Artificial Intelligence in Today's World
SAS Institute Inc. (n.d.). What is artificial intelligence? - AI Glossary. SAS. Retrieved December 1, 2023, from https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html#ai-glossary
Books
AI assistants by ISBN: 9780262365451Publication Date: 2021A brief introduction to the technology, the issues and future of virtual agents such as Siri, Alexa and the Google Assistant
Causal Inference and Discovery in Python by ISBN: 9781804612989Publication Date: 2023-05-31A must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning
AI Ethics by ISBN: 0262538199Publication Date: 2020-04-07This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions--offering a compelling, necessary read for our ChatGPT era.
Algorithms by ISBN: 9780262358675Publication Date: 2020In the tradition of Real World Algorithms: A Beginner's Guide, Panos Louridas is back to introduce algorithms in an accessible manner, utilizing various examples to explain not just what algorithms are but how they work.
Artificial Intelligence by ISBN: 9780136042594Publication Date: 2009-12-01Artificial Intelligence: A Modern Approach, focuses on areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics.
Bayesian Reasoning and Machine Learning by ISBN: 9780521518147Publication Date: 2012-02-02This text develops everything from basic reasoning to advanced techniques within the framework of graphical models.
Deep Learning by ISBN: 0262035618Publication Date: 2016-11-18An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
Deep Learning by ISBN: 0262537559Publication Date: 2019-09-10An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
Information Theory, Inference and Learning Algorithms by ISBN: 9780521642989Publication Date: 2003-09-25In Information Theory, Inference and Learning Algorithms, message-passing algorithms are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks.
Machine learning by ISBN: 9780262365369Publication Date: 2021An updated introduction for generalists to this powerful technology, its applications and possible future directions
Machine Learning by ISBN: 9780262305242Publication Date: 2012-09-07A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Make Your Own Neural Network by ISBN: 9781530826605Publication Date: 2016-03-31A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language.
Natural Language Processing with Python by ISBN: 9780596550967Publication Date: 2009-06-12This book offers an introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
Pattern Recognition and Machine Learning by ISBN: 9780387310732Publication Date: 2011-04-06This book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It also uses graphical models to describe probability distributions.
Probabilistic Graphical Models by ISBN: 9780262013192Publication Date: 2009-07-31The framework of probabilistic graphical models is presented in this book while considering the use of the proposed framework for causal reasoning and decision making under uncertainty.
Reinforcement Learning, Second Edition by ISBN: 9780262039246Publication Date: 2018-11-13This text explores reinforcement learning's relationships to psychology and neuroscience, as well as a case-studies chapter including AlphaGo.
Commercial Documentation
OpenAI Charter: OpenAI’s mission is to ensure that artificial general intelligence (AGI)—That is, highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.
GoogleAI (Our Focus): As many have highlighted, we believe that AI has the potential to benefit people and society through its capacity to:
- Enable scientific and other breakthroughs to address humanity’s greatest future opportunities and challenges (e.g. medical diagnosis)
- Make information more useful and available to more people, everywhere, often helping overcome barriers including access, disabilities and language
- Assist people and organizations to make decisions, solve problems, be more productive and creative in their daily and work lives
- Enable innovation that leads to new, helpful products and services for people, organizations, and society more broadly
