Definition
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
AI as a concept refers to computing hardware being able to essentially think for itself, and make decisions based on the data it is being fed. AI systems are often hugely complex and powerful, with the ability to process unfathomable depths of information in an extremely quick time in order to come to an effective conclusion.
warning that hyper-intelligent machines are going to slaughter us and equally frightening, if less dire, warnings that AI and robots are going to take all of our jobs.
How AI is used!
Strong AI, weak AI and everything is about difference
It turns out that people have very different goals with regard to building AI systems, and they tend to fall into three camps, based on how close the machines they are building line up with how people work.
For some, the goal is to build systems that think exactly the same way that people do. Others just want to get the job done and don’t care if the computation has anything to do with human thought. And some are in-between, using human reasoning as a model that can inform and inspire but not as the final target for imitation.
The work aimed at genuinely simulating human reasoning tends to be called “strong AI,” in that any result can be used to not only build systems that think but also to explain how humans think as well. However, we have yet to see a real model of strong AI or systems that are actual simulations of human cognition, as this is a very difficult problem to solve. When that time comes, the researchers involved will certainly pop some champagne, toast the future and call it a day.
The work in the second camp, aimed at just getting systems to work, is usually called “weak AI” in that while we might be able to build systems that can behave like humans, the results will tell us nothing about how humans think. One of the prime examples of this is IBM’s Deep Blue, a system that was a master chess player, but certainly did not play in the same way that humans do.
Somewhere in the middle of strong and weak AI is a third camp (the “in-between”): systems that are informed or inspired by human reasoning. This tends to be where most of the more powerful work is happening today. These systems use human reasoning as a guide, but they are not driven by the goal to perfectly model it.
A good example of this is IBM Watson. Watson builds up evidence for the answers it finds by looking at thousands of pieces of text that give it a level of confidence in its conclusion. It combines the ability to recognize patterns in text with the very different ability to weigh the evidence that matching those patterns provides. Its development was guided by the observation that people are able to come to conclusions without having hard and fast rules and can, instead, build up collections of evidence. Just like people, Watson is able to notice patterns in text that provide a little bit of evidence and then add all that evidence up to get to an answer.
Likewise, Google’s work in Deep Learning has a similar feel in that it is inspired by the actual structure of the brain. Informed by the behavior of neurons, Deep Learning systems function by learning layers of representations for tasks such as image and speech recognition. Not exactly like the brain, but inspired by it.
The important takeaway here is that in order for a system to be considered AI, it doesn’t have to work in the same way we do. It just needs to be smart.
Narrow AI vs. general AI
There is another distinction to be made here — the difference between AI systems designed for specific tasks (often called “narrow AI”) and those few systems that are designed for the ability to reason in general (referred to as “general AI”). People sometimes get confused by this distinction, and consequently, mistakenly interpret specific results in a specific area as somehow scoping across all of intelligent behavior.
Systems that can recommend things to you based on your past behavior will be different from systems that can learn to recognize images from examples, which will also be different from systems that can make decisions based on the syntheses of evidence. They may all be examples of narrow AI in practice, but may not be generalizable to address all of the issues that an intelligent machine will have to deal with on its own. For example, I may not want the system that is brilliant at figuring out where the nearest gas station is to also perform my medical diagnostics.
The next step is to look at how these ideas play out in the different capabilities we expect to see in intelligent systems and how they interact in the emerging AI ecosystem of today.
Artificial intelligence basic components
- VISION
- TEXT
- HEARING
- DIALECT
There are the basic components where AI will react/respond with environment.
Many of AI’s revolutionary technologies are common buzzwords, like “natural language processing,” “deep learning,” and “predictive analytics.” Cutting-edge technologies that enable computer systems to understand the meaning of human language, learn from experience, and make predictions, respectively.
Understanding AI jargon is the key to facilitating discussion about the real-world applications of this technology. The technologies are disruptive, revolutionizing the way humans interact with data and make decisions, and should be understood in basic terms by all of us.
Machine Learning (Learning from experience)
Machine learning, or ML, is an application of AI that provides computer systems with the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of algorithms that can analyze data and make predictions. Beyond being used to predict what Netflix movies you might like, or the best route for your Uber, machine learning is being applied to healthcare, pharma, and life sciences industries to aid disease diagnosis, medical image interpretation, and accelerate drug development.
Deep Learning (Self-educating machines)
Deep learning is a subset of machine learning that employs artificial neural networks that learn by processing data. Artificial neural networks mimic the biological neural networks in the human brain.Multiple layers of artificial neural networks work together to determine a single output from many inputs, for example, identifying the image of a face from a mosaic of tiles. The machines learn through positive and negative reinforcement of the tasks they carry out, which requires constant processing and reinforcement to progress.Another form of deep learning is speech recognition, which enables the voice assistant in phones to understand questions like, “Hey Siri, How does artificial intelligence work?”
Neural Network (Making associations)
Neural networks enable deep learning. As mentioned, neural networks are computer systems modeled after neural connections in the human brain. The artificial equivalent of a human neuron is a perceptron. Just like bundles of neurons create neural networks in the brain, stacks of perceptrons create artificial neural networks in computer systems.Neural networks learn by processing training examples. The best examples come in the form of large data sets, like, say, a set of 1,000 cat photos. By processing the many images (inputs) the machine is able to produce a single output, answering the question, “Is the image a cat or not?”This process analyzes data many times to find associations and give meaning to previously undefined data. Through different learning models, like positive reinforcement, the machine is taught it has successfully identified the object.
Natural Language Processing (NLP) (Understanding the language)
Natural Language Processing or NLP, allows computers to interpret, recognize, and produce human language and speech. The ultimate goal of NLP is to enable seamless interaction with the machines we use every day by teaching systems to understand human language in context and produce logical responses.Real-world examples of NLP include Skype Translator, which interprets the speech of multiple languages in real-time to facilitate communication.
Computer Vision (Understanding images)
Computer vision is a technique that implements deep learning and pattern identification to interpret the content of an image; including the graphs, tables, and pictures within PDF documents, as well as, other text and video. Computer vision is an integral field of AI, enabling computers to identify, process and interpret visual data.Applications of this technology have already begun to revolutionize industries like research & development and healthcare. Computer Vision is being used to diagnose patients faster by using Computer Vision and machine learning to evaluate patients’ x-ray scans.
Additional Supporting technologies for Artificial Intelligence
- Graphical Processing Units or GPUs are a key enabler of AI, providing the massive computing power necessary to process millions of data and calculations quickly.
- The Internet of Things, or IoT, is the cumulative network of devices that are connected to the internet. The IoT is predicted to connect over 100 billion devices in the coming years.
- Intelligent data processing is being optimized using advanced algorithms for faster multi-level analysis of data. This is the solution to predict rare events, comprehending systems and unique situations.
With the integration of Application Processing Interfaces or APIs, aspects of artificial intelligence can be plugged into existing software, augmenting its normal function with AI.
Artificial Intelligence is a diverse topic
As we have learned, AI is describing a set of different technologies. Each of these technologies require detailed explanation. Staying up to date and understanding the differences of these technologies is a difficult task. Keep up with the latest changes and stay tuned for our upcoming posts. in the next AI post we will be knowing about “difference between AI/ML/DL(Artificial intelligence/Machine learning/Deep learning). thank you! please do follow!!
Author: Arcot Gautham
Edited By: Nivedita.M
Cognive 