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Super Bowl Neuroscience

Subutai AhmadSubutai AhmadVP Research
Super Bowl Neuroscience

We are slowly recovering from the disappointing 49er loss to the Ravens in the Superbowl. For those who watched it, it was a tremendously exciting game. In fact the events of the last few minutes had a significant effect on our Sparse Pool result!

We promised to explain how the Sparse Football Pool relates to the brain, the CLA (Numenta’s Cortical Learning Algorithm) and Intelligence. The CLA relies on Sparse Distributed Representations, a form of information representation where you have a bunch of 0s and 1s. Most of the numbers are 0s and a few of them are 1s (hence the term “Sparse”). In fact, each entry to the pool was an SDR—nine 1s and twenty-one 0s. SDRs are also the fundamental way our brain represents information. At any point in time, most of the neurons in our brain are quiet and a small percentage of them are firing. It turns out that this form of representation can have some really interesting properties. I constructed the Football Pool specifically so I could highlight some of these points. In the brain the numbers are, of course, far larger and the situation is a lot more complex, but we can illustrate the basic concepts using the Pool.

Numerical Properties Of SDRs

Even though there are only a small number of 1s, systems using SDRs can uniquely represent a massive number of patterns. Let’s ask the following question: given that you can only select 9 True answers out of 30, how many unique entries are there? The answer turns out to be larger than you might guess: there are 14,307,150 possible unique entries! (I know that sounds like a lot—it’s based on a concept called "binomial coefficients"). In Grok our patterns have 40 bits on out of 2048. The number of unique patterns is an unimaginably large 2.37 x 10^84! Give or take 10^80.

What is the chance someone else can have the exact same answer as you? Assuming everything is random, that’s just the flip side of the above question: it is 1 in 14,307,150. Even your identical twin would have a hard time guessing your answer.

What is the chance of someone getting a perfect score? There were 12 Trues in the final answer. Picking any 9 out of those 12 would be fine. There are 220 possible perfect answers, so again, if everything was random, the chance of getting a perfect score is about 220/14,307,150, or about 1 in 65,032.

OK, even if you don’t get a perfect score, what is a good score? The chance of getting a score of 8 is 1 in 28,903. The chance of getting a 7 is still very rare: only 1 in 18,065. Now, here’s a puzzler: we had less than 150 entries yet two people had a score of 7. How is this possible? Either the constellations were lined up perfectly, or there is something else going on. Turns out we can ignore astrology—there is another answer. The fact that a highly improbable event occurred tells us that there is something really non-random happening.

Semantic Properties Of SDRs

The world is not random, and neither were the questions. The questions were grouped into similar semantic categories and the SDR corresponding to each entry represents these semantics. Here are four aspects of this:

SDRs can represent specific information: Each person’s answer reveals something about the way they thought about the game. This could be very specific. Suppose you answered True to Question 11 (Frank Gore will rush for more yards than Ray Rice). That tells us you predicted Frank Gore would do well.

SDRs can represent complex information: Suppose you answered True to questions 2, 3, 5, 14, 17, 21, and 22 (Question Group 1 below). This probably means you predicted it would be an exciting game. On the other hand if you answered True to questions 6, 9, 10, 11, 16, 19, 23, 24, and 26 (Question Group 2 below) it means you predicted the 49ers were going to dominate the Ravens. So, SDRs can represent something specific but can also implicitly represent complex high level information. Isn’t that a key to intelligence and intelligent representations?

SDRs represent subtle variations using a distributed code: The questions are “overlapping” and so the information is distributed. For example, questions 2, 3, and 5 in Group 1 have a lot in common. This means you don’t have to answer True to all the questions in Group 1. Even answering True to, say, any 3 or 4 of them would be sufficient to tell us you thought the game was going to be exciting. You can convey more or less subtlety by choosing exactly which ones you answer as True. A partial answer tells us something about your thoughts and no particular answer is critical. This is exactly analogous to the brain: a sparse set of active neurons can represent lots of subtlety and complexity.

SDRs can simultaneously represent multiple independent concepts: If you answered True to, say, any 4 of the first set, and any 5 from the second set it means you believed both propositions: the game was going to be exciting, but in the end the 49ers were going to beat the Ravens. The ability to simultaneously represent independent concepts is another property of SDRs. It is particularly important when you are making predictions. Grok’s algorithms (and neurons) use this property to make simultaneous predictions about the future in a single step.

Can we use all this to say something about our winners? If you look at our winner, Ryan, it looks like he basically guessed that the game would be pretty exciting and that the 49er stars would have a good statistical game. Our second place entry (also with a score of 7) guessed that the game would be exciting and that the Ravens would win. Their guesses weren’t perfect, but their basic hunches ended up being correct and hence they had a high overlap score. This could only have happened in a non-random world, with meaningful SDRs. Conversely, if you answered the questions randomly, you probably didn’t do too well!

We have touched on a few properties of SDRs, some of them subtle. You can represent semantic properties and concepts. You can represent both very specific and very subtle concepts. You can represent multiple concepts simultaneously and this can be used in prediction. Grok (and our brain) relies on all of these properties and more. Of course, there are many other aspects of Grok (such as learning SDRs) that we didn’t cover here.

To wrap up, I hope you gained some insight into the power of SDRs and how our brains represent information. Most importantly, I hope you had fun with this as I did. Next time your spouse complains you are watching too much football, let them know you are actually involved in the greatest possible scientific quest: understanding human intelligence. Go Niners!

Question Group 1 - Overall Game Excitement

  1. There will be a lead change in the first half
  2. There will be three lead changes in the game
  3. The team leading at the end of the first half will lose the game
  4. There will be more than 10 total points in the first half
  5. There will be a punt or kickoff return that is greater than 40 yards
  6. There will be a score in the final 2 minutes of the first half
  7. There will be a score in the final 2 minutes of regulation time

Question Group 2 - 49er Domination

  1. Colin Kaepernick will have at least one run greater than 20 yards
  2. Patric Willis will get more tackles than Ray Lewis (solo + assisted)
  3. Aldon Smith will finally get a sack in the playoffs
  4. Frank Gore will rush for more yards than Ray Rice
  5. San Francisco will score first
  6. Colin Kaepernick will have a higher QB Rating than Joe Flacco
  7. Frank Gore will score the first touchdown
  8. Vernon Davis will score a touchdown.
  9. The San Francisco Forty Niners claim their 6th Superbowl Trophy!
Subutai AhmadSubutai AhmadVP Research