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.

Wrapping up Web Summit

According to The Atlantic, it’s the place “where the future goes to be born.” The always descriptive New York Times has dubbed it “a grand conclave of the tech industry’s high priests.” After three consecutive trips to Web Summit, we’ll go ahead and call it one of the most thought-provoking events we attend year-after-year. We’ve already marked our calendars for 2019 and can’t wait to see faces, old and new, next November in Lisbon.

This year’s highlights included a DefinedCrowd pitch on the Growth Summit Stage, a fascinating panel on the future of AI featuring our founder, Daniela Braga, alongside Sam Liang (AISense), Jean-Francois Gagné (Element AI), and Vasco Calais Pedro (Unbabel), all leaders of scaling startups with their fingers on the pulse at the crossroads of technological innovation and global enterprise. It was also a pleasure to share the stage with António Mexia, CEO of EDP, which is the largest energy provider in Portugal as well as one of our clients and investors.

“We learn during years and years. We go to school, we read many books. It takes a life 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, with the cloud and with our platform, in 3 months it is possible to combine thousands of human brains in the same computational memory. “ 

-Daniela Braga DefinedCrowd Founder and CEO 

Missed those speeches and panels? Don’t worry. Daniela spent a lot of time off-stage sharing thoughts (like the one quoted above) on the present and future of AI with outlets such as Expresso, RTP, Journal Economico, Noticias ao Minuto, TVI 24, and Dinheiro Vivo. We’ve linked them all here [in the original Portuguese] for your convenience. Go ahead and have a look!

Of course, the most enjoyable part of events like Web Summit is the conversations that are sparked after a speech, panel, or simply as a result of wandering in front of the right booth. We know how valuable those discussions can be as they develop into formalized relationships with future investors, clients, thought-leaders, and partners.

We’re happy to have started a lot of these dialogues with contacts across industries and media at booth G111. To those we met, it was a pleasure. Let’s keep talking. To those we didn’t (or those who misplaced our business cards), we’re always available at pr@definedcrowd.com. We’re looking forward to hearing from you.

See you all next year!

See You at Web Summit 2018

 

Three years in a row makes for a tradition. Web Summit is here again! Taking place next week, November 5th-8th in Lisbon. We feel like we’ve grown up alongside this event, and know that it’s been critical to our success thus far. At last year’s conference, we launched our public API and met investors like Evolution Equity Partners, who ended up leading our series A. We can’t wait to see what doors open this year.

We’ll be spending most of our time at Growth Summit, which focuses on rapid-growth startups. Wednesday, November 7th, at 10:12am, founder and CEO, Daniela Braga, will take the Growth Stage to discuss her insights from three years spent at the helm of DefinedCrowd. Shortly thereafter, at 11:00am, she’ll return to discuss broad industry themes as part of the Hyper Growth in AI panel.

Finally, in the afternoon from 1-2pm,  Daniela will meet with three female entrepreneurs for 20-minute one-on-one sessions as part of Web Summit’s Women in Tech Mentor program. 

We’re excited for a busy, productive week and can’t wait to see you there. If you’re unable to make these events, or just want to chat in-person, we’ll be at booth G111 all day November 7th.

In summary:

  • Daniela will present DefinedCrowd on the Growth Stage November 7th at 10:12 am;
  • Shortly thereafter, she’ll serve on the Hyper Growth In AI panel at 11:00am, also on the Growth Stage;
  • From 1-2pm, Daniela will meet 1-on-1 with participants in Web Summit’s Women in tech Mentor Program;
  • We’ll be at booth G 111 all day November 7th.

We’re looking forward to spending the first part of November in sunny Lisbon. Stop by our booth, drop us a line on social, or email us at sales@definedcrowd.com if you’re looking to get in touch. See you all next week!

 

We’re teaming up with IBM to make high-quality training data more accessible than ever

Our product Integration with Watson Studio means researchers can now access high-quality training data to train, test and build models all on one platform.

Today marks another huge product milestone here at DefinedCrowd; I’m very excited to announce our product integration with IBM’s Watson Studio. A lot of hard work went into my being able to type those lines this morning, all well worth it for us and our end-users. With DefinedCrowd’s data solutions embedded within Watson Studio, customers can now source, structure, and enrich training data directly from the Watson Studio.

From a logistical perspective, users will be able to set up DefinedCrowd’s customizable data workflows through a dedicated user interface unique to the Watson Studio, the goal being to offer a seamless, one-stop solution for researchers looking to build, train, and test AI models all on the same platform.

In addition to more accessible data, we’re also providing users with quality guarantees that will ensure high-performing results. We’re launching this collaboration with two of our most in-demand workflows, Image Tagging and Text Sentiment Analysis. It’s crucial that these sorts of datasets are sourced and delivered with precision and accuracy, as we’ve detailed in our series on data-labeling. Earning a tech giant like IBM’s trust in handling such critical workflows is a real testament to our work so far.

It’s been a treat to work alongside IBM during this process. A product integration like this doesn’t just increase our exposure externally. Internally, we also get a chance to really examine our core product offerings and focus on how they can be expanded and improved. My team is hard at work on exactly that, and we can’t wait to share all the things we’re building with you. I promise you’ll be hearing from me again very soon.

This integration with IBM fits with an emerging pattern of joint-initiatives between various tech leaders and DefinedCrowd. Earlier this year, we were chosen as an official Amazon Alexa Skills partner, and we’ll be announcing another big collaboration later this year. Joint-efforts like these are an enormous part of our efforts to improve our product offerings to serve a wider array of clients.

We’re ambitious here. Our goal is— and always has been— to stake our claim as the first-choice service provider for high-quality AI training data. Product integrations like this one with IBM are massive for expanding our product capabilities—always my number one priority— and diversifying our client roster. We’re well on our way on both fronts.

So, if you’re an IBM Watson Studio user, we’ll be right there to help the next time you’re building an image recognition or text sentiment analysis model.

Not a Watson Studio customer? Or, need something other than image tagging and sentiment annotation services? Worry not. Check out our wide array of data solutions or email us at sales@definedcrowd.com.

Why precision data-labeling remains the essential ingredient for successful AI

When we talk about AI, things like driverless cars, lightning-speed medical diagnoses, and smart infrastructure tend to dominate the conversation. That makes sense. On any given day, you might find us talking about those same scenarios around our water coolers too. But, we’d be naive to think that future use-cases like these are simple inevitabilities. For AI to deliver on its potential, we’ll have to peel back the curtain and scrutinize how these models are made.

It’s no secret that the vast majority (90%) of data floating through the digital realm is unstructured. As a result, it’s critical that AI models are properly trained to make sense of that ever-growing pile of text, images, audio and video.

That’s why precisely annotated data is to AI models as high-quality ingredients are to a fine meal. With strong datasets as a base, AI “chefs” can confidently focus on their craft. Without it, they’re trying to make French Onion Soup with no butter and a bag of rotten onions. Things can only end badly.

While we’ve had thousands of years to perfect the art of cultivating produce and harvesting grains, we’re not so far along when it comes to AI training data. Of course, everybody knows that better ingredients make for better products, but as an industry, we’re still in the early years. Right now, that means we’re constantly finding out all the minute seemingly inconsequential details that can cause a training dataset to “spoil.”

AI and ML scientists know this all too well. Right now, most of them spend more than half their time retroactively “scrubbing” tainted training data, trying to salvage what they can.

Take text sentiment annotation, for example. The goal is deceptively simple: Does this sentence express positivity, negativity, or neutrality? However, when you consider the domain-specific, ever-in-flux slang that dominate subcultures across social channels, you start to understand all the ways that can go wrong.

To illustrate the point, let’s consider the following two sentences. “What a screamer!” and “What a howler!” On the surface, those are two sentences with the same structure and meaning. Agree? Good. But now, let’s pretend we’re tweeting about the World Cup Final. In soccer lingo, a “screamer” connotes an epic goal, while a “howler” indicates a boneheaded mistake. Those two sentences we agreed were effectively the same now have completely opposing meanings and correlated sentiments.

That seemingly small variation would make a world of difference for, say, a Sports Marketing firm deciding when to put a jersey on sale, or unfortunately but more crucially, where a police force might need to deploy extra protective measures after a big match.

Soccer Player executes overhead kick
If he makes it? It’s a “screamer,” If he shanks it off his foot? A “howler.” In soccer parlance, the two are worlds apart.

Not only do data researchers need to be cognizant of specific social contexts, they also need to pay close attention to the biases that arise out of common social contexts. In the realm of computer vision, particularly facial recognition technology, we’ve seen how harmful poorly considered datasets can be in perpetuating inequity by excluding people from access to new technologies.

Truth is that, while data annotation may not garner the same buzz as the sci-fi future use-cases we all know so well, if we don’t really scrutinize and refine our processes for cultivating precision datasets, we’re going to see a lot of firms trying to serve full tasting menus with empty pantries.

That’s why at DefinedCrowd, we’re always pushing for new ways to scrutinize data collection and annotation processes and anticipating the edge cases that other firms tend to let slide through the cracks. Check out our use-cases to learn more about how we’re able to guarantee clients high-quality data at speed and scale.  To see what high-quality data can do for you, request a trial or email us at sales@definedcrowd.com.