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Cognitive neuro.computing with our homeomorph topological technology

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TOPOLOGY SHOWS US THAT ALL DATA HAS a underlying shape.TOPOLOGY STUDIES the shape of data. THE DEGREE OF SIMLARITY BETWEEN TWO SHAPES (PATTERN) CAN BE EXPRESSED AS THE AMOUNT OF STRETCHING. TOPOLOGICAL DATA ANALYSIS PROVIDES A GENERAL FRAMEWORK TO EXTRACT INFORMATION FROM DATA-SETS WHICH ARE HIGHDIMENSIONAL, INCOMPLETE NOIS NOISY. TDA PROVIDES A GENERAL FRAMEWORK TO ANALYZE SUCH DATA IN A MANNER THAT IS INSENSITIVE TO THE METRIC CHOSEN AND PROVIDES DIMENSIONALITY REDUCTION AND ROBUSTNESS TO NOISE.


Winter is coming...

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Since Alan Turing first posed the question "can machines think?" in his seminal paper in 1950, "Computing Machinery and Intelligence", Artificial Intelligence (AI) has failed to deliver on its promise. That is, Artificial General Intelligence. There have, however, been incredible advances in the field, including Deep Blue beating the world's best chess player, the birth of autonomous vehicles, and Google's DeepMind beating the world's best AlphaGo player. The current achievements represent the culmination of research and development that occurred over more than 65 years. Importantly, during this period there were two well documented AI Winters that almost completely debunked the promise of AI.


An example of what's holding back general intelligence research • /r/artificial

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Its interesting but ultimately to really'under' 'stand' language in a deep way, the systems will need representations based on lower-level (possibly virtual) sensory inputs. That is one of the main enablers for truly general intelligence because its based on this common set of inputs over time, i.e. senses. The domain is sense and motor output and this is a truly general domain. Its also a domain that is connected to the way the concepts map to the real physical world. So when the advanced agent NN systems are put through their paces in virtual 3d worlds by training on simple words, phrases, commands, etc. involving'real-world' demonstrations of the concepts then we will see some next-level understanding.


OpenAI cofounder Greg Brockman on the transformative potential of artificial general intelligence

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Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.


What is Artificial General Intelligence? And has Kimera Systems made a breakthrough?

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The field of artificial intelligence has spawned a vast range of subset fields and terms: machine learning, neural networks, deep learning and cognitive computing, to name but a few. However here we will turn our attention to the specific term'artificial general intelligence', thanks to the Portland-based AI company Kimera Systems' (momentous) claim to have launched the world's first ever example, called Nigel. The AGI Society defines artificial general intelligence as "an emerging field aiming at the building of "thinking machines"; that is general-purpose systems with intelligence comparable to that of the human mind (and perhaps ultimately well beyond human general intelligence)". AGI would, in theory, be able to perform any intellectual feat a human can. You can now perhaps see why a claim to have launched the world's first ever AGI might be a tad ambitious, to say the least.