The global use and further development of AI continued to grow in 2021 as enterprises found more ways to deploy it and developers discovered new ways to capture its possibilities for business users.
So, what might 2022 bring for AI and a wide range of related IT fields from MLOps to security, cloud and edge computing, open source, the metaverse and more?
To answer that question, we received a wide range of predictions from IT industry experts who shared their thoughts with EnterpriseAI. We are publishing them here, edited for clarity and brevity, to give our readers an early look at what may come in 2022 in enterprise AI and related technologies.
IT leaders should see more Robotic Process Automation (RPA) efforts mature and deliver business results. Here are four things to look for
Robotic Process Automation (RPA) in 2022 won't be about what's new and shiny, but rather the evolution and maturation of trends already underway.
This should be welcome news for IT and business leaders who see RPA as a single tine in a multi-prong automation strategy. 'New and shiny' does not necessarily produce results. But 2022 in general is likely to be a year where boards, investors, customers, and other stakeholders ask: Where are the results?
To put it more specifically: Where are the results from those outsized investments you've been making in digital transformation, AI/ML, cloud, and elsewhere?
The pace of technological change increased in 2021, and if history is any guide, will continue to accelerate in 2022.
At the leading edge of high tech are data science and artificial intelligence, two disciplines that promise to keep the pace of change at a high level.
Interest in AI, machine learning, and data science is extremely high, if the number of predictions on these topics is any indication. We start this batch of predictions with DataKitchen CEO Chris Bergh, who notes that the global AI market is projected to grow at a compound annual growth rate (CAGR) of 33% through 2027. But that significant growth comes with a hidden risk: reputational harm due to bias and a lack of accountability in AI processes.
In today's digital age, artificial intelligence (AI) and machine learning (ML) are emerging everywhere: facial recognition algorithms, pandemic outbreak detection and mitigation, access to credit, and healthcare are just a few examples.
But, do these technologies that mirror human intelligence and predict real-life outcomes build a consensus with human ethics? Can we create regulatory practices and new norms when it comes to AI? Beyond everything, how can we take out the best of AI and mitigate the potential ill effects? We are in hot pursuit of the answers.
AI/ML technologies come with their share of challenges. Globally leading brands such as Amazon, Apple, Google, and Facebook have been accused of bias in their AI algorithms. For instance, when Apple introduced Apple Card, its users noticed that women were offered smaller lines of credit than men. This bias seriously affected the global reputation of Apple.
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