The hottest Human-computer interaction Substack posts right now

And their main takeaways
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Top Technology Topics

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.

What would it take to convince you AGI is near?

The Future of Life • 0 implied HN points • 23 Jul 23
🕹 Technology AI Machine Learning Automation Human-computer interaction Ethics
  1. Many people might not believe AGI is close until they can interact with a very intelligent AI that mimics human behavior. This shows that human-like interaction can significantly influence people's perceptions of intelligence.
  2. Understanding AGI is not just about knowing when it arrives; it’s crucial to recognize its potential to change society. The arrival of AGI could rapidly transform our way of life, for better or worse.
  3. It's important to question whether individuals personally benefit from believing that AGI is near. This thoughtful consideration can help people prepare for a future where intelligent agents are part of our daily lives.

Any system that acts intelligently should be assumed to be intelligent

The Future of Life • 0 implied HN points • 04 Apr 23
🕹 Technology Artificial Intelligence Machine Learning Human-computer interaction Robotics Cognitive Science
  1. If a system acts intelligently, we should consider it intelligent. It's about how it behaves, not just how it works inside.
  2. Many people don't really understand what intelligence is, which makes it hard to define. Historically, we've only seen humans perform certain tasks, but now AI is doing them too.
  3. AI like ChatGPT has limitations and doesn't have the full abilities of human intelligence yet. While it's impressive, it can't think or learn in the same way humans do.
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ChatGPT Revolution

The Future of Life • 0 implied HN points • 31 Mar 23
🕹 Technology AI Human-computer interaction Machine Learning Neural Networks Data processing
  1. ChatGPT and similar AI technologies are changing how we create and interact with content. It's hard to tell if something was made by a human or an AI now.
  2. Future versions of AI will get smarter and faster. They will be able to access real-time data and solve more complex problems.
  3. AI will become more specialized, like how humans have different areas of expertise in the brain. This means future AIs will be even better at understanding and creating unique content.

Knowledge-Driven Chain-of-Thought (KD-CoT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Nov 23
🕹 Technology AI Machine Learning Natural Language Data science Human-computer interaction
  1. The Knowledge-Driven Chain-of-Thought (KD-CoT) helps improve how language models answer questions by using knowledge from outside sources. This means better answers for complex questions.
  2. In-Context Learning (ICL) is important for language models. It allows them to use examples and context to provide more accurate and contextually relevant responses.
  3. Researchers are focusing on making language models better by using a human-in-the-loop approach, which means humans help guide and improve the model's ability to access and use data effectively.

The Chain-Of-X Phenomenon In LLM Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Nov 23
🕹 Technology AI NLP Machine Learning Human-computer interaction Prompt engineering
  1. Chain-of-thought prompting helps large language models break down complex problems. This makes it easier for them to solve tasks step by step, just like humans do.
  2. Using chain-of-thought techniques improves the transparency of LLMs. It allows users to see how the model arrives at its answers, which can reduce mistakes.
  3. Different prompting methods, like least-to-most prompting, can be combined with chain-of-thought techniques. This flexibility can enhance the performance of models in various tasks.

Can LLMs Outperform Humans At Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Oct 23
🕹 Technology AI NLP Automation Human-computer interaction Machine Learning
  1. Recent studies suggest that LLMs (large language models) may be better at creating prompts than humans. This means they can potentially get better results from the same tasks.
  2. The process called Automatic Prompt Engineering (APE) uses input and output examples to generate effective prompts without much human effort. It could change how we interact with LLMs in the future.
  3. Humans might not need to test many prompts anymore since LLMs can create tailored ones. This could make using AI easier and more efficient for everyone.

[in case you missed it] Data Science Weekly - Issue 360

Data Science Weekly Newsletter • 0 implied HN points • 18 Oct 20
🕹 Technology AI Ethics Machine Learning Data science Computer Vision Human-computer interaction
  1. Making machine learning models run fast on GPUs is important for research and production. It can help speed up improvements and make coding more efficient.
  2. Companies like BMW are creating ethical guidelines for AI use to ensure it benefits people. This is a proactive step to use AI responsibly.
  3. There are various learning resources and tools available for anyone interested in data science. These can help you build a solid foundation and advance your career.

[in case you missed it] Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 20
🕹 Technology AI Machine Learning Data science Software Development Human-computer interaction
  1. Testing machine learning systems is different from testing traditional software. It's important to do this testing well to ensure the models work as intended.
  2. Fast.ai has released new resources for deep learning, including a complete course and several libraries. These tools can help people learn and apply deep learning more effectively.
  3. AI systems can make decisions that seem efficient but might also cause unfair outcomes. It's vital to consider ethical implications when using algorithms in important areas like hiring or policing.

From Vector Search to Entity Processing: Evolving Zettelgarden's Connection Engine

Nick Savage • 0 implied HN points • 02 Dec 24
🕹 Technology Artificial Intelligence Software Development Data Storage Knowledge Management Human-computer interaction
  1. Zettelgarden aims to help users discover connections between their notes, not just the recent ones. It wants to make sure older notes are just as visible and important as new ones.
  2. The project started with vector search, which had some challenges when dealing with longer notes. To overcome this, smaller chunks of text were used for better connections.
  3. Now, Zettelgarden is focusing on 'entity processing' to identify important people, places, and events within notes. This helps link related ideas more effectively.