What a year it's been for AI.
Anticipating what's next sounds very perilous, but I'll give it a try.
Here are eight AI predictions for 2024🔮
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1) AI smart glasses become a thing 😎
As multimodality rises, leading AI companies will double down on AI-first wearable devices
And what’s better than the glasses form factor to host an AI-assistant ?
Branches are close to the ears to deliver audio, cameras are close to the eyes to capture ego-centric input, plus they’re hands-free and comfortable
Meta is leading the way with Ray Ban, but think about the recent Open AI x Snapchat rumor 👀
We’re only getting started...
2) ChatGPT won't be to AI assistant what Google is to search
2023 started with ChatGPT taking all the light and ends with Bard, Claude, Llama, Mistral and thousands of derivatives
As commoditization continues, ChatGPT will fade as THE reference ➡️ valuation correction
3) So long LLMs, hello LMMs
Large Multimodal Models (LMMs) will keep emerging and oust LLMs in the debate; multimodal evaluation, multimodal safety, multimodal this, multimodal that
Plus, LMMs are a stepping stone towards truly general AI-assistant
4) No significant breakthrough, but improvements on all fronts
New models won't bring real breakthrough (👋GPT5) and LLMs will remain intrinsically limited and prone to hallucinations. We won’t see any leap making them reliable enough to "solve basic AGI" in 2024
Yet, iterative improvements will make them “good enough” for various tasks.
Improvements in RAG, data curation, better fine-tuning, quantization, etc, will make LLMs robust/useful enough for many use-cases, driving adoption in various services across industries
5) Small is beautiful
Small Language Models (SLMs) are already a thing, but cost-efficiency and sustainability considerations will accelerate this trend
Quantization will also greatly improve, driving a major wave of on-device integration for consumer services
6) An open model beats GPT-4, yet the open vs closed debate progressively fades
Looking back at the dynamism and progress made by the open source community over the past 12 months, it’s obvious that open models will soon close the performance gap.
We’re ending 2023 with only 13% left between Mixtral and GPT-4 on MMLU
But most importantly, open models are here to stay and drive progress, everybody realised that. They will coexist with proprietary ones, no matter what OS detractors do.
7) Benchmarking remains a conundrum
No set of benchmarks, leaderboard or evaluation tools emerge as THE one-stop-shop for model evaluation.
Instead, we’ll see a flurry of improvements (like HELM recently) and new initiatives (like GAIA), especially on multimodality.
8) Existential-risks won't be much discussed compared to existing risks
While X-risks made the headlines in 2023, the public debate will focus much more on present risks and controversies related to bias, fake news, users safety, elections integrity, etc.
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