United have won nine of their 10 matches in all competitions in the 22-year-old's absence, with Sancho currently following an individual training programme devised to get him back to the level required to play in the Premier League.

Impersonating someone is hardly a revolutionary type of fraud, but this summer Patrick Hillmann, chief communications officer at cryptocurrency exchange Binance, found himself victim of a new approach to spoofing – using an artificial intelligence (AI) generated video also known as a deepfake. In August, Hillmann, who has been with the company for two years, received several online messages from people claiming that he had met with them regarding "potential opportunities to list their assets in Binance" – something he found odd because he didn't have oversight of Binance's listings. Moreover, the executive said, he had never met with any of the people who were messaging him. In a company blog post, Hillmann claimed that cybercriminals had set up Zoom calls with people via a fake LinkedIn profile, and used his previous news interviews and TV appearances to create a deepfake of him to participate in the calls. He described it as "refined enough to fool several highly intelligent crypto community members."

Artificial intelligence can reveal with incredible accuracy which individuals may develop dementia, new research has found. AI has a 92% accuracy rating for predicting which memory clinic attendees will have dementia within two years, according to the study, published Thursday in the journal JAMA Network Open. The findings are based on data from over 15,300 US patients. Authors say the algorithmic accuracy of AI predictions may be able to reduce the amount of false dementia diagnoses -- and possibly help doctors intervene earlier. "We know that dementia is a highly feared condition. Embedding machine learning in memory clinics could help ensure diagnosis is far more accurate, reducing the unnecessary distress that a wrong diagnosis could cause," said study co-author and University of Exeter research fellow Janice Ranson in a press release.

Borussia Dortmund returned to winning ways in the Bundesliga in emphatic style on Matchday 22 with a thumping 4-0 win away to fierce local rivals Schalke, but it was Erling Haaland, and his first of two goals in particular, that grabbed the headlines. BVB have slid down the table in recent weeks, winning just one of their six league outings preceding Saturday's trip to Schalke, but served a reminder to their local neighbours that they remain the Ruhr region's top dogs, scoring twice in each half to condemn the struggling Royal Blues to a 15th defeat of the season. And Haaland was instrumental in that. Schalke had fought doggedly for much of the first half, but Jadon Sancho's opener on 42 minutes had given Dortmund a deserved lead before Haaland set his seal on the game on the stroke of half-time. Standing at 6'4" and weighing 194 pounds it is usually not a fair fight for defenders, but if not all players are created made equal, neither are all goals. Seeing Sancho receive the ball on the left-hand edge of the Schalke penalty area, Haaland instantly began to peel away from his marker, Bastian Oczipka. Sancho spotted it and floated a cross towards his teammate, who hung in the air before executing a sublime sideways scissors-kick with his left foot into the net from 16 yards. "It was a nice goal," Haaland told bundesliga.com "Obviously it was a good assist from Jadon.

General Game Playing (GGP) is a framework in which an artificial intelligence program is required to play a variety of games successfully. It acts as a test bed for AI and motivator of research. The AI is given a random game description at runtime which it then plays. The framework includes repositories of game rules. The Inductive General Game Playing (IGGP) problem challenges machine learning systems to learn these GGP game rules by watching the game being played. In other words, IGGP is the problem of inducing general game rules from specific game observations. Inductive Logic Programming (ILP) has shown to be a promising approach to this problem though it has been demonstrated that it is still a hard problem for ILP systems. Existing work on IGGP has always assumed that the game player being observed makes random moves. This is not representative of how a human learns to play a game. With random gameplay situations that would normally be encountered when humans play are not present. To address this limitation, we analyse the effect of using intelligent versus random gameplay traces as well as the effect of varying the number of traces in the training set. We use Sancho, the 2014 GGP competition winner, to generate intelligent game traces for a large number of games. We then use the ILP systems, Metagol, Aleph and ILASP to induce game rules from the traces. We train and test the systems on combinations of intelligent and random data including a mixture of both. We also vary the volume of training data. Our results show that whilst some games were learned more effectively in some of the experiments than others no overall trend was statistically significant. The implications of this work are that varying the quality of training data as described in this paper has strong effects on the accuracy of the learned game rules; however one solution does not work for all games.