Akihiro Kishimoto is a research staff member at IBM Research – Ireland working on a range of projects in artificial intelligence, parallel and distributed computing and search. His interest in these technical fields grew from his passion for board games. And while a student at the University of Tokyo, he and three of his fellow classmates designed ISshogi, a program to play the incredibly complex (and ancient) Japanese board game, Shogi. ISshogi won the World Computer Shogi Championships four times from 1997-2005. While studying AI at the University of Alberta, Akihiro was a member of the GAMES group (Game-playing, Analytical methods, Minimax search and Empirical Studies) in the Department of Computing Science, and worked with Jonathan Schaeffer and others to solve Checkers.

DVDs were first launched in Japan, Travelocity opened up as the first online booking agent, eBay, Ask Jeeves also opened their online doors and the Spice Girls had their first UK number-one. You get the point (well except the Spice Girls bit) – it was an inflection point with technology, and 20 years later we are that same point again with AI, sometimes referred to as cognitive technologies. At the time I was fortunate to work for a very innovative company who had developed the first generation of SaaS solution for managing supply chains. So as you can imagine I spent a lot of my time sat in meetings trying to convince executives the internet was going to change the world and they needed to innovate. What we forget (which seems silly now) is many of these execs either dismissed the technology, or worse, had convinced themselves they get it and have a solid plan.

London: An intelligent and highly sophisticated robot developed by a research team of University of Birmingham in the UK is joining the world of work as a trainee office manager. The robot, named Betty, will greet guests at reception and carry out tasks at the Transport Systems Catapult, based in Milton Keynes, in the UK, for a two-month trial period. Betty's duties will include patrolling the offices, assessing how many staff members are in the office outside working hours and monitoring the environment by collating data on clutter, office temperature, humidity and noise. She will also check fire doors are closed and desks are clear. A highly sophisticated robot, Betty runs Artificial Intelligence-driven software developed by an international research team led by the University of Birmingham.

Apple CEO Tim Cook opened up the company's WWDC week by noting that his keynote would focus on the company's four device platforms: Apple Watch, Mac, iPhone/iPad, and TV. The operating systems that power these products have been the foundation of Apple's enviable technology ecosystem--its collection of hardware, software, and services that complement each other and enhance the value of the aggregate even more than each component. Microsoft has finally brought the same version of Windows to desktop and phone, and Google has allowed Android apps to invade Chrome OS. By contrast, Apple remains committed to a unique user experience on each of its devices. It has, for example, refused to bring touch screens to the Mac or allow iOS apps to run on the Mac.

Japan's electronics makers are beefing up efforts to recruit hard-to-find artificial intelligence experts -- a critical resource as connected technologies and services loom large on the industry's path forward. Growth in the number of college graduates with AI expertise -- typically math whizzes or engineers with additional knowledge of programming languages and data analysis -- has failed to keep pace with rising demand for such talent. The global supply is only in the tens of thousands, pitting companies expanding AI research operations against each other in the search for top recruits. Sony will next spring begin bulking up its ranks of such new graduates with a specialized recruiting framework for research and development in AI and machine learning. No limit will be set on the number of staff that can be hired, unlike under the company's normal recruiting system.

In Machine learning and statistics, a common task is to fit a model to a set of training data. This model can be used later to make predictions or classify new data points. When the model fits the training data but does not have a good predicting performance and generalization power, we have an overfitting problem. Regularization is a technique used to avoid this overfitting problem. The idea behind regularization is that models that overfit the data are complex models that have for example too many parameters.

The team at Facebook discusses DeepText, their engine that analyses posts and comments on Facebook, to make a better product. Just imagine what the equivalent at Google does with all your phone conversations. "Text is a prevalent form of communication on Facebook. Understanding the various ways text is used on Facebook can help us improve people's experiences with our products, whether we're surfacing more of the content that people want to see or filtering out undesirable content like spam. With this goal in mind, we built DeepText, a deep learning-based text understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages. DeepText leverages several deep neural network architectures, including convolutional and recurrent neural nets, and can perform word-level and character-level based learning. We use FbLearner Flow and Torch for model training. Trained models are served with a click of a button through the FBLearner Predictor platform, which provides a scalable and reliable model distribution infrastructure. Facebook engineers can easily build new DeepText models through the self-serve architecture that DeepText provides."

A robot that intentionally draws blood can be a real prick. Created by roboticist Alexander Reben, this robot suits this very classification. He's made a robot that he claims to be the first robot to break Isaac Asimov's (an American science fiction author) First Law. The law states that a robot may not harm a human, nor may it be a bystander in any harm brought upon a human. Reben's robot is composed of a uArm where the actuated gripper has been replaced with a small prick.

Romit Shah, of Japanese financial holding company Nomura, has raised his rating on the shares in silicon specialist Nvidia, after spending time with CEO Jen-Hsun Huang and hearing about the company's plans for the future. The Wall Street analyst said that Huang "was very enthusiastic about the prospects for the data center business, as hyperscale companies quickly adopt throughput computing in an effort to accelerate workload performance." Shares jumped three percent on the news. Nvidia's year-on-year data center revenues grew by 63 percent last quarter, mostly due to the "broad adoption" of Tesla M40 GPU accelerator. Nomura believes the company has an 80 percent share of the accelerator market, which accounts for less than one percent of overall data center spending.

In my last post about Bot Framework, I wrote an overview about how you can create a weather bot.. in this post, I'm starting a series where I'll go deep into each part of building a bot using the Bot Framework.. In this post, I'll deep-dive into Language Understanding Intelligence Service (LUIS), and how we can use it to understand the user message. LUIS is an API built by Microsoft and introduced as a part of Microsoft Cognitive Services (A set of intelligence services).. it's a Machine Learning API that allows you to create an app with some intents and entities and then train it on some utterances and then start using it in your apps or websites and most importantly… your bots! In this post, I'll guide you to create a LUIS app focusing on the bots scenarios, let's start! Well, this the most important part of building an intelligent service than can help you understanding the language, what's your intents?