Of all the subdisciplines of AI, some of the most exciting advances have been made within machine learning. In short, machine learning is the ability for a program to absorb huge amounts of data and create predictive algorithms. If you've ever heard that AI allows computers to learn over time, you were likely learning about machine learning. Programs with machine learning discover patterns in data sets that help them achieve a goal. As they analyze more data, they adjust their behavior to reach their goal more efficiently.

Computers cannot think, though increasingly, they can do things only humans were able to do. A product of the field of research known as artificial intelligence, cognitive technologies have been evolving over decades. It is now possible to automate tasks that require human perceptual skills, such as recognizing handwriting or identifying faces, and those that require cognitive skills, such as planning, reasoning from partial or uncertain information, and learning. Technologies able to perform tasks such as these, traditionally assumed to require human intelligence, are known as cognitive technologies. Businesses are taking a new look at them because some have improved dramatically in recent years, with impressive gains in computer vision, natural language processing, speech recognition, and robotics, among other areas.

The Ibex Pro is one supercharged machine that will probably hurt your electric bill. It's got the same number of GPUs as Nvidia's superfast DGX-1, which is being used for deep learning. System76 is targeting the Ibex Pro -- which is a rack server -- at the same market as the DGX-1. The server has fewer, but newer, CPUs, compared to the DGX-1. An entry-level Ibex Pro priced at US $9,575 will run Ubuntu Server 16.10, with a six-core Intel Xeon E5-2603v4 chip, 16GB of memory, a Tesla K40 GPU, and 250GB of storage.

Robots may be used to care for elderly people lacking family aid, according to a team of academics who estimated the highly advanced artificial intelligence could be available within three years, BBC News reported Monday. The international team, sponsored by the European Union and Japanese government, have begun a $2.5 million project in an attempt to develop robots capable of looking after older people. More than 1 million elderly people were lacking the daily living assistance they need, according to charity Age UK. Researchers said robots would not only help out with physical tasks, but also provide companionship with uniquely programmed, culturally sensitive personalities. "Assistive, intelligent robots for older people could relieve pressures in hospitals and care homes as well as improving care delivery at home and promoting independent living for the elderly," Professor Irena Papadopoulos, an expert in transcultural nursing, told BBC. "It is not a question of replacing human support but enhancing and complementing existing care."

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.