NVIDIA AI experts predict a rapid transformation across industries as companies accelerate AI rollouts and begin to build best practices for adopting generative AI. Generative AI, which can ingest text, voice and video to create new content, has the potential to revolutionise productivity, innovation and creativity. Deep learning algorithms like OpenAI’s ChatGPT could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 business use cases, according to McKinsey & Company. NVIDIA experts predict that 2024 will see increased partnerships with cloud service providers and data storage companies to efficiently handle and deploy big data.
Generative AI: The Future of Industries
Generative AI has emerged as a transformative technology, with many companies racing to harness its ability to ingest text, voice, and video to create new content that can revolutionize productivity, innovation, and creativity. Deep learning algorithms like OpenAI’s ChatGPT, further trained with corporate data, could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 business use cases, according to McKinsey & Company. However, managing massive amounts of internal data has been cited as the biggest obstacle to scaling AI. NVIDIA experts predict that 2024 will be about creating partnerships and collaborations with cloud service providers, data storage and analytical companies, and others with the know-how to handle, fine-tune and deploy big data efficiently.
Customization and Open-Source Software in Enterprises
Customization is coming to enterprises. Companies won’t have one or two generative AI applications — many will have hundreds of customized applications using proprietary data that is suited to various parts of their business. Companies like Amdocs, Dropbox, Genentech, SAP, ServiceNow, and Snowflake are already building new generative AI services using retrieval-augmented generation (RAG) and large language models (LLMs). Open-source pre-trained models are leading the charge, with generative AI applications that solve specific domain challenges becoming part of businesses’ operational strategies.
AI and Microservices: The Future of Software Development
Generative AI has spurred the adoption of application programming interface (API) endpoints, which make it easier for developers to build complex applications. In 2024, software development kits and APIs will level up as developers customize off-the-shelf AI models using AI microservices such as RAG as a service. This will help enterprises harness the full potential of AI-driven productivity with intelligent assistants and summarization tools that can access up-to-date business information.
AI as a National Treasure and Quantum Computing
AI is set to become the new space race, with every country looking to create its own center of excellence for driving significant advances in research and science and improving GDP. Government-funded generative AI centers of excellence will boost countries’ economic growth by creating new jobs and building stronger university programs to create the next generation of scientists, researchers, and engineers. Enterprise leaders will launch quantum computing research initiatives based on two key drivers: the ability to use traditional AI supercomputers to simulate quantum processors and the availability of an open, unified development platform for hybrid-classical quantum computing.
From RAG to Riches and Multimodality
Retrieval-augmented generation (RAG) is expected to be embraced by enterprises in 2024. As companies train LLMs to build generative AI applications and services, RAG answers the inaccuracies or nonsensical replies that sometimes occur when the models don’t have access to enough accurate, relevant information for a given use case. Text-based generative AI is set to become a thing of the past, with many industries embracing multimodal LLMs that allow consumers to use a combination of text, speech, and images to deliver more contextually relevant responses to a query about tables, charts, or schematics.
AI Safety and the Democratization of Development
Collaboration among leading AI organizations will accelerate the research and development of robust, safe AI systems. Standardized safety protocols and best practices will be adopted across industries, ensuring a consistent and high level of safety across generative AI models. On the other hand, virtually anyone, anywhere will soon be set to become a developer. As computing infrastructure becomes increasingly trained on the languages of software development, anyone will be able to prompt the machine to create applications, services, device support, and more.
AI in Healthcare and Enterprise Data Management
AI surgical assistants and generative AI drug discovery factories are emerging in the healthcare sector. Combining instruments, imaging, robotics, and real-time patient data with AI will lead to better surgeon training, more personalization during surgery, and better safety with real-time feedback and guidance even during remote surgery. On the enterprise side, companies learned from 2023 that building LLMs from scratch isn’t easy. Cloud service providers, colocation providers, and other businesses that handle and process data for other businesses will help enterprises with full-stack AI supercomputing and software.
Generative AI in Retail and Industrial Digitalization
Retailers are gearing up to introduce cutting-edge, generative AI-powered shopping advisors, which will undergo meticulous training on the retailers’ distinct brand, products, and customer data to ensure a brand-appropriate, guided, personalized shopping journey that mimics the nuanced expertise of a human assistant. In the industrial sector, the fusion of industrial digitalization with generative AI is poised to catalyze industrial transformation. Generative AI will make it easier to turn aspects of the physical world — such as geometry, light, physics, matter, and behavior — into digital data.
Modernizing the Vehicle Production Lifecycle and Building Anew with Generative AI
The automotive industry will further embrace generative AI to deliver physically accurate, photorealistic renderings that show exactly how a vehicle will look inside and out — while speeding design reviews, saving costs, and improving efficiencies. In the architecture, engineering, construction, and operations (AECO) industry, generative AI is being used as a guidepost. Hundreds of generative AI startups and customers in AECO and manufacturing will focus on creating solutions for virtually any use case, including design optimization, market intelligence, construction management, and physics prediction.
AI in Networking and Cybersecurity
A renewed focus on networking efficiency and performance will take off as enterprises seek the necessary network bandwidth for accelerated computing using GPUs and GPU-based systems. In cybersecurity, the pivot from app-centric to data-centric security is in full swing. Data is the fundamental supply chain for LLMs and the future of generative AI. Enterprises are just now seeing the problem unfold at scale. Companies will need to reevaluate people, processes, and technologies to redefine the secure development lifecycle (SDLC).
AI in Telecoms and Financial Services
After five years of 5G, network coverage and capacity have boomed — but revenue growth is sluggish and costs for largely proprietary and inflexible infrastructure have risen. The new year will be about aggressively pursuing new revenue sources on existing spectrum to uncover new monetizable applications. In financial services, firms will undergo a strategic shift toward a highly scalable, hybrid combination of on-premises infrastructure and cloud-based computing, driven by the need to mitigate concentration risk and maintain agility amid rapid technological advancements.
Physics-ML for Faster Simulation and The Rise of Robotics Programmers
Energy companies will increasingly turn to physics-informed machine learning (physics-ML) to accelerate simulations, optimize industrial processes, and enhance decision-making. In the robotics industry, LLMs will lead to rapid improvements for robotics engineers. Generative AI will develop code for robots and create new simulations to test and train them.
“Generative AI started the year as a blip on the radar but ended with a splash. Many companies are sprinting to harness its ability to ingest text, voice and video to churn out new content that can revolutionize productivity, innovation and creativity.” – CLIFF EDWARDS
“One size doesn’t fit all: Customization is coming to enterprises. Companies won’t have one or two generative AI applications — many will have hundreds of customized applications using proprietary data that is suited to various parts of their business.” – MANUVIR DAS, Vice President of Enterprise Computing
“National treasure: AI is set to become the new space race, with every country looking to create its own center of excellence for driving significant advances in research and science and improving GDP.” – IAN BUCK, Vice President of Hyperscale and HPC
“From RAG to riches: Expect to hear a lot more about retrieval-augmented generation as enterprises embrace these AI frameworks in 2024.” – KARI BRISKI, Vice President of AI Software
“Target lock on AI safety: Collaboration among leading AI organizations will accelerate the research and development of robust, safe AI systems. Expect to see emerging standardized safety protocols and best practices that will be adopted across industries, ensuring a consistent and high level of safety across generative AI models.” – NIKKI POPE, Head of AI and Legal Ethics
“The democratization of development: Virtually anyone, anywhere will soon be set to become a developer. Traditionally, one had to know and be proficient at using a specific development language to develop applications or services. As computing infrastructure becomes increasingly trained on the languages of software development, anyone will be able to prompt the machine to create applications, services, device support and more.” – RICHARD KERRIS, Vice President of Developer Relations, Head of Media and Entertainment
“AI surgical assistants: The day has come when surgeons can use voice to augment what they see and understand inside and outside the surgical suite.” – KIMBERLY POWELL, Vice President of Healthcare
