Intersection Of AI Research Articles

Facing Immersive “Post-Truth” in AIVR?

In recent years, prevalent global societal issues related to fake news, fakery, misinformation, and disinformation were brought to the fore, leading to the construction of descriptive labels such as “post-truth” to refer to the supposedly new emerging era. Thereby, the (mis-)use of technologies such as AI and VR has been argued to potentially fuel this new loss of “ground-truth”, for instance, via the ethically relevant deepfakes phenomena and the creation of realistic fake worlds, presumably undermining experiential veracity. Indeed, unethical and malicious actors could harness tools at the intersection of AI and VR (AIVR) to craft what we call immersive falsehood, fake immersive reality landscapes deliberately constructed for malicious ends. This short paper analyzes the ethically relevant nature of the background against which such malicious designs in AIVR could exacerbate the intentional proliferation of deceptions and falsities. We offer a reappraisal expounding that while immersive falsehood could manipulate and severely jeopardize the inherently affective constructions of social reality and considerably complicate falsification processes, humans may neither inhabit a post-truth nor a post-falsification age. Finally, we provide incentives for future AIVR safety work, ideally contributing to a future era of technology-augmented critical thinking.

Artificial Intelligence and Innovation Ethics

This symposium examines key questions posed by teaching ethics to artificial intelligence for business settings. A general question is how to balance the benefits and risks of AI, which is a significant concern with technological change. That concern is made more severe by the large-scale implications of AI on human life, including our understanding of what it is to be a human being and what entities can be properly treated as right holders. More specifically, several topics arise in the intersection of AI and Ethics that this panel will address. Fairness in the use of AI for business: When AI is used at a large scale for business, there is always a concern that it may also lead to drastic and large scale discrimination against some groups of people. For example, deep-learning systems may deny mortgage loans to members of certain groups when others with comparable financial resources receive loans, and this may occur even if none of the training data indicate group membership. It is thus crucial to design tools that can monitor the AI’s performance to continuously test for bias. A second crucial goal is to design methods to mitigate any such biases to the maximum extent possible. This research direction will involve both making fundamental contributions to AI and statistics in terms of developing these tools, and impactful use in business in many applications. A large ethics literature has carefully analyzed concepts of fairness, and this body of thought can be applied to AI. Many statistical measures of bias have been proposed, some of which are inconsistent with others. An ethical analysis can help evaluate whether measures have normative justification. Ethically grounded value alignment: Deep learning systems are frequently designed to reflect human values so as to avoid recommending decisions inconsistent with these values. Values are typically ascertained, however, in the much the same empirical way as facts and predictions - in this case, by analyzing large datasets that reflect human beliefs and preferences. Yet the AI community is coming to realize that a purely empirical approach can reflect biases and prejudices as well as acceptable moral values. There is no substitute for grounding value alignment in ethical principles that are independently derived, a manoeuvre that avoids the philosophically famous “naturalistic fallacy” of deriving ethical conclusions from purely factual premises. The deontological tradition in ethics provides the intellectual resources to develop rigorously defined and grounded principles that can be used to screen training sets or otherwise direct learning procedures. Human-Centered Explainable AI (XAI): Many industry experts have pointed out the critical need for human oriented explanation by AI systems. According to an IBM survey, about 60% of 5,000 executives were concerned “about being able to explain how AI is using data and making decisions.” However, the most successful algorithms in use today are not transparent. All of these models are fundamentally “black boxes” that include many layers of complex, typically nonlinear, transformations of inputs. It can be quite difficult for anyone to understand the algorithm’s output and/or why the model makes key predictions. Given these challenges, efforts to develop more interpretable, explainable, or intelligible algorithms comprise a key area of current research. The explainability of an algorithm plays a key role in detecting, enabling, and improving auditability, fairness, trust, and reliability. However, the definition of interpretability and desiderata of what makes a good explanation remain elusive and different researchers use different, often problem- or domain-specific, definitions. More alarmingly, this XAI research rarely involves systematic investigation of human responses with regard to a “What is a good explanation for machine learning output?” AI generates a variety of ethical questions at three interconnected levels. The first is the legal dimension: what laws should be enacted to govern AI? Should some particular aspect of AI be subject to legal regulation at all? Do we need to fashion specific legislation to address AI issues or rely on more general legal standards? The second is the social dimension, which raises questions about the social morality that should be cultivated concerning AI. What sort of culture will develop in response to AI? A third level is concerned with issues that arise for individuals and associations in their engagement with AI. That connects with corporations and associations, which still need to exercise their own moral judgment.

The Intersection of AI and precision medicine at RSNA 2018

The Intersection of AI and precision medicine at RSNA 2018

Natural language translation at the intersection of AI and HCI

Old questions being answered with both AI and HCI.

Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics

Researchers in AI and Robotics have in common the desire to “make robots intelligent”, evidence of which can be traced back to the earliest AI systems. One major contribution of AI to Robotics is the model-centered approach, whereby intelligence is the result of reasoning in models of the world which can be changed to suit different environments, physical capabilities, and tasks. Dually, robots have contributed to the formulation and resolution of challenging issues in AI, and are constantly eroding the modeling abstractions underlying AI problem solving techniques. Forty-eight years after the first AI-driven robot, this article provides an updated perspective on the successes and challenges which lie at the intersection of AI and Robotics.

Intelligent Learning Technologies Part 2: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges

The fall and winter issues of AI Magazine present articles on some of the most interesting projects at the intersection of AI and education. Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game‐based learning environment as well as technology‐focused components such as student models and data mining. This issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

Intelligent Learning Technologies: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges

The fall and winter issues of AI Magazine present articles on some of the most interesting projects at the intersection of AI and education. Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game‐based learning environment as well as technology‐focused components such as student models and data mining. The winter issue will conclude with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

Book review: From Animals to Animats 2. Edited by Jean-Arcady Meyer, Herbert L. Roiblat, and Stewart W. Wilson (MIT Press)

It is time animals lived side by side with animals. They can live, learn, build maps, evolve, and be our domesticated pets too. In the intersection of AI, Robotics, Philosophy and Psychology, traditions of building ever more complex systems that try to exhibit intelligent behavior or solve puzzles are giving way to systems that live simple lives, without pretending to reason deeply, or to act as if they were real animals. Instead, the system designer builds simple agents and endows them with basic shallow abilities to interact with the world. The point is that the designer doesn't solve all the problems that the system may encounter beforehand. Nor does the designer build a system that acts as if it is trying to talk to itself outloud. Many of the traditional symbolic systems are designed so that they can be made to generate a transcript of human understandable steps they take to decide on performing each and every action. Once deployed in a complex world, the animat (just as a device would) uses its repertoire of interactive behaviors as they become needed (e.g., a microwave oven responds to buttons pushed). The interactions that follow may appear intelligent. Such agents can also be made to learn from these interactions and enrich their range of capabilities.