Expert Interview (Part 1): Gregory Piatetsky-Shapiro on Exciting and Worrisome Advances in Artificial Intelligence
In Part 1 of this two-part interview, Gregory Piatetsky-Shapiro of KDnuggets (@KDnuggets) discusses the how today’s advances in deep learning are cause for excitement and concern.
Tracking Big Data’s Evolution in Data Science, AI & Machine Learning
Machines have always fascinated Piatetsky-Shapiro – ever since he was a kid reading stories about robots by Isaac Asimov and other sci-fi authors.
He discovered his love for programming while studying computer science at Technion, where he spent a few weeks in the summer of his first year programming a computer (in APL) to play battleships. “I was soundly defeated by my own program,” he says. “That gave me an appreciation for the abilities of technology. I became more interested in creating programs than playing them.”
Piatetsky-Shapiro’s passion for understanding data and helping others stay up to date on developments in databases led him to launch the first Knowledge Discovery in Databases workshop in 1989, which later grew into full-fledged KDD conferences.
In 1993, after the third KDD workshop, he started KDnuggets News, an e-newsletter focused on data mining and knowledge discovery. The first issue went to 50 researchers who attended the workshop. Today, the KDnuggets brand has more than 200,000 subscribers across email, Twitter, Facebook and LinkedIn. With over 500,000 visitors in October 2017, KDnuggets.com has become a go-to resource for data science and analytics news, software, jobs, courses, education and more.
Piatetsky-Shapiro is one of the leading voices in Big Data – a field he says is somewhat amorphous, encapsulating infrastructure and database management, and closely connected to data science, machine learning and artificial intelligence.
(Note: what is now called “data science” was earlier called “data mining” or “knowledge discovery” but it refers to the same field dedicated to analyzing and understanding data and extracting useful knowledge from it.)
Exciting AI Advances with Deep Learning
“What is really most exciting now is deep learning,” he says.
While the concept of multi-level (deep learning) neural networks has been around since the 1960s, there wasn’t enough data, computer power or clever algorithms to use them effectively. But in the past few years, this approach– rebranded as “deep learning”– received sufficient data and processing powers and has been achieving amazing feats almost every week.
Examples of Deep Learning Breakthroughs
There are many examples of deep learning being deployed today.
Consumers who speak to their smartphone assistants like Siri or Cortana, or to Amazon Alexa or Google Home, are getting good results thanks to deep learning.
Google’s recent advances in machine translation are another big advance, thanks to deep learning.
It used to be that computers would do machine-based translation by using hand-crafted rules derived by thousands of linguistic experts. However, powered by large amounts of text and advanced Deep Learning network, in 2016 Google switched to Google Neural Machine Translation, which eliminates all manual rules and translates entire sentences at a time. This has significantly improved the quality of translations.
Finally, Piatetsky-Shapiro mentioned AlphaGo, a computer program developed by Google DeepMind to play the ancient Chinese game of Go. In 2016, AlphaGo, trained partly on thousands of human championship games, defeated world champion Lee Sedol 4:1.
In 2017, an improved version called AlphaGo Zero, combined Deep Learning and Reinforcement Learning methods and learned to play from scratch, entirely by self-play. After three days and a few million games, the new version reached the level of program that defeated Go world champion in 2016. After 40 days, AlphaGo Zero achieved superhuman level and defeated the previous version 100:0.
Today, it’s considered the strongest Go player in history.
“It’s very exciting and it’s also very scary,” Piatetsky-Shapiro says. As AlphaGo Zero improved its game play, it began choosing very different moves than human experts on a more frequent basis.
Be sure to continue to Part 2 of this interview, where Piatetsky-Shapiro discusses self-driving Artificial Intelligence and how businesses can approach it.
For a more Big Data insights, check out our report, 2018 Big Data Trends: Liberate, Integrate & Trust, to see what every business needs to know in the upcoming year, including 5 key trends to watch for in the next 12 months!