Robots applications in our daily life increase at an unprecedented pace. As robots will soon operate "out in the wild", we must identify the safety and security vulnerabilities they will face. Robotics researchers and manufacturers focus their attention on new, cheaper, and more reliable applications. Still, they often disregard the operability in adversarial environments where a trusted or untrusted user can jeopardize or even alter the robot's task. In this paper, we identify a new paradigm of security threats in the next generation of robots. These threats fall beyond the known hardware or network-based ones, and we must find new solutions to address them. These new threats include malicious use of the robot's privileged access, tampering with the robot sensors system, and tricking the robot's deliberation into harmful behaviors. We provide a taxonomy of attacks that exploit these vulnerabilities with realistic examples, and we outline effective countermeasures to prevent better, detect, and mitigate them.

In an economy where data is changing how companies create value -- and compete -- experts predict that using artificial intelligence (AI) at a larger scale will add as much as $15.7 trillion to the global economy by 2030. As AI is changing how companies work, many believe that who does this work will change, too -- and that organizations will begin to replace human employees with intelligent machines. This is already happening: intelligent systems are displacing humans in manufacturing, service delivery, recruitment, and the financial industry, consequently moving human workers towards lower-paid jobs or making them unemployed. This trend has led some to conclude that in 2040 our workforce may be totally unrecognizable. Are humans and machine really in competition with each other though?

This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment. We argue that a major limit of current work in AI is that it is missing this perspective, both theoretically and experimentally. Finally, we discuss the promising approach of developmental artificial intelligence, modeling infant development through multi-scale interaction between intrinsically motivated learning, embodiment and a fastly changing socio-cultural environment. This paper takes the form of an interview of Pierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI - K{\"{u}}nstliche Intelligenz special issue in developmental robotics.

This recent TechRadar article explores the evolution of AI technologies that could conceivably outperform humans in creative disciplines previously perceived as uniquely human. How should the law deal with liability for infringement of third party materials used in the creation of independent AI-generated outputs? Read more about the topics of discussion this article has raised, how intellectual property law might be affected by AI technologies and AI-generated output, and the benefits and uncertainties facing the field, in our Cookie Jar article here. Thanks to developments in AI, the days of the human creative may be numbered...

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. ROME – Pope Francis urged governments on Monday to use the coronavirus crisis as a revolutionary opportunity to create a world that is more economically and environmentally just -- and where basic health care is guaranteed for all. Francis made the appeal in his annual foreign policy address to ambassadors accredited to the Holy See, an appointment that was postponed for two weeks after he suffered a bout of sciatica nerve pain that made standing and walking difficult. Francis urged the governments represented in the Apostolic Palace to contribute to global initiatives to provide vaccines to the poor and to use the pandemic to reset what he said was a sick economic model that exploits the poor and the Earth. Pope Francis delivers his blessing from his studio window overlooking St. Peter's Square, at the Vatican, Sunday, Feb. 7, 2021.

Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static - i.e., they do not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for dramatic performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data - as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/retention algorithms in NS, which integrate a formalized learning process. Using a full-blown operational model, we demonstrate that NS achieves an order of magnitude improvement in memory access performance for two representative applications when compared to traditional content-based memory.

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

Definitions of the term'Artificial Intelligence' tend to fit one of the following categories: While all of these options are similar in that they deal with'intelligent behavior' in computers, they are also quite different. The first refers to a research discipline, while the second and third describe what that research discipline seeks to create. The ways in which the term'AI' can be used depend on which of these definitions you consider valid. For instance, news articles often have titles to the effect of "Google's new AI learned X" or "A new AI can do Y," such as: But, such usage ("An AI Developed", "AI can now", etc.) is only valid with that third'intelligent entity' definition. If the first'field of research' definition is chosen instead, these titles would have to be rewritten as "Google's new AI algorithm learned X" or "A new AI system can do Y."

There was a time when we heard terms like Artificial Intelligence and Machine Learning only in sci-fi movies. But today, technological advances have brought us to a point where businesses across verticals are not only talking about, but also implementing artificial intelligence and machine learning in everyday scenarios. AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI has evolved from being a research topic to being at the early stages of enterprise adoption.

An important feature of human intelligence is the ability to learn. The amazing learning abilities of the human brain enable babbling babies to grow into learned and easy-to-talk adults. For human beings, learning is an innate ability. The universal existence of this ability makes us ignore its strangeness and preciousness. As far as artificial intelligence research is concerned, how to make machines possess the most universal capabilities in the human world is a very challenging research direction. In different research paths, the subjects, contents and methods of learning are quite different.