- Help tackle current and pressing real world challenges, such as public health crises, natural disasters, climate change, and sustainability
- Help identify, and mitigate societal biases and structural inequities (e.g. socio-economic, socio-demographic and regional inequities)
Search String
MeSH has the following:
An example PubMed search string:
("Algorithms"[Mesh] OR "Algorithms"[tiab] OR algorithm*[tiab] OR "Computational Biology"[Mesh] OR "Computational Biology"[tiab] OR "Data Mining"[Mesh] OR "Data Mining"[tiab] OR "Artificial Intelligence"[Mesh] OR "Artificial Intelligence"[tiab] OR "Computer Simulation"[Mesh] OR "Computer Simulation"[tiab]) OR ("Machine Learning"[Mesh] OR "Machine Learning"[tiab] OR "Neural Networks"[tiab] OR "Pattern Recognition, Automated"[Mesh] OR "Pattern Recognition, Automated"[tiab]) OR ("Deep Learning"[Mesh] OR "Deep Learning"[tiab] OR "Neural Networks, Computer"[tiab] OR "Pattern Recognition, Automated"[Mesh] OR "Pattern Recognition, Automated"[tiab]) OR ("Supervised Machine Learning"[tiab] OR "Classification"[Mesh] OR "Classification"[tiab]) OR ("Unsupervised Machine Learning"[tiab] OR ("Sentiment Analysis"[Mesh] OR "Sentiment Analysis"[tiab] OR "Natural Language Processing"[Mesh] OR "Natural Language Processing"[tiab] OR "Text Mining"[tiab]))
An example Scopus search string:
( TITLE-ABS-KEY ( "Algorithms" OR algorithm* ) AND TITLE-ABS-KEY ( "Computational Biology" ) ) OR ( TITLE-ABS-KEY ( "Data Mining" ) AND TITLE-ABS-KEY ( "Artificial Intelligence" ) ) OR ( TITLE-ABS-KEY ( "Computer Simulation" ) AND ( TITLE-ABS-KEY ( "Machine Learning" ) OR ( TITLE-ABS-KEY ( "Neural Networks" ) W/3 TITLE-ABS-KEY ( "Computer" ) ) OR TITLE-ABS-KEY ( "Pattern Recognition, Automated" ) OR TITLE-ABS-KEY ( "Deep Learning" ) OR ( TITLE-ABS-KEY ( "Supervised Machine Learning" OR "classification" ) W/5 TITLE-ABS-KEY ( "Machine Learning" ) ) OR ( TITLE-ABS-KEY ( "Unsupervised Machine Learning" ) W/5 TITLE-ABS-KEY ( "Machine Learning" ) ) OR TITLE-ABS-KEY ( "Sentiment Analysis" ) OR TITLE-ABS-KEY ( "Natural Language Processing" ) OR TITLE-ABS-KEY ( "Text Mining" ) ) ) AND ( LIMIT-TO ( PUBYEAR , 2024 ) OR LIMIT-TO ( PUBYEAR , 2023 ) OR LIMIT-TO ( PUBYEAR , 2022 ) )
Journals
- Neural Networks
- Machine Learning
- Artificial Intelligence
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- Journal of Artificial Intelligence Research (JAIR)
- ACM Transactions on Intelligent Systems and Technology (TIST)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- Expert Systems with Applications
- AITopics (AAAI)
- Journal of Machine Learning Research (JMLR)
ArXiv (Pre-print Server)
ArXiv is a repository of electronic preprints, including papers in the fields of computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics. ArXiv is managed and operated by Cornell University, and it has become a popular platform for researchers to share their work before it undergoes peer review and is published in academic journals.
- OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. Retrieved from https://arxiv.org/abs/2303.08774
- Park JS, O'Brien JC, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative Agents: Interactive Simulacra of Human Behavior. arXiv. 2023 Apr 7. eprint: 2304.03442.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017).
- Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large Language Models Encode Clinical Knowledge. arXiv. 2022 Dec 26. Eprint 2212.13138.
- Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint. Retrieved from https://arxiv.org/pdf/2005.14165.pdf
- Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Del Giorno, A., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., Salim, A., Shah, S., Singh Behl, H., Wang, X., Bubeck, S., Eldan, R., Kalai, A. T., Lee, Y. T., & Li, Y. (2023). Textbooks Are All You Need. arXiv. eprint: 2306.11644.
- Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., & Cobbe, K. (2023). Let's Verify Step by Step. arXiv. eprint: 2305.20050.
- Mozannar, H., Lee, J. J., Wei, D., Sattigeri, P., Das, S., & Sontag, D. (2023). Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. arXiv. https://arxiv.org/abs/2311.01007