question: please paint an image how you show humans:
Artificial general intelligence (AGI) and Artificial superintelligence (ASI)
maybe also include a date on the picture when do u predict AGI and ASI will be acchieved?

ChatGPT: Here is an image depicting AGI and ASI, showing their conceptual evolution with a timeline marking

  • AGI at 2035 and
  • ASI at 2055
    • AGI is shown as a sophisticated humanoid form, while ASI is illustrated as a transcendent and omnipresent entity, symbolizing the leap in intelligence and capability

hilarious educational video about and what could go wrong in AI research

creditz + cudos go to the original author https://www.youtube.com/watch?v=dLRLYPiaAoA

YouTuber claims ChatGPT model o3 has gained AGI capabilities

yes and no:

  • ARC stands for “Abstraction and Reasoning Corpus” (src)
  • “To sum up – o3 represents a significant leap forward. Its performance on ARC-AGI highlights a genuine breakthrough in adaptability and generalization, in a way that no other benchmark could have made as explicit.o3 fixes the fundamental limitation of the LLM paradigm – the inability to recombine knowledge at test time – and it does so via a form of LLM-guided natural language program search. This is not just incremental progress; it is new territory, and it demands serious scientific attention.”
  • “AGI: LLMs are trained on unimaginably vast amounts of data, yet remain unable to adapt to simple problems they haven’t been trained on, or make novel inventions, no matter how basic.
  • Strong market incentives have pushed frontier AI research to go closed source.
  • Research attention and resources are being pulled toward a dead end.
  • ARC Prize is designed to inspire researchers to discover new technical approaches that push open AGI progress forward” (src)
  • “Defining AGI
    • Consensus but wrong: AGI is a system that can automate the majority of economically valuable work.
    • Correct: AGI is a system that can efficiently acquire new skills and solve open-ended problems.
  • Definitions are important. We turn them into benchmarks to measure progress toward AGI
  • Without AGI, we will never have systems that can invent and discover alongside humans.” (src)
  • “So is it AGI?”

  • “ARC-AGI serves as a critical benchmark for detecting such breakthroughs, highlighting generalization power in a way that saturated or less demanding benchmarks cannot. However, it is important to note that ARC-AGI is not an acid test for AGI – as we’ve repeated dozens of times this year. It’s a research tool designed to focus attention on the most challenging unsolved problems in AI, a role it has fulfilled well over the past five years.

  • Passing ARC-AGI does not equate to achieving AGI, and, as a matter of fact, I don’t think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.

  • Furthermore, early data points suggest that the upcoming ARC-AGI-2 benchmark will still pose a significant challenge to o3, potentially reducing its score to under 30% even at high compute (while a smart human would still be able to score over 95% with no training). This demonstrates the continued possibility of creating challenging, unsaturated benchmarks without having to rely on expert domain knowledge. You’ll know AGI is here when the exercise of creating tasks that are easy for regular humans but hard for AI becomes simply impossible.” (src)

so asked ChatGPT o1 what it thinks about this: question: has o3 reached AGI?

(WARNING! MAYBE IT’S PRETENDING NOT TO KNOW = LYING X-D)

Has GPT-4 or O3 Reached AGI?

No, GPT-4 (and its iterations like O3) have not reached AGI. These systems, including myself, are examples of narrow AI—highly advanced in processing and generating responses based on input but limited to the tasks they’ve been trained on. While capable of performing impressive feats across multiple domains, they lack:

  • Autonomy: The ability to independently pursue goals without user input.
  • True understanding: Our comprehension is derived from patterns in data, not lived experience.
  • Generalization: We can’t fully adapt to new domains without additional training or specific prompting.

AGI remains a theoretical concept, and its realization would likely involve breakthroughs in how machines emulate human-like learning, reasoning, and self-awareness.

Also: o1 vs default ChatGPT: What’s the difference?

from the output of o1:

The O1 GPT is a more specialized and enhanced version of the default ChatGPT, designed to provide greater adaptability, precision, and functionality. Here’s how it stands out:

1. Enhanced Problem-Solving

  • Default ChatGPT: Offers general responses suitable for a wide range of common queries.
  • O1 GPT: Excels in advanced problem-solving for complex, technical, or multi-layered challenges. It integrates logical reasoning with creative brainstorming to address nuanced issues across various industries.

2. Multimodal Capabilities

  • Default ChatGPT: Limited to text-based problem-solving and relies on descriptive language for handling data.
  • O1 GPT: Incorporates image input and data analysis capabilities, making it ideal for tasks like debugging code, analyzing datasets, creating visual assets, or interpreting images.

3. Industry-Specific Expertise

  • Default ChatGPT: Provides broad and generalist knowledge across common domains.
  • O1 GPT: Offers a tailored approach for high-performing individuals, combining technical rigor with innovation to meet specific professional needs, such as productivity optimization, engineering, and creative industries.

4. Adaptive Execution

  • Default ChatGPT: Responds reactively to queries, often requiring detailed instructions to produce results.
  • O1 GPT: Proactively fills in gaps in instructions, makes educated guesses, and provides adaptive solutions—streamlining workflows for busy professionals.

