As artificial intelligence improves, machines will soon be equipped with intellectual and practical capabilities that surpass the smartest humans. But not only will machines be more capable than people, they will also be able to make themselves better. That is, these machines will understand their own design and how to improve it – or they could create entirely new machines that are even more capable. The human creators of AIs must be able to trust these machines to remain safe and beneficial even as they self-improve and adapt to the real world. This idea of an autonomous agent making increasingly better modifications to its own code is called recursive self-improvement.

Technology is advancing at record speed as innovations that were a foggy prediction came to life one after the other. This passing month, I decided to explore "future studies" and browsed the web for the latest advancements in the tech world, and especially AI, IT, and SaaS. An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor -- outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. People have started noticing self-driving Uber cars in downtown San Francisco, fueling speculation the ridesharing company could soon be deploying autonomous vehicles for commercial use right where it all started, in the Bay Area.

The society of intelligent agents now has a new member. His name is Ozlo, from the Palo Alto, California-based company of the same name. According to co-founder and CEO Charles Jolley, he's the only independent intelligent agent left, now that Samsung has scooped up Viv. But the key differentiator, Jolley told me, is that Ozlo is "the only assistant that can link together competing sources of information." As an example, Jolley recalled that he wanted "some steak and live music" while on a recent trip to Las Vegas.

With the increasing volume of interactions with customers over social media channels, KLM Royal Dutch Airline is the first airline to test how artificial intelligence could assist customer service agents. "We have 100,000 mentions a week on social media," says Tjalling Smit, senior vice president of Digital at KLM Royal Dutch Airlines. "We handle around 15,000 customer service cases a week and we answer our customers 24/7 in 10 different languages." As social channels proliferate, KLM makes sure it is present where its customers live online. "We were the first airline to allow customers to get their boarding passes and flight confirmation through Facebook Messenger," says Smit. KLM is piloting DigitalGenius' GPU-accelerated AI system that is integrated directly into KLM's Customer Relationship Management tool, and provides a layer of deep learning and artificial intelligence to service agents in real-time.

If you're a video game fan of a certain age, you may remember Edge magazine's controversial review of the bloody sci-fi shooting game, Doom. Perhaps you enjoyed a good laugh, as many first-person shooter fans have, at the writer's much-mocked assertion: "if only you could talk to these creatures, then perhaps you could try and make friends with them, form alliances ... Now that would be interesting." Of course, we all know what happened. There would be no room in the Doom series, nor any subsequent first-person blast-'em-up, for such socio-psychological niceties. Instead, we enjoyed 20 years of shooting, bludgeoning and stabbing, the ludicrous idea of diplomacy cast roughly aside. But during this era, something else was happening in game design, and in academic thinking around video games and artificial intelligence. Buoyed by advances in AI research and aided by increasingly powerful computer processors, developers were beginning to think about the possibilities of non-player characters (NPCs) who could think and act in a more complex and human way – who could provide the emotional feedback that the Edge reviewer was thinking about.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving.

Verification and validation of agentic behavior have been suggested as important research priorities in efforts to reduce risks associated with the creation of general artificial intelligence (Russell et al 2015). In this paper we question the appropriateness of using language of certainty with respect to efforts to manage that risk. We begin by establishing a very general formalism to characterize agentic behavior and to describe standards of acceptable behavior. We show that determination of whether an agent meets any particular standard is not computable. We discuss the extent of the burden associated with verification by manual proof and by automated behavioral governance. We show that to ensure decidability of the behavioral standard itself, one must further limit the capabilities of the agent. We then demonstrate that if our concerns relate to outcomes in the physical world, attempts at validation are futile. Finally, we show that layered architectures aimed at making these challenges tractable mistakenly equate intentions with actions or outcomes, thereby failing to provide any guarantees. We conclude with a discussion of why language of certainty should be eradicated from the conversation about the safety of general artificial intelligence.

It is hypothesized by some thinkers that benign looking AI objectives may result in powerful AI drives that may pose an existential risk to human society. We analyze this scenario and find the underlying assumptions to be unlikely. We examine the alternative scenario of what happens when universal goals that are not human-centric are used for designing AI agents. We follow a design approach that tries to exclude malevolent motivations from AI agents, however, we see that objectives that seem benevolent may pose significant risk. We consider the following meta-rules: preserve and pervade life and culture, maximize the number of free minds, maximize intelligence, maximize wisdom, maximize energy production, behave like human, seek pleasure, accelerate evolution, survive, maximize control, and maximize capital. We also discuss various solution approaches for benevolent behavior including selfless goals, hybrid designs, Darwinism, universal constraints, semi-autonomy, and generalization of robot laws. A "prime directive" for AI may help in formulating an encompassing constraint for avoiding malicious behavior. We hypothesize that social instincts for autonomous robots may be effective such as attachment learning. We mention multiple beneficial scenarios for an advanced semi-autonomous AGI agent in the near future including space exploration, automation of industries, state functions, and cities. We conclude that a beneficial AI agent with intelligence beyond human-level is possible and has many practical use cases.

This article reports on the first international Competition of Distributed and Multiagent Planners (CoDMAP). The competition focused on cooperative domain-independent planners compatible with a minimal multiagent extension of the classical planning model. The motivations for the competition were manifold: to standardize the problem description language with a common set of benchmarks, to promote development of multiagent planners both inside and outside of the multiagent research community, and to serve as a prototype for future multiagent planning competitions. The article provides an overview of cooperative multiagent planning, describes a novel variant of standardized input language for encoding mutliagent planning problems and summarizes the key points of organization, competing planners and results of the competition.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).