Industry executives and experts share their predictions for 2024.  Read them in this 16th annual VMblog.com series exclusive.

AIML makes its way to the edge, the factory floor, and beyond

By Krishna Rangasayee, CEO and founder of SiMa.ai

As we enter 2024, a cautious sense of optimism can be felt across the technology landscape. The past year saw tremendous advances, particularly around generative AI, that promise to usher in a new era of innovation. However, with such rapid change also comes uncertainty.

While the full impact of these developments is yet unknown, they foreshadow an exciting shift towards more decentralized and autonomous intelligence. As models and decisions increasingly move to the edge rather than the cloud, it raises new opportunities and challenges around governance, privacy, and reliability.

Here are my 2024 AIML predictions to look out for:

1. Generative AI Comes to the Edge: As we move further into the digital age, the convergence of AI and physical devices is poised to drive transformative changes across various industries. The first wave of generative AI happened in the cloud, in mostly the form of a consumer-like experience. The second - and more meaningful - will happen at the edge; OpenAI's recent pause on ChatGPT Plus sign-ups was a glaring indicator that the cloud can't handle the scale and performance required to succeed in supporting the mission critical work happening at the edge (think what such a "pause" would do to unmanned drones or medical devices actively in use). While much remains to be determined when it comes to the look and feel of multimodal AI at the edge, one thing is clear - there's no doubt 2024 will bring fundamental changes to the machines humans rely on.


2. Buyers Will Prioritize Software Flexibility Over Incumbent Loyalty:  Historically, chips have been designed as a one-size-fits-all leaving customers building at the edge in a power and performance conundrum. As more and more workloads migrate off the cloud and to the edge, software that enables a company to easily and quickly AI-enable their product or service will prevail. This shift emphasizes the importance of software compatibility and accessibility to hardware resources. Customers are recognizing the limitations of incumbent vendors in powering edge AI for the long term; similarly, they are foregoing legacy hardware bias in pursuit of a software experience that radically simplifies their life and meets both the frames/second/watt performance and accuracy bar that AI and ML at the edge demands. 


3. Deglobalization Raises the Bar for Data Security: Centralized computing at the edge offers companies more opportunity to keep data within their own walls. As concerns of software supply chain hacks and security threats continue to grow, and deglobalization continues to lead to stricter data residency laws, we'll see companies opt to keep data on their devices vs. the cloud, as the edge removes the need to constantly move large amounts of data. This also increases reliability, privacy, and compliance with ever-evolving regulatory requirements. Ultimately, the edge gives enterprises an additional layer of self-governance and autonomy over their information.


4. Industry 4.0 Comes to the Factory Floor (Yes, Finally): Advanced technologies - sensors, machine learning, computer vision, robotics, edge computing, etc. - have proven to increase supply chain resiliency for manufacturers who adopt them. While robotics and industrial automation companies have touted these capabilities for years, 2024 is the year they become real. As tech companies realize the need to diversify their operations and embrace Industry 4.0 technology to be more resilient, factories will become smart manufacturing ecosystems, where AI-driven systems are seamlessly integrated into every stage of the production process. 


5. Machine Learning Model Accuracy Will Underpin Responsible AIML in 2024 and Beyond: Chatbots, agents and copilots have taken off in 2023, despite near-constant hallucinations. In 2024, responsible AIML will become a topic du jour, particularly as use cases expand and more demographics begin to interact with generative AI. That push will be driven largely by a focus on the accuracy of ML models, particularly as generative AI continues to push into highly regulated fields like healthcare and finance. As edge ML becomes more prominent, this will get easier - cloud-based models are generally operating on pre-processed data, making it much harder for developers to understand why their models make the decisions they do, and even more challenging to correct. 2024 will bring increased scrutiny on companies like OpenAI and Anthropic, which will trickle down to smaller providers, as well as open source developers, as engineers utilize edge AIML, smaller models and further fine-tuning.

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ABOUT THE AUTHOR

Krishna Rangasayee, SiMa.ai Founder and CEO

Krishna Rangasayee is Founder and CEO of SiMa.ai. Previously, Krishna was COO of Groq and at Xilinx for 18 years, where he held multiple senior leadership roles including Senior Vice President and GM of the overall business, and Executive Vice President of global sales. While at Xilinx, Krishna grew the business to $2.5B in revenue at 70% gross margin while creating the foundation for 10+ quarters of sustained sequential growth and market share expansion. Prior to Xilinx, he held various engineering and business roles at Altera Corporation and Cypress Semiconductor. He holds 25+ international patents and has served on the board of directors of public and private companies.