Computers Really Are About To Take Over The World

| August 22, 2019 | Reply

We’ve had some interesting services reviewed over the summer. We started with Bets2Profit and Bets2Profit Lays and rounded out July with the impressive Charlie Mears Racing. There was also one football service in the month, actually its two-in-one, Goals Goals Goals and Last 15 Winners. Among the remainder, we went down under with Oz Racing and looked at a couple of each-way services as well.

June finished with a review of Ice Diamond from Betfan, which is an amalgam of four of their existing services in one package. Early in July we also updated the performance of Tipster Warehouse, another readymade portfolio from the desk of Betting Gods. It really is worth visiting the reviews section regularly!

Remember also to visit our partner site Tipstars for daily updates, monthly rankings, historic results, tipster bios and the latest news on over 180 tipster services across a range of sports.

Superhuman Intelligence?

I’ve just finished reading “The Big 9” by Professor Amy Webb, who lectures at New York University’s Stern School of Business, but more interestingly is also the founder of the Future Today Institute…

The Future Today Institute conducts research on emerging technologies and, as part of that research, Professor Webb and her team have developed a data-driven method of forecasting the future. Many of the references below are taken from the book.

The book concludes, amongst other things, that artificial intelligence (AI) will have achieved artificial super intelligence (ASI) by 2070.

At the moment AI is applied to very specific tasks and, although it can learn within that sphere, it is still limited to that domain. This is known as artificial narrow intelligence (ANI). The book looks, however, at how ANI will be used to construct more general systems that can tackle a wider range of tasks. They term this artificial general intelligence (AGI). At this point, AI will begin to approach parity with humans in terms of overall intelligence.

From there, the sky will be the limit…

Like present-day AI programs, AGI systems will be able to improve themselves continuously and at a breathtaking pace. This will eventually allow them to outperform the human mind – not just by a little, but by trillions of times its level of intelligence. At that point, AI will have achieved artificial super intelligence or ASI.

The possible applications and the potential implications of ASI paint a pretty dark picture for the future. It’s not as much science-fiction as you might think!

I am absolutely fascinated by the whole area of AI, and thoroughly recommend The Big 9 as a real thought-provoking read.

To understand where this links with sports betting we need to return to the main focus of the book…

All of the predicted advances in AI are built on the development of deep neural networks or DNNs. The precise mechanics of how these work are rather complicated, but the basic idea behind them is fairly simple…

Similar to the human brain, a DNN consists of thousands of simulated neurons linked together and arranged into hundreds of complex layers. By sending and receiving signals to and from each other, these layers of neurons are able to do something called “deep learning”. That means they can teach themselves how to do things with little or no human supervision; they don’t have to be taught by their human creators!

One of the benchmarks in the development of AI has been its ability to play Go, an ancient Chinese board game many times more complex than chess, and which requires the ability to be able to engage in creative, responsive and on-the-fly strategic thinking to win a game against a skilled human opponent.

In 2014, a tech start-up company called AlphaMind unveiled its creation AlphaGo. This DNN-powered programme defeated a professional Go player by five games to nil. The biggest hurdle in the development of AI so far had been surpassed.

The original AlphaGo, was loaded with an initial data set of 100,000 previously played games of Go. By sifting through this library, the DNNs in AlphaGo were able to develop a sense of judgment about how to play. As remarkable as this achievement was, it was eclipsed just three years later with the emergence of AlphaGo Zero.

Unlike its predecessor, AlphaGo Zero was not loaded with any initial data. Instead, the program just started playing Go against itself, without even knowing the rules for placing the pieces. By seeing what worked and didn’t work in each game, it was able to develop its own sense of judgment, which soon surpassed that of its predecessor. Just 40 days after its electronic birth, AlphaGo Zero was able to beat the latest version of the original AlphaGo in 90 percent of its games!

Even more astounding was the fact that in those 40 days, AlphaGo Zero not only figured out all of the strategies that human Go masters had painstakingly learned over thousands of years. It also discovered brand new strategies that had never before been witnessed. Freed from AI’s previous reliance on a human-generated data set, AlphaGo Zero was able to surpass the limits of human knowledge and think in novel, non-human ways about the game.

In a certain sense, AlphaGo Zero can, therefore, be said to have achieved superhuman intelligence; it developed a type of thinking that was both different from and better than ours.

Now, to sports betting and tipping…

Since the turn of the century the use of algorithms, computer modelling, machine learning etc. has grown exponentially in trying to beat the bookmakers. But they simply employ their own data scientists and analysts to keep one step ahead.

But think about, in the same way that AlphaGo was furnished with a library of data from past games of Go, so the current use of computers to predict sporting events is based on feeding the machine with data and variables that are chosen and prioritised by the human maker. Whilst computers can undoubtedly handle many times more data and manipulate it in fractions of a second, they are still relying on the initial human input to set the strategies and select the perceived pertinent data.

Imagine now a machine like AlphaGo Zero, utilising powerful DNNs, put to use to analyse and predict sporting events. Without providing any historic data, any strategies or any other information, let the machine start to capture data and variables from sporting events, be that horse racing, football, cricket, tennis, golf, US sports or tiddlywinks! Could it, like AlphaGo Zero, discover new strategies and think in novel, non-human ways about the sport?

Initially, a DNN-powered system might focus on just one sport in a very ANI way. But what’s to stop the application widening so that the same machine looks at a multitude of sports and adopts learning from one event to apply to others? Effectively moving from ANI to AGI. From there its but a small step to ASI.

Could superhuman intelligence finally be the holy grail of punters?

I’ll leave you to decide if its all science-fiction or a real picture of the not too distant future.


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