5 Use Cases for AI in Utilities

Use Case 1: Energy Theft Prevention

An industry-wide shift to advanced metering infrastructure (AMI) has been a boon for the utilities industry, allowing providers to handle peak-load conditions and meet demand fluctuations in real time. What’s more, corresponding technologies like IoT and digital twins have vastly improved maintenance efficiency while reducing total operational costs. Overall, AMI has been a huge win for the sector as a whole.  

One downside? Anti-fraud technology has failed to keep pace with the rate of infrastructure change, leaving these automated systems highly susceptible to theft as a result. Around the world, the numbers speak for themselves. In Canada,  B.C. hydro has seen electricity theft increase from 500 GWh in 2006 to 850 GWh today. Developing countries, like Brazil, have to contend with the fact that 1/5 of all generated electricity is stolen.   In the US, $6 billion worth of electricity is lost to piracy every year, making energy the third most stolen good in the country (credit card information and cars rank 1 and 2). All told, energy theft accounts for almost $100 billion in losses each year around the globe. That’s a terrible number for energy producers, but an even worse statistic for consumers, who end up bearing the brunt of those costs.  

Artificial intelligence and machine learning technologies can combat energy piracy by enabling utilities providers to leverage the vast data sets produced through AMI upgrades. Pattern-detection models equipped with entity-recognition capabilities can scan individual customer profiles and flag suspicious discrepancies between billing and usage data. In fact, automated theft-prevention pilots are already making real headway here. One such project in Brazil can accurately pick out fraud at a 65% hit rate, which outpaces similar tools on the market. 

Thanks to AI, soon enough, energy thieves will have nowhere left to hide. 

Energy Theft Prevention

Use Case 2: Sentiment Analysis in Digital Marketing

Building out individual consumer profiles and keeping tabs on long-term behavior doesn’t just allow utilities companies to flag potential bad actors, it also opens up opportunities to identify, reward, and retain legitimate consumers, while informing strategic initiatives to grow a company’s overall customer base.   

Sentiment analysis models can produce both macro-level analysis (sifting through social media postings and online reviews) and micro-level insights (keeping tabs on individual customer interactions with customer-service representatives and virtual agents) that allow utilities providers to better serve targeted marketing campaigns to specific geographical locales and  individual customers.

Who’s at risk of churning? Who may be looking to upgrade their services? These are all questions AI can help answer.       

Digital Marketing

Click here for an infographic with all the 5 Use Cases.


Use Case 3: Virtual Agents and Chatbots

As digital technologies lower barriers to entry in just about every sector, asset-intensive businesses, like utilities, increasingly face challenges from new competition. With a consumer base spoiled for choice, customer-service capabilities and consumer-engagement initiatives have become all the more critical to long-term success. It’s no surprise that in Gartner’s “2019 CIO and CEO Agenda: A Utilities Perspective,” industry decision makers ranked “Customer Experience” as a top business priority for 2019.

We’ve spoken at length about how chatbots can meet growing customer expectations saving companies time and money today, while  setting the foundations for one-to-one communication channels down the line.     Utilities providers can draw inspiration from cutting-edge chatbots in other sectors, who do much more than regurgitate store locations and opening hours. Bank of America’s Erica helps customers understand their financial habits through animated spending paths, while Shell’s virtual avatars Emily and Ethan  use Natural Language Processing to assist customers in finding specific goods in massive product databases.

Utilities chatbots could help customers track their energy usage, provide useful pointers on increasing efficiency and, through product marketing partnerships with appliance makers, even offer recommendations for energy efficient appliance upgrades while projecting the long-term savings resulting from their installation. 

Virtual Agents and Chatbots

Use Case 4: Predictive Maintenance

The crown jewel of AI in utilities has long been predictive maintenance. The goal? To create automated systems that continuously monitor critical infrastructure in order to:

A) Notify technicians of minor issues (think a loose screw on a wind turbine) before those issues compound into major costs (think that loose screw leading to permanent damage on the turbine’s rotor blades).

B) Analyze mass data sets from IoT-enabled infrastructure and accurately predict the likelihood that any particular asset will need to be replaced/repaired in the near to mid-term.  

The potential benefits here are enormous. Companies like Duke energy are already saving tens of millions of dollars through early detection of dips in asset performance, which allows them to nip minor issues in the bud before they get a chance to snowball. As the technology becomes more advanced, predictive maintenance will solve other industry pain-points too, such as redundancies in backup infrastructure purchases.



Click here to discover how DefinedCrowd’s data expertise puts EDP on the path to predictive maintenance.


Predictive Maintenance

Use Case 5: Document Management

At DefinedCrowd, we’re proud to have worked with visionary companies like EDP, who are leveraging expertly-annotated computer vision training data to implement predictive maintenance practices. For more, check out our case study linked above. 

