The State of AI in 2023: Key Developments and What’s Next
The annual?State of AI Report?offers an in-depth look at the latest AI trends across research, industry, policy, and safety. This year’s report highlights rapid advances, especially in large language models (LLMs) like GPT-4. However, risks around bias, robustness, and governance are also mounting.
Key take-outs:
GPT-4 Stands Unchallenged (For Now)
GPT-4 demonstrates unmatched performance, surpassing all other LLMs on benchmarks and human exams. This validates the power of proprietary architectures and reinforcement learning from human feedback.
Researchers are urgently working to match GPT-4 through new approaches, including smaller models, better datasets, and longer context. However, scaling may be constrained by available human-generated data.
Real-World Impact Spreads
LLMs and diffusion models enable advances in areas like drug discovery and synthetic biology. NVIDIA’s booming earnings highlight the compute intensity of AI research. Startups leverage GPUs for competitive edge, while chip vendors create export-control proof offerings amidst trade restrictions.
Generative AI’s Breakout Year
Led by ChatGPT, generative AI across modalities from text to video saw $18B in funding. However, retention remains a challenge versus incumbent apps. Adoption faces hurdles around bias, misinformation, and job displacement that need addressing.
Intensifying Policy Debates
Governments worldwide are acting on AI governance, but deep divisions remain. With fragmented regulatory approaches, progress on global coordination is slow. AI labs are stepping in, but evaluation and oversight mechanisms need maturing alongside capabilities.
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Mainstreaming Safety Research
Debates around AI existential risk have entered the mainstream. But many models prove easy to ‘jailbreak’, posing robustness challenges. Alternatives like self-alignment show promise. Vetting capabilities through rigorous evaluation is essential.
What’s Next in 2024?
Here are 5 predictions for the state of AI continuing rapid evolution in 2024:
1. Multimodal AI assistants will approach human capabilities across limited domains.
2. Specialized AI chips will be increasingly commoditized.
3. Governments will establish national AI research initiatives and funding on par with space programs.
4. Cryptographic techniques will emerge to certify and trace AI model provenance.
5. Formal verification will become standard practice for safety-critical AI systems.
In closing, realizing AI’s benefits requires collaboration amongst all stakeholders. With thoughtful leadership, AI can positively transform society. But we must continue advancing AI safety and ethics at the same pace as capabilities…