Artificial intelligence: the role of evolution in decision-making – Telegraph.co.uk

Strategy matters in war. But behind every good strategy is good data. Take Korean War veteran and US Air Force officer John Boyd as an example.

He was tasked with analysing the outcome of dogfights aerial battles between fighter planes conducted at close range and come up with a way to save the lives of more American pilots. He did.

What Boyd created was a framework for decision-making that is known as the OODA loop. OODA refers to the recurring cycle of four actions: observe, orient, decide and act. He discovered that the pilots who came out of dogfights most successfully were those who had processed the loop as quickly and as often as possible.

Their experience of reacting to lots of different situations meant they could best adapt their battle strategies to unfolding events.

The theory of the OODA loop is central to the activities of Sentient, which is the worlds most funded artificial intelligence (AI) company

What Boyd was telling us was that surviving, learning and adapting were key to winning, says Antoine Blondeau, the co-founder and co-chairman of Sentient Technologies. Since then, that framework has been pervasive within the US armed forces. If you train, train and train, you go through that loop as often as possible.

The theory of the OODA loop is central to the activities of Sentient, which is the worlds most funded artificial intelligence (AI) company and the inventor of the technology behind Siri, Apples intelligent personal assistant.

When it builds a decision-making system, it aims to embed AI at every stage of the OODA loop. It does this by using deep-learning neural networks that have algorithms inspired by the function and structure of the human brain.

And as far as decision-making mechanisms go, there is no better example than the human brain. We humans are a winning design, Mr Blondeau points out. We are a species that has adapted very, very well to our environment to the point of domination.

In the space of a few milliseconds, artificial intelligence is in effect emulating an evolutionary process that took place over millions of years. But applying that approach at scale requires going through the OODA loop as often as possible, which in turn requires a huge amount of computing power.

The next stage of AIs evolution is the involvement of AI systems in the development of other AI systems

This explains why Sentients neural networks rely on the rapid mathematical calculations of 5,000 graphics processing units (GPUs). Most personal computers do not need more than one GPU.

Sentient is focused on developing commercial AI technology for three particular sectors: e-commerce, financial markets trading and insurance. Theres a lot of interest in implementing AI within the world of insurance, says Mr Blondeau, not only for risk prediction and developing better models of risk, but also for universal intelligence problems understanding the what and the how that make customers buy.

Last year Sentient launched Ascend, its own universal intelligence product. Ascend is essentially a self-evolving website that retailers can use to give their customers a personalised shopping experience. It tests different images, messaging and button placements to come up with the best combination for a particular shopper.

The result of this degree of personalisation is a striking uplift in sales, according to Mr Blondeau, who cites the example of a US mobile phone reseller that has already rolled out the technology. You can get a 40pc increase in lead generation and a 30pc increase in conversions within a few weeks. Thats the power of evolution within this loop, where the system is learning every minute.

The next stage of AIs evolution is the involvement of AI systems in the development of other AI systems. Needless to say, Sentient is already working on this technology, which Mr Blondeau predicts will help to dramatically reduce the cost of developing smart systems in future.

Yet he does note that there needs to be an ethical debate around regulating for augmenting but not replacing humans.

You can quickly evolve to intelligent systems that are addressing complex problems quickly and efficiently, with a feedback loop that operates not just at the application level but at the network structure level as well, says Mr Blondeau. We call that neuro evolution. Its effectively a marriage of deep learners and evolution.

While the decision-making ability of AI has the potential to be of huge benefit to humans, relieving us of the burden of having to make certain decisions ourselves, Mr Blondeau believes that aspect of the technology is widely misunderstood.

People think of AI as big data, he says. Then they think of it as insights and predictions. At Sentient, we tend to think of it as making decisions. Whatever data and insight humans gain every day is in order to make decisions. So true AI has to be able to make decisions not just look at data or predict trends. It has to be part of the decision-making process. Ideally AI is a decision-maker.

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Artificial intelligence: the role of evolution in decision-making - Telegraph.co.uk