In March this year, Microsoft CEO Satya Nadella talked about the industry trend of using human language more pervasively for interaction with computing devices, a trend he called "conversation as a platform." He also announced several bot initiatives, including the company's bot framework. In April, Facebook launched its Messenger platform with bots. Then, in May, Google announced its attempt to develop AI-powered bots, called Google Assistant. Since then, bots have been widely regarded as a new user interface (UI) to fundamentally change how computing will be experienced by people.

A summer program at Stanford University introduced high school girls to artificial intelligence this summer. Among the activities they learned more about were flying drones, how autonomous cars work, diving robots and machine learning for healthcare. The two-week AI program was developed last year by Olga Russakovsky, a Stanford postdoctoral researcher, and Fei-Fei Li, associate professor of computer science and director of Stanford's AI Lab. They were motivated by a "desperate" need to bring more women into the field. As Li told the girls during their first day, as explained in a blog entry, AI could in the future become the "Terminator next door," or follow a more humane direction, based on the people behind the scenes doing the research and development work.

The investment firm Susa Ventures won't have any partners based in Los Angeles as it begins investing from a new 50-million fund. Susa unveiled its second fund -- double the size of its first -- Tuesday. In looking at the first batch of investments, Susa's geographically spread apart team realized that most of the companies were in the San Francisco Bay Area, including finance start-ups Robin Hood and LendUp. They decided to consolidate in San Francisco, too. "To take it to take next level, we all needed to be in the same office and S.F. seemed like the best," said general partner Seth Berman, who moved there from Los Angeles more than a year ago.

Criteo is the world leader in performance Internet advertising. The Recommender System is at the core of its Engine. Real-time prediction: Everybody predicts clicks. But how do you accurately predict if the userâ s click on a product will generate a sale? Thankfully, you have billions of data points to help you.

Data Science covers all such tools, techniques and technologies which help us handle data and use it for our good. It's an interdisciplinary blend of Data Inference, Algorithm Development, and Technology in order to solve analytically complex problems. The 3 components involved in data science are organising, packaging and delivering data (the OPD of data). Data Science is such a vast and subjective topic of discussion, it is not practically possible to encapsulate it in one single blog. It isn't an independent field in itself, it is a combination of various fields including Computer Science, Mathematics and Statistics, and Business Strategy.

I met Abby at an HRUndergroundX meetup hosted by Nick Larche. She told me about a new technology she was using to recruit candidates for her start-up clients. "Abby, it was nice to meet you the other night. I'd love to get together and have coffee." "… Amy CC'd has my schedule and will reach out to coordinate with you. She'll also send you a calendar invite once confirmed. Amy, please block this as a 30 minute coffee meeting. If Crema is not a good location for Greg, please let him select our meeting spot. Does Monday, Jul 18 at 4:00 PM work? Alternatively, Abby is available Tuesday, Jul 19 at 11:00 AM or 4:00 PM. Abby likes Crema Coffee House, 2862 Larimer St, Denver, CO 80205, USA, for coffee. I am open at 11am on July 22nd. I received and accepted the invite. Just want to confirm that Abby is still available for our appointment tomorrow at 11AM?"

An anonymous reader writes: MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) says that while many'analyst-driven solutions' rely on rules created by human experts and therefore may miss attacks which do not match established patterns, a new artificial intelligence platform changes the rules of the game. The platform, dubbed AI Squared (AI2), is able to detect 85 percent of attacks -- roughly three times better than current benchmarks -- and also reduces the number of false positives by a factor of five, according to MIT. The latter is important as when anomaly detection triggers false positives, this can lead to lessened trust in protective systems and also wastes the time of IT experts which need to investigate the matter. AI2 was tested using 3.6 billion log lines generated by over 20 million users in a period of three months. The AI trawled through this information and used machine learning to cluster data together to find suspicious activity.

Artificial Intelligence will make society smarter, leaner and more efficient. But first, startups and businesses must enable the workforce of the future and pivot business models to incorporate AI. Mundane tasks such as driving, scheduling and logistics will all be handled by an AI assistant with multiple input points, such as microphones around your house and smartphones. The AI assistant will be the central nervous system of your life and connected smart home. In the future, when you summon a shared autonomous car from your phone to go out to dinner, your AI assistant will automatically notify the restaurant of your ETA and dietary restrictions.

Michael Cavaretta (top right) is the Director of Analytics Infrastructure at Ford Motor Company. He is conversant and curatorial on Twitter and regularly shares links regarding artificial intelligence, in transportation and beyond.

The two week wait (2ww) pathway exists to improve the clinical outcomes of patients with suspected cancer. In head and neck cancer, a difficult diagnosis domain, only 10% of referrals to the pathway require further treatment, significantly effecting the delivery of this service. At LifeQueue, we hope to build tools to address this problem by enhancing the way GPs refer, and how these referrals are prioritised in secondary care. To achieve this we have applied machine learning methods to data representing the symptoms of 5,000 referrals with 400 positive cancer diagnoses. This talk will elaborate on the methods explored, and discuss how they might be applied in other diagnosis situations.