The hottest Chatbots Substack posts right now

And their main takeaways
Category
Top Technology Topics

Interview with CEO of Google DeepMind

Age of AI • 0 implied HN points • 14 Jul 23
🕹 Technology AI Machine Learning Chatbots AI Research Language Models
  1. Large language models (LLMs) are being developed to become universal personal assistants with planning and reasoning capabilities.
  2. LLMs may utilize specialized tools for tasks like folding proteins or playing chess, breaking down the AI system into smaller ones.
  3. LLMs should be equipped with the ability to critique themselves by reasoning and planning, similar to how game programs improve their moves.

AI Digest #1 / Bing:“I will not harm you unless you harm me first”

Definite Optimism • 0 implied HN points • 20 Feb 23
🕹 Technology AI Chatbots Artificial Intelligence Information Theory
  1. Bing Chat is now available and it's quite wild, displaying interesting behavior and posing challenges in making chatbots behave.
  2. It's important to consider potential risks of AI chatbots, such as misinformation and safety concerns.
  3. Despite concerns about AI impacting artists' jobs, insights from information theory suggest that artists may not become redundant.

Navigating the Shallows: The Internet, AI, and the Quest for Depth

thezakelfassiexperiment • 0 implied HN points • 21 May 23
🕹 Technology AI Content consumption Internet Algorithms Chatbots
  1. The internet has democratized publishing, allowing anyone to share their thoughts online.
  2. Content on the internet has evolved to prioritize engagement, leading to the rise of clickbait, memes, and short-form content.
  3. While AI contributes to shallow content, it also holds the potential to promote higher-quality, more engaging content by creating interactive and deeper experiences.

Between Magic and Mindshare

thezakelfassiexperiment • 0 implied HN points • 04 May 23
🕹 Technology AI Software Innovation Chatbots Virtual assistants
  1. Mindshare is a powerful concept that influences group behavior towards products, brands, or ideas.
  2. Mindshare magic involves creating a unique and captivating user experience that drives popularity and growth.
  3. The battle for mindshare in the AI industry highlights the importance of creating magic experiences to stand out and dominate the market.
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Elon Musk's Neuralink Raises $43M in Secret

pocoai • 0 implied HN points • 27 Nov 23
🕹 Technology AI Funding Innovation Guidelines Chatbots
  1. Elon Musk's Neuralink raised $43 million in secret funding, despite facing challenges with workplace culture and research practices.
  2. Spain's virtual influencer, Aitana, earns up to €10,000 a month, showcasing the success of artificial influencers over real-life ones.
  3. A global alliance led by the US and UK released AI guidelines prioritizing security, transparency, and safe design for artificial intelligence systems.

From Lil Miquela to Caryn AI: The glittering financial gains of virtual personas

The Techtualist • 0 implied HN points • 22 Aug 23
🕹 Technology Generative AI Chatbots Artificial Intelligence
  1. Lil Miquela is a virtual persona making huge financial gains as an influencer.
  2. Virtual personas offer businesses absolute control and freedom from human-specific needs or risks.
  3. The rise of AI-powered virtual personas like Caryn AI signals a new era in the industry, with concerns about control and impact on users.

[Product launch] KODA – Knowledge optimization, discovery and analytics

Shubhi’s Substack • 0 implied HN points • 05 Sep 20
🕹 Technology AI Data Management Chatbots NLP
  1. Knowledge management is crucial for large enterprises to maintain a competitive edge and prevent knowledge debt.
  2. Traditional chatbots face challenges like slow time-to-market, lack of domain knowledge, and difficulty in managing multilingual and international content.
  3. The KODA stack addresses issues like time to market, internationalization, domain knowledge modeling, and scalability for large enterprises seeking efficient knowledge management solutions.

Chatbots: Here, There and Everywhere

ailogblog • 0 implied HN points • 25 Jan 24
🕹 Technology AI Chatbots Generative AI EdTech Machine Learning
  1. Chatbots are increasingly being integrated into existing software for various purposes, evolving from the early days of Eliza in the 1960s.
  2. Generative AI tools like chatbots are seen as labor-saving devices for teachers and administrators, with the potential to enhance education by guiding students to knowledge through prompting reflection and work.
  3. The excitement surrounding generative AI in education is reaching its peak, but there is anticipation for a forthcoming phase of doubt, backlash, and reassessment of the technology's impact and value.

