An intelligent machine capable of anticipating your next move minutes in advance sounds like the stuff of nightmares – but is now a reality.
Researchers have taught an AI to recognise patterns in people’s actions, allowing it to accurately predict the next move in a sequence minutes in advance.
The software, which was built by a team at the University of Bonn in Germany, was taught to anticipate actions by watching hours of cooking videos.
Dr Jürgen Gall believes the intelligent software will eventually be able to prophesize your actions ‘hours before they happen’.
If the team manages to fine-tune the algorithm to be able to anticipate actions that far in advance, it’s possible to imagine a slew of real-world application, from home automation gadgets, to Big Brother-esque surveillance.
To teach the AI to accurately predict actions before they take place, Dr Jürgen Gall and his team focused on cooking videos.
Using pre-recorded videos of people preparing a meal, the researchers were able to teach the machine to recognise each action being performed on-screen, including cutting tomatoes, adding salt and flipping a pancake.
In total, some 40 videos were used to teach the AI.
Each of these recordings was around six minutes long and contained some 20 different actions.
After four hours, the algorithm was able to recognise the sequence of events needed to prepare a dish, which is far from trivial given the variety in approaches and recipes in the pre-recorded clips.
‘Then we tested how successful the learning process was,’ explained Dr. Jürgen Gall.
‘For this we confronted the software with videos that it had not seen before.’
Like before, the machine was told what was happening in the video for the first 20 or 30 per cent of the clip.
The algorithm was then asked to predict the next action before it took place on-screen.
The machine flagged-up its ‘observation’ before the action, drawing on its knowledge of the recipe and its understanding of how similar sequences have played out before.
The AI was able to correctly anticipate actions in the near future with surprising accuracy.
Dr Gall said: ‘Accuracy was over 40 percent for short forecast periods, but then dropped the more the algorithm had to look into the future.’
For activities which were more than three minutes in the future, the algorithm was only able to accurately predict the outcome in 15 per cent of cases.