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IS BRAINCHIP AKIDA TECHNOLOGY THE NEXT BIG BANG IN Ai???
IN MY OPINION IT ALREADY IS BUT WHAT WOULD I KNOW BUT EVERYONE I READ WHO HAS THE CREDENTIALS TO KNOW ARE SCREAMING AT THE TOP OF THEIR LUNGS THAT POWER AND HEAT HAVE REACHED UNSUSTAINABLE LEVELS IN THE TECHNOLOGY WORLD AND AKIDA SOLVES BOTH ISSUES BEFORE IT EVEN STARTS TO DISPLAY ITS REVOLUTIONARY BEAST LIKE PROCESSING ABILITIES INCLUDING AS WE ALL KNOW ONE SHOT/SEVERAL SHOT/INCREMENTAL LEARNING. ON CHIP CONVOLUTION, CNN TO SNN CONVERSION AND CAPACITY TO OPERATE COMPLETELY SELF CONTAINED WITHOUT CLOUD CONNECTION WHILE REMAINING PROCESS AND SENSOR AGNOSTIC:
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Oct 18, 2022,05:30am EDT
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Aleksa Gordic, an AI researcher with DeepMind
ALEKSA GORDIC
Transformers work by splitting text into small units, called tokens, and mapping them onto high-dimension networks - often thousands of dimensions. We humans cannot envisage this. The space we inhabit is defined by three numbers – or four, if you include time, and we simply cannot imagine a space with thousands of dimensions. Researchers suggest that we shouldn’t even try.
There are various ways in which machines can discover the relationships, or vectors, between tokens. In supervised learning, they are given enough labelled data to indicate all the relevant vectors. In self-supervised learning, they are not given labelled data, and they have to find the relationships on their own. This means the relationships they discover are not necessarily discoverable by humans. They are black boxes. Researchers are investigating how machines handle these dimensions, but it is not certain that the most powerful systems will ever be truly transparent.
The first of these image-generation models were Generative Adversarial Networks, or GANs. These were pairs of models, with one (the generator) creating imagery designed to fool the other into accepting it as original, and the second system (the discriminator) rejecting attempts which were not good enough. GANs have now been surpassed by Diffusion models, whose approach is to peel noise away from the desired signal. The first Diffusion model was actually described as long ago as 2015, but the paper was almost completely ignored. They were re-discovered in 2020.
IN MY OPINION IT ALREADY IS BUT WHAT WOULD I KNOW BUT EVERYONE I READ WHO HAS THE CREDENTIALS TO KNOW ARE SCREAMING AT THE TOP OF THEIR LUNGS THAT POWER AND HEAT HAVE REACHED UNSUSTAINABLE LEVELS IN THE TECHNOLOGY WORLD AND AKIDA SOLVES BOTH ISSUES BEFORE IT EVEN STARTS TO DISPLAY ITS REVOLUTIONARY BEAST LIKE PROCESSING ABILITIES INCLUDING AS WE ALL KNOW ONE SHOT/SEVERAL SHOT/INCREMENTAL LEARNING. ON CHIP CONVOLUTION, CNN TO SNN CONVERSION AND CAPACITY TO OPERATE COMPLETELY SELF CONTAINED WITHOUT CLOUD CONNECTION WHILE REMAINING PROCESS AND SENSOR AGNOSTIC:
Has There Been A Second AI Big Bang?
Calum ChaceContributor
"The AI guy"
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Oct 18, 2022,05:30am EDT
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Aleksa Gordic, an AI researcher with DeepMind
ALEKSA GORDIC
The first Big Bang in 2012
The Big Bang in artificial intelligence (AI) refers to the breakthrough in 2012, when a team of researchers led by Geoff Hinton managed to train an artificial neural network (known as a deep learning system) to win an image classification competition by a surprising margin. Prior to that, AI had performed some remarkable feats, but it had never made much money. Since 2012, AI has helped the big technology companies to generate enormous wealth, not least from advertising.A second Big Bang in 2017?
Has there been a new Big Bang in AI, since the arrival of Transformers in 2017? In episodes 5 and 6 of the London Futurist podcast, Aleksa Gordic explored this question, and explained how today’s cutting-edge AI systems work. Aleksa is an AI researcher at DeepMind, and previously worked in Microsoft’s Hololens team. Remarkably, his AI expertise is self-taught – so there is hope for all of us yet!Transformers
Transformers are deep learning models which process inputs expressed in natural language and produce outputs like translations, or summaries of texts. Their arrival was announced in 2017 with the publication by Google researchers of a paper titled “Attention is All You Need”. This title referred to the fact that Transformers can “pay attention” simultaneously to large corpus of text, whereas their predecessors, Recurrent Neural Networks, could only pay attention to the symbols either side of the segment of text being processed.Transformers work by splitting text into small units, called tokens, and mapping them onto high-dimension networks - often thousands of dimensions. We humans cannot envisage this. The space we inhabit is defined by three numbers – or four, if you include time, and we simply cannot imagine a space with thousands of dimensions. Researchers suggest that we shouldn’t even try.
Dimensions and vectors
For Transformer models, words and tokens have dimensions. We might think of them as properties, or relationships. For instance, “man” is to “king” as “woman” is to “queen”. These concepts can be expressed as vectors, like arrows in three-dimensional space. The model will attribute a probability to a particular token being associated with a particular vector. For instance, a princess is more likely to be associated with the vector which denotes “wearing a slipper” than to the vector that denotes “wearing a dog”.There are various ways in which machines can discover the relationships, or vectors, between tokens. In supervised learning, they are given enough labelled data to indicate all the relevant vectors. In self-supervised learning, they are not given labelled data, and they have to find the relationships on their own. This means the relationships they discover are not necessarily discoverable by humans. They are black boxes. Researchers are investigating how machines handle these dimensions, but it is not certain that the most powerful systems will ever be truly transparent.
Parameters and synapses
The size of a Transformer model is normally measured by the number of parameters it has. A parameter is analogous to a synapse in a human brain, which is the point where the tendrils (axons and dendrites) of our neurons meet. The first Transformer models had a hundred million or so parameters, and now the largest ones have trillions. This is still smaller than the number of synapses in the human brain, and human neurons are far more complex and powerful creatures than artificial ones.Not by text alone
A surprising discovery made a couple of years after the arrival of Transformers was that they are able to tokenise not just text, but images too. Google released the first vision Transformer in late 2020, and since then people around the world have marvelled at the output of Dall-E, MidJourney, and others.The first of these image-generation models were Generative Adversarial Networks, or GANs. These were pairs of models, with one (the generator) creating imagery designed to fool the other into accepting it as original, and the second system (the discriminator) rejecting attempts which were not good enough. GANs have now been surpassed by Diffusion models, whose approach is to peel noise away from the desired signal. The first Diffusion model was actually described as long ago as 2015, but the paper was almost completely ignored. They were re-discovered in 2020.
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