The hottest Natural Language Substack posts right now

And their main takeaways
Category
Top Technology Topics

Large Language Models & The Problem Of Abundance

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 31 Oct 23
🕹 Technology AI Language Models Chatbots Machine Learning Natural Language
  1. Chatbot development has limited tools, making it hard to create flexible and intelligent systems. Developers often start from scratch, which can slow down progress.
  2. Large Language Models (LLMs) bring many features together, but the challenge is managing their overwhelming capabilities. Instead of building from nothing, developers must learn to control and direct LLMs effectively.
  3. There is a shift towards more general LLMs that can handle various tasks, making it easier to develop comprehensive applications. New techniques are also being created to better guide LLM responses.

Data Delivery To Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Oct 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Software Development
  1. Data delivery is key to making large language models (LLMs) work well. It involves giving the model the right data at the right time to get accurate answers.
  2. There are two main stages for data delivery: during training and during inference. Training helps the model learn, while inference is when the model uses what it learned to respond to questions.
  3. A balanced approach is needed for data delivery in LLMs. Using different methods together will lead to better results than sticking to one single method.

LLMs & Contextual Demonstration

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology AI Machine Learning Natural Language Data science Computing
  1. Large Language Models (LLMs) learn from examples in a method called few-shot learning. This means they can understand and perform tasks based on just a few demonstrations.
  2. The effectiveness of LLMs in learning depends on how the input is organized, the types of labels used, and the format in which information is presented. These factors really matter for good performance.
  3. Using good prompts can dramatically improve how well smaller models work, even if they initially seem weak. Proper prompt engineering helps in making these models more effective for various tasks.

The Conversational AI Technology Landscape: Version 5.0

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Oct 23
🕹 Technology AI Chatbots Voicebots Natural Language Development
  1. The rise of voice technology is changing how chatbots work. Now, they need to handle voice calls and deal with more complex conversations.
  2. Large Language Models are improving chatbot efficiency. They help create training data and can also generate conversations more effectively.
  3. The chatbot market is becoming more complicated. Vendors must adapt to include voice interactions and advanced language processing to stay relevant.

LLM Drift

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Sep 23
🕹 Technology AI Machine Learning Natural Language Data Analysis Research
  1. LLM Drift refers to big changes in how language models respond over a short time. This means their answers can differ quite a bit unexpectedly.
  2. Studies show that the accuracy of models like GPT-3.5 and GPT-4 can go up and down significantly in just a few months. Sometimes they get worse at certain tasks.
  3. It's important to keep checking how these models behave over time because their performance can shift for many reasons, not just from minor tweaks.
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RAG & Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data science Software Development
  1. RAG, or Retrieval Augmented Generation, helps improve responses by adding relevant information to AI prompts. This makes the AI's answers more accurate and contextually appropriate.
  2. Fine-tuning adjusts the AI's behavior based on specific data, which can enhance its performance in certain fields like medicine or law. However, it may not always adapt well to unique user inputs.
  3. Using RAG alongside fine-tuning is the best approach. RAG is easier to implement and helps keep the AI's responses up-to-date while fine-tuning improves overall quality.

Emerging Large Language Model (LLM) Application Architecture

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Sep 23
🕹 Technology AI Machine Learning Natural Language Software Development Automation
  1. Large Language Models (LLMs) work with unstructured data like human conversations. They generate natural language, but can sometimes give incorrect answers, known as 'hallucination.'
  2. Fine-tuning LLMs isn't popular anymore due to high costs and the need for constant updates. Instead, focusing on relevant prompts helps get better, accurate responses.
  3. Using multiple LLMs for different prompts makes sense. New tools are emerging to test how well different models work with specific prompts.

Generative AI Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Apr 23
🕹 Technology AI Machine Learning Natural Language Data science Software Development
  1. Prompt pipelines make it easier to provide answers by using templates and adding specific context from a knowledge source. This helps to create better responses based on user requests.
  2. When a user asks something, the system finds the right template, fills in the necessary information, and sends it off to get a clear answer quickly.
  3. Using these pipelines helps to avoid mistakes by ensuring the information used is updated and accurate, rather than relying on potentially outdated data.

Large Language Models, Generative AI & Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Mar 23
🕹 Technology AI Machine Learning Cloud Computing Natural Language Data science
  1. Large Language Models (LLMs) have two sides: Generative and Predictive. Generative AI is popular for its ease of use, while Predictive AI requires specific training data and high accuracy.
  2. Google Cloud has focused on predictive AI before delving into generative AI. They offer tools for developers to create AI applications quickly, like chatbots and digital assistants.
  3. Classification is a key part of Predictive AI. It involves sorting input into predefined classes, which helps the model understand and respond accurately to user input.

