The discourse on responsible artificial intelligence (AI) regulation is understandably dominated by risk-focused assessments and analyses. This approach reflects the fundamental uncertainty policymakers face when determining appropriate responses to current, emerging and novel AI systems. In this article, we argue that by operationalising the concept of freedom - the philosophical counterpart to responsibility - a complementary approach centred on the potential societal benefits of AI systems can be developed. The result is a discursive framework grounded in freedom as capability and freedom as opportunity, which represent the two main intellectual traditions of interpreting freedom. We contend that the complexity, ambiguity and contestation involved in regulating AI systems make a deliberative paradigm more useful than the conventional technical one. The resulting framework is structured around coordinative, communicative and decision spaces, each with sequential focal points and associated outputs.

Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We apply this approach to the task of discovering public value expressions in US AI patents. We collect a database comprising 154,934 patent documents using an advanced Boolean query submitted to InnovationQ+. The results are merged with full patent text from the USPTO, resulting in 5.4 million sentences. We design a framework for identifying and labeling public value expressions in these AI patent sentences. A prompt for GPT-4 is developed which includes definitions, guidelines, examples, and rationales for text classification. We evaluate the quality of the labels and rationales produced by GPT-4 using BLEU scores and topic modeling and find that they are accurate, diverse, and faithful. These rationales also serve as a chain-of-thought for the model, a transparent mechanism for human verification, and support for human annotators to overcome cognitive limitations. We conclude that GPT-4 achieved a high-level of recognition of public value theory from our framework, which it also uses to discover unseen public value expressions. We use the labels produced by GPT-4 to train BERT-based classifiers and predict sentences on the entire database, achieving high F1 scores for the 3-class (0.85) and 2-class classification (0.91) tasks. We discuss the implications of our approach for conducting large-scale text analyses with complex and abstract concepts and suggest that, with careful framework design and interactive human oversight, generative language models can offer significant advantages in quality and in reduced time and costs for producing labels and rationales.

Environmental, social and governance (ESG) factors should be key considerations for CTOs and technology leaders scaling next generation companies from day one. Investors are increasingly prioritizing startups that focus on ESG, with the growth of sustainable investing skyrocketing. It's simple: Consumers are no longer willing to support companies that don't prioritize sustainability. According to a survey conducted by IBM, the COVID-19 pandemic has elevated consumers' focus on sustainability and their willingness to pay out of their own pockets for a sustainable future. In tandem, federal action on climate change is increasing, with the U.S. rejoining the Paris Climate Agreement and a recent executive order on climate commitments.

A cabal of over 1,000 experts in the field of computing, engineering, artificial intelligence, and even prominent officers in the US Army, have signed an open letter, hosted by the Future of Life Institute, imploring the military to deprioritise its implementation of artificial intelligence. The signatories of the letter, entitled Research Priorities for Robust and Beneficial Artificial Intelligence, believe that "intelligent agents," or "systems that perceive and act in some environment," are not yet compatible with current AI technology, and that the social benefits of AI should be examined and tested further before military use is explored. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008-09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do.

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents -- systems that perceive and act in some environment. In this context, "intelligence" is related to statistical and economic notions of rationality -- colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research.

GlobalData research has found the top artificial intelligence (AI) influencers based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in artificial intelligence on Twitter during Q2 2020. Evan Kirstel is a B2B thought leader with extensive experience across enterprises sales, alliances, and business development. He currently serves as chief digital officer and advisor of NYDLA.ORG, a remote, distance/digital learning and collaboration association. Kirstel is of the opinion that the role of artificial intelligence accelerates the opportunity for increased customer and agent engagement alike.

The National Artificial Intelligence Research Institutes is launching today to fund $200 million in grants over the coming years, which shall go towards benefiting society. "Sustained R&D investments are needed to advance trust in AI systems to ensure they meet society's needs" Building trustworthy AI is at the top of the funding priorities for the new program led by the National Science Foundation (NSF) and supported by a whole bunch of government agencies and their many acronyms. Together, these six areas shall prove to have a profound impact on our lives in the future -- from the management of our food supply to curing and preventing diseases and making giant leaps in our understanding of our place in the cosmos. "Sustained R&D investments are needed to advance trust in AI systems to ensure they meet society's needs and adequately address requirements for robustness, fairness, explainability, and security," according to the National Artificial Intelligence Research and Development Strategic Plan: 2019 Update. NSF Director France Cordova stated, "Advances in AI are progressing rapidly and demonstrating the potential to transform our lives.

Technological advances have changed the way people work, consume information, and live. Innovations in technology gave impetus to the development of Artificial Intelligence (AI). In recent years, AI has generated controversy. Proponents cite AI's track record of improving operational efficiency, enhancing target identification efforts, and increasing productivity through automation and big data analytics. Conversely, AI opponents blame automation for fueling unemployment and rising inequality; some have even suggested AI is a threat to humanity.

I think often of Ender's Game these days. In this award-winning 1985 science-fiction novel by Orson Scott Card (based on a 1977 short story with the same title), Ender is being trained at Battle School, an institution designed to make young children into military commanders against an unspecified enemy (http://bit.ly/2hYQMDF). Ender's team engages in a series of computer-simulated battles, eventually destroying the enemy's planet, only to learn then that the battles were very real and a real planet has been destroyed. I got involved in computing at age 16 because programming was fun. Later I discovered that developing algorithms was even more enjoyable.

Music follows a set of patterns that can extract feelings from its listeners and provoke emotional responses. While machines can now make music too, they don't give much consideration to the emotional response of their audience. But now a team of researchers has developed a machine-learning device that can detect the emotional state of listeners and make new songs that provoke new feelings. In the study, participants listened to music while wearing wireless headphones that contained brain wave sensors. The team of researchers, under the support of Osaka University's Center of Innovation (COI) program, developed the AI that detects users' brain state and provides a means for activating it through music.