AI-bits.org

• GPT 4omni — multi-modal generative language model and twin offerings from Microsoft; competitive offerings by Google (Gemini) and Anthropic (Claude)
  Text-to-Speech that understands (almost) everything (even with little context)
  image and video generators that turn every scribbler into an artist
  "Data Analyst" for breathtaking analyses
  programming assistant
  Text-to-speech with emotion — undistinguishable from humans

  Many versions and features have been free for the last year, seemingly to take advantage of the moment to win the broadest possible audience.

  Head-to-head-race between OpenAI, Google, Anthropic and xAI and challenged by open source models from DeepSeek, Qwen and half a dozen others.

  (Google) DeepMind should be credited for releasing models that play in entirely different leagues and often significantly outperform competitors (whether human or conventional programming).
  (AlphaFold protein folding and more, weather models with orders of magnitude better efficiency,…)

• Even though clickbait headlines suggest otherwise: Open Source running locally is usually second best

  For a local model to be similarly good, it currently needs to be well beyond 13b parameters. Then it is relatively slow on normal hardware.

• But already the improved idea: Continue Plugin for VS Code and Ollama environment for a model combo (Codestral 22b Chat and "fill in the middle", Llama 3 7b v 70b and Starcoder 2)

• If the code is not a company secret, one indulges in ChatGPT 4o for free and/or risks a $10 for GitHub Copilot

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• Google NotebookLM

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• RAG via GUI or framework (e.g., LlamaIndex)

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Simple minds postulated "prompt engineering" as the next big job — as if it wasn't obvious that LLMs were destined to do a much better job in that area than humans.

• Prompt generation support in OpenAI chatGPT playground

  The Generate button in the Playground lets you generate prompts, functions, and schemas from just a description of your task.

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• Digital twins (of robots, factories, and eventually the earth)

  for planning and/or maintenance (comparison of reality v model in real-time)

• Robots (various providers) trained by the dozens in extreme time-lapse with Nvidia Isaac Sim and commanded in natural language

• BMW is a long-known Nvidia showcase example for the digital factory twin

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• AI was supposed to learn everything from examples alone and failed miserably at basic arithmetic.

  Students can use calculators. AI could not. Exams are often "open book". AI had no Internet access. The situation has changed.

• AI agents (very trendy!) for weather forecasts with the information clarity / ambiguity of icons for "variable" and an accuracy achievable with weather in April

  The same example questions have been asked of Siri, Google, and Alexa for years, such as:
  What's the weather (going to be¹⁾? (¹⁾ English is a crazy language!)
  The only good answer came from Amazon years ago with a TV spot featuring a hidden joke, where the attentive observer wonders why the actor doesn't look out the window.
  Ahh, he's blind! And now the question asked actually makes sense.

• AI — at university level in many areas — often misused for everyday platitudes or sheer nonsense, featured in dozens of examples on chatbot homepages

  "Explain the concept of nostalgia to a preschooler"

• Logic puzzles linguistically maximally convoluted

  Simplified: Three murderers. One more murderer enters and kills a murderer. How many murderers are there?
  Three? Four? (Does a dead murderer count as a murderer?)

  Simplified: Mary travelled to the kitchen. Sandra journeyed to the kitchen. Mary went back to the garden. Where is Mary?
  Llama3-8B-1.58-100B-tokens gets it wrong. ChatGPT 4o knows that "travelled" and "journeyed to the kitchen" are bad choices.
  Double obfuscation for the sake of what?