This topic doesn't have much to do with nicer code, but there is probably some overlap in interest. However, some of the topics that we cover arise naturally here, so read on! MCMC is simply an algorithm for sampling from a distribution. The term stands for "Markov Chain Monte Carlo", because it is a type of "Monte Carlo" (i.e., a random) method that uses "Markov chains" (we'll discuss these later). MCMC is just one type of Monte Carlo method, although it is possible to view many other commonly used methods as simply special cases of MCMC.

Salesforce has been talking about building AI into its wide-ranging cloud portfolio for the last couple of years, and teased the Einstein brand more recently. After acquiring a long list of startups developing cognitive computing or AI technologies and assembling a team of 175 data scientists, it's ready to demonstrate what Einstein looks like beyond a cartoon version of the world's most famous German scientist. With the release, Salesforce is bringing to its CRM and other line of business software tools the AI layer that others in the industry are also looking to provide. Putting rapidly-scalable cloud computing power towards crunching huge data sets seems to be a focus for many vendors, as John Ball, senior vice-president and general manager of Einstein at Salesforce says. "Everyone and everything is connected. All those connections are generating vastly more data than ever before and thanks to the cloud, we have access to vast computing capability," he says.

According to Elo's research, the biggest threat for the well-being of Finnish entrepreneurs seem to be the challenge of time management and the lack of supporting networks. This is one of the challenges hackers in'Elo Data Science Hack' hackathon are set to solve. "The aim of our hackathon is to help entrepreneurs flourish and maintain balance in their working life. As Finland's most popular employment pension insurer, we feel passionate about Finnish entrepreneurs' well-being. We have over 85,000 customers and the only way to reach each and every one of them is with digital solutions. This also calls for a new kind of intelligent services," tells Satu Huber, CEO of Elo Mutual Pension Insurance Company.

Ben Robinson, chief strategy & marketing officer at banking software firm Temenos, explains how the future of finance will be shaped by artificial technology. Google's DeepMind triumph this year over one of the world's highest ranked champions at Go is a sign: computers with artificial intelligence (AI) are learning how to outperform us. If computers can learn to beat us at a game, why not at things that we can't afford to get wrong – medical diagnostics, risk analysis, legal and investment advice? And what effect over time – say 20 years – will this have on the way a host of services, such as banking, are delivered? "Service will be all about data and algorithms," David Brear, co-founder and CEO at 11:FS recently suggested.

Chatbots are not new, especially for this generation. In fact, back in 2000, if any of you had ever been an avid user of AOL Instant Messenger, you probably tried chatting with its bot, SmarterChild. I used it to request movie times and to be honest, called him names too (apparently, he didn't mind). SmarterChild was the only bot at that time and was loved for so many reasons. Who knew he would rest in peace so soon?

Scientists at Sony have claimed that they have created "first-ever entire songs composed by Artificial Intelligence". The two songs'Daddy's Car' and'Mister Shadow' have been developed using'FlowMachines', an AI system that learns music styles from a vast database of songs to compose new songs. 'Daddy's Car' has been composed in the style of the English band'The Beatles'.

Sony recently released two songs composed by AI and French composer Benoît Carré arranged, produced the songs, and wrote the lyrics. Sony has also announced a full album made by their AI to be released in 2017. Earlier, AI had written the screenplay for a short film (though it doesnt make too much sense, for now). It is already known that many media sources, including Yahoo, have been using AI to write articles for their websites. AI at Google has attempted its hand at poetry and with good result.

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.