Disclaimer: Readers be aware that the word AI, short for Artificial Intelligence, is being used and has been used for the past few years as click bait and by people who don’t really know what it means. In addition to non technical executives who are riding the wave of propaganda that the advances in AI “are going to change the world” or, “AI will be the most important technology of our lifetime.” Many of these folks who “know nothing” on the subject and are often being misled. In addition to the misleading advertising on the internet, a number of corporations, including Apple and Google, are investing large sums of money in and developing the technology.
This hyped AI is in fact a branch of computer science called Machine Learning (ML). If we want to be more technical and specific; we can state that the hype is about the use of various ML computer algorithms (accordingly computer code) in limited real world applications like translation, classifying images. To be more specific, developers (computer programmers) use Artificial Neural Networks (ANN) more specifically Convolution Neural Networks (CNN) with a method called back propagation. What is ironic is that John Launchbury, the Director of DARPA‘s Information Innovation Office (I2O) once called these ML methods “Glorified Excel Sheets”.
After extensive research for the past three years on the topic of AI, I present you with the following list. If you are interested in what most people refer to as “AI” (actually ML) and what is may bring to our world, you will need to follow these people, simply because these are people who “kinda” know what they are doing.
1- Jeff Hawkins of Numenta
A bit of an odd choice to start, but Jeff’s thousand brain theory and his work on exploring neuroscience and its relationship to computer algorithms, are in our opinion one of the most promising paths to achieving something that is going to be relevant to real world applications. His company Numenta has published several papers and computer codes that are based on this work.
2- Mostafa Dehghani of Google Brain
Dehighani is working on machine translation projects at Google Brain, and is exploring new more advanced algorithms for language understanding. It is definitely worth it to follow his work if you are bored of google Deepmind’s AlphaGO and Alpha Zero video games achievements.
3- Andrej Karpathy of Tesla
Karpathy is working on ML for self-driving cars at Tesla. Before this he worked on machine vision at OpenAI and Google. Anthony’s ultimate goal is help create ML algorithms (neural nets) that mimic human driving behaviour. This is much more applicable to the real world than many other people on this list.
4- Ian Goodfellow of Apple
Goodfellow invented a ML algorithm called Generative Adversarial Networks (GANs) which is being used in image filters and to create fake videos. Goodfellow previously worked for Google, and he is now exploring what is next after GANs.
5- George Hotz of comma.ai
Hotz is best known for hacking, from iphones to playStations, and for being member of Google’s project X hacking for good team. He is now working for his company comma.ai trying to solve self driving cars and making how to make them cheap and mainstream.
6- Ray Kurzweil of Google
It is not very clear what does Kurzweil does at Google (which is unsettling). In his own words he is trying to bring natural language understanding to Google, he had many successes before, such as inventing a scanner, optical character recognition, and text-to-speech.
7- Geoffrey Hinton of Google and University of Toronto
Hinton is famous for being called the Father of deep learning. Hinton pioneered back propagation use for CNN which enabled all people on the list do their work. Hinton is working on ideas around capsule theory (capsule networks) to find better ML algorithms after admitting that CNN with back propagation is not how intelligence work. He works for Google and University of Toronto.
8- Andrew NG of deeplearning.ai
NG spends his time helping AI start-ups get off the ground. He teaches ML courses, advises ML graduate students and advocates for using graphics cards in ML computation.
9- Yoshua Bengio of MILA and CIFAR
Bengio helped pioneer deep learning with Hinton, he now works on advising students to help find principles of intelligence.
10- Rich Sutton of Google Brain and University of Alberta
Sutton worked on a branch of ML algorithms called reinforced learning. He worked on policy gradient methods which helped Google AlphaGo and AlphaZero achieve success. He works on researching general principles of intelligence and making choices.
To Conclude , AI has been extensively discussed in the context of the recent political and economic debates. A large proportion of the articles we reviewed are written by people who clearly know little or nothing about the topic in question, and have no particular expertise in such fields. The good news is, if you follow the people I mentioned above, you will have a much clearer sense of what this AI world is really like, and will be motivated to look into the pressing real questions and make recommendations to solve real problems.