ChatGPT Is Letting Users Customize the Experience
OpenAI is now letting users build custom versions of ChatGPT to accomplish their specific personal and professional tasks in an effort to stand out in an increasingly crowded market.
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ChatGPT users will now be able to quickly create their own specialized versions of ChatGPT — simply called GPTs — that can help teach math to a child or explain the rules of a board game, the company said.
OpenAI plans to introduce a store later this month where users can find GPTs developed by other users, similar to the way people search for apps in Apple's App Store.
OpenAI also said it is introducing a preview version of GPT-4 Turbo, a more powerful and speedier version of its most recent large language model, the technology that underpins ChatGPT. OpenAI said the Turbo version of GPT-4 was built with a trove of online data running through April of this year, giving it a greater awareness of current events.
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Elon Musk Debuts 'Rebellious' Grok AI Bot to Challenge ChatGPT
Elon Musk revealed his own AI bot to challenge ChatGPT, claiming the prototype is already superior to ChatGPT 3.5 across several benchmarks.Dubbed Grok, it's the first product of Musk's xAI company and is now in testing with a limited group of US users.
Grok is being developed with data from Musk's X, formerly Twitter, and is thus better informed on the latest developments than alternative bots with static datasets, the company's website said. It's also designed to answer "with a bit of wit and has a rebellious streak," according to the announcement.
How AI Is Revolutionizing Science
Artificial intelligence is transforming the scientific process, unlocking new discoveries that were previously out of reach through human effort alone, and paving the way for future breakthroughs. Watch more from the Bloomberg Originals series AI IRL.
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Social & Cultural policy director at Iran's National Center of Cyberspace
11 个月S. Mehran M. Ziabary Esmahan Hakkak Moslem T.
Formerly Research Associate UBC
11 个月We need innovative tools to address the challenges associated with hallucination in currently available chatbots built on large language models (LLMs). We also need innovative strategies to minimize hallucination, gaslighting, and parroting in currently available LLM chatbots. It is utmost important to train these models with diverse and representative data: Strategy: Ensure training data used for LLMs are diverse and representative of various cultures, backgrounds, and perspectives. Rationale: Biases in AI often stem from biased or limited training data. By incorporating diverse datasets, the LLM can learn from a wide range of sources, minimizing biases and hallucinations. Bias-Aware Algorithms and Data Balancing: Strategy: Develop algorithms that are aware of biases and deploy techniques like data balancing to remove skewed or discriminatory patterns. Rationale: By identifying and addressing biases within the algorithms and training data, the LLM can generate responses that are less likely to be influenced by hallucinations and distortions.