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After a lot of brain storming and extensive research we conclude that artificial intelligence has the best application in sentiment analysis.

Understanding the human sentiments by evaluation of textual data will help us to control the devices that work on AI better, to synchronize them in a more effective way to operate among humans and to ensure a better environment for both the human and the machine. A lot of path breaking research has already been carried on in this direction and much more is on its way. This is what humanity needs. This is the future.

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Questionaire:-

1) Is artificial intelligence really the next level of human achievement?

2) What are the costs at that we have to pay. And will we be able to meet the challenges.

3) Are we prepared, if something worse comes out of it all.

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Vocabulary:-

1)Lexicon:- A lexicon is the vocabulary of a person, language or branch of knowledge.

2)Maudlin: - Sentimental in a self pitying way.

3) plucky:- courageous in the face of difficulties.

4) Impregnable:- A fortified situation. Impossible to penetrate.

5) Acumen:- Shrewdness shown by keen insight.

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Survey :-

Q) How do you think can a robot or quad-copter using artificial intelligence help in future ?

Q) Do you think that artificial intelligence can be dangerous if used in military and air traffic control ?

Q) Of course , will it reach to common people as well ?

Q) How can artificial intelligence be used in crime cases ? Think.

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Application of AI in future which should be feasible are :-

• Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics,
• Architectures, algorithms and techniques for distributed AI systems, including multi-agent based control and holonic control,
• Decision-support systems,
• Aspects of reasoning: abductive, case-based, model-based, non-monotonic, incomplete, progressive and approximate reasoning,
• Applications of chaos theory and fractals,
• Metaheuristics and their applications in intelligent automation: Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, etc.,
• Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems,
• Neural networks, fuzzy systems, neuro-fuzzy systems,
• Perception, e.g. image processing, pattern recognition, vision systems, tactile systems, speech recognition and synthesis,
• Aspects of software engineering, e.g. intelligent programming environments, verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability,
• Intelligent fault detection, fault analysis, diagnostics and monitoring,
• Self-organizing, emerging or bio-inspired system,
• Industrial experiences in the application of the above techniques, e.g. case studies or benchmarking exercises.

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Vocabulary :-

Impregnable - strong enough to resistor withstand attack; not to be taken by force,unconquerable.

Collaborator - a person who works jointly on an activity or project; an associate.

Automation - the use or introduction of automatic equipment in a manufacturing or other process or facility.

Rudimentary - relating to an immature, undeveloped, or basic form.

Ethical - relating to moral principles or the branch of knowledge dealing with these.

Accretion - an increase by natural growth or addition.

Dearth - an insufficient quantity or number.

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Applications of Artificial Intelligence in neural computing as recommended by me:-

1) Imbibing the capability of self thinking and operation in various electronic devices.

2) Understanding the neural functioning of human brain using electronics. This will help unravel some of the mysteries of human brain which have long puzzled the biologists.

3) Creating units of armed robots who are programmed to behave in a discreet and ethical manner. These units can make hits in terrorist bases which are almost impregnable, without causing any civilian casualties. Lives of lot of soldiers will be saved too.
4) In understanding sentiment analysis. Solving problems related to emotional contents.

5) We can have self driving cars, machines we have only seen or heard of in fiction and advanced ways of handling so many of our persisting problems using Artificial Intelligence.

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See how quadcopters use artificial intelligence to behave like athletes

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The astounding athletic power of quadcopters | Raffaello D'Andrea

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Means of information:-

1)Google

2)Youtube

3)TED Talks

4)Quora

5)Scholarly Articles

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Future of AI

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The long-term future of AI(and what we can do about it): Daniel Dewey at TEDxVienna

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Daniel Dewey is a research fellow in the Oxford Martin Programme on the Impacts of Future Technology at the Future of Humanity Institute, University of Oxford. His research includes paths and timelines to machine superintelligence, the possibility of intelligence explosion, and the strategic and technical challenges arising from these possibilities.

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Proof:-

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The Daily Galaxy --Great Discoveries Channel

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I would be remiss if I didn't start this article out preaching some sort of dire warnings about the threat of artificial intelligence and robotic overlords. And when you hear of the US Defense Advanced Research Projects Agency (DARPA) creating an AI that can run air traffic control stations, you begin to wonder how wise it is letting DARPA have any money whatsoever.

Thankfully, it isn't as scary as it sounds - although, the simple fact is, DARPA produces technologies that almost always filter through to the mainstream.

