/10 AI Predictions For The Next 10 Years

10 AI Predictions For The Next 10 Years

Key idea: The technology behind AI continues to evolve at a fast pace, and I believe this will only accelerate in the future.

Original author and publication date: Nir Caldero – June 2, 2022

Futurizonte Editor’s Note: It seems that we see AI (and not ourselves) as both the future and the enabler of the future.

From the article:

1. Reaping rewards will be among the most important trends over the next 10 years. I believe companies will be rewarded for their efforts moving from models either in pilots or MVPs to full deployment at a large scale. Companies will fully complete the first phase of becoming data-driven or speeding-to-insight through the full consolidation of their data into the multicloud and hybrid cloud environment. This will allow them to harness the power of their data and AI for every strategic and important business problem and decision. Importantly, moving to the cloud will not be a massive radical operation anymore.

2. The cloud will gain even more presence, but there will be a shift toward open-source technology services blended into a company’s cloud environment. Among the major reasons, cloud consumption cost is becoming too expensive as we scale up models in production. It reaches close to a point where the benefits are channelized by the major costs, and companies will have to figure this out. Still, I believe current cloud service costs will decline over time following the tendency of any technology as it proliferates. In contrast, though, this trend will also be met with more powerful yet pricey services around operations at the scale of AI.

3. When it comes to MLOps at the core and scale, we will see massive movement in terms of skills from “building a model” to “fully deploying models.” As this trend peaks, however, we must also pair its evolution of the field by increasing the number of machine learning (ML) engineers to prevent employment bottlenecks.

4. We will continue to see more blending methods and inputs for a greater good.

Already, we see how blending different data inputs and techniques can help to create better models that are also less expensive to train. Image recognition blended with natural language processing (NLP) techniques will continue to dominate and improve model performances as we move closer to a digital world with infinite interactions to leverage over “movements” (images) and both written and spoken text.

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