Amid Japan's rapidly aging population, a burgeoning industry is targeting families expecting a death in the near future. Services on offer range from seminars on funerals and inheritance arrangements to a board game that prepares players for the financial implications of age-related decline. With more and more elderly Japanese living alone, consultations are also offered on how to obtain guardianship needed for time in hospital, as well as how to bequeath assets to individuals and organizations other than legal heirs. Pip Robot Technology Co.'s "Kokorozumori" ("Making Preparations in One's Head") is a dice board game that gets families to think about the costs and implications of caring for aging, ailing relatives. The Osaka-based manufacturer of robot dolls intended as companions for elderly people created the game in 2015, in collaboration with a research team at the University of Tsukuba. Players form pairs -- one elderly and the other a younger family member -- and earn an income in the form of pension and salary as they progress on the board.

So needless to say, coming back to it there has been a lot to learn. In the future, I will write a bit more about some of the available tools and libraries that exist these days (both expanding on the traditional AI Stanford libraries I have mentioned previously with my tweet sentiment analysis, plus newer frameworks that cover "deep learning"). Anyway, inspired by this post, I thought it would be a fun Sunday night refresher to write my own neural network. The first, and last, time that I wrote a neural network was for my final year dissertation (and that code is long gone), so was writing from first principals. The rest of this post will be a very straightforward introduction to the ideas and the code for a basic single layer neural network with a simple sigmoid activation function.

Gearing up for three days of coverage of BigDataNYC 2016 at 37 Pillars in New York City, the SiliconANGLE Media team and NVIDIA Corp. hosted The Future: AI-Driven Analytics, An Evening of Deep Learning. This event kicked off the conversation about deriving benefits from Big Data to advanced Artificial Intelligence (AI) and Machine Learning (ML). An event panel met to talk about deep learning, what it means, where it's headed and implications for next-gen apps. Panelists Jim McHugh, VP and GM of NVIDIA Corp.; Randy Swanberg, distinguished engineer at IBM; Ram Sriharsha, product manager, Apache Spark, at DataBricks, Inc.; and Josh Patterson, director of Field Engineering at Skymind joined host George Gilbert, (@ggilbert41), Big Data analyst at Wikibon and theCUBE cohost (from the SiliconANGLE Media team), to talk about deep learning and where is going in the future. Gilbert began the panel discussion by saying that the real advance that is impending right now is the magnitude of cores that use GPUs (Graphics Processing Unit) as auxiliary processing units, which he feels is going to change the future of where computation will go.

Accenture has teamed up with a coder, an orchestral composer and a datavis company to put artificial intelligence (AI) and big data to music, in a project called Symphonologie. Looking to combine data with art, Symphonologie's visual music piece has three influences: business, technology and the new digital world. To combine all of these into one artistic experience, Accenture looked to Hannah Davis, a musician and creative technologist; Rare Volume, an interactive design and data company; and composer Mathieu Lamboley. The project began with Davis' thesis, which dealt with putting music to words. She built a computer programme called TransProse, which reads bodies of text and determines the "densities" of eight emotions: joy, sadness, anger, disgust, anticipation, surprise, trust, and fear.

One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It's a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result. The network seems like it is able to learn concepts about 3D space and the structure of the objects it's drawing, and because it's generating images rather than numbers it gives us a better sense about how the network "thinks" as well. I happened to stumble upon the Radboud Faces Database some time ago, and wondered if something like this could be used to generate and interpolate between faces as well. To implement this, I adapted a version of the "1s-S-deep" model from the chairs paper.

The crew in Redmond has revealed that Office 365 is wielding cloud-based AI to automate many tasks. Tap for Word and Outlook surface relevant content from your company to help finish a project, for instance. PowerPoint and Sway will have a QuickStarter feature that gives you curated outlines for given topics, saving you the trouble of creating the foundation of a presentation from scratch. Excel, meanwhile, will have a way to turn raw geographic data into Bing-based maps. Some of these intelligent features are available now, although you'll have to wait until later this year to get the Excel and PowerPoint helpers.

NVIDIA (NVDA) is already the clear leader in the artificial intelligence chip segment, and the company scored yet another win by getting International Business Machines (IBM) to use its NVIDIA Tesla P100 Pascal GPUs through NVIDIA NVLink. Last week IBM revealed a series of Linux-based servers which, according to the company, deliver higher levels of performance and greater computing efficiency than any other x86 based server. IBM has been steadily building its cognitive division, and its analytics segment alone is now nearing 5 billion in quarterly revenues. Since the decision to put Watson at the center of things the company has been expanding its AI capabilities, and the decision to use NVIDIA's products clearly underscores the lead NVIDIA has created in this segment. "The open and collaborative model of the OpenPOWER Foundation has propelled system innovation forward in a major way with the launch of the IBM Power System S822LC for High Performance Computing," said Ian Buck, vice president of Accelerated Computing at NVIDIA.

Artificial intelligence is an increasingly popular term that lacks a unified, concrete definition. Nils J Nilsson, one of the founding researchers in the field of AI gave us one. "Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment." AI today is a buzzword in technology that has everyone sitting up to pay attention. With every other billboard in the Bay area talking about AI and machine learning, it appears like the time of Jarvis from Iron Man, Samantha from'Her' and even the creepy'HAL' from 2001 – a space odyssey is fast approaching.

Microsoft wants the world to know it's not falling behind in the race for artificial intelligence. The technology giant--like Google goog, Facebook fb, and IBM ibm --has been pushing into the trendy field of artificial intelligence, catch-all for various technologies that help computers recognize patterns from massive amounts of data. Microsoft CEO Satya Nadella emphasized A.I. on Monday at the company's annual IT conference in Atlanta by describing how his company is using the technology and how it plans to do so a lot more in future. "We are not pursuing A.I. to beat humans at games," said Nadella, taking a subtle hit at competitors like Google and IBM, whose A.I. technologies got some attention for beating humans at the ancient Chinese board game Go and on the game show Jeopardy. Microsoft's overarching goal is to "democratize A.I.," which Nadella explained has something to do with analyzing the mountains of data produced by consumers and businesses and then presenting the findings to people who have far less free time than they used to have.

The analysis has identified nearly 200 real-world enterprise use cases for AI that are classified into 25 industry sectors. The firm forecasts that revenue for enterprise AI applications will increase from 358 million in 2016 to 31.2 billion by 2025, representing a compound annual growth rate (CAGR) of 64.3%. Artificial intelligence (AI) technologies are quickly gaining mindshare among corporate executives around the world, driving a proliferation of use cases that touch virtually every industry. AI technologies, which include deep learning, machine learning, natural language processing (NLP), and computer vision, among others, are designed to endow computers with human-like faculties such as hearing, seeing, reasoning, and learning. But AI enables computers to do some things better than humans, especially when it comes to processing very large amounts of data quickly, efficiently, and accurately.