DefinedCrowd announces ISO 27001 certification

By João Freitas, CTO of DefinedCrowd

To deliver on our promise of high-quality data, we’re pleased to announce we’re now ISO 27001 certified, another step forward in our commitment to protecting our client’s data. We went through the rigorous process of obtaining ISO 27001 certification, one of the best-known standards in information security, to safeguard our business and our customers from cyber-attacks, loss or corruption of data, and the potential damages that could accompany these issues. We have also assigned a Data Security Officer who will be responsible for overseeing our compliance efforts as a top priority. 

“Data is the core of our business, so ensuring its safety is integral to our core values. Considering our clients are big corporations, we are not taking any chances when it comes to data security. Receiving the ISO 27001 certification is an important step, that proves our global commitment to the highest international standards,” states our Founder and CEO Daniela Braga.   

ISO 27001 is a consistent and centrally controlled management system standard for protecting information and reducing threats to all business processes through effective monitoring and control of IT security risks. Obtaining this certification involves the auditing and evaluation of a variety of internal processes, from information security guidelines, asset management, staff security and access control to systems maintenance, compliance, and cryptography. With this certification, DefinedCrowd ensures compliance with the international standards of the industry, assuring the best practices for information security controls are in place. Going forward, the certification undertakes annual audits and ongoing surveillance to ensure continued optimization of processes to safeguard data at every touchpoint. 

Being ISO 27001 certified, alongside our GDPR compliance efforts, reinforces our promise to deliver – and protect – high quality data for our clients. 

Curing pain points: the role of AI in healthcare

AI is a compelling fit for healthcare, especially when there’s promise to advance diagnosis, assist surgery and enhance drug discovery. However, the narrative of AI in healthcare can be slightly skewed. Breakthrough developments such as early cancer detection understandably make headlines; but they’re often some way from leaving research labs and making it to real-world datasets. As an industry, healthcare also comes with certain challenges to achieving an AI-centered approach – among them questions around access to healthcare data, standardization of regulations and AI performance critical situations. Nonetheless, AI has a lot to offer and is already gaining notable traction in the healthcare industry, both behind the scenes and directly with patients.

Gartner believes AI is on the cusp of becoming one of the most important transformational forces in healthcare. By 2026, AI has the potential to save the US healthcare industry $150 billion annually and truly augment the capabilities of human health workers. As health institutions cope with an aging population, increasing patient demands and a steep shortage of physicians, investing in AI will be crucial to helping tackle some of these pain points.

To get a clearer picture of AI in healthcare, let’s look at the applications providing the most value in the industry today:

Reducing admin time

Completing routine duties such as chart notes, treatment plans, prescriptions and billing is critical for any health institution. However, excessive administrative tasks keep medical professionals from giving more time to patient care. With the shortage of physicians expected to reach 120,000 by 2030 and the number of people aged 65 and over set to double within the next 40 years, solutions are needed to avoid worrying delays as time becomes inevitably scarcer.

Subsets of AI, natural language processing (NLP) and speech recognition are readily used across health institutions to handle repetitive, time-consuming tasks. From transcribing text to extracting and structuring information, these technologies streamline clinical documentation. For example, speech recognition is allowing medical professionals to transcribe voice into text, while NLP is being used to structure and analyze large amounts of medical research. As well as saving time, these applications ensure a higher level of accuracy, fewer errors and better consistency across patients’ history and recommended treatment plans.

Improving care with virtual nursing

Preventing unnecessary hospital visits not only lessens the load for medical professionals, it also leads to a happier patient. So, when looking to advance care outside facilities, AI is a frontrunner. Powered by NLP and using voice or text, virtual nursing assistants are now supporting patients around the clock by quickly responding to concerns and monitoring recovery progress. As a result, they’re also helping prevent readmissions through frequent communication, extracting critical insights and alerting a medical professional on-duty if any action is required. Harvard estimates that AI-powered virtual nurse assistants could save $20 billion annually and 20% of nurses time.

Enhancing surgery with AI-assisted robotics

Leading the value potential for AI in healthcare, AI-assisted robotic surgery is becoming increasingly popular with the potential to save the industry $40 billion annually. Using machine learning to combine pre-op data with real-time metrics, the technology physically guides surgeons during procedures. Bringing several talents to the table, AI-assisted robotics enhances instrument precision, reduces variations across patients and improves future surgery through data collection. Considered minimally invasive, patients’ hospital stay post-surgery is cut by 21% due to significantly faster healing times. A win-win for both the patient and medical staff.

