Who is behind it?: Two leading AI labs cited building AGI in their mission statements: OpenAI, founded in 2015, and DeepMind, a research lab founded in 2010 and acquired by Google in 2014. Still, the concept might have stayed on the margins if not for the same wealthy tech investors interested in the outer limits of AI. Musk invested in DeepMind and introduced the company to Google co-founder Larry Page. Musk brought the concept of AGI to OpenAI's other co-founders, like CEO Sam Altman.

Concerns about the growing abilities of chatbots trained on large language models, such as OpenAI's GPT-4, Google's Bard and Microsoft's Bing Chat, are making headlines. Experts warn of their ability to spread misinformation on a monumental scale, as well as the existential risk their development may pose to humanity. As if this isn't worrying enough, a third area of concern has opened up – illustrated by Italy's recent ban of ChatGPT on privacy grounds. The Italian data regulator has voiced concerns over the model used by ChatGPT owner OpenAI and announced it would investigate whether the firm had broken strict European data protection laws. Chatbots can be useful for work and personal tasks, but they collect vast amounts of data.

As part of that job, he's planned a round-the-world goodwill tour to talk with politicians and people using OpenAI's technology. The month-long campaign -- which will take him to Canada, Brazil, Nigeria, Europe, Singapore, Japan, Indonesia and Australia, among other stops -- comes as debate over AI's impact on the world is heating up. The Italian government temporarily banned OpenAI in March, citing concerns about privacy and data collection.

It's been a whirlwind few months in the world of large language models (LLMs), better known to most people as chatbots. Since the release of ChatGPT by OpenAI in Nov. 2022, we've seen billions upon billions of dollars being poured into the development and implementation of generative AIs such as Google's Bard and Microsoft's Bing chatbots--and it's easy to see why. Chatbots like ChatGPT or image generators like DALL-E and Midjourney can feel like magic. With the right prompts, you can get it to do things you wouldn't have imagined a few years ago like craft late night monologue-ready jokes and creating award-winning pieces of "art." It's no surprise that since the public launch of ChatGPT, tech companies have been working to cash in on this modern-day gold rush.

Imagine if it were possible to produce infinite amounts of the world's most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result? This is a reality today. It is called synthetic data. Synthetic data is not a new idea, but it is now approaching a critical inflection point in terms of real-world impact. It is poised to upend the entire value chain and technology stack for artificial intelligence, with immense economic implications. Data is the lifeblood of modern artificial intelligence. Getting the right data is both the most important and the most challenging part of building powerful AI.

対話型 AI と音声認識を使って雑談してみる 音声認識を使って OpenAI の対話型 AI 用モデルと音声で雑談してみるサンプル実装です。音声認識部分はWhisper Micの実装をほぼそのまま利用しています。また、マイクに話しか...

Artificial Intelligence (AI) has made incredible strides in transforming the way we live, work, and interact with technology. Recently, that one area that has seen significant progress is the development of Large Language Models (LLMs), such as GPT-3, ChatGPT, and GPT-4. These models are capable of performing tasks such as language translation, text summarization, and question-answering with impressive accuracy. While it's difficult to ignore the increasing model size of LLMs, it's also important to recognize that their success is due largely to the large amount and high-quality data used to train them. In this article, we will present an overview of the recent advancements in LLMs from a data-centric AI perspective, drawing upon insights from our recent survey papers [1,2] with corresponding technical resources on GitHub.

Helping to reduce costs and enhance productivity are both things that your employer will look kindly upon. But what if you use an external tool for those tasks and the tasks involve confidential data that ended up on a server outside of the control of your company? As a news writer at Tom's Hardware reported there were 3 incidents in 20 days where Samsung staff shared confidential information with ChatGPT. In other organizations, an executive cut and pasted their firm's 2023 strategy document into ChatGPT and asked it to create a PowerPoint deck, and a doctor submitted his patient's name and their medical condition and asked ChatGPT to craft a letter to the patient's insurance company. All of these actions were performed with the best of the organization in mind, but ended up taking confidential information outside of the company.

Large Language Models (LLMs) like OpenAI ChatGPT are called foundational models because even though they are trained for a relatively small set of tasks, they work exceptionally well for multiple unseen downstream tasks. While there is still some debate on how they are so good, at a high level it is quite easy to under what they do -- they just predict the next word (read tokens). And all the cool tools you see built using these models, are nothing but the smart application of this feature. All said and done, they are also quite infamous for a lot of things -- sometimes the generations don't make sense, the models hallucinate (generating the same things over and over again), generations are not factually correct, they can't do maths well, and more. While one research paradigm believes training bigger models with more data can fix some of these problems, another approach is to improve the existing model by connecting them with external apps.

For the first time, OpenAI may face a lawsuit over ChatGPT-generated defamation. An Australian mayor named Brian Hood, who according to Reuters is peeved about the fact that ChatGPT wrongfully identified him as a guilty party in a "foreign bribery scandal involving a subsidiary of the Reserve Bank of Australia in the early 2000s," apparently claiming that Hood had even served prison time for his so-called crime. Hood was involved in the scandal -- but as the whistleblower, not the crime-doer. Yeah, we'd be pissed, too. Per Reuters, Hood's lawyers sent a "letter of concern" to OpenAI back on March 21 demanding that the company fix its chatbot's error within 28 days.