A team of researchers have developed a new algorithm that could help search and rescue teams locate people lost at sea using ocean currents, wind speed, and wave direction. The project was a joint effort from scientists at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech, who tested their method using human manikins in the ocean off the coast of Martha's Vineyard. Unlike current search and rescue models--which also use data about ocean currents and wind to calculate the likely location of a missing person by simulating one single linear path--the team's new system is focused on identifying multiple points of'attraction' in the ocean, which can sometimes change dramatically over time. Using a system they called Transient Attracting Profiles (TRAPS), the team tracks these attraction points, which they behave like'moving magnets' pulling people in the water toward them. Instead of mapping out a single, linear path, the TRAPS model identifies many different attraction points, or'traps,' in the ocean that will likely have pulled a person in multiple directions as they drift through the waters.

AAI's leadership underwent a major change in March of this year. Martha Pollack, who had been serving as AAAI president since July 2009 resigned her position, and Henry Kautz, who had been serving as AAAI president-elect assumed the duties and responsibilities of the president. As stipulated in the AAAI bylaws, Kautz will serve in this capacity until the 2010 AAAI annual business meeting, after which he will begin his full two-year term as president, starting one year ahead of schedule. In addition, Eric Horvitz, who has already served one year as AAAI past president, has agreed to serve one additional year so that the position will remain filled throughout Kautz's tenure as president. An election will be held this year for the now-open position of president elect.

The social care crisis – as demonstrated with the Conservative Party's disastrous manifesto pledge – is one of the biggest issues facing the NHS. Spending squeezes have led to bed-blocking in hospitals; while low wages and poor working conditions are causing carers to leave the industry in droves. One social care startup thinks that a solution might lie in AI. Cera, founded by Ben Maruthappu, a former innovation adviser to NHS England, is a startup that wants to provide smarter social care using an Uber-style platform to match carers and patients. The startup has to date raised £2.7 million in early funding, and in March signed a partnership with Barts Health NHS Trust, as well as several clinical commissioning groups and hospitals in London to test its solution.

Can AI overhaul the social care system? The startup has today taken the tiniest baby steps to launch an AI chatbot that it hopes will, at an unspecified point in future, be able to assist carers with recommendations for home care of people with conditions such as dementia. And even potentially steer off medical emergencies via pre-emptive alerts. The far more basic reality of the chatbot it's launching today is that Martha (as it's called) will be able to recommend care packages to potential customers. Which underlines how the inflated promises of AI really do hinge on data acquisition.

Early on an unusually blustery day in June, Kevin Esvelt climbed aboard a ferry at Woods Hole, bound for Nantucket Island. Esvelt, an assistant professor of biological engineering at the Massachusetts Institute of Technology, was on his way to present to local health officials a plan for ridding the island of one of its most persistent problems: Lyme disease. He had been up for much of the night working on his slides, and the fatigue showed. He had misaligned the buttons on his gray pin-striped shirt, and the rings around his deep-blue eyes made him look like a sandy-haired raccoon. Esvelt, who is thirty-four, directs the "sculpting evolution" group at M.I.T., where he and his colleagues are attempting to design molecular tools capable of fundamentally altering the natural world. If the residents of Nantucket agree, Esvelt intends to use those tools to rewrite the DNA of white-footed mice to make them immune to the bacteria that cause Lyme and other tick-borne diseases. He and his team would breed the mice in the laboratory and then, as an initial experiment, release them on an uninhabited island. If the number of infected ticks begins to plummet, he would seek permission to repeat the process on Nantucket and on nearby Martha's Vineyard. More than a quarter of Nantucket's residents have been infected with Lyme, which has become one of the most rapidly spreading diseases in the United States. The illness is often accompanied by a red bull's-eye rash, along with fever and chills. When the disease is caught early enough, it can be cured in most cases with a single course of antibiotics. For many people, though, pain and neurological symptoms can persist for years. In communities throughout the Northeast, the fear of ticks has changed the nature of summer itself--few parents these days would permit a child to run barefoot through the grass or wander blithely into the woods. "What if we could wave our hands and make this problem go away?" Esvelt asked the two dozen officials and members of the public who had assembled at the island's police station for his presentation. He explained that white-footed mice are the principal reservoir of Lyme disease, which they pass, through ticks, to humans.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

This paper describes a method of cutting off reasoning when all of the answers to a problem have been found. Briefly, the method involves keeping and maintaining information about the sizes of important sets.