Deep Learning Pioneer Yoshua Bengio Says AI Is Not Magic And Intel AI Experts Explain Why And How Posted on : Sep 20 - 2019

Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation “AI is not magic.” Winner of the 2018 Turing Award (with the other “fathers of the deep learning revolution,” Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the “amazing progress in AI” while stressing the importance of understanding its current limitations and recognizing that “we are still very far from human-level AI in many ways.”

Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this “learning component,” and deep learning develops intuitive knowledge “by acquiring that knowledge from data, from interacting with the environment, from learning. That’s why current AI is working so much better than the old AI.”

At the same time, classical AI aimed to allow computers to do what humans do—reasoning, or combining ideas “in our mind in a very explicit, conscious way,” concepts that we can explain to other people. “Although the goals of a lot of things I’m doing now are similar to the classical AI goals, allowing machines to reason, the solutions will be very different,” says Bengio. Humans use very few steps when they reason and Bengio contends we need to address the gap that exists between our mind’s two modes of thought: “System 1” (instinctive and emotional) and “system 2” (deliberative and logical). This is “something we really have to address to approach human-level AI,” says Bengio.

To get there, Bengio and other AI researchers are “making baby steps” in some new directions, but “much more needs to be done.” These new directions include tighter integration between deep learning and reinforcement learning, finding ways to teach the machine meta-learning or ”learning to learn”—allowing it to generalize better, and understand better the causal relations embodied in the data, going beyond correlations.

Bengio is confident that AI research will overcome these challenges and will achieve not only human-level AI but will also manage to develop human-like machines. “If we don’t destroy ourselves before then,” says Bengio, “I believe there is no reason we couldn’t build machines that could express emotions. I don’t think that emotions or even consciousness are out of reach of machines in the future. We still have a lot to go… [to] understand them better scientifically in humans but also in ways that are sufficiently formal so we can train machines to have these kinds of properties.”

At present, however, “organizations that are starting to use AI sometimes don’t realize the limitations of what we currently have,” noted Bengio.

At the MIT event, I talked to two Intel VPs—Gadi Singer and Carey Kloss—who are very familiar with what companies do today with the current form of AI, deep learning, with all its limitations. “Enterprises are at a stage now where they have figured out what deep learning means to them and they are going to apply it shortly,” says Singer.  “Cloud Service Providers deploy it at scale already. Enterprise customers are still learning how it can affect them,” adds Kloss. View More