ChatGPT is another large language model (LLM) inline but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT and other LLMs. In contrast, this study focuses on the important aspects that are mostly overlooked, i.e. sustainability, privacy, digital divide, and ethics and suggests that not only chatGPT but every subsequent entry in the category of conversational bots should undergo Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This paper discusses in detail about the issues and concerns raised over chatGPT in line with aforementioned characteristics. We support our hypothesis by some preliminary data collection and visualizations along with hypothesized facts. We also suggest mitigations and recommendations for each of the concerns. Furthermore, we also suggest some policies and recommendations for AI policy act, if designed by the governments.

In 2019, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) formulated a plan for the integration of artificial intelligence with education, and suggested that AI has the potential to create a more equitable, efficient, and innovative educational system (1). Although this statement was made prior to Covid-19's rapid proliferation, it warrants renewed consideration in light of the calamitous effects the pandemic has had on the education system. The abrupt shift to remote learning and subsequent return to in-person instruction have revealed significant decreases in learning outcomes and readiness to meet academic standards (2); decreases that a return to normalcy has little hope of addressing. Now, if ever, is the time to look towards innovative means of enhancing education, and artificial intelligence could prove to be the change required to bring about meaningful academic growth. Recent studies indicate that in the United States, school closures and remote learning have resulted in significant decreases in educational outcomes for K-5 students that roughly equate to a five month deficit in math skills, and a four month deficit in reading comprehension (2).

The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice.

Although the potential for artificial intelligence to transform healthcare in lower income countries has been much hyped, the technology is proving genuinely useful in helping Africa overcome difficulties in tackling diseases. Such technology can automate medical tasks and help doctors to do more with limited resources. It can even accelerate advances if certain barriers are overcome. The work of minoHealth AI Labs, the Ghana-based data science start-up that I founded, offers one example. By collecting medical images, we are seeking to automate radiology through the use of deep learning.

Last week, a video went viral on social media around the world. It shows a robotic arm picking up a bowling ball, spinning its arm around, and hurling the ball down the lane at speed, sending all the bowling pins flying. It soon emerged that the very real-looking video was in fact fake, the work of a motion graphics designer who had hash-tagged it with words such as animation, rendering and CGI to indicate that it was computer-generated, before sharing on social media. Nonetheless, from the reactions it was clear that not everyone picked up the clues; many if not most people thought it was real. The impossibility of distinguishing between fake and real in images and videos is an everyday occurrence now, something we just have to live with.

One measure of the status of civilization is the complexity of tools used by the society. As societies have progressed, tools and machines used by them have become increasingly complex. Despite their rising complexity the current set of tools and machines still need humans to create and use them and they can only do things what humans have pre-programmed them to do or control them to do. In particular, current set of machines cannot learn and enhance their knowledge. However, a new set of machines are emerging that can learn and they need minimal human intervention to operate.

In response to a world in which cancer is a growing global health challenge, there is now a greater need for US Medical Physicists and other Radiation Oncology professionals across institutions to work together and be more globally engaged in the fight against cancer. There are currently many opportunities for Medical Physicists to contribute to alleviating this pressing need, especially in helping enhance access to Medical Physics Education/training and Research Excellence across international boundaries, particularly for low and middle-income countries (LMIC), which suffer from a drastic shortage of accessible knowledge and quality training programs in radiotherapy. Many Medical Physicists aremore » not aware of the range of opportunities that even with small effort could have a high impact. Faculty at the two CAMPEP-accredited Medical Physics Programs in New England: the University of Massachusetts Lowell and Harvard Medical School have developed a growing alliance to increase Access to Medical Physics Education/training and Research Excellence (AMPERE), and facilitate greater active involvement of U.S. Medical Physicists in helping the global fight against cancer and cancer disparities. In this symposium, AMPERE Alliance members and partners from Europe and Africa will present and discuss the growing global cancer challenge, the dearth of knowledge, research, and other barriers to providing life-saving radiotherapy in LMIC, mechanisms for meeting these challenges, the different opportunities for participation by Medical Physicists, including students and residents, and how participation can be facilitated to increase AMPERE for global health.