Jasper AI (Free trial, or $49/mo) @heyjasperai http://
jasper.ai Jasper AI has claimed to have read 10% of the internet, and is trained by copywriters to write emotional, persuasive copy. Rated 4.9/5 stars, 80k+ users. https://
loom.com/share/5526c0b0
5ad646d9a29279d0e4b1d87c
…
LLMS
-
Jasper AI copywriting tool overview
By
–
-
Overview of OpenAI’s Playground and Davinci model text generation
By
–
Open AI Playground ($0.02 per 750 words) @OpenAI http://
openai.com Text generation based on any prompt you write. Answers questions, writes copy, simplifies, and expands. Test out the powerful Davinci text model, or less costly legacy models in the "Playground". -
LangChain 0.0.16: Chain Input/Output Comparison and Model Evaluation
By
–
version 0.0.16: compare inputs/outputs on a whole chain Useful for evaluation when you're doing more than just a simple call to an LLM Github: https://
github.com/hwchase17/lang
chain
… How do different models do in @OfirPress
's self-ask w/ search example? https://
colab.research.google.com/drive/1atz4xfZ
LpIHJKD2kf38WnxHv61XU-dwb
… -
Addressing AI-Generated Content Challenges in Education
By
–
OpenAI can write college papers and pass a plagiarism check What will be some ways for educators to combat this?
-
Unlocking Reasoning Capabilities in Smaller Language Models
By
–
Unlocking reasoning in smaller language models is a great direction
-
General-Purpose Neural Networks Through Simple Scalable Objectives
By
–
when the core unlock was achieving a kind of general-purpose computer neural net via simple scalable objectives that have strong training signal (many bits of contraints per training example). Like language modeling, and not like reinforcement learning.
So that was interesting 😀 -
Scaling Language Models: Neural Networks as General-Purpose Text Computers
By
–
TLDR: LMs have been around forever. Not obvious finding: turns out that if you scale up the training set and use a powerful enough neural net (Transformer), the network becomes a kind of general-purpose computer over text.
-
A Decade of AI Progress: Language Models and Neural Networks
By
–
I wrote this thread because I spent the last ~decade, obsessing over directions that would make fastest progress in AI, and was very interested in language models (e.g. my semi-famous 2015 post "The Unreasonable Effectiveness of Recurrent Neural Networks" https://
karpathy.github.io/2015/05/21/rnn
-effectiveness/
…) -
Transformer Training Objective Difficulty Forces Weight Space Optimization
By
–
The second critical ingredient is that while a Transformer seems ~able to act as a general-purpose computer in principle, the training objective has to be hard enough to actually force the optimization to discover and converge onto it in the "weights space" of the network.
-
Language Modeling as Universal Learning Objective Through Text Compression
By
–
Turns out language modeling (i.e. ~next word prediction; equivalent to compression) of internet text is this excellent objective – v simple to define and collect data for at scale. It forces the neural net to learn a lot about the world, "multi-tasking" across many domains.