In a recent interview, Jeff Dean, a leading researcher at Google, shared some insightful views that are relevant to both AI development and
environmental concerns. Firstly, he criticized the idea that just
scaling up AI models and datasets will lead to major breakthroughs in
AI. Instead, he suggests that a qualitative leap is necessary, the
nature of which is still unknown.

Regarding environmental concerns related to AI, Jeff Dean addressed the criticism about Google's increasing carbon footprint due to AI research. He clarified that:

- The transition to renewable energy sources is inherently incremental: new large-scale energy capacities must be built and integrated, and while energy consumption is steadily increasing, the carbon footprint can initially rise but is expected to drop sharply once new energy solutions are operational.

- AI's energy consumption is a small fraction of the total energy usage by tech giants, which is dominated by traditional services for billions of users and cloud services for businesses globally. The challenge, therefore, is not to make AI specifically "clean" in terms of energy but to improve the energy efficiency of data centers at large.
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Elon Musk tweeted that they started training on the newly built supercluster X.AI in Memphis, Tennessee.

This data center is equipped with 100,000 H100 GPUs, which is a substantial number compared to META's recently launched clusters of 24,576 GPUs each, and GPT-4 was rumored to have been trained on 25,000 cards.
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رویداد رونمایی از ابزار هوش مصنوعی دادماتولز به صورت اوپن سورس

در این رویداد ابزار NLP دادماتولز به صورت رسمی آزادرسانی شده و برنامه‌های توسعه جمعی با مشارکت دانشگاه و بخش خصوصی و حمایت‌های دولتی مطرح می‌شود.



⏳زمان:
دوشنبه ۱۵ مرداد ساعت ۱۰ الی ۱۲

📍مکان:
صندوق نوآوری و شکوفایی، سالن آمفی تئاتر

📎لینک ثبت نام:
https://evand.com/events/dadmatools


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Meta AI released a new version of the Llama model today

The new version - 3.1 comes with a new model of 405B parameters and an upgrade of the previous versions - the 70B and 8B models. According to the release notes, the new model capabilities are similar to the GPT-4, GPT-4o, and Claude 3.5 Sonnet models.

Release notes: https://ai.meta.com/blog/meta-llama-3-1/

Model on Hugging Face: https://huggingface.co/meta-llama/Meta-Llama-3.1-405B


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Instant Intelligence is wild with Llama 3.1 8B + Groq
Source


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Elon Musk says xAI's new supercomputer in Memphis was installed in just 19 days and will be used to train Grok 3, which is expected by December, and will be the most powerful AI in the world
Source

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Huge announcement from Meta. Welcome Llama 3.1!

This is all you need to know about it:

The new models:
- The Meta Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction-tuned generative models in 8B, 70B, and 405B sizes (text in/text out).

- All models support long context length (128k) and are optimized for inference with support for grouped query attention (GQA).

- Optimized for multilingual dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.

- Llama 3.1 is an auto-regressive language model with an optimized transformer architecture, using SFT and RLHF for alignment. Its core LLM architecture is the same dense structure as Llama 3 for text input and output.

- Tool use, Llama 3.1 Instruct Model (Text) is fine-tuned for tool use, enabling it to generate tool calls for search, image generation, code execution, and mathematical reasoning, and also supports zero-shot tool use.


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Google’s new weather prediction system combines AI with traditional physics

Weather and climate experts are divided on whether AI or more traditional methods are most effective. In this new model, Google’s researchers bet on both.
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Google DeepMind’s new AI systems can now solve complex math problems

AlphaProof and AlphaGeometry 2 are steps toward building systems that can reason, which could unlock exciting new capabilities.
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ChatGPT Advanced Voice Mode 😱😱😱

Counting as fast as it can to 10, then to 50 (this blew my mind - it stopped to catch its breath like a human would)
Source


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How machines that can solve complex math problems might usher in more powerful AI

Google DeepMind’s AlphaProof and AlphaGeometry 2 are milestones for AI reasoning.
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Intel has worst day on Wall Street in 50 years, falls to lowest price in over a decade

Intel shares had their biggest drop since 1974 on Friday after the chip-maker reported a big miss on earnings in the June quarter and said it would lay off more than 15% of its employees.

The stock is trading at its lowest since 2013.

Asian names including Samsung and TSMC closed lower, with European chip firms such as ASML also dropping.

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AI companies promised to self-regulate one year ago. What’s changed?

The White House’s voluntary AI commitments have brought better red-teaming practices and watermarks, but no meaningful transparency or accountability.
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When allocating scarce resources with AI, randomization can improve fairness

*The use of machine-learning models to allocate scarce resources or opportunities can be improved by introducing randomization into the decision-making process.*
*Researchers from MIT and Northeastern University argue that traditional fairness methods, such as adjusting features or calibrating scores, are insufficient to address structural injustices and inherent uncertainties.*
*The introduction of randomization can prevent one deserving person or group from always being denied a scarce resource, and can be especially beneficial in situations involving uncertainty or repeated negative decisions.*
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Painter Van Gogh's work was animated with artificial intelligence.

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MIT researchers advance automated interpretability in AI models

Understanding AI Systems
Artificial intelligence models are becoming increasingly prevalent, and understanding how they work is crucial for auditing and improving their performance. MIT researchers developed MAIA, a system that automates the interpretation of artificial vision models. MAIA can label individual components, identify biases, and even design experiments to test hypotheses.
MAIA in Action
MAIA demonstrates its ability to tackle three key tasks, including labeling individual components, cleaning up image classifiers, and hunting for hidden biases. For example, MAIA was asked to describe the concepts that a particular neuron inside a vision model is responsible for detecting. MAIA uses tools to design experiments and test hypotheses, providing a comprehensive answer.
Limitations and Future Directions
While MAIA is a significant step forward in interpretability, it has limitations. For instance, its performance is limited by the quality of the tools it uses and can sometimes display confirmation bias. Future directions include scaling up the method to apply it to human perception and developing tools to overcome its limitations.
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As artificial intelligence models become increasingly prevalent, and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Imagine if we could directly investigate the human brain by manipulating each of its individual neurons to examine their roles in perceiving a particular object.
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