“Enterprises lift bespoke LLMs into the cloud: One thing enterprises learned from 2023 is that building LLMs from scratch isn’t easy. Companies taking this route are often daunted by the need to invest in new infrastructure and technology and they experience difficulty in figuring out how and when to prioritize other company initiatives.” – CHARLIE BOYLE, Vice President of DGX Platforms
“Generative AI shopping advisors: Retailers grapple with the dual demands of connecting customers to the products they desire while delivering elevated, human-like, omnichannel shopping experiences that align with their individual needs and preferences.” – AZITA MARTIN, Vice President of Retail, Consumer-Packaged Goods and Quick-Service Restaurants
“Industrial digitalization meets generative AI: The fusion of industrial digitalization with generative AI is poised to catalyze industrial transformation.” – REV LEBAREDIAN, Vice President of Omniverse and Simulation Technology
“Modernizing the vehicle production lifecycle: The automotive industry will further embrace generative AI to deliver physically accurate, photorealistic renderings that show exactly how a vehicle will look inside and out — while speeding design reviews, saving costs and improving efficiencies.” – XINZHOU WU, Vice President and General Manager of Automotive
“Building anew with generative AI: Generative AI will allow organizations to design cars by simply speaking to a large language model or create cities from scratch using new techniques and design principles.” – BOB PETTE, Vice President of Enterprise Platforms
“AI influx ignites connectivity demand: A renewed focus on networking efficiency and performance will take off as enterprises seek the necessary network bandwidth for accelerated computing using GPUs and GPU-based systems.” – GILAD SHAINER, Vice President of Networking
“Clarity in adapting the security model to AI: The pivot from app-centric to data-centric security is in full swing. Data is the fundamental supply chain for LLMs and the future of generative AI. Enterprises are just now seeing the problem unfold at scale. Companies will need to reevaluate people, processes and technologies to redefine the secure development lifecycle (SDLC). The industry at large will redefine its approach to trust and clarify what transparency means.” – DAVID REBER JR., Chief Security Officer
“Running to or from RAN: Expect to see a major reassessment of investment cases for 5G.” – RONNIE VASISHTA, Senior Vice President of Telecoms
“AI-first financial services: With AI advancements growing exponentially, financial services firms will bring the compute power to the data, rather than the other way around.” – MALCOLM DEMAYO, Vice President of Financial Services
“Physics-ML for faster simulation: Energy companies will increasingly turn to physics-informed machine learning (physics-ML) to accelerate simulations, optimize industrial processes and enhance decision-making.” – MARC SPIELER, Senior Director of Energy
“The rise of robotics programmers: LLMs will lead to rapid improvements for robotics engineers. Generative AI will develop code for robots and create new simulations to test and train them.” – DEEPU TALLA, Vice President of Embedded and Edge Computing
Summary
Generative AI, which can process text, voice and video to create new content, is predicted to revolutionise productivity and innovation across industries. Large language models (LLMs) and retrieval-augmented generation (RAG) are expected to become more accessible and easily deployed, potentially adding trillions to business revenues annually.
- Generative AI, which can process text, voice and video to create new content, has seen rapid growth and adoption across industries.
- Companies such as Amdocs, Dropbox, Genentech, SAP, ServiceNow, and Snowflake are developing new generative AI services using retrieval-augmented generation (RAG) and large language models (LLMs).
- NVIDIA experts predict that 2024 will see increased collaboration with cloud service providers and data storage companies to manage and deploy big data efficiently.
- AI capabilities like RAG, autonomous intelligent agents, and multimodal interactions are expected to become more accessible and easily deployed.
- Quantum computing research initiatives are predicted to increase, driven by the ability to simulate quantum processors using traditional AI supercomputers and the availability of a unified development platform for hybrid-classical quantum computing.
- AI safety research and development is expected to accelerate, with emerging standardized safety protocols and best practices being adopted across industries.
- Generative AI is predicted to revolutionise various sectors, including healthcare, finance, retail and manufacturing, by enabling more accurate applications and sophisticated, context-sensitive chatbots and personalised content recommendation systems.
- The automotive industry is expected to further embrace generative AI to deliver physically accurate, photorealistic renderings, speeding design reviews, saving costs and improving efficiencies.
- Generative AI is also predicted to enable breakthroughs in autonomous vehicle (AV) development, including turning recorded sensor data into fully interactive 3D simulations.