A Stanford undergrad's AI-based chatbot has already helped us with our parking tickets and various legal issues, but now his DoNotPay bot is taking on an even bigger, trickier issue. After receiving acclaim for the bot, which challenged over 160,000 tickets, Joshua Browder taught the program how to help homeless people in the UK claim their right to public housing. The user simply asks for help, and the bot will ask them a series of questions to determine how best to help them. Usually this will involve crafting a claim letter, which the bot fills in with the information that's been provided. In the end, people can save hundreds of dollars in legal advising fees when they need that money the most.

Suppose we'd like to visualize a set of joint probabilities realizable by distributions of 3 binary variables. It is a 7 dimensional regular simplex, and we could draw a lower dimensional section of it. As an example, suppose we want to find a good 2 dimensional section of a 3 dimensional regular simplex. It seems reasonable to restrict attention to sections that go through simplex center and two other points, each of which is a vertex or a center of some edge or face. There are just 2 such sections determined up to rotation/reflection For 7 dimensional simplex, we could similarly take 3 dimensional sections determined by simplex center and 3 points, each a centroid of some set of vertices.

The bot ecosystem is changing, and so are the use-cases. Fueled by the development work of Apple, Facebook, Google, Microsoft, and others, bots are becoming more intelligent and therefore capable of performing tasks previously done by humans. Not to be confused with robots, a bot is a software program designed to imitate the behavior of humans. Most bots are designed to do a specific task, whether that's crawling the web to index sites, making robocalls, serving up a weather report, or responding to a simple customer query. However, orchestration layers are required to enable seamless, transparent, rich services.

GeckoSystems' BaseBot demonstrating several times real time sense and avoid of unmapped, moving obstacles with a person unexpectedly getting in front of the mobile robot.

Apple Inc. acquired artificial intelligence startup Turi Inc. for about 200 million, according to people familiar with the situation, in the latest deal by the iPhone maker to accumulate advanced computing capabilities for its products and services. Turi helps developers create and manage software and services that use a form of AI called machine learning. It also has systems that let companies to build recommendation engines, detect fraud, analyze customer usage patterns and better target potential users, according to the Seattle-based startup's website. Apple could use this to more rapidly integrate the technology with future products. Apple's move Friday is part of a broader battle among Google, Facebook Inc. and Amazon.com

It's also a question of making models behave in line with organizational strategic objectives AND ethics. Following this logic, an essential part of effective model management really becomes outcomes assessment, not just in terms of accuracy, but are we ethically in line and producing ethical outcomes. We have a realistic chance to teach desired behaviors in our predictive models. In this age of analytics sandboxes and playpens, what kind of structures can we envision?

Artificial intelligence enables Staples to automate ordering and customer service through its Easy Button. Staples Inc. is testing technology enabling business customers to order products by voice via its Easy Button. The office supplies retailer is applying machine learning technology to the button, allowing customers to press it to order or reorder a product by voice, or to ask common order-related questions, such as when an order will be delivered or the status of a return. The move is part of a big push by Staples Inc., No. 21 in the Internet Retailer 2016 B2B E-Commerce 300, to use machine learning to automate ordering and customer service, says Ryan Bartley, director of mobile for Staples. Machine learning refers to computer programs that teach themselves to grow and change when exposed to new data, without being programmed by an individual.

MIT Media Lab director Joi Ito recently published a thoughtful essay titled "Society-in-the-Loop Artificial Intelligence," and has kindly credited me with coining the term. Now that it is out there, I wanted to elaborate a little on what I mean by "society in the loop," and to highlight the gap that it bridges between the humanities and computing. What I call "society in the loop" is a scaled up version of an old idea that puts the "human in the loop" (HITL) of automated systems. In HITL systems, a human operator is a crucial component of a control system, handling challenging tasks of supervision, exception control, optimization and maintenance. Recently, a number of articles have been written about the importance of applying HITL thinking to Artificial Intelligence (AI) and machine learning systems (e.g. HITL AI has been going on for a while.

It is dangerous to attribute too much intelligence to these systems. When I enrolled in Computer Science in 1995, Data Science didn't exist yet, but a lot of the algorithms we are still using already did. And this is not just because of the return of the neural networks, but also because probably not that much has fundamentally changed since back then. At least it feels to me this way. Which is funny considering that starting this year or so AI seems to finally have gone mainstream.

Apple and Intel acquired machine learning startups. Palantir purchased a data visualization startup. And AWS rolled out an analytics service for streaming real time data. We've got all this and more in our Big Data Roundup for the week ending August 14, 2016. The company acquired Turi, an artificial intelligence (AI) and machine learning startup, in a deal reportedly worth 200 million.