Are.na
There are some promising transfer and semisupervised learning techniques that may provide alternatives to gathering a great deal of labeled data,
Ash Fontana • The AI-First Company: How to Compete and Win with Artificial Intelligence
So, the condition of superabundance creates a need for aggregation. LLMs will amplify that abundance. It’s a good bet that this strengthens the strategic importance of aggregation.
Gordon Brander • LLMs and information post-scarcity
The second challenge we call the illusory progress gap: mistaking progress in AI on easy problems for progress on hard problems.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
Deep learning is greedy. In order to set all the connections in a neural net correctly, deep learning often requires a massive amount of data.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
OK, so let’s say one’s settled on a certain neural net architecture. Now there’s the issue of getting data to train the network with. And many of the practical challenges around neural nets—and machine learning in general—center on acquiring or preparing the necessary training data. In many cases (“supervised learning”) one wants to get explicit ex
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