The state of AI and what's next
PLUS: Jailbreaks for GPT4-V, 3D objects in 15 min, FaceSwap, SVG icon generator and more
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The state of AI and what’s next
GPT-4 still has the throne over all other LLMs
Most models are closed systems for security reasons (GPT4, Claude, PalM2), but MetaAI and a few others keep the open-source flame burning
Open-source is more vibrant than ever: in the last 30 days Llama models have been downloaded more than 32M times on Hugging Face
“Subjectivity” as a benchmark: Although there are many benchmarks (mostly academic) to evaluate these models, users often prefer to listen or follow their *own* subjective reasoning for their use-case
GPU demand skyrocketed: NVIDIA went right into the $1T market cap club and their chips are used 19x more in AI research than all the alternatives combined.
Big Tech led the investments in AI, and not VC firms: Microsoft invested in OpenAI, AWS in Anthropic, Google in Inflection.. etc.
ChatGPT is the fastest growing product on the planet. But latest research suggest that retention is very low for AI products
What’s next — 2024 predictions
Their predictions are accurate, and some are already coming true. We may include more predictions in future newsletters around multimodality and speech.
Hollywood will incorporate GenAI for visual effects
AI media company will be investigated for misuse in the 2024 elections
AI agents will crush SOTA in a complex enviroment (e.g. AAA game)
One major IPO listing for an AI company (e.g. Databricks)
$1 billion+ spent to scale large language models
Competition investigations around Microsoft/OpenAI deal
Limited progress on global governance
Banks will launch GPU debt funds to replace VC funding
AI generated song breaks into the Billboard Hot 100 Top 10
Large AI company (e.g. OpenAI) acquires an AI chip company (this might happen very soon!)
🚨 What else is going on
AI image detectors are used to discredit war images. But, these tools are not a good way to determine if an image is real or AI generated. They're not very explainable — they're black boxes that just come up with an answer from a limited training data, and can make a lot of mistakes on low res or new images.
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