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For the last few years, Intel (INTC) has been shifting its focus away from PC to data-centric businesses. It's looking to tap future technologies such as AI, autonomous vehicles, and 5G networking infrastructure. NVIDIA (NVDA) is a leader in the AI space. Intel has identified NVIDIA as its AI competitor, as data centers prefer the latter's Tesla GPUs (graphics processing unit) for their AI workloads. Intel has tried to compete with NVIDIA's Tesla GPUs with its Altera field-programmable gate arrays, Xeon Phi processors, and traditional Core processors.

Welcome to the Semantic Web - Chris Skinner's blog. Vincent Fournier/Gallerystock By Toby Walsh However you look at it, the future appears bleak. The world is under immense stress environmentally, economically and politically. The novelist who inspired Elon Musk. Elon Musk, the world's most restless entrepreneur, has embarked on yet another venture.

You have to wonder whether Nvidia is going to get sick of winning all the time. The company's stock price is up to $178--69% more than this time last year. Nvidia is riding high on its core technology, the graphics processing unit used in the machine-learning that powers the algorithms of Facebook and Google; partnerships with nearly every company keen on building self-driving cars; and freshly announced hardware deals with three of China's biggest internet companies. Investors say this isn't even the top for Nvidia: William Stein at SunTrust Robinson Humphrey predicts Nvidia's revenue from selling server-grade GPUs to internet companies, which doubled last year, will continue to increase 61% annually until 2020. Nvidia will likely see competition in the near future.

Intel has announced a neuromorphic artificial intelligence (AI) test chip named Loihi, which it said is aimed at mimicking brain functions by learning from data gained from its environment. According to Intel, its efforts in "comparing machines with the human brain" have resulted in a self-learning, energy-efficient AI chip that uses asynchronous spiking to take inferences from its environment and become constantly smarter. Loihi has digital circuits mimicking the basic mechanics of the brain, corporate vice president and managing director of Intel Labs Dr Michael Mayberry said in a blog post on Tuesday, which requires lower compute power while making machine learning more efficient. "Neuromorphic chip models draw inspiration from how neurons communicate and learn, using spikes and plastic synapses that can be modulated based on timing. This could help computers self-organise and make decisions based on patterns and associations," Mayberry explained.

Before looking ahead and seeing what tomorrow has in store, let's take a step back to find out about the history of artificial intelligence and what the main reasons behind its creation are. The first steps that led to the birth of this discipline date back to the 1600s. It was only in the last century, however--in 1959 to be exact--that Arthur Samuel, a pioneer in artificial intelligence, suggested that, instead of receiving everything they needed from programmers, computers could learn autonomously. This belief was consolidated over the years, and with the spread of the Internet and the increased use of sensors and mobile devices, it became possible to create and aggregate huge amounts of data from which machines are now able to extract meaningful information. In the world of artificial intelligence, machine learning (or automatic learning) represents a further step forward.

Intel's artificial intelligence efforts have been scattered over many different units but are now being united into a single operating group. The Artificial Intelligence Products Group will focus on the development of chips and software products tied to machine learning, algorithms, and deep learning. The new group could become Intel's single most important group as companies implement machine learning into operations. Intel is tweaking more chips and developing software to take on workloads like analytics, image recognition, and automation. Intel is designing a new Xeon Phi chip code-named Knights Mill that will focus on machine learning. Additionally, it is applying its wide portfolio of FPGAs (field programmable gate arrays) to artificial intelligence.

Lux Capital's portfolio companies sound like they ripped their product concepts from the pages of science fiction. Among these are self-driving car startup Zoox, a company gathering oceanographic data from fleets of autonomously operating boats called Saildrone, Desktop Metal whose 3-D printers that can make objects out of alloys on the spot, and 3Scan, a startup that makes detailed 3-D models of human tissue to help doctors better diagnose diseases, and several neuroscience startups including Kallyope, Halo Neuroscience and Cala Health. Today Lux Capital partners revealed that they have closed their fifth fund at $400 million to continue investing in the startups that are "inventing the future without destroying humanity." Lux Capital also announced that long-time venture investor, attorney (and karate black belt) Renata Quintini is also joining the firm as a partner. She previously worked at Felicis Ventures, was an investment manager for Stanford's endowment, and was an investor in Cruise Automation and Dollar Shave Club, among others.

This year was far from the perfect environment for many startups looking to grow their business. But despite uncertainty here in the US and overseas, a slew of entrepreneurs took the plunge and started their own businesses. Success came to founders and investors who focused on high-growth areas. Those include the ride-sharing app Juno, which competes with big players like Uber and Lyft, as well as several companies focused on cybersecurity and virtual reality, among others. So what will the number one startup trend be in 2017?

Its expected over 50 billion devices will be connected by 2020, these includes wearables, vehicles, city infrastructure and probably sensors in every home appliance. Intel is working hard towards this vision and identified these areas the chipmaker will be focusing advancely for 2017. Artificial intelligence, 5G networks, automated driving and virtual reality/merged reality. The global robotics and AI market is estimated to grow to $153 billion by 2020, which includes $83 billion for robotics and $70 billion for AI-based analytics. The technology supporting some nascent AI applications, such as natural language processing and bots will greatly improve, paving the way for more widespread adoption of AI.