Even with advanced document management systems in place, information workers (lawyers, paralegals, accountants, and compliance officers) still waste 10% of their time chasing down improperly filed documents, or recreating lost files altogether. 

In the utilities sector, document management issues are often compounded by the vast number of subcontractors and independent distributors present in the field, all of whom bring their own unique invoicing and documentation procedures into the fray. It’s not unheard of for invoicing and other record keeping to be delivered in hand-written form at times, particularly in rural areas.

NLP models capable of entity recognition, document segmentation, and digital transcription can order the chaos by digitizing hand-written documents and segmenting contracts and invoices into individual sections and clauses. From day one, these capabilities can exponentially improve contract organization, and give those information workers back their lost time. Down the line, these models can also assist with contract creation and negotiation by executing the legal research grunt work that arms teams with provisions, briefs, and court filings relevant to specific contract disputes.


Click here for an infographic with all the 5 Use Cases.

10 Tips For Building a Successful Chatbot

“Building a bot is easy. Building a bad bot is even easier”-  Norm Judah (CTO-Microsoft)

Intro:

Globally, businesses spend $1.3 trillion on 265 billion customer service calls every year. As a result, brands across industries are investing in chatbots as a way to save time (99% improvement in response times) and money, (30% average drop in cost-per-query resolution) while increasing customer satisfaction.

But, that holy trifecta only comes to fruition if the bot gets things right every single time. Without precision training data, models trip up on simple tasks, consumers get frustrated, and the whole thing falls apart. 

While an average company may look at chatbots simply as a means of cutting costs, industry-leaders understand that AI opens the door for entirely new and innovative products. Take banking customers, for example, who identified their top priorities in a study by CGI group as follows:

  • To be rewarded for their business
  • To be treated like a person
  • To be able to check their balance anytime they wish
  • To be provided with wealth-building advice
  • To be shown spending habits and given advice on how to save

Forward-thinking banks know that by investing in a chatbot today, they’re laying the groundwork for a technology that, down the line, will allow them to hit every single one of those customer priorities. They’re investing accordingly and according to the McKinsey Global Institute, they’re building an insurmountable advantage as a result.

With that in mind. Here are my top 10 tips for keeping a chatbot initiative on the road to long-term success:

1. Know The Story:

Intents are the fundamental building blocks of task-oriented chatbots. Think of them as the problems that your agent will need to be able to resolve. In a banking scenario, these could be anything from checking an account’s balance, to wiring money, or checking branch hours. You need to understand your customers’ needs and map them out into well-defined actions (intents). Make flowcharts that delineate every possible flow of a conversation from point A to point B. Understand how the customers intents are interlinked, and determine whether there is a logical order between them. If you don’t do this exhaustively, your bot will be thrown by even the slightest variations.

2. Get Your Entities Straight

If intents define the broad-level context that determines a chatbot’s capabilities, entities are the specific bits of information the bot will need in order to execute those actions. That means when a bot recognizes an intent, like wiring money let’s say, it also needs to know the recipient and monetary amount to be transferred (at the very least). Intents can be as complex as needed, containing both mandatory and optional entities (like source account or currency, in the money wiring scenario).

3. Divide To Conquer

Don’t expect intents to come with all their requisite entities in just one turn. People leave things out. Nobody types, “I’m looking to wire $500 from my savings account to Mike Watson.” Things like “Wire $500” are much more common. Consider what further steps your bot will need to take in order to fill in the gaps. Zoom in on those flowcharts from step 1 and, for each intent, map out all the possible entity combinations. Design the conversation flow accordingly.

4. “If I Remember Correctly …”

Your bot needs to remember things! Keep track of recent interactions (intents and entities). People tend to ask follow-up questions, and it’s a nice touch to be able to answer without the redundancy of requesting information they’ve already provided. Imagine that a customer asks for a specific bank branch address. The bot successfully responds to the intent, and then the user asks: “And when does it open?” The best chatbots will answer immediately, understanding that the conversational subject is still that same branch. Keep in mind that the same can be true of intents: A customer may ask “What are the Greenwood branch hours?” followed by “What about Capitol Hill?”

5. Know What To Do When You Don’t Know What To Do

Prepare to not understand everything your customer wants, and know how to respond accordingly. You can simply say, “Sorry, I didn’t get that,” but the best bots (like the best customer service reps) provide more useful responses, such as “I didn’t quite catch that. Do you want me to perform an online search?” Or, “I didn’t quite catch that. Do you mind asking the question a different way? Or shall I connect you to an agent?”

6. “Let’s Run It From The Top”

Even though you’ll do everything in your power to avoid it, your bot could get lost in complex conversations where customers express a high number of unique intents. That’s why users should always have the option to restart the conversation from scratch. A clean slate beats a long stream of frustrating interactions from which you won’t be able to recover.