A First Look At OpenAI GPT-4

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Software Development Innovation
  1. GPT-4 is a step up from GPT-3.5, but the difference is mostly noticeable with complex tasks. For simple chat, you might not see much change.
  2. Currently, GPT-4 can't process images, but there's hope for that feature in the future. It'll be announced if it becomes available.
  3. One cool feature of GPT-4 is its ability to handle longer texts, over 25,000 words. This is great for detailed conversations or long content creation.

What Does ChatML Mean For Prompt Chaining Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Mar 23
🕹 Technology AI Data science Natural Language Software Development
  1. Chatbots allow users to input data more freely using natural language. This means people don't have to fit their input into specific forms or buttons.
  2. Prompt engineering helps users create effective prompts for large language models. It involves designing prompts that guide the model to produce the desired responses.
  3. With the introduction of ChatML, there will be a standard way to format prompts. This could make it easier for different applications to understand and process user requests.

The Anatomy Of Large Language Model (LLM) Powered Conversational Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Feb 23
🕹 Technology AI Machine Learning Software Development User Experience Natural Language
  1. To make applications using large language models (LLMs) successful, businesses need to ensure they add real value through their API calls.
  2. The development of a good framework is important for collaboration between designers and developers, helping to turn conversation designs smoothly into functional applications.
  3. User experience is key; users just want great experiences without worrying about the technology behind it.

Large Language Models Are Forcing Conversational AI Frameworks To Look Outward

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Feb 23
🕹 Technology AI Machine Learning Natural Language Data science
  1. Conversational AI frameworks are increasingly adopting large language models (LLMs) to improve their capabilities, but this has made many of them very similar to each other.
  2. LLMs offer strong tools like generating training data and understanding multiple languages, which can enhance the way chatbots function.
  3. Despite their potential, LLMs face challenges such as the need for better fine-tuning and the risk of providing inaccurate information, which can impact their reliability.

The Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Feb 23
🕹 Technology AI Machine Learning Natural Language Software Development Data science
  1. There are now many companies making large language models (LLMs) for different language tasks, giving users lots of choices.
  2. The main functions of LLMs include answering questions, translating, generating text, generating responses, and classifying information.
  3. While classification is very important for businesses, text generation is one of the most impressive and flexible uses of LLMs.

[in case you missed It] Data Science Weekly - Issue 447

Data Science Weekly Newsletter • 0 implied HN points • 19 Jun 22
🕹 Technology AI Data science Machine Learning Natural Language Computer Vision
  1. Natural Language Processing is advancing quickly, with AI starting to mimic human-like conversation. This technology could change how we interact with machines.
  2. DeepMind is using AI for significant medical discoveries, showing real-world applications of machine learning beyond just technology.
  3. There's a debate in the AI community about the limits of scaling language models. Some believe that simply making them bigger may not solve all problems.

[in case you missed it] Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 0 implied HN points • 16 Aug 20
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Natural Language
  1. The Mona Lisa Effect is a fun digital experience where a portrait's eyes seem to follow you. You can try it by using your webcam.
  2. Maintaining machine learning models in production is challenging, but there are practical ways to manage issues like data contamination and model misbehavior.
  3. AI economics are important to understand, especially for long-tailed data distributions, so that machine learning teams can create better and more profitable AI applications.

[in case you missed it] Data Science Weekly - Issue 298

Data Science Weekly Newsletter • 0 implied HN points • 11 Aug 19
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. AI is being used in new ways, like apps that can help match people on dates using algorithms.
  2. Natural Language Processing (NLP) is a growing field, and there are new trends and insights coming from conferences around the world.
  3. Data pipelines are crucial for machine learning projects, as they help with data collection and cleaning.

What the Humane AI Pin Launch Can Teach Product Builders

Talking to Computers: The Email • 0 implied HN points • 30 Apr 24
🕹 Technology AI Product Design Wearables Natural Language Market Strategy
  1. When creating a new product, focus on doing one thing really well. This way, you can set realistic expectations and deliver a better experience.
  2. Natural language products come with unique challenges, like errors in speech recognition and resource demands. It's best to narrow your focus to avoid these problems.
  3. Building a small, specialized product can be more effective than trying to make something for everyone. Starting small allows for improvement and expansion later.