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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. Here’s what’s involved in making that happen.

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The importance of diversity in AI isn’t opinion, it’s math

IBM Journey to AI blog

How might this insight affect evaluation of AI models? Model (in)accuracy To quote a common aphorism, all models are wrong. This holds true in the areas of statistics, science and AI. Models created with a lack of domain expertise can lead to erroneous outputs. How are you making your model explainable?

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How to responsibly scale business-ready generative AI

IBM Journey to AI blog

According to Precedence Research , the global generative AI market size valued at USD 10.79 in 2022 and it is expected to be hit around USD 118.06 Generative AI and risky business There are some fundamental issues when using off-the-shelf, pre-built generative models. by 2032 with a 27.02% CAGR between 2023 and 2032.

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The most important AI trends in 2024

IBM Journey to AI blog

2022 was the year that generative artificial intelligence (AI) exploded into the public consciousness, and 2023 was the year it began to take root in the business world. iii] “AI models haven’t had that kind of data before. Those models will just have a better understanding of everything.”

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MLOps and the evolution of data science

IBM Journey to AI blog

Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). For instance, human experts bring domain knowledge and expertise that can complement AI systems. Explaining Explanations in AI || SSRN Onose, E.

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What Leaders Want: Shifting to AI-Driven Healthcare

DataRobot Blog

In our previous healthcare blog , Sally Embrey explained how the integration of health and care services is gathering pace globally and how the creation of Integrated Care Systems (ICSs) by England’s National Health Service (NHS) is the latest example of services being organized around a local population. Learn More.