Doug Lenat has worked in diverse parts of AI – natural language understanding and generation, automatic program synthesis, expert systems, machine learning, etc. – for going on 40 years now, just long enough to dare to write this article. His 1976 Stanford PhD thesis, AM, demonstrated that creative discoveries in mathematics could be produced by a computer program (a theorem proposer, rather than a theorem prover) guided by a corpus of hundreds of heuristic rules for deciding which experiments to perform and judging "interestingness" of their outcomes. That work earned him the IJCAI Computers and Thought Award, and sparked a renaissance in machine learning research. Dr. Lenat was on the CS faculty at CMU and Stanford, was one of the founders of Teknowledge, and was in the first batch of AAAI Fellows. He worked with Bill Gates and Nathan Myhrvold to launch Microsoft Research Labs, and to this day he remains the only person to have served on the technical advisory boards of both Apple and Microsoft.

Process integrated mechanisms (PIM) offer a new approach to the problem of coordinating the activity of physically distributed systems or devices. Current approaches to coordination all have well-recognized strengths and weaknesses. We propose a novel architecture to add to the mix, called the Process Integrated Mechanism (PIM), which enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to its rejection in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously.

Since late 2016, the Chinese government has subjected the 13 million ethnic Uyghurs and other Turkic Muslims in Xinjiang to mass arbitrary detention, forced political indoctrination, restrictions on movement, and religious oppression. Credible estimates indicate that under this heightened repression, up to one million people are being held in "political education" camps. The government's "Strike Hard Campaign against Violent Terrorism" (Strike Hard Campaign, 严厉打击暴力恐怖活动专项行动) has turned Xinjiang into one of China's major centers for using innovative technologies for social control. This report provides a detailed description and analysis of a mobile app that police and other officials use to communicate with the Integrated Joint Operations Platform (IJOP, 一体化联合作战平台), one of the main systems Chinese authorities use for mass surveillance in Xinjiang. Human Rights Watch first reported on the IJOP in February 2018, noting the policing program aggregates data about people and flags to ...

The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Mission planning for space is challenging because of the mixture of goals and constraints. Every space mission tries to squeeze all of the capacity possible out of the spacecraft. For Orbital Express, this means performing as many experiments as possible, while still keeping the spacecraft safe. Keeping the spacecraft safe can be very challenging because we need to maintain the correct thermal environment (or batteries might freeze), we need to avoid pointing cameras and sensitive sensors at the sun, we need to keep the spacecraft batteries charged, and we need to keep the two spacecraft from colliding... made more difficult as only one of the spacecraft had thrusters.

The ability to say "no" in a variety of ways and contexts is an essential part of being socio-cognitively human. Through a variety of examples, we show that, despite ominous portrayals in science fiction, AI agents with human-inspired noncompliance abilities have many potential benefits. Rebel agents are intelligent agents that can oppose goals or plans assigned to them, or the general attitudes or behavior of other agents. They can serve purposes such as ethics, safety, and task execution correctness, and provide or support diverse points of view. We present a framework to help categorize and design rebel agents, discuss their social and ethical implications, and assess their potential benefits and the risks they may pose.

Artificial Intelligence has impacted almost every aspect of human life, leaving large amounts of manual jobs to computers, and allowing humans to pursue intelligent jobs. However, in the future, there may be a time when intelligent jobs are also done artificially. Even more unsettling is the possibility that machines will become more intelligent than humans. Although there is little consensus on this topic, such an event is almost certain to happen. We can only hope that we have found a way to make AI "friendly", or share humans interests beforehand.

From Shakey the Robot to self-driving cars, from the personal computer to personal assistants on our phones, the Defense Advanced Research Projects Agency (DARPA) has led the development of integrated artificial intelligence (AI) systems for more than half a century. From the earliest days of AI, it was apparent that a robust, generally intelligent system should include a complete set of capabilities: perception, memory, reasoning, learning, planning, and action; and when DARPA initiated AI research in the 1960s, ambitious projects such as Shakey the Robot went after the complete package. As DARPA realized the challenges, they backed away from the ultimate goal of integrated AI and tried to make progress on the individual problems of image understanding, speech and language understanding, knowledge representation and reasoning, planning and decision aids, machine learning, and robotic manipulation. Yet, even as researchers struggled to make progress in these subdisciplines, DARPA periodically resurrected the challenge of integrated intelligent systems and pushed the community to try again. In the 1980s, DARPA's Strategic Computing Initiative took on challenges of integrated AI projects such as the Autonomous Land Vehicle and the Pilot's Associate.

Bridges Health Services is merging with Las Vegas-based home health and hospice companies Gentlecare Home Health and Renaissance Hospice and Los Angeles-based home health and hospice companies Supreme Healthcare Inc. and Supreme Hospice Inc. Bridges Health Services was founded by Carolyn Romero, a home health and hospice nurse. The more than 10-year-old company has a national footprint, delivering hospice and home health care primarily seniors covered by Medicare. Along with the merger, the company has also implemented a new artificial intelligence platform designed to help them identify patients who are in most need of their services as early as possible in their disease trajectory. "The traditional approach of our health care delivery system focuses on treatment rather than prevention," Bridges founder Romero said. Artificial intelligence technology is in its infancy, but hospice organizations are already applying the technology to provide patient support, reduce costs, and improve efficiency.

As fires get larger and more dangerous, various government and private agencies have turned to AI to detect, and potentially predict wildfires. The National Guard has been carrying out reconnaissance flights in California during the late summer and fall for the pew years, but now the drones used to carry out these flights have received upgrades with AI algorithms intended to automatically generate maps of fires within a particular region. Creating fire maps is an incredibly difficult process that requires data analysis to map constantly changing fires as they move over rugged terrain. Both air and ground observations are used to make fire maps, and fire maps are typically only updated once every day or so. Large fires can move as far as 15 miles during a single day, as witnessed by some of the fires this fire season.

The jargon used to describe the latest cybersecurity solutions has proliferated - "machine learning", "blockchain", "DevSecOps" and "shift left" - but the same problem remains. It is difficult to sift through the noise and find tools that actually work. In a founders' market, startups can take advantage of the easy funding environment by throwing buzzwords into an investor pitch deck, still warm from the heat generated from the RSA theme du jour. Without a doubt, security remains a huge market and an increasingly important one. So how can we identify where true value resides in this market?