The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Self-Refine Is An Iterative Refinement Loop For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology Data science
  1. Self-Refine improves LLM output without needing extra training data. It does this by refining the output through feedback in a loop.
  2. The approach mimics how humans recheck their work to find better ways to express ideas, like improving an email draft or optimizing code.
  3. Quality of results gets better with more iterations, but it's important to balance this with potential delays and costs. Stronger models produce better refinements.

A New Prompt Technique From DeepMind Called Optimisation by PROmpting (OPRO)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology Data science
  1. A new technique called Optimisation by PROmpting (OPRO) helps improve the performance of language models by using specific prompts. This method aims to make prompts more effective without changing the underlying models.
  2. OPRO can generate multiple prompt options at once, allowing the system to find the best one more efficiently. This strategy is helpful for solving tasks and provides better stability in results.
  3. The prompts created with OPRO can perform 8% to 50% better than those designed by humans, showing it can be more efficient in certain tasks. It's a new way to help machines understand and respond more accurately.

Context-Aware Meta-Learning For Foundation Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology Data science
  1. Large Language Models can learn quickly from little information during use, without needing extra training. This makes them very flexible in understanding and generating text.
  2. Currently, images don't learn as easily as text when it comes to recognizing new things on the spot. Improving this could allow visual models to learn like language models do.
  3. The new method called Context-Aware Meta-Learning helps visual models learn new concepts right away without extra setup. This can lead to exciting new applications that connect text and images better.

Data Delivery To Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Oct 23
🕹 Technology Data science
  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.

A New Prompting Approach From DeepMind Called Analogical Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology Data science
  1. DeepMind's Analogical Prompting helps language models recall similar past problems to solve new ones. This way, models can learn from existing knowledge without needing specific examples every time.
  2. This approach allows models to create their own relevant examples, reducing the need for human labeling and making the problem-solving process more efficient.
  3. By generating tailored examples, DeepMind's method improves the accuracy of solutions while also simplifying the training process for the models.
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LLMs & Contextual Demonstration

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology Data science
  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.

Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Oct 23
🕹 Technology Data science
  1. Large Language Models (LLMs) are evolving and diversifying, leading to the rise of Foundation Models that can handle various types of data like text and images. This means they can do more complex tasks now.
  2. There's a shift in how LLMs are used, with a focus on improving their functions like text analysis, speech recognition, and dialog generation. New techniques help these models perform better in their designated tasks.
  3. The market is seeing exciting new opportunities, especially in tools that help businesses use LLMs effectively, like data discovery and user-friendly interfaces. These tools can help companies tap into the potential of LLMs better.

A New Prompt Engineering Technique Has Been Introduced Called Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Oct 23
🕹 Technology Data science
  1. Step-Back Prompting helps Large Language Models find better answers by simplifying complex questions. It turns a detailed question into a more generic one that's easier to tackle.
  2. This technique can be combined with other methods to improve accuracy and effectiveness. It shows promise in fixing errors from traditional approaches.
  3. Using Step-Back Prompting requires careful thought and might work best with autonomous systems. It's a more advanced method compared to static prompting.

Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Oct 23
🕹 Technology Data science
  1. Using Retrieval Augmented Generation (RAG) helps improve how language models work by allowing them to learn from additional, relevant data.
  2. RA-DIT is a new method that combines fine-tuning of the language model with updates to the retriever, making both more aligned and effective.
  3. A human approach to training the retriever with curated data ensures ongoing improvement and better responses in real conversations.

RAG & Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology Data science
  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.

Agents, LLMs & Multihop Question Answering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Apr 23
🕹 Technology Data science
  1. Agents can use different tools based on user requests. This gives them the flexibility to respond to questions that don't fit a typical sequence.
  2. Prompt chaining involves linking prompts together to create a more complex response. However, it can struggle with unexpected user queries.
  3. For better responses, it's important for an Agent to have clear instructions on which tool to use. Fine-tuning these instructions can improve how well the Agent answers questions.

