Enabling the generative AI revolution with intelligent computing everywhere
This post originally appeared on World Economic Forum on January 15, 2024.
- Generative artificial intelligence is era-defining and could benefit the global economy to the tune of $2.6 to $4.4 trillion annually.
- To realize the full potential of generative AI, it will be distributed across the cloud and edge devices such as smartphones, PCs, vehicles and industrial IoT.
- On-device generative AI will provide increased responsiveness, more precise personalization, greater reliability and enhanced privacy.
- Intelligent computing everywhere fosters greater opportunities for participation in the digital economy.
The generative artificial intelligence (AI) revolution is here. The pace of innovation and development in generative AI — as well as its adoption — is unprecedented, and its impact will be transformative as generative AI applications become indispensable companions and business enablers.
In fact, more than 93% of employers expect to use generative AI within the next five years to increase innovation and creativity, automate repetitive tasks and boost learning.1 According to McKinsey, the technology’s total economic benefit could add $2.6 to $4.4 trillion annually across more than 60 use cases — by comparison, the UK’s GDP in 2021 was $3.1 trillion.2
While the majority of generative AI development has been focused in the cloud — and the cloud will continue to play an indispensable role — it is quickly evolving to run directly on devices, including smartphones, PCs, vehicles, mixed reality and IoT devices, Wi-Fi access points and more. This will be key to realizing generative AI’s potential as an accelerant to digital transformation.
Starting this year, we expect to see dramatic growth in a new generation of affordable devices that can run generative AI models locally, including smartphones, PCs, vehicles, mixed reality, IoT devices and network equipment. Devices have a unique role to play, as executing generative AI on device allows for increased responsiveness, more precise personalization, greater reliability and enhanced privacy.
The performance and efficient AI capabilities of devices will also allow generative AI to run pervasively and operate proactively. Users can have digital assistants that anticipate their needs, rather than just reacting to requests through clicks and taps. Applications are on the way to make use of these capabilities — enabling entirely new experiences and applications focused on productivity, content creation, education, research and development, enterprise applications and more.
More personalized experience
With on-device and edge AI, applications can run continuously, enabling them to learn about the user, their preferences and behaviors, as well as utilize complimentary external data. This invaluable context and content can enable more relevant, specialized and individualized responses to users, including for key topics such as education and healthcare.
Also, since computations are performed locally, on-device AI avoids the potential for latency, while increasing reliability by being able to execute a query anywhere and anytime. This quicker response is crucial for applications requiring fast decision-making, such as voice assistants, augmented reality and gaming.
Increased privacy
As generative AI is adopted, it will be vital for confidential and personal information to remain private. A key benefit of on-device and edge AI is that queries and personal and proprietary information can remain on device (or on premise using private edge clouds). This enhanced privacy and security is essential for wide-scale trust and adoption across both consumer and enterprise applications.
It also helps address the requirement to comply with privacy regulations, such as the European Union’s GDPR. Certainly, careful implementation will be required to balance benefits with protecting user data.
Intelligent computing everywhere
Generative AI is one of the biggest transformations in computing — from cloud to devices. The cloud and devices will work together to elevate human capacity.
In a hybrid AI approach, workloads are distributed and coordinated among cloud and edge devices to provide optimal performance and efficiency across use cases. When both the cloud and device use the same generative AI model, the device can provide the cloud a head start. The data on the device also enables the AI application to be more precise because it has real-time context about the user.
Making use of distributed computing and processing more AI on device or in a hybrid approach will help manage costs associated with data centers.
Reduced infrastructure and environmental costs
On-device and edge AI reduces the burden of the data centers’ infrastructure and environmental costs. The annual global AI data center cost could top $76 billion by 2028.3 However, according to Tirias Research, if 20% of generative AI processing workloads could be offloaded by running on the device or through hybrid processing, the cost of global AI data center would decline by $15 billion.4
Further, a study found that one AI-generated image created in the cloud can require as much power as charging a smartphone.5 To illustrate the energy efficiency of running AI on mobile devices, we tested a commercial smartphone and were able to generate more than 400 images on a single battery charge using an optimized AI model.
Enabling generative AI globally
As a new generation of always connected, smarter and more capable devices scale at the edge, working collaboratively with the cloud, they will help communities to drive sustainable growth and innovation, unlock efficiencies, increase productivity and enable new business models. The widespread availability of smartphones, along with PCs, smart vehicles and other devices, represents a significant opportunity for individuals, enterprises and nations to take part in the benefits of generative AI. Enabling intelligent computing everywhere fosters greater opportunities for participation in the digital economy.
References:
1: Amazon Staff. (December 7, 2023). A new study reveals 5 ways AI will transform the workplace as we know it. Retrieved on January 15, 2024 from: https://www.aboutamazon.com/news/aws/how-ai-changes-workplaces-aws-report
2: Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (June 14, 2023). The economic potential of generative AI: The next productivity frontier. McKinsey Digital. Retrieved on January 15, 2024 from: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
3 and 4: McGregor, J. (July 27, 2023). GenAI Breaks The Data Center (Part II): Moving GenAI To The Edge Through On-Device Computing. Forbes. Retrieved on January 15, 2024 from: https://www.forbes.com/sites/tiriasresearch/2023/07/27/genai-breaks-the-data-center-part-ii-moving-genai-to-the-edge-through-on-device-computing/?sh=50b604a253c3
5: Luccioni, A.S., Jernite, Y., & Strubell, E. (November 28, 2023). Power Hungry Processing: Watts Driving the Cost of AI Deployment?. Cornell University. Retrieved on January 15, 2024 from: https://arxiv.org/pdf/2311.16863.pdf
