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C-suite leaders enjoy a unique vantage point when it comes to discovering, understanding and harnessing enterprise and industry developments. Here, top executives from Dell, Lenovo, Intuit, SAS and others share their insights about where generative AI is headed next - in the singular manner that only they can.
While generative AI has sparked incredibly creative ideas of how it will transform business and the world, there are very few real-world, scaled generative AI activities. As we move into 2024, we will see the first wave of generative AI enterprise projects reach levels of maturity that will expose important dimensions of generative AI not yet understood in the early phases.
We will see the cost of training foundation large language models (LLMs) decrease rapidly as silicon is optimized to train them at a rate of 50% every two years. This is making it possible for more companies to develop and deploy more of their own LLMs. As a result, we can expect to see a proliferation of new LLM-based applications in the coming years. Also, the current hype surrounding LLMs is likely to slow down in 2024 as companies begin to face the challenges of building domain-specific AI assistants. Overall, LLMs are poised to have a major impact on businesses and society in 2024 and beyond.
Natural language interfaces (NLIs) powered by generative AI will be expected for new products and more than half will have this by default by the end of 2024. Generative AI will also be leveraged in B2B interactions with users demanding more contextualized, personalized and integrated solutions. Generative AI will offer APIs, interfaces and services to access, analyze and visualize data and insights, becoming pervasive across areas such as project management, software quality and testing, compliance assessments and recruitment efforts. As a result, observability for AI will grow.
We will also see the rise of specialized, domain-specific AI models and a shift to smaller, specialized LLMs with higher levels of accuracy, relevancy, precision and niche domain understanding. For instance, LLaMA-7B models – often used for code completion and few-shotting – will see increasing adoption.
AI is moving beyond the large language model text world of ChatGPT and the landscapes of Midjourney to Large Multimodal Models (LMMs), systems that can reason across different media types. This is opening up new types of applications and possibilities, such as image-based inventory or virtual product support assistants for small businesses, and may help to ground future AI systems on more real-world examples that mitigate the potential of hallucination. We expect many more applications over the next 12 months, and as generative AI learns with sound, vision, and other senses, the near future may bring with it AI systems that can distinguish between reality and fiction.
As organizations scramble to leverage the full potential of AI, expect to see a surge in the hiring of chief AI officers (CAIOs) over the next year. A recent study by Foundry showed that 11% of midsize to large organizations have already designated an individual for this role, and another 21% of organizations are actively seeking one. The CDO Club is getting a jump on 2024 by holding The CAIO Summit 2023 this month, billed as the world's first-ever event specifically designed for chief artificial intelligence officers (CAIOs).
This trend will echo the initial rise of chief cloud officers (CCOs) during the early days of the cloud computing boom. A CAIO may be a good investment, given the attention required to the unknowns, risks and criticality of rolling out an effective AI strategy. However, as it was with CCOs, this trend will be short-lived.
The need for specialized CAIOs will dissipate as AI becomes more deeply embedded in business operations and strategy. Responsibilities once thought to require a dedicated AI executive will eventually fall within the purview of CIOs, or the role may converge with that of the chief data officer at some point in the future. This shift will reflect a broader, more integrated understanding of AI's role across various business functions.
The complexity of generative AI will spark the application of new software architectures that orchestrate information flow across enterprise systems, predictive models and enhance conversational experiences. Retrieval-augmented generation (RAG) is an AI framework for retrieving and incorporating up-to-date information with LLMs. This is a great first step, but this architecture will be limited to a certain scale and complexity of use cases in the organization. Agent-based frameworks - like the pioneering work of AutoGen from Microsoft - facilitate building networks of roles and functions that leverage RAGs, LLMs, and enterprise systems to meet the complexity of today’s organizations.
Generative AI will make it easier to turn aspects of the physical world — such as geometry, light, physics, matter and behavior — into digital data. Democratizing the digitalization of the physical world will accelerate industrial enterprises, enabling them to design, optimize, manufacture and sell products more efficiently. It also enables them to more easily create virtual training grounds and synthetic data to train a new generation of AIs that will interact and operate within the physical world, such as autonomous robots and self-driving cars.
Also, 3D interoperability will take off: From the drawing board to the factory floor, data for the first time will be interoperable. The world’s most influential software and practitioner companies from the manufacturing, product design, retail, e-commerce and robotics industries are committing to the newly established Alliance for OpenUSD. The universal language between 3D tools and data, OpenUSD will break down data silos, enabling industrial enterprises to collaborate across data lakes, tool systems and specialized teams easier and faster than ever to accelerate the digitalization of previously cumbersome, manual industrial processes.
Although generative AI is reimagining how we interact with machines, there are some immediate concerns that will be particularly challenging in the early years of widespread AI and language model adoption. For a lot of people involved in what we loosely call 'knowledge work,' quite a few of their jobs are going to vaporize. Rapid change makes it hard to quickly absorb displaced workers elsewhere in the workforce, and as a result, both the private sector and governments will need to step up.
Deepfakes are also another hurdle, and we can expect increased attacks on what we humans collectively think of as our reality — resulting in a world where no one can, or should, trust a video of you because it may be AI-generated.
Finally, advances in AI will exacerbate the digital divide that has been happening over the past 20 to 30 years between the haves and have nots, and will further increase inequality across the globe. I can only hope that by making information more accessible, this emerging technology leads to a new generation of young adults who better understand the issues and potential, and can counter that risk.
Companies deploying AI will become more cognizant about the risks and underlying nature of AI and we will see more businesses taking targeted actions to mitigate this. For example, new patterns such as Retrieval Augmented Generation can help LLMs generate results from authoritative sources. Additional techniques such as ensuring the quality and fidelity of training data and keeping a human in the loop for both training (reinforcement learning based on human feedback), and inference for the most sensitive scenarios are ways of balancing the augmented intelligence that generative AI provides.
We will also see an increase in robust governance policies, processes and tools including testing and validation for AI-generated content, embedding monitoring throughout the entire system. Having a clear AI policy that lays out the criteria to determine what is ethical, responsible and inclusive will guide the use of AI. This coupled with education so that teams working in this space can learn the skills necessary to implement the guidance, will be the cornerstone from which we will see businesses executing tangible AI plans.
Read more about:ChatGPT / Generative AI
Ben Wodecki is the Jr. Editor of AI Business, covering a wide range of AI content. Ben joined the team in March 2021 as assistant editor and was promoted to Jr. Editor. He has written for The New Statesman, Intellectual Property Magazine, and The Telegraph India, among others. He holds an MSc in Digital Journalism from Middlesex University.
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