Darktrace, a global leader in cyber security AI, today announced that it has won the AI & Machine Learning Award at the 2022 Go:Tech Awards. Backed by Business Leader, one of the UK's leading business titles, the annual Go:Tech Awards celebrate the UK's tech pioneers and innovators. Darktrace was named winner in the AI & Machine Learning category, an award which showcases pioneers in the artificial intelligence space. Darktrace was the first to apply artificial intelligence to the challenge of cyber security when it brought'Self-Learning AI' to market in 2013. The technology works by learning a sense of'self' for the organization it is defending, enabling it to understand if a cyber-attack is occurring and then to interrupt the malicious activity in real time.

Darktrace, a global leader in cyber security AI, today announced that it has won the AI & Machine Learning Award at the 2022 Go:Tech Awards. Backed by Business Leader, one of the UK's leading business titles, the annual Go:Tech Awards celebrate the UK's tech pioneers and innovators. Darktrace was named winner in the AI & Machine Learning category, an award which showcases pioneers in the artificial intelligence space. Darktrace was the first to apply artificial intelligence to the challenge of cyber security when it brought'Self-Learning AI' to market in 2013. The technology works by learning a sense of'self' for the organization it is defending, enabling it to understand if a cyber-attack is occurring and then to interrupt the malicious activity in real time.

Because its concise and its minimal, it does not include topics like GANs. Reinforcement learning etc. tt also does not cover Bayesian approaches n detail What is the difference between machine learning and deep learning?

Running a business optimally is not only about finding and convincing customers to buy your product. It's also about retaining customers who've already bought your product, so they will remain loyal to your business. Stopping customer "churn" has long been a big business behind the scenes, but historically it's been a slow, tedious process. Boston, Massachusetts-based marketing-tech startup OfferFit, which announced $14 million in series A funding today, has made halting customer churn its life's ambition in part by replacing standard manual A/B testing with an AI implementation that's faster to respond to customers, more accurate, and more efficient to use. A/B testing (also known as split-testing) is the process of comparing two versions of a web page, email, or other marketing asset and measuring the difference in performance.

Darktrace plans to expand its AI-powered security offerings to include attack prevention by the end of 2021, the company told VentureBeat. On Tuesday, executives from the company described plans for upcoming product updates that will expand the Darktrace portfolio to include proactive security AI capabilities, joining the company's detection and response technologies. The upcoming launch of "prevent" capabilities will extend Darktrace "into the offensive area for the first time ever," said Nicole Eagan, chief strategy officer and AI officer at Darktrace, while speaking at the virtual Gartner Security & Risk Management Summit -- Americas conference on Tuesday. In a statement provided to VentureBeat, Eagan said that "development of this breakthrough innovation known as our'prevent' capability is on track, and we expect this to be released to early adopters by the end of this calendar year." Founded in 2013, the Cambridge, U.K.-based firm went public in April and now has a market capitalization of $4.25 billion.

Israeli AI mobility company Autobrains, the developer of a first-of-its-kind self-learning artificial intelligence technology for assisted and autonomous driving, announced Monday that it has raised $101 million in Series C funding round led by Temasek, a global investment company headquartered in Singapore. Additional participants in the round included new investors Knorr-Bremse AG, a leading automotive player and VinFast, as well as existing investor BMW and long-term strategic partner, German automotive part manufacturer Continental AG. The announcement reflects the anticipation from global leaders, including Tier 1 suppliers and OEMs, as well as sophisticated investment funds, that Autobrains is disrupting the advanced driver-assistance systems (ADAS) and autonomous vehicle marketplace with its alternative approach to AI for automation. The company will use the funding to grow its commercial reach into new global markets. Autobrains' self-learning AI is fundamentally different from other deep learning systems.

Alphabet's Loon, the team responsible for beaming internet down to Earth from stratospheric helium balloons, has achieved a new milestone: its navigation system is no longer run by human-designed software. Instead, the company's internet balloons are steered around the globe by an artificial intelligence -- in particular, a set of algorithms both written and executed by a deep reinforcement learning-based flight control system that is more efficient and adept than the older, human-made one. The system is now managing Loon's fleet of balloons over Kenya, where Loon launched its first commercial internet service in July after testing its fleet in a series of disaster relief initiatives and other test environments for much of the last decade. Similar to how researchers have achieved breakthrough AI advances in teaching computers to play sophisticated video games and helping software learn how to manipulate robotic hands in lifelike ways, reinforcement learning is a technique that allows software to teach itself skills through trial and error. Obviously, such repetition is not possible in the real world when dealing with high-altitude balloons that are costly to operate and even more costly to repair in the event they crash.

"This is the single most important problem to solve in AI today," says Yann LeCun, chief artificial intelligence scientist at Facebook Inc. It is a Manhattan Project-like effort that will go on for years, if not decades. At Facebook, Alphabet Inc.'s Google and other companies and universities around the world, scientists are working to create better AI that learns through self-supervision, teaching itself about the world the way people do. The immediate goal is broader AI that can perform multiple tasks, but that could one day lead to artificial general intelligence, or machines with humanlike thinking. A look at how innovation and technology are transforming the way we live, work and play.

Machine learning is becoming so smart that algorithms designed to set prices in online marketplaces are mirroring each others' behaviour to raise prices. Algorithms using self-learning AI are popular systems that have become adopted by Amazon to constantly learn and set the best prices in order to drive website profit. An experiment by researchers in Bologna used algorithms similar to those manipulated by online shopping sites and found they were able to'collude' to artificially hike up prices. The researchers showed that this could happen entirely out of human control, as the independent AI systems were able to learn each others' behaviours. Machine learning is becoming so smart that online price setting algorithms are mirroring each others' behaviour to raise prices and with a goal to raise profits.

A team of researchers from Belgium think that they are close to extending the anticipated end of Moore's Law, and they didn't do it with a supercomputer. Using an artificial intelligence (AI) algorithm called reservoir computing, combined with another algorithm called backpropagation, the team developed a neuro-inspired analog computer that can train itself and improve at whatever task it's performing. Reservoir computing is a neural algorithm that mimics the brain's information processing abilities. Backpropagation, on the other hand, allows for the system to perform thousands of iterative calculations that reduce error, which lets the system improve its solution to a problem. "Our work shows that the backpropagation algorithm can, under certain conditions, be implemented using the same hardware used for the analog computing, which could enhance the performance of these hardware systems," Piotr Antonik explains.