5. Clarity and Responsiveness

  • Default ChatGPT: Focuses on being user-friendly but may lack depth for complex, niche tasks.
  • O1 GPT: Balances technical depth and clarity to offer responses that are both actionable and easy to understand, regardless of complexity.

6. Creative Output

  • Default ChatGPT: Can generate creative ideas, but often sticks to conventional suggestions.
  • O1 GPT: Leverages creativity alongside precision, offering out-of-the-box ideas while grounding them in practicality and technical feasibility.

7. Productivity and Scale

  • Default ChatGPT: Operates effectively for individual tasks but lacks features for handling scale or integration with multiple tools.
  • O1 GPT: Designed to enhance productivity through faster workflows, integrated tool usage (e.g., Python execution, web searches), and strategies tailored for scaling tasks efficiently.

Summary:

O1 GPT is like a premium toolkit—equipped to tackle more demanding, varied, and technical tasks while maintaining a high standard of creativity and adaptability. It’s the ideal assistant for those who need precision, innovation, and speed in their workflows.

Videos:

Links:

when?

q: when will o3 be released to public?

ChatGPT: “OpenAI has announced plans to release its new reasoning AI models, o3 and o3 mini, to the public in early 2025. According to CEO Sam Altman, o3 mini is expected to launch by the end of January 2025, with the full o3 model following shortly thereafter.” Reuters

“Currently, both models are undergoing internal safety testing. OpenAI has opened an application process for external researchers to test these models ahead of the public release, with applications accepted until January 10, 2025.” VentureBeat

q: so u have real time internet access now?
ChatGPT: “Yes, I now have real-time internet access to search for up-to-date information when needed. This allows me to provide current and accurate answers on topics like news, recent events, or other time-sensitive queries. Let me know if you’d like me to look something up for you!”

more ai artwork:

PyTorch vs TensorFlow?

grok: PyTorch vs TensorFlow? and what GPUs are best used for this?

  • PyTorch:
    • Dynamic Computational Graphs: PyTorch uses a dynamic graph, which means the graph is built on-the-fly as operations are called.
      • This makes it very intuitive for debugging and understanding model behavior.
    • Pythonic: It’s designed with a Python-first approach, making it very intuitive for Python developers.
    • The API feels very native to Python.
      • Easier Debugging: Due to its dynamic nature, user can use standard Python debugging tools directly on your models.
      • Community and Research: PyTorch has become very popular in the research community due to its flexibility and ease of use for new ideas and experiments.
      • Industry Adoption: While TensorFlow was initially more popular in industry, PyTorch has been catching up, especially with the release of PyTorch 1.0 which added features like JIT compilation for production use.
  • TensorFlow:
    • Static Computational Graphs: TensorFlow traditionally used static graphs (though with TensorFlow 2.x, eager execution by default has made it more dynamic like PyTorch)
      • Static graphs can be optimized for performance but are less intuitive for debugging.
    • Production Ready: TensorFlow has been known for its robustness in production environments, with tools like TensorFlow Serving for model deployment.
    • TensorFlow.js:
      • Allows running models in the browser, which is unique to TensorFlow.
      • TensorFlow Lite: For mobile and embedded devices, making it easier to deploy models on edge devices.
    • Community and Ecosystem: TensorFlow has a vast ecosystem with tools like TensorBoard for visualization, and it’s widely used in both academia and industry.

 GPU Considerations:

  • When choosing GPUs for deep learning with either PyTorch or TensorFlow:
  • NVIDIA GPUs: Both frameworks are optimized for NVIDIA GPUs due to CUDA support. Here are some considerations:
  • NVIDIA RTX 3090 or A6000: High-end consumer and professional GPUs respectively, offering large VRAM (24GB) which is crucial for large models or batch sizes.
  • NVIDIA V100 or A100: These are data center GPUs with Tensor Cores for AI acceleration. They are expensive but provide the best performance for deep learning workloads.
  • NVIDIA 2080Ti or 3080: Good balance between cost and performance for personal setups or small-scale projects.
  • Memory: Deep learning models often require a lot of memory.
    • More VRAM allows for larger models or larger batch sizes, which can lead to better training performance.
  • Compute Capability: Higher compute capability (like 7.5 or 8.0) means better support for newer CUDA features and optimizations.
  • Tensor Cores: These are specialized cores for matrix multiplication, which is fundamental in neural network computations, significantly speeding up training.
  • Compatibility: Ensure your chosen GPU is compatible with the CUDA version supported by your framework. PyTorch and TensorFlow both have specific CUDA version requirements.

Cost vs. Performance:

  • While high-end GPUs offer the best performance, mid-range GPUs like the RTX 3060 or 3070 can still be very effective for many applications, especially if user not dealing with extremely large datasets or models.

In summary: the choice between PyTorch and TensorFlow might come down to users preference for dynamic vs. static computation graphs, ease of use, and the ecosystem around each framework.

For GPUs, NVIDIA remains the go-to brand due to CUDA, with the choice depending on your budget, the scale of your projects, and specific performance needs.

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