To assist a child we must provide him with an environment which will enable him to develop freely. There's a kindergarten I walk past on the way to work, and I can't help but peek inside everyday. The classroom -- packed with toys and puzzles, music and books, flower planters and even an occasional cat -- was obviously crafted to be a rich and bustling world for kids to interact and play in. Contrary to its meaning, child's play is far from simple. Playing in a diverse, exciting universe is how we nurture a child's budding intelligence.

Artificial intelligence technologies might find a considerable hurdle in privacy rights, obligations and sanctions provided by the EU General Data Protection Regulation. As part of the series of blog posts on the major changes introduced by the EU Data Protection Regulation, here is an article on how it might affect artificial intelligence. What are artificial intelligence technologies? The perfect example of an artificial intelligence is a self driving car which needs to take autonomous decision based on whatever happens on a street. And a confirmation of the current concerns (and prejudices) around AI is a new study from Germany's Federal Highway Research Institute which found that the autopilot feature of the Tesla Model S constitutes a "considerable traffic hazard".

This year delivered a whole host of body blows to the UK's thriving startup sector. There has been an investor slowdown, few notable IPOs, increased market volatility and shining lights sold abroad. Then there was the biggest punch of them all, the Brexit vote to leave the European Union and all of the issues this brings up for a sector reliant on foreign talent. So what can the UK tech startup scene expect in 2017? It will no doubt be a year of further turmoil, and startups will need to be prepared for the worst, but there are also green shoots of hope for smart founders and investors who are focusing on the right areas.

The Internet of Things is here, they cried. Great, but do I really have to pull out my smartphone to do everything? Pushing a button was so much easier. Can't I just talk to my house now? Can I scream "red alert" and have my lights flash red? For decades we've been shown that voice is the future.

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As Italy is slowly recovering from a series of deadly earthquakes, researchers at the Italian Institute of Technology of Genoa are developing a new generation of robots to work in disaster zones. The key to the four-legged robot, called HyQ, an acronym for "Hydraulic Quadruped", is its mobility. September ... Teton Capital Partners long / short strategy delivered 8.1% In 2014 Pacific Investment Management Company (PIMCO) Emerging Markets Bond Fund Suffers Losses on Russian Bets Congress Could Be About to Give this Asset Class a Huge Promotion – Muni Bonds It appears there's no shortage of investor love for municipal bond funds.

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In the race to build the best AI, there's already one clear winner As Google, Facebook, Microsoft, and Baidu take turns leapfrogging each other in artificial intelligence innovation, one company stands to profit from any outcome: Nvidia. Graphics processor units, the company's biggest moneymaker, have become the industry standard for deep learning, a flavor of artificial intelligence widely used by tech companies to build personal virtual assistants, image recognition for tagging photos, and even the software behind self-driving cars. Despite talks from Microsoft and Google about developing their own proprietary chips, almost every major tech company is partnered up with Nvidia and uses its hardware. Last month, Microsoft announced a partnership to work with Nvidia's AI-tailored DGX-1 supercomputer, and Google's recently revamped cloud services will offer the option to run on Nvidia GPUs in 2017. Facebook's open-source Big Sur design for their server racks also rely on Nvidia hardware.

Folding laundry, it turns out, is really hard to automate. Researchers from the UK, Czech Republic and Greece have used this seemingly simple task to extend the limits of machine learning and robotics. Andreas Doumanoglou, a PhD Student at Imperial College London, and his team programmed a two-armed robot to identify and fold laundry through a series of steps, each one with it's own challenges.

Chess is less complex than Go, but it still has too many possible configurations to solve by brute force alone. Instead, programs cut down their searches by looking a few turns ahead and judging which player would have the upper hand. In Go, recognizing winning and losing positions is much harder: stones have equal values and can have subtle impacts far across the board. To interpret Go boards and to learn the best possible moves, the AlphaGo program applied deep learning in neural networks -- brain-inspired programs in which connections between layers of simulated neurons are strengthened through examples and experience. It first studied 30 million positions from expert games, gleaning abstract information on the state of play from board data, much as other programmes categorize images from pixels.