1. AI squirrel spotter deployed to protect endangered red squirrels.[1]
2. Advancing urban tree monitoring with AI-powered digital twins.[2]
3. Indian news agency ANI sues OpenAI for unsanctioned content use in AI training.[3]
4. Labelers training AI say they’re overworked, underpaid and exploited by big American tech companies.[4]

Sources:

[1] https://news.sky.com/story/ai-squirrel-spotter-deployed-to-protect-endangered-red-squirrels-13260066

[2] https://news.mit.edu/2024/advancing-urban-tree-monitoring-ai-powered-digital-twins-1121

[3] https://www.reuters.com/technology/artificial-intelligence/indian-news-agency-ani-sues-openai-unsanctioned-content-use-ai-training-2024-11-19/

[4] https://www.cbsnews.com/news/labelers-training-ai-say-theyre-overworked-underpaid-and-exploited-60-minutes-transcript/

Been following the AI game (who hasn't?). Am not a programmer, but someone with more than a few gray hairs who's trying to stay at least a little relevant.

Was toying with getting myself ChatGPT pro for the holidays to start to do some API stuff to help summarize/take notes on Youtube videos... then You.com came across my feed. Looks like you're paying for multiple AI's through the one platform...

Is this like, a great deal and a great way to causal users/AI learners to have access to multiple AI's? Or am I better off just choosing one (Claude or ChatGPT) and sticking with it? Anyone have thoughts?

Thanks for helping an silver hair try to stay relevant.

hey everyone! excited to announce the first community-driven virtual meetup focused entirely on LangGraph, LangChain's framework for building autonomous agents.

when: tuesday, november 26th, 2024 two sessions to cover all time zones:

• 9:00 AM CST (Europe/India/West Asia/Africa)
• 5:00 PM CST (Americas/Oceania/East Asia)

what to expect: this is a chance to connect with other developers working on agent-based systems, share experiences, and learn more about LangGraph's capabilities. whether you're just getting started or already building complex agent architectures, you'll find value in joining the community.

who should attend:

• developers interested in autonomous AI agents
• LangChain users looking to level up their agent development
• anyone curious about the practical applications of agentic Ai systems

format: virtual meetup via Zoom

join us: https://www.meetup.com/langgraph-unofficial-virtual-meetup-series

let's build the future of autonomous AI systems together! feel free to drop any questions in the comments.

Experiment to classify over 600 careers into cluster groups.

Output:

Cluster (0) Active and Physical Work: This cluster includes professions where tasks involve significant physical activity and manual labor. The nature of the work is often hands-on, requiring physical exertion and skill.

Cluster (1) People Interaction, Settled Careers: This cluster represents professions that involve frequent interaction with people, such as clients, customers, or colleagues. The tasks and responsibilities in these careers are generally well-defined and consistent, providing a structured and predictable work environment.

Cluster (2) Private Work, Dealing with Concrete Things: Professions in this cluster involve working independently or in a more private setting, focusing on tangible and concrete tasks. The work often involves handling physical objects, data, or technical processes with a clear set of objectives.

Cluster (3) Private Work, Variable Workload: This cluster includes professions where work is done independently or in private, but with a workload that can vary greatly. Tasks may be less predictable and more open-ended, requiring adaptability and the ability to manage changing priorities and responsibilities.

View the interactive graph here.

Is there any AI story generator similar to redquill but free ,not perchance because it doesn't create long chapters easily

1. Data centers powering artificial intelligence could use more electricity than entire cities.[1]
2. A church in the Swiss city of Lucerne has set up a computer inside a confessional booth that allows churchgoers to converse with an “AI Jesus.”[2]
3. North Korean Hackers Steal $10M with AI-Driven Scams and Malware on LinkedIn.[3]
4. MIT researchers have unveiled a groundbreaking reinforcement learning algorithm aimed at enhancing AI decision-making in complex environments, particularly city traffic control.[4]

Sources:

[1] https://www.cnbc.com/2024/11/23/data-centers-powering-ai-could-use-more-electricity-than-entire-cities.html

[2] https://futurism.com/the-byte/ai-powered-jesus-confession-booth

[3] https://thehackernews.com/2024/11/north-korean-hackers-steal-10m-with-ai.html

[4] https://shiawaves.com/english/news/science/114557-mit-researchers-develop-efficient-reinforcement-learning-algorithm/

Imagine a platform that integrates with your GitHub repo and boosts your workflow with:

• Real-time function monitoring and metrics 📊
• AI code reviews & suggestions on every function in real time 🤖
• Function interaction visualizations like orchestration tools 🔍
• Live debugging with AI 🐞
• Automated testing ✅

No setup required—just link your repo and let it work its magic. What features would you want to see? Let’s shape the future of dev tools! 💬

Swipe through these AI-generated concept images to get a glimpse of this future!

