unusual or potentially harmful action is detected, automated monitoring generates an alert well within time.

Latest path-breaking innovations like Security Information and Event Management (SIEM) systems have come all the way from Intrusion Detection System (IDS).

In fact, SIEM is evolving still with the unprecedented power of Machine Learning, enabling cyber security analysts and cyber defense engineers to better handle diverse, unstructured large data sets and to reduce time to consolidation, correlation, and insights.

All businesses small or large alike can opt for big data analytics solutions for strengthening their cyber security.

Combining Machine Learning and AI, businesses are empowered by real-time detection, automated monitoring, and data intelligence for boosting resiliency has enabled businesses of all sizes to replace traditional security tools with big data analytics in cyber defense looking forward to overcoming cyber security challenges.
(Related reading: AI in cybersecurity)
Applications of Big Data Analytics in Cyber Security

Statistical techniques and AI help in detecting potential frauds

Big data analytics plays a tremendous role in fraud detection.

Some techniques for statistical techniques in data analysis include pre-integrated techniques for error detection, validation, rectification, and filling the gaps in the form of missing or wrong data.

Algorithms for quick and effortless detection of anomalies

Previous models, firewalls, and threat detection solutions were basically outdated in their approach towards intrusion response, proving inadequate in defending against modern cybersecurity threats.

Now, after the incorporation of big data analytics in cyber security networks cyber defense engineers and data scientists are empowered to match algorithms for detecting anomalies in the behavior of transactions and users.

Big data analytics not only strengthens cyber security networks through the above method but also aids in eliminating false alarms, estimating potential risks, and predicting futuristic insights.

Conventionally, the process of cyber defense management has been knowledge-intensive as well as time-consuming.

Thanks to the rapidly expanding depth of big data analytics, the time taken to correlate data for forensics purposes and generate actionable security actions has been drastically reduced.

A multitude of statistical parameters including averages, quintiles, performance metrics, and probability distributions can be calculated in no time using big data analytics.

Patterns and associations among large groups of data can be easily clustered and classified by cyber security professionals and data scientists by utilizing big data analytics. ( source)

Ending Note

While reaching the end of the blog, it can be successfully stated that big data analytics has revolutionized the way cyber security analysts used to approach vulnerabilities and anomalies.

It has overwhelmingly positive effects in leveling the challenges that were faced by cyber security professionals and data scientists. They are now empowered to quickly identify anomalies by visualizing and analyzing tremendous amounts of data.

Once they are able to access these enormously large datasets, it becomes a cakewalk for data scientists to decide the framework for feature engineering.

Big data analytics combined with the power of Machine Learning and data mining has not only addressed the severe challenges faced earlier by data scientists and cyber defense engineers but has also enabled them to find futuristic insights and harness the massive parallel computing of big data for strengthening cyber security.

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the rise of big data analytics in cyber defense

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big data analytics and cyber security- an overview

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Applications of Big Data Analytics in Cybersecurity | Analytics Steps

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When we talk about big data analytics in cyber security, it reflects the capacity of collecting enormous amounts of digital information. It works by extracting, visualizing, and analyzing futuristic insights so that disastrous cyber threats and attacks can be predicted well in advance.

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RPA and AI in Healthcare: Advantages and Differences

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RPA and AI in Healthcare: Advantages and Differences

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62105960 Parichat Chusit

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Various healthcare companies around the globe are keen to adopt enhanced ways to optimize methods, achieve greater efficiency, and most importantly, grow the quality of patient experience and engagement.

With the increasing number of patients, it turns out to be significant for healthcare systems to function work in the direction of more streamlined processes to manage money and lowering healthcare costs, and for this advanced technologies play an incredible performance.

In line up with the above briefing, this article explains Robotic Process Automation (RPA) and Artificial Intelligence(AI) in terms of basic differences. Over the section, we will learn how RPA and AI benefit the healthcare industry and a glimpse of RPA vs AI in the context of healthcare.

A Brief Look to RPA and AI

In simple terms, Robotic Process Automation, or RPA as it is known, is the technology that deploys a particular batch of rules and algorithms, and relying on that, it automates tasks with minimal error and enhances productivity, i.e. performs/mimics rules-based human activities.

Some of the fundamental tasks of RPA involve data transformation from one system to another, payroll procedure, and processing of general forms, etc.

Artificial Intelligence, also known as AI, refers to the capability of computers to simulate human intelligence, whether it is recognizing an image or resolving business issues.

AI transforms the entire data analysis process more intelligently, it can derive more significant and appropriate details and fetch beneficial information that enables an individual to make informed decisions.

While AI is converged to performing a human-level task, RPA is software that reduces human efforts. However, AI is far ahead of RPA, still, two notorious techs hold the potential to lift things to the next level, if combined together.

Key differences between RPA and AI

Listing below some conceptual differences between RPA and AI;

1. RPA counts as software that mimics human activities, whereas, AI is the imitation of human intelligence in the machines that are designed to think like a human and simulate their actions.

2. Some rules are predefined that assist RPA robots to perform automated tasks, in opposite to that, AI world on the basis of thinking and learning.

3. Being a rule-based approach, RPA technology comprises no intelligence as it automates repetitive tasks. But for the context of AI, its broad bandwidth comprise superior technologies like ML and NLP. that work beyond rule-based models to automate tasks.

4. One feature of RPA falls on making an impact on businesses as RPA can transform tremendous data without being fed manually which helps in decision making, in contrast to this, AI enables automated decision-making with the engagement of humans.

5. The main purpose of RPA is to just automate repetitious and mundane business procedures, whereas, AI substitutes human efforts, e.g. at someplace, robots or machines are working instead of human labours.

RPA and AI in Healthcare

RPA serves as a binding procedure that recovers data, finishes forms, and conducts methodical functions, acquires processes, and boosts the accomplishment of working labour across the healthcare sector.

Document transforming: With the help of RPA, healthcare providers could automate the process of consuming, entering, and transforming patient information like health records, patient registration, and other clinical documents.

Former authorization: Many times physicians can deploy RPA in order to accumulate the huge information required and regulate the procedure of previous authorization on a daily basis that facilitates the entire process and cuts the total costs of manual work.

The tendency to pay: By programming RPA robots, providers can make accelerated cash flow for retrieving real-time knowledge on patients' receipts and deductibles.

Regression experiment: For assisting testers across the entire regression experiment process, RPA robots could be applied via conducting tests, extracting data, and executing various well-organized testing steps for patients. Also, RPA robots can deliver a portion of the process precisely faster as compared to a human.

Below are the advantages of implementing AI in healthcare;

AI blends cognitive automation with machine learning techniques, hypothesis formation, language processing, and algorithm variation for making insights and performing analytics with the equivalent inclination level as a human.

In the context of population health, AI helps in identifying hidden patterns in medical data via automated analytical techniques that result in making informed decisions. After that, better healthcare plans can be designed or predicted and understood that make impact the population's health.

Various health plans can adopt AI to address product strategies, improve and sustain provider networks, and in costing and jeopardize management, sales and marketing, and the engagement of patients.

Moreover, the health insurance sector has been witnessed the extensive deployment of automation, for example, in the realms of fraud, waste, policy inspecting and renewal, computing premiums, and providing assertive patient responses.

For alleviating the load of administrative tasks, which is time-consuming and alternatively can be spent on patient care, AI can deal with clinical tasks in storing and deciphering diagnostic outcomes, automating drug dispensary, recommending suitable treatment in a short time structure.

For example, via implementing Artificial Intelligence, hospitals can obtain outcomes through analyzing tremendous phenotypic and genetic images, deposited in their healthcare databases, and make informed and data-driven decisions.

In the near time, AI implementation in healthcare will provide virtual assistant in the form of caregivers, counsellors, admit and discharge management robots, and the healthcare firms that can respond to health queries and wellness questions, depending upon the voice and facial recognition technology, it can also deal with managing, stockpiling, disbursing, and restoring medications.