Scaleable Prompt Pipelines For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Apr 23
🕹 Technology Data science
  1. Creating good prompts for AI needs context. A well-structured prompt includes clear instructions, context, the user's question, and the expected answer format.
  2. To handle many prompts at once, automation is key. Using tools to automatically search and retrieve the right context for prompts will save time and improve responses.
  3. For AI to work well in specific areas, it needs accurate and well-organized data. This data helps improve the AI’s answers, especially in narrow topics.

Generative AI Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Apr 23
🕹 Technology Data science
  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.

Multi-Label Text Classification With Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Mar 23
🕹 Technology Data science
  1. Google Cloud Vertex AI allows for multi-label text classification, which means multiple tags can be assigned to a document. This helps in better organizing and processing text data.
  2. Training a model on Vertex AI can take a long time, especially with large datasets. For example, using nearly 12,000 training items can take over four hours to complete.
  3. The system's interface for managing training data and labels can be complex and a bit confusing. This makes it harder to easily update and manage the training data.

Training & Testing Text Classification Models with Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Mar 23
🕹 Technology Data science
  1. Google's AutoML makes it easy to build classification models without needing much technical know-how. It simplifies the process, allowing more people to create models.
  2. Vertex AI can classify text into single or multiple categories, but it doesn't support complex class structures. So, simple classifications work best.
  3. While AutoML speeds up model creation, training times can be long. It's important to plan your data splits and annotation sets for better model performance.

Creating Training Data For Text Classification In Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Mar 23
🕹 Technology Data science
  1. Creating training data for AI is a crucial first step in making it work well. It involves careful organization and structuring of data to help the AI learn effectively.
  2. A data-centric approach requires ongoing exploration and refinement of the training data. This means continuously checking the data for patterns and making adjustments as needed.
  3. Using human labelers to categorize data can be costly and complex. It's often easier to automate this process with human oversight rather than sending data out for labeling.

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 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.

Three Ways In Which Whisper Is Advancing ChatGPT

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Mar 23
🕹 Technology Data science
  1. Speech to text has unique challenges, like disfluencies that happen when people talk. These differences can help improve how ChatGPT understands and processes voice input.
  2. Whisper can provide ChatGPT with access to lots of audio data. This means it can learn from a wider variety of information, which can make responses better.
  3. The future of AI models includes using different types of data, not just text. This shift towards multi-modal models means ChatGPT can eventually handle audio, images, and more, making it more versatile.

Large Language Models, Foundation Models & Multi-Modal Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Mar 23
🕹 Technology Data science
  1. Large Language Models (LLMs) are being developed into Foundation Models that can handle tasks beyond just language, like images and voice. This shows how technology is evolving to be more versatile.
  2. GPT-4 is now seen as a Multi-Modal Model that combines different types of data, allowing it to work with text, images, and more. This expands the possibilities for AI applications.
  3. As the use of LLMs increases, there will be more focus on creating fine-tuned models. This means turning unstructured data into structured data for better interaction and understanding.

What Does ChatML Mean For Prompt Chaining Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Mar 23
🕹 Technology Data science
  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.

Chaining Large Language Model (LLM) Prompts Via Visual Programming

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Feb 23
🕹 Technology Data science
  1. Chaining LLM prompts can make complex tasks easier to handle. It allows many prompts to work together for better results.
  2. Using templates for prompts helps to save time and keep things organized. They allow you to reuse parts of your prompts easily.
  3. There's a growing opportunity to combine traditional logic with LLMs. This mix can enhance chatbot and AI systems in powerful ways.

Solving For The Long Tail Of Intent Distribution

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Feb 23
🕹 Technology Data science
  1. The long tail of intent distribution has a lot of important customer conversations that can be often overlooked. These conversations are key to understanding what users really want.
  2. Using existing customer data like conversation transcripts and reviews can help identify these overlooked intents. Analyzing this data properly allows for better understanding and response design.
  3. Aligning chatbot intents with actual customer conversations is crucial for success. This ensures that the chatbot effectively meets user needs and improves overall interaction.

