I had the pleasure of meeting Amy Ingram quite by accident. She was copied on an emailed meeting request, and followed up quickly and courteously. I asked the company I was dealing with when they decided to hire a new assistant. Honestly, I felt a little foolish when I learned that Amy was AI. Not that I felt foolish, but that it was done so well I had no idea.

Earlier this week, Amazon unveiled its 50 internet-connected personal assistant "so you can add Alexa to any room in your home." Alexa is the online giant's artificial-intelligence powered bot that listens to what you say and answers your commands and questions: What's the weather? Can you order me a large pepperoni pizza? And the low priced Echo Dot, about the size of a hockey puck, means many more homes will soon have on-command digital listening devices that eavesdrop on – and store – family conversations, holiday celebrations, and even off-color comments (and also bickering siblings or quarreling spouses). Sure, it has its conveniences and Star Trek-like appeal and maybe you're OK with potential privacy implications.

In 2014, The European Union introduced its RoboLaw, a 1.9 million funded project to prepare relevant guidance of legal and ethical aspects for robotics. The RoboLaw has come to light with the help of engineering, philosophy, law, technology and human enhancement experts, and topics covering soft and tough regulations on privacy issues, data protection, and ethical aspects. The European Parliament is also talking about AI replacing jobs and ways of protecting society from unemployment. Mady Delvaux, the EU Parliament's Rapporteur on Civil Law Rules on Robotics, however, found a positive side of AI taking jobs, in her opinion piece she claims: 'If industry uses more automation robotics, it will become more efficient and competitive, allowing companies to relocate their production back to Europe. Of course, this will eliminate certain kinds of jobs, but it will also create new ones.'

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.