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This infographic helps explain deep learning in context to AI and ML. Deep-learning models that are based on a single deep-learning architecture are solo deep-learning models. Models that are produced by connecting two or more deep-learning architectures are hybrid deep-learning (HDL) models. HDL can also be formed by combining one deep-learning model with machine-learning models for classification purposes. This is called the hybridization process

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This model explains all the different types of AI and what they might be used for.

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Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.

When companies today deploy artificial intelligence programs, they are most likely using machine learning - so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

"In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done," said MIT Sloan professor Thomas W. Malone, the founding director of the MIT Center for Collective Intelligence. "So that's why some people use the terms AI and machine learning almost as synonymous ... most of the current advances in AI have involved machine learning."

With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year.

From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency. "Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations," said MIT computer science professor Aleksander Madry, director of the MIT Center for Deployable Machine Learning.

While not everyone needs to know the technical details, they should understand what the technology does and what it can and cannot do, Madry added. "I don't think anyone can afford not to be aware of what's happening."

That includes being aware of the social, societal, and ethical implications of machine learning. "It's important to engage and begin to understand these tools, and then think about how you're going to use them well. We have to use these [tools] for the good of everybody," said Dr. Joan LaRovere, MBA '16, a pediatric cardiac intensive care physician and co-founder of the nonprofit The Virtue Foundation. "AI has so much potential to do good, and we need to really keep that in our lenses as we're thinking about this. How do we use this to do good and better the world?"

What is machine learning?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit "intelligent behaviors" like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as "the field of study that gives computers the ability to learn without explicitly being programmed."

The definition holds true, according to Mikey Shulman, a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or "software 1.0," to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow.

But in some cases, writing a program for the machine to follow is time-consuming or impossible, such as training a computer to recognize pictures of different people. While humans can do this task easily, it's difficult to tell a computer how to do it. Machine learning takes the approach of letting computers learn to program themselves through experience.

Machine learning starts with data - numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.

From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane's website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong - as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.)

Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.

Successful machine learning algorithms can do different things, Malone wrote in a recent research brief about AI and the future of work that was co-authored by MIT professor and CSAIL director Daniela Rus and Robert Laubacher, the associate director of the MIT Center for Collective Intelligence.

"The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take," the researchers wrote.

There are three subcategories of machine learning:

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.

In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren't explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

In the Work of the Future brief, Malone noted that machine learning is best suited for situations with lots of data - thousands or millions of examples, like recordings from previous conversations with customers, sensor logs from machines, or ATM transactions. For example, Google Translate was possible because it "trained" on the vast amount of information on the web, in different languages.

In some cases, machine learning can gain insight or automate decision-making in cases where humans would not be able to, Madry said. "It may not only be more efficient and less costly to have an algorithm do this, but sometimes humans just literally are not able to do it," he said.

Google search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. "That's not an example of computers putting people out of work. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans."

Machine learning is also associated with several other artificial intelligence subfields:
Natural language processing
Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
Neural networks
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
Deep learning
Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the "weight" of each link in the network - for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those features appear in a way that indicates a face.

Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics.

"The more layers you have, the more potential you have for doing complex things well," Malone said.

Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability.

How businesses are using machine learning

Machine learning is the core of some companies' business models, like in the case of Netflix's suggestions algorithm or Google's search engine. Other companies are engaging deeply with machine learning, though it's not their main business proposition.

Others are still trying to determine how to use machine learning in a beneficial way. "In my opinion, one of the hardest problems in machine learning is figuring out what problems I can solve with machine learning," Shulman said. "There's still a gap in the understanding."

In a 2018 paper, researchers from the MIT Initiative on the Digital Economy outlined a 21-question rubric to determine whether a task is suitable for machine learning. The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human.

Companies are already using machine learning in several ways, including:

Recommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. "[The algorithms] are trying to learn our preferences," Madry said. "They want to learn, like on Twitter, what tweets we want them to show us, on Facebook, what ads to display, what posts or liked content to share with us."

Image analysis and object detection. Machine learning can analyze images for different information, like learning to identify people and tell them apart - though facial recognition algorithms are controversial. Business uses for this vary. Shulman noted that hedge funds famously use machine learning to analyze the number of cars in parking lots, which helps them learn how companies are performing and make good bets.