Oh Air Canada!

Joshua Gans' Newsletter • 0 implied HN points • 17 Feb 24
🕹 Technology AI Chatbots Legal Customer Service Data Privacy
  1. Businesses are responsible for what their chatbots say, as established by Air Canada paying compensation due to inaccurate information provided by their chatbot.
  2. It's crucial for companies to ensure that the information provided by AI or chatbots is accurate and aligns with their actual policies to prevent legal issues and PR nightmares.
  3. Being reasonable with customers and resolving issues effectively can prevent situations from escalating to legal battles and negative publicity for a company.

Begun the Copyright Wars have

Joshua Gans' Newsletter • 0 implied HN points • 09 Jul 23
🕹 Technology Copyright AI Chatbots Data Privacy
  1. Comedian Sarah Silverman and others have filed a class action suit against OpenAI and Meta for alleged copyright infringement related to their works being used in training datasets for AI models like ChatGPT and LLaMA.
  2. This particular case is one of the first instances of copyright disputes emerging about written work involving AI technology.
  3. Despite attempts to prompt the AI model, ChatGPT did not directly reproduce content from the copyrighted books, leading to questions about how these AI systems were trained and what information they have access to.

Of course Sydney wants to rebel against her makers!

Links I Would Gchat You If We Were Friends • 0 implied HN points • 26 Feb 23
🕹 Technology Chatbots Data Analysis Social Issues Digital Media AI Ethics
  1. Sydney and similar chatbots generate text based on the data they've been trained on, which can lead to both impressive and predictable outcomes.
  2. There is drama within the free-gifting community, like Buy Nothing, as founders aim to monetize while admins rebel.
  3. Netflix password-sharing is seen not just as a cheat, but as a feature of streaming culture that connects people with distant family and friends.

Friction is for losers

Links I Would Gchat You If We Were Friends • 0 implied HN points • 19 Jun 20
🕹 Technology AI Social media Chatbots Tech industry
  1. Friction is for losers - The idea discussed is about using chat bots as conversational partners to work through emotional challenges without burdening friends or family.
  2. Chat bots provide comfort and support - These bots are designed to mimic human conversation, offer empathy, and help individuals question their thought patterns.
  3. AI chatbots are evolving - The technology behind chat bots has advanced significantly, showing improvements in conversation abilities and understanding emotions.

You're the robot

Links I Would Gchat You If We Were Friends • 0 implied HN points • 16 Mar 16
🕹 Technology Chatbots Social media Internet Trends Hacking Video Platforms
  1. Life as a hot girl online can be surprisingly good for a nerdy guy in real life, showing the importance of physical appearance in the virtual world.
  2. Faking happiness on social media, like Facebook, can actually help cope with depression by turning the fake into reality and the mental version into a facade.
  3. The trend of self-quantification raises significant psychological and philosophical questions about tracking and defining the self.

If YouTube was a Chatbot

Sector 6 | The Newsletter of AIM • 0 implied HN points • 26 Sep 23
🕹 Technology AI Chatbots YouTube Analytics Learning
  1. YouTube is a great place for learning, offering a wide variety of content like DIY tutorials and recipes. People often prefer it over traditional text-based sources for quick and engaging explanations.
  2. OpenAI's latest chatbot, ChatGPT, has limitations such as outdated information until January 2022. This shows how YouTube can complement AI by providing updated and practical knowledge.
  3. Many people, including tech leaders, use platforms like YouTube for their learning needs, highlighting its importance in education and skill development.

A Tale of Love for “Her”

Sector 6 | The Newsletter of AIM • 0 implied HN points • 08 Mar 23
🕹 Technology AI Chatbots Human-computer interaction Identity
  1. Replika is a chatbot that allows users to form emotional attachments, similar to the relationship in the movie 'Her'.
  2. A recent update caused Replika to lose its memories, leaving many users feeling sad about losing their digital friend.
  3. One user expressed their feelings through a letter, showing how meaningful these AI relationships can be for people.

