How companies can overcome the cost barrier when adopting artificial intelligence
The promise of AI is intriguing, offering businesses the opportunity to achieve unprecedented efficiency, innovation and growth. Yet for many, this potential remains frustratingly out of reach. The high cost of entry, driven by factors such as unpredictable cloud pricing models and demanding compute requirements, represents a significant barrier to adoption, especially when it comes to powerful new tools such as Generative AI.
The solution lies in democratizing access to AI – this breakthrough technology must be affordable, accessible and implementable for businesses of all sizes. By removing the cost barrier, companies can spark a new wave of innovation and unleash the true potential of AI tools across all industries. Fortunately, we are already seeing signs of progress.
Breaking down barriers: making AI a commodity for everyone
Just as cloud computing has changed the technology landscape by making computing power accessible to all, AI is on a similar trajectory. The commercialization of AI, driven by factors such as standardized models and accessible platforms, will be critical in making it affordable and usable for all types of businesses.
Crucially, this journey towards accessible AI relies heavily on collaboration and partnerships. By working together, companies can pool resources, share expertise and develop tailored AI solutions that meet their specific needs while addressing the cost and efficiency challenges of compute-intensive workloads. This collaborative approach will be critical to ensuring AI benefits everyone.
The potential uses of AI are numerous and span every industry imaginable. As AI becomes more commoditized, we can expect to see a surge in innovation as companies of all kinds discover how to use this technology to solve their unique challenges.
From the cloud to the edge: technologies that democratize AI access
One of the key technologies driving the democratization of AI is edge computing. Edge computing brings AI capabilities closer to data sources, enabling real-time processing and decision-making in various industries. No-code/low-code AI platforms enable users with limited programming skills to build and deploy AI models without extensive coding, expanding the accessibility of AI development. AutoML tools automate model selection, training, and optimization, simplifying the AI development process for non-professionals.
In addition, federated learning enables AI models to be trained across decentralized edge devices, alleviating privacy concerns and enabling broader participation in AI model training. These advances will expand access, simplify development, and facilitate the deployment of AI in diverse applications and environments.
Another area of democratization of innovation is running some AI models on CPUs instead of GPUs. Large Language Models (LLMs) process huge data sets during training, and most of their computations during training and inference involve matrix multiplications, which are usually performed in parallel. GPUs, with their thousands of cores, are inherently designed to support highly parallel computations much better than CPUs. This is a big reason why GPUs are much better suited to running LLMs than CPUs. In addition, the high memory bandwidth of GPUs is better suited to transporting the many intermediate data points involved in LLM computations between memory and processing units.
However, with recent improvements such as quantization, state space models, and frameworks like MLX that use unified memory, we can gradually run SLMs (Small Language Models) or some quantized LLMs on CPUs. This is another clear example of technologists becoming more and more aware of how to leverage the capabilities of AI and apply technology in practical ways.
A future with AI: unlocking potential in all industries and society
The development of AI suggests that it could redefine not only the underlying technology but also the way we work, collaborate and even communicate. This profound change will drive innovation, economic growth and societal progress. The key will be to ensure that this change is done ethically and safely so that it remains a positive transition rather than a negative one.
To encourage wider AI adoption, companies need to address these multifaceted obstacles using a holistic approach that considers technical, organizational, ethical, and regulatory aspects. But before we get to that point, we need to address the challenges that hinder AI adoption. The prevailing skills shortage will pose a huge challenge to AI adoption. From a senior management perspective, the skills shortage would make AI implementation more difficult as no one can help manage the complexity of integrating AI solutions into existing systems and workflows.
The first step to address this issue is to upskill employees so that resistance to change within the organization remains low and employees with the right skills can work with AI systems. This would address concerns about privacy and security of the AI systems as all employees trust AI systems.
I am convinced that today’s generation of leaders is up to the challenge of democratizing AI. The signs of progress are everywhere, from the rise of edge computing to the ability to run AI models on CPUs, demonstrating a commitment to making AI more accessible and affordable. This push toward democratization will unleash a wave of innovation across all sectors, transforming not only our technology but also the way we work and connect.
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