One of the core components of the cloud upgrade enabled through the AWS partnership is the use of 65 years’ worth of historical race data to train deep learning models. Aside from simply giving a greater analytical breakdown of how races have evolved and showing how teams can optimise their strategies, these models will also be able to make race predictions and analyse data in real time.

This data will also add value to the fan experience, giving spectators a real-time, data-driven feed of why teams may be adopting a particular strategy and just what is going on underneath the car’s exterior. As the sport looks to make a broader digital push, with a renewed focus on social media and digital platforms, the capability to provide fans with a level of insight that is usually limited to the teams could massively change the experience.

Potentially the largest disruption that artificial intelligence and machine learning could bring to Formula One is the advent of driverless vehicles. Even though driverless vehicles are still fairly early into development, making it unlikely that we will see such vehicles capable of Formula One speeds and manoeuvres any time soon, the prospect of autonomous vehicles could transform racing.

Companies such as Roborace are already showing that the concept is viable and that as machine learning develops and more and more data can be fed in (assisted by services such as AWS), cars could eventually become smart enough to fully replace human drivers.

The idea is not without its criticism of course; there are some who believe that in order for Formula One to remain the sport it is it needs the contest between humans and that what makes the sport exciting is the contest of human will and skill. There is certainly some debate as to how you would establish true competition between autonomous vehicles that would presumably be either hopelessly split in their capability or so close as to remove the excitement of content.

However, with the increased safety alone, it is hard not to imagine that we will at some point see driverless cars take to the tracks.

In March, Lewis Hamilton took second place in the first race of the Australian Grand Prix after something went wrong with his car’s software. In essence, the Mercedes team’s software told them they had a far more comfortable gap in which to operate than they ended up with. A four-second miscalculation saw Sebastian Vettel take first place.

Mercedes team boss Toto Wolff explained the issue saying: "We were always within this three to four-second margin and then suddenly the cameras showed us the pit exit and Sebastian came out in front of us. The drivers always oscillate in the time delta within one second and he did absolutely nothing wrong. It was down to a software bug or an algorithm that was simply wrong."

While such challenges obviously add tension and excitement to F1 races, they also leave room for improvement by enhanced machine learning. While the algorithm may have told the Mercedes team to expect Vettel to spend 15 seconds in the pit, the new data point received from that race could now be fed into the software to watch out for the Ferrari team’s pit times.

The smarter the software becomes, the more it can start to account for just how a driver can and should react to any given situation. This raises the question of whether we may reach a point where the sport is so data-driven it may as well feature autonomous cars – but for now, there is nothing akin to the thrill of human skill.

The underlying concept of Photobox’s AI is for it to tell the customer’s story with them, not for them. When customers were presented with an option that can create an entire album automatically in a matter of seconds, they rejected it.

"They actually want to spend the time doing it,” said Orme. “That's surprising to a technologist who is like 'quicker, faster, faster', but what it helped us to realise is that actually we should use the AI to solve the emotional challenges our customers feel."

One such emotional challenge is the anxiety customers reported when first starting a photo album. This anxiety stemmed from the daunting prospect of 50 empty pages or the overwhelming choices available in design.

“It's up to us then to build a technology to be sympathetic to that and actually to help them be creative, so make suggestions rather than fulfil the customer journey."

Photobox instead used AI for the low-value, monotonous work such as removing duplicates and blurred photos. AI also helps to overcome the first hurdle by suggesting photographs and layouts, which Orme describes as allowing the customer to be the 'director of the movie', having a say on the final cut.

However, research by Photobox found that 45% of 16-24-year-olds believe that AI will be better at finding a gift for a loved one within the next two years compared to just 13% of over-55s, suggesting that future generations may be happier to relinquish more control to AI.

AI can automatically assemble a photo album, but its decision-making process is going to be very different to that of a human. Where an AI would create an album based on categories, such as a colour or theme, a human would make emotional decisions that make no sense to an AI.

But could that change in the future? Orme thinks it unlikely.

“Human beings are very complex and our emotions are driven by an incalculable number of inputs on any given day.

“So do I think AI will truly love, or hate or fear? I think probably not. But I think we'll probably be able to do a reasonable job of making you think that it is. Whether or not we should do that, I think is the interesting question.”

However, Orme does believe that AI can be used to determine someone’s emotional state, which could help brands understand the impact that the technology is having. This could be used to provide feedback to brands and allow them to modify its approach in real-time.

The ability to do so is some way off, though, and many obstacles remain. It is commonly acknowledged, for example, that diversity is needed in the technology industry. Diversity becomes increasingly important when designing AI.

"We have to get more opinions, we have to get diverse perspectives as we're building these technologies. We have to get more diverse datasets to train them on to truly represent what is culturally correct and what's ethically correct."