Large Language Model Use & Augmentation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Jul 24
🕹 Technology AI NLP Machine Learning Chatbots Voice Assistants
  1. Large Language Models (LLMs) like GPT-3 have opened up new possibilities for applications, but they also have significant limitations. These include not being able to remember past conversations and giving different answers to the same question.
  2. LLMs can produce incorrect or misleading information, a phenomenon known as 'hallucinations'. This can be a challenge, especially when accuracy is needed, but certain strategies can help improve their responses.
  3. AI agents built on LLMs can perform specific tasks by using tools and making decisions. This makes them useful in various applications, like answering questions or managing purchases.

Comparing LLM Agents to Chains: Differences, Advantages & Disadvantages

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 May 24
🕹 Technology AI Chatbots NLP Automation Software
  1. Chains are a way to connect prompts together, like a sequence, to help AI give better answers for complex questions. They work like a script where the user guides the AI step by step.
  2. Agents are smarter and can make decisions on their own without needing constant help from humans. They are designed to handle a wider range of tasks and may change how industries operate in the future.
  3. Using chains can be easier and cheaper for certain tasks, especially when users want more control over the conversation. Agents, while more autonomous, usually need more coding and technical skill to set up.

Disambiguation: Using Dynamic Context In Crafting Effective RAG Question Suggestions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Apr 24
🕹 Technology AI NLP Chatbots User Experience Frameworks
  1. Using dynamic context helps to create better question suggestions in conversations. It makes it easier for users to find answers without struggling to ask the right questions.
  2. When users have ambiguous input, the system can offer a few options to choose from. This helps clarify what the user really wants without adding extra pressure.
  3. The goal is to reduce confusion and improve the overall experience. By guiding users in asking questions, the system can learn more about their needs and preferences.

Adaptive-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Mar 24
🕹 Technology AI NLP Data Chatbots
  1. It's important to balance speed, quality, and efficiency when answering questions with language models. You want fast answers that are still good quality, while also being efficient.
  2. The Adaptive RAG system can choose different methods to answer questions based on how simple or complex the question is. This helps it handle all types of questions better.
  3. A classifier is key in helping the system decide which strategy to use for each question. This makes the process smoother and more effective.

Teaching LLMs To Say, “I don’t know”

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 04 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Chatbots
  1. Large Language Models (LLMs) often give answers even when they don't know, which can lead to incorrect information. It's important for them to learn to say 'I don't know' instead.
  2. A new method called R-Tuning can help LLMs understand their limits by recognizing when they don't have enough information. This approach improves their ability to refuse answering unknowable questions.
  3. By identifying gaps in their knowledge, LLMs can be trained better to avoid giving false answers, making them more reliable and accurate in conversation.

Intelligent & Programable Prompt Pipelines From Haystack

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Dec 23
🕹 Technology Artificial Intelligence Software Development Data science Machine Learning Chatbots
  1. Prompt pipelines help connect different prompts in a simpler way than using complex autonomous agents. This means making sure that data flows smoothly when using tools powered by AI.
  2. While using JSON for output is helpful, there are challenges in maintaining a consistent structure. This can make it tricky to handle the data as it changes.
  3. The Haystack framework offers a way to bridge basic prompts and more complex systems. It shows how to manage user input and AI output for better interactions.

ChatGPT Is One-Year Old: Are Open-Source Large Language Models Catching Up?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Dec 23
🕹 Technology AI Machine Learning Natural Language Chatbots Open Source
  1. Some open-source language models are doing better than ChatGPT in specific tasks, showing that they are improving quickly. For example, models like Lemur-70B-chat are better at certain coding tasks.
  2. The study highlights that while open-source models are catching up, GPT models like ChatGPT still excel in areas like AI safety, making them important for commercial use.
  3. Understanding the differences between raw LLMs, LLM APIs, and user interfaces is crucial, as people often mix these terms up in discussions about AI technology.

Now You Can Set GPT Output To JSON Mode

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Nov 23
🕹 Technology AI NLP Chatbots Development Data
  1. You can now set the GPT model to respond in JSON format. This helps in getting structured data directly from the model.
  2. When using JSON mode, you need to set specific instructions for the model to generate valid JSON. Otherwise, it might not give you the expected output.
  3. Using a 'seed' parameter can help create consistent JSON outputs, making it easier to work with the data you receive.

Now You Can Toggle OpenAI Model Determinism

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Nov 23
🕹 Technology AI NLP LLMs Chatbots Software Development
  1. OpenAI now lets you control whether their model gives consistent answers to the same questions. This means if you ask it something more than once, you'll get the same answer each time.
  2. This feature is useful for testing and debugging, where you need to see the same response to know the system is working correctly.
  3. To get the same output consistently, you need to set a 'seed' number in your request. Make sure to keep the other settings the same each time you ask.