Fraud detection. Machines can analyze patterns, like how someone normally spends or where they normally shop, to identify potentially fraudulent credit card transactions, log-in attempts, or spam emails.

Automatic helplines or chatbots. Many companies are deploying online chatbots, in which customers or clients don't speak to humans, but instead interact with a machine. These algorithms use machine learning and natural language processing, with the bots learning from records of past conversations to come up with appropriate responses.

Self-driving cars. Much of the technology behind self-driving cars is based on machine learning, deep learning in particular.

Medical imaging and diagnostics. Machine learning programs can be trained to examine medical images or other information and look for certain markers of illness, like a tool that can predict cancer risk based on a mammogram.
Read report: Artificial Intelligence and the Future of Work
How machine learning works: promises and challenges

While machine learning is fueling technology that can help workers or open new possibilities for businesses, there are several things business leaders should know about machine learning and its limits.
Explainability
One area of concern is what some experts call explainability, or the ability to be clear about what the machine learning models are doing and how they make decisions. "Understanding why a model does what it does is actually a very difficult question, and you always have to ask yourself that," Madry said. "You should never treat this as a black box, that just comes as an oracle ... yes, you should use it, but then try to get a feeling of what are the rules of thumb that it came up with? And then validate them."

This is especially important because systems can be fooled and undermined, or just fail on certain tasks, even those humans can perform easily. For example, adjusting the metadata in images can confuse computers - with a few adjustments, a machine identifies a picture of a dog as an ostrich.

Madry pointed out another example in which a machine learning algorithm examining X-rays seemed to outperform physicians. But it turned out the algorithm was correlating results with the machines that took the image, not necessarily the image itself. Tuberculosis is more common in developing countries, which tend to have older machines. The machine learning program learned that if the X-ray was taken on an older machine, the patient was more likely to have tuberculosis. It completed the task, but not in the way the programmers intended or would find useful.

The importance of explaining how a model is working - and its accuracy - can vary depending on how it's being used, Shulman said. While most well-posed problems can be solved through machine learning, he said, people should assume right now that the models only perform to about 95% of human accuracy. It might be okay with the programmer and the viewer if an algorithm recommending movies is 95% accurate, but that level of accuracy wouldn't be enough for a self-driving vehicle or a program designed to find serious flaws in machinery.
Bias and unintended outcomes
Machines are trained by humans, and human biases can be incorporated into algorithms - if biased information, or data that reflects existing inequities, is fed to a machine learning program, the program will learn to replicate it and perpetuate forms of discrimination. Chatbots trained on how people converse on Twitter can pick up on offensive and racist language, for example.

In some cases, machine learning models create or exacerbate social problems. For example, Facebook has used machine learning as a tool to show users ads and content that will interest and engage them - which has led to models showing people extreme content that leads to polarization and the spread of conspiracy theories when people are shown incendiary, partisan, or inaccurate content.

Ways to fight against bias in machine learning including carefully vetting training data and putting organizational support behind ethical artificial intelligence efforts, like making sure your organization embraces human-centered AI, the practice of seeking input from people of different backgrounds, experiences, and lifestyles when designing AI systems. Initiatives working on this issue include the Algorithmic Justice League and The Moral Machine project.

Putting machine learning to work

Shulman said executives tend to struggle with understanding where machine learning can actually add value to their company. What's gimmicky for one company is core to another, and businesses should avoid trends and find business use cases that work for them.

Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations.
Aleksander Madry MIT Computer Science Professor
The way machine learning works for Amazon is probably not going to translate at a car company, Shulman said - while Amazon has found success with voice assistants and voice-operated speakers, that doesn't mean car companies should prioritize adding speakers to cars. More likely, he said, the car company might find a way to use machine learning on the factory line that saves or makes a great deal of money.

"The field is moving so quickly, and that's awesome, but it makes it hard for executives to make decisions about it and to decide how much resourcing to pour into it," Shulman said.

It's also best to avoid looking at machine learning as a solution in search of a problem, Shulman said. Some companies might end up trying to backport machine learning into a business use. Instead of starting with a focus on technology, businesses should start with a focus on a business problem or customer need that could be met with machine learning.

