Employees of Datatonic, a Europe-based data analytics consultancy, recently participated in a week-long hackathon ("Data in Motion Hack Week") organized by Traffic for London (TfL), that city's official transport authority. As you might expect, the goals of the hackathon included stimulating developer creativity to overcome, through innovative use of public-cloud infrastructure and open data, high-priority TfL challenges such as limited overall transport capacity, endemic road congestion and air-quality degradation. Most of the other teams chose to focus on data mashups or visualizations to give London residents information for making better route decisions during their commutes. The Datatonic hackers, in contrast, looked to machine learning (ML). By augmenting real-time data visualization with an ML model, they found they could predict areas of congestion during the morning and evening commutes, which currently stand at 30 million daily journeys, and more than 1 million net-new journeys expected by 2018.

Deep learning algorithms are modeled on biological neural networks and enable machines to learn and mimic human-like responses. Think: smartphone assistants answering your questions, or Amazon recommending products based on your search and purchasing history.

Slow economic growth is the mantra of political campaigns and economic angst. Growth in economic output per hour ("labor productivity") achieved an annual pace of 3 percent for a full half-century between 1920 and 1970. Since 1970 that rate has slowed to about 1.5 percent, and in the last six years productivity growth has slowed further to a lamentable 0.5 percent annual rate. Growth in the middle of the 20th century was propelled by the invention in the late 19th century of electricity, the internal combustion engine, the telephone, chemicals and plastics, and the diffusion to every urban household of clear running water and waste removal. America made a transition from 50 percent of the working population on farms to a largely urban nation, and the drudgery of household work – carrying water in and out, doing laundry on a scrub board – made a transition to modern bathrooms and kitchens by the 1950s.

You've all heard how machine learning algorithms can improve efficiency, decrease costs and lead to better decision making… But what can Artificial Intelligence really bring to your organisation and which technology should be used for which process? The event will be focused towards large enterprise from Utility, Telecom, Retail, Insurance and Financial Services – some of Europe's largest customer facing organisations. With innovative case studies that will resonate with the end user. Don't get left behind your competitors.

After researching and looking into the different algorithms associated with Machine Learning, I've found that there is an abundance of great material showing you how to use certain algorithms in a specific language. However what's usually missing is the simple mathematical explaination of how the algorithm works. In all cases this may not be possible without a strong mathematical background, but for some I know I would definitely find it useful. This post requires just basic mathematics knowledge and an interst in data science and machine learning. I will be talking about Naive Bayes as a classifier and explaining in simple terms how it works and when you might use it.

When I wrote up'Asynchronous methods for deep learning' last month, I made a throwaway remark that after Go the next challenge for deep learning systems would be to win an esports competition against the best human teams. Can you imagine the theatre! Since those are team competitions, it would need to be a team of collaborating software agents playing against human teams. Which would make for some very cool AI technology. Today's paper isn't quite at that level yet, but it does show that progress is already being made on playing first-person shooter (FPS) games in 3D environments.

A standard 6-person meeting room (C) is adjacent to the brainstorm room covered with whiteboards (D). A variety of meeting spaces is an essential part of the WeWork experience, but finding the right combination can be challenging. How many meeting rooms do you need in an office? It's a simple question, but one that is very difficult to answer. Even experienced architects and designers struggle to allocate the correct number of meeting spaces, relying mainly on rules of thumb and intuition to overcome the lack of empirically verified guidance.

Online learning is becoming the norm in universities across the globe, bringing sweeping changes to the way we learn. But earlier this year on online graduate class at Georgia Tech took things a stage further. "Our Teaching Assistants are getting bogged down answering routine questions," said Ashok Goel, who teaches a graduate science course. Students in the class typically post 10,000 messages a semester on the Piazza forum for the course, many of which are either variations on a theme or simple logistical questions. To address this problem, Ashok turned to IBM Watson to create a virtual TA called Jill Watson who was trained on 40,000 posts and released to the wild on the live forum in March as an addition to the other eight TAs.

Seeing the long queues outside ATMs and the confusion and chaos about the availability of cash in the wake of the demonetisation, two engineering graduates from Coimbatore's Government College of Technology decided to do something to help the struggling people. "With the ongoing mess in the country, we thought'why not build something to help ease the situation'. Though there are other interfaces that dispense similar information, ATMBot is different because it is crowd-funded. Users need not download an extra application on their phone to use it," says Abishek Muthian, who founded the city-based start-up Timebender Technologies India Private Limited along with Aravindhan Ramachandran. After his graduation, Abishek decided to venture out on his own.

At Inbound 2016, HubSpot's co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing. Here's what I learned from their keynote presentation and a brief interview. So predicts Halligan, adding "in five years, you will do a lot less navigating through apps and more just asking questions and chatting back and forth with bots… the next thing you know, we like it and it's easier and more efficient than waiting for the sales rep to call you back." Shah notes that businesses started building websites in the 1990s so they can answer customer questions 24/7. "Soon," he says, "they will start building bots. They won't replace the websites, but they will power them. The shortest time between a customer question and the answer will be a bot. It's not human vs. bot, it's human to the bot powered."