The gaming sector has performed well in 2016, with many of the top names in the space, fromNintendo(NASDAQOTH: NTDOY) to chipmaker NVIDIA(NASDAQ: NVDA), posting solid returns.Well-received new content, impressive new technology, and renewed ideas about how to get both of those to consumers have helped the sector grow this year. Here were some of the winners in 2016, and what the future could hold for these companies. Forget GE! Heres how to play the largest growth opportunity in history Forget GE! Heres how to play the largest growth opportunity in history Though it was up around 80% before a mid-December sell-off, the company has still had a remarkable year. Nintendo's resurgence follows the company's decision to finally develop a mobile strategy and bring its timeless characters and stories into a modern age. Nintendo was one of the big winners with the Pokemon Go phenomenon that exploded worldwide this year, and the company announced a new game console called Switch to come out in 2017, which has helped to raise its stock price as well.

To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an "AI supercomputer in a box" is a welcome development. Early customers of Nvidia's DGX-1, which combines machine-learning software with eight of the chip maker's highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services. Data scientists have been leveraging GPUs to accelerate deep learning--an AI technique that mimics the way human brains process data--since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren't possible. It costs $129,000, more than systems that companies could assemble themselves from individual components.

Self-driving electric cars could make car parks obsolete within the next 10 years and reduce air pollution to almost zero in Scotland's cities, an expert has predicted. The vehicles are likely to be commonplace by 2030, said Simon Tricker, of "smart cities" specialist UrbanTide, which uses technology and data to improve city planning. "Scottish local authorities are already thinking about what city streets will look like in a decade's time - and the answers are pretty astounding," he said. Private companies can't be trusted on driverless cars, minister warns Ford to offer driverless commercial vehicles by 2021 Driverless car safety revolution could stall over moral dilemma Ministers pledge to put UK at heart of driverless car revolution Private companies can't be trusted on driverless cars, minister warns "Self-driving cars won't need parking spaces in cities - they're likely to be rented rather than owned and will just head off and carry out their next journey after dropping passengers off. Many car parking spaces which we now take for granted will simply become obsolete. "The pace at which electric vehicle technology is developing means they're also likely to be electric, so will produce zero emissions as they're driven.

Hewlett Packard Enterprise (NYSE:HPE) announced a new release of its flagship unstructured data analytics engine, HPE IDOL, featuring advanced Natural Language Question Answering. The new version of HPE IDOL leverages advanced machine learning functionality to improve the effectiveness and contextual accuracy of human interactions with computers. Among the biggest challenges facing organizations trying to leverage Big Data is providing answers to users' questions in a natural, effective manner without cumbersome user interfaces or extensive training. Interactive voice assistants and online chatbots have recently simplified this process for consumers, however developers have had a difficult time adapting this approach to enterprise-class tasks due to the complexity and context of the questions, trustworthiness of the source, specificity of the information needed and accuracy of the answer. HPE Natural Language Question Answering deciphers the intent of a question and provides an answer or initates an action drawing from an organization's own structured and unstructured data assets in addition to available public data sources to provide actionable, trusted answers and business critical responses.

The paper tackles the problem of teaching AI to recognize objects using simulated images, which are easier to use than photos (since you don't need a human to tag items) but poor for adapting to real-world situations. The trick, Apple says, is to use the increasingly popular technique of pitting neural networks against each other: one network trains itself to improve the realism of simulated images (in this case, using photo examples) until they're good enough to fool a rival "discriminator" network. Ideally, this pre-training would save massive amounts of time and account for hard-to-predict situations that don't always turn up in photos. This doesn't mean that Apple is suddenly an open book. It could take years before it's clear how transparent Apple has become with its scientific findings.

Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn't happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side's perspectives and paradigms. The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4].

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

More Open AI and Machine Learning Toolsets Arrive by - Dec. 02, 2016 Google's Open Embedded Projector is a Cool Data Visualization Tool Google Collects Open Artificial Intelligence Demos, Invites You to Contribute The Renaissance Continues for Open Source Artificial Intelligence Microsoft Open Sources Transformative Speech Recognition Toolkit Google Open Sources Powerful Image Recognition Tool Recently, in an article for TechCrunch, Spark Capital's John Melas-Kyriazi weighed in on how startups can leverage artificial intelligence and machine learning to advance their businesses or even give birth to brand new ones. As a corollary avenue on that topic, it's worth noting that some very powerful artificial intelligence and machine learning engines have recently been open sourced. Quite a few of them have been tested and hardened at Google, Facebook, Microsoft and other companies, and some of them may represent business opportunities. Just recently, two new open source entries on this front have emerged, and they are worth investigating. Health Catalyst has created healthcare.ai as a repository of healthcare-focused open source machine learning software, with an eye toward encouraging the healthcare industry to tap into the power of AI and machine learning.

To learn more about the state of AI today and where we might be headed in coming years, download the free report "What is Artificial Intelligence?," by Mike Loukides and Ben Lorica. There are four basic ingredients for making AI: data, compute resources (i.e., hardware), algorithms (i.e., software), and the talent to put it all together. In this era of deep learning ascendancy, it has become conventional wisdom that data is the most differentiating and defensible of these resources; companies like Google and Facebook spend billions to develop and provide consumer services, largely in order to amass information about their users and the world they inhabit. While the original strategic motivation behind these services was to monetize that data via ad targeting, both of these companies--and others who are desperate to follow their lead--now view the creation of AI as an equally important justification for their massive collection efforts. While all four pieces are necessary to build modern AI systems, what we'll call their "scarcity" varies widely.