Preventing (cyber) viruses

The healthcare industry is relying on internet-connected devices more than ever, playing a vital role in hosting medical records, billing and patient care. The downside? Every new device opens a new door to cyberattacks, so protecting sensitive patient data is a top priority. While attacks are on the rise across all industries, healthcare is really feeling the brunt of it, the worst-case scenario being the loss of patient records and history, posing a dangerous threat in emergency situations. Beyond the conventional cybersecurity systems, AI and machine learning can help identify and prevent cyber viruses and attacks – detecting suspicious files, websites and domains and isolating them before they can do harm. Predictive analysis can highlight future suspicious behavior and NLP can support prevention by selecting useful information from scanned data on previous attacks, enabling better risk analysis and management.  

Looking to the future

So what barriers are impeding the safe and ethical progress of the more transformative, breakthrough AI initiatives in healthcare? Firstly, access to large healthcare data is limited and high-quality data – a necessity for efficient applications – is extremely difficult to come by outside the medical research profession. Secondly, regulations and guidelines are currently ambiguous and lack stringent management, meaning stronger restrain towards AI growth. And finally, the legal risk of algorithms undertaking critical tasks and their failure to perform. However, the future is bright and the life-saving potential of those initiatives is most certainly worth the wait.

Meanwhile, as long-term innovations continue to progress behind the scenes, AI capabilities have started to make a marked difference across many healthcare touchpoints. For now, the focus for providers should be towards areas where AI is mature and can deliver value today. Looking forward, as barriers are addressed and developments gain momentum, we can expect an even fuller realization of AI’s potential to truly revolutionize the healthcare industry.

7 AI trends in retail

Changing how we search, discover and shop, digitalization is transforming the retail landscape. Although there’s still a place for brick and mortar stores, e-commerce has brought fierce competition, along with new standards of customer expectations. To strive ahead of new entrants and keep up with a full digital transformation, retailers can employ new technologies, more specifically AI, to maintain relevancy in a crowded market.     

Tackling many retail challenges head on, AI can be used to personalize shopping experiences, optimize the supply chain and increase conversion through large amounts of customer data. AI is also helping more traditional brands remain competitive and allowing physical stores gain an advantage. So what popular applications of AI are we seeing in retail? Let’s look at 7 trends making an impact across the industry. 

New format retail 

1. Micro-fulfillment centers 

Given that fast delivery is vital for any e-commerce strategy, micro-fulfilment centers are proving effective. Micro-fulfilment centers are small-scale warehouses, generally located in urban areas near the end consumer. Not only do these centers hold more than regular supermarkets, but they can also be 94% smaller than a traditional warehouse. Within these vertically stacked centers, AI is implemented to advise the best location for goods on the shelves. It’s also used to prioritize tasks and navigate ground robots to collect and organize goods. Israel’s Rami Levy, America’s Walmart and UK’s Ocado, are all retailers who are implementing micro-fulfillment centers globally.  

2. Amazon’s Grab and Go 

Many are aware of Amazon’s recent attempt to shake up the retail space with its Grab and Go convenience store. The bold move is heralding a new way for retail with no cash or cashier. This concept uses AI, but not in the most obvious way. Instead of facial recognition (due to privacy concerns), computer vision is used to pick up a shopper’s physical presence. Tracking their every move, computer vision also identifies items removed from the shelf. By sending all this data to a centralized system, Amazon can then charge people accordingly as they exit the store. But is this concept a novelty or an actual transformation for in-store retail? Well for some it may feel like a gimmick, however CB Insights identifies over 150 companies working to transform brick-and-mortar stores to a human-free environment with the help of computer vision and automation.  

Search and discovery  

3. Neiman Marcus’s image recognition app 

AI is supporting search and discovery in a saturated retail landscape. Retail stores are using image recognition to make it easier for customers to acquire items for which they are searching. Neiman Marcus’s Snap. Find. Shop. app allows customers to quickly browse inventory in search of the same or similar products. Similarly, Target has used this approach via a partnership with Pinterest. Using Pinterest Lens, customers upload an image of any product and are presented recommended products that are similar and available at Target. Both these approaches use machine learning to identify similarities in items, whether it’s the subject of an image or the visual patterns that match the likes of other images. And for Target, partnering with a leading company in visual search was a forward-thinking move, not only saving an enormous amount of time but also capitalizing on the influence of social media in consumer decision-making. 

