We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications. — Read More
#strategy, #chatbotsRecent Updates Page 188
The Beatles will release a new and ‘final record’ this year, Paul McCartney says — with a little help from AI
It’s the news fans of the Fab Four thought they would never see: The Beatles will release a new song this year featuring vocals from John Lennon, with a little help from artificial intelligence, Paul McCartney said Tuesday.
Speaking to BBC Radio 4, the 80-year-old McCartney confirmed that the band — whose cultural influence may have been unmatched in the 20th century — will release “the final Beatles record” this year, having used cutting-edge technology to extract Lennon’s voice from an old demo recording.
“We just finished it up and it’ll be released this year,” he said. — Read More
Loneliness, insomnia linked to work with AI systems
Employees who frequently interact with artificial intelligence systems are more likely to experience loneliness that can lead to insomnia and increased after-work drinking, according to research published by the American Psychological Association.
Researchers conducted four experiments in the U.S., Taiwan, Indonesia and Malaysia. Findings were consistent across cultures. The research was published online in the Journal of Applied Psychology. — Read More
The Study
Geoffrey Hinton – Two Paths to Intelligence
Why trying to “shape” AI innovation to protect workers is a bad idea
Instead, we should empower workers and create mechanisms for redistribution.
I’ve been to a number of meetings and panels recently where intellectuals from academia, industry, media, and think tanks gather to discuss technology policy and the economics of AI. Chatham House Rules prevent me from saying who said what (and even without those rules, I don’t like to name names), but one perspective I’ve encountered increasingly often is the idea that we should try to “shape” or “steer” the direction of AI innovation in order to make sure it augments workers instead of replacing them. And the economist Daron Acemoglu has been going around advocating very similar things recently:
According to Acemoglu and [his coauthor] Johnson, the absence of new tasks created by technologies designed solely to automate human work will…simply dislocate the human workforce and redirect value from labour to capital. On the other hand, technologies that not only enhance efficiency but also generate new tasks for human workers have a dual advantage of increasing marginal productivity and yielding more positive effects on society as a whole… — Read More
Perfectly Secure Steganography Using Minimum Entropy Coupling
Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques. In this work, we show that a steganography procedure is perfectly secure under Cachin (1998)’s information theoretic-model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure is maximally efficient if and only if it is induced by a minimum entropy coupling. These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees with non-trivial efficiency; additionally, these algorithms are highly scalable. To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines — arithmetic coding, Meteor, and adaptive dynamic grouping — using GPT-2, WaveRNN, and Image Transformer as communication channels. We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints. In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling. — Read More
Meta’s Open-Source ‘MusicGen’ AI Is Like ChatGPT for Tunes
Meta has released another open-source AI model trained on hundreds of thousands of music tracks online.
AI has managed to intrude on most artistic endeavors, and now it’s fully come for the music industry. Meta has now announced the release of the open source version of its music generation AI model that uses simple prompts to generate music like ChatGPT or other large language model-based AI generate text.
… [T]he model uses an EnCodec audio tokenizer based on a transformer language model. Users can demo MusicGen through Hugging Face’s API, though, generating some music could take some time depending on how many users are using it at once. You can use the Hugging Face site to create your own instance of the model for much faster outputs. Otherwise, you can download the code and run it yourself if you have the know-how and the rig to support it. — Read More
People Are Pirating GPT-4 By Scraping Exposed API Keys
Why pay for $150,000 worth of OpenAI access when you could just steal it?
People on the Discord for the r/ChatGPT subreddit are advertising stolen OpenAI API tokens that have been scraped from other peoples’ code, according to chat logs, screenshots and interviews. People using the stolen API keys can then implement GPT-4 while racking up usage charges to the stolen OpenAI account. — Read More
The Rise and Rise of Voice AI
I was wondering about the relative frequency of certain topics that intrigue me in regard to contemporary, technology-oriented sound studies. For a quick glimpse, first I charted “machine listening” in Google Trends, then adding “audio surveillance” and, for a broader swath, “audio deepfake.” The three terms were almost identical in the narrow band of popularity they populated for the past few years. I sought to expand the subject matter with a fourth item, something related to artificial intelligence.
Needless to say, we’re in the midst of AI Summer. These days “AI + [anything]” — following the rise in chatter around DALL-E 2, Midjourney, Stable Diffusion, ChatGPT, DeepMind, Bard, and OpenAI, among other projects — is going to be more popular now than it was even six or seven months ago. — Read More
Hundreds attend church service generated by ChatGPT
The artificial intelligence chatbot asked the believers in the fully packed St. Paul’s church in the Bavarian town of Fuerth to rise from the pews and praise the Lord.
The ChatGPT chatbot, personified by an avatar of a bearded Black man on a huge screen above the altar, then began preaching to the more than 300 people who had shown up on Friday morning for an experimental Lutheran church service almost entirely generated by AI. — Read More
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