As we stand on the brink of a new year, the potential of applied AI in 2022 beckons. Let's delve into the possibilities, exploring the transformative power of AI and how it might reshape our world in the coming months.
AI adoption has skyrocketed
More people are working with AI, it’s now being taken seriously, and maturity is increasing
- This means AI is no longer a game that researchers play – it’s becoming applied, taking center stage for the likes of Microsoft and Amazon and beyond
Large language models, multimodal models, and hybrid AI
LLMs can internalize basic forms of language, whether it’s biology, chemistry, or human language, and we’re about to see unusual applications of LLMs grow.
- We think LLMs will see increased adoption and lead to innovative products in 2022 in a number of ways: through more options for customization of LLM like GPT-3
- More options for building LLMs
- Through LLMs-as-a-service offerings
Applied AI in Health Care and Manufacturing
O’Reilly’s AI Adoption in the Enterprise 2021 survey cites technology and financial services as the two domains leading AI adoption.
- Health care is third due to the advent of COVID-19 and the rise of AI in biology and health care
- Manufacturing is last because it suffers a labor shortage that AI can help alleviate.
AI chips
So-called AI chips, a new generation of hardware designed to optimally run AI-related workloads, are seeing explosive growth and innovation
- Cloud mainstays such as Google and Amazon are building new AI chips for their datacenters
- What this means for applied AI is that choosing where to run AI workloads no longer means just deciding between Intel CPUs and Nvidia GPUs
MLOps and data centricity
Selecting what hardware to run AI workloads on can be thought of as part of the end-to-end process of AI model development and deployment, called MLOps – the art and science of bringing machine learning to production.
- In 2021, with lessons learned from operationalizing AI, the emphasis is now shifting from shiny new models to perhaps more mundane, but practical, aspects such as data quality and data pipeline management.