The AI stylist 

4. Expert advice online 

Although e-commerce has boomed in the past decade, customers still value brick-and-mortar stores where they can touch an item and try on different sizes before purchasing. North Face is trying to bridge the gap between the physical and online store with its Expert Personal Shopper. The app mimics a retail expert, helping customers navigate the e-commerce store while receiving advice similarly to an in-store experience. For online shoppers, extra support and further guidance is maybe what’s needed with nearly 70 percent of shopping carts being abandoned before purchase is completed.

5. Fitting rooms of the future 

Tech is vamping up the traditional fitting room with a more exciting, streamlined experience for shoppers. Instead of waving down an employee or venturing out of a cubicle in an uncomfortable item, American Eagle customers can ask for alternative sizing directly to the room. As well as these requests, customers use AI enabled touch screens to receive personalized product recommendations based on the items they’ve selected.

Not your regular Bot 

6. H&M integrate bot into popular messaging app 

As the modern-day consumer seeks to connect with brands wherever they are (emails, social media, forums), brands need to be available on numerous channels. Fashion brand H&M launched a chatbot within the trending message app Kik – a popular app in the States, used by 40% of American teenagers. The bot is not your regular AI-powered communication tool. Customers can buy items directly, and can also receive styling tips after the bot learns what the user likes.

7. Macy’s enhance the in-store experience with AI 

Ever walked into a department store and felt overwhelmed by the sheer number of products on display? Macy’s On Call app saves customers from a daunting shopping experience. After entering a store, users open the app and begin chatting with an AI bot. It’s not the everyday bot experience to receive directions to a specific in-store item. The bot can also check whether an item is in stock and alert a human employee if it senses the customer becoming frustrated. Here, the bot uses sentiment analysis, the process of understanding and categorizing opinions expressed through language (text and voice), to read the customer’s emotion. This tool is one of the most valuable AI solutions for brands and proven extremely powerful, allowing for real-time monitoring about what people are thinking or feeling.  

The retail landscape is in a time of shift. And as new ways of shopping continue to evolve, AI will be crucial for traditional retailers to provide exceptional value. AI in retail is mature; and thanks to the abundance of use cases, retailers have plenty of inspiration so they too can apply AI to their businesses and yield strong returns on investment.  

AI in banking: 3 use cases

The highly competitive banking sector is seeing some of the most transformative effects of AI, with mostly larger banks such as Wells Fargo, JP Morgan, Bank of America, Citibank putting it to work across key areas of their business operations. Analysts predict that if AI is properly deployed, it has the potential to reduce banks’ costs by 25% and increase revenues by 30% within 5 to 7 years. AI fits extremely naturally with banking as it thrives on data. And as banks deal with enormous amounts of data, these technologies can transform all aspects of how banks work, from how they operate on the backend, to how they interact internally and externally.

So, what main concerns is AI addressing, and what AI-driven applications are being used to tackle them? Here are 3 ways AI is showing global traction in the banking industry: 

Customer Service 

Alongside new technology comes new ways of communicating, and these days it’s common to stumble across a voice or chatbot that delivers a surprisingly personalized customer service. And with the growing availability of choice when it comes to financial institutions, it’s more and more critical for banks to deliver excellent customer service on-demand to build loyalty.

Chatbots, interactive voice response (IVR) and virtual assistants are popular AI-enabled tools. And as the capabilities of AI such as natural language processing and speech recognition increase, banks will continue to adopt these solutions. Banks are not only employing these solutions to minimize costs, by up to 30%, but also to reduce end-to-end communication time with clients. For routine inquiries, bots are shown to improve response times by 99%, reducing time-to-resolution from hours to just a few minutes. The end result? A happier customer, faster. 

Royal Bank of Canada’s (RBC) NOMI is a great example of an AI-driven virtual assistant that is improving overall customer experience. The assistant responds to customers’ requests and queries and also provides other support features, such as: informing about available funds, alerting to anomalies or unusual activity and providing personalized insights and advice on financial management. Results from NOMI show not only increased usage of the banks’ mobile app and opening of savings accounts by 20%; but also a wealth of invaluable insights into their customer base.  