What Are Realistic GPT-4 Size Expectations?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Feb 23
🕹 Technology Data science
  1. GPT-4 is likely to have around 1 trillion parameters, which is much smaller than the rumored 100 trillion. This is based on how language models have grown over time.
  2. Experts suggest that it's not just about the number of parameters. The quality of training data is equally important for improving performance in language models.
  3. There is a limited supply of high-quality language data. If better data sources don’t emerge, the growth of model sizes may slow down significantly.

Bootstrapping A Chatbot With A Large Language Model

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Feb 23
🕹 Technology Data science
  1. To build a chatbot, you can organize unstructured data by clustering it into themes called intents. This helps make sense of lots of information and sets the stage for training the bot.
  2. Once the bot receives a user's message, it uses semantic search to match the message with the right intent. This helps in retrieving the most relevant information quickly.
  3. The bot then generates a response using the matched intent and the user's question. This process allows the chatbot to provide accurate and context-aware answers.

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 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 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.

How To Create HuggingFace🤗 Custom AI Models Using autoTRAIN

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Feb 23
🕹 Technology Data science
  1. autoTRAIN lets you build custom AI models without needing to code. It's user-friendly and has both free and paid options.
  2. You can easily upload your data in different formats like CSV, TSV, or JSON. The platform keeps your data private and secure.
  3. As your model trains, you can see real-time results about its accuracy. This helps you understand how well it's performing and make necessary adjustments.

How to use Google’s CausalImpact

Logos • 0 implied HN points • 23 Dec 21
🕹 Technology Data science
  1. Google's CausalImpact helps you see how actions, like a marketing campaign, affect outcomes like sales. It predicts what would have happened without that action, making it easier to understand its impact.
  2. Using CausalImpact requires some basic coding in R, but even beginners can follow along. You'll collect data in a simple format, run the analysis, and see results visually and in tables.
  3. When using CausalImpact, it's crucial to choose the right control variables. They should correlate with your main outcomes but not be influenced by the actions you're analyzing.

Coming soon

DataSyn’s Substack • 0 implied HN points • 27 Aug 24
🕹 Technology Data science
  1. A new Substack for DataSyn is launching soon. It will likely share information about synthetic data and its uses.
  2. Subscribing to this Substack could provide useful insights in the field of data science.
  3. The focus seems to be on artificial intelligence and large language models.

LLMs can only generate

Sunday Letters • 0 implied HN points • 14 Jul 24
🕹 Technology Data science
  1. Generative models like LLMs can only create new content from scratch. They can't just fix mistakes in the specific part we want; they'll regenerate everything instead.
  2. Reliability is key for these systems to be useful. Unlike humans, who can iterate and refine work step by step, generative models don't have that ability to just modify a piece.
  3. When using generative models, it's important to clearly scope the work. You should restrict what you want the model to generate to avoid unexpected changes, using coding to help manage the tasks.

[in case you missed it ] Data Science Weekly - Issue 472

Data Science Weekly Newsletter • 0 implied HN points • 11 Dec 22
🕹 Technology Data science
  1. Machine learning can have unintended biases if the training data includes wrong patterns. It's important to check how models make decisions to avoid mistakes.
  2. You can use machine learning in Google Sheets without any coding or data sharing. There are easy tools available that let anyone analyze data and make predictions.
  3. Realtime machine learning is becoming a trend in tech companies, which means they want to make their data analysis and model scoring faster and more efficient.

[in case you missed it] Data Science Weekly - Issue 471

Data Science Weekly Newsletter • 0 implied HN points • 04 Dec 22
🕹 Technology Data science
  1. MLOps is important for automating machine learning products. It helps researchers and practitioners understand the roles and workflows needed in machine learning.
  2. Companies face challenges when moving to realtime machine learning. They need to balance performance, cost, and complexity in their ML pipelines.
  3. The FDA has outlined guiding principles for using AI in medical devices. These principles aim to ensure safety and effectiveness in tech for healthcare.