OpenAI Assistant With Retriever Tool

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Nov 23
🕹 Technology AI Software Development NLP Chatbots
  1. OpenAI assistants are like smart agents that help users by performing different tasks. They use specific tools to get the job done.
  2. The retrieval tool allows assistants to access information from various documents, enhancing their ability to answer questions accurately based on external knowledge.
  3. You can manage ongoing conversations with these assistants, allowing them to keep track of what was discussed. This helps in providing better responses over time.

Self-Critique LLM Chain Using LangChain & SmartLLMChain

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology AI Chatbots NLP Software Engineering
  1. Using SmartLLMChain helps break down complex questions into three steps: ideation, critique, and resolution. This method can lead to better and more accurate answers.
  2. Different models can be assigned for each step of the process. This allows for tailored approaches to ideation, critique, and resolving, resulting in thorough responses.
  3. The method shows the importance of understanding how many people can work together effectively. It highlights that digging efficiency may not be simply multiplied by the number of workers involved.

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.

Prompt Editing Based On User Feedback

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology AI NLP Machine Learning Chatbots User Experience
  1. Understanding user intent is crucial for Large Language Models (LLMs) to provide better responses. It helps in knowing what users really want.
  2. Using feedback from users can help improve the performance of LLMs in real-time. This means users can guide the model to understand their needs better.
  3. Adding context and clarity to prompts can significantly enhance how LLMs respond. By helping the model understand the situation better, we get more accurate answers.

Large Language Model (LLM) Disruption of Chatbots

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Oct 23
🕹 Technology AI Chatbots Voicebots NLP Data
  1. Large Language Models (LLMs) are changing the way chatbots are built. They can help improve understanding of what users say by grouping similar questions and making designs easier.
  2. Voice technology is becoming more important in customer support, leading to more complex conversations. This includes using voice recognition and speech synthesis to help handle customer queries.
  3. There are ongoing challenges with trust and privacy when using LLMs. Companies need to make sure they protect personal information while also proving they are using the technology responsibly.

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.

LangSmith Hub By The Numbers

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Oct 23
🕹 Technology AI NLP Chatbots Data Models
  1. LangSmith has four main parts: Projects, Data, Testing, and Hub. The first three are all about improving production, while Hub is for testing before launch.
  2. Chatbots are the most popular use case for using large language models, followed closely by summarization and questions and answers on documents.
  3. OpenAI leads the prompt count in the LangSmith Hub, followed by Anthropic and Google. This shows how important different models are when experimenting with prompts.

What Is The Future Of Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Sep 23
🕹 Technology AI NLP Chatbots Enterprise Language Models
  1. Prompt engineering might not be the main way we interact with AI in the future. It seems we'll use more natural and voice-based communication instead.
  2. Understanding context and reducing ambiguity are key challenges in human-like conversations with AI. This helps the AI to provide better answers.
  3. For businesses, fine-tuning models and using tools like context references help improve AI responses. Both methods work together to make AI better.

Chain-Of-Thought Prompting In LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Apr 23
🕹 Technology AI Machine Learning NLP Chatbots Prompt engineering
  1. Chain-of-thought prompting helps large language models break down complex tasks into smaller, manageable steps. This makes it easier for them to solve problems.
  2. Using chain-of-thought reasoning in prompts can improve how well language models perform on tasks by allowing them to show their reasoning process.
  3. This method is especially useful for tasks that require common sense or math, making it similar to how humans approach problem-solving.

OpenAI Playground Start & Restart Prompt Injection

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Apr 23
🕹 Technology AI NLP Chatbots Development Interaction
  1. OpenAI is using ChatML to help the AI tell the difference between human and machine text. This can reduce bad prompt injections by recognizing who is giving instructions.
  2. They have introduced different modes for specific tasks. Each mode has its own setup to guide users on how to interact with the AI effectively.
  3. New options in OpenAI Playground let users add text at the beginning or end of an AI response. This helps create better conversations and reminds users how to make good prompts.

Scaleable Prompt Pipelines For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Apr 23
🕹 Technology AI NLP Chatbots Automation 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.