Kirjahaku

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

An insightful exploration of Chat GPT and other advanced AI systems—how we got here, where we’re headed, and what it all means for how we interact with the world.In ChatGPT and the Future of AI, the sequel to The Deep Learning Revolution, Terrence Sejnowski offers a nuanced exploration of large language models (LLMs) like ChatGPT and what their future holds. How should we go about understanding LLMs? Do these language models truly understand what they are saying? Or is it possible that what appears to be intelligence in LLMs may be a mirror that merely reflects the intelligence of the interviewer? In this book, Sejnowski, a pioneer in computational approaches to understanding brain function, answers all our urgent questions about this astonishing new technology.Sejnowski begins by describing the debates surrounding LLMs’ comprehension of language and exploring the notions of “thinking” and “intelligence.” He then takes a deep dive into the historical evolution of language models, focusing on the role of transformers, the correlation between computing power and model size, and the intricate mathematics shaping LLMs. Sejnowski also provides insight into the historical roots of LLMs and discusses the potential future of AI, focusing on next-generation LLMs inspired by nature and the importance of developing energy-efficient technologies.Grounded in Sejnowski’s dual expertise in AI and neuroscience, ChatGPT and the Future of AI is the definitive guide to understanding the intersection of AI and human intelligence.

How deep learning from Google Translate to driverless cars to personal cognitive assistants is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.