The power of AI is its ability to turn unstructured data into structured data
Matt Hartmanmatthewhartman.substack.com
The power of AI is its ability to turn unstructured data into structured data
To the extent that large language models (and I should note that while I’m focusing on image generation, there are a whole host of companies working on text output as well) are dependent not on carefully curated data, but rather on the Internet itself, is the extent to which AI will be democratized, for better or worse.
Ben Thompson • The AI Unbundling
sari added
There is so much more structure in the world than we tend to believe. And LLMs and other AI models are structure discovery machines.
In the current state of the art, models are only as good as the data they’re trained on and the models can’t generate new training data themselves. AI-generated output is based on the data that trained the AI in the first place, so any data it creates will be a regurgitation of the data it already has. That means that humans
... See morePacky McCormick • Differentiation
sari added
While LLMs continue to devour web-scraped data, they’ll increasingly consume their own digital progeny as AI-generated content continues to flood the internet. This recursive loop, experimentally confirmed, erodes the true data landscape. Rare events vanish first. Models churn out likely sequences from the original pool while injecting their own un... See more
Azeem Azhar • 🔮 Open-source AI surge; UBI surprises; AI eats itself; Murdoch’s empire drama & the internet’s Balkanisation ++ #484
MargaretC added
Almost every company I talk to, and basically every solution vendor, has been pushing for people to use AI to “talk-to-your-data,” an approach allows the AI to retrieve content from a company’s proprietary databases and then work with the documents and data it retrieves. The problem is that AIs hallucinate, or make up plausible information, all the... See more
Ethan Mollick • Almost an Agent: What GPTs can do
Nicolay Gerold added
When it comes to identifying where generative AI can make an impact, we dig into challenges that commonly:
- Involve analysis, interpretation, or review of unstructured content (e.g. text) at scale
- Require massive scaling that may be otherwise prohibitive due to limited resources
- Would be challenging for rules-based or traditional ML approaches
Developing Rapidly with Generative AI
Nicolay Gerold added
There is no AI Strategy without a Data Strategy
Training AI models requires data. Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial.
Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithm... See more
Training AI models requires data. Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial.
Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithm... See more
Jason Cohen • AI startups require new strategies: This time it's actually different
Nicolay Gerold added
I would differentiate between data for existing behaviors vs new behaviors. The world and our tools are constantly changing. I think AI is one of those drivers. I think the startups who can either facilitate new behaviors the best (e.g. Cursor for AI-assisted coding) or who can create entirely new behaviors have a great way to generate unique training data.
Taking existing data and making it workable with AI is a lot of work and takes skilled labor (AI + domain expertise). I wouldn’t say incumbents have the complete edge. They have the edge in certain applications and behaviors under the assumptions these behaviors are not changing to render their applications useless.
sari and added