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. 

Our Series A round in the News

What a week it’s been! In case you missed it, on July 31st, we announced the closing of an $11.8 million Series A Funding round. That’s a huge milestone for us, especially considering we were founded less than three years ago. We’ve been having a lot of exciting discussions internally as we strategize on how to deploy this capital to stimulate growth, improve our product offerings and increase our global market share.

We’re glad to hear elements of that discussion taking place outside of these walls. Our Series A got some fantastic coverage from an outstanding, and geographically diverse, group of publications.

It really was an honor to see our story in flagship publications like Venturebeat, The Seattle Times, and Built In Seattle for our English-speaking audience, and Expresso and Público for our Portuguese-speaking friends.

We’re also thankful for the coverage from major names in financial and business news like FortuneYahoo FinanceMarket Insider, and the Puget Sound Business Journal in English and Jornal de NegociosDinheiro Vivo, and ECO in Portuguese.

Whichever is your language (and if it’s both, cheers) curl up with some of this fantastic reading this weekend, or better yet, this evening.

And hey, if all this press means you’re hearing about DefinedCrowd for the first time, or the first time in a while, welcome! We’re glad you’re here. The articles linked here provide some excellent information on our company. But, we’ll introduce ourselves here too.

In a nutshell, we’re an AI and ML training-data company. We collect, enrich, and structure training data to fuel AI initiatives at an array of Fortune 500 companies across industries.

Our unique combination of customizable workflows and human-in-the-loop data collection processes means we provide higher quality training data (98% accuracy capabilities) at faster speed (5x-10x faster than competition) and larger scale (46+ languages covered) than our competitors.

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

And, if you want to build with us, great news! We’re hiring. Keep tabs on our Career Page for current and future openings.

Happy reading!

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.

Bringing Inspiration Home: Our COO Reports on Microsoft Inspire

Hi everybody. Happy to be writing you from the cool PNW air after spending the first part of the week at the Microsoft Inspire Conference in Vegas.

Inspire was a fantastic opportunity to get face-time with colleagues from the Microsoft Partner Network’s startup ecosystem. I made fantastic business contacts, had great discussions, and most importantly, got a few days to dedicate my focus to DefinedCrowd’s long-term growth next to a lot of smart people with firsthand knowledge of the pains startups experience when they begin to scale.

Microsoft Inspire first day

I also had the opportunity to attend presentations and think about the AI revolution we spend our day-to-day grinding toward on a macro-level. I was—I’ll just say it— inspired by a presentation on Microsoft’s AI Earth initiative and the development of ML models that improve conservation, farming practices, and understandings of diverse ecosystems. At DefinedCrowd, we’re proud to be leading the way to a future where AI plays an essential role in solving some of our biggest societal ills.

That’s not just some line I’m being fed from marketing. For us, it’s a pillar. Before founding DefinedCrowd, our CEO Daniela spent years developing and advocating for widespread use of speech interfaces as the basis of assistive technologies (Cortana and Alexa have since proved her right) mainly because she saw the benefits to the blind, elderly and infirm. We believe in what we’re building and in the possibility for a world where fateful calls from freeway shoulders disappear, and no doctor has to say “if only we’d caught it earlier” ever again.

However, we know the AI revolution only gets us to that future if there’s a moral code at its foundation. That’s why I want to close with the idea of a “Hippocratic Oath” for AI, first proposed by Microsoft’s Brad Smith and Harry Shum in their book The Future Computed, and further drawn out by Oren Etzioni at TechCrunch (If you don’t read them you should. Right now. Start here, or here, or even here to see what they’ve been saying about us.)

I won’t lay it out entirely. Instead, here are some lines that stuck with me:

“1. [We] will apply, for the benefit of the humanity, all measures required, avoiding those twin traps of over-optimism and uniformed pessimism.

2. [We] will remember that there is an art to AI as well as science, and that human concerns outweigh technological ones.

3. My [Our] AI will seek to collaborate with people for the greater good, rather than usurp the human role and supplant them.

DefinedCrowd will grow this year. A lot. At Inspire, I heard a ton of buzz surrounding “new” use cases for AI in industries like Fintech, Food & Drink, Retail, and more. None of those use cases surprised me. Because our product offering is, and always has been, industry agnostic, we’ve been doing work across industries like these since day one. We’re in a fantastic position as more kinds of businesses find more solutions in AI and ML models. Just know that as we grow, we’re thinking hard about how to do it the right way.

A talk at Microsoft Inspire about Artificial Intelligence and Machine Learning

Thanks to everyone at Microsoft who helped put Inspire on, and who took some time to meet me in Vegas. I can’t wait to see where we are this time next year.

In the meantime, if you want to know how AI can solve problems for you, Check out  our  solutions. And, speaking of growth, we’re hiring. A lot. Check out our  Careers page for opportunities to build incredible things with us at DefinedCrowd.

We’re looking forward to hearing from you.

Smart data has arrived in Tokyo!

We are thrilled to announce that DefinedCrowd has now an office in Tokyo! This is a huge milestone and we couldn’t be more honored and humbled to bring smart data to the top Japanese corporations who are leading the way in the Artificial Intelligence development.

I am also super excited to announce that Aya Zook will be the General Manager of the Japanese subsidiary and the Asia-Pacific region as a result of more than one year of building our business and reputation in the Asian market.

We celebrated this important event with a cocktail reception in Tokyo, that counted with the presence of more than 55 key people from top Japanese corporations. It was a great moment to talk not only about business but also to connect and reconnect with incredibly talented and smart people, who are leading the way when it comes to the future of AI.

The Japanese market is one of the world’s largest economies and historically one of the most advanced in technology. We are truly honored and humbled to help the top Japanese corporations in their path to AI, with our expertise in Artificial Intelligence, Machine Learning, Speech Technologies, Natural Language Understanding and Computer Vision.

And with a new office, new opportunities arise. If you want to join us at the Tokyo office, check out our current openings at our Careers page. Who knows if you’re the next member of this growing and worldwide spread family that is DefinedCrowd.

Let’s do this!