Sidewalk Labs, a secretive subsidiary of Alphabet, wants to radically overhaul public parking and transportation in American cities, emails and documents obtained by the Guardian reveal. Its high-tech services, which it calls "new superpowers to extend access and mobility", could make it easier to drive and park in cities and create hybrid public/private transit options that rely heavily on ride-share services such as Uber. But they might also gut traditional bus services and require cities to invest heavily in Google's own technologies, experts fear. Sidewalk is initially offering its cloud software, called Flow, to Columbus, Ohio, the winner of a recent 50m Smart City Challenge organized by the US Department of Transportation. Using public records laws, the Guardian obtained dozens of emails and documents submitted to Challenge cities by Sidewalk Labs, detailing many technologies and proposals that have not previously been made public.

When I first decided to focus on machine learning, I didn't really know what to expect. With experience already in python, I did my research and found that sklearn seemed to be a popular library. Within one hour, I had an up and running SVM fitted to some randomly-generated data, swimming in predictions galore. This was definitely not it! As I explored further, I realized that there was so much models and concepts in machine learning and I'd never felt so intimidated.

In a match last October, the AlphaGo program developed by Google's "DeepMind" subsidiary beat, 5 games to 0, the French professional player Fan Hui,1 who is ranked 2 dan (on the professional scale from 1 dan to 9 dan) and is today Europe's best player. The story was related by the journal Nature.2 This was the first time that a computer beats a professional player. But in the world of artificial intelligence, the progress demonstrated by the AlphaGo victory wasn't expected for another ten years or so. The moment of truth, however, will take place between March 9-15 in Seoul, where AlphaGo will face the South Korean Lee Se-dol, who is 9 dan, and is considered the best player in the world as well as a Go living legend. This new game, which will be broadcast live on the Web, comes with a 1,000,000 prize for the human champion if he wins.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. But a different kind of AI is becoming prevalent in nearly all of the sciences. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One place machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet.

It's all about trying to teach computers to make connections, similar to those humans make instinctively when growing up, in distinguishing objects. When it comes to video content, machine learning can help solve one of the growing issues in the industry. Barry Schwarz calls it'the paradox of choice' which he describes in his book and his excellent TED talk. Simply put, there has been an explosion of high quality video content production over the last decade. In 2014, Annalect reported that US consumers wanting to watch episodic TV had over 350 to choose from. Yet, consumers are less happy now than when they had fewer choices. It turns out that too many choices just make decisions harder. So, as an industry, we must come up with new ways of getting a better understanding of what each consumer wants to watch and create tools that will make discovery and recommendation more seamless and effective. In fact, machine learning could very well be the driver of a completely new set of content discovery and hyper-personalized services that will dramatically improve viewer satisfaction.

A clash between robots and workers is unlikely. Rather, disruptive technology can make workers more efficient without replacing them, and raise profits, while maintaining or increasing a company's workforce. Disruptive innovation, if not well-managed and regulated, can have a negative impact on jobs and working conditions. The digital economy and the shifts it causes are moving at a fast pace across all sectors. It bears both opportunities for productivity and well-being, and risks for certain job profiles, specifically routine heavy tasks.

Artificial Intelligence (AI) is the next wave of revolution in the medical health care industry. The wide scale use of AI had been relatively limited until now, mainly due to the shortage of high computing power and advanced processors. AI used to be mainly limited to supercomputers for research processes rather than being available for the mainstream industry. But now that we have access to affordable and advanced computing, seamless connectivity and innovative developments in the AI sector, the healthcare industry stands on the verge of a revolution. Many visionary companies employing AI applications have already been a part of a funding and major M&A activities across the globe.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

AlphaGo, the board-game-playing AI from Google's DeepMind subsidiary, is one of the most famous examples of deep learning – machine learning using neural networks – to date. So it may be surprising to learn that some of the code that led to the machine's victory was created by good old-fashioned humans. The software, which beat Korean Go Champion Lee Sedol 4–1 in March, taught itself to play the ancient Asian game by running millions of simulations against itself. AlphaGo is one of two neural networks, taught by a mixture of supervised learning (studying previous games played by humans) and reinforcement learning (playing against itself and learning from its mistakes). But some things, it turns out, just can't be taught.

Last week MPs warned that the UK is facing a digital skills crisis. Its report explained that 90% of jobs require digital skills to some degree, but flagged that the UK needs another 745,000 workers with digital skills by 2017 in order to remain competitive against other countries. At the moment the skills gap is reportedly costing the UK around 63bn a year. In tandem, we are seeing more companies invest in automation and artificial intelligence (AI), which reduces the need for humans in the decision making process. For example, Twitter recently announced the acquisition of machine learning startup, Magic Pony Technology.