AI Expert Marva Bailer explains to Fox News Digital Hank Azaria's opinion piece about humanity and AI matters. "The Simpsons" star Hank Azaria has voiced his fears over artificial intelligence in a new opinion piece. The actor, who has been with the show since 1989, wrote an opinion essay for The New York Times, worrying AI "will be able to recreate the sounds of the more than 100 voices I created for characters on'The Simpsons.'" He continued, "It makes me sad to think about it. Not to mention, it seems just plain wrong to steal my likeness or sound -- or anyone else's."

We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the scenario in which one agent is human, while the other is an autonomous agent. We run experiments with human subjects in a simple grid domain, which simulates the single track road problem. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. However, we show that an agent that tries to maximize a linear combination of the human's utility and its own utility, achieves a high score, and significantly outperforms other baselines, including an agent that tries to maximize only its own utility. While humans can cope with new situations quite easily, even state-of-the-art algorithms trouble with new situations that they haven't been trained on. Unfortunately, when it comes to autonomous vehicles the results may be devastating. One example for an uncommon, yet important scenario for autonomous vehicles is the problem of a single track road. In this problem two vehicles in opposite directions must cross a narrow road, which is not wide enough to allow both vehicles to pass at the same time.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Fisher Stevens really regrets appearing as an Indian character in brownface for the 1986 movie "Short Circuit" and its subsequent sequel. Stevens, who was born in Chicago, plays Ben Jabituya in the science fiction comedy about two scientists whose advanced robot gains sentience. While the Johnny 5 robot character is still revered as a staple of 1980s comedies, Stevens' role and the subsequent darkening of his skin to appear as an Indian man is often criticized to this day for portraying a stereotype and taking a role away from an Indian actor.

Natural language programming is a promising approach to enable end users to instruct new tasks for intelligent agents. However, our formative study found that end users would often use unclear, ambiguous or vague concepts when naturally instructing tasks in natural language, especially when specifying conditionals. Existing systems have limited support for letting the user teach agents new concepts or explaining unclear concepts. In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations. Users can also define new procedures and concepts by demonstrating and referring to contents within GUIs of existing mobile apps. We demonstrate this approach in PUMICE, an end-user programmable agent that implements this approach. A lab study with 10 users showed its usability.

Chatbots have been a core measure of AI since Turing has presented his test for intelligence, and are also widely used for entertainment purposes. In this paper we present a platform that enables users to collaboratively teach a chatbot responses, using natural language. We present a method of collectively detecting malicious users and using the commands taught by these users to further mitigate activity of future malicious users.

Artificial intelligence (AI) has a lot to offer over human beings as a brand representative. It doesn't need incentives, bonuses, or stock options. However, just like your junior brand manager, it can sometimes tweet abhorrent content you would rather forget. One crisp spring Wednesday, Microsoft unveiled Tay, an artificial intelligence chatbot meant to simulate an energetic young woman with "zero chill." The experiment ended quickly, and poorly, when Tay became a crude, racist monster.

Preventive maintenance is essential for the smooth operation of any equipment. Still, people occasionally do not maintain their equipment adequately. Maintenance alert systems attempt to remind people to perform maintenance. However, most of these systems do not provide alerts at the optimal timing, and nor do they take into account the time required for maintenance or compute the optimal timing for a specific user. We model the problem of maintenance performance, assuming maintenance is time consuming. We solve the optimal policy for the user, i.e., the optimal timing for a user to perform maintenance. This optimal strategy depends on the value of user's time, and thus it may vary from user to user and may change over time. %We present a game Based on the solved optimal strategy we present a personalized maintenance agent, which, depending on the value of user's time, provides alerts to the user when she should perform maintenance. In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent significantly improves user performance.

The number of people with disabilities is continuously increasing. Providing patients who have disabilities with the rehabilitation and care necessary to allow them good quality of life creates overwhelming demands for health and rehabilitation services. We suggest that advancements in intelligent agent technology provide new opportunities for improving the provided services. We will discuss the challenges of building an agent for the health care domain and present four capabilities that are required for an agent in the health care domain: planning, monitoring, intervention and encouragement. We will discuss the importance of personalizing all of them and the needto facilitate cooperation between the automated agent and the human care givers. We will review recent technology that can be used toward the development of agents that can have these capabilities and their promise in automating services such as physiotherapy, speech therapy and cognitive training.