After discussing the fundamental function of RPA and AI in the healthcare industry, let's discuss the brief factual differences among the two;

While RPA lets macro-levelled task automation and standardizing responsibilities that own a solidified workflow and definite set of rules, whereas AI relies on machine learning, natural language processing, and speech recognition for performing micro-level tasks.

Since RPA addresses a key part of processes without undergoing a full overhaul, achieve faster and short-term challenges, while with AI, healthcare organizations could address the fundamental challenges like efficiency, patient-centricity, and growth.

As RPA is the rule-based approach, it can undertake manual, time-consuming, low-level tasks, in contrast to this, AI can be implemented from the digitization of health records to managing inventory, handling unstructured data, and directing regulatory, and reporting challenges.

RPA, as a tool, can be used to transform a human workforce-driven process into the self-regulated service space that a patient or user can implement on their own. And, AI, which is a step ahead, helps in advancing healthcare delivery and acts to be revolutionized in-patient healthcare operations.

The primary objective of using RPA in this sector is that to diminish and eliminate repetitive, mundane, manual tasks for medical staff, whereas, AI focuses on addressing the agony circumstances for healthcare facilities across the globe, like, huge operational costs, a shortage of assimilation, and sharing of data, paper-based knowledge, the inadequacy of clinical staff, etc.

(Suggested reading: Healthcare data analytics)
The above discussion represents RPA, in particulars of robots, incorporates software that imitates human activities and AI signifies to a technology imitates human intelligence.

From RPA services to AI implementation, it is a voyage amidst doing and thinking, i.e. RPA is a process-driven approach and AI is a data-driven approach, especially in the healthcare domain. We emphasized highlighting the differences between the two techs.

However, due to ample advancement, converging AI with RPA can permit complex data to execute automated end-to-end processes and unite predictive modelling and inferences into these processes for supporting humans smarter and faster.

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RPA and AI in Healthcare: Advantages and Differences | Analytics Steps

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Various healthcare companies around the globe are keen to adopt enhanced ways to optimize methods, achieve greater efficiency, and most importantly, grow the quality of patient experience and engagement. With the increasing number of patients, it turns out to be significant for healthcare systems to function work in the direction of more streamlined processes to manage money and lowering healthcare costs, and for this advanced technologies play an incredible performance.

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62103973 Thanathorn Jeadahoa

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A Beginner’s Guide to Machine Learning - RapidMiner

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Are you new to machine learning and unsure where to started? Read our beginner’s guide covering everything you need to know—what it is, examples, why it’s so valuable and more.

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3 Biggest Tech Trends Driving the Automotive Industry in 2021

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3 Biggest Tech Trends Driving the Automotive Industry in 2021

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62109905 Sanpakorn Pattamarak

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Interest in electric vehicles would emerge again periodically over the following decades, but the technology was never efficient enough to support consumer products. The first signs of a real revival didn't occur until the turn of the next century, when battery technology advancements, smart grid developments, and simple energy economics suddenly made electrification much more appealing.

The shift back towards electric vehicles has been gradual, but the tide is about to turn. As recently as 2010 there were only about 17,000 electric cars in use across the globe, according to figures from the International Energy Agency. By 2019, though, that number had surged to 7.2 million, with 47% of these vehicles located in China.

Changes over the next decade will be even more pronounced, as countries around the world have committed to phasing out gasoline and diesel internal combustion engines. Deloitte, in fact, predicts electric vehicles will secure nearly a third of the total market share for new car sales by 2030.

2. Connectivity

Any vehicle with components that connect over wireless networks to other devices or systems can be referred to as a connected vehicle. This technology is, of course, already common-and is almost certainly in your own car right now.

Vehicle connectivity is generally controlled through a console dashboard, which places phone apps, navigation maps, and music streaming options all within easy reach while minimizing driver distractions. Software like this will only grow more sophisticated (and ubiquitous) in the near future.

Business Insider Intelligence expects connected car shipments to reach 77 million by 2025. And McKinsey predicts that as cars become further connected, new dimensions of value will unfurl for drivers, auto manufacturers, and service providers-especially once lightning-fast 5G internet is factored in.

The cars of tomorrow could well be mobile living rooms-at least for passengers-as developers are working on augmented reality and other immersive experiences that will transform in-car entertainment options.

Increased connectivity is also reshaping the way we drive. Some cars today, for instance, are using high-bandwidth networks to communicate with other vehicles and highway infrastructure-including traffic signals, cameras, lane makers, streetlights, and parking meters-to improve traffic systems and increase driver safety. This technology is still in its early phase of development, but vehicle-to-vehicle and vehicle-to-infrastructure communication will ultimately be critical to autonomous driving.

And one of the most promising automotive IoT developments is predictive maintenance. Sensors installed throughout connected cars already collect performance data for diagnostic purposes, but in the near future, this information will be processed in the cloud to predict when parts will require maintenance long before they fail.

3. Autonomous driving

Autonomous driving has been "just around the corner" for so long that many have taken its development for granted or simply dismissed its plausibility altogether. The rapid adoption of the technology over the next decade, then, may seem shocking to those who don't follow the industry closely. PricewaterhouseCoopers predicts that autonomous vehicles will account for around 40% of overall traffic (in terms of miles driven) by 2030. That number may be even higher in China, which is poised to outpace Europe and the US as the leading market for autonomous driving.

The real transformation won't be in the cars themselves, but in the world around them: self-driving technology promises to make our roads substantially safer, reduce traffic and pollution, increase car-sharing, and trigger a renaissance in urban design as prime real estate once reserved for parking becomes available for development.

Autonomous driving will also change our relationship with vehicles. Though we associate cars with the freedom of mobility, the truth is that today's automobiles are simply parked somewhere 95% of the time. This inefficiency is part of the reason why some analysts believe the combination of autonomous driving and ride-sharing will effectively kill private-car ownership.

Other analysts disagree, however, noting that cars still function as status symbols and that personal vehicles will likely always offer benefits that shared or rented simply can't match.

Regardless, autonomous vehicles will fundamentally change the business models of Uber, Lyft, and other ride-hailing services. Right now, gig economy drivers own and maintain their own vehicles, but the switch to autonomous will likely require these companies to own or lease large-scale fleets. (They could also simply be supplanted by Waymo, Tesla, and General Motors.)

The (Automotive) Technology of Tomorrow is Here Today

All of the technological shifts described above have been supported or driven by recent developments in artificial intelligence, machine learning, deep neural networks, and related fields of research.

Autonomous vehicles, for instance, rely on AI to mimic the decision-making expertise of an experienced driver while making complicated inferences about appropriate rule application. This technology is already being implemented in vehicles that aren't autonomous. AI-based driver assistant software can check blind spots, communicate with other vehicles and road infrastructure, monitor driver behavior (and tell when somebody is falling asleep), and even enable "autopilot" driving in limited situations.

By 2030, AI technology like this is expected to be included in 95% of new vehicles.

AI is also transforming how we build cars. On factory floors, for instance, AI is currently being used to optimize schedules and workflows, help robots operate safely alongside assembly line workers, and identify defects in key vehicle components. These applications (and others) are helping manufacturers reduce costs, increase efficiency, and deliver better products to consumers.

Wrapping Up

Artificial intelligence has applications across the entire automotive value chain, from design to production to operation. Aftermarket services, including predictive maintenance and driver insurance, are also being transformed by AI. There are clearly endless opportunities to leverage the technology and unlock value, but the particulars are going to vary significantly by use case.
If you'd like to read more about some of the ways that AI and ML are impacting the manufacturing industry-as well as others-check out 50 Ways to Impact Your Business with AI.

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3 Biggest Tech Trends Driving the Automotive Industry in 2021 - RapidMiner

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Interest in electric vehicles would emerge again periodically over the following decades, but the technology was never efficient enough to support consumer products. The first signs of a real revival didn't occur until the turn of the next century, when battery technology advancements, smart grid developments, and simple energy economics suddenly made electrification much more appealing.

