Toronto-based IT software developer announced today the pilot program, Karen by Karen.ai, a cognitive recruiting chatbot that engages candidates throughout the application process, matches candidates to alternative positions and provides screening support before, during and after the recruiting process. "Karen derives concepts and personality traits from candidates' resumes and cover letters and assesses the level of engagement and gains deeper insights through a simple text chat conversation. This conversation improves the candidate's experience of the recruitment process and represents the company's brand," said founder Noel Webb. Webb also noted Karen can follow up with candidates during and after the requisition and provide alternative options within the company for which they may be better suited. While automated solutions have existed in recruitment for nearly a decade, Karen takes these automations one step further, because Karen can learn from conversations with candidates and predict other positions for which they might be a match.

Google announced today that it will extend funding for AI research at the Montreal Institute for Learning Algorithms (MILA). The company will invest a total of C$4.5 million, or about US$3.4 million, to fund seven faculty members across the institute, as well as MILA faculty at the University of Montreal and McGill University. That funding will also continue to support Yoshua Bengio, one of the few deep-learning experts currently working. There's no doubt that Google is hoping to expand its AI and deep-learning expertise by investing in folks like Benigo. Google also announced it will be opening up a deep-learning and AI research group at its offices in Montreal.

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration. At the Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, will explore how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient. For World Cities Day 2016, I asked Michael a few questions to learn more about utilising data science in energy, creating a smarter grid, political challenges, and more. What are the main transformative technologies that will help create a smarter grid?

BellHawk Systems Corporation announces the availability of a new white paper "Artificial Intelligence and the Smart Industrial Warehouse." This white paper is available for download from the front page News section of www.BellHawk.com. In the popular press, there is much ado made about Artificial Intelligence (AI) being used in robots that buzz about high volume retail warehouses, automatically picking consumer products that are being shipped overnight in response to orders made over the Internet. But this misses all the ways that AI can be used to inexpensively improve the operation of industrial warehouses without a major investment in robots or other expensive materials handling equipment by providing the information and advice that managers, supervisors, and material handlers need to do their jobs efficiently. This white paper examines how AI based operations tracking and management systems, such as BellHawk, can be used to improve the efficiency of industrial warehouses, prevent mistakes, and enable customer orders to be shipped on time.

NEW YORK & LAS VEGAS--(BUSINESS WIRE)--Today at Money 20/20, Mastercard announced its plans to launch artificial intelligence (AI) bots that allow consumers to transact, manage finances, and shop via messaging platforms. According to research firm Gartner, nearly $2 billion in online sales will be performed exclusively through mobile digital assistants by the end of 2016.1 Mastercard is developing bots for both its merchant and bank partners, which will use chat, messaging and natural language interfaces to communicate with consumers. With the Mastercard bots, partners can have a true dialogue with consumers and provide personalized service, seamless user experience and contextual offers and rewards. Mastercard KAI, the Mastercard bot for banks, will seamlessly extend Mastercard services to customers on messaging platforms and make financial information and decisions part of consumers' everyday lives. In this testing phase on Messenger, Mastercard is partnering with Kasisto, the company that created KAI Banking, the conversational artificial intelligence (AI) platform, to power branded virtual assistants and smart bots for financial services and is a current participant in the Mastercard Start Path Global program.

"Intel sees AI transforming the way businesses operate and how people engage with the world," the company said in a statement yesterday. "Intel is assembling the broadest set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars." Dubbed Intel Nervana, the new platform comes courtesy of the company's acquisition of the two-year-old Nervana Systems announced three months ago. The platform will be optimized for AI workloads with an emphasis on both speed and ease of use. The first product in the platform, a chip codenamed "Lake Crest," will begin testing in the first half of next year and will eventually be available to key customers later that year, according to Intel.

Intel has flexed its AI muscles and beefed up its services with a bunch of new products and collaborations, in an effort to adapt to the technological upheaval of intelligent software. At Intel's first "AI Day" in San Francisco, Brian Krzanich, CEO, said the company is "continuing to evolve" and working to provide an "end-to-end AI solution" to allow companies to easily integrate intelligence into their infrastructures. As data generated by companies continues to pile up, the interest in analyzing that data using machine learning and AI has been piqued. The largest technology companies are all making big investments and staking their claims in AI. But while companies such as Google and Microsoft have developed libraries of machine learning tools such as TensorFlow and Cognitive Toolkit, Intel is more focused on updating servers to cope with the intense computation required to process and train AI systems.

A product from Japan created quite the stir at Consumer Electronics Show in Las Vegas and CEATEC JAPAN in Tokyo this year. The "harmony" of clothing analysis, artificial intelligence (AI), and robotics blend together to produce a "fully automatic clothes folding machine." Japan technological alliance "seven dreamers laboratories' is the developer. The product details have been released in various places, so I won't get into that, but as the name says, "It's a robot that folds clothes. No further explanation is needed." The company announced a partnership with Panasonic (TSE:6752) and Daiwa House (TSE:1925) last year, and together established the joint venture Seven Dreamers Laundroid with plans to begin sales by reservation for their first machine "Laundroid 1" in March of 2017. The developer, Seven Dreamers, announced on November 14th the securement of 6 billion yen (around $60 million US) in funds from SBI Investment, in addition to Panasonic and Daiwa House. The shareholding ratios and payment date remain undisclosed. The concept began in 2005, and with the realization of "folding" from 2013, Laundroid was born. I heard from Seven Dreamers CEO Shin Sakane about the road it took to get here. I came today with the idea of asking straight out, "What happened to make robots fold the laundry?" Well, to be straight, "It's now possible to recognize clothes using artificial intelligence," is maybe the simplest answer I can give. Let's go through the process. How did the idea first come to you? Before that, first permit me to talk a little about what criteria the Seven Dreamers esteem. For us, there are three criterion for "Things that have not been realized yet but could change our lives, and also enrich them." The technological hurdles are high and our policy is to clear them. You've made something that sets high hurdles. Since first coming up with the idea, I was thinking about different markets to satisfy all the criteria. Looking around we see many products targeted at men. Starting now and into the future, 'women', 'the elderly', and'children' are the keywords that will become important. After thinking, the idea that maybe the answer lies within the home came to me and, while I don't usually talk with my wife about work, I casually mentioned it to her. What do you wish you had? She came back just as fast, "Of course, it has to be a machine that folds the laundry.

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings – if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.

Few technologies have the transformative potential to reshape how we live, move, and work. Electricity and the Internet were two technologies that fundamentally transformed life in the 20th century. Artificial intelligence (AI) is the 21st century equivalent of electricity and the Internet. According to a new white paper from Tractica, AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. Tractica's white paper analyzes 10 key trends that are influencing the development of the global artificial intelligence market, and is available for free download on the firm's website.