7. Control what You Can Control

You can’t control what the customer is going to say, but you sure can control how your bot will respond. Invest in variability. Different greeting and parting phrases are a nice touch, as is addressing customers by name.

8. Quality Is Variability. Variability Is Quality.

People express the same intents and entities in a multitude of different ways. Investing in data collection that gathers comprehensive variants for how people express certain bits of information is one of the most important steps on the road to building successful virtual agents. Only then will your bot understand that “How much did I spend between November 1st and November 31st” is the same as “How much have I spent this month.”

9. Sound like a Local 

People in the Pacific Northwest might refer to their savings accounts as “rainy-day” funds, whereas customers in the deep south may prefer the term “honey-pot.” On the global scale, in the US, people like to say “checking account,” but in the UK, “main” or “current” are the more popular terms. A globalized company looking to serve a broad customer-base needs to understand how different consumer blocs speak at a granular level. That way, their bot can properly interface with every customer. Here, once again, the world’s most clever algorithm won’t save you. It’s all about the data.

10. Precision. Precision. Precision. 

To quote Google’s Peter Novig, “More data beats better algorithms, but better data beats more data.” Collecting a lot of variants and running them through intent classifiers and entity-taggers only works if that data is annotated correctly. When a customer says, “check balance,” your bot needs to understand that “check” can serve as both a noun and a verb depending on the context. Otherwise, your costumers will be ramming their head against the wall with something as simple as checking the balance of their savings account. All the data in the world does you no good if it’s improperly annotated.

An Interview With Dinheiro Vivo

At last week’s Web Summit, we were lucky enough to sit down with Dinheiro Vivo, a leading financial publication in Portugal. Our conversation touched on everything from the quality-focused approach to training data to AI use-cases across industries. Watch the full interview here (in Portuguese), or check out the English transcription below:

Dinheiro Vivo [DV] – For those who still do not know your work, what does DefinedCrowd do?

Daniela Braga [DB] – We are a data collection and cleaning platform for Machine Learning and for Artificial Intelligence. Our platform combines crowdsourcing with machine learning. A mixture of people and machines working at the same time.

Artificial Intelligence is the imitation of a human brain, artificially. To develop our own intelligence, we go to school, we read many books. It takes a lifetime for a person to be able to react and make decisions in their daily lives. The way machines learn is similar. But with the computational capacity that is currently possible using the cloud and our platform, in just 3 months we can combine thousands of human brains in the same computational memory.

Our platform combines crowdsourcing with machine learning. A mixture of people and machines working at the same time.

DV – And then (the data) is used in applications that we use every day?

DB– Namely Apple Siri, Google Assistant, Alexa. Self-driving cars and even more industrial applications like machines that are doing quality control instead of having people doing it. Or at airports with automatic flight controllers.

DV – And this year was a dazzling year, an investment round, new partners, an office in Tokyo. What have you done to achieve this success?

DB – Especially in the United States, our largest market is still the United States, followed by Japan and followed by Europe. Clients are more open, they’re investing in Artificial Intelligence.

There are many companies in machine learning but there is practically no one doing data cleaning and treatment like us. It’s like the spades and pickaxes of the gold rush. We are making the shovels and picks of AI, of modern times.

DV- And coming to Web Summit also makes all the difference, right?

DB– Our growth milestones have been basically aligned with those of Web Summit. We have been here since 2016, which was the first year Portugal hosted Web Summit. We had closed our seed series. Last year (2017) we basically met the group of investors to close the series A. And this year we are here to be on the list of the top 10 AI companies in the world.

DV – And finally, when you leave Web Summit, what do you expect to take with you?

DB – This year it’s basically a visibility and recruitment maneuver- we are in an aggressive recruitment phase. We want to demonstrate that this is really the best place to work in Portugal. We’re also looking to continue developing partnerships, and solidify go-to-market strategy. Next year, I would like for 50% of our revenue to come from partnerships.

DefinedCrowd Closes $11.8 million Series A Funding Round

Big news here at DefinedCrowd this week! Less than three years after our founding in August 2015, I’m beyond proud to announce the closing of an $11.8 million Series A funding round led by New York/Zurich based Evolution Equity Partners.

We’re delighted to welcome Kibo Ventures, EDP Ventures and Mastercard as new investors and are happy that Sony, Portugal Ventures, Amazon and Busy Angels have continued to put their faith in what we’re building.

It’s been a big year for us so far. In January, we publicly unveiled our SaaS platform, which helps data scientists collect, enrich, and structure data to train AI and ML models.

People have noticed.

