Collokia, machine learning company that builds seamless collaboration solutions for software developers, announced the beta launch of its new, collaborative machine-learning platform, designed to foster collaboration within software teams by enabling improved access to information and knowledge. Software teams spend a great deal of time researching technical solutions, especially when the technology they are working with is new to the team. But sifting through the vast array of information available on the Internet is cumbersome. Collokia's collaborative machine-learning platform offers an easy-to-use solution that allows software teams to easily identify, catalog, and share useful information, reducing the time spent on research and promoting increased knowledge transfer between, and within, development teams. Most collaboration platforms have cumbersome systems for information sharing, placing an extra burden on already busy teams, Collokia's platform for knowledge mapping, collection, and distribution is designed to be completely transparent and effortless.

On Thursday July 28, 2016, Yann LeCun took part in a Quora Session, a Q&A session hosted by Quora roughly analogous to Reddit's AMA format. LeCun was posed numerous questions, and provided insightful answers to many. What follows are a few excerpts from the session, including questions posed and partial LeCun answers. What are some recent and potentially upcoming breakthroughs in deep learning? The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks).

Some might remember the 2000 sci-fi film The 6th Day. Arnold Schwarzenegger famously instructs his refrigerator to order more milk when it informs him that the milk had expired. While at the time this convenience might have seemed preposterous, today smart products like this are quickly filling our homes. Insights firm Gartner suggests that the average household could be equipped with more than 500 smart devices by 2022. Samsung has recently launched one such device called the Family Hub Refrigerator.

Thank you for your missive to the Market at the start of August. I must say I totally agree with your headline message, that "London needs to be more imaginative in its quest for innovation". You also rightfully point out this will not be an easy journey for various reasons, be they political, technological or inertial. And you are on the money when it comes to not simply running after the latest fad or a shiny bit of kit. Also, of course, you hit the nail on the head with your point that the priority should not always be looking for ways to replace solid, reliable legacy systems.

CAIT writes your social media content for you. Utilizing the latest AI technology, the CAIT app produces high quality, personalized content for your social accounts and schedules it. CAIT will launch in mid-Spetember During Denver Startup Week.

Instructors returning to high school and college classes this fall, take note: Grading your students' work is about to get a lot easier. A UC Berkeley professor and three former graduate students are putting the finishing touches on an artificial intelligence technology that groups answers and allows them to be graded en masse. The AI-boosted capability, now wrapping up beta testing before becoming available this fall, will be the newest feature of the online grading application Gradescope. The team launched the app as a company two years ago, in part to stem cheating. Having a digital record of a graded paper makes it hard to alter written answers and argue the paper was incorrectly graded.

The University of Nottingham is developing an artificially-intelligent sensor system to clean food manufacturing equipment more precisely, which could save 100m a year for the UK industry alone. This revolutionary AI-driven monitoring system could lead to greater production capacity and therefore cheaper food prices for consumers. Food and drink production is the largest manufacturing sector in Britain and the highest industrial user of water at approximately 430 million litres a day. As current technologies cannot accurately determine exactly how dirty food and drink processing equipment is inside, cleaning can last up to five hours a day - to minimise food safety risks. Cleaning accounts for 30 per cent of energy and water use and leads to excessive productivity down time and over-use of chemicals, at huge cost to manufacturers and the environment.

Behind the scenes, Yelp is improving its restaurant listings using your food porn pictures. That photo you took of you eating soft serve at your local ice cream shop? From that photo alone, the AI can determine whether the ice cream shop has outdoor seating, the ambience, and whether it's for couples of families. The AI will eventually be able to identify dishes in the photos by analyzing the color, texture, and shapes of the images. The result is photos and restaurant profiles that will be auto-captioned with phrases like, "good for kids, casual and loud atmosphere," with a percentile accuracy rating.

Each fall, groggy teenagers resign themselves to another year of fighting their body clocks so they can get to class on time. It's well known that teens who don't get at least eight hours of sleep a night face a slew of problems. That's why both the American Academy of Pediatrics and the Centers for Disease Control recommend shifting middle- and high-school start times to 8:30 a.m. or later. Yet during the 2011-12 school year -- the most recent statistics available -- only 17.7 % of the nation's public middle, high and combined schools met the 8:30 a.m. In California, the average start time was 8:07 a.m.

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.