One of the most fascinating examples of social innovation I've been tracking recently was the We Counter Hate platform, by Seattle-based agency POSSIBLE (now part of Wunderman Thompson Seattle) that sought to reduce hate speech on Twitter by turning retweets of these hateful messages into donations for a good cause. Here's how it worked: Using machine learning, it first identified hateful speech on the platform. A human moderator then selected the most offensive and most dangerous tweets and attached an undeletable reply, which informed recipients that if they retweet the message, a donation will be committed to an anti-hate group. In a beautiful twist this non-profit was Life After Hate, a group that helps members of extremist groups leave and transition to mainstream life. Unfortunately (and ironically) on the very day I reached out to the team, Twitter decided to allow users to hide replies in their feeds in an effort to empower people faced with bullying and harassment, eliminating the reply function which was the main mechanism that gave #WeCounterHate its power and led to it being able to remove more than 20M potential hate speech impressions.

You should attend Adweek's Elevate: AI summit March 6 in New York. Hate speech isn't a new problem, but in the social media age, the rate at which it can be spread has exponentially increased. But can artificial intelligence help stop it? Agency Possible and its longtime partners at social media marketing software company Spredfast wanted to do something to slow the rate at which hate can spread online. That's why they launched WeCounterHate, a campaign which "counters" such messages on Twitter with a donation to a nonprofit organization fighting hate for every retweet.

With technology moving as fast as it is, we're all scrambling to stay relevant. And marketers are no exception. Every day, we're met with stories about robots taking over our jobs and AI-less businesses soon being left in the dust. And in this dramatically shapeshifting panorama, it's easy to get all shifty-eyed and assume your competitors are nailing the whole AI thing, poised to bury you. Naturally, you look for solutions to put your marketing capabilities on steroids, vowing to tap the power of AI.

The Second Annual Robotics Competition and Exhibition was held in July 1993 in conjunction with the National Conference on Artificial Intelligence. This article reports some of my experiences in helping to design and run the contest and some reflections, drawn from post mortem abstracts written by the competitors, on the relation of the contest to current research efforts in mobile robotics. The competition, which attracted teams from many of the top mobile robotics research laboratories in the United States (see side bar), was first proposed by Thomas Dean and held at the 1992 NCAI conference. Dean's concept was to further the research into the skills such robots need--sensing, interpretation, planning, and reacting--by bringing together interested parties in a cooperative and challenging environment. Ideas should be tested in the real world, not just the controlled conditions of the laboratory.

CARMEL's software design is hierarchical in Object recognition is done using a single camera and a one-pass algorithm to detect horizontally striped, bar-code-like tags on each of the 10 objects. A distance and a heading for each object are returned. Recalibrating the robot's position is done by triangulating from three objects with known locations. FLAKEY's basic software design is distributed: Articles On the perception side, modules add raw sonar and structured-light information to LPS, treating it as an occupancy grid. Other interpretive processes use this information to construct and maintain higher-order structures, parsing the data into surface segments, recognizing objects, and so on.

The first Robotics Exhibition and Competition sponsored by the American Association for Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future. Advanced sensors and efficient actuators and power systems are now available for a wide range of applications. Related technology in vision, planning, and learning has also matured, and the time is ripe for a marriage of these technologies. Further, the growing economic incentives for robotic systems point the way to challenging research.

Hunting a serial killer is, according to experts, a fundamentally different type of detective work than any other type of homicide investigation. For decades the top investigators in this hyper-specialized field have turned to technology. In 2017 this means AI, and just like everything else, it's revolutionizing the industry. It's impossible to know how many active serial killers there are in the US right now. Due to law enforcement and other government reporting failures, miscategorized evidence, and genuine mystery the best estimate we have is somewhere between 25 and 340.

There has been a lot of buzz around artificial intelligence, which uses logic to mimic the human brain. In the advertising space, many shops have created an AI or a cognitive technology division. But for the time being, AI's real impact on various marketing fields seems to be limited. People often use the term AI interchangeably with machine learning, but they are different. AI is the broad concept of teaching machines with data to do things in an efficient way, while machine learning is the technique -- using algorithms to process data, learn from insights and make predictions -- that trains AI, according to agency executives. Machine learning is typically considered as a subset of AI, they said.

Would you go on a business date at work? Would you think I was weird if I told you I did? Some apps are making this possible, so I decided to try it out. As a family man who has just entered his 40s, I am not going to be arranging romantic liaisons on my smartphone. But maybe I haven't completely missed the boat when it comes to the thrill of swiping, matching and meeting up with strangers. Shapr is an app that works like the dating app Tinder, but it's for making business connections rather than romantic ones.

Event[0] is a game about a stranded astronaut talking to an artificial intelligence to help them get back to earth. Like Firewatch before it, much of this solitary adventure is centered around conversing with your colleague. Unlike Firewatch, your colleague is a computer recalling 2001's HAL. Also unlike Firewatch, you get to manually type in the questions you'd like to ask. There's no prescribed dialogue trees here, folks.