[in case you missed it] Data Science Weekly - Issue 470

Data Science Weekly Newsletter • 0 implied HN points • 27 Nov 22
🕹 Technology Data science
  1. Recommender systems often focus on increasing user engagement, but this can lead to unintended negative effects like addiction. A new understanding of user preferences could help create better recommendations.
  2. GitLab's Data Team Handbook shares valuable information on how data is used in various business functions. It's organized into helpful sections that explain dashboards, team operations, and current projects.
  3. Deep learning is being used to test video games like Candy Crush for more human-like gameplay. This approach is explored by researchers from gaming companies, highlighting the potential for better game design.

[in case you missed it] Data Science Weekly - Issue 469

Data Science Weekly Newsletter • 0 implied HN points • 20 Nov 22
🕹 Technology Data science
  1. Learning machine learning can be a challenging but rewarding journey, and it often involves continuous effort to improve skills and practices.
  2. Robotics and AI are making a big impact in industries like fulfillment, but there are still many challenges to overcome as the technology scales.
  3. Emerging AI capabilities, particularly in large language models, are becoming increasingly action-driven, resembling more advanced forms of intelligence.

[in case you missed it] Data Science Weekly - Issue 468

Data Science Weekly Newsletter • 0 implied HN points • 13 Nov 22
🕹 Technology Data science
  1. Before leaving Twitter, it's a good idea to download and save your data. This way, you can analyze important trends and insights you might miss if you just leave.
  2. The command line can make data processing easier and more readable. New tools like SPyQL help bridge familiarity with SQL and Python for better data analytics.
  3. Federated learning allows multiple users to train models without sharing their raw data. This technology can enhance privacy while still allowing valuable insights from diverse data sources.

[in case you missed it] Data Science Weekly - Issue 467

Data Science Weekly Newsletter • 0 implied HN points • 06 Nov 22
🕹 Technology Data science
  1. Startups using large language models should focus on improving user experience, as it's currently their biggest hurdle, not the data or algorithms.
  2. Data science notebooks have evolved significantly since they were first created, and there are predictions for how they'll continue to develop in the future.
  3. OpenAI is supporting new AI startups by offering $1 million each and early access to their systems, which could help boost innovation in the field.

[in case you missed it] Data Science Weekly - Issue 466

Data Science Weekly Newsletter • 0 implied HN points • 30 Oct 22
🕹 Technology Data science
  1. Teaching science should start with the values and virtues of being a good scientist rather than just tools and techniques. Focusing on qualities like curiosity and creativity is key.
  2. Creating a data dictionary before collection is crucial. It helps guide your data collection and makes interpreting results easier later on.
  3. Open source reinforcement learning is evolving with new organizations to improve standardization and support. This effort aims to enhance the quality and usability of available tools.

[in case you missed it] Data Science Weekly - Issue 464

Data Science Weekly Newsletter • 0 implied HN points • 16 Oct 22
🕹 Technology Data science
  1. Building a community of R users can greatly enhance collaboration and knowledge sharing, especially in specialized fields like pharmaceuticals.
  2. Generating research ideas often starts with identifying gaps in existing literature, which can be guided by specific frameworks to improve the quality of ideas.
  3. Data cleaning is crucial for model accuracy, and its success relies on effective ETL processes and organizational commitment to maintaining high-quality data.

[in case you missed it] Data Science Weekly - Issue 463

Data Science Weekly Newsletter • 0 implied HN points • 09 Oct 22
🕹 Technology Data science
  1. To explore a large CSV file, you should use handy tools and methods to quickly understand the data without getting overwhelmed.
  2. AI can help convert messy unstructured text into organized data, speeding up tasks that would usually take a long time manually.
  3. Building a career in data science involves learning not just the technical skills but also how to navigate job opportunities and project management.