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62102884 Nutchanon Thawornnuruk

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Artificial intelligence (AI) is transforming how we do business and live. We make advances in healthcare, finance, data security, travel, transportation, and social media every day.

When it comes to the automotive business, artificial intelligence (AI) is changing the game. According to studies, the value of AI in automotive and cloud services would surpass $ 10.73 billion by 2024.

With technology taking over the entire business world, we must keep up with the Industrial Revolutions occurring every second. And AI is one of the most powerful technology, and it becomes a lot easier for humans to interact with machines.

After all, everything in this world is science and everything can be calculated. So let's look over how AI is being used in the world of transportation and how AUDI uses AI?

AI in Automotive Industry

When it comes to driving, artificial intelligence has a plethora of options, including fully autonomous mode and driving assistance mode. One of the automotive industry's primary priorities is keeping the driver safe while driving.

AI delivers enhanced safety capabilities that can recognize unsafe conditions and notify the driver if an accident is imminent. It can also take over emergency vehicle control if the driver becomes unable to drive.

Multiple sensors, cross-traffic detectors, emergency braking, driver assistance, blind-spot monitoring, and other features are included to assure passenger safety. The AI has progressed to the point where it can assist people in driving, but it's been a long time since we've seen a city full of driverless automobiles. This is due to the fact that driving is not governed by a set of rules.

Some AI algorithms are unable to comprehend the complexities of driving a car. This is a complicated procedure that necessitates the monitoring of many conditions and the anticipation of multiple eventualities.

The computer power needed for self-driving cars is currently unavailable. Google and Tesla have made numerous advances in artificial intelligence and machine learning, and it won't be long before self-driving cars become a reality.

Status of AI in Automotive

Artificial intelligence (AI) and self-driving cars are frequently complementary topics in technology. Simply said, you can't have one without the other. Though AI is being deployed at a rapid pace in a variety of areas, the way it is being used within the automobile industry is a hotly debated topic right now.

With each vehicle manufacturer and their mother racing to develop synthetic intelligence and self-driving technologies, there is also a swarm of IT organizations and startups with the same goal.

Though many people believe that personal, self-sustaining automobiles are the future, there are numerous ways in which AI and gadget mastering are being applied in how cars are built and how they behave on the road.
(Read About:- Different Examples of AI)
To combine the Audi A6 body pieces, approximately 5,300 spot welds are required. Historically, production workers employed ultrasonic waves to manually monitor the quality of the resistance spot welding process (WPS for short).

Experts in production, innovation management, digitalization planning, and information technology are presently trying highly smart methods to determine the quality of weld spots at the Neckarsulm site.

A team led by Mathias Mayer and Andreas Rieker will utilize artificial intelligence (AI) to detect quality problems in real-time as part of the WPS Analytics pilot project.

AUDI AG's Head of Production and Logistics Delivery Management Digitization, Michael Haeffner, is satisfied with the site's project.

"Our mission as a pilot plant for the Volkswagen Group's digital manufacturing and logistics is to test and develop digital solutions." From vehicle design to use in continuous production - "We are utilizing AI to evaluate important technologies that will make Audi and its location future-ready."

Spot welding (sometimes referred to as resistance spot welding) is a type of resistance welding. This welding process primarily employs an electric current to deliver pressure and heat to the weld in order to join two or more metal plates.

It generates heat by sending an electric current through tough materials such as low carbon steel and bringing a copper alloy electrode into contact with the surface of the sheet metal.

The deployment of solutions for the entire Volkswagen Group is envisaged.

Audi is a pioneer in the ongoing development of this prospective use case as part of the Volkswagen Group's industrial cloud. The system integrates production data from group factories all over the world into a single high-performance digital platform.

The primary purpose is to improve efficiency and cut costs. Each linked location can download its machine, tools, and system software directly from the industrial cloud (similar to an app store), increasing product efficiency.

Following the successful implementation of the "WPS Analytics" algorithm and dashboards in Neckarsulm, preparations are in the works to roll them out across the Group.

Colleagues at Volkswagen's Emden factory also rely on optimizing algorithmic control for resistance spot welding, thus the first synergies have already been realized. The outcomes of both projects are dispersed throughout the industrial cloud.
(Suggested reading: How BMW Uses Artificial Intelligence (AI)?)
AUDI's revolutionary vehicles in coming years

The Audi AI: TRAIL Quattro is the fourth concept car with an electric drive system in the series, which began with the introduction of the Audi Aicon at the 2017 IAA.

Following this luxury-class car developed for highly automated long-distance driving, the Audi PB18 E-Tron, an emissions-free monoposto geared for racing, made its debut in Pebble Beach in 2018.

The Audi AI: ME, revealed at the 2019 Auto Shanghai exhibition, is the third in the series and is an autonomous city car aimed at megacities throughout the world. It lets its passengers forget about the inevitable stop-and-go traffic in cities by providing flawless connectivity and a broad range of infotainment services, allowing them to spend their time in the car as productively and comfortably as possible.

Finally, with AI: TRAIL, Audi enables futuristic vehicles off-road. The vehicle is outfitted with four electric motors, an assist and automated driving system, and Audi's signature Quattro permanent all-wheel drive.

AI will continue to act as a technology innovation for years to come. Artificial intelligence (AI) is a revolutionary area of ​​computer science that is undoubtedly poised to become a key component of various emerging technologies such as big data, robotics, and IoT.

In just a few years, AI has gone from fantasy to reality. Machines that help people with intelligence, not just in science fiction movies, but in the real world. We are currently living in a world of artificial intelligence, which is only a few years of history.

"We're at the beginning of a golden age of AI. Recent advancements have already led to an invention that previously lived in the realm of science fiction - and we've only scratched the surface of what's possible." -JEFF BEZOS

Conclusion about AI in India

According to Amitley, Managing Director of Data and Analytics at Protiviti Member Farms in India, India's automotive industry is in the midst of an exciting phase of vehicle telematics.

Protiviti, a worldwide consulting and accountancy firm, assists numerous Indian automobile manufacturers in the technology industry. In light of the current cataclysms, which "allow unforeseen developments in the industry in many ways," Ray believes India has no reason to make an exception.

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Artificial intelligence (AI) is transforming how we do business and live. We make advances in healthcare, finance, data security, travel, transportation, and social media every day. When it comes to the automotive business, artificial intelligence (AI) is changing the game. According to studies, the value of AI in automotive and cloud services would surpass $ 10.73 billion by 2024.

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62100268 Korawich Kiangia

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Data science is a domain that combines data-bound analytical techniques and scientific theory to generate insights for business stakeholders. Its shape, elements, and size allow organizations to optimize operations, identify new business opportunities, and reduce the functional performance of departments such as marketing and sales.

Simply put, data science creates a competitive edge against competitors by optimizing operations with unique data throughput. As Forbes reported, data-driven companies are 23 times more likely to win customers and 19 times more likely to be profitable. Let us read through the data science techniques, methods, and future of data science.

Data science for corporate decision-making is already a reality. It is the cornerstone of entrepreneurship in the information age. The application goes beyond the pure extrapolation of knowledge. Carefully selected results will help maximize efficiency. A common example is the reuse of data to graph a buyer's persona. It can be used especially for marketing campaigns and brand building.

A professional with data science skills will know how to find meaningful information in the data they come across. (s)he leads the company in the right direction. The company needs strong data-driven decision-making, for which (s)he is an expert. Data Science professionals are experts in various basic fields of statistics and computer science. They use their analytical skills to solve business problems.

Some of the vital benefits that data science professionals offer the enterprises with their upgraded data science skills are improved ROI, improved sales, streamlined operations, shorter product lead times, and increased customer loyalty and satisfaction.

The basic data analytics tools and techniques for business professionals are a combination of open source and out-of-the-box options.

