Artificial Intelligence 2020 - is there progress? - page 52
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Speaking of transistors - these too will need to be modified for the analogue board to work. After all, transistors store bits - i.e. "bits of a number", while you will need to store the whole number as a voltage (like a battery), because the analogue number is the current amplitude, not the interrupt.
Elementary: int: 4 bytes of 8 bits = 32 bits of digital information encoded in their states (1 or 0) give a value space of over 4 billion. If this is converted into analogue form, the amplitude (voltage) accuracy of the signal must have 9 zeros after the decimal point, otherwise the transmitted/received signal will distort the original number.
How to do it on a 22nm crystal - I can't imagine, it's so fine there - you can't trim it...
So there will be no high precision, as they said.
Grids can already write programs
Translated general information about GPT-3: (there are translation defects)
Wikipedia:
Generative Pre-trained Transducer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third generation language prediction model in the GPT-n series created by OpenAI, an artificial intelligence research lab in San Francisco. [2] The full version of GPT-3 has a capacity of 175 billion machine learning parameters, two orders of magnitude more than its predecessor GPT-2. [1]: 14 GPT-3, which was introduced in May 2020, and has been in beta testing since July 2020. [3] is part of a trend in natural language processing (NLP) systems towards "pre-trained language representations". [1] Prior to the release of GPT-3, the largest language model was Microsoft's Turing NLG, introduced in February 2020, with ten times less bandwidth than GPT-3. [4]
The quality of text produced by GPT-3 is so high that it is difficult to distinguish it from human written text, which has both advantages and risks. [4] Thirty-one OpenAI researchers and engineers submitted a source paper dated 28 May 2020 presenting the GPT-3. In their paper, they warned of the potential dangers of GPT-3 and called for research to mitigate the risks. [1]: 34 David Chalmers, an Australian philosopher, described GPT-3 as "one of the most interesting and important AI systems ever created. "[5] GPT-3 can create websites, answer questions and prescribe drugs. [6]
According to The Economist, improved algorithms, powerful computers and an increase in digitised data have sparked a revolution in machine learning, with new techniques leading to "rapid improvements in tasks" in the 2010s, including language manipulation [7]. Software models are trained using thousands or millions of examples in a "structure ... loosely based on the neural architecture of the brain." [7] The architecture most commonly used in natural language processing (NLP) is the neural network. [8] It is based on a deep learning model that was first introduced in 2017, a transformer machine learning model. [8] GPT-n models are based on this deep learning neural network architecture. There are a number of NLP systems capable of processing, analysing, organising, linking, contrasting, understanding and generating answers to questions [9].
History:
On 11 June 2018, OpenAI researchers and engineers published their original paper on generative models - language models - artificial intelligence systems that can be pre-trained with a huge and diverse corpus of text using datasets in a process they called generative pre-training. training (GP). [10] The authors described how language comprehension performance in natural language processing (NLP) was improved in generative pre-training (GPT-n) through a process of "generative pre-training of a language model on a diverse corpus of unlabelled text, followed by discriminative adjustments to each specific task". This eliminated the need for human supervision and time-consuming manual labelling [10].
In February 2020, Microsoft unveiled its Turing Natural Language Generation (T-NLG), which was then "the largest language model ever published with 17 billion parameters." [11] It performed better than any other language model in a variety of tasks that included summarising texts and answering questions
Capabilities:
In an arXiv preprint dated 28 May 2020, a team of 31 OpenAI engineers and researchers described the development of a "modern language model" called GPT-3 [1][4] or Generative Pretrained Transformer 3, a third generation language model. The team succeeded in increasing GPT-3's capacity by over two orders of magnitude compared to its predecessor, GPT-2, making GPT-3 the largest non-sparse language model to date. [1]: 14 [2] The GPT-3's greater number of parameters provides a higher level of accuracy than previous versions with lower capacity. [12] The GPT-3 has ten times the capability of the Microsoft Turing NLG. [4]
Sixty percent of the weighted pre-training dataset for GPT-3 comes from a filtered version of Common Crawl, consisting of 410 billion byte pair-coded tokens. [1]: 9 Other sources are 19 billion tokens from WebText2, which is 22% weighted sum, 12 billion tokens from Book1, which is 8%, 55 billion tokens from Book2, which is 8%, and 3 billion tokens from Wikipedia, which is 3%. [1]: 9 GPT-3 has been trained on hundreds of billions of words and is capable of coding in CSS, JSX, Python, etc. [3] Because GPT-3's training data was comprehensive, it did not require further training for different language tasks. [3]
On June 11, 2020, OpenAI announced that users could request access to the GPT-3 user-friendly API, a "suite of machine learning tools" to help OpenAI "explore the strengths and weaknesses" of this new technology [13][14]. ] The invitation described this API as having a universal text-input-output interface that could perform almost "any English-language task", instead of the usual single use case. [13] According to one user who had access to a closed early release of the OpenAI GPT-3 API, GPT-3 is "frighteningly good" at writing "amazingly coherent text" with just a few simple prompts [15].
Because GPT-3 can "generate news articles that human evaluators have difficulty distinguishing from articles written by humans" [4] GPT-3 has "the potential to promote both useful and harmful applications of language models" [1]: 34 In their May 28, 2020 article, researchers detailed the potential "harmful effects of GPT-3" [4], which include "misinformation, spam, phishing, abuse of legal and government processes, fraudulent academic essay writing and social engineering pretexts. "[1]. The authors draw attention to these dangers to call for research on risk reduction. [1]:
OpenAI's new GPT-3.