For the moment however, the DARPA research is concentrating on providing assistance to US Military Air Fields with an AI that will help automate some procedures. The Generalized Integrated Learning Architecture (GILA) is intended to supplement and probably eventually takeover human controls to manage the load of increased air traffic, unmanned vehicles and other airborne weapons.

Currently, air control centers use what is known as Airspace Control Orders, which provide guidance in the use of messages intended to communicate airspace control information among locations. Naturally, commands that are read incorrectly or are that provide unclear instructions can cause a lot of trouble, and possible harm to passengers, pilots and people on the ground.

The GILA project - being developed by Lockheed Martin's Advanced Technology Laboratories - is intent on learning flight controllers' tasks by example so that in the future they can create and control these orders. Naturally providing a massive computer with computing power and speed well above what a human can produce will be a benefit.

According to DARPA, GILA will learn by constructing its knowledge base from different sources, including generating knowledge by its own reasoning. According to a Military & Aerospace item, such software has to combine limited observations with subject expertise, general knowledge, reasoning, and by asking what-if questions.

"Eventually [GILA] will outperform the novice human planner by 125% while giving the inexperienced user an embedded, accelerated training capability," says Lockheed.

Without trying to draw corollary lines between GILA and SkyNet, from the popular Terminator series, and thus showing us our own doom, this will hopefully help in the long run. And all my joking aside there better be someone out there who is monitoring all these robotic and AI developments so that we do not end up with a cataclysmic apocalyptic future.

Posted by Josh Hill.

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Artificial Intelligence to Control Air Traffic

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I would be remiss if I didn't start this article out preaching some sort of dire warnings about the threat of artificial intelligence and robotic overlords. And when you hear of the US Defense Advanced Research Projects Agency (DARPA) creating...

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Proof:-

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The wonderful and terrifying implications of computers that can learn | Jeremy Howard

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Sources of information :-

1)Article from MIT University

2)News articles from daily galaxy

3)scholarly articles as well

4) research paper on 'application of AI in air traffic control automation' by DA Spencer.

5)TED talk by Jeremy Howard on how computers can learn (AI)

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Rado Kotorov is the chief innovation officer for BI and analytics provider Information Builders.
VP Marketing: "Siri, should I open this email?"

Siri: "Don't. Your boss sounds very angry. And, by the way, the tone of the email that you are writing to the product management team is very negative. Please change it."

Many people delegate more and more to Siri (the electronic genie 'in' the iPhone) - from dialing phone numbers and finding directions, to taking dictation for SMS and emails. What if Siri could help us modulate the emotional tone of our messages? Would it make the world a better place with less conflict and argument, or would it leave piles of unread messages?

Perhaps, but to be able to help in this aspect Siri will have to learn to do sentiment analysis: read text and determine its emotional charge.

Today, there is a lot of hype and buzz about sentiment analysis even without Siri's involvement. Like any new technology it has its advocates and its skeptics. And since the technology is in the early stages of evolution the skeptics can find plenty of failures and the proponents plenty of hope.

Let us start with the skeptics.

Emotions are perceived as uniquely human and difficult to read even by human beings. The Spanish philosopher Unamuno believed that emotions distinguished humans from the animal world - he had never seen a cat crying! So how can a machine accurately predict the emotions in messages?

The problem is further exacerbated by the subtleties of language and expressions, by unwritten but implied insinuations in short messages, by the symbols and abbreviations that constantly change and evolve in social media posts, and by the fact that messages can express more than one emotion. All of these are perfectly valid concerns and problems that do not have definite solutions today.

The 'mad' scientists argue that these are minor issues raised by those uninitiated in the arts of semantic analysis. And they have stacks of papers, journals, books, examples, programs, and so on to show you how machines are as smart as people in understanding and assessing written and spoken language. But is it so? Didn't HP just write off $8 billion from the acquisition of Autonomy - the company that pioneered semantic search and was promising to be able to read and understand text better than lawyers do?

Isn't this an admission that we are not quite there yet?

I think that both sides are wrong and a more pragmatic approach that recognizes both the needs and the limitations is more beneficial. The driver for sentiment analysis is early detection of warning signs and opportunities. Detecting that a customer is getting very frustrated with customer service can be used to prevent customer churn, and detecting early that negative posts have the potential of going viral can trigger a counter action to prevent severe reputational damage.

The reason why we are seeking the help of machines is because the volume of online communications is so large that organizations cannot put enough employees to sift through all of them. Naturally when there is a need, people will be looking for solutions ... and hype and skepticism are corollaries to every search for a solution.

So what do I think is the practical solution given these limitations?

