ChatGPT as a Python Programming Assistant by Matthew Mayo ChatGPT: Everything You Need to Know by Nisha Arya Explainable AI: 10 Python Libraries for Demystifying Your Model's Decisions by Maryam Miradi How to Use Python and Machine Learning to Predict Football Match Winners by Vaishnavi Amira Yada 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 by Matthew Mayo

The Jobs of the Future Are a Thing of the Past. "You may have read about the outsourcing issue, the great X-factor in American politics today, in cover articles in Time, Wired, Business Week.... In New Hampshire, John Kerry was asked about the problem. His answer: 'We have to create the next wave of those kinds of jobs that come from the fact that we're highly educated and deeply committed to science and technology education.' He mentioned artificial intelligence--and drew a laugh from a computer science professor who noted that artificial intelligence, the gleaming dream of the 1990s, has hardly created a single job in the world."

This issue of the AI Magazine initiates a new and (we hope) regular feature, Reviews of Books. Before presenting our first book review, a few comments about the aims of this feature are in order. Visions of applications of computer technology can serve as a stimulus for discussions of new ideas and directions in this field. Consequently, in addition to reviewing the standard literature produced by those in the AI community, we also seek to encourage reviews and discussions by people outside the field of AI to characterize where we seem to be going and what we are likely to achieve in the future. Reviews of this type can focus on such things as the technical accuracy of these popular treatments, and/or moral conclusions, either explicit or implied.

Each row, each column, and each 3x3 box must contain every number from 1 to 9. Additional number clues can be found by answering these questions:. Each row, each column, and each 3x3 box must contain each of the 9 different letters. If completed properly, a nine letter word will be revealed. Puzzle solutions can be found on page 107.

The first feature is a special section of competition reports, edited by Sven Koenig and Robert Morris. In this issue the section highlights results from three competitions: the Seventh International Planning Competition, the International SAT Solver Competitions, and the Trading Agent Competition. Recurring competitions play an important role in the progress of many AI areas, and Koenig and Morris hope to make coverage of such competitions -- including how the competitions illuminate the state of the art -- an ongoing section in AI Magazine. I thank them for initiating this exciting feature and encourage future submissions. Please see the solicitation and guidelines included in this issue on page 10.

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never-ending search for increasing student bang-for-the-buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade.

Social-emotional intelligence is an essential part of being a competent human and is thus required for humanlevel AI. When considering alternatives to the Turing test it is therefore a capacity that is important to test. We characterize this capacity as affective theory of mind and describe some unique challenges associated with its interpretive or generative nature. Mindful of these challenges we describe a five-step method along with preliminary investigations into its application. We also describe certain characteristics of the approach such as its incremental nature, and countermeasures that make it difficult to game or cheat.

The Association for the Advancement of Artificial Intelligence now serves a global audience, and our members, meeting participants, councilors, and officers reside in countries throughout the world. Worldwide AI is designed to meet our expanded audience's interests. In the columns that will appear in this and forthcoming issues, readers will find a continuing source of news and information on significant research projects and accomplishments, academic and community events, and experiences fielding notable applications of AI. We expect that increased awareness about AI activities around the world will fuel new opportunities for communication and collaboration. The inaugural columns in this issue of Worldwide AI describe artificial intelligence trends in India and South Africa.

The infrastructure and tools necessary for largescale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data miner or domain expert can rightly be expected to exercise caution when required (for example, around misleading conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model-building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge that gives reason to ...

An equally important dimension of big data is variety, where the focus is to process highly heterogeneous data sets. We describe how we use semantics to address the problem of big data variety. We also describe Karma, a system that implements our approach and show how Karma can be applied to integrate data in the cultural heritage domain. In this use case, Karma integrates data across many museums even though the data sets from different museums are highly heterogeneous. Volume refers to the problem of how to deal with very large data sets, which typically requires execution in a distributed cloud-based infrastructure.