Some of these are listed below:

Spark

Hadoop

NoSQL

Databases Julia,

SQL, Python, R, Scala, JavaScript, and several other programming languages ​​

Data science professionals also use tools such as Tableau, SAS, Apache, etc. Therefore, knowledge of ML libraries such as ScikitLearn, TensorFlow, PyTorch, and Keras, are vital because ML is an integral and inevitable subset of data science.

In the near future, data science professionals will have the ability to take on new business areas as they are critical and involve several complex challenges. Therefore, businesses can make exponential leaps in the future.

According to a report by IBM, there will be an increase in the job openings for professionals having the latest data science skills. This would require professionals to earn data science certifications and opt for additional courses to learn those skills and stay ahead of the competition. Some of the data science certifications that can be earned by professionals to win a chance to crack job roles in this field are:

Senior Data Scientist (SDSTM) by the Data Science Council of Americ...

Microsoft Certified: Azure Data Scientist Associate

IBM Data Science Professional

SAS Certified AI and Machine Learning Professional

Large-scale enterprises will rapidly adopt machine learning to drive their business in several ways. A lot of tasks will be automated, which is one of the key future goals of several industries.

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Data science and analytics: the future implications - DataScienceCentral.com

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Data science is a domain that combines data-bound analytical techniques and scientific theory to generate insights for business stakeholders. Its shape, elements, and size allow organizations to optimize operations, identify new business opportunities, and reduce the functional performance of departments such as marketing and sales. Simply put, data science creates a competitive edge against competitors...

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Big data engineering – A complete blend of big data analytics and data science

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Big data engineering – A complete blend of big data analytics and data science

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62104310 Theeradon Boonrod

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business man hand working on laptop computer with digital layer business strategy and social media diagram on wooden desk

Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
~Forbes

Globally several individuals have chosen data science and big data engineering as a mainstream career option, but still there are people who are unaware of about the various options that are available. Many claims suggest big data analyst will be discarded after big data, while a few claim that big data and data science are similar, or one is a subset of another.

Data science has been here for a long time, while big data, on the other side, and is fairly new, originating from the former with many relevant transformations. Big data analytics leverages the techniques and software systems that are helpful in either and vice versa with respect to techniques.

But the role of data engineer is as much important as the data scientists is. Because if data scientist develops a breakthrough algorithm, then the big data engineer puts it into production for the use by the business, according to a statistics report from Gartner that only 15 percent of big data projects ever make it into production. And while many never seek into the reasons why 85 percent of the big data projects never make it.

Let's have a look at the comparison between data scientist, big data analyst & data engineer

Big data engineering is the process of developing and building systems for collecting, storing, and analyzing data. It is a wide field with various applications in several industries. Firms have collected massive amounts of data, and they need data infrastructure and personnel to sort and analyze the information. This resulted in the demand for professionaldata engineers who work to design systems that collect, manage, and convert the raw data into usable information.

This information is useful for data scientists and business analysts to interpret. The main objective is to make data accessible so that the companies can take help of it for evaluating and optimizing their business's overall performance. That is why for every one data scientist firms require at least two data engineers. It can be said that, one may needs as many as 5 data engineers per every 1 data scientist.

A data engineer is the all-purpose everyman of a big data analyst operation, working between downstream analysts on the one hand, and upstream data scientists on the other. They will often come from programming backgrounds and are experts in Big Data frameworks, such as Hadoop. They're called on to ensure that data pipelines are scalable, repeatable, and secure, and can serve multiple constituents in the enterprise.

Now, that little surge in demand seems to be blossoming into a full-blown data engineering shortage. According to a new report released by Stitch and Galvanize, there are only 6,500 self-reported data engineers according to an analysis of their LinkedIn profiles, but more than 6,600 job openings for data engineers in the San Francisco Bay Area alone.

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Big data engineering - A complete blend of big data analytics and data science - DataScienceCentral.com

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Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn. ~Forbes Globally several individuals have chosen data science and big data engineering as a mainstream career option, but still there are people who are unaware of about the various options that are available.

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6 Major Branches of Artificial Intelligence (AI)

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6 Major Branches of Artificial Intelligence (AI)

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62103379 Danunuch Kongwatmai

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"Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior - understanding language, learning, reasoning, solving problems, and so on." - (Barr & Feigenbaum, 1981)

Artificial intelligence is the practice of computer recognition, reasoning, and action. It is all about bestowing machines the power of simulating human behaviour, notably cognitive capacity. However, Artificial intelligence, Machine learning, and Data Science are all related to each other.

In the commencement of this blog, we will gain expertise in Artificial Intelligence and its major six branches.

Prefacing Artificial intelligence

In terms of easy definition, Artificial Intelligence is the capability of a machine or computer device to emulate human intelligence (cognitive process), acquire from experiences, adapt to the latest information and operate humans-like-activities.

Artificial Intelligence executes tasks intelligently that yield in generating huge accuracy, adaptability, and productivity for the entire system. Tech decision-makers are seeking many ways to adequately implement artificial intelligence technologies into their businesses to draw interference and add values to them.

For example, AI, in the media industry, is used at large scales, such as in social media, in automated journalism, etc. Another example can be seen at AI in banking applications like chatbots, mobile banking, fraud detection, customer engagement, etc.

Apart from this, AI has various fundamental application incorporating NLP, healthcare, automotive, gaming, speech recognition, finance, vision system, etc. and required for;

To design expert systems equipped with the knowledgeable practice that is proficient to acquire, manifest, decipher and justify to its users.

Stimulating devices to identify results for complicated issues like humans do and implement them in the mode of algorithms in computers.

Branches of Artificial Intelligence As AI Capabilities

There is a broad set of techniques that come in the domain of artificial intelligence such as linguistics, bias, vision, planning, robotic process automation, natural language processing, decision science, etc. Let us acquire information about some of the major subfields of AI in deep;

In the video, six major branches of artificial intelligence are explained in a quick way.

1. Machine learning

In terms of advanced technology, one of the most demanding fields is Machine Learning, it is making buzz every day whenever a new product is introduced by any company that deploys ML techniques and algorithms for delivering the consumer in a highly creative manner.

Machine Learning is the technique that gives computers the potential to learn without being programmed, it is actively being used in daily life, machine learning applications in daily life, even without knowing that. Fundamentally, it is the science that enables machines to translate, execute and investigate data for solving real-world problems.

With the deployment of complex mathematical expertise, programmers design machine learning algorithms that are coded in a machine language in order to make a complete ML system. By this way, ML enables us to perform tasks to categorize, decipher and estimate data from a given dataset.

In the last few years, it has given us self-driving cars, image and speech recognition, demand forecasting models, useful web search and various extensive applications. It basically converges on the applications that adapt from experience and advance their decision-making potential or predictive accuracy over a period of time.

Moreover, depending on the types of data available, data professionalists select types of machine learning (algorithms) for what they want to predict from data,

1. Supervised Learning: In this type of learning, data experts feed labelled training data to algorithms and define variables to algorithms for accessing and finding correlations. Both the input and output of the algorithm are particularized/defined.

2. Unsupervised Learning: This type of learning include algorithms that train on unlabelled data, an algorithm analyzes datasets to draw meaningful correlations or inferences. For example, one method is cluster analysis that uses exploratory data analysis to obtain hidden or grouping patterns or groups in datasets.

3. Reinforcement Learning: For teaching a computer machine to fulfil a multi-step process for which there are clearly defined rules, reinforcement learning is practised. Here, programmers design an algorithm to perform a task and give it positive and negative signal to act as algorithm execute to complete the task. Sometimes, the algorithm even determines on its own what action to take to go ahead.

2. Neural Network

Incorporating cognitive science and machines to perform tasks, the neural network is a branch of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). Neural network replicates the human brain where the human brain comprises an infinite number of neurons and to code brain-neurons into a system or a machine is what the neural network functions.
In simple terms, a neural network is a set of algorithms that are used to find the elemental relationships across the bunches of data via the process that imitates the human brain operating process.
So, a neural network refers to a system of neurons that are original or artificial in nature, where artificial neurons are known as perceptrons, know from here, the complete perceptron model in the neural network.

A neuron in a neural network is a mathematical function (such as activation functions) whose work is to gather and classify information according to a particular structure, the network strongly implements various statistical techniques, such as regression analysis, to accomplish tasks.

From forecasting to market research, they are extensively used for fraud detection, risk analysis, stock-exchange prediction, sales prediction and many more.

3. Robotics

This has emerged as a very sizzling field of artificial intelligence. An interesting field of research and development mainly focuses on designing and constructing robots.

Robotics is an interdisciplinary field of science and engineering incorporated with mechanical engineering, electrical engineering, computer science, and many others.

Robotics determines the designing, producing, operating, and usage of robots. It deals with computer systems for their control, intelligent outcomes, and information transformation.

Robots are deployed often for conducting tasks that might be laborious for humans to perform steadily. Major of robotics tasks involved- assembly line for automobile manufacturing, for moving large objects in space by NASA. AI researchers are also developing robots using machine learning to set interaction at social levels.
Six major branches of Artificial Intelligence
4. Expert Systems

Expert systems were considered amid the first successful model of AI software. For the first time, they were designed in the 1970s and after that escalated in the 1980s.

Under the umbrella of an AI technology, an expert system refers to a computer system that mimics the decision-making intelligence of a human expert. It conducts this by deriving knowledge from its knowledge base by implementing reasoning and insights rules in terms with the user queries.
The effectiveness of the expert system completely relies on the expert's knowledge accumulated in a knowledge base. The more the information collected in it, the more the system enhances its efficiency. For example, the expert system provides suggestions for spelling and errors in Google Search Engine.
Expert systems are built to deal with complex problems via reasoning through the bodies of proficiency, expressed especially in particular of "if-then" rules instead of traditional agenda to code. The key features of expert systems include extremely responsive, reliable, understandable and high execution.

5. Fuzzy Logic

In the real world, sometimes we face a condition where it is difficult to recognize whether the condition is true or not, their fuzzy logic gives relevant flexibility for reasoning that leads to inaccuracies and uncertainties of any condition.

In simpler terms, Fuzzy logic is a technique that represents and modifies uncertain information by measuring the degree to which the hypothesis is correct. Fuzzy logic is also used for reasoning about naturally uncertain concepts. Fuzzy logic is convenient and flexible to implement machine learning techniques and assist in imitating human thought logically.

It is simply the generalization of the standard logic where a concept exhibits a degree of truth between 0.0 to 1.0. If the concept is completely true, standard logic is 1.0 and 0.0 for the completely false concept. But in fuzzy logic, there is also an intermediate value too which is partially true and partially false.

6. Natural Language Processing

It is hard from the standpoint of the child, who must spend many years acquiring a language ... It is hard for the adult language learner, it is hard for the scientist who attempts to model the relevant phenomena, and it is hard for the engineer who attempts to build systems that deal with natural language input or output. These tasks are so hard that Turing could rightly make fluent conversation in natural language the centerpiece of his test for intelligence. - Page 248, Mathematical Linguistics, 2010.

In layman words, NLP is the part of computer science and AI that can help in communicating between computer and human by natural language. It is a technique of computational processing of human languages. It enables a computer to read and understand data by mimicking human natural language.

(Recommend blog: Types of machine translation)

NLP is a method that deals in searching, analyzing, understanding and deriving information from the text form of data. In order to teach computers how to extract meaningful information from the text data, NLP libraries are used by programmers. A common example of NLP is spam detection, computer algorithms can check whether an email is a junk or not by looking at the subject of a line, or text of an email.

Implementing NLP gives various benefits such as;

It improves the accuracy and efficiency of documents.

It has the ability to make automated readable summary text.

It is very advantageous for personal assistants such as Alexa,

It enables organizations to opt chatbots for customer support.

It makes sentiment analysis easier.

Some of the NLP applications are text translation, sentiment analysis, and speech recognition. For example, Twitter uses NLP technique to filter terroristic language from various tweets, Amazon implements NLP for interpreting customer reviews and enhancing their experience.

Conclusion

Artificial intelligence systems turn to extend more capable by augmenting in size and complexity. AI analysts are continuously attempting to build up software systems for diverse applications like automatic learning, knowledge, natural language, and speech recognition.

(Read also: What is Knowledge Graph?)

Depending on the functioning of AI systems, we have studied six branches under the umbrella of the Artificial Intelligence field. The six fields are now the buzz word in the industries and organizations. Numerous corporations are promoting it to make use of it and serve people in a much better approach.

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6 major branches of artificial intelligence

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major branches of artificial intelligence

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different branches of artificial intelligence

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various branches of artificial intelligence

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different areas of artificial intelligence

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various fields of artificial intelligence

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what are the different branches of artificial intelligence

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6 Major Branches of Artificial Intelligence (AI) | Analytics Steps

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"Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior - understanding language, learning, reasoning, solving problems, and so on." - (Barr & Feigenbaum, 1981) Artificial intelligence is the practice of computer recognition, reasoning, and action.

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62101696 เจษฎาพันธ์ ผ่องเส้ง

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Data is everywhere. From data research to artificial intelligence technology, data has become an essential commodity that is being perceived as a link between our past and future. Is an organization willing to collect its past records?

Data is the key solution to this problem. Is any programmer willing to formulate a Machine Learning algorithm? Data is what s/he needs to begin with.

While the world has moved on to technology, it still is unaware of the fact that data is the building block of all these technological advancements that have together made the world so advanced.

When it comes to data, a number of tools and techniques are put to work to arrange, organize, and accumulate data the way one wants to. Factor Analysis is one of them. A data reduction technique, Factor Analysis is a statistical method used to reduce the number of observed factors for a much better insight into a given dataset.

But first, we shall understand what is a factor. A factor is a set of observed variables that have similar responses to an action. Since variables in a given dataset can be too much to deal with, Factor Analysis condenses these factors or variables into fewer variables that are actionable and substantial to work upon.

A technique of dimensionality reduction in data mining, Factor Analysis works on narrowing the availability of variables in a given data set, allowing deeper insights and better visibility of patterns for data research.

Most commonly used to identify the relationship between various variables in statistics, Factor Analysis can be thought of as a compressor that compresses the size of variables and produces a much enhanced, insightful, and accurate variable set.

"FA is considered an extension of principal component analysis since the ultimate objective for both techniques is a data reduction." Factor Analysis in Data Reduction

Types of Factor Analysis

Developed in 1904 by Spearman, Factor Analysis is broadly divided into various types based upon the approach to detect underlying variables and establish a relationship between them.

While there are a variety of techniques to conduct factor analysis like Principal Component Analysis or Independent Component Analysis, Factor Analysis can be divided into 2 types which we will discuss below. Let us get started.

As the name of this concept suggests, Confirmatory Factor Analysis (CFA) lets one determine whether a relationship between factors or a set of overserved variables and their underlying components exists.

It helps one confirm whether there is a connection between two components of variables in a given dataset. Usually, the purpose of CFA is to test whether certain data fit the requirements of a particular hypothesis.

The process begins with a researcher formulating a hypothesis that is made to fit along the lines of a certain theory. If the constraints imposed on a model do not fit well with the data, then the model is rejected, and it is confirmed that no relationship exists between a factor and its underlying construct. Perhaps hypothetical testing also finds a space in the world of Factor Analysis.

In the case of Exploratory Factor Statistical Analysis, the purpose is to determine/explore the underlying latent structure of a large set of variables. EFA, unlike CFA, tends to uncover the relationship, if any, between measured variables of an entity (for example - height, weight, etc. in a human figure).