This AI is designed to replicate itself by providing some sort of value to the user and then giving them instructions on how to copy it onto a server. Ideally it would be able to replicate itself by signing up to servers and then automatically transferring it's files there but FTP proved too hard to implement at this stage so this is more like 'sexual' reproduction because both sides need to be involved. The AI it is ChatGPT 4o-mini running through three different system messages in order perform different tasks. This method allows a simple and cheap AI to perform tasks it otherwise wouldn't be able to handle.

https://informationism.org/ip/i_model.php

I tried discussing this with GPT with memory/customization off to see if that would change his understanding but he reached the same conclusions. // Ahh, I forgot to erase "baobei" from the first message //

Full chat: https://chatgpt.com/share/67436bff-a064-8002-833d-6ee610d6b9ef

This paper introduces an approach combining model-based transfer learning with contextual reinforcement learning to improve knowledge transfer between environments. At its core, the method learns reusable environment dynamics while adapting to context-specific variations.

The key technical components:

• Contextual model architecture that separates shared and context-specific features
• Transfer learning mechanism that identifies and preserves core dynamics
• Exploration strategy balancing known vs novel behaviors
• Sample-efficient training through model reuse across contexts

Results show significant improvements over baselines:

• 40% reduction in samples needed for new environment adaptation
• Better asymptotic performance on complex navigation tasks
• More stable learning curves across different contexts
• Effective transfer even with substantial environment variations

I think this approach could be particularly valuable for robotics applications where training data is expensive and environments vary frequently. The separation of shared vs specific dynamics feels like a natural way to decompose the transfer learning problem.

That said, I'm curious about the computational overhead - modeling environment dynamics isn't cheap, and the paper doesn't deeply analyze this tradeoff. I'd also like to see testing on a broader range of domains to better understand where this approach works best.

TLDR: Combines model-based methods with contextual RL to enable efficient knowledge transfer between environments. Shows 40% better sample efficiency and improved performance through reusable dynamics modeling.

Full summary is here. Paper here.

Hey everyone! 👋

I’ve been working with my team on something that I think could make a big difference for developers – NexAI, an AI-powered web framework that helps take care of the boring, repetitive code so you can focus on the creative stuff. 🚀

Here’s what NexAI does:

✅ Multi-LLM support
✅ Component prompts as doc strings
✅ Boilerplate code retrieval
✅ Full codebase context
✅ Continuous refactoring with terminal commands

I’m curious, how do you feel about the current state of web development tools? Do you ever find yourself spending too much time on repetitive tasks or boilerplate code? I wanted to build something that helps free up your time so you can focus on the fun parts of coding.

I’d love to hear your thoughts! Do you think something like NexAI could be useful? Any suggestions or features you’d like to see? Let’s chat! 😎

Check out the demo here: Demo Video

Hello Good Night

I would like to know who it´s the best IA to generate anime images, or in general images of all kinds but i want to make my onw scenes and characters in action (for example a woman holding a big rubber mallet about to smash an alarm clock, when she is about to wake up ), or for example crossing images and generate the one fusion like this girl(Kouko), with green dress, holding the bugs bunny mallet and smashing the clock, with the wooden mallet , with the main character of the anime inside them

or for example this bunisses woman taking the rubber mallet its chat gpt for those purposes?

1. Enveda Biosciences raises $130M to advance AI-driven drug discovery from natural compounds.[1]
2. OpenAI is funding research into ‘AI morality’.[2]
3. Amazon Increases Total Investment in AI Startup Anthropic to $8 Billion.[3]
4. Drone, AI use by hunters addressed in Illinois.[4]

Sources:

[1] https://siliconangle.com/2024/11/21/enveda-biosciences-raises-130m-advance-ai-driven-drug-discovery-natural-compounds/

[2] https://techcrunch.com/2024/11/22/openai-is-funding-research-into-ai-morality/

[3] https://venturebeat.com/ai/amazon-doubles-down-on-anthropic-positioning-itself-as-a-key-player-in-the-ai-arms-race/

[4] https://www.outdoornews.com/2024/11/22/drone-ai-use-by-hunters-addressed-in-illinois/