While not all banks are introducing virtual assistants to help with the multitude of customer demands, chatbots are a common and more simplified option, helping with everyday requests and decreasing response time. Other banks who have similarly implemented virtual assistants and chatbots include Bank of America, with Erica, and Wells Fargo has been piloting an AI-driven chatbot through Facebook messenger, both delivering a highly personalized customer service.

Process Optimization 

A key solution provided by AI-powered tools is process optimization. And a valuable use case in banking is using AI to enhance robotic process automation (RPA), the process in which software mimics human actions rather than AI which simulates human intelligence. When these two technologies are implemented together, the result is powerful: AI enables RPA to perform more complex automation such as interpreting, decision-making, and analyzing across various processes. The big benefit? It gives back time, reducing employees’ hours spent on mundane and repetitive tasks, and allows for more focus on high-value projects. 

Banking is among one of the biggest adopters of these initiatives and there are several applications being used to transform departments. A great example of a company using AI to optimize processes is American bank, JP Morgan. Their internal IT team use bots to respond to requests such as changing an employee’s password. With over 1.7 million minor requests year on year, these bots are highly valued especially for one of the largest banks in the US. 

JP Morgan has also launched a program called COiN (short for Contract Intelligence). The system reduces the time to review documents and has also proven to limit human error that occurs in loan servicing. Prior to the implementation of COiN, JP Morgan would review 12,000 commercial credit agreements taking nearly 360,000 hours. When dealing with large amounts of documents, mistakes could often arise; but now, thanks to their machine learning system, this task can be completed with a higher performance rate and in a matter of seconds.      

AI has shown tremendous potential to increase process optimization. Banks are already seeing successful outcomes, moving their employees’ time from small insignificant tasks to more valuable opportunities, essentially bringing more critical thinking into banking businesses. Not to mention, a more engaged and motivated workforce.  

Compliance and Risk Management 

Keeping up with the challenging environment of banking compliance and risk management is not only time consuming but also costly. And with the average bank spending $120 million annually on compliance and customer onboarding procedures, as well as tackling the increased frequency and complexity of cyber-attacks, there is enormous potential for AI technologies to support this area.

Banks need to respond to large amounts of unstructured data that emerge from difficult regulatory demands. AI has proven particularly effective in dealing with this data in daily tasks such as automating legal, compliance and risk documentation, as well as analyzing data sets that train machine learning algorithms to track credit card fraud or money laundering. A lot of these tasks involve excessive manual work; by moving them to an AI-powered system instead, banks can free up employees to deal with more complex decisions.   

Global financial group, Citibank, partnered with data science company, Feedzai, leaders in the market for real-time risk management in banking, to implement a transaction monitoring platform. Powered by machine learning technology, the system adjusts automatically to monitor discrepancies and changes in payment behaviors, thus enabling banks to manage risk and keep their customers safe from fraudsters.

Compliance and risk management has always been an important focus area for banking, and thanks to AI, there have been game changing developments. As AI continues to make considerable inroads in these areas, banks will be able to focus on more analytics, rather than spending their time avoiding risk or dealing with increased compliance regulations.   

Beyond the hype, AI is showing clear development with ample use cases and substantial return. And as banks continue to fight for customer loyalty, having the right technical solutions on the backend will be key to sustaining a competitive advantage. With AI use cases starting to appear from leading banks, others soon will follow suit. Over the next few years, we can expect to see further widespread adoption of AI in banking, and from not just the bigger players.   

AI bias and data scientists’ responsibility to ensure fairness

As artificial intelligence creeps out of data labs and into the real world, we find ourselves in an era of AI-driven decision-making. Whether it’s an HR system helping us sort through hundreds of job applications or an algorithm that assesses the likelihood of a criminal becoming a recidivist, these applications are helping shape our future.   

AI-based systems are more accessible than ever before. And with its growing availability throughout industries, further questions arise surrounding fairness and how it is ensured throughout these systems. Understanding how to avoid and detect bias in AI models is a crucial research topic, and increasingly important as its presence continuously expands to new sectors. 

AI Systems are only as good as the data we put into them.”

