The Turing machine (TM) was proposed by Alan Turing in 1936, and it is a mathematical model of computation made up of an infinitely long tape and a head that interacts with the tape by reading, editing, and moving symbols on it. It works by manipulating symbols on the strip according to a predefined set of rules. The tape is made up of an endless number of cells, each of which can contain one of three symbols – 0, 1, or blank (" "). Therefore, this is referred to as a three-symbol Turing machine. Regardless of how simple it seems, it is capable of simulating any computer algorithm, regardless of complexity. The tape that these computations are done on can be considered to be the machine's memory, akin to how our modern-day computers have memory. However, the Turing machine differs from modern-day computers as it has limited...
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You're reading from Hands-On Mathematics for Deep Learning
Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
Read more about Jay Dawani
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Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
Read more about Jay Dawani