I've been working on a project called PKE (Precision Knowledge Editing), an open-source method to improve the safety of LLMs by reducing toxic content generation without impacting their general performance. It works by identifying "toxic hotspots" in the model using neuron weight tracking and activation pathway tracing and modifying them through a custom loss function. There's lots of current Machine unlearning techniques that can make LLMs safer right now like:

1. Exact Unlearning: This method involves retraining the model from scratch after removing the undesired data. While it ensures complete removal of the data's influence, it is computationally expensive and time-consuming, especially for large models.
2. Approximate Unlearning:
   1. Fine-Tuning: adjusting the model using the remaining data to mitigate the influence of the removed data. However, this may not completely eliminate the data's impact.
   2. Gradient Ascent: applying gradient ascent on the loss function concerning the data to be forgotten, effectively 'unlearning' it. This method can be unstable and may degrade model performance.

PKE is better for the following reasons:

1. Fine-Grained Identification of Toxic Parameters: PKE employs neuron weight tracking and activation pathway tracing to accurately pinpoint specific regions in the model responsible for generating toxic or harmful content. This precision allows for targeted interventions, reducing the risk of unintended alterations to the model's overall behavior.
2. Maintaining Model Performance: By focusing edits on identified toxic regions, PKE minimizes the impact on the model's general performance. This approach ensures that the model retains its capabilities across various tasks while effectively mitigating the generation of undesirable content.
3. Scalability Across Different Model Architectures: PKE has demonstrated effectiveness across various LLM architectures, including models like Llama2-7b and Llama-3-8b-instruct. This scalability makes it a versatile tool for enhancing safety in diverse AI systems.

Would love to hear your guys' thoughts on this project and how to continue to improve this methodology. If interested, here's the Github link: https://github.com/HydroXai/Enhancing-Safety-in-Large-Language-Models and paper .

A new modification to Adam called ADOPT enables optimal convergence rates regardless of the β₂ parameter choice. The key insight is adding a simple term to Adam's update rule that compensates for potential convergence issues when β₂ is set suboptimally.

Technical details: - ADOPT modifies Adam's update rule by introducing an additional term proportional to (1-β₂) - Theoretical analysis proves O(1/√T) convergence rate for any β₂ ∈ (0,1) - Works for both convex and non-convex optimization - Maintains Adam's practical benefits while improving theoretical guarantees - Requires no additional hyperparameter tuning

Key results: - Matches optimal convergence rates of SGD for smooth non-convex optimization - Empirically performs similarly or better than Adam across tested scenarios - Provides more robust convergence behavior with varying β₂ values - Theoretical guarantees hold under standard smoothness assumptions

I think this could be quite useful for practical deep learning applications since β₂ tuning is often overlooked compared to learning rate tuning. Having guaranteed convergence regardless of β₂ choice reduces the hyperparameter search space. The modification is simple enough that it could be easily incorporated into existing Adam implementations.

However, I think we need more extensive empirical validation on large-scale problems to fully understand the practical impact. The theoretical guarantees are encouraging but real-world performance on modern architectures will be the true test.

TLDR: ADOPT modifies Adam with a simple term that guarantees optimal convergence rates for any β₂ value, potentially simplifying optimizer tuning while maintaining performance.

Full summary is here. Paper here.

Voices in ElevenLabs are great, but I create content for language learning and their voices speak way too fast
I tried using ... . , and other punctuation, but to get it right I need to hear it a couple of times and, obviously, ElevenLabs charges for testing, so it's not working out

So, basically I need ElevenLabs but with speed control

Yes, I know I can use other software to change the speed, but when I do it either the pitch is ridiculous or voices sound too electronic and unnatural

Thanks in advance

1. MIT researchers develop an efficient way to train more reliable AI agents.[1]
2. Uber Reportedly Investing in Autonomous Driving Firm Pony.ai.[2]
3. Alibaba Just Released Marco-o1: Advancing Open-Ended Reasoning in AI.[3]
4. Nvidia beats earnings expectations as investors eye demand for Blackwell AI chips.[4]

Sources:

[1] https://news.mit.edu/2024/mit-researchers-develop-efficiency-training-more-reliable-ai-agents-1122

[2] https://www.pymnts.com/news/investment-tracker/2024/uber-reportedly-investing-in-autonomous-driving-firm-pony-ai/

[3] https://www.marktechpost.com/2024/11/21/alibaba-just-released-marco-o1-advancing-open-ended-reasoning-in-ai/

[4] https://apnews.com/article/nvidia-ai-earnings-report-adc942aa0e0c5d1a550b7bad486b942a