It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving. A dermatologist uses a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.

Intel and Chinese telecoms company ZTE claim to have achieved a new record – more than 1000 images per second in facial recognition – with what is known as'theoretical high accuracy' achieved for their custom topology. "Perception, such as recognising 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 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, where ZTE's engineers used Intel's Arria 10 FPGA for a cloud inferencing application using a convolutional neural networks (CNN) algorithm. According to the company, the deep learning designs can be migrated from the Arria 10 FPGA family to the Intel Stratix 10 FPGA family, and users can expect up to nine times performance boost.

Artificial intelligence can boost business productivity and profitability -- as well as find ways to foster human happiness, according to the director of Hitachi's artificial intelligence laboratory. Interest in AI traditionally has centered on purpose-built technology crafted for a particular reason, such as Google's AI program that last year beat a champion player at the Chinese strategy game Go, said Kazuo Yano during a 14 December presentation at AAAS headquarters. The address was part of the Hitachi lecture series, which has brought speakers to AAAS for nearly a decade to examine a wide range of issues related to science and society. Yano, who serves as Hitachi's chief corporate scientist, noted that AI increasingly is being used to address the needs of business. To cope with changing variables like customer behavior and marketplace position, businesses now can take advantage of the flexibility offered by "general-purpose AI," which can be added to existing systems, he said.

MyPolly is the world's first product that introduces continuous conversation via natural language understanding. MyPolly, which is currently in closed beta testing for developers and bot builders, enables virtual assistants and bots to interpret a human's input, making MyPolly "smarter" over time. Jack Crawford, Founder and CEO of datalog.ai and Malaikannan Sankarasubbu, Founder and CTO, started the company last year to equip bots and virtual assistants to intelligently remember associations between words and things. For example, you might say to your virtual assistant, "My dog's name is Sebastian." Later in the dialogue, MyPolly would recall your dog's name.

In 1971, Intel, then an obscure firm in what would only later come to be known as Silicon Valley, released a chip called the 4004. It was the world's first commercially available microprocessor, which meant it sported all the electronic circuits necessary for advanced number-crunching in a single, tiny package. It was a marvel of its time, built from 2,300 tiny transistors, each around 10,000 nanometres (or billionths of a metre) across – about the size of a red blood cell. A transistor is an electronic switch that, by flipping between "on" and "off", provides a physical representation of the 1s and 0s that are the fundamental particles of information. In 2015 Intel, by then the world's leading chipmaker, with revenues of more than $55bn that year, released its Skylake chips. The firm no longer publishes exact numbers, but the best guess is that they have about 1.5bn–2 bn transistors apiece. Spaced 14 nanometres apart, each is so tiny as to be literally invisible, for they are more than an order of magnitude smaller than the wavelengths of light that humans use to see.

Google has cool technology to recognize images and speech, and IBM's hardware can diagnose diseases and beat humans in Jeopardy. Combine the two, and you get a powerful computer with serious brains. IBM is merging Google's artificial intelligence tools with its own cognitive computing technologies, allowing deep-learning systems to more accurately find answers to complex questions or recognize images or voices. Google's open-source TensorFlow machine-learning tools are being packed into IBM's PowerAI, which is a toolkit for computer learning. The two can be combined to improve machine learning on IBM's Power servers.

AI-powered chatbots, VR, and immersive storytelling trends will leave a permanent mark on the industry. Every year, the web design industry goes through some sort of evolution cycle to stay relevant and inspiring. Last year, I wrote about the top web design trends to watch in 2016, many of which have taken root and changed the way we understand design forever. This year, like a number of UX experts, I'm betting on AI-powered chatbots, VR, and immersive storytelling trends to leave a permanent mark on the industry. Undoubtedly, we will also see a lot of last year's trends continue to shape and influence the web design space.

If you're worried about the possibility of skin cancer, you might not have to depend solely on the keen eye of a dermatologist to spot signs of trouble. Stanford researchers (including tech luminary Sebastian Thrun) have discovered that a deep learning algorithm is about as effective as humans at identifying skin cancer. By training an existing Google image recognition algorithm using over 130,000 photos of skin lesions representing 2,000 diseases, the team made an AI system that could detect both different cancers and benign lesions with uncanny accuracy. In early tests, its performance was "at least" 91 percent that of its flesh-and-blood counterparts. The algorithm would have to be refined and rigorously tested before put to use in the medical world.

A true genius, Alan Turing was played brilliantly by Benedict Cumberbatch inThe Imitation Game -- the movie about his life and role in ending WWII -- which introduced him to a whole new generation of admirers. It wasTuring who predicted machine learning would play a big role in modern computing in his article the "Turing Test," way back in 1950. Indeed, Turing was way ahead of his time, which was a major theme in the movie, but now the world has caught up. The major advancements in readily accessible computing power, the quantity of data available, and algorithms that truly make machine learning possible are driving our ability to process data, analyze it, and act on it in ways that would make Mr. Turing proud. These advances have completely changed the machine learning game: The fundamental concept remains the same, but now it's far more sophisticated, efficient, and easily deployable. Beyond the big headline-grabbing examples of how machine learning will impact our lives -- such as through driverless cars -- it has exciting potential to put an end to the bland and sometimes ineffective customer experiences that many retailers are delivering to their customers.

It does look like a cheat sheet with everything you need to know in terms of formulas used in standard machine learning algorithms. It would be helpful if someone could add some meat to this document, in particular, adding some context and explanations for each formula. Many of these formulas are nicely illustrated here (scroll to the bottom of the page after clicking on the link.)