Abdalla Kablan All this stimulating talk makes me happy because I can see many brilliant concepts, and brilliant minds working within the sphere, solving the same problem using different approaches, and each one is truly innovative in its own way. The adaptive approach solves a core AI problem that’s existed for a while. The biggest problem with AI and solving games like chess etc., is that we still depend on the ‘vast and super’ kind of computing, and calculating all the possible permutations that can be played by the opponent. Yet that’s a very narrow form of intelligence, the machines are struggling at solving a wider form of intelligence problem. For example, the machine can beat a human at chess, but the machine would struggle to make a cup of tea in a kitchen that it’s never seen before. Whereas for a human we go to a new flat and a new kitchen, we open the cupboard, get a mug and teabag and pour hot water. The machine struggles with that because it’s a wider form of problem. Using adaptive approaches that learn on the go and connect all the dots and move out of the usual correlation to causality. What Ben was describing is, when you employ the causation not the correlation. The biggest problem with traditional data analytic approaches is that they were looking at big data sets and trying to find a correlation and build a predictive system based on correlation analysis. Meaning that if one set moved up - we know they’ve been heavily correlated in the past - the other one is going to move up as well, which
does not identify the causation. What Ben described is looking at the degrees of separation of a problem, looking at different pools of players and what would be the effect of one team acquiring a certain player that would add value to another pre-existing player. This is to increase or decrease their performance so an elasticity forms between the data which is apparent between one entity, in this case a player, and another. In Finance we’re building what we call a ‘cheat sheet’ of six degrees of financial separation. In identifying the relationships between various markets, we have nodes and hubs, we have huge markets, and small markets built around them. If we want to anticipate when one major event occurs in a minor market, how long would it take until it would ripple to a big hub or big exchange, or any other sub-hubs around it and what would be the impact of that? For example, if something happens in coffee bean commodity stocks in Brazil what would the effect of that be, beyond the spice market in India? It’s similar to the six degrees of separation concept in social media which is when you choose two random people on the planet, you find they will be connected to each other in less than six degrees. In Finance we can measure the size of impact between one event in a certain market and another event in another market. We’ve introduced a weighted approach in giving different weights to each decision that we make. We decide, based on a particular set of information, what the confidence rate is, and that confidence rate is dictated by how much weight it will have given that form of information. It’s not only big data it’s linked data, and linked by varying degrees of separation.
83