Industry-wide, we know that sentiment scores can be assigned to about 40% to 50% of messages. Essentially, sentiment analysis allows us to classify accurately messages at both ends of the bell curve, the extremely positive and the extremely negative. Given the human condition, very angry messages have simply a very angry tone. And the focus of the messages is directed entirely to the expression of the strong frustration. The same is true for the very happy messages. So we can safely claim that the technology will help us to filter the messages at both tails of the bell curve, and this is not a small gain.

Now let us figure how to further augment it, so that we can gain more insight. What if we pair sentiment analysis with search technologies? In other words, we assign sentiment to a message, but also index all the messages in a search index. Then, once we find an angry message, search can show us related messages. It is true that those messages will be related by content, but even an inexperienced user can quickly sift through similar messages and pick up the ones that are of interest.

Let us take it one step further.

Search already comes packed with sophisticated analyzers that can extract words from text. Those words and their frequencies can be displayed in various visualizations - tag clouds, lexical diversity graphs, streaming graphs. With this enhancement a user can filter the messages on sentiment (detecting the most angry ones), see the related messages (using search), analyze the aggregate word distributions in the related messages (using text graphs) and drill down on the graphs to further filter the messages. At the end they can read the few remaining messages and know exactly both the sentiment and the content.

The combination of the three technologies allows analysis in fractions of a second to detect cases that need attention. And this is operationally invaluable without being outrageously esoteric.

Siri, can you save me some time?

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Sentiment analysis and artificial intelligence: Siri, should I open this email?

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Rado Kotorov is the chief innovation officer for BI and analytics provider Information Builders. VP Marketing: "Siri, should I open this email?" Siri: "Don't. Your boss sounds very angry. And, by the way, the tone of the email that you are writing to the product management team is very negative.

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ScaleOut Software: What is Operational Intelligence?

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This video shows what Operational Intelligence means using an e-commerce example. That is, the ability to scalably analyze live data as it changes. In this case a user is looking for a new BBQ and an e-commerce company misses out on a larger sale because they cannot analyze his data as its changing.

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Sentiment Analysis of Social Media Texts Part

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How to do real-time Twitter Sentiment Analysis (or any analysis)

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This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too.

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This is an introduction for the layman to how sentiment analysis in machines works.

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This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided. See the README file contained in the release for more details.

When using this dataset, please cite our ACL 2011 paper [ bib].

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This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided.

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As part of Salesforce Einstein, Einstein powers the world's smartest CRM by embedding state-of-the-art deep learning technology into the Salesforce Platform.

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Proofs:-

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SAAIP'16: Sentiment Analysis Where AI Meets Psychology

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In recent times, research activities in the areas of Opinion, Sentiment, Emotion and/or Mood in natural language texts, speech, music and other media have become the mainstream research under the umbrella of subjectivity analysis and affective computing. These tasks are considered vital since a decade from various academic and commercial perspectives.

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Sources of information

1) Report of SAAIP on sentiment analysis.

2) Document from University Of Hawaii.

3) Other scholarly articles from Standaford university,

4) Metamind corporate website

5) YouTube videos

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ds

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Q) What is the function of controllers in an automated ATC ?

A) These are the main functions of a controller :-

1. coordinating with other sectors,

2. navigating aircraft,

3. issuing altitude clearances, and

4. maintaining aircraft separation.

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Q) Why use of artificial intelligence in automation of air traffic control?

A) AI can help by reducing human effort. Currently these ATCs(Air Traffic Control) are run by human controllers.Increasing number of flights is however straining the system.

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The article which I post is about application of Artificial Intelligence in Aviation and Au . It particularly talks about air traffic control automation. Air traffic control automation deals with basically aircraft traffic and prevent collisions. The major collaborator on this paper is D.A. Spencer.

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Q) How can Artificial Intelligence help?

A) AI can identify positive and negative polarity of evaluative text. This will help in development of devices that will recognize, display and model emotions from text matter.

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Q) What is the objective of sentiment analysis?

A) In recent time when the social media is flexing its muscle there is a huge amount of data available for the analysts. Chats on WhatsApp and Facebook or blogs and reviews, Efforts are made to understand the polarity of human sentiments and incorporating them in devices.

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Q) How to perform sentiment classification?

A) Sentiment analysis can be performed on text or conversations and is basically lexico based or corpus based.

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This article is about application of Artificial Intelligence in Neural Computing. This talks about application of sophisticated algorithms to understand human sentiments. The major collaborators on this paper are Xiaolong Wang , Qingcai Chen and Shusen Zhou.