We are releasing an API to access the new AI models developed by OpenAI. Unlike most AI systems, which are designed for a single use case, today's API provides a universal input-output interface, allowing users to try it out on almost any English-language task. You can now request access to integrate the API into your product, develop a completely new application, or help us explore the strengths and weaknesses of the technology.
For any text request, the API will return the completion of the text, trying to match the pattern you have specified. You can 'program it' by showing just a few examples of what you want it to do; its success usually varies depending on how complex the task is. The API also allows you to customise performance on specific tasks by training the datasets you provide (small or large) or by learning from users or developers.
We designed the API to be easy to use and flexible to make machine learning teams more productive. In fact, many of our teams now use the API so they can focus on machine learning research rather than distributed systems issues. Today, API is running models with weights from the GPT-3 family with many speed and throughput improvements. Machine learning is evolving very rapidly and we are constantly updating our technology to keep our users up to date.
The pace of progress in this area means that there are often unexpected new applications of AI, both positive and negative. We will stop API access for known malicious uses such as stalking, spamming, radicalisation or astroturfing. But we also know we can't foresee all the possible outcomes of this technology, so today we're launching a private beta version rather than a public one, building tools to help users better control the content returned by our API, and exploring security issues. We'll share what we've learned so that our users and the wider community can build more human artificial intelligence systems.
In addition to being a source of revenue that helps us cover costs as we pursue our mission, the API has pushed us to focus on the universal technology of artificial intelligence - advancing the technology, ensuring its use, and considering its impact in the real world. We hope that API will significantly lower the barrier to making useful artificial intelligence products, leading to tools and services that are difficult to imagine today.
Interested in learning more about APIs? Join companies like Algolia, Quizlet and Reddit, and researchers from organisations like the Middlebury Institute in our private beta.
If you want to try out GPT-3 today, you'll need to apply to the OpenAI whitelist. But the applications for this model seem endless - ostensibly you can use it to query a SQL database in plain English, automatically comment code, automatically create code, write fancy article titles, write viral tweets and much more.
But what's going on under the bonnet of this incredible model? Here's a (brief) look inside
GPT-3 is a neural network-based language model. A language model is a model that predicts the probability of a sentence existing in the world. For example, a language model might mark the phrase: "I'm taking my dog for a walk" as more likely to exist (i.e. online) than the phrase: "I'm taking my banana for a walk". This is true for both sentences and phrases and, more generally, for any sequence of characters.
Like most language models, GPT-3 is elegantly trained on an unlabeled set of text data (in this case, the training data includes, among others, Common Crawl and Wikipedia). Words or phrases are randomly removed from the text, and the model has to learn to fill them using only the surrounding words as context. This is a simple learning task which results in a powerful and versatile model.
The architecture of the GPT-3 model itself is a transformer based neural network. This architecture became popular about 2-3 years ago, and was the basis of the popular BERT NLP model and the predecessor of GPT-3, GPT-2. In terms of architecture, GPT-3 is actually not very new!
What makes it so special and magical?
It's really big. I mean really big. With 175 billion parameters, it's the largest language model ever created (an order of magnitude larger than its closest competitor!) And it has been trained on the largest dataset of all language models. This seems to be the main reason why GPT-3 is so impressively "smart" and human-sounding.
But here's the really magical part. Thanks to its sheer size, the GPT-3 can do what no other model can (well) do: perform specific tasks without any special configuration. You can ask the GPT-3 to be a translator, programmer, poet or famous author, and it can do it with its user (you) providing less than 10 training examples. Shit.
That's what makes GPT-3 so fascinating to machine learning practitioners. Other language models (like BERT) require a complex fine-tuning step, where you collect thousands of examples of (say) French-English sentence pairs to teach it how to do the translation. To tailor a BERT for a specific task (e.g. translation, summarization, spam detection, etc), you need to go out and find a large training dataset (on the order of thousands or tens of thousands of examples), which can be cumbersome or unwieldy. sometimes impossible, depending on the task. With GPT-3, you don't need to do this fine-tuning step. That's its essence. That's what attracts people to GPT-3: customisable language tasks with no data to learn.
GPT-3 is in private beta today, but I can't wait to get my hands on it.
This article was written by Dale Markowitz, an applied artificial intelligence engineer at Google, based in Austin, Texas, where she works on applying machine learning to new fields and industries. She also enjoys solving her life's problems with AI and talks about it on YouTube.
Honestly, I'm blown away by this GPT-3. Cool stuff.))))
However, something much cooler is coming, and soon. Why? Because GPT-3 is inefficient as hell in terms of efficiency.
None of us, crammed billions of combinations of words into our heads in sentences scanning the internet, yet, can write books, think logically and critically, and solve immeasurably more complex and ambiguous problems. How?
Human learning is a different level of information assimilation and processing. GPT-3 is severely lacking something inside, like a backbone, an archetype, an internal engine... and it's not clear what else...
The learning approach of this network is flawed compared to human learning and we need to figure out what it is.
Honestly, I'm blown away by this GPT-3. Cool stuff. ))))
nothing new in the algorithms, but the power gives new possibilities and levels of model quality. 175 yds is not 5000 words))))
nothing new in the algorithms, but the power gives new possibilities and levels of model quality. 175 yards is not 5000 words))))
That's the thing, I haven't heard anything new about the algorithms. All these network training methods were already there - the only difference is the scale.
Tried to find video examples of it working and this is what impressed:https://twitter.com/sharifshameem
This thing creates an interface based on a verbal description along with partial functionality. At first I thought it was nonsense, but when I looked closer I realized I was wrong. However, I still couldn't fully understand the limits of the possibilities.
The scope of the GPT-3 is unclear.