A basic understanding of machine learning is important, LaRovere said, but finding the right machine learning use ultimately rests on people with different expertise working together. "I'm not a data scientist. I'm not doing the actual data engineering work - all the data acquisition, processing, and wrangling to enable machine learning applications - but I understand it well enough to be able to work with those teams to get the answers we need and have the impact we need," she said. "You really have to work in a team."

Sign-up for a Machine Learning in Business Course.

Watch an Introduction to Machine Learning through MIT OpenCourseWare.

Read about how an AI pioneer thinks companies can use machine learning to transform.

Watch a discussion with two AI experts about machine learning strides and limitations.

Take a look at the seven steps of machine learning.
Read next: 7 lessons for successful machine learning projects

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Machine learning is a powerful form of artificial intelligence that is affecting every industry. Here's what you need to know about its potential and limitations and how it's being used.

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All the different types of AI and interpritation of how they might grow to become more intelligent

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this model shows 4 different types of Ai

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Robotics and Artificial Intelligence are two terms that are often confused and sometimes not distinguished, but they actually refer to different technologies. A rtificial intelligence is a discipline that focuses on enabling machines to develop the same intellectual capabilities as humans. Robotics, on the other hand, is the science of designing and building physical robots to improve automation and innovation.

What exactly are robotics and Artificial Intelligence?

Robotics and AI are two related fields of science and technology, but with several differences. Robotics is the discipline that deals with designing machines capable of automating tasks. In this sense, robotics experts also create, programme and operate these automated elements to develop certain skills and tasks.

Meanwhile, artificial intelligence is a branch of computing that studies how machines can mimic the cognitive processes of humans, learning and reasoning in order to solve problems and carry out specific tasks, just as a human being would. AI experts design algorithms so that machines are able to learn autonomously, solve problems, understand language and reason using logic.

Difference between robotics and Artificial Intelligence

The main difference between robotics and Artificial Intelligence lies in the approach. Robotics focuses on the manipulation of the physical area, while AI is oriented towards the internal or digital part.

Another difference is the area of application. On the one hand, robotics creates machines that have their own mobility and can interact with the environment. They are generally used to perform repetitive, high-speed or high-precision tasks, e.g. in industrial sectors of chain production or in the field of medicine. On the other hand, artificial intelligence focuses on data processing and algorithm design. In this sense, it is applied in a variety of contexts, from personalised care to education.

On the other hand, robots are programmed to follow a set of instructions in a repetitive way and are perfect for improving the productivity of companies in a number of sectors. AI, on the other hand, although it can also be used in multiple contexts, is more dynamic. For example, an AI system can be used to process banking data and make investment decisions, but it can also be used to analyse medical information and prepare for surgery.

Moreover, they have evolved unevenly throughout history. Robotics as we know it today has been around for decades, although there were mechanisations as early as the 17th and 18th centuries. Furthermore, the term robot dates back to 1921, coined by the Czech writer and playwright Karel Capek. Today, there are world-renowned scientific institutes devoted specifically to robot design. In contrast, AI is a relatively new technology, which emerged in 1956 at the Dartmouth Conference. It is often developed by small companies, as its origin has raised concerns about privacy, data processing and the potential displacement of human labour.

Relationship between artificial intelligence and robotics

Although there are quite a few differences between robotics and Artificial Intelligence, they are two branches that benefit from each other. Primarily, AI is used to improve skills such as movement, adapting to the environment, optimising performance, diagnosing errors and performing autonomous tasks of machines, i.e. it improves the learning and application capabilities of robots.

Both robotics and AI aim to automate tasks and facilitate processes for humans, and use data collected by input and output sensors to facilitate decision-making.

In this sense, it is increasingly common to see work environments where machines collaborate with people to improve different tasks. This human-machine collaboration is embodied in cobots or collaborative robots, which are specifically designed to perform tedious tasks that require greater effort. Their applications are useful in almost any sector, and they are gradually being adapted to different environments.

Both technological fields require specific knowledge for their correct manipulation, which is why experts working in these areas have studied computer science, physics or engineering.

