Experiments with NLP and GPT-3

This Substack explores the potential and challenges of working with GPT-3 and NLP technologies through various experiments. It covers creating local language models, building and utilizing the AI stack with embeddings and vector databases, understanding neural networks, generating educational content, and developing efficient encoding methods for text.

Natural Language Processing Machine Learning GPT-3 Embeddings Vector Databases Neural Networks Educational Content Generation Large Language Models AI in Content Creation

The hottest Substack posts of Experiments with NLP and GPT-3

And their main takeaways

The LLM stack

7 implied HN points • 23 Jun 23
🕹 Technology AI Data Pipelines
  1. The LLM App stack is important in the AI world today.
  2. Embeddings from OpenAI and Huggingface play a key role in giving meaning to data.
  3. VectorDBs like Pinecone and Vespa are crucial for managing embeddings in the AI stack.

The use cases of large language models(LLMs)

1 HN point • 12 Mar 23
🕹 Technology AI NLP Automation Text generation
  1. Large language models are not AGI but are making significant advancements in solving various NLP problems.
  2. LLMs excel in tasks like parts of speech tagging, semantic parsing, named entity recognition, and question answering.
  3. LLMs can automate back office work and offer solutions for tasks like stemming, lemmatization, relationship extraction, summarization, keyword extraction, and text generation.
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Building a new embedding(bit vector) for text

1 HN point • 09 Feb 23
  1. Embeddings play a crucial role in NLP research and solutions.
  2. Experimenting with alternative embeddings like the KE Sieve method can lead to significant size reduction and efficient search operations.
  3. By building an embedding space from scratch using methods like TF-IDF and KE Sieve, it's possible to create unique and effective sentence embeddings for various applications.

AI and the art of doing with little-Part 1

0 implied HN points • 05 Mar 24
🕹 Technology AI Data Innovation Research Open Source
  1. In the AI field, access to large amounts of compute power and data is crucial, but it can be expensive and a barrier for many. This can lead to a reliance on funding and resources, putting a focus on money as a determinant of success.
  2. The author emphasizes the importance of simpler, more accessible experiments in AI research, drawing inspiration from V.S. Ramachandran's approach in neuroscience. Small, innovative solutions may offer promising alternatives to standard big science methods.
  3. There is a push for exploring new ways to tackle AI challenges beyond the current reliance on GPUs and deep learning models. The idea of creating open-source datasets and involving young talents from India in research signifies a shift towards more inclusive and collaborative approaches.

What does $2 for 1 million tokens get you

0 implied HN points • 09 Mar 23
🕹 Technology AI NLP API
  1. For $2, 1 million tokens can generate a variety of content like code, articles, novels, tweets, and more.
  2. Generating content using AI may not always result in high-quality or unique output; success may involve integrating AI into existing processes.
  3. The key is to leverage generative AI as a part of the creative pipeline rather than relying solely on the AI to do all the work.

Taking on OpenAI

0 implied HN points • 11 Jun 23
🕹 Technology AI NLP Algorithms Models Mathematics
  1. Sama believes building foundational models to compete with OpenAI's ChatGPT is hopeless without significant investment.
  2. The current approach depends heavily on data and compute resources, which OpenAI has in abundance.
  3. The author plans to build foundational models using the KESieve algorithm, focus on math, involve students, and avoid traditional funding methods.