IBM Research

AI builds upon the data it is fed. While AI can often be relied upon to improve human decision-making, it can also inadvertently accentuate and bolster human biases. What is AI bias? AI bias occurs when a model reflects implicit human prejudice against areas of race, gender, ideology and other characteristic biases.  

Google’s ‘Machine Learning and Human Bias’ video provides a tangible example of this idea. Picture a shoe. Your idea of a shoe may be very different from another person’s idea of a shoe (you might imagine a sports shoe whereas someone else might imagine a dressy shoe). Now imagine if you teach a computer to recognize a shoe, you might teach it your idea of a shoe, exposing it to your own bias. This is comparable to the danger of a single story.  

The single story creates stereotypes, and the problem with stereotypes is not that they are untrue, but that they are incomplete. They make one story become the only story.”

himamandaNgozi Adichie

So, what happens when we provide AI applications with data that is embedded with human biases? If our data is biased, our model will replicate those unfair judgements. 

Here we can see three examples of AI replicating human bias and prejudice:  

  • Hiring automation tools: AI is often used to support HR teams by analyzing job applications and some tools rate candidates through observing patterns in past successful applications. Where bias has appeared is when these automation tools have recommended male candidates over female, learning from the lack of female presence. 
     
  • Risk assessment algorithms: courts across America are using algorithms to assess the likelihood of a criminal re-offending. Researchers have pointed out the inaccuracy of some of these systems, finding biases against different races where black defendants were often predicted to be at a higher risk at re-offending then others.  
     
  • Online social chatbots: several social media chatbots built to learn language patterns, have been removed and discontinued after the posting of inappropriate comments. These chatbots, built using Natural Language Processing (NLP) and Machine Learning, learned from interactions with trolls and couldn’t filter through indecent language.   

The three scenarios above illustrate AI’s potential to be biased against groups of people. And the key underlining factor of these results is biased data. Although inadvertently, they did exactly what they were trained to do — they made sense of the data they were given.   

Data reflects social and historical processes and can easily operate to the disadvantage of certain groups. When trained with such data AI can reproduce, reinforce, and most likely exacerbate living biases. As we move into an era of AI-driven decision-making, it is more and more crucial to understand the biases that exist and take preventive measures to avoid discriminatory patterns. 

Understanding the types of biases, and how to detect them is crucial for ensuring equality. Google identifies three categories of biases:

  • Interaction bias: when systems learn biases from the users driving the interaction. For example, chatbots, when they are taught to recognize language patterns through continued interactions.  
  • Latent bias: When data contains implicit biases against race, sexuality, gender etc. For example, risk assessment algorithms which show examples of race discrimination. 
     
  • Selection bias: When the data you use to train the algorithm is over-represented by one population. For example, where men are over-represented in past job applications and the hiring automation tool learns from this.    

So how can we become more aware of these biases in data? In Machine Learning literature, ‘fairness’ is defined as “A practitioner guaranteeing fairness of a learned classifier, in the sense that it makes positive predications on different subgroups at certain rates.” Fairness can be defined in many ways, depending on the given problem. And identifying the criteria behind fairness requires social, political, cultural, historical and many other tradeoffs.  

Let’s look at understanding the fairness of defining a group to certain classifications. For example, is it fair to rate different groups loan eligibility even if they show different rates of payback? Or is it fair to give them loans comparable to their payback rates? Even a scenario like this, people might disagree as to what is fair or unfair. Understanding fairness is a challenge and even with a rigorous process in place, it’s impossible to guarantee. And, for that reason, it is imperative to measure bias and, consequently, fairness.   

Strategies of measuring bias are present across all society sectors, in cinema for example the Bechdel test assesses whether movies contain a gender bias. Similarly, in AI, means of measuring bias have started to arise. Aequitas, AI Fairness 360, Fairness Comparison and Fairness Measures, to name a few, are resources data scientists can leverage to analyze and guarantee fairness. Aequitas, for example, facilitates auditing for fairness, helping data scientists and policymakers make informed and more equitable decisions. Data scientists can use these resources to evaluate fairness and help make their predications more transparent.  

The Equity Evaluation Corpus (EEC) is a good example of a resource that allows Data Scientists to automatically assess fairness in an AI system. This dataset, which contains over 8,000 English sentences, was specifically crafted to tease out biases towards certain races and genders. The dataset was used to automatically assess 219 NLP systems for predicting sