The Impact of Large Language Models and Deep Learning

With an estimated impact of $9.5T -$15.4T annually it is hard to overstate the value of artificial intelligence. Much of this value is predicated on the promise of AI which includes:

• Faster time to market with higher quality products
• Lower overall costs and higher net profitability
• Greater customer satisfaction, acquisition, and retention
  • Like how Nike addressed 60% of its customers wearing the wrong size shoe
• Faster time to decision
• Achieving higher degrees of compliance with less overhead

Deep Learning, which uses artificial neural networks to ingest and process unstructured data like text and images, goes well beyond what was possible with earlier Machine Learning (ML) models and is driving an increasing share of that opportunity. Deep Learning leverages some of the world’s largest models and massive datasets to deliver unprecedented accuracy and insights, unlocking previously inaccessible computational insights with AI.

28% of organizations are already achieving “high outcomes” with AI, according to Deloitte . And if you are not part of that 28%, you’re risking your company’s competitiveness and financial success.

Deep Learning is reshaping every market, every industry, and society as a whole. To take advantage of this opportunity, organizations must solve the challenges found in early AI deployments, all of which are compounded by Deep Learning and Large Language Models if you pursue a Do It Yourself AI model (DIY AI). These include:

1. Finding and retaining AI talent
2. Installing and maintaining the infrastructure required to run the models
3. Building the models in time to extract value from them

Finding and retaining top AI talent

Every organization, outside of a few select hyperscalers, are challenged with hiring and retaining data scientists that are capable of building and training the AI models they need. The complexity of building large machine learning models means that there are a very limited number of people that can successfully build and train them at scale. With Deep Learning, even fewer data scientists can do so and trying to recruit them puts organizations in direct competition for talent with the largest hyperscalers.

Installing and maintaining infrastructure

GPUs have enabled the first wave of AI, successfully training mid-size AI models. GPUs have worked very well for this task, as long as the models are not too big or the data sets too large. Outside of the mid-range for machine learning, GPUs have struggled. The complexity of Deep Learning further pushes GPUs to the point where it is estimated that current GPUs can only run 1% of Deep Learning models.

Building the models in time to extract value from them

Don’t underestimate the challenge of creating models fast enough to extract value from them. According to a report by Harvard Business Review, it typically takes a minimum of 18 months for an organization to transform into one that is AI enabled.

Large language models are growing at the rate of 10X every 12 months, and the compute requirements are doubling every 3.4 months. The result is that by the time a model is ready to deploy, it is already out of date, so you are never really ready.

Problems solved

The challenges presented all highlight hurdles that every organization must individually overcome. There is a better way that can lead to results in weeks, not years.

Join SambaNova for an upcoming announcement where we will demonstrate how we have solved each of these challenges to finally usher in the AI enabled era. Learn how SambaNova is, for the first time, making it possible for organizations to enjoy the benefits that Deep Learning and Large Language Models offers, while slashing time to value from months to only weeks.

Learn more about how large language models enable you to unlock the value of AI and Deep Learning.