Intel Corp. INTC 0.34 % signaled it wants a bigger role in artificial intelligence, revealing plans to modify a line of chips to target a fast-growing market turning into a battleground for technology suppliers. The company told technology developers Wednesday that it plans next year to deliver a new version of the Xeon Phi processor--a product line previously targeted at scientific applications--with added features designed to accelerate tasks associated with what Silicon Valley calls artificial intelligence. Intel said the technology will help accelerate a technique called deep learning, increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Intel's Xeon processors already are a fixture in data centers, and have a role in nearly all deep-learning tasks carried out there. But some users also install auxiliary processors for artificial-intelligence tasks, notably chips called GPUs that rival Nvidia Corp. NVDA 0.42 % has long sold for videogames.

In the healthcare industry, natural language processing has many potential applications. NLP can enhance the completeness and accuracy of electronic health records by translating free text into standardized data. It can fill data warehouses and semantic data lakes with meaningful information accessed by free-text query interfaces. It may be able to make documentation requirements easier by allowing providers to dictate their notes, or generate tailored educational materials for patients ready for discharge.

Over the past few weeks, we've started to learn more and more about machine learning and the role it plays in computer vision, image classification, and deep learning. We've seen how Convolutional Neural Networks (CNNs) such as LetNet can be used to classify handwritten digits from the MNIST dataset. We've applied the k-NN algorithm to classify whether or not an image contains a dog or a cat. And we've learned how to apply hyperparameter tuning to optimize our model to obtain higher classification accuracy. However, there is another very important machine learning algorithm we have yet to explore -- one that can be built upon and extended naturally to Neural Networks and Convolutional Neural Networks.

My intern at TCL is over soon. Before going back to the campus for graduation, I have decided to build myself a personal deep learning rig. I guess I cannot really rely on the machines either in the company or in the lab, because ultimately the workstation is not mine, and the development environment may be messed up (It already happened once) . With a personal rig, I can conveniently use teamviewer to login my deep learning workstation at any time. And I got the chance to build everything from scratch.

It seems like there isn't a day which goes by at the moment without a new robotic invention in the news, with promises around how these inventions will not only revolutionise our lives, but threaten our jobs. In the outsourcing sector robots are most definitely on the way, or in some cases, already here. And it is, therefore, vital that businesses operating in this sector seriously consider how some robotic processes can enhance their operations – there's no doubt competitors are also considering the same issue. These robotic innovations can be roughly split into two separate areas: robotic process automation (RPA) and artificial intelligence (AI). RPA, simply speaking, is a process which enables computer software to partially or fully automate human activities which are manual, repetitive and rule-based.

IBM created a computer chip that works like a brain. Now, Samsung has used it to create a biologically inspired digital eye. The IBM chip, called TrueNorth, is built of 4,096 tiny computing cores that form about a million digital brain cells and 256 million connections. Together they act like the brain's neurons, sending short messages to one another to process data. The design, known as neuromorphic computing, marks a dramatic departure from traditional chips that run software packaged into strict sequences of instructions.

Every summer, technologists turn to Gartner's Hype Cycle for Emerging Technologies, which has become a barometer of sorts for gauging the state of various hardware and software innovations that are expected to impact business and society over the next decade. This year, Gartner analysts have their eye on all manner of artificial intelligence technologies, including "smart machines" that can learn by themselves. After relieving "big data" from its hype-cycle duties last year–ostensibly due to the all-encompassing pervasiveness of data in this pervasively digital age–Gartner analysts this year are talking up a swath of related "smart machine" technologies. Together, Gartner refers to these technologies as key enablers of "the perceptual smart machine age" that is currently unfolding. "Smart machine technologies," the analyst group says in a press release, "will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks that will allow organizations with smart machine technologies to harness data in order to adapt to new situations and solve problems that no one has encountered previously."

In 2011, a supercomputer won 1 million on Jeopardy! In 2016, that same supercomputer is tackling a challenge quantified not in millions of dollars but in millions of cancer patients. The goal is to use Watson's natural language processing to mine the medical literature and a patient's records to provide treatment advice. And this month the Watson computer system is drastically expanding its reach--from one hospital in Thailand to six in India and a planned 21 more in China. This instantiation of Watson, dubbed Watson for Oncology, is an artificial intelligence system that has access to millions of pages of medical textbooks and journal articles.

The hottest news last week came out of the Intel INTC Developer Forum (IDF) 2016. But Facebook's FB decision to open source its artificial intelligence (A.I.) research, Alphabet's GOOGL Duo and Oracle ORCL asking for a retrial in its case against Alphabet over Java also made headlines. This was a mega event where Intel talked about everything but PCs. The company seems to think that the next big thing driving chip growth for PCs is VR capabilities (since it's a computationally intensive exercise, it's a good way to sell its chips). To that end, it announced Project Alloy, a reference design for cordless headgear that merges the AR and VR worlds and runs on Microsoft's MSFT Windows Holographic OS.

The bank has paid A 300,000 for Chip, one of only three REEM robots from Spanish firm PAL Robotics in the world, according to the Australian. Chip is at the centre of a new two year academic partnership with University of Technology Sydney (UTS), the Australian Technology Network of Universities (ATN), and property development giant Stockland. Commbank's Sydney innovation lab will be used as a testing environment for students and academics of Australia's leading technology universities to conduct research and development in social robotics using Chip. Kelly Bayer Rosmarin, group executive, institutional banking and markets, CommBank, says: "The development of robotics and artificial intelligence will affect all of us in the future...This research will help us better understand the impact social robotics will have on the lives of people, customers and industries across Australia." In Japan robots have already started to make their way into bank branches.