Results for “Hon Weng Chong”

My entry into this space came in 2019 when I read a paper by Demis Hassabis of DeepMind advocating for machine learning and AI researchers to go back to their roots in neuroscience. I took that literally — I went back to the neuroscience department at my alma mater, the University of Melbourne, and asked what was exciting them.

You cannot expend more energy than you can consume. That's a fundamental law of physics. If you do, you starve, you die, and you remove yourself from the gene pool. Biological systems have therefore been under enormous selective pressure to develop highly efficient intelligence.

Think about a rabbit sitting in a field. If that rabbit saw a hawk circling above and decided to wait for the back-propagation step before responding, it would be dead. It has been eliminated from the gene pool. The better you model the world, and the faster you can act on that model, the more likely your genes are to survive.

The brain is definitely not doing computation in the purest sense. We are not crunching numbers in binary ones and zeros in our heads. When people ask me how our system compares to an NVIDIA GPU in terms of FLOPS, I tell them they're asking the wrong question. A more important question is: what are your inputs, what output do you want, and how intelligently can the system get from one to the other?

I think intelligence is best understood as an entity that has the ability to improve a metric through repeated exposure over time. By that definition, machine learning algorithms are learning systems — they get better with more data and more exposures. A dog is a learning system. A cat is a learning system — you teach it a trick, reward it a few times, and it just does it from there on. And humans, of course, are the ultimate example of a learning system.

Think about a rabbit sitting in a field. If that rabbit saw a hawk circling above and decided to wait for the back-propagation step before responding, it would be dead. The better you model the world, and the faster you can act on that model, the more likely your genes are to survive.

You cannot expend more energy than you can consume. That's a fundamental law of physics. If you do, you starve, you die, and you remove yourself from the gene pool. Biological systems have therefore been under enormous selective pressure to develop highly efficient intelligence.

The things that are trivially easy for humans turn out to be extraordinarily difficult for machines, and vice versa. I cannot do the square root of a large number in my head, but my pocket calculator can do that instantly. But my pocket calculator still cannot make me a cup of coffee.

I think intelligence is best understood as an entity that has the ability to improve a metric through repeated exposure over time. By that definition, machine learning algorithms are learning systems — they get better with more data and more exposures.

The brain is definitely not doing computation in the purest sense. We are not crunching numbers in binary ones and zeros in our heads. A more important question is: what are your inputs, what output do you want, and how intelligently can the system get from one to the other?