Microsoft's attempt to use machine learning to improve on the Duckworth-Lewis method in cricket has been dismissed by the current custodian of the system. For the uninitiated, the Duckworth-Lewis method, or D/L method for short, is used to calculate the score that the team batting second in a limited overs cricket match needs to reach if the match is affected by rain. For example, if the team batting first scores 400 in 50 overs for five wickets, but rain reduces the second team's innings to 40 overs, the D/L method may put forward a score of 300. It was invented by statisticians Frank Duckworth and Tony Lewis and was first used in 1997 in a match between England and Zimbabwe. It is now regularly used at matches at all levels.

Hiding behind pixelated and blurred faces will soon be a thing of the past, thanks to artificial intelligence. Researchers at the University of Texas at Austin and Cornell Tech has come out with a paper which outlines the working of a software that can reconstruct faces and objects that have been masked using techniques like blurring and pixelation. A blurred number plate or a pixelated human face, anything can be decoded by this image recognition software. What's even more scary is that the researchers didn't even have to come up with any new image reconstructing algorithms to make it work. Instead, the team used mainstream machine learning methods where the computer is trained with a set of example data rather than programming.

According to newly published research from Juniper, RTB -- where advertising inventory is bought and sold on a per-impression basis, via programmatic instantaneous auction -- has now been made accessible and affordable to a wide range of stakeholders by machine learning. The analyst firm says that machine learning promises to transform the RTB segment of the digital advertising market, with algorithms able to predict the success outcome of an impression, and adjust bid amounts dynamically. By way of example, Juniper explains that the fact that the end-user has recently been delivered a similar advert may impact their likelihood of clicking on another and, meanwhile, other behavioural and contextual attributes are used to predict how successful an impression may be on an individual level. "Typical RTB allows the advertiser to target demographics or various population subsets," research author Steffen Sorrell says. "Adding machine learning into the mix effectively allows RTB networks to target the individual. This is a much more powerful tool."

Baidu Inc, the Chinese internet search giant, has created a US 200 million venture capital unit to invest in artificial intelligence projects. The new unit, Baidu Venture, is chaired by chief executive Robin Li Yanhong and will focus on projects also in augment reality and virtual reality. "The first-phase investment is planned at US 200 million and the fund will invest in projects that are at their early stage," the company said. The unit will be independently run from Baidu's existing investment teams, allowing it to make fast decisions without having to go through complicated internal approval process, it added. Baidu is betting big on AI technology as a fresh income stream amid a slowdown in its search and advertising business.

The corporate learning environment has been experiencing a great deal of development over recent years. It shows no signs of stopping as learners become more involved in their own education. Gone are the days when the organization dictated what should be learned and how. Learners are now demanding that they are educated in a way that works for them. The teams usually responsible for corporate learning within companies, human resources, are also undergoing a period of change as they identify areas where they need to come up to speed to deliver the most tangible results for their companies.

When we see two people meet, we can often predict what happens next: A handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. Momentum by TNW is our New York technology event for anyone interested in helping their company grow.

Toronto-based AI startup Diligen has entered the battle for a share of the legal due diligence market. In what is now a rapidly growing legal tech market, Canadian company Diligen is joining a number of startups targeting law firms with an AI-driven due diligence capability. The eight-person company already has several law firm clients, though at present isn't at liberty to name them. The company only started last year and says it has not, as yet, 'officially launched', though it is in full operation. Founders, Laura van Wyngaarden and former Baker & McKenzie associate Konrad Pola, said that their company's project management focus is what differentiates them from other AI-driven due diligence providers.

A retirement home in north Toronto is preparing to welcome an unusual resident: Ludwig, an artificially intelligent robot. Adorned with spiky mauve-coloured hair, green-tinged eyes and a few quirky facial expressions to mimic a range of emotions, the two-foot-tall robot is made to look and act like a little boy. By drawing his elderly neighbours into conversation, Ludwig's creators say he can track and monitor signs of Alzheimer's disease or dementia. He's so good, he can detect subtle changes in speech and vocal patterns that might escape retirement home staff, says Isaac Weinroth, executive director of One Kenton Place, where Ludwig will begin trials next month. "Even things like the time gap between verbs, or the use of verbs, or lack of verbs, the time gap between sentences, between words in sentences," he says.

Not quite breathtaking speed, yet events of 1895 were still history in the making. It's hard to imagine anything that could transform the way we move around quite as much as this. Yet industry experts recently gathered at Frost & Sullivan's Intelligent Mobility conference in London to consider innovations that could be every bit as history making as the arrival of the first motor cars. From the rise of electric vehicles to the arrival of a super-connected world, to a shift towards car-sharing apps and a need to keep crowded cities moving, mobility is rapidly evolving. Add artificial intelligence (AI) to the equation, and the changes ahead seem even more dramatic. Our forefathers gazed in wonder at the horseless carriage – are we now staring at a world once more about to be transformed?

Managing and, preferably, optimizing your full financial portfolio, an investment portfolio, a pension plan and so on is traditionally a human task. It is done by various possible people such as investment managers, wealth managers, financial advisers and even accountants. Since just a few years a range of new fintechs hit the market with so-called robo-advisers or automated financial advice tools. They have been popping up (and keep popping up) in no time and some already even dissapeared. Automated financial advice and optimization is a hot and diverse market with the launch of several platforms, digital and/or mobile in a really short timeframe.