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There is a significant gap between the organization’s ambitions to use artificial intelligence (AI) and the reality of how those projects turn out, said Intel’s chief data scientist. Melvin Greer said this in a conversation with VentureBet founder and CEO Matt Marshall at the Trans 0Rm 2021 Virtual Conference last week.

One of the main areas is emotional intelligence and mindfulness. The epidemic highlighted this gap: the way people had responsibilities at home and at work meant their ability to focus and concentrate could be compromised, Greer said. This can be a problem when AI is used in cybertech, such as when someone tries to use chatbot or some other anti-machine learning technique against us.

“Our ability to reach the heart of what we are trying to achieve can be compromised when we are not in an emotional state and mindful and present,” Greer said.

Align AI with cloud projects

In a recent Harvard Business Review survey of 3,000 executives in 14 industry sectors, only 20% said they actually used AI as part of their core business.

To bridge the gap between ambition and reality in AI, it is “absolutely critical” that organizations align AI with their cloud computing and cybersecurity initiatives, Greer said. While organizations think about other ongoing digital transformation initiatives – cybersecurity and cloud computing, for example – and align them with the AI ​​initiative, which becomes a force multiplier, Greer said. This initiative does not require the same skills, the same pace or the same goals, but it fits together. Cloud computing, as a place where a lot of data is stored, can be a catalyst for AI. Cyber ​​security is another because data, data models, dolls and algorithms need to be protected.

He added, “What we’re seeing is that there’s a trend point, and what we need to do is think more clearly about our digital transformation or all the other initiatives going on in artificial intelligence projects.”

Quantum Vs. Neuromorphic

Enterprise leaders have to stay up to date with trends as the sector develops rapidly, but some emerging trends are still years away from practical use. Quantum computing and neuromorphic computing are two very exciting research areas, Greer said, but not yet at the point of having commercial applications. In 2017, Intel built its neuromorphic research community with nearly 100 universities and 50 industry partners. Greer said researchers have access to hardware and computing platforms, with software development kits specifically designed as software optimization mechanisms.

“We’ll see commercial applications and neuromorphic brain-inspired computing with Quantum sooner rather than later,” Greer predicted.

Over the past few years, Intel has built itself a data-centric organization that focuses on AI as a key competency. While many companies are working to develop AI for a variety of uses, Greer said there is a significant gap between the ambitions that organizations seek to achieve and the realities associated with the insights given by data and programs. For example, Greer said organizations need to start thinking about AI’s emotional intelligence and mindfulness. In the current phase of the Covid-19 epidemic, individuals need to work on multiple tasks at once; This way the ability to focus and concentrate can sometimes be compromised.

Increasing AI capabilities

Gray noted that while AI initiative investments have tripled since 2001, many of them have gone for fear of losing rather than succeeding in AI development and deployment. Organizations need a pragmatic approach, putting aside enthusiasm, investment and activity around AI, Greer said.

One thing to keep in mind is that in some cases, AI is not the right option, he said. It is important to be “absolutely crystal clear” about solving a problem before trying to figure out whether to run deep education programs.

Understanding employees – which means there are different teams involved in the development and distribution of these capabilities, Greer said. The “lack of different talents” everyone needs to tend to represent a very homogeneous people that creates a talent pool, ”he said.

Having a data strategy

Another gap that enterprises often overlook is the amount of data they have and what they can do with it. Many enterprises do not have access to the data they need to succeed. Greer estimates that 85% of the data scientist’s job is making data available, managed and administered so that it can be used. Data needs to be categorized, managed and labeled at the point of creation. Given that data is being created at 3.7 terabytes per day, it is not easy to go back and clear the data later. Before an organization can develop an AI strategy, it must first create a data strategy.

“We’re still in a situation where if we have really bad data, we’ll do stupid things quickly with machines, and we’ll train them for things that are inherently erroneous or biased,” Greer said.

Researchers, scientists and developers must take a human-centered approach to data and AI systems. “A.I. Has published its ethical principles or human rights policy around how it should be used, and has engaged with NGOs and international organizations on how to make the best use of AI, Greer said.

“Because no, data is not oil. And data is not fuel. Data is people, ”Greer said.

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