Do you understand the main types of AI, how they work, and where they add value? Let's break down machine learning, deep learning, natural language processing, computer vision, and explainable AI
The Enterprisers Project
"Artificial intelligence (AI) is redefining the enterprise's notions about extracting insight from data. Indeed, the vast majority of technology executives (91 percent) and 84 percent of the general public believe that AI is the 'next technology revolution,' according to Edelman's 2019 Artificial Intelligence (AI) Survey. PwC has predicted that AI could contribute $15.7 trillion to the global economy by 2030.
AI, in short, is a pretty big deal. However, it's not a monolithic entity: There are multiple flavors of cognitive capabilities. Understanding the various types of AI, how they work, and where they might add value to the business is critical for both IT and line-of-business leaders..."
Giant AI chips like the Cerebras WSE are dazzlingly fast and could transform AI models, but how soon is the question for CIOs. Experts mull the merits of small vs. big AI chips
"New types of AI chips that adopt different ways of organizing memory, compute and networking could reshape the way leading enterprises design and deploy AI algorithms," opines
George Lawton in SearchEnterpriseAI
"At least one vendor, Cerebras Systems, has begun testing a single chip about the size of an iPad that moves data around thousands of times faster than existing AI chips. This could open opportunities for developers to experiment with new kinds of AI algorithms..."
"This is a massive market opportunity and I see a complete rethink of computer architecture in progress," said Ashmeet Sidana, chief engineer at Engineering Capital, a VC firm..."
IBM and Oracle both forecast that by 2020, 80% of interaction between businesses and users will be automated
"Although that prediction may be a bit too optimistic, Robotic Process Automation (RPA) is currently widely used across industries to speed-up mundane processes and improve customer relations in the form of virtual assistants, such as chatbots. The next logical step is the implementation of RPA-assisted 'voice bots', however adoption has been slow. So, why don't more organizations and businesses deploy voice assistants to further automate customer interactions?..." - insideBIGDATA
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