Saturday, July 17, 2021

g-f(2)380 The Big Picture of Business Artificial Intelligence (7/17/2021), VB, The future of deep learning, according to its pioneers




ULTRA-condensed knowledge


Opportunity, The fabulous progress of deep learning highlights our natural intelligence

Lesson learned, The ultimate goal of AI
  • The ultimate goal of AI scientists is to replicate the kind of general intelligence humans have. 
        Lesson learned, Humans don’t suffer from the problems of current deep learning systems 
        • “Humans and animals seem to be able to learn massive amounts of background knowledge about the world, largely by observation, in a task-independent manner,” Bengio, Hinton, and LeCun write in their paper. 
        • “This knowledge underpins common sense and allows humans to learn complex tasks, such as driving, with just a few hours of practice.”
        • “Humans can generalize in a way that is different and more powerful than ordinary iid generalization: we can correctly interpret novel combinations of existing concepts, even if those combinations are extremely unlikely under our training distribution, so long as they respect high-level syntactic and semantic patterns we have already learned.”
                        Lesson learned, Scientists provide various solutions to close the gap between AI and human intelligence

                        Lesson learned, Promising advances in deep learning

                        Lesson learned, There are improvements in deep learning of different kinds with new techniques (i.e., contrastive learning, self-supervised learning, capsule networks)


                        Genioux knowledge fact condensed as an image


                        Extra-condensed knowledge


                        The ultimate goal of AI scientists is to replicate the kind of general intelligence humans have. And we know that humans don’t suffer from the problems of current deep learning systems.
                        • Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers of deep learning argue in a paper published in the July issue of the Communications of the ACM journal.


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                        Condensed knowledge


                        Opportunity, The fabulous progress of deep learning highlights our natural intelligence

                        Lesson learned, The ultimate goal of AI
                        • The ultimate goal of AI scientists is to replicate the kind of general intelligence humans have. 
                              Lesson learned, Humans don’t suffer from the problems of current deep learning systems 
                              • “Humans and animals seem to be able to learn massive amounts of background knowledge about the world, largely by observation, in a task-independent manner,” Bengio, Hinton, and LeCun write in their paper. 
                              • “This knowledge underpins common sense and allows humans to learn complex tasks, such as driving, with just a few hours of practice.”
                              • “Humans can generalize in a way that is different and more powerful than ordinary iid generalization: we can correctly interpret novel combinations of existing concepts, even if those combinations are extremely unlikely under our training distribution, so long as they respect high-level syntactic and semantic patterns we have already learned.”
                                              Lesson learned, Scientists provide various solutions to close the gap between AI and human intelligence

                                              Lesson learned, Promising advances in deep learning

                                              Lesson learned, There are improvements in deep learning of different kinds with new techniques (i.e., contrastive learning, self-supervised learning, capsule networks)

                                              1. The ultimate goal of AI scientists is to replicate the kind of general intelligence humans have. And we know that humans don’t suffer from the problems of current deep learning systems.

                                              • Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers (Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, recipients of the 2018 Turing Award) of deep learning argue in a paper published in the July issue of the Communications of the ACM journal.
                                              2. “Humans and animals seem to be able to learn massive amounts of background knowledge about the world, largely by observation, in a task-independent manner,” Bengio, Hinton, and LeCun write in their paper. 

                                              3. “This knowledge underpins common sense and allows humans to learn complex tasks, such as driving, with just a few hours of practice.”

                                              4. “Humans can generalize in a way that is different and more powerful than ordinary iid generalization: we can correctly interpret novel combinations of existing concepts, even if those combinations are extremely unlikely under our training distribution, so long as they respect high-level syntactic and semantic patterns we have already learned.”

                                              5. Scientists provide various solutions to close the gap between AI and human intelligence.

                                              6. One approach that has been widely discussed in the past few years is hybrid artificial intelligence that combines neural networks with classical symbolic systems. 

                                              • Symbol manipulation is a very important part of humans’ ability to reason about the world. It is also one of the great challenges of deep learning systems.
                                              7. Bengio, Hinton, and LeCun do not believe in mixing neural networks and symbolic AI.

                                              8. The deep learning pioneers believe that better neural network architectures will eventually lead to all aspects of human and animal intelligence, including symbol manipulation, reasoning, causal inference, and common sense.

                                              9. Promising advances in deep learning

                                              • In their paper, Bengio, Hinton, and LeCun highlight recent advances in deep learning that have helped make progress in some of the fields where deep learning struggles.
                                              • One example is the Transformer, a neural network architecture that has been at the heart of language models such as OpenAI’s GPT-3 and Google’s Meena. 
                                              • One of the benefits of Transformers is their capability to learn without the need for labeled data.
                                              10. There are improvements in deep learning of different kinds with new techniques (i.e., contrastive learning, self-supervised learning, capsule networks).

                                              • “There’s still a long way to go in terms of our understanding of how to make neural networks really effective. And we expect there to be radically new ideas,” Hinton told ACM.


                                              Category 2: The Big Picture of the Digital Age

                                              [genioux fact deduced or extracted from geniouxfacts]

                                              This is a “genioux fact fast solution.”

                                              Tag Opportunities those travelling at high speed on GKPath

                                              Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).

                                              Type of validity of the "genioux fact". 

                                              • Inherited from sources + Supported by the knowledge of one or more experts.


                                              Authors of the genioux fact

                                              Fernando Machuca


                                              References




                                              ABOUT THE AUTHORS


                                              Ben Dickson

                                              Ben Dickson is a software engineer and the founder of TechTalks, a blog that explores the ways technology is solving and creating problems. He writes about technology, business and politics. Follow him on Twitter: @BenDee983.  


                                              TechTalks


                                              At Tech Talks, we examine trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for.


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