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Artificial Intelligence, Machine Learning, and Neural Networks

(MIT Dome, Yu-Chih Ko)


Artificial Intelligence: Fueling the Next Wave of the Digital Era


Artificial Intelligence


Artificial Intelligence (AI) and Machine Learning (ML) principles have been around for decades. AI's recent surge in popularity is a direct result of two factors. First, AI/ML algorithms are computationally intensive. The availability of cloud computing has made it feasible to run these algorithms practically. Second, training AI/ML models requires massive amounts of data. The availability of big data platforms and digital data have improved the effectiveness of AI/ML, making them better in many applications than humans.

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.  

AI offers broad technological capabilities that can be applied to all industries, profoundly transforming the world around us. AI has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast. AI enabled technologies are already shifting how we communicate, how we work and play, and how we shop and care for our health. For businesses, AI has become an absolute imperative for creating and maintaining a competitive edge.


The Rise of Machine Learning


Machine Learning is a current application of AI. The technology is based on the idea that we should really just be able to give machines access to data, and let them learn for themselves. 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. 


Deep Learning and Deep Neural Networks


Deep Learning 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. 

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.




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