New advancements in data science often spark dire predictions about how powerful new technologies will transform the world. Yet, as writer Stephen Shankland reminds us, technologies like Open AI's new Chat GPT (short for chat-based Generative Pretrained Transformer) are created by humans. Chat GPT is a chatbot that is "trained with human assistance to deliver more useful, better dialog." The people assisting that training--those who create the models and assemble the data used to train chatbots--make a difference in the technologies that will go on to shape our lives. Computer scientist Joy Buolamwini, an early critic of racial bias in facial recognition software, said technology should "be more attuned to the people who use it and the people it's used on."

The 4th Industrial Revolution is the culmination of the digital age. Nowadays, technologies such as robotics, nanotechnology, genetics, and artificial intelligence promise to transform our world and the way we live. Artificial Intelligence Ethics and Safety is an emerging research field that has been gaining popularity in recent years. Several private, public and non-governmental organizations have published guidelines proposing ethical principles for regulating the use and development of autonomous intelligent systems. Meta-analyses of the AI Ethics research field point to convergence on certain principles that supposedly govern the AI industry. However, little is known about the effectiveness of this form of Ethics. In this paper, we would like to conduct a critical analysis of the current state of AI Ethics and suggest that this form of governance based on principled ethical guidelines is not sufficient to norm the AI industry and its developers. We believe that drastic changes are necessary, both in the training processes of professionals in the fields related to the development of software and intelligent systems and in the increased regulation of these professionals and their industry. To this end, we suggest that law should benefit from recent contributions from bioethics, to make the contributions of AI ethics to governance explicit in legal terms.

This paper presents a web-based demonstration of Emotion Twenty Questions (EMO20Q), a dialog game whose purpose is to study how people describe emotions. EMO20Q can also be used to develop artificially intelligent dialog agents that can play the game. In previous work, an EMO20Q agent used a sequential Bayesian machine learning model and could play the question-asking role. Newer transformer-based neural machine learning models have made it possible to develop an agent for the question-answering role. This demo paper describes the recent developments in the question-answering role of the EMO20Q game, which requires the agent to respond to more open-ended inputs. Furthermore, we also describe the design of the system, including the web-based front-end, agent architecture and programming, and updates to earlier software used. The demo system will be available to collect pilot data during the ACII conference and this data will be used to inform future experiments and system design.

Recent years have seen the increasing popularity of certificates as a means for data scientists to build new skills. Companies, such as Google, have even begun offering their own, with promising results. This popularity may be a result of the fact that a machine learning certificate can offer similar amounts of knowledge for less time and costs. There are numerous other benefits as well. There is a lot that can be learned on the job.

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Everywhere you turn today is some unbelievable technological advancement on a variety of fronts. In our everyday lives, we hear or experience things about autonomous vehicles, warehouse robots, chatbots, Alexa, Siri, Uber, automated email responses, robotic surgeries, Netflix recommendation systems, smart factories, smart buildings and search retargeting. Technology giants are becoming the most valuable companies on the planet, and we see our lives shifting from what we thought was kids and their smartphone addictions and gaming to encroaching on our daily lives across the board. They are typically revolving around a variety of enabling technology layers, namely, cloud computing, computational systems, networks and sensors, robotics, material sciences, digital manufacturing and artificial intelligence. However, at the center of it, is AI that permeates many of the other advances in some shape or form in creating intelligent systems on top of advances in core products or technologies.

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Future telescopes like the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) are designed to sample the chemistry of exoplanet atmospheres. Ten years from now, spectra of alien skies will be coming in by the hundreds, and the data will be of a higher quality than is currently possible. Astronomers agree that new analysis techniques, including machine learning algorithms, will be needed to keep up with the flow of data and have been testing options in advance. An upcoming study in Monthly Notices of the Royal Astronomical Society trialed one such algorithm against the current gold standard method for decoding exoplanet atmospheres to see whether the algorithm could tackle this future big-data problem. "We got really good agreement between [the answers from] our machine learning method and the traditional Bayesian method that most people are using," said Matthew Nixon.

"In 1998, Tata Motors' tryst with destiny in the passenger car business began with the launch of Indica. Despite all the attention, Indica failed to sizzle, with sales falling 20% the next year as customers complained that the car was rolled out despite glitches. Tata Motors carried out a survey to identify the problems, and discovered that the components being procured from Tier 1 and Tier 2 suppliers were of sub-standard quality. Amit Bhingurde, chief operations officer, TAL Manufacturing Solutions, a Tata Motors subsidiary, explains the reason: 'The manufacturers were not able to produce high quality components as they couldn't afford automation'." The need of automation is the result of today's phenomenon - Artificial Intelligence.

The USC Center for Artificial Intelligence in Society (CAIS)--a joint venture of the USC Suzanne Dworak-Peck School of Social Work and USC Viterbi School of Engineering--will host its first Visiting Fellows Program this summer focused on employing AI to help solve complex societal problems. As part of the Fellows Program, visiting researchers from all over the world will come to USC this summer for up to three months to learn from a working model established by the Center's co-founders, Eric Rice of the USC Suzanne Dworak-Peck School of Social Work and Milind Tambe of the USC Viterbi School of Engineering. The two had successfully collaborated by employing AI to ensure that homeless youth shared important public health information among peers in the youths' own social networks. "Using artificial intelligence to promote the greater good is an emerging area of study with huge potential," said Eric Rice, co-director of CAIS and associate professor at the USC Suzanne Dworak-Peck School of Social Work. "Our goal in establishing this fellowship is to bring together the best and brightest scholars in artificial intelligence and social work to explore breakthrough solutions to age-old problems plaguing many of our cities and communities." Topics to be studied by fellows this summer include suicide prevention among college students; social support for North Korean refugees to help their integration into South Korean society; wildlife conservation through poaching prevention in developing nations' national parks; HIV and substance abuse prevention for homeless youth; and predicting and reducing gang violence in Los Angeles.