In a recent op-ed for The New York Times, Microsoft Principal Researcher Kate Crawford discusses what she sees as the a very real problem with artificial intelligence today: "Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to." Below are a few excerpts from "Artificial Intelligence's White Guy Problem." Police departments across the United States are also deploying data-driven risk-assessment tools in "predictive policing" crime prevention efforts. In many cities, including New York, Los Angeles, Chicago and Miami, software analyses of large sets of historical crime data are used to forecast where crime hot spots are most likely to emerge; the police are then directed to those areas. At the very least, this software risks perpetuating an already vicious cycle, in which the police increase their presence in the same places they are already policing (or overpolicing), thus ensuring that more arrests come from those areas.

The White House and New York University's Information Law Institute, with support from Google Open Research, Microsoft Research and the MacArthur Foundation will host a major public symposium to address the near-term impacts of AI technologies across social and economic systems. The focus will be the challenges of the next 5-10 years, specifically addressing four themes: social inequality, labor, healthcare, and ethics. Leaders from industry, academia, and civil society will share ideas for technical design, research and policy directions. This event will be live-streamed. Check back on July 7th for a live feed.

Iyad Rahwan was the first person I heard use the term society-in-the-loop machine learning. He was describing his work which was just published in Science, on polling the public through an online test to find out how they felt about various decisions people would want a self-driving car to make - a modern version of what philosophers call "The Trolley Problem." The idea was that by understanding the priorities and values of the public, we could train machines to behave in ways that the society would consider ethical. We might also make a system to allow people to interact with the Artificial Intelligence (AI) and test the ethics by asking questions or watching it behave. Society-in-the-loop is a scaled up version of human-in-the-loop machine learning - something that Karthik Dinakar at the Media Lab has been working on and is emerging as an important part of AI research.

DOING THE ROBOT So, you're thinking about bringing artificial intelligence technology to your financial institution - what do you need to take into consideration? First, decide if you are betting for a chatbot or conversational UI in your app, or both. Regardless of the previous choice, the successful uptake of a natural language interface will largely depend on the culture of the market in which you operate. Messaging might become the dominant model of interacting with the world, as Uber DevEx lead Chris Messina predicts in his clarion call to #ConvComm. However, this culture isn't yet universal by any means - and there are, of course, major generational differences around messaging behaviours.

Someone at Google really likes sneakers: The company has just bought a French machine learning startup that taught a computer how to recognize 15,000 different types of them. Paris-based Moodstocks builds image and object recognition software using deep learning techniques, and offered an Android app and visual search API that could recognize certain kinds of object. By analyzing video from a smartphone camera, and correlating it with accelerometer readings to determine how the camera is moving around, the software is able to infer information about the three-dimensional shape of objects in the video, facilitating their recognition. In February 2015 the company demonstrated its ability to identify sneakers through its app. Three months later, after training the software using 15,000 photos of shoes from an online retailer's website, Moodstocks claimed to be able to shop online for all the sneakers on sale in a Macy's store. Google has been introducing elements of machine learning into its existing online services, including Google Translate and Inbox, a next-generation interface for Gmail.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

The artificial intelligence software is learning how to recognize early signs of two eye diseases.Video provided by Newsy A link has been sent to your friend's email address. The artificial intelligence software is learning how to recognize early signs of two eye diseases.Video provided by Newsy Newslook

Alejandro (Sasha) Vicente Grabovetsky, Co-founder of Avalon AI, discusses the ways in which machine learning is improving the rates of failed dementia clinical trials and improving the lives of those living with the disease. The idea for Avalon AI came together when my Co-founder Olivier van den Biggelaar and I realised that we shared the same aim, which was to help defeat ageing. Following that, what immediately came to mind was dementia because it's a disease that has not been successfully tackled yet. Lots of age related diseases like diabetes and cancer receive a lot of funding and are being heavily addressed, while dementia is under-funded partly due to failed clinical trials. Very few dementia clinical trials have succeeded and we noticed that a lot of the past trials were targeting late-stage dementia, where a lot of brain damage had already occurred.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Two weeks after Twitter acquired Magic Pony to advance its machine learning smarts for improving users' experience of photos and videos on its platform, Google is following suit. Today, the maker of Android and search giant announced that it has acquired Moodstocks, a startup based out of Paris that develops machine-learning based image recognition technology for smartphones whose APIs for developers have been described as "Shazam for images." Moodstocks' API and SDK will be discontinued "soon", according to an announcement on the company's homepage. "Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription," the company noted. Terms of the deal were not disclosed and it's not clear how much Moodstocks had raised: CrunchBase doesn't note any VC money, although when we first wrote about the company back in 2010 we noted that it had raised 500,000 in seed funding from European investors.