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Use Case Focus: AI in Action, by H2O.ai’s Vinod Iyengar

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Vinod Iyengar is Director of Marketing at California-based developer H2O.ai. H2O.ai are the makers behind H2O, the leading open source machine learning platform for smarter applications and data products. They work across a number of mission critical applications, including predictive maintenance, operational intelligence, security, fraud, auditing, credit scoring, user based insurance, ICU monitoring and more in over 5,000 organizations. And with customers including Capital One, PricewaterhouseCoopers, Comcast, Nielsen Catalina Solutions, Macy’s and Aetna – to name just a select few – they are clearly in a prominent position in this space.

 

 Writing for AIBusiness.org, Vinod details some key use cases of artificial intelligence in specific industries, while also sharing H2O.ai’s vision for AI and the challenges we face in adopting it…

 

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Vinod Iyengar of H2O.ai

Across industries and business disciplines, businesses use artificial intelligence to increase revenue or reduce costs by performing tasks more efficiently than humans could do unaided.

The following five examples demonstrate the versatility and wide applicability of artificial intelligence:

Preventing fraud. With more than 150 million active digital wallets than $200 billion in annual payments, PayPal leads the online payments industry. At that volume, even low rates of fraud can be very costly; early in its corporate history, the company was losing $10 million per month to fraudsters. To address the problem, PayPal built a top team of researchers, who used state-of-the-art artificial intelligence techniques to build models that can identify fraudulent payments in real time.

 

Recommending content. For customers of Comcast’s X1 interactive TV service, Comcast provides personalized real-time recommendations for content based on each customer’s prior viewing habits. Working with billions of history records, Comcast uses artificial intelligence techniques to develop a unique taste profile for each customer, then groups customers with common tastes into clusters. For each cluster of customers, Comcast tracks and displays the most popular content in real time, so customers can see what content is currently trending. The net result: better recommendations, higher utilization and more satisfied customers.

 

Building better cars. New cars built by Jaguar Land Rover have 60 on-board computers that produce 1.5 gigabytes of data every day across more than 20,000 metrics. Engineers at the company use artificial intelligence to distil the data and understand how customers actually use the vehicle. By working with actual usage data, designers can predict part failure and potential safety issues; this helps them to engineer vehicles appropriately for expected conditions.

 

Targeting best prospects. Marketers use “propensity to buy” models as a tool to determine the best sales and marketing prospects and the best products to offer. With a vast array of products to offer, from routers to cable TV boxes, Cisco’s marketing analytics team trains 60,000 models and scores 160 million prospects in a matter of hours. By experimenting with a range of techniques the team has greatly improved the accuracy of the models. That translates into more sales, fewer wasted sales call and more satisfied sales reps.

 

Improving health care delivery. For hospitals, patient readmission is a serious matter, and not just out of concern for the patient’s health and welfare. Medicare and private insurers penalize hospitals with a high readmission rate, so hospitals have a financial stake in making sure that they discharge only those patients who are well enough to stay healthy. The Carolinas Healthcare System, CHS, uses artificial intelligence to construct risk scores for patients, which case managers use to make discharge decisions. This system enables better utilization of nurses and case managers, prioritizing patients according to risk and complexity of the case. As a result, CHS has lowered its readmission rate from 21 percent to 14 percent.

 

VISION

At H2O.ai, we believe that AI will become as ubiquitous, easy-to-use and powerful as search. Google, Yahoo, and others helped unleash the power of the Web for ordinary users by making it easy to find relevant results from a seemingly limitless number of pages. Similarly, artificial intelligence will allow businesses of all kinds to tap into the power of modern data sets by making it easy to get to valuable insights.

 

CHALLENGES

However, we’re obviously not there yet. There are two sets of challenges confronting organizations looking to adopt AI: technical challenges and business challenges. On the technical side, organizations often find that there is a lack of readily-available relevant data sets. Even where data is available, it is often not in a usable format. A lot of the data is unstructured, messy, filled with inaccuracies and lacking relevant metadata. Large data sets require a great deal of munging, the process of cleansing data and converting it into a clean usable format. In addition, many organizations run into a lack of awareness among lay business users about how AI can deliver results for them and why they should adopt it.

 

FUTURE

It’s difficult to predict exactly when the change will occur completely, but we’re already beginning to see it happen. I suspect that in the next decade we’ll see a radical reimagining of what AI is – from the stuff of science fiction to a practical tool used by individuals and businesses every day. To get there we’re going to need a willingness rework traditional rule-based systems and regulatory frameworks. It will also require further investments — both from artificial intelligence developers like H2O.ai, and from business users whose volumes of data and needs for analysis outstrip conventional methods. By working together, vendors and business users can make that happen!

 

H2O.ai

Our focus at H2O.ai is currently on building out our Steam AI engine. Steam streamlines the process of building machine learning models and deploying them into production. The goal is to enable the rapid operationalizing of insights. We’re also focussed on being the best machine learning technology available for Spark. And finally, we endeavour to take all of the work we do and present it in a visually intuitive manner that helps organizations get the most of their data and models.

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