On the other hand, AI allows robots to communicate intelligently, not only with human operators, but also with other robots. Thus, machines can understand needs and work collaboratively to solve problems.

It is also important to note that the application of artificial intelligence in robotics is still challenging, both technologically and ethically. For example, the safety of autonomous decision-making by robots has not been guaranteed. To this end, the involvement of a human capable of supervising the tasks is necessary. Although machines do not usually make mistakes, there is always a certain margin of error.

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Differences between robotics and Artificial Intelligence

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More about the difference between robotics and AI, a discipline that develops software capable of learning tasks performed by humans.

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This diagram is an example of the 3 types of machine learning and a description of what each one does.

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this graphic could be useful because it displays information on all 3 types of machine learning

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image tells you about machine and deep learning

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Benefits of AI: How it Contributes to Our Society and Economy

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Benefits of AI: Examples for Industries, Businesses, Society and Economy. Innovations in the AI space have led to several benefits.

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here is 7 branches of AI

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What are neural networks?

Neural networks try to emulate the human brain, combining computer science and statistics to solve common problems in the field of AI

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Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google's search algorithm.

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Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

∑wixi + bias = w1x1 + w2x2 + w3x3 + bias

output = f(x) = 1 if ∑w1x1 + b>= 0; 0 if ∑w1x1 + b < 0

Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it "fires" (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.

Let's break down what one single node might look like using binary values. We can apply this concept to a more tangible example, like whether you should go surfing (Yes: 1, No: 0). The decision to go or not to go is our predicted outcome, or y-hat. Let's assume that there are three factors influencing your decision-making:

1. Are the waves good? (Yes: 1, No: 0)

2. Is the line-up empty? (Yes: 1, No: 0)

3. Has there been a recent shark attack? (Yes: 0, No: 1)

Then, let's assume the following, giving us the following inputs:

X1 = 1, since the waves are pumping

X2 = 0, since the crowds are out

X3 = 1, since there hasn't been a recent shark attack

Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome.

W1 = 5, since large swells don't come around often

W2 = 2, since you're used to the crowds

W3 = 4, since you have a fear of sharks

Finally, we'll also assume a threshold value of 3, which would translate to a bias value of -3. With all the various inputs, we can start to plug in values into the formula to get the desired output.

Y-hat = (1*5) + (0*2) + (1*4) - 3 = 6

If we use the activation function from the beginning of this section, we can determine that the output of this node would be 1, since 6 is greater than 0. In this instance, you would go surfing; but if we adjust the weights or the threshold, we can achieve different outcomes from the model. When we observe one decision, like in the above example, we can see how a neural network could make increasingly complex decisions depending on the output of previous decisions or layers.

In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1. Since neural networks behave similarly to decision trees, cascading data from one node to another, having x values between 0 and 1 will reduce the impact of any given change of a single variable on the output of any given node, and subsequently, the output of the neural network.

As we start to think about more practical use cases for neural networks, like image recognition or classification, we'll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we'll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In the equation below,

i represents the index of the sample,

y-hat is the predicted outcome,

y is the actual value, and

m is the number of samples.

𝐶𝑜𝑠𝑡 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛= 𝑀𝑆𝐸=1/2𝑚 ∑129_(𝑖=1)^𝑚▒(𝑦 ̂^((𝑖) )−𝑦^((𝑖) ) )^2

Ultimately, the goal is to minimize our cost function to ensure correctness of fit for any given observation. As the model adjusts its weights and bias, it uses the cost function and reinforcement learning to reach the point of convergence, or the local minimum. The process in which the algorithm adjusts its weights is through gradient descent, allowing the model to determine the direction to take to reduce errors (or minimize the cost function). With each training example, the parameters of the model adjust to gradually converge at the minimum.

See this IBM Developer article for a deeper explanation of the quantitative concepts involved in neural networks.

Most deep neural networks are feedforward, meaning they flow in one direction only, from input to output. However, you can also train your model through backpropagation; that is, move in the opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the parameters of the model(s) appropriately.

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Neural networks can be classified into different types, which are used for different purposes. While this isn't a comprehensive list of types, the below would be representative of the most common types of neural networks that you'll come across for its common use cases:

The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958.

Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we've primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it's important to note that they are actually comprised of sigmoid neurons, not perceptrons, as most real-world problems are nonlinear. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks.

