Latest machine learning publications
While machine learning has transformed many industries over the past decade, one area that is still playing catch-up is insurance. It’s a sector used to finding itself trailing behind other industries’ tech adoption, where high running costs of legacy systems squeeze budgets to such an extent that it’s hard for firms to stump up the cash necessary for driving innovation. While online comparison services have proliferated in recent years, signing up to and managing the policy invariably involves the pens, paper, and printers that other digitally-transformed industries have long since left behind.
While the cloud has enabled companies of all stripes to harness the power of machine learning, AI workloads like machine learning training and inference are computationally demanding and expensive.
According to some estimates, machine learning inference, the process of using a trained model to make predictions, can represent up to 90 percent of overall operational costs for running ML workloads.
Facial recognition technology and 3D athlete-tracking to enhance the viewing experience of the Olympic Games will be used during Tokyo 2020, Intel has said.
The computer chip-maker, which is a leading partner of the major international multi-sport event, will be able to identify more than 300,000 people at the Games in Japan, including athletes, volunteers, media and other staff.
No other technology has captured the world’s imagination quite like AI, and there is perhaps no other that has been as disruptive. AI has already transformed the lives of people and businesses and will continue to do so in endless ways as more startups uncover its potential. According to a recent study, venture capital funding for AI startups in the UK increased by more than 200 percent last year, while a Stanford University study observed a 14-times increase in the number of AI startups worldwide in the last two years.
Cyber AI specialist Darktrace has launched a new cyber security tool that emulates real life thought processes to investigate cyber threats at a fraction of the speed of humans.
The UK security firm has been developing the technology behind Cyber AI Analyst for three years at its R&D centre in Cambridge, where researchers used a combination of unsupervised, supervised and deep learning to obtain an algorithmic thumbprint of human intuition and cyber analyst know-how. 100 “world-class” cyber analysts representing a variety of customer deployments were studied as part of the research, the company said.