“DefinedCrowd’s SaaS platform has very quickly positioned the company as an innovative leader to solve AI/ML’s global most pressing problem, the need for continuous access to highly accurate data,” says Dennis Smith, Founder and Managing Partner at Evolution Equity Partners and the newest member of DefinedCrowd’s Board of Directors.

Turns out he’s not the only one who’s been unable to resist jumping on board. In April, after opening our fourth office (the first in Tokyo), Stephen Rauch — a former Starbucks, HBO, and Microsoft Executive — joined DefinedCrowd as our VP of Product.

This Series A round is a real milestone for DefinedCrowd. Again, it’s only been three years since we started on this road, and we’re thrilled to have gotten ourselves to this mile-marker in such a short period of time.

It also means we’re raring to fuel up and drive on. After all, with over 500,000 processed units/day, a growing crowd on Neevo that’s already 45,000+ strong, and data collected in over 46 languages,  we’re used to moving fast around here.

AI models are like high-performance vehicles. Data is the fuel that keeps them running smoothly. Imagine you’re driving your brand new $500,000 Ferrari off the lot. You pull up to a gas station to fill up. Would you risk damaging that beautiful, 700 horsepower engine with unleaded fuel? No. You’re choosing premium. Only the highest quality will do.

That’s where we come in. We’ve been fueling the AI initiatives of Fortune 500 companies from day one. This new capital means we’ll be able to continue doing so, at larger scale, as we offer more clients more solutions to their AI needs.

Expect to hear more from us as we develop our product offering, double our team by December 31st, increase revenue six-fold, and rapidly increase our global market share through strategic partnerships (More coming on this soon!).

We also have big plans to grow and qualify our crowd on Neevo and ensure data security through GDPR compliance and ISO certifications.

2018’s already been a big year at DefinedCrowd, and the future looks bright. More big news coming. Stay tuned!

In the meantime, if you’re building something of your own, check out  our solutions, or write to us at sales@definedcrowd.com.

If you want to build with us, check our Careers Page for current openings.

Who’s Wearing the Sunglasses? Reflections from ACL

Networking, Recruiting, and how DNN demands will drive our future growth

This past week, I had the tremendous opportunity to travel to Melbourne, Australia for the 56th annual meeting of the Association of Computational Linguistics (ACL). This is the single largest gathering for the NLP and ML communities in the world, which meant a busy week for Daniela and I. We made great new contacts, attended a fascinating keynote address on the development of Deep Neural Networks, and met some brilliant talent spread across enterprise and the academy that will drive AI into the future.

With three years of steep growth under our belt, this year’s ACL was a chance for us to define our leadership role in driving this industry toward that future. Our booth had a steady stream of visitors, many representing some of the biggest names in AI on the planet. We weren’t surprised. It’s been a bit of pattern for us this year as we’ve formed a wide array partnerships and rapidly expanded our client roster in 2018.

Booth_Image

More businesses are realizing the value of sourcing high-quality, scalable training data (and the high costs of settling for anything less). We’re uniquely positioned to provide exactly that, which means we’ve been having some fascinating conversations with some incredible companies lately. As always, stay tuned for more on this soon!

For now, as we return to our bases in Seattle and Lisbon we’ll certainly be discussing Anton van den Hegel’s invited talk, “Deep Neural Networks and what they’re not very good at.” As Daniela’s been saying for years, Deep Neural Networks (DNN’s) have long been the “holy grail” of machine-learning development, as they offer the clearest path to self-learning AI that can truly improve on-the-fly.

DNN’s are already responsible for major breakthroughs in fraud detection and manufacturing optimization. But, for all they’ve accomplished, DNNs still fall short in tasks that require contextual interpretation.

Take the image below:

Who's wearing the sunglasses?
Photo By Heather Shwartz, sourced from Unsplash

 

If I asked you, “Who’s wearing the sunglasses?” You’d say “the pineapple” without second thought. DNN models? Not so much. They’re not capable of integrating the linguistic, visual, and contextual understandings necessary to come up with the correct response. At least… not yet.

As always, the barriers to these capabilities are falling. The brilliant scientists and engineers we met at ACL are…well… brilliant after all.

But, for DNN’s to obtain these contextual reasoning and interpretation capabilities, they’ll need incredibly precise, accurate and complexly structured data. DefinedCrowd is the only firm that can deliver the kind of job-tailored high-quality data necessary to train and test these kinds of models. It’s an exciting time to be here!

Which brings me to my final point. As more demand for our data grows, our team will too. Actually, it already is. Right now, we have 23 open positions across our offices in Seattle, Lisbon, Porto, and Tokyo. We met a lot of great talent at ACL. To all of you who stopped by our booth, it was a pleasure. If you passed on a resume, you’ll hear from us soon.

And if we missed you? There’s still time! Go to our careers page to see how to come build amazing things with us.