Convolutional neural networks (CNNs) are similar to feedforward networks, but they're usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.

Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.

Deep Learning and neural networks tend to be used interchangeably in conversation, which can be confusing. As a result, it's worth noting that the "deep" in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers-which would be inclusive of the inputs and the output-can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.

To learn more about the differences between neural networks and other forms of artificial intelligence, like machine learning, please read the blog post " AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference? "

The history of neural networks is longer than most people think. While the idea of "a machine that thinks" can be traced to the Ancient Greeks, we'll focus on the key events that led to the evolution of thinking around neural networks, which has ebbed and flowed in popularity over the years:

1943: Warren S. McCulloch and Walter Pitts published " A logical calculus of the ideas immanent in nervous activity (PDF) (link resides outside ibm.com)" This research sought to understand how the human brain could produce complex patterns through connected brain cells, or neurons. One of the main ideas that came out of this work was the comparison of neurons with a binary threshold to Boolean logic (i.e., 0/1 or true/false statements).

1958: Frank Rosenblatt is credited with the development of the perceptron, documented in his research, " The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain " (link resides outside ibm.com). He takes McCulloch and Pitt's work a step further by introducing weights to the equation. Leveraging an IBM 704, Rosenblatt was able to get a computer to learn how to distinguish cards marked on the left vs. cards marked on the right.

1974: While numerous researchers contributed to the idea of backpropagation, Paul Werbos was the first person in the US to note its application within neural networks within his PhD thesis (PDF) (link resides outside ibm.com).

1989: Yann LeCun published a paper (PDF) (link resides outside ibm.com) illustrating how the use of constraints in backpropagation and its integration into the neural network architecture can be used to train algorithms. This research successfully leveraged a neural network to recognize hand-written zip code digits provided by the U.S. Postal Service.

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Explains the differences between AI and ML

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Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.

Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.

Computer programmers and software developers enable computers to analyze data and solve problems - essentially, they create artificial intelligence systems - by applying tools such as:

machine learning

deep learning

neural networks

computer vision

natural language processing

Below is a breakdown of the differences between artificial intelligence and machine learning as well as how they are being applied in organizations large and small today.

What Is Artificial Intelligence?

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.

Today, artificial intelligence is at the heart of many technologies we use, including smart devices and voice assistants such as Siri on Apple devices. Companies are incorporating techniques such as natural language processing and computer vision - the ability for computers to use human language and interpret images - to automate tasks, accelerate decision making, and enable customer conversations with chatbots.

What Is Machine Learning?

Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.

By studying and experimenting with machine learning, programmers test the limits of how much they can improve the perception, cognition, and action of a computer system.

Deep learning, an advanced method of machine learning, goes a step further. Deep learning models use large neural networks - networks that function like a human brain to logically analyze data - to learn complex patterns and make predictions independent of human input.

How Companies Use AI and Machine Learning

To be successful in nearly any industry, organizations must be able to transform their data into actionable insight. Artificial Intelligence and machine learning give organizations the advantage of automating a variety of manual processes involving data and decision making.

By incorporating AI and machine learning into their systems and strategic plans, leaders can understand and act on data-driven insights with greater speed and efficiency.

Efficiency is key to the success of an organization in the manufacturing industry. Artificial intelligence can help manufacturing leaders automate their business processes by applying data analytics and machine learning to applications such as the following:

Data privacy and security are especially critical within the banking industry. Financial services leaders can keep customer data secure while increasing efficiencies using AI and machine learning in several ways:

The health care field uses huge amounts of data and increasingly relies on informatics and analytics to provide accurate, efficient health services. AI tools can help improve patient outcomes, save time, and even help providers avoid burnout by:
Learn more about how AI is changing the world of health care.
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Artificial Intelligence (AI) vs. Machine Learning

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Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI. Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.

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Demonstrates some of the branches and uses of AI including Machine learning. 

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This shows the similarities and differences between Ai and ML while also giving a better understanding of how Deep learning realates to both of these.

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Shows how a Decision Tree works.

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This shows all of the things that go into AI and what it can do

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Shows what weak AI does is based on.

