Three big bets on the future of AI.

In April 2023, Goldman Sachs released a report estimating that advancements in generative AI have the potential to drive a 7% (or approximately $7 trillion) “increase in global GDP and lift productivity growth by 1.5 percentage points over 10 years.” This prospect so clearly highlights why it is important to get the future of generative AI right, especially as it relates to data — the key piece that is arguably the heart of this technology.

So, what does the future of generative AI look like? A big part of it will be split-second curation, consolidation across multiple data sources and types, and providing context to LLMs. To thrive and function their best, LLMs will need fresh, curated data and context for applications — all of which needs to happen in milliseconds, snaps of real-time. Let’s dive a bit deeper into the three tenets I am betting as the future of AI:

1. An ensemble of LLMs.

LLMs, or large language models, are “deep learning algorithms that can recognise, summarise, translate, predict, and generate content using very large datasets.” LLMs are the backbone of generative AI. As this technology continues to evolve, there is not going to be one universal LLM that dominates the market. Instead, organisations will leverage an ensemble of LLMs to power use cases, something we already see emerging today. For example, GPT4 is rumored to be not just one massive model but a collection of 10+ different models, each with 100 billion parameters all stitched together. Consequently, enterprises will have to have a combination of LLMs or foundational models that they start to leverage. I believe enterprises will hedge their bets and costs by utilising multiple foundational models that accomplish specific tasks better than others. This includes both open-source LLMs like Llama 2 and Hugging Face, and private LLMs like OpenAI, Anthropic , and Cohere.

2. AI data planes emerge.

For businesses, I believe there will be an AI data plane that sits between their ensemble of LLMs and their corporate data. Incorporating an AI data plane provides additional context and clean data to an ensemble of LLMs for instant responses based on data within the enterprise firewalls. These data planes will have to have the ability to ingest, store, and process vector embeddings — along with other data types and structures, including hybrid search. This includes managing data access, security, and governance, as well as a thin layer of intelligence that helps prototype and build applications rapidly and easily.

3. Real-time AI will increasingly become the norm.

As AI proliferates and we start to interface with more audio and video-enabled AI,  businesses will demand access to fresh data in real-time (milliseconds) to provide the right context for foundational models. LLMs and other multi-structured foundational models will need to respond to requests in real-time and, in turn, will need their data planes to have real-time capabilities to process and analyse data in diverse formats. To execute real-time AI, enterprises need to continuously vectorise data streams as they are ingested and utilise those for AI applications. Consequently, organisations will increasingly move toward a zero ETL philosophy to minimise data movement, complexities, and latencies to power their AI apps.

Conclusion

The world of AI and generative AI is fast evolving. Newer applications, foundational and business models, and supporting technologies are quickly emerging. Understanding the small pieces that make up the larger puzzle is key to getting the generative AI revolution right — and creating a future where this technology can be used to elevate human lives.

Through our expertise in technology solutions and data management, Logixal empowers businesses to leverage generative AI effectively, driving innovation and enhancing human lives.

Logixal can help organisations navigate the future of generative AI by:

1. Developing AI data planes that seamlessly integrate LLMs with corporate data sources, ensuring data quality, security, and governance.
2. Facilitating the transition to real-time AI through data streaming and processing solutions, minimising latency, and enabling continuous insights for AI applications.

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