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Hype versus hope: Upcoming applications of AI

Panelists at this year’s AI media panel at PARTNERS 2017 debated the ins and outs of AI.

2017년 11월 7일 4 최소 읽기

What is artificial intelligence? Ask multiple people and you’ll likely get multiple answers. Is it chatbots? Is it really good analytics on an unimaginably large data set? Is it a computer beating a human at chess?

Everyone has a different perspective, but one thing it’s certainly not is under-hyped.

Panelists at this year’s AI media panel at PARTNERS 2017 debated the ins and outs of AI, reframing the conversation to get past how trendy the topic has become and focusing on the promise it holds today for enterprises.

Overcoming the hype

The panelists agreed that a business must have a strong use case defined before it throws itself into AI adoption.

“Don’t just get shiny new toy syndrome,” warned Andrew Stephen, associate dean of research at the Said Business School at University of Oxford. There is a substantial contrast between buying an Amazon Echo for $100 and placing an eight-figure bet on an AI system that requires a board of directors write-off, he said. The difference is that analytics for enterprise must scale — and executives must learn and adapt quickly to create validity around their AI investments.

Yasmeen Ahmad, director at Think Big Analytics, a Teradata company, agreed that business problems create essential focus around AI for enterprise.

“You need to do some experimentation and pilots, but focus them on a business problem or use case you have in mind,” she said. That gives companies the drive to apply new techniques and do something revolutionary. Then they can build out a sustainable AI roadmap, she said. Key to applying AI at scale, though, is less hands-on interaction from data scientists.

“It’s not just about deploying one model,” Ahmad said. Businesses must deploy “many [models] that can self-learn and adapt.”

Stephen Brobst, chief technology officer at Teradata said automation is the key differentiator between what people are calling AI and machine learning versus its much older cousin, linear mathematics.

“Automation discovers new features. That ‘learning’ [in machine learning] is important, because before data mining was manual.” And in a post-big data world, that model is unsustainable.

Human skillsets in an AI world

So once businesses successfully build an AI use case and invest in machine learning and deep learning applications, what skills will humans need to complement it?

Surprisingly, the panelists didn’t advocate for a future where we all need to learn how to code. Instead, they said to focus on developing critical thinking skills. Then, when AI requires less feature engineering from humans, businesses can rest assured that their models aren’t degrading without anyone noticing.

“For the marketer of the future, that means you have to know how to interact with those machines or model outputs,” says Ahmed. “You will have to understand what machines and algorithms are doing. You have to add a human element to that.”

Ensuring machine learning models aren’t operating in a black box is critically important to Danske Bank right now. The company uses a deep learning model implemented by Teradata to monitor fraud, and if a customer calls wondering why their transaction was turned down, Nadeem Gulzar, head of global analytics for Danske Bank, says he needs to have an answer.

“We have to trust that the AI model will work, but you also have to be critical,” he said. “[If the] model is saying you should go left, but you have the strongest feeling you should go right, question it. Is it really working or is something wrong?”

AI’s top applications right now

Moderator Martin Willcox, Teradata’s director of its Big Data Centre of Excellence, asked each of the four panelists what their favorite application of AI is right now, and there was across-the-board enthusiasm for health care applications.

Brobst described a future where predictive analytics could determine when someone is going to need emergency care, or even intercede to deter future health problems.

Stephen, who has focused some of his research on how social media affects mood, was interested in day-to-day health, helping people spend their time more wisely to maximize their psychological well-being.

Ahmad, who worked in life sciences before becoming a data scientist, anticipates a day where automated experiments could prove as valid as physical experiments, so researchers could virtually simulate the validity of drug trials or gene therapy. Then, health care could pivot from its one-drug-fits-all approach. “To get down to personalized medicine, you need that compute power to analyze at scale,” she said.

Gulzar emphasized the efficacy of health care AI applications versus other industries.

“The amount of data we are able to produce and monitor, you end up saving lives. That’s a big thing,” he said. “Yes you can upsell, but saving lives is actually out of this world.”

For resources on how to implement AI in your enterprise, go to the Teradata AI Insights page.

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약 Martin Willcox

Martin has over 27-years of experience in the IT industry and has twice been listed in dataIQ’s “Data 100” as one of the most influential people in data-driven business. Before joining Teradata, Martin held data leadership roles at a major UK Retailer and a large conglomerate. Since joining Teradata, Martin has worked globally with over 250 organisations to help them realise increased business value from their data. He has helped organisations develop data and analytic strategies aligned with business objectives; designed and delivered complex technology benchmarks; pioneered the deployment of “big data” technologies; and led the development of Teradata’s AI/ML strategy. Originally a physicist, Martin has a postgraduate certificate in computing and continues to study statistics.

모든 게시물 보기Martin Willcox

약 Yasmeen Ahmad

A strategist and change leader, Yasmeen Ahmad has worked on executive teams with focus on defining and leading strategy, driving priorities with a sense of urgency and leading cross-functional initiatives. Yasmeen has held roles including VP of Enterprise Analytics, Head of Global Communications and Chief of Staff to a CEO. Her creativity, ideas and execution have supported organizations to move quickly to deliver on key transformation objectives, including pivots to analytics, as-a-service, subscription and cloud. 
 
Yasmeen is a strong communicator, well versed in connecting business and technical disciplines. Her keynote presentations, articles and published materials are demonstration of her thought leadership and ability to simplify complex concepts. She is regarded as an expert in the enterprise data and analytics domain, having successfully consulted to deliver multi-million dollars of value within Fortune 500 companies. Yasmeen leads with a passion for being customer obsessed and outcome focused. A strong people leader, Yasmeen has driven change management and people initiatives to foster a culture of growth and continuous improvement. Yasmeen is a strong proponent for transparency, diversity, inclusiveness and authentic leadership. 
 
Yasmeen has a PhD in Life Sciences from the Wellcome Trust Centre in Gene Regulation and Expression and has studied on executive programs related to Disruptive Innovative and Strategic IQ at Harvard Business School. Yasmeen has been named as one of the top 50 data leaders and influencers by Information Age and Data Scientist of the Year by Computing magazine, as well as being nominated as a Finalist for Innovator of the Year in the Women in IT Awards. Finally, Yasmeen is part of the exclusive Executive Development Program at Teradata.
 

모든 게시물 보기Yasmeen Ahmad

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