We’ve come a long way since forming in 2015. Starting out as a small team, we now have four offices worldwide – Lisbon, Porto, Tokyo, and Seattle – and continue to grow every day.
Our unique platform has helped many successful companies feed their artificial intelligence applications with training data. Using human intelligence coupled with machine-learning, we deliver project-specific, quality-guaranteed data.
Today, we’re proud to announce that DefinedCrowd is among CB Insights’ third annual list of 100 AI startups. A research team from CB Insights selected 100 startups based on the following factors: investor profile, market potential, partnerships, competitive landscape, and team strength.
Companies are categorized by focus area. These focus areas aren’t mutually exclusive and include core sectors such as telecommunications, government, retail, healthcare and enterprise tech sectors such as training data (where we sit), software development, data management, and cybersecurity.
We are pleased to be among this group of incredible AI startups, selected from an extensive list of 3k+ AI companies, and look forward to seeing these companies grow.
It´s been a great start to 2019. And, we´re very thankful to everyone who has helped get us here.
Earlier this week, DefinedCrowd was Featured in Jornal Económico, a premium financial publication in Portugal. We’ve translated the article from the original Portuguese for our English- speaking friends. Enjoy!
Founded in Seattle, USA, DefinedCrowd is a startup specializing in training data for Artificial Intelligence. The company counts Amazon, IBM, and EDP as investors and clients.
DefinedCrowd provides services so that data scientists can gather, structure, and enrich datasets for Artificial Intelligence, helping companies improve speed to market and the overall quality of their AI products. DefinedCrowd accelerates enterprise AI initiatives by combining machine learning technology with human-in-the-loop collection processes. Founded in August 2015 by entrepreneur Daniela Braga, the company is headquartered in Seattle, has R&D centers in Lisbon and Porto, and a sales office in Tokyo.
Three months after its founding, the company opened their first R&D office at Startup Lisbon. Since then, DefinedCrowd has blossomed from an initial team of three employees to a workforce of more than 70 that is still growing.
“It is important to raise capital if we want to move fast, especially in the technological sector.”
Daniela Braga to Jornal Económico
This influx of capital is being used to accelerate product development and accelerate team growth. Two-thirds of DefinedCrowd’s 70 employees work out of Portugal. The company expects to add 80 more team members by the end of 2019.
Over the past six months, DefinedCrowd has announced three partnerships: a formal designation as an Amazon Alexa Skills partner, a product integration with IBM Watson Studio; and participation as a featured vendor in Microsoft‘s co-sell program.
DefinedCrowd’s platform provides industry-agnostic data services and can support text, audio, and image annotation. The company’s clients span industries as a result: from Fintech, to Retail, Healthcare, Utilities, and the Internet of Things. Their client portfolio consists mostly of Fortune 500 companies, including BMW, MasterCard, EDP, José de Mello Saúde, SoftBank, Yahoo Japan, Randstad, and Nuance.
DefinedCrowd’s goals are ambitious. The company aims to become the world’s number one AI data provider through expanding their client-base and forging new partnerships with industry leaders.
With a degree in Portuguese Language and Literature, Daniela Braga has spent her career examining the rigorous use of language, the perfect foundation for her business. “We deal daily with data in 70 languages and dialects. Our clients need, at a minimum, native-level speakers and sometimes even require linguists or specialists in language sciences for all of them” says the entrepreneur.
After graduating with a master’s degree in applied linguistics, she went on to earn a PhD in Speech Technologies at the Faculty of Engineering at the University of Porto and taught at the University of A Coruña for two years before joining Microsoft (whom she worked for in Portugal, China and the United States).
After leaving Microsoft in 2013, Daniela moved to American company Voicebox. Simultaneously, she was invited to teach Data and Crowdsourcing for Speech Technologies at the University of Washington. It was during this time that she saw the gap between the Artificial Intelligence data scientists wanted to develop and the training data available to build it. She decided to found her own company as a result.
Waving a well-paid job goodbye, and with few personal resources, she started meeting with investors in Seattle, and quickly received an initial check: $ 200,000 in financing to start her business. A business that is now signing contracts with some of the largest companies in the world.
DefinedCrowd is in constant growth and employee numbers have been updated to reflect our current position.
In the market for some last-minute gift recommendations for a machine learning “geek?” (we use the term affectionately around here). DefinedCrowd’s got you covered with our “machine learning lover’s book list,” hand-selected by our ML Team. From the ins and outs of speech and language processing to broad-level theoretical overviews of the machine learning field, these texts cover the wide-ranging topics we discuss in our office every day. Enjoy! And happy holidays from all of us at DefinedCrowd.
Summary: In this excellent intro to speech and language processing tecnologies, Jurafsky presents an empirical approach that comprehensively covers language technology based on applying statistical and machine-learning algorithms alongside modern technologies. The book largely emphasizes scientific evaluation and practical applications.
Summary: A fantastic introduction to statistical natural language processing that uses an analytical approach to cover a range of mathematical and linguistic foundations for NLP technologies. Foundations of Statistical Natural Language Processing proves further useful in presenting theoretical and algorithmic building blocks for NLP technologies.