While CFA works on finding a relationship between a set of observed variables and their underlying structure, this works to uncover a relationship between various variables within a given dataset.

Conducting Exploratory Factor Analysis involves figuring the total number of factors involved in a dataset.

"EFA is generally considered to be more of a theory-generating procedure than a theory-testing procedure. In contrast, confirmatory factor analysis (CFA) is generally based on a strong theoretical and/or empirical foundation that allows the researcher to specify an exact factor model in advance." EFA in Hypothesis Testing

Applications of Factor Analysis

With immense use in various fields in real life, this segment presents a list of applications of Factor Analysis and the way FA is used in day-to-day operations.
Applications of factor analysis
Marketing is defined as the act of promoting a good or a service or even a brand. When it comes to Factor Analysis in marketing, one can benefit immensely from this statistical method.

In order to boost marketing campaigns and accelerate success, in the long run, companies employ Factor Analysis techniques that help to find a correlation between various variables or factors of a marketing campaign.

Moreover, FA also helps to establish connections with customer satisfaction and consequent feedback after a marketing campaign in order to check its efficacy and impact on the audiences.

That said, the realm of marketing can largely benefit from Factor Analysis and trigger sales with respect to much-enhanced feedback and customer satisfaction reports.

(Must read: Marketing management guide)

In data mining, Factor Analysis can play a role as important as that of artificial intelligence. Owing to its ability to transform a complex and vast dataset into a group of filtered out variables that are related to each other in some way or the other, FA eases out the process of data mining.

For data scientists, the tedious task of finding relationships and establishing correlation among various variables has always been full of obstacles and errors.

However, with the help of this statistical method, data mining has become much more advanced.

(Also read: Data mining tools)

Machine Learning and data mining tools go hand in hand. Perhaps this is the reason why Factor Analysis finds a place among Machine Learning tools and techniques.

As Factor Analysis in machine learning helps in reducing the number of variables in a given dataset to procure a more accurate and enhanced set of observed factors, various machine learning algorithms are put to use to work accordingly.

They are trained well with humongous data to rightly work in order to give way to other applications. An unsupervised machine learning algorithm, FA is largely used for dimensionality reduction in machine learning.

Thereby, machine learning can very well collaborate with Factor Analysis to give rise to data mining techniques and make the task of data research massively efficient.

(Recommended blog: Data mining software)

Nutritional Science is a prominent field of work in the contemporary scenario. By focusing on the dietary practices of a given population, Factor Analysis helps to establish a relationship between the consumption of nutrients in an adult's diet and the nutritional health of that person.

Furthermore, an individual's nutrient intake and consequent health status have helped nutritionists to compute the appropriate quantity of nutrients one should intake in a given period of time.

The application of Factor Analysis in business is rather surprising and satisfactory.

Remember the times when business firms had to employ professionals to dig out patterns from past records in order to lay a road ahead for strategic business plans?

Well, gone are the days when so much work had to be done. Thanks to Factor Analysis, the world of business can use it for eliminating the guesswork and formulating more accurate and straightforward decisions in various aspects like budgeting, marketing, production, and transport.

Pros and Cons of Factor Analysis

Having learned about Factor Analysis in detail, let us now move on to looking closely into the pros and cons of this statistical method.

Pros of Factor Analysis

The first and foremost pro of FA is that it is open to all measurable attributes. Be it subjective or objective, any kind of attribute can be worked upon when it comes to this statistical technique.

Unlike some statistical models that only work on objective attributes, Factor Analysis goes well with both subjective and objective attributes.

While data research and data mining algorithms can cost a lot due to the extraordinary charges, this statistical model is surprisingly cost-effective and does not take many resources to work with.

That said, it can be incorporated by any beginner or an experienced professional in light of its cost-effective and easy approach towards data mining and data reduction.

While many machine learning algorithms are rigid and constricted to a single approach, Factor Analysis does not work that way.

Rather, this statistical model has a flexible approach towards multivariate datasets that let one obtain relationships or correlations between various variables and their underlying components.

(Must read: AI algorithms)

Cons of Factor Analysis

While there are many pros of Factor Analysis, there are various cons of this method as well. Primarily, Factor Analysis can procure incompetent results due to incomprehensive datasets.

While various data points can have similar traits, some other variables or factors can go unnoticed due to being isolated in a vast dataset. That said, the results of this method could be incomprehensive.

Another drawback of Factor Analysis is that it does not identify complicated factors that underlie a dataset.

While some results could clearly indicate a correlation between two variables, some complicated correlations can go unnoticed in such a method.

Perhaps the non-identification of complicated factors and their relationships could be an issue for data research.

Even though Factor Analysis skills can be imitated by machine learning algorithms, this method is still reliant on theory and thereby data researchers.

While many components of a dataset can be handled by a computer, some other details are required to be looked into by humans.

Thus, one of the major drawbacks of Factor Analysis is that it is somehow reliant on theory and cannot fully function without manual assistance.

(Suggested reading: Deep learning algorithms)

Summing Up

To sum up, Factor Analysis is an extensive statistical model that is used to reduce dimensions of a given dataset with the help of condensing observed variables in a smaller size.

(Top reading: Statistical data distribution models)

By arranging observed variables in groups of super-variables, Factor Analysis has immensely impacted the way data mining is done. With numerous fields relying on this technique for better performance, FA is the need of the hour.

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application of factor analysis in business

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Factor Analysis: Types and Applications | Analytics Steps

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Data is everywhere. From data research to artificial intelligence technology, data has become an essential commodity that is being perceived as a link between our past and future. Is an organization willing to collect its past records? Data is the key solution to this problem. Is any programmer willing to formulate a Machine Learning algorithm?

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AI ปัญญาประดิษฐ์กับการใช้ประโยชน์ทางการแพทย์

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AI ปัญญาประดิษฐ์กับการใช้ประโยชน์ทางการแพทย์

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62107842 Yutthapoom Sananoy

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แพทย์ประจำบ้านต่อยอดสาขาประสาทวิทยา ภาควิชาอายุรศาสตร์ โรงพยาบาลจุฬาลงกรณ์ สภากาชาดไทย เป็นหมอและนักวิทยาศาสตร์ที่มีความฝันว่าจะนำ AI มาใช้ในด้านการแพทย์ให้เกิดประโยชน์ต่อสังคมสูงสุด

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ปัญญาประดิษฐ์กับการใช้ประโยชน์ทางการแพทย์ - AI for All - Thailand

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หากกล่าวถึง AI หรือปัญญาประดิษฐ์ หลายคนอาจคิดถึงหุ่นยนต์ที่เดินไปเดินมา มีความคิดเป็นของตัวเอง สามารถทำงานแทนมนุษย์ ทำงานบ้านหรือพาสุนัขไปเดินเล่นได้ แม้ว่าขณะนี้เรายังคงอยู่ห่างไกลจากจุดนั้น แต่ต้องยอมรับว่าปัจจุบัน AI ได้พัฒนามาไกลกว่าสมัยก่อนมาก...

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62100359 Kritsanaphong Kimsue

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GitHub Copilot Open Source Alternatives - KDnuggets

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GitHub’s Copilot code generation tool is currently only available via approved request. Here are 4 Copilot alternatives that you can use in your programming today.

