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AI Applications Can Benefit From Autonomous Optimization

Jacob Simkovich

Brand and Content Manager, Intel Granulate

The rapid growth of artificial intelligence (AI) tools such as ChatGPT has started to revolutionize industries around the world. In fact, Grand View Research predicts AI will see annual growth of 37.3% from 2023 to 2030, and according to Forbes, 97% of business owners believe ChatGPT will help their business.

While chat-based AI tools are taking the world by storm, they need incredible amounts of data to run. This means they can be slow or need to impose restrictions on users, something that certainly won’t improve productivity in the way 97% of businesses hope it will. 

The solution is autonomous optimization, which can improve the performance of an application, even if it’s working with a large data set. This frees up engineers to focus on more complex tasks while reducing costs and improving the software as a whole.

The 2023 Data and AI Trends report from Google Cloud states that by 2026, 82% of organizations are looking to ensure that all capabilities supporting the full data and AI workflow are tightly integrated into their cloud data platform. Eliminating silos and storing data in one place is incredibly powerful but it takes a lot of computational resources to manage.

To put this into context, ChatGPT is trained on 100 trillion parameters and 300 billion words and works from 570 gigabytes of text data. This amount of data, on top of requests from millions of users, can hamper performance and drive up costs. Midjourney, the AI image generator, has 14.5 million registered users with 1.1 million of them active at any given time — that means Midjourney is processing huge amounts of data, especially at peak times.

It’s a similar story for other generative AI tools, including Google Bard and Open AI’s DALL-E. As these tools become more widespread, they’ll be even more demand on the amount of data they can process and how quickly.

Enhancing Performance in AI Applications   

To avoid performance issues, software engineers can use autonomous optimization to identify contended resources and adapt the runtime level resource management to better fit the needs of the application and its users. 

The benefits of optimizing this process include:

  • Reduce costs: With the application using fewer resources, you can lower third-party costs.
  • Increase performance: Reduce runtime and bottlenecks so users don’t need to wait as long for responses.
  • Drive efficiency: Minimize the risk of performance degradation at high utilization rates, ensuring users can use the app effectively at all times.
  • Improve stability: Handle peak traffic so users can generate AI responses even when the application goes viral.
  • Reduce fatigue: Reduce the need for alerts and monitoring.
  • Free up staff: Free up your team to work on more complex updates and tasks, allowing the app to continue to stand out even as new players enter the market.

Generative AI Application Optimization Use Cases

The types of AI applications that can benefit from autonomous optimization include:

  • Image processing tools such as plant identification apps
  • Natural language processing tools such as intelligent email security plugins
  • Real-time AI chatbots such as ChatGPT
  • Facial recognition software used in secure buildings
  • Autonomous vehicle software and other advanced driver assist systems
  • Data analytics tools such as those designed to support business decision making

All these tools deal with a lot of data and need to be quick in order to provide their key features while ensuring the experience for the end user is seamless. 

Increasing Efficiency and Resource Management

The goal of autonomous optimization is to improve resource management in a way that boosts efficiency. In reducing processing time and CPU utilization without changing any code, organizations can ensure their AI tools run as efficiently as possible. In reducing the computational strain it’s possible to reduce management overhead, improve stability, and drive efficiency. 

The Future of AI and its Dependence on Optimization

Artificial intelligence tools are here to stay but as they become even more widespread, it’s going to be vital that they’re working as expected for all users. Automated optimization can mitigate the need for increased resources as popularity increases. Recent research from PwC shows that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. It puts this down to the use of AI and how it will increase product variety with increased personalization and attractiveness.

So whether businesses are using AI tools for content, insights, process improvements, or personalization, those tools need to keep evolving to handle larger data sets in more complex ways than ever before – something that won’t be possible without automated optimization.

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