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Equipping AI with emotional intelligence can improve outcomes

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There is a significant gap between an organization’s ambitions for using artificial intelligence (AI) and the reality of how those projects turn out, Intel chief data scientist Dr. Melvin Greer said in a conversation with VentureBeat founder and CEO Matt Marshall at last week’s Transf0rm 2021 virtual conference.

One of the key areas is emotional intelligence and mindfulness. The pandemic highlighted this gap: The way people had to juggle home and work responsibilities meant their ability to stay focused and mindful could be compromised, Greer said. This could be a problem when AI is used in a cyberattack, like when someone is trying to use a chatbot or some other adversarial machine learning technique against us.

“Our ability to get to the heart of what we’re 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, just 20% of respondents said they have actually implemented AI as part of their core business.

In order 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. When organizations think about other ongoing digital transformation initiatives — cybersecurity and cloud computing, for example — and align them with AI initiatives, that becomes a force multiplier, Greer said. These initiatives don’t require the same skills, move at the same pace, or achieve the same goals, but they do fit together. Cloud computing, as a place where lots of data is stored, can be a catalyst for AI, he said. Cybersecurity is another because the data, data models, and algorithms need to be protected.

“What we are seeing is that there is an inflection point, and what it requires us to do is to think more clearly around all the other initiatives that are going on in our digital transformation or artificial intelligence projects,” he added.

Quantum vs. neuromorphic

Enterprise leaders have to stay up to date with trends because the field is evolving rapidly, but some of the emerging trends are still years away from practical use. Quantum computing and neuromorphic computing are two very exciting research areas, Greer said, but neither is at a point of having commercial applications yet. In 2017, Intel formed its neuromorphic research community with about 100 universities and 50 industry partners. Researchers get access to hardware and computing platforms, along with a software development kit specifically designed as a software optimization mechanism, Greer said.

“We will see commercial applications and neuromorphic brain-inspired computing much sooner than we will with quantum,” Greer predicted, but noted that was still five to 10 years out.

In the past few years, Intel has made itself a data-centric organization that focuses on AI as a core competency. While many companies have been working on developing AI for different uses, Greer said, there is a significant gap between the ambition that organizations want to achieve and the reality associated with insights those data and programs delivered. For example, Greer said organizations need to start thinking about the emotional intelligence and mindfulness of AI. In the current stage of the COVID-19 pandemic, individuals need to work on multiple tasks at the same time; thus the ability to stay focused and be mindful may sometimes be compromised.

Growing AI capabilities

Greer noted that while investments in AI initiatives have tripled since 2016, many of those are driven by the fear of missing out, rather than successes in the development and deployment of AI. The enthusiasm, investment, and activity around AI aside, organizations need a pragmatic approach, Greer said.

One thing to consider is that in some cases, AI is not a suitable option, he said. It is important to be “absolutely crystal clear” about the problem to solve before trying to figure out whether to run deep learning applications.

Understanding the workforce — which means having diverse teams in the development and distribution of AI capabilities — is the most critical, Greer said. The lack of diverse talent “requires us to pretend everybody is representative of the very homogeneous people that make up the talent pool,” he said.

Having a data strategy

Another gap enterprises often overlook is the amount of data they have and what they can do with it. Many enterprises don’t have the access to manage the data they need to become successful. Greer estimated that 85% of a data scientist’s job is making data available, manageable, and governable so it can be used. Data needs to be classified, managed, and labeled at the point it is being created. Considering that data is being created at 3.7 terabytes per person every day, it isn’t easy to go back and clean data later. Before an organization can develop an AI strategy, it has to first create a data strategy.

“We’re still very much in a situation where if we have really bad data, we will simply do stupid things faster with machines, and we will train them to do things which are inherently erroneous or bias,” Greer said.

It is imperative that researchers, scientists, and developers take a human-centric approach to data and AI systems. Intel has published its ethical principles, or human rights policy, around how AI should be used, and is engaged with non-governmental and international organizations on how to use AI for good, Greer said.

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

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Categories
AI

How AI can enable better health care outcomes

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Artificial intelligence isn’t just a tool for pure tech — health care providers can use it too. Clinical practice and AI go together, three top health care leaders at national enterprises agreed during a panel at Transform 2021 hosted by VentureBeat general manager Shuchi Rana.

Using data to reduce medical waste and over-testing can help hospital systems save money, said Dr. Doug Melton, head of clinical and customer analytics at Evernorth, a subsidiary of insurance giant Cigna. “Before, we had unsupervised learning, and it was harder to do. You had to be prescriptive in your hypotheses,” Melton said.

AI has the potential to help clinicians improve patient outcomes, said Dr. Taha Kass-Hout, director and chief medical officer at Amazon Web Services. Medical records can be a great source of data to develop algorithms, speech recognition, and decision-making tools that could help doctors and nurses identify risk factors for serious illnesses such as congestive heart failure.

Early breast and lung cancer detection is another outcome that not only helps patients, but also benefits enterprise leaders. At Evernorth, Melton’s team used machine learning to analyze pre-certifications for radiology and past claims data, identifying who was at higher risk of developing more serious health issues down the line. ML improves prevention and holistic management, Melton said, and improves cost savings for both the patient and provider by as much as 3 times.

Data analytics are also key to reducing other hospital costs, said Dr. Joe Colorafi, system VP of clinical data science and analytics at Commonspirit Health. By crunching the numbers, researchers can find which hospital stays last too long and when clinicians are over-assigned to a patient.

Collecting additional data from users can also help providers determine a holistic health care plan, Melton said. For instance, information on stressors in patients’ lives and other social determinants of health, such as access to fresh food and stable housing, can anchor plans to improve health outcomes. “When we do that, I think we can have whole-person medicine instead of acute care management,” Melton said.

Think of AI as a toolbox to understand the information presented to health care providers, Kass-Hout said. Using machine learning to narrow down symptoms and diagnoses also means building a repository of information to improve health systems. For instance, the accuracy of Amazon Web Services’ model to predict congestive heart failure increased by 4% as the algorithms took in notes about how physicians were treating the condition and monitoring patients for symptoms.

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Repost: Original Source and Author Link