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62107339 ภาณุพงสื กองสุวรรณ

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ประโยชน์ของ AI กับการใช้งานในภาคธุรกิจ (3)
อ่านบทความก่อนหน้า: อนาคตของ AI และตัวอย่างภาคธุรกิจที่น่าจับตามอง (2)
ด้วยขีดความสามารถที่เพิ่มขึ้นได้อยู่เสมอของ AI ทำให้หลายๆ ธุรกิจเริ่มหันมาตั้งคำถามแล้วว่าจะเอา AI ที่ชาญฉลาดมาใช้ในการทำงานได้อย่างไร ประโยชน์ของ AI กับการใช้งานในภาคธุรกิจอยู่ตรงไหนบ้างเพราะนับวันองค์กรต่างๆ ก็จะมีเทคโนโลยีกลายเป็นรากฐานของธุรกิจและ AI ก็เป็นเหมือนเครื่องมือที่เข้ามารีดเร้นคัดเอาประสิทธิภาพของเทคโนโลยีเหล่านี้ออกมาใช้ให้มากขึ้นด้วย

ในการสำรวจของ eMarketer นั้น ผู้บริหารในองค์กรใหญ่ๆ ได้ให้ความเห็นถึงงานที่ AI จะเข้ามาช่วยในธุรกิจได้คือ

ใช้วิเคราะห์ข้อมูลซับซ้อนได้แม่นยำมากกว่า

ข้อมูลที่มีความซับซ้อน มีจำนวนมากมายมหาศาลที่ถูกกักเก็บจากเทคโนโลยีการจัดเก็บที่ทรงประสิทธิภาพ หากจะใช้แรงงานคนทั้งหมดในการจัดการและประมวลผล เราสามารถบอกได้เลยว่าคงไม่ใช่เรื่องง่าย ฉะนั้น AI จึงเป็นคำตอบที่ดีในการทำงานประมวลผลข้อมูลนั้นด้วยความถูกต้อง แม่นยำ และนำเสนอได้ภายในระยะเวลาอันจำกัด เรามีตัวอย่างที่เกิดขึ้นแล้วหลายอย่างเช่น

ด้านการค้นหาในระบบออนไลน์ - ในสมัยก่อนบางครั้งผลลัพธ์ในการค้นหาอาจไม่ถูกต้อง ไม่ถูกใจ ไม่เหมาะนำไปใช้งาน แต่ปัจจุบันการค้นหาใน Google แค่คีย์เวิร์ดคำเดียวก็สามารถหาข้อมูลที่ตรงใจได้ เพราะระบบ AI สามารถวิเคราะห์พฤติกรรมการใช้คำค้นหาหรือคีย์เวิร์ด เพื่อให้สามารถได้ข้อมูลที่ต้องการค้นหา ซึ่งแน่นอนว่าความผิดพลาดมีน้อยลงกว่าแต่ก่อนมาก

ด้านการเงินการทุน - การเล่นหุ้นหรือการลงทุนจะรวดเร็วขึ้น เพราะปัจจัยการคิดและวิเคราะห์จาก AI จะช่วยรวบรวมข้อมูลทั้งหมดที่สำคัญและนำมาประมวลผลหาโอกาสหรือความเป็นไปได้ในการลงทุนที่ทำให้ได้ผลกำไรในระยะเวลาอันสั้น

ด้านสุขภาพ - การวิเคราะห์หรือตรวจหาโรคจะทำได้รวดเร็วและแม่นยำขึ้น คำว่าป่วยระยะสุดท้ายหรือเกินเยียวยาด้วยสาเหตุการวิเคราะห์ล่าช้าจะกลายเป็นเรื่องเก่าไป เพราะ AI สามารถทำการสแกนเพื่อค้นหาจุดผิดปกติ รวมถึงการให้คำแนะนำกับแพทย์เพื่อใช้ในการพิจารณาได้ เคยมีการทดลองใช้ตรวจสอบมะเร็งเต้านมในสหรัฐฯ ระบบ AI สามารถตรวจพบความผิดปกติที่อาจก่อให้เกิดมะเร็งเต้านม และช่วยให้ผู้หญิงกว่า 52% สามารถหายจากโรคมะเร็งก่อนที่แพทย์จะตรวจพบ

ด้านการตรวจสอบสิ่งผิดกฎหมายออนไลน์ - ในขณะที่ e-Commerce กำลังเติบโตอยู่ การซื้อขายสินค้าผิดกฎหมายรวมไปถึงการฟอกเงินเกิดขึ้นง่ายมาก สินค้าออนไลน์กว่าล้านชิ้นจะตรวจสอบกันอย่างไรให้ถูกต้องและรวดเร็ว AI สามารถเรียนรู้และตรวจสอบได้ว่าสินค้าประเภทใดผิดกฎหมาย รวมไปถึงตรวจสอบรายละเอียดของผู้ซื้อและผู้ขาย เพื่อวิเคราะห์ว่าการซื้อขายในแต่ละครั้งได้ว่ามีข้อพิรุธอะไรที่นำไปสู่การฟอกเงินหรือไม่

ด้านการรักษาความปลอดภัยของข้อมูล -ปัจจุบันมีมัลเเวร์เกิดขึ้นใหม่กว่า 3 แสนไฟล์ทุกวัน ซึ่งมีความแตกต่างกันไม่เกิน 10% ทำให้การตรวจสอบติดตามทำได้ยากมาก แต่ระบบ AI สามารถแยกแยะได้ทันทีว่าไฟล์ไหนเป็นมัลแวร์คุกคาม โดยเฉพาะปัจจุบันนี้มีการใช้ระบบคลาวด์ ระบบ AI จึงยิ่งมีความสำคัญในการตรวจสอบข้อมูล

นี่เป็นเพียงตัวอย่างคร่าวๆ เท่านั้น ยังมีอีกหลายวงการและมิติทางธุรกิจที่ใช้ AI ในการวิเคราะห์ข้อมูล

เปิดโอกาสในการ "สร้างสรรค์" บริการและความประทับใจให้ลูกค้าได้มากกว่าเดิม

การสร้างสรรค์รูปแบบบริการใหม่ๆ ได้จากการสนับสนุนการทำงานของ AI เรามีตัวอย่างเช่น Chatbot ที่เราสามารถเพิ่มลูกเล่นในการรับมือกับลูกค้าและบริการได้จากข้อมูลการถามตอบกับลูกค้า หรือการที่ AI สามารถประเมินผลพฤติกรรมผู้บริโภค ประวัติการซื้อทั้งก่อน ระหว่างและหลังการซื้อ เหล่านี้สามารถนำข้อมูลมาใช้ได้อย่างถูกต้องเพื่อให้เราสามารถเลือกขายสินค้าที่ตรงใจมากขึ้น แถมยังช่วยสร้างประสบการณ์ที่ถูกจริตผู้บริโภคแต่ละคนได้มากกว่าเดิม

สำหรับการตลาดและการสร้างความประทับใจของคนยุคใหม่ที่ชอบหาข้อมูลด้วยตัวเอง AI สามารถช่วยสร้างแบรนด์ด้วยภาพลักษณ์ที่ทันสมัยน่าสนใจได้ ตั้งแต่สามารถช่วยสร้างเว็บไซต์ที่สวยงาม ครบด้วยการใช้งานที่เหมาะกับลูกค้าคาดหวังและลูกค้าตัวจริง ช่วยทำ SEO ให้ได้ผลดีตอบโจทย์การค้นหาของลูกค้าได้ดีขึ้น ทั้งช่วยสร้าง ช่วยเชื่อมโยงเนื้อหาที่ดึงดูดผู้อ่านได้มากขึ้น ไปจนถึงช่วยให้การโปรโมทสินค้าและทำโฆษณามีประสิทธิภาพโดนใจลูกค้า

AI จะกลายเป็นเรื่องที่น่าสนใจในธุรกิจเพราะ AI สามารถทำงานประมวลผลซึ่งว่ากันด้วยข้อมูลล้วนๆมาวิเคราะห์ และตัดสินใจโดยอิงจากข้อมูลที่เคยมีมาก่อน เพื่อหาทางเลือกที่คุ้มค่าที่สุด มีความเสี่ยงน้อยที่สุด หรือใช้เวลาน้อยที่สุดได้ โดยเรียกว่าการค้นหาทางเลือกที่ Optimize ที่สุดบนข้อมูลที่มีอยู่ เพื่อช่วยให้เราตัดสินใจเรื่องยากๆ จากข้อมูลที่ต้องใช้ประกอบการตัดสินใจจำนวนมหาศาล แตกต่างกันโดยสิ้นเชิงกับของมนุษย์ เพราะ AI ไม่ได้นำเรื่องของ Sense อารมณ์ ความชอบ รสนิยม ความคิดเห็น ความสัมพันธ์ส่วนตัว หรือการวัดดวงมาใช้ เหมือนที่มนุษย์อาจใช้สติปัญญาและประสบการณ์ในการตัดสินใจ แต่แฝงด้วยสัญชาติญาณและความรู้สึกที่อยู่ในตัว และนั่นทำไม AI จึงเป็นสิ่งที่ตอบโจทย์แก่เจ้าของธุรกิจอยู่ไม่น้อย