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Helps explain machine learning

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Most of us would find it hard to go a full day without using at least one app or web service driven by machine learning. But what is machine learning (ML), exactly?

Though the term machine learning has become increasingly common, many people still don't know exactly what it means and how it is applied, nor do they understand the role of machine learning algorithms and datasets in data science. Here, we will examine the question "what is ML?"

We will provide insight into how machine learning is used by data scientists and others, how it was developed, and what lies ahead as it continues to evolve.

Definition of Machine Learning

The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns ( view a visual of machine learning via R2D3). Machine learning techniques leverage data mining to identify historic trends and inform future models.

The typical supervised machine learning algorithm consists of roughly three components:

For example, if you're building a movie recommendation system, you can provide information about yourself and your watch history as input. The algorithm will take that input and learn how to return an accurate output: movies you will enjoy. Some inputs could be movies you watched and rated highly, the percentage of movies you've seen that are comedies, or how many movies feature a particular actor. The algorithm's job is to find these parameters and assign weights to them. If the algorithm gets it right, the weights it used stay the same. If it gets a movie wrong, the weights that led to the wrong decision get turned down so it doesn't make that kind of mistake again.

Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention - empowering the algorithm to uncover hidden insights without being specifically programmed to do so.

Types of Machine Learning

There are many types of machine learning models defined by the presence or absence of human influence on raw data - whether a reward is offered, specific feedback is given, or labels are used.
According to Nvidia.com, there are different machine learning models such as:
Finally, there's the concept of deep learning, which is a newer area of machine learning that automatically learns from datasets without introducing human rules or knowledge. This requires massive amounts of raw data for processing - and the more data that is received, the more the predictive model improves.

Commonly Used Machine Learning Algorithms

The purpose of machine learning is to use machine learning algorithms to analyze data. By leveraging machine learning, a developer can improve the efficiency of a task involving large quantities of data without the need for manual human input. Around the world, strong machine learning algorithms can be used to improve the productivity of professionals working in data science, computer science, and many other fields.

There are a number of machine learning algorithms that are commonly used by modern technology companies. Each of these machine learning algorithms can have numerous applications in a variety of educational and business settings.

Read on to learn about many different machine learning algorithms, as well as how they are applicable to the broader field of machine learning.

Linear Regression

Linear regression is an algorithm used to analyze the relationship between independent input variables and at least one target variable. This kind of regression is used to predict continuous outcomes - variables that can take any numerical outcome. For example, given data on the neighborhood and property, can a model predict the sale value of a home? Linear relationships occur when the data relationship being observed tends to follow a straight line overall - and as such, this model can be used to observe whether a data point is increasing, decreasing, or remaining the same relative to some independent variable, such as time elapsed or position.

Machine learning models can be employed to analyze data in order to observe and map linear regressions. Independent variables and target variables can be input into a linear regression machine learning model, and the model will then map the coefficients of the best fit line to the data. In other words, the linear regression models attempt to map a straight line, or a linear relationship, through the dataset.

Logistic Regression

Logistic regression is a supervised learning algorithm that is used for classification problems. Instead of continuous output like in linear regression, a logistic model predicts the probability of a binary event occurring. For example, given an email, can a model predict whether the contents are spam or not?

Machine learning algorithms can use logistic regression models to determine categorical outcomes. When given a dataset, the logistic regression model can check any weights and biases and then use the given dependent categorical target variables to understand how to correctly categorize that dataset.

Neural Networks

Neural networks are artificial intelligence algorithms that attempt to replicate the way the human brain processes information to understand and intelligently classify data. These neural network learning algorithms are used to recognize patterns in data and speech, translate languages, make financial predictions, and much more through thousands, or sometimes millions, of interconnected processing nodes. Data is "fed-forward" through layers that process and assign weights, before being sent to the next layer of nodes, and so on.

Crucially, neural network algorithms are designed to quickly learn from input training data in order to improve the proficiency and efficiency of the network's algorithms. As such, neural networks serve as key examples of the power and potential of machine learning models.

Decision Trees

Decision trees are data structures with nodes that are used to test against some input data. The input data is tested against the leaf nodes down the tree to attempt to produce the correct, desired output. They are easy to visually understand due to their tree-like structure and can be designed to categorize data based on some categorization schema.

