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Artificial Intelligence and Machine Learning

MIT_Dome_DSC_0871
(MIT Dome, Yu-Chih Ko)

 Innovation in the AI Era

 

 "AS Computing Reshapes Our World, MIT Intends to Help Make Sure It Does So for the Good of All." -- MIT President L. Rafael Reif

Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning (ML) is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. Deep Learning (DL) is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations.

AI is a broad field with a long history. It went through ups and downs, successes and failures, optimism and disappointment, big enthusiasm with large funding, and then cutting funding and so on and so forth. AI is now maturing. Today, as data will drive future discoveries and alleviate the complexity of AI.

As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) - images, text, transactions, mapping data, you name it. 

The most important thing to understand about AI is that it is not a static formula to solve. It’s a constantly evolving system designed to identify, sort, and present the data that is most likely to meet the needs of users at that specific time, based on a multitude of variables that go far beyond just a simple keyword phrase. 

AI is trained by using known data, such as: content, links, user behavior, trust, citations, patterns, and then analyzing that data using user experience, big data, and machine learning to develop new ranking factors capable of producing the results most likely to meet user needs.

The goal of Artificial Intelligence (AI) is to understand intelligence by constructing computational models of intelligent behavior. This entails developing and testing falsifiable algorithmic theories of (aspects of) intelligent behavior, including sensing, representation, reasoning, learning, decision-making, communication, coordination, action, and interaction. AI is also concerned with the engineering of systems that exhibit intelligence. 

 

The Foundation of AI

 

Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category. 

Generalized AIs – systems or devices which can in theory handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art. 

AI is an interdisciplinary field and several other disciplines have contributed to its progress and this includes mathematics, economics, linguistics, neuroscience, control theory and cybernetics, psychology, computer engineering, and finally philosophy. Given how broad the field is, it won't be possible to go every single contribution. However, let's go through the main concept introduced by each of these disciplines.

  • With no surprise philosophers were the first AI contributors. They formulated ideas for AI, starting with Aristotle in 400 BC. They considered the mind as a machine, our physical system operating as a set of logical rules. There have been different philosophy movements including rationalism, dualism, materialism, empiricism, induction, etc. 
  • Mathematicians provided the tools to formalize and manipulate logic. They also worked out the details of propositional logic and first order logic. Mathematics also lead the ground for algorithms for logical deduction to draw valid conclusions. Finally mathematicians also contributed with the theory of probability invaluable to help deal with uncertainty in the real world.
  • Economists provided the formal theory of rational decisions to maximize what they call payoff or utility. They combine decision theory and probability theory for decision making and uncertainly. They also address game theory in which an agent is planning to maximize its utility in the presence of an opponent who is aiming or planning against him. Economists also formalize mark of decision processes as a class of sequential decision problems with the mark of property.
  • Neuroscience contributed to AI progress by addressing how brain functions and how brains and computers are similar or dissimilar. A good progress has been made so far in understanding how the brain functions. And we could expect more involvement in AI in the next decades or so.
  • Psychologists care about how we think and act. Cognitive psychology specifically perceives the brain as an information processing machine. It lead to the development of the field of cognitive science.
  • Computer engineering cares about how to build powerful machines to make AI possible. For example although the idea of self driving cars or autonomous driving has been there for decades, it's became only possible today thanks to advances in computer engineering.
  • Control theory and cybernetics aim to design simple optimal agents receiving feedback from the environment. Today modern control theory design systems that maximize
    an objective functions over time, which gets AI and control theory today closer disciplines than ever.
  • Linguistics cares about how our languages and thinking related. And today modern linguistics and AI format we call computational linguistics or natural language processing which is a very important piece in natural language understanding in AI. 
 

The Rise of Machine Learning


Machine learning is a technique in which we train a software model using data. The model learns from the training cases and then we can use the trained model to make predictions for new data cases.
 
Machine learning provides the foundation for artificial intelligence (AI). Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has. One of these was the realization that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves. The second was the emergence of the Internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.

Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the Internet to give them access to all of the information in the world.
 
Machine learning is concerned with the scientific study, exploration, design, analysis, and applications of algorithms that learn concepts, predictive models, behaviors, action policies, etc. from observation, inference, and experimentation and the characterization of the precise conditions under which classes of concepts and behaviors are learnable. Learning algorithms can also be used to model aspects of human and animal learning. Machine learning integrates and builds on advances in algorithms and data structures, statistical inference, information theory, signal processing as well as insights drawn from neural, behavioral, and cognitive sciences. 

 

 

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