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AI (Artificial Intelligence)

Machines simulating human characteristics and intelligence.
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Justin Searls blog.testdouble.com

How to tell if AI threatens YOUR job (and 3 simple rules to keep it)

Justin Searls dives deep into whether AI tools like ChatGPT actually threaten knowledge worker jobs and provides helpful ideas around what to do about it.

Having spent months programming with GitHub Copilot, weeks talking to ChatGPT, and days searching via Bing Chat as an alternative to Google, the best description I’ve heard of AI’s capabilities is “fluent bullshit.” And after months of seeing friends “cheat” at their day jobs by having ChatGPT do their homework for them, I’ve come to a pretty grim, if obvious, realization:

The more excited someone is by the prospect of AI making their job easier, the more they should be worried.

Practical AI Practical AI #214

End-to-end cloud compute for AI/ML

We’ve all experienced pain moving from local development, to testing, and then on to production. This cycle can be long and tedious, especially as AI models and datasets are integrated. Modal is trying to make this loop of development as seamless as possible for AI practitioners, and their platform is pretty incredible!

Erik from Modal joins us in this episode to help us understand how we can run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without our own infrastructure.

Practical AI Practical AI #213

Success (and failure) in prompting

With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs.

Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips.

Practical AI Practical AI #212

Applied NLP solutions & AI education

We’re super excited to welcome Jay Alammar to the show. Jay is a well-known AI educator, applied NLP practitioner at co:here, and author of the popular blog, “The Illustrated Transformer.” In this episode, he shares his ideas on creating applied NLP solutions, working with large language models, and creating educational resources for state-of-the-art AI.

Practical AI Practical AI #209

3D assets & simulation at NVIDIA

What’s the current reality and practical implications of using 3D environments for simulation and synthetic data creation? In this episode, we cut right through the hype of the Metaverse, Multiverse, Omniverse, and all the “verses” to understand how 3D assets and tooling are actually helping AI developers develop industrial robots, autonomous vehicles, and more. Beau Perschall is at the center of these innovations in his work with NVIDIA, and there is no one better to help us explore the topic!

Python github.com

ImaginAIry imagines & edits images from text inputs

This is a Pythonic wrapper around stable diffusion with image editing by InstructPix2Pix. The four images featured below (top) are generated by the following command:

imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"

Then they are edited (bottom) with the following commands:

>> aimg edit scenic_landscape.jpg "make it winter" --prompt-strength 20
>> aimg edit dog.jpg "make the dog red" --prompt-strength 5
>> aimg edit bowl_of_fruit.jpg "replace the fruit with strawberries"
>> aimg edit freckled_woman.jpg "make her a cyborg" --prompt-strength 13
ImaginAIry imagines & edits images from text inputs

Python github.com

A library for building apps with LLMs through composability

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications.

LangChain is designed to help with prompts, chains (sequences of calls), data augmented generation, agents, memory & evaluation tasks.

Practical AI Practical AI #207

Machine learning at small organizations

Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.

AI (Artificial Intelligence) watcher.guru

Microsoft wants to acquire a 49% stake in ChatGPT

This escalated quickly. I don’t know about you, but I’m a daily user of ChatGPT. Just yesterday, I asked “What options does Linux offer for fast RAID 0 software RAID?” and I had an entire conversation that settled on Btrfs as a good option and I learned how to create and configure the array, mount it, and most importantly scrub it for errors. I’ll still use ZFS, of course. But, I’ve never had that experience using Google (nor can you).

…according to a report by Semafor, Microsoft Corp is discussing the possibility of acquiring OpenAI, the parent company of ChatGPT. The tech-industry giant is ready to pay upwards of $10 billion for the acquisition.

Clearly, Microsoft sees the bigger picture here for Bing, Microsoft 365, GitHub Copilot, and more. This also speaks to the conversation we had with Swyx about AI’s future being tied to capitalism and eventually being controlled by the FAANGs.

Practical AI Practical AI #205

NLP research by & for local communities

While at EMNLP 2022, Daniel got a chance to sit down with an amazing group of researchers creating NLP technology that actually works for their local language communities. Just Zwennicker (Universiteit van Amsterdam) discusses his work on a machine translation system for Sranan Tongo, a creole language that is spoken in Suriname. Andiswa Bukula (SADiLaR), Rooweither Mabuya (SADiLaR), and Bonaventure Dossou (Lanfrica, Mila) discuss their work with Masakhane to strengthen and spur NLP research in African languages, for Africans, by Africans.

The group emphasized the need for more linguistically diverse NLP systems that work in scenarios of data scarcity, non-Latin scripts, rich morphology, etc. You don’t want to miss this one!

Changelog Interviews Changelog Interviews #519

GPT has entered the chat

To wrap up the year we’re talking about what’s breaking the internet, again. Yes, we’re talking about ChatGPT and we’re joined by our good friend Shawn “swyx” Wang. Between his writings on L-Space Diaries and his AI notes repo on GitHub, we had a lot to cover around the world of AI and what might be coming in 2023.

Also, we have one more show coming out before the end of the year — our 5th annual “State of the log” episode where Adam and Jerod look back at the year and talk through their favorite episodes of the year and feature voices from the community. So, stay tuned for that next week.

Ars Technica Icon Ars Technica

Stability AI plans to let artists opt out of Stable Diffusion 3 image training

On Wednesday, Stability AI announced it would allow artists to remove their work from the training dataset for an upcoming Stable Diffusion 3.0 release. The move comes as an artist advocacy group called Spawning tweeted that Stability AI would honor opt-out requests collected on its Have I Been Trained website. The details of how the plan will be implemented remain incomplete and unclear, however.

This seems like a step in the right direction, but it appears that artists will have to proactively register and manually flag matched images in the database. Ain’t nobody got time for that!

History dynomight.net

Historical analogies for large language models

How will large language models (LLMs) change the world?

No one knows. With such uncertainty, a good exercise is to look for historical analogies—to think about other technologies and ask what would happen if LLMs played out the same way.

I like to keep things concrete, so I’ll discuss the impact of LLMs on writing. But most of this would also apply to the impact of LLMs on other fields, as well as other AI technologies like AI art/music/video/code.

What follows are 13 examples of technological innovations that changed the world and description of how they affected they way people work. Here’s an example analogy of Feet and Segways:

First, there was walking. Then the Segway came to CHANGE THE NATURE OF HUMAN TRANSPORT. Twenty years later, there is still walking, plus occasionally low-key alternatives like electric scooters.

In this analogy, LLMs work fine but just aren’t worth the trouble in most cases and society doesn’t evolve to integrate them. Domain-specific LLMs are used for some applications, but we start to associate “general” LLMs with tourists and mall cops. George W. Bush falls off an LLM on vacation and everyone loses their minds.

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