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Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust--in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. No library descriptions found. |
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We are making amazing tools, thats certain, but most people see the latest improvements in solving a very specific problem and extrapolate from that to believe we are close to having a general purpose AI, or True AI.
It compares classical AI with modern ML driven AI and talks about the strengths and weaknesses of both. Modern AI is amazing, but the flawed classical AI with all of it's impracticality is the only way we can ever achieve AGI, not by improving neural networks or making ML more efficient.
My belief is that when it comes to developing true AI we need to go back to the drawing board and classical AI is better at helping achieve it than the modern approaches that are being overhyped by Media with hyperboles, clickbait and in some cases fraud. If you share this belief than you'll enjoy this book. (