Today, deep learning is the talk of the town. There is no shortage of media coverage, papers, books, and events on deep learning. Yet deep learning is not new. Its roots go back almost to the early days of artificial intelligence and computing. While the field received the cold shoulder for decades, there were a few scientists and researchers who plodded forward, keeping faith that the idea of artificial neural networks would one day bear fruit. And we are seeing the fruits of deep learning in everyday applications, such as search, chat, email, social media, and online shopping.

AI is suffering the consequences of being too successful. A few months ago I wrote an article entitled "5 Reasons Why I Left the AI Industry" in which I criticized AI's flaws upfront -- probably the reason the post went viral. It was more a rant than a well-thought article, but I still stand by most of what I wrote then. However, something was missing in that article. I attacked the industry but never proposed improvement solutions. It moves a lot of money because it gives unparalleled power -- military, economic, and even social.

The buzz around Deep Learning often misleads layman people to think that it is a newly invented technology, but it comes as a shock for them when they know that foundations of Deep Learning were laid down as early as in the 1940–1950s. There is a long history of deep learning where most of the popular deep neural network architectures and theories were already proposed throughout the latter half of the 20th century. If it was such the case, then you may ask why the Deep Learning revolution is taking place in the current times and why not a few decades back. The short answer is that the right hardware and compute power, required to train the large neural networks efficiently, did not exist during those times; thus all the theories were mostly on papers without practical support. There was a time when if you were researching on neural networks, you would not have been taken seriously by the machine learning research community.

Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. Whereas for the classical ML, domain experts and data scientists often have to write hand-crafted algorithms to extract and represent high-dimensional features from the raw data, deep learning models, on the other hand, automatically extracts and work on these complex features. A lot of theory and mathematical machines behind the classical ML (regression, support vector machines, etc.) were developed with linear models in mind. However, practical real-life problems are often nonlinear in nature and therefore cannot be effectively solved using those ML methods.

Imagine a computer that can read your handwriting (even if it's as bad as mine). Or one that can read a tiny street sign in a grainy picture you snapped on your phone. Or better yet, one that can do this and immediately translate the results into 100 different languages. In the last few years, all these things suddenly became possible. This is the power of modern, Deep Learning driven Optical Character Recognition (OCR). OCR is the process of using machine vision, letter recognition and other techniques to automatically extract text from an image.

Imagine a computer that can read your handwriting (even if it's as bad as mine). Or one that can read a tiny street sign in a grainy picture you snapped on your phone. Or better yet, one that can do this and immediately translate the results into 100 different languages. In the last few years, all these things suddenly became possible. This is the power of modern, Deep Learning driven Optical Character Recognition (OCR). OCR is the process of using machine vision, letter recognition and other techniques to automatically extract text from an image.

Artificial intelligence is a branch of engineering that has traditionally ignored brains, but recent advances in biologically inspired deep learning have dramatically changed AI and made it possible to solve problems in vision, speech planning and natural language. If you talk to Alexa or use Google Translate, you have experienced deep learning in action. In this lecture, explore the past, present and future of deep learning with Terrence J. Sejnowski from the Salk Institute for Biological Studies. Arntzen Grand Challenges Lecture Series: The Deep Learning Revolution Presented by Terrence J. Sejnowski Tuesday, November 5, 2019 Lecture: 5 p.m. Reception: 6 p.m. Interdisciplinary Science and Technology Building IV (ISTB4) Marston Exploration Theater, Tempe campus [map] Register to attend! Light hors d'oeuvres and an open bar will be provided.

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steelmaking. Bessemer decided to license his patent to a handful of steelmakers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steelmakers from agreeing to favorable licensing terms. In an effort to drive adoption, Bessemer opened his own steel making plant with the intention of undercutting competitors.

To the general public, today's "AI" technologies are nothing short of magic. Algorithms that can eerily understand video, images, speech, and text, translate between languages with uncanny accuracy, drive cars, play video games, find cancer and even best humans at complex strategy games by developing novel moves that no human had ever devised. Groundbreaking new milestones are crossed almost daily. Computer science programs are flooded with students eager to become AI experts and companies can't hire enough AI programmers. It would seem the era of AI has truly arrived.

The symbiosis of the two fields, how they overlap, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, consciousness, general AI, spiking neural networks, data science, and a lot more.