Earlier this month, Apple announced that it would allow its artificial intelligence researchers to publish research papers -- a major shift in the notoriously secretive company's policy. Now, just a few weeks later, the first of these papers has been made public on the preprint server arXiv. The paper -- titled "Learning from Simulated and Unsupervised Images through Adversarial Training"-- deals with intelligent image recognition technology. Specifically, it describes a technique that would enable a program to recognize and decipher computer-generated images. So far, this has not been possible because, as the researchers from Apple note, "synthetic data is often not realistic enough," and increasing the realism is "computationally expensive."

About two years ago, AT&T named Affirmed Networks a Domain 2.0 supplier for its virtual evolved packet core (vEPC), and the 5-year-old startup has taken off ever since. The company's claim to fame was jump-started by its vEPC running production traffic in LTE networks. This is important because at the time, not even companies like Ericsson or Alcatel Lucent were commercially shipping their vEPCs. Evolved packet cores are the complex network hardware that cellular data and voice networks rest on. And to be able to virtualize that was quite the accomplishment and performance enhancer for telcos.

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What Does Artificial Intelligence Mean in Human Resources and Recruiting? One of the most talked about trends in HR and recruiting in the second half of 2016 has been AI and artificial intelligence. Artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal." It's a branch of computer science that uses machine learning algorithms that mimic cognitive functions; making machines more human-like. And who doesn't need more human-like functions in an industry that is called human resources?

The video game industry has been a surprising growth market this year, fueled by popular games and new technology. These companies stood out in 2016. The gaming sector has performed well in 2016, with many of the top names in the space, from Nintendo (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.

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.