Podium, a trailblazing company known for developing innovative AI tools for podcasters, has unveiled a revolutionary product—Podbook. This AI-powered application transforms podcast episodes into fully-fledged books, providing a seamless way for creators to repurpose their content, engage with their audience in a new format, and generate an additional revenue stream. With sophisticated language processing, Podbook ensures the transformed content reads like a book, enhancing the reader’s experience. Currently, Podbook is in closed beta, with the waitlist now open for all podcasters interested in this cutting-edge product. — Read More
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Monthly Archives: June 2023
AI Is a Lot of Work
As the technology becomes ubiquitous, a vast tasker underclass is emerging — and not going anywhere.
A few months after graduating from college in Nairobi, a 30-year-old I’ll call Joe got a job as an annotator — the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. In Joe’s case, he was labeling footage for self-driving cars — identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of — frame by frame and from every possible camera angle. It’s difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.
Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers.
… [I]t was a job in a place where jobs were scarce, and Joe turned out hundreds of graduates. After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn’t really a problem because they rarely knew themselves. — Read More
How existential risk became the biggest meme in AI — “Ghost stories are contagious.”
Who’s afraid of the big bad bots? A lot of people, it seems. The number of high-profile names that have now made public pronouncements or signed open letters warning of the catastrophic dangers of artificial intelligence is striking.
… Concerns about runaway, self-improving machines have been around since Alan Turing. Futurists like Vernor Vinge and Ray Kurzweil popularized these ideas with talk of the so-called Singularity, a hypothetical date at which artificial intelligence outstrips human intelligence and machines take over.
But at the heart of such concerns is the question of control: How do humans stay on top if (or when) machines get smarter? — Read More
A.I. human-voice clones are coming for the Amazon, Apple, Google audiobook
Annual audiobook sales could reach over $30 billion within a decade, and time and cost of production suggest AI will play a bigger role in the future.
Google Play and Apple Books utilize AI-generated voices to some extent already, though there are high hurdles to recreating human voice pacing, intonation and emotion.
Voice actors say opportunities to clone their voices for speedier, cheaper production on some forms of audiobooks can’t be ignored. — Read More
The Curse of Recursion: Training on Generated Data Makes Models Forget
Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet. — Read More
#training, #transfer-learningNo, GPT4 can’t ace MIT
A paper seemingly demonstrating that GPT-4 could ace the MIT EECS + Math curriculum recently went viral on twitter, getting over 500 retweets in a single day. Like most, we were excited to read the analysis behind such a feat, but what we found left us surprised and disappointed. Even though the authors of the paper said they manually reviewed the published dataset for quality, we found clear signs that a significant portion of the evaluation dataset was contaminated in such a way that let the model cheat like a student who was fed the answers to a test right before taking it.
We think this should call into greater question the recent flurry of academic work using Large Language Models (LLMs) like GPT to shortcut data validation — a foundational principle in any kind of science, and especially machine learning. These papers are often uploaded to Arxiv and widely shared on Twitter before any legitimate peer review. In this case, potentially spreading bad information and setting a poor precedent for future work. — Read More
Run open-source LLMs on your computer. Works offline. Zero configuration.
GPT-4 Can Use Tools Now—That’s a Big Deal
… Earlier this week, OpenAI built tool use right into the GPT API with an update called function calling. It’s a little like a child’s ability to ask their parents to help them with a task that they know they can’t do on their own. Except in this case, instead of parents, GPT can call out to external code, databases, or other APIs when it needs to.
Each function in function calling represents a tool that a GPT model can use when necessary, and GPT gets to decide which ones it wants to use and when. This instantly upgrades GPT capabilities—not because it can now do every task perfectly—but because it now knows how to ask for what it wants and get it. — Read More
Introducing Voicebox: The first generative AI model for speech to generalize across tasks with state-of-the-art performance
Meta AI researchers have achieved a breakthrough in generative AI for speech. We’ve developed Voicebox, the first model that can generalize to speech-generation tasks it was not specifically trained to accomplish with state-of-the-art performance.
Like generative systems for images and text, Voicebox creates outputs in a vast variety of styles, and it can create outputs from scratch as well as modify a sample it’s given. But instead of creating a picture or a passage of text, Voicebox produces high-quality audio clips. The model can synthesize speech across six languages, as well as perform noise removal, content editing, style conversion, and diverse sample generation. — Read More
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Exclusive: Xi Jinping tells Bill Gates he welcomes U.S. AI tech in China
Chinese President Xi Jinping discussed the global rise of artificial intelligence with Bill Gates on Friday and said he welcomed U.S. firms including Microsoft bringing their AI tech to China, two sources familiar with the talks said. — Read More
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