Decision trees are one method of supervised learning, a field in machine learning that refers to how the predictive machine learning model is devised via the training of a learning algorithm.

Random Forest

Random forest models are capable of classifying data using a variety of decision tree models all at once. Like decision trees, random forests can be used to determine the classification of categorical variables or the regression of continuous variables. These random forest models generate a number of decision trees as specified by the user, forming what is known as an ensemble. Each tree then makes its own prediction based on some input data, and the random forest machine learning algorithm then makes a prediction by combining the predictions of each decision tree in the ensemble.

What Is Deep Learning?
Deep learning models are a nascent subset of machine learning paradigms. Deep learning uses a series of connected layers which together are capable of quickly and efficiently learning complex prediction models.
If deep learning sounds similar to neural networks, that's because deep learning is, in fact, a subset of neural networks. Both try to simulate the way the human brain functions. Deep learning models can be distinguished from other neural networks because deep learning models employ more than one hidden layer between the input and the output. This enables deep learning models to be sophisticated in the speed and capability of their predictions.

Deep learning models are employed in a variety of applications and services related to artificial intelligence to improve levels of automation in previously manual tasks. You might find this emerging approach to machine learning powering digital assistants like Siri and voice-driven TV remotes, in fraud detection technology for credit card companies, and as the bedrock of operating systems for self-driving cars.

Machine Learning (ML) vs. Artificial Intelligence (AI)

Trying to make sense of the distinctions between machine learning vs. AI can be tricky, since the two are closely related. In fact, machine learning algorithms are a subset of artificial intelligence algorithms - but not the other way around.

To pinpoint the difference between machine learning and artificial intelligence, it's important to understand what each subject encompasses. AI refers to any of the software and processes that are designed to mimic the way humans think and process information. It includes computer vision, natural language processing, robotics, autonomous vehicle operating systems, and of course, machine learning. With the help of artificial intelligence, devices are able to learn and identify information in order to solve problems and offer key insights into various domains.

On the other hand, machine learning specifically refers to teaching devices to learn information given to a dataset without manual human interference. This approach to artificial intelligence uses machine learning algorithms that are able to learn from data over time in order to improve the accuracy and efficiency of the overall machine learning model. There are numerous approaches to machine learning, including the previously mentioned deep learning model.

Why Is Machine Learning Important?

Machine learning and data mining, a component of machine learning, are crucial tools used by many companies and researchers. There are two main reasons for this:

Who Is Using Machine Learning?

Companies leveraging algorithms to sort through data and optimize business operations aren't new. Leveraging algorithms extends not only to digital business models such as web services or apps, but also to any company or industry where data can be gathered, including the following:

Amazon, Facebook, Netflix, and, of course, Google have all been using machine learning algorithms to drive searches, recommendations, targeted advertising, and more for well over a decade. For example, Uber Eats shared in a GeekWire article that they use data mining and machine learning to estimate delivery times.

Evolution of Machine Learning

Although advances in computing technologies have made machine learning more popular than ever, it's not a new concept. According to Forbes, the origins of machine learning date back to 1950. Speculating on how one could tell if they had developed a truly integrated artificial intelligence (AI), Alan Turing created what is now referred to as the Turing test, which suggests that one way of testing whether or not the AI is capable of understanding language is to see if it's able to fool a human into thinking they are speaking to another person.

In 1952, Arthur Samuel wrote the first learning program for IBM, this time involving a game of checkers. The work of many other machine learning pioneers followed, including Frank Rosenblatt's design of the first neural network in 1957 and Gerald DeJong's introduction of explanation-based learning in 1981.

In the 1990s, a major shift occurred in machine learning when the focus moved away from a knowledge-based approach to one driven by data. This was a critical decade in the field's evolution, as scientists began creating computer programs that could analyze large datasets and learn in the process.

The 2000s were marked by unsupervised learning becoming widespread, eventually leading to the advent of deep learning and the ubiquity of machine learning as a practice.

Milestones in machine learning are marked by instances in which an algorithm is able to beat the performance of a human being, including Russian chess grandmaster Garry Kasparov's defeat at the hands of IBM supercomputer Deep Blue in 1997 and, more recently, the 2016 victory of the Google DeepMind AI program AlphaGo over Lee Sedol playing Go, a game notorious for its massively large space of possibilities in game play.

