Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. Focal points have included the use of general purpose and specialized CPUs, GPUs, custom ASICs, and more recently, FPGAs. As the battle to match the correct hardware devices for these training workloads continues, another has flared up on the deep learning inference side. Training neural networks has its own challenges that can be met with accelerators, but for inference, the efficiency, performance, and accuracy need to be in balance. One developing area in inference is in the use of binarized neural networks (BNNs)--an approach that has implications on the low-precision side for both hardware and software.

Natural language processing, big data, machine learning -- these are all big topics. Seriously, Google it or listen to a Ted talk, it's everywhere. But let's break it down, because these buzzwords are often thrown around and rarely explained at the fundamental level. What exactly is natural language processing (NLP) and why is it relevant to you and your business? I spoke with our resident linguist Ascander Dost here at SalesforceIQ to get down to the basics.

Mark Zuckerberg envisions a software system inspired by the "Iron Man" character Jarvis as a virtual butler managing his household. The Facebook founder's dream is about artificial intelligence, which is slowly but surely creeping into our daily lives, no longer just science fiction. Artificial intelligence or AI is getting a foothold in people's homes, starting with the Amazon devices like its Echo speaker which links to a personal assistant "Alexa" to answer questions and control connected devices such as appliances or light bulbs. Analyst Carolina Milanesi of the research firm Creative Strategies said that "2016 was the year about raising awareness, and exposing consumers to the idea of AI in a more mass market way." Milanesi said it may take time for the technology to fulfill its potential, noting that companies need "a strong hook" to bring large numbers of consumers into this world.

Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). Developed with ZTE, a leading technology telecommunications equipment and systems company, the image recognition technology is what many companies in Internet search and AI are trying to advance. Perception, such as recognizing a face in an image, is one of the essential goals of the ZTE 5G System," said Duan Xiangyang, vice president of the ZTE Wireless Institute. "Deep learning technology is very important as it can enable such perception in mobile edge computing systems, thus making ZTE's 5G System smarter." The test took place in Nanjing City, China, where ZTE's engineers used Intel's midrange Arria 10 FPGA for a cloud inferencing application using a CNN algorithm. ZTE has achieved a new record – beyond a thousand images per second in facial recognition – with what is known as "theoretical high accuracy" achieved for their custom topology. Intel's Arria 10 FPGA accelerated the raw design performance more than 10 times while maintaining the accuracy. The Arria 10 FPGA provides up to 1.5 teraflops (TFLOPs) single precision floating-point processing performance, 1.15 million logic elements, and more than a terabit-per-second high-speed connectivity. Such deep learning designs can be seamlessly migrated from the Arria 10 FPGA family to the high-end Intel Stratix 10 FPGA family, and users can expect up to nine times performance boost. Besides the impressive increase in performance, the team at the ZTE Wireless Institute sped design time with the use of the OpenCL programming language. With the Intel reference design, and using the Intel SDK for OpenCL to program the FPGA, our development time was greatly shortened," said Xiong Tiankui, chief engineer, ZTE Wireless Institute.

Stephen Hawking and Elon Musk have joined prominent artificial intelligence researchers in pledging support for principles to protect mankind from machines and a potential AI arms race. An open letter published by the Future of Life Institute (FLI) on Monday outlined the Asilomar AI Principles--23 guidelines to ensure the development of artificial intelligence that is beneficial to humanity. For decades, science fiction writer Isaac Asimov's'Three Laws of Robotics' were a cornerstone for the ethical development of robots and artificial intelligence machines. First laid out in his 1942 short story Runaround, Asimov's three principles stated: A robot must not harm a human through action or inaction; a robot must obey humans; and a robot must protect its own existence. Each rule takes precedence over the rules that follow it in order to ensure a human's life is protected over the existence of a robot.

Although AI hasn't yet taken off in the enterprise market, it soon may. AI could be used to enhance customer service, provide companies with recommendations based on data analytics, root out fraud or help manufacturers find defects in products before they're shipped. The market for AI in the business world is going to heat up, according to research firm IDC, which predicts that the market for cognitively enabled applications and software is going to be worth $40 billion in 2020. One kind of AI that has gotten a lot of attention lately is machine learning, an artificial intelligence that can learn from and make predictions based on data it accesses. Google is building the technology into the company's cloud product in hopes of winning enterprise business.

Big Data Lab (BDL) BDL is led by Dr. Tong Zhang. BDL focuses on large-scale machine learning algorithms and applications in areas such as predictive analytics, large data structure algorithms, and intelligent systems research. BDL's mission is to make people's lives better through big data. Institute of Deep Learning (IDL) Baidu launched the Institute of Deep Learning in 2013. The team's focus areas include image recognition, machine learning, robotics, human-computer interaction, 3D vision and heterogeneous computing.

AI is a powerful force and a reality for everybody. It improves our healthcare, shopping and travel experiences and is starting to make inroads into the workplace and government. With that, comes a responsibility to make sure AI is a force for good, and that its tremendous power does not create a new chasm in our society. Many areas of public policy, from education and the economic safety net, to defense, environmental preservation, and criminal justice, will see new opportunities and new challenges driven by the continued progress of AI. Government must continue to build its capacity to understand and adapt to these changes.

Robotic cars are great at monitoring other cars, and they're getting better at noticing pedestrians, squirrels, and birds. The main challenge, though, is posed by the lightest, quietest, swerviest vehicles on the road. "Bicycles are probably the most difficult detection problem that autonomous vehicle systems face," says UC Berkeley research engineer Steven Shladover. Nuno Vasconcelos, a visual computing expert at the University of California, San Diego, says bikes pose a complex detection problem because they are relatively small, fast and heterogenous. "A car is basically a big block of stuff. A bicycle has much less mass and also there can be more variation in appearance -- there are more shapes and colors and people hang stuff on them."

Artificial intelligence has reached a new milestone, with a program beating four professional players in a poker tournament lasting 20 days. Libratus, an AI program developed by a team of researchers at Carnegie Mellon University, took on Dong Kim, Jimmy Chou, Daniel McAulay and Jason Les at no-limit Texas Hold'em in a Pittsburgh casino, eventually taking $1.76 million (£1.4 million) in chips. It's been hailed as a milestone for AI, with Libratus co-creator Tuomas Sandholm declaring, "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans." Boston Dynamics describes itself as'building dynamic robots and software for human simulation'. It has created robots for DARPA, the US' military research company Deep Blue, a computer created by IBM, won a match against world champion Garry Kasparov in 1997. Apple's virtual assistant for iPhone, Siri, uses artificial intelligence technology to anticipate users' needs and give cheeky reactions Xbox's Kinect uses artificial intelligence to predict where players are likely to go, an track their movement more accurately Its human opponents had been sharing notes in an effort to expose Libratus' weaknesses, but the AI grew stronger as the tournament went on.