The idea is similar to Grid Search, but instead of trying all possible combinations we will just use randomly selected subset of the parameters. Instead of trying to check 100,000 samples we can check only 1,000 of parameters. Now it should take a week to run hyperparameter optimization instead of 2 years. Let's sample 100 two-dimensional data points from a uniform distribution. In case if there are not enough data points, random sampling doesn't fully covers parameter space.

It is common, at the beginning of the year, to ponder upon what the year ahead will bring. Several experts have published their predictions for trends we can expect in legal technology, in 2017. So, what are they saying? Generally speaking, they expect lawyers to become more mobile, more collaborative (using the cloud do to do), and more responsive (using social media to engage with clients and potential clients). Cybercrime & Cyberwarfare, too, will remain in the news.

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In surpassing 30,000 attendees - up from 19,000 the year previous - AWS re:Invent 2016 continues to capture the imagination of the partner, customer and developer communities. Yet despite the bumper crowds, it was intelligence exhibited by machines that stole the show in Las Vegas. Artificial intelligence to be precise, heralded as the next great disrupter in cloud, and the weapon of choice for vendors fighting for increased market share. While nothing is certain in life but death and taxes - well, perhaps for some - when it comes to public cloud, the dominance of Amazon Web Services is both predictable and undeniable. Yet the battle for control of the skies has been raised a notch further with the tech giant enhancing its services across its broad portfolio, with its new cloud-native database offerings designed to lure large enterprise accounts.

This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently. When your car drives you to the parking lot you get to the level four, when there is no need for a human operator for a specific use case or a part of a journey.

Beginning with the status of Artificial Intelligence, we will continue by envisioning the development of AI over the coming years, before discussing the implications of Rise of AI for us as a whole. See our preliminary program and check out our AI Topics.

AI's next frontier requires ethics built through policy. With one foot in its science fiction past and the other in the new frontier of science and tech innovations, AI occupies a unique place in our cultural imagination. Will we live into a future where machines are as intelligent -- or frighteningly, more so -- than humans? We have already witnessed AI predict the outcome of the latest U.S. presidential election when many policy wonks failed. Perhaps we are further along than we thought.

Video viewability is a top priority for video publishers who are under pressure to verify that their audience is actually watching advertisers' content. In a previous post How Deep Learning Video Sequence Drives Profits, we demonstrated why image sequences draw consumer attention. Advanced technologies such as Deep Learning are increasing video Viewability through identifying and learning which images make people stick to content. This content intelligence is the foundation for advancing video machine learning and improving overall video performance. In this post, we will explore some challenges in viewability and how deep learning is boosting video watch rates.

IBM has produced a glossy multimedia website (see below) to expound the possibilities under the label "IBM 5 in 5: five innovations that will help change our lives within five years." And analog, MEMS and sensors are prominent. But digital electronics will also have its say in the form of big data and artificial intelligence. The full list is artificial intelligence; hyperspectral imaging; microfluidic lab-on-chips, networks of novel sensors and something IBM calls "macroscoping." Amongst the developments IBM researchers are predicting – and a slightly disconcerting prediction at that – is that what we say and write could be monitored and used as indicators of our mental health and physical well-being. Patterns of speech and writing and how they change over time, analysed by cognitive systems, could provide tell-tale signs of early-stage developmental disorders, mental illness and degenerative neurological diseases.

With China making rapid progress in artificial intelligence (AI)-based research, it is imperative that India view AI as a critical element of its national security strategy, recommends an August 2016 report titled India and the Artificial Intelligence Revolution. Thanks to the increasingly digital economy, fuelled by improving education and globalization, the Indian consumer is unknowingly the country's biggest beneficiary of recent advances in AI, notes the report. From utilizing various applications powered by AI to using a range of online services such as Amazon Marketplace and Netflix that learn from consumers' online behaviour to make intelligent product and service recommendations, consumers are readily engaged with the proliferation of AI in India, whether they appreciate it or not. Indian academics, public researchers, labs, and entrepreneurs face a different challenge than the corporations that dominate the space--the infrastructure necessary for an AI revolution in India has been neglected by policymakers. While lack of physical infrastructure is certainly a major impediment, India's AI development also suffers from the paucity of the necessary cultural infrastructure, which is key for recent advances from lab to marketplace in AI.