Massive moonshot and short-term investments in R&D to make progress on AI alignment and evaluations of capabilities, current and anticipated harms and longer-term risks; answers need to arrive and be implemented by regulators before the advances in capabilities create significant dangers (the timeline for which is highly uncertain but trends are worrisome). This requires both national and multilateral efforts as well as incentives for AI developers through regulatory requirements.

Setting up global principles for the democratic and multilateral governance and oversight of Frontier AI labs to avoid unfettered concentration of AI-driven power and protect democracies from a major increase in AI-driven disinformation campaigns, especially as 4 billion people on earth are expected to be sent to the polls in 2024. This includes the development of regulation and treaties as well as oversight mechanisms giving a voice not just to the national government of the AI Frontier lab but also civil society, independent academics and representatives of the international community.

Setting up government-funded AI Frontier Labs with a non-profit mission and democratic and multilateral governance (as per (2)), in order to help achieve UN SDGs not addressed by commercial AI efforts and protect the public from national security risks and mitigate other potentially catastrophic outcomes with future AI systems harboring malicious goals (set by nefarious humans or of AIs whose control would have been lost).

In 2024, we need to do a better job making sure our technology serves everyone. The development of models and the training datasets have been heavily biased towards English-speaking and Western European countries, offering little representation of languages from the Global South or Asia, where much of the world’s population lives. I predict with projects like Cohere for AI’s AYA LLMs will vastly improve in terms of breadth of languages use to train models, significantly democratizing the technology.

2024 will also be a year for research bets. Multi-modal will become a ubiquitous term as we move away from sub-fields dedicated to language, computer vision and audio in isolation. Models will become more akin to our human intelligence – able to process multiple sensory inputs at once. It will also be the year we focus on the size of models – looking for efficiency rather than size – as it becomes critical to deploy models that are available in resource-constrained environments. Our lab is already pushing the limits of efficiency at scale and adaptive computing (both data pruning and mixture of experts).

Finally, 2024 should also be a year when we  examine the defaults of where, how and by whom research is done. To date, state of art models have come from a handful of labs and researchers. We will begin to make critical strides and begin to close the significant gaps with regard to who participates in research. In 2024, I hope we create better incentive structures for broadening of participants and cultural backgrounds deeply involved in AI research. 

Rise of small language models (SLMs). Doesn’t mean that the large language models are going away. The world needs LLMs. The world also needs SLMs. Particularly for task specific or domain specific capabilities, I see a world of large and small language models and applications using a combination of these models to optimize price, performance and accuracy as appropriate.

Hype around AI though will still be there, is going to be a little more grounded. There is still going to be a lot of excitement around AI.  Everybody is going to be thinking about how they leverage AI in whatever they are doing. At the same time, if you are a builder you are going to be more focusing on use cases and business models that are sustainable. If you are a customer, you are going to be thinking about “show me the money” or “what is the ROI” for me? If you are an investor you are going to want to see business model, traction and monetization to go hand in hand with technology innovation.

Nvidia is the industry’s best friend (except maybe for Intel, AMD and a few others). Whether you are large technology company or a hyperscale cloud provider or a startup, Nvidia is here to help with chips, hardware, software, cash (largest AI investor in 2023). It is both an amazingly enviable position to be in and a super responsible position to be in to continue driving innovation and staying ahead of the curve.

LLMs Reimagine the Data Stack - We believe that LLMs will meaningfully transform the data stack. These transformative models will elevate the efficiency of diverse data professionals by fostering collaboration and automating tasks that were traditionally manual—particularly those demanding considerable effort, domain expertise, and time. Beyond serving as copilots for routine tasks, LLMs will unlock innovative capabilities, such as Data Spelunking (disambiguating data, semantic similarity, entity resolution), validating unstructured data quality, and optimizing queries, among other advancements.

New Forms of Feedback (RL’X’F) on the Block - In 2023, Reinforcement Learning from Human Feedback (RLHF) has proven surprisingly effective for aligning models towards general human preferences. However, RLHF is expensive, time-consuming and difficult to scale in specialized domains. We expect to see new forms of interactive feedback emerge for domain-specific tasks and non-sensory data like RL (Reinforcement Learning) through AI Feedback, RL through Experimental Feedback, RL through Expert Feedback and more. We will see a new wave of startups address these challenges for enterprises training and fine-tuning their models.

RAG evolves from Semantic Similarity to more sophisticated approaches - Though semantic similarity is the current consensus-based approach, it doesn’t work optimally for all RAG use cases. The approach will evolve from semantic similarity towards ontology-based representations (e.g. graph networks) over the next 12-24 months. For example, representing a code base in a graph ontology captures the relationships between embedded and/or interconnected functions in a way that cosine similarity likely doesn’t

Will the GPU craze continue? We’re in the first inning of a very long and exciting game. The demand profile for GPU hardware is growing so fast, it will far outpace anything we expect today. Compute requirements to train cutting-edge AI are doubling every 5-6 months and I think that over the next several years, the degree to which it is going to become part of our everyday life hasn't even been explored yet. 

As Jensen Huang said, we’re seeing two simultaneous platform shifts at the same time – accelerated computing and generative AI. The world is moving away from general purpose computing, the one-size-fits-all infrastructure for the broadest range of use cases. This new era of generative AI demands upgraded data centers and specialized cloud providers. Industries like AI, VFX, and life sciences need a modern, specialized cloud, purpose-built for GPU-intensive workloads.

A modern software stack will also prove to be the differentiator for the next generation of cloud companies, to support the most complex and intensive workloads, like generative AI, with a product that's more efficient and more performant at scale compared to legacy platforms. 

LMMs per Domain, Industry and Culture - In 2024 we will see a surge of specialized, domain specific  and cultural-aware LMMs. The current LMMs are knowledge generalists from a business, industry and culture domain standpoint. The cloud infrastructure and the abundant offer of commercial and open source LMMs and tools (Azure, AWS, Google) released in 2023, offer an opportunity to develop with speed and ease tailored AI solutions in 2024.

LMM Context Management improvement  - 2024 will see the arrival of more efficient new and creative techniques and tools that surpass the actual limitations of prompt engineering and fine-tunning methods. The key success factor will be to have a more and better cost/effective and technology efficient LMM context and thought management.

Cognitive Psychology techniques - This prediction is more personal biased. Although still in its infancy in academic research, the use of cognitive psychology (and computational psychology) techniques into the LMM frameworks, to resemble or to get closer to a real AGI, would provide a promising alternative to enhance the actual LMM models.   

I think that AI community will face the first big data leakage, this leak will open the door to different solutions more open-source and with more transparency.

As well as for web3 economy, the ecological cost of AI will be a major point in 2024, improvements in less energy-intensive LLMs will be a key for AI sustainability.

The AI for all will become a very important subject and this will require accessibility to the greatest number,( unfortunately not everyone is able to use Hugging Face)

An LLM trained predominately on Mandarin text will rival GPT4 in performance.

A medium sized European country will demonstrate how to effectively integrate generative AI into the running of public services and the good conduct of government. The larger NATO nations will continue to focus on how to apply AI to weapons systems, but not focus on building efficiencies within their large bureaucracies.

The first large scale GenAI induced redundancies will be reported in the media. This will be a big story, but as ever, the reality will be quite distinct from the media narrative.