AI: In the Beginning was the Algorithm

Estimated reading time: 3 minutes

Hardly a day goes by without the media reporting on the latest amazing advances in Artificial Intelligence. But are we always talking about the same thing?

The purpose of Artificial Intelligence is to perform certain cognitive functions in place of humans. But is the same kind of Artificial Intelligence used in a video game or a social network, or to help make a medical diagnosis, conduct a military operation or operate an air traffic control centre?

The spectacular progress we read about in the media is mainly happening in consumer applications of AI. These applications use algorithms like deep learning, which correlate vast quantities of data to solve certain problems such as pattern recognition.

Artificial Intelligence used for air traffic management or military operations is not the same as that used for video games or social network.

But a company like Thales has customers and partners who are engaged in particularly complex activities of vital importance for individual citizens and society at large — smart cities, transportation networks, security services and defence systems. They need to make time-sensitive decisions in extremely constrained environments, and those decisions have a direct impact on human life, physical security and the ability of businesses and major infrastructure to operate. That makes all the difference when it comes to developing and using AI technologies.

The critical nature of these tasks poses a whole range of specific challenges that very few technology companies are capable of meeting.

In the beginning was the algorithm. Under the hood of every form of AI there are mathematical algorithms, some of them data-driven, others based on models, laws of physics and mathematical principles. The AI that's talked about most — AI for consumer applications — is data-driven. It uses deep learning algorithms that need to be fed phenomenal quantities of data on a permanent basis.

In critical environments, the situation is a little more complex. In some cases, critical systems and their many sensors generate even more data than consumer applications. But in other cases, there may be very little data available — or none at all, quite simply because the situations we are trying to understand and master have never happened before. Data-driven AI can work well in those first cases, but it has a fundamental flaw in that it doesn't explain what it's doing. Indeed it can even produce false results or be misled by its data, and nobody understands why. In these cases, model-based AI can be extremely useful.

One of Thales's major strengths is the ability to develop both these complementary types of algorithms, supported by expertise in the technologies that are driving the digital revolution today, namely connectivity, IoT and cybersecurity. Cybersecurity expertise, in particular, is a key differentiator for the Thales Group, making it possible to capture, analyse and transmit data securely and reliably in applications where security is a fundamental requirement.

Thales researchers working on the future of artificial intelligence are also helping to save the planet.

One aspect of AI that receives less media attention is energy consumption. In consumer AI applications, data is stored and analysed in gigantic datacentres, preferably located in cold climates because of the enormous amount of energy they consume.

In Thales's markets, energy constraints are even more critical, particularly for onboard systems. Imagine the complexity, for example, of embedding AI applications on board a fighter aircraft!

Here too, Thales research teams are exploring new avenues of investigation. In particular, the CNRS/Thales joint physics laboratory, directed for many years by Nobel prize-winner Albert Fert, is researching ways to minimise the amount of energy that AI consumes so it can be used in constrained environments like an aircraft cockpit.

The energy challenge is crucial for the future, and not only in the digital world. The Internet consumes more energy today than the much-maligned air transport system. And an AI system consumes between 10,000 and 1 million times more energy than the neurons of a human brain.

It's not as strange as it may appear that Thales researchers working on the future of artificial intelligence are also helping to save the planet.