OpenAI releases text-to-video model Sora for ChatGPT Plus and Pro users
Dec 9 (Reuters) - OpenAI said on Monday it has released its artificial intelligence model, which creates video from text, to ChatGPT Plus and Pro users, expanding its foray into multimodal AI technologies.
The Microsoft-backed (MSFT.O), opens new tab company, which kicked off a generative AI craze with the launch of its ChatGPT chatbot in November 2022, aims to target similar text-to-video tools from Meta (META.O), opens new tab and Alphabet's (GOOGL.O), opens new tab Google, along with Stability AI's Stable Video Diffusion.
Dec 9 (Reuters) - OpenAI said on Monday it has released its artificial intelligence model, which creates video from text, to ChatGPT Plus and Pro users, expanding its foray into multimodal AI technologies.
The Microsoft-backed (MSFT.O), opens new tab company, which kicked off a generative AI craze with the launch of its ChatGPT chatbot in November 2022, aims to target similar text-to-video tools from Meta (META.O), opens new tab and Alphabet's (GOOGL.O), opens new tab Google, along with Stability AI's Stable Video Diffusion.
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Introducing the Realtime API
Today, we're introducing a public beta of the Realtime API, enabling all paid developers to build low-latency, multimodal experiences in their apps. Similar to ChatGPT’s Advanced Voice Mode, the Realtime API supports natural speech-to-speech conversations using the six preset voices(opens in a new window) already supported in the API.
We’re also introducing audio input and output in the Chat Completions API(opens in a new window) to support use cases that don’t require the low-latency benefits of the Realtime API. With this update, developers can pass any text or audio inputs into GPT-4o and have the model respond with their choice of text, audio, or both.
From language apps and educational software to customer support experiences, developers have already been leveraging voice experiences to connect with their users. Now with Realtime API and soon with audio in the Chat Completions API, developers no longer have to stitch together multiple models to power these experiences.
Today, we're introducing a public beta of the Realtime API, enabling all paid developers to build low-latency, multimodal experiences in their apps. Similar to ChatGPT’s Advanced Voice Mode, the Realtime API supports natural speech-to-speech conversations using the six preset voices(opens in a new window) already supported in the API.
We’re also introducing audio input and output in the Chat Completions API(opens in a new window) to support use cases that don’t require the low-latency benefits of the Realtime API. With this update, developers can pass any text or audio inputs into GPT-4o and have the model respond with their choice of text, audio, or both.
From language apps and educational software to customer support experiences, developers have already been leveraging voice experiences to connect with their users. Now with Realtime API and soon with audio in the Chat Completions API, developers no longer have to stitch together multiple models to power these experiences.
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Will LLMs always hallucinate?
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
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AI Prediction in 2025
1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).
2. Mistral will be absorbed into Meta
3. Cohere will be bought by Google
4. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.
5. Only OpenAI and XAI may remain as independent companies.
6. Mainstream Adoption of AI Agents.
7. Proliferation of Specialized Large Language Models.
1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).
2. Mistral will be absorbed into Meta
3. Cohere will be bought by Google
4. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.
5. Only OpenAI and XAI may remain as independent companies.
6. Mainstream Adoption of AI Agents.
7. Proliferation of Specialized Large Language Models.
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Artificial_Intelligence,_Game_Theory_and_Mechanism_Design_in_Politics.pdf
2.8 MB
Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023
Tshilidzi Marwala, 2023
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Fundamentals_of_Deep_Learning_Designing_Next_Generation.pdf
15.9 MB
Fundamentals of Deep Learning
Nithin Buduma, 2022
Nithin Buduma, 2022
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Modern_Deep_Learning_for_Tabular_Data_Novel_Approaches.pdf
51.8 MB
Modern Deep Learning for Tabular Data
Andre Ye, 2023
Andre Ye, 2023
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