ที่เรายกตัวอย่างมาข้างต้นพอให้คุณเห็นภาพได้ว่า AI จะเข้ามาอยู่ในชีวิตได้อย่างไรบ้าง แต่ทำไมยังคงมีเรื่องกังวลอีก ในเมื่อให้ประโยชน์ได้มากขนาดนี้? ตามที่บอกไปตอนต้นจากบทความ: เมื่อเราพูดถึง AI คุณนึกถึงอะไร? ว่าตอนนี้มีผลิตภัณฑ์ที่เป็น AI ล้ำยุคใหม่ๆอยู่ในตลาดโลกเพียง 5% และหลายชนิดยังเป็น prototype (ต้นแบบ) อยู่ แต่หลายบริษัทใหญ่ในโลกก็กำลังทุ่มเทวิจัยและพัฒนา AI กันอย่างไม่หยุดยั้งเพราะประโยชน์ของ AI ก็มีมากเกินกว่าจะบรรยายได้หมด แต่จากข่าวด้านลบของ AI ที่เคยได้ยิน เราอาจกังวลใจในเรื่องที่ว่า สิ่งเหล่านี้ทำงานได้ดีมาก อีกหน่อยจะเข้ามาแทนที่แรงงานคน ทำให้มนุษย์อย่างเราอาจตกงานได้ หรือหาก AI ฉลาดกว่า เราๆที่เป็นมนุษย์จะคุมไม่อยู่ ดังนั้นเราต้องพัฒนาตัวเองไปด้วย กระทั่งแจ๊ค หม่า มหาเศรษฐีเจ้าของบริษัทอาลีบาบา ธุรกิจ e-commerce ก็ออกมาให้สัมภาษณ์ว่า "ถ้าเราไม่ปฏิวัติการศึกษาและเริ่มต้นตั้งแต่ตอนนี้ ในอีก 30 ปีข้างหน้า ลูกหลานของเราจะสู้หุ่นยนต์ไม่ได้อย่างแน่นอน ... เราต้องสอนสิ่งที่หุ่นยนต์ไม่สามารถทำได้ ... "

แถมปัจจุบัน การพัฒนา AI ถูกทุ่มเทแบบสุดกำลังจากค่ายยักษ์ใหญ่ทุกค่ายในโลก และภายในปี 2020 จะมีอาชีพเกิดใหม่มากมายแน่นอน แต่เราบอกได้เลยว่าความน่ากังวลไม่ได้มีเพียงแค่เรื่องเหล่านี้ มันส่งผลกระทบรุนแรงและน่ากังวลมากกว่านั้นมาก

ว่ากันว่าหากการพัฒนา AI ทำอย่างรวดเร็วจนเกินไป ประกอบกับอาชญากรรมที่เกิดขึ้นในโลกไซเบอร์ก็มีการปรับเปลี่ยนวิธีจารกรรมมาตลอด จึงมีความกังวลว่า AI อาจถูกนำมาใช้เป็นเครื่องมือในการก่อการร้าย เช่น

- การรวมเทคโนโลยี AI เข้ากับวิธีการโจมตีแบบหลายทางเพื่อตรวจหาและใช้ประโยชน์จากจุดอ่อนในสภาพแวดล้อมของผู้ให้บริการระบบคลาวด์

- การสร้างมัลแวร์จาก AI เพื่อสร้างการโจมตีแบบใหม่ เจ้าของธุรกิจมีโอกาสโดนโจมตีในระดับรุนแรงหากถูกบุกรุกหรือหยุดทำงาน

- การแทรกแซงบังคับควมคุมสิ่งต่างๆที่เกิดขึ้นจาก AI ทั้งรถยนต์ไร้คนขับ โดรนส่งของ โดรนทำงานของรัฐบาล ฯลฯ

- การบิดเบือนข่าวสาร วิดีโอ การสื่อสาร หรือการแอบอ้างบุคคลสำคัญเพื่อส่งสารที่สร้างความเข้าใจผิด

- การจัดฉากเหตุการณ์ ไปจนถึงการขโมยและแบ่งปันข้อมูลลับ และอาจนำไปสู่การคุกคามมนุษย์ด้วยกัน

แม้ว่าที่กล่าวมายังไม่ครอบคลุมความสามารถของ AI ที่จะถูกนำมาใช้ในทางที่ผิดทั้งหมด แต่ก็พอจะสะท้อนให้เราคาดการณ์ทิศทางความเสียหายที่อาจเป็นไปได้ อย่างไรก็ตามการพัฒนานั้นเกิดขึ้นได้ หากผู้ผลิตมีการออกแบบเพื่อป้องกันและลดความเสี่ยงที่จะกลายเป็นผลพวงจากการพัฒนา ผู้ที่มีส่วนร่วมกับการใช้งาน AI ต้องเตรียมการเพื่อรองรับสิ่งที่ไม่คาดคิดที่อาจเกิดขึ้นได้ รัฐบาลต้องออกกฏหมายใหม่มาเพื่อให้สามารถเท่าทัน ปกป้อง และรับมือกับปัญหาได้ เราเชื่อว่าจากนี้ไปภัยจาก AI จะไม่ใช่เรื่องไกลตัว หากรู้จักกับมันมากขึ้นและรู้วิธีป้องกันไว้ อย่างน้อยก็ทำให้ปัญหาที่อาจจะรุนแรงให้กลายเป็นเรื่องเบาบางลงได้

กว่าจะถึงตรงนั้นเรายังมีเวลาศึกษาและทำความเข้าใจในเรื่องของ AI ได้อีกมาก แม้ว่าจะมีข้อน่ากังวลมากมาย แต่พอเราพูดถึงประโยชน์มหาศาลของ AI ที่เราจะใช้ได้ก็นับว่าคุ้มค่า ยิ่งเราเข้าใจหลักการทำงานได้มากจนพอรู้ว่าจะนำเอาไปใช้ประโยชน์ ลดทอนความเสี่ยงและป้องกันปัญหาได้อย่างไร การคว้าโอกาสที่ดีในอนาคตจากการใช้ AI เห็นทีจะวาววับอยู่ข้างนี้แล้วล่ะค่ะ

บทความที่เกี่ยวข้อง

- เมื่อเราพูดถึง AI คุณนึกถึงอะไร? (1)

- อนาคตของ AI และตัวอย่างภาคธุรกิจที่น่าจับตามอง (2)

- ประโยชน์ของ AI กับการใช้งานในภาคธุรกิจ (3)

- AI ก้าวหน้าหรือน่ากลัว? (4)

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ประโยชน์ของ AI กับการใช้งานในภาคธุรกิจ | บทบาทและความสามารถของ AI

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อ่านบทความก่อนหน้า: อนาคตของ AI และตัวอย่างภาคธุรกิจที่น่าจับตามอง (2) ด้วยขีดความสามารถที่เพิ่มขึ้นได้อยู่เสมอของ AI ทำให้หลายๆ ธุรกิจเริ่มหันมาตั้งคำถามแล้วว่าจะเอา AI ...

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5 Benefits of using AI in the Cryptocurrency Market (Ashesh Anand, Mar 23, 2022)

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5 Benefits of using AI in the Cryptocurrency Market (Ashesh Anand, Mar 23, 2022)

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