AI's Impact on Careers in STEM
Articles
- A.I. Is Coming for Mathematics, TooSiobhan Roberts, New York Times, Jul. 2, 2023
- Advancing mathematics by guiding human intuition with AIAlex Davies et al., Nature, 2021
- The AI Biologist: DeepMind's Demis Hassabis explains how artificial intelligence solved one of the biggest problems in biologyTanya Lewis, Scientific American, Feb., 2023
- Artificial intelligence: A powerful paradigm for scientific researchYongjun Xu et al., The Innovation, Nov. 2021
- Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunitiesAbdullahi Saka et al., Advanced Engineering Informatics, Jan. 2023
- Eric Schmidt: This is how AI will transform the way science gets doneEric Schmidt, MIT Technology Review, Jul. 5, 2023
- Impact of Artificial Intelligence on Engineering: Past, Present and FutureRobert Blake et al., Procedia CIRP, 2021
- It’s the End of Computer Programming as We Know It. (And I Feel Fine.)Farhad Manjoo, New York Times Opinion, Jun. 2, 2023
- No, AI Is Not “Disrupting” ArchitectureKate Wagner, The Nation., Mar. 17, 2023
- A symposium at UT Austin stakes out initial signposts for architecture’s future with artificial intelligenceAaron Seward, The Architect's Newspaper, Mar. 10, 2023
Videos
- Using AI to Accelerate Scientific DiscoveryInstitute for Ethics in AI Oxford
Books in the Library
Artificial Intelligence by Example by
Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key Features AI-based examples to guide you in designing and implementing machine intelligence Build machine intelligence from scratch using artificial intelligence examples Develop machine intelligence from scratch using real artificial intelligence Book Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learn Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate Understand chained algorithms combining unsupervised learning with decision trees Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph Learn about meta learning models with hybrid neural networks Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging Building conversational user interfaces (CUI) for chatbots Writing genetic algorithms that optimize deep learning neural networks Build quantum computing circuits Who this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.
Artificial Intelligence in Games by
This book covers all the necessary topics that a professional game AI programmer needs to know, from math and steering behaviours to terrain analysis, pathfinding and decision-making. Written to be easily accessible, each topic is accompanied by an example game that allows the reader to add their own code to see the effects their changes have. Each chapter is split into two parts. The first part covers the necessary theory in a friendly, conversational manner, using visual examples and fictional game scenarios to give additional context. The second part is a coding tutorial in C# for the topic at hand. Each chapter has its own example game available to download, written in C# in the Unity Game Engine. This book will be suitable for students and aspiring games programmers looking to gain a grounding in game AI techniques.
Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research by
The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions. This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity. Additionally, it explores measures to expedite the integration of AI into research in developing countries. A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance.