Today, researchers are hard at work to expand on these achievements. As machine learning and artificial intelligence applications become more popular, they're also becoming more accessible, moving from server-based systems to the cloud. At Google Next 2018, Google touted several new deep learning and machine learning capabilities, like Cloud AutoML, BigQuery ML, and more. During the past few years, Amazon, Microsoft, Baidu, and IBM have all unveiled machine learning platforms through open source projects and enterprise cloud services. Machine learning algorithms are here to stay, and they're rapidly widening the parameters of what research and industry can accomplish.

What Is the Future of Machine Learning?

Machine learning algorithms are being used around the world in nearly every major sector, including business, government, finance, agriculture, transportation, cybersecurity, and marketing. Such rapid adoption across disparate industries is evidence of the value that machine learning (and, by extension, data science) creates. Armed with insights from vast datasets - which often occur in real time - organizations can operate more efficiently and gain a competitive edge.

The applications of machine learning and artificial intelligence extend beyond commerce and optimizing operations. Following its Jeopardy win, IBM applied the Watson algorithm to medical research literature, thereby "sending Watson to medical school." More recently, precision medicine initiatives are breaking new ground using machine learning algorithms driven by massive artificial neural networks (i.e., "deep learning" algorithms) to detect subtle patterns in genetic structure and how one might respond to different medical treatments. Breakthroughs in how machine learning algorithms can be used to represent natural language have enabled a surge in new possibilities that include automated text translation, text summarization techniques, and sophisticated question and answering systems. Other advancements involve learning systems for automated robotics, self-flying drones, and the promise of industrialized self-driving cars.

The continued digitization of most sectors of society and industry means that an ever-growing volume of data will continue to be generated. The ability to gain insights from these vast datasets is one key to addressing an enormous array of issues - from identifying and treating diseases more effectively, to fighting cyber criminals, to helping organizations operate more effectively to boost the bottom line.

The universal capabilities that machine learning enables across so many sectors make it an essential tool - and experts predict a bright future for its use. In fact, in Gartner's "Top 10 Technology Trends for 2017," machine learning and artificial intelligence topped the list:

"AI and machine learning [...] can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously." The article also notes: "The combination of extensive parallel processing power, advanced algorithms and massive datasets to feed the algorithms has unleashed this new era."

Machine Learning and UC Berkeley School of Information

In recognition of machine learning's critical role today and in the future, datascience@berkeley includes an in-depth focus on machine learning in its online Master of Information and Data Science (MIDS) curriculum.

The foundation course is Applied Machine Learning, which provides a broad introduction to the key ideas in machine learning. The emphasis is on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra is important. Students learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

The advanced course, Machine Learning at Scale, builds on and goes beyond the collect-and-analyze phase of big data by focusing on how machine learning algorithms can be rewritten and extended to scale to work on petabytes of data, both structured and unstructured, to generate sophisticated models used for real-time predictions.

In the Natural Language Processing with Deep Learning course, students learn how-to skills using cutting-edge distributed computation and machine learning systems such as Spark. They are trained to code their own implementations of large-scale projects, like Google's original PageRank algorithm, and discover how to use modern deep learning techniques to train text-understanding algorithms.
Learn more about the datascience@berkeley curriculum or explore the online Machine Learning short course from the UC Berkeley School of Information, which requires no programming or coding experience, and prepares working professionals to implement machine learning in their organization. Citation for this content: datascience@berkeley, the online Master of Information and Data Science from UC Berkeley.
Last updated February 2022.

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Whether you know it or not, you've probably been taking advantage of the benefits of machine learning for years. Most of us would find it hard to go a full day without using at least one app or web service driven by machine learning. But what is machine learning?

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Gives context for what ML is and why its important in AI.

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this image explains what artificial intelligence is and how it works

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What is the difference between ML and AI? Is there one? Are they the same? How do you know? There's two ways to answer that. 1. Pay attention in class. 2. Watch this video. That's why I posted it.

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this image briefly shows AI, Machine learning and, Deep learning work.

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