Summary: An essential read for anyone interested in learning more about crowdsourcing training data for speech models. Offers a comprehensive overview from the basics of setting up a task, to tips for task interfaces and methodologies for quality assessment.
Summary: This book offers a great introduction to what many consider the “Holy Grail” of Machine Learning. Topics covered, range from mathematical and conceptual background to deep learning techniques. The “research perspectives” that book-end chapters with specific case-studies make Deep Learning a great resource for students and software engineers alike.
Summary: For those looking to master deep learning for image recognition and classification, Deep Learning for Computer Vision offers practical walk-throughs, hands-on tutorials and a direct teaching style. Useful for both beginners and for the seasoned deep learning pro looking to brush up on the fundamentals.
The growth and development of computer programs supported by artificial intelligence has led to intense debate around regulatory difficulties and because of the technology’s potential effects on employment. Are people’s concerns in these areas warranted?
From the earliest days of civilization, man, as a single thinker on earth, sought to reduce the need for physical work by inventing tools. First came the wheel and the transportation of food over farther distances with less manpower. We have been taught to evolve by creating more with less effort. For many, this was negative in the short term. Those who were once freight carriers lost their jobs. The wheel was invented around 3000 BC. You might think I´m crazy to start a technological discussion about this historical moment, but the historical reference is useful to be made in order to demystify the discussion, and to then further analyze the data we have.
Moving forward some years later, at the start of the industrial revolution, millions of people protested in the streets of England and the United States against the introduction of weaving machinery. On the surface, the “destruction” of jobs seemed quite high. However, in truth, these jobs were never really destroyed, but rather professionally reformed. Factories, with a drastic boost in production, were increasing the salaries of those who adapted to the machinery while simultaneously reducing their overhead costs. Both countries’ wealth grew as a result due to increases in disposable income for families, and more jobs created to support the burgeoning count weaving and spinning industry. Indeed, the number of people employed in weaving jumped from 7,900 to over 320,000 after the invention of the weaving machine.
Now after a little history revision, let’s return to the present.
Recently PWC, one of the world’s largest consultants, launched a global study in which they estimated that artificial intelligence in the UK will replace 20% of today’s jobs within the next 20 years. However, they also estimated that artificial intelligence will create just as many jobs as it replaces. Sectors at high risk include law, finance, insurance, drivers and white-collar workers. Areas like education, science, information, communication and computing are among those that will be most valued in the future.
Nowadays, from the moment we wake up and look at our mobile phones, until the moment we lay down and check our Facebook feed for the last time, we’re in constant contact with artificial intelligence that gives us the kind of information that allows us to make better decisions. We need to accelerate the transformation of educational systems, adapting them to the new realities of the fourth technological revolution with a particular focus on programming disciplines. We also need to find ways to support professional training programs that respond to the demands of the labor market.
Ultimately, there is no future in which machines will be able to replace what binds human beings: creativity, intuition and love. At the end of the day, perhaps AI will make us even more human.
Before founding DefinedCrowd, our CEO Daniela Braga had a long career as a researcher at companies like Voicebox and Microsoft. A pioneer in speech technology, she became one of the earliest advocates for voice-enabled technology as a primary user interface (long before Alexa, Siri, and Cortana, proved her right).
Her stance was rooted in a passion for uncovering the crossroads where technology and human experience collide. Convinced that Automated Speech Recognition (ASR) could improve all of our interactions with the world at large, she strove to build the models that would make that vision into a reality.
Quickly, she butted up against technological limits. The lack of high-quality training data so critical to constructing effective models was chief among them. She founded DefinedCrowd in 2015 as a direct result of that frustration, envisioning a company that would leverage cutting-edge technology, dynamic workflows, and innovative crowd-management practices to deliver the exact data sets researchers would need to build high-performance models.
Her passion for AI as a means of enhancing human experience permeates everything we do. We take the goal seriously. As our COO, Walter Benadof, wrote so eloquently just a few months ago, it’s imperative that every practitioner in this field maintains a core set of ethics and values as they continue to develop and mature.
That word, “practitioner,” is no accident. I use it to reinforce a concept we’ve touched on before, a “Hippocratic Oath” for AI, first proposed in Microsoft’s The Future Computed, and further elaborated upon by Oren Etzioni at TechCrunch. We’ve been thinking hard about how our past and future work fits with the values stated therein and the values of our company as a whole.
We’re proud that our core competencies in Natural Language Processing, Computer Vision and Automated Speech Recognition are already making workplaces, classrooms, and ultimately the world at large more accessible, safer, and easier to navigate.
On top of that, we’re also thrilled to be in the process of developing several inspiring pilots for use cases as diverse as improving healthcare interfaces to detecting preventable natural disasters (think wildfires) before they have a chance to spread out of control.
The AI sector as a whole is just scratching the surface of how the technology we create can improve the human experience. We look forward to continuing our partnerships, and forging new ones, with companies we truly do consider as beacons of our industry. We can’t wait we work side-by-side to unlock new use cases and technologies that truly can make our world a better place.
That’s why we do what we do. It has been from the very beginning.
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.
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.
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.
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.
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.
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.
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.