cross-posted from: https://kbin.social/m/[email protected]/t/62462

This is the potential development in AI I'm most interested in. So naturally, I tested this when I first used ChatGPT. In classic ChatGPT fashion, when asked to make a directed acyclic graph representing cause and effect, it could interpret that well enough to make a simple graph...but got the cause and effect flow for something as simple as lighting a fire. Haven't tried it again with ChatGPT-4 though.

cross-posted from: https://kbin.social/m/[email protected]/t/68397

I’ve been following the development of the next Stable Diffusion model, and I’ve seen this approach mentioned.

Seems like this is a way in which AI training is analogous to human learning - we learn quite a lot from fiction, games, simulations and apply this to the real world. I’m sure the same pitfalls apply as well.

cross-posted from: https://programming.dev/post/154634

TL;DR (by GPT-4 🤖):

• Use of AI Tools: The author routinely uses GPT-4 to answer casual and vaguely phrased questions, draft complex documents, and provide emotional support. GPT-4 can serve as a compassionate listener, an enthusiastic sounding board, a creative muse, a translator or teacher, or a devil’s advocate.

• Large Language Models (LLM) and Expertise: LLMs can often persuasively mimic correct expert responses in a given knowledge domain, such as research mathematics. However, the responses often consist of nonsense when inspected closely. The author suggests that both humans and AI need to develop skills to analyze this new type of text.

• AI in Mathematical Research: The author believes that the 2023-level AI can already generate suggestive hints and promising leads to a working mathematician and participate actively in the decision-making process. With the integration of tools such as formal proof verifiers, internet search, and symbolic math packages, the author expects that 2026-level AI, when used properly, will be a trustworthy co-author in mathematical research, and in many other fields as well.

• Impact on Human Institutions and Practices: The author raises questions about how existing human institutions and practices will adapt to the rise of AI. For example, how will research journals change their publishing and referencing practices when AI can generate entry-level math papers for graduate students in less than a day? How will our approach to graduate education change? Will we actively encourage and train our students to use these tools?

• Challenges and Future Expectations: The author acknowledges that we are largely unprepared to address these questions. There will be shocking demonstrations of AI-assisted achievement and courageous experiments to incorporate them into our professional structures. But there will also be embarrassing mistakes, controversies, painful disruptions, heated debates, and hasty decisions. The greatest challenge will be transitioning to a new AI-assisted world as safely, wisely, and equitably as possible.

Quick Read: https://www.marktechpost.com/2023/05/21/this-ai-paper-proposes-a-latent-diffusion-model-for-3d-ldm3d-that-generates-both-image-and-depth-map-data-from-a-given-text-prompt/

Paper: https://arxiv.org/abs/2305.10853

Quick Read: https://www.marktechpost.com/2023/05/26/stanford-researchers-introduce-sophia-a-scalable-second-order-optimizer-for-language-model-pre-training/

Paper: https://arxiv.org/abs/2305.14342

Quick Read: https://www.marktechpost.com/2023/06/04/say-goodbye-to-costly-auto-gpt-and-langchain-runs-meet-rewoo-the-game-changing-modular-paradigm-that-cuts-token-consumption-by-detaching-reasoning-from-external-observations/

Paper: https://arxiv.org/abs/2305.18323

Github link: https://github.com/billxbf/ReWOO

Quick Read: https://www.marktechpost.com/2023/06/10/a-new-ai-research-introduces-recognize-anything-model-ram-a-robust-base-model-for-image-tagging/

Paper: https://arxiv.org/abs/2306.03514

Github: https://github.com/xinyu1205/Recognize_Anything-Tag2Text

Project: https://recognize-anything.github.io/

Quick Read: https://www.marktechpost.com/2023/06/14/researchers-from-harvard-introduce-inference-time-intervention-iti-an-ai-technique-that-improves-the-truthfulness-of-language-models-from-32-5-to-65-1/

Paper: https://arxiv.org/abs/2306.03341

Github link: https://github.com/likenneth/honest_llama

In this episode, Some More News looks at the hype surrounding artificial intelligence, the way corporations are desperate to use it to save money, and the theory among some rich people that it's going to destroy civilization.

Been a while since posting here! I've just finished my PhD at Cambridge where I was lucky enough to work on both AI and 3D printing in potentially one of the coolest subject combinations out there (was great fun). Thought the community might be interested in the research and getting involved!

During my research, I focused on developing AI-powered self-aware 3D printers that use vision to detect nearly any type of error (large or small) on any machine using any material. The AI could then predict the causes of the error and either try to fix the errors autonomously on the fly, or fix them for the next print! Correction video: https://youtu.be/OODy-dI52Zg

Excitingly, the AI can even learn how print materials it has never seen before without any human intervention, from TPU and Carbon Fibre filled Nylon to silly things like ketchup. For context we only trained this AI on PLA printed on Creality printers... its crazy that it can then generalise to other setups.

I'm super excited to continue working on this area, and would love the amazing printing community to get involved. I can see some exciting (and obvious) directions in creating printers that can dynamically adapt to unforeseen challenges

leading to no more troubleshooting or slicer profile and material tuning. But maybe there are more interesting applications???

Will keep people posted on updates. Hopefully releasing some code in the coming weeks and will perhaps setup a Discord or something if people are interested in bouncing ideas around and getting involved in a Beta maybe?

For those who are overly interested, some open publications: https://www.nature.com/articles/s41467-022-31985-y

https://www.sciencedirect.com/science/article/pii/S2214860422002378

https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202200153

Code: https://github.com/brjathu/LART

Project Page: http://people.eecs.berkeley.edu/~jathushan/LART

BBF learns with only 2 hours of gameplay, which is the same amount of practice time that human testers can use in the benchmark. Thus, the model-free learning algorithm achieves human learning efficiency and requires significantly less computational power than older methods.

As England prepares to run two pilot studies on Universal Basic Income, the Tories have been talking it down just as much as they can.

But is it that bad? A recent study in Finland would suggest that it’s actually very good for people, no matter what Politics Live panellist Lee Rowley might say.

Link to the tool: https://github.com/CiaraStrawberry/TemporalKit

Link to creator's yt channel: https://www.youtube.com/@tamulur/videos

This is a Skyrim VR mod I am working on which lets you talk to NPCs using ChatGPT, xVASynth, and Whisper (speech-to-text). NPCs have their own tailored prompts based on their unique backgrounds which allows ChatGPT to roleplay as that character. I have a basic memory system set up to allow NPCs to remember past conversations with the player. In-game events such as the time of day and the NPC's location are also passed to ChatGPT to give context.

Here is the full video: https://youtu.be/Gz6mAX41fs0

Link to clip: https://tube.xy-space.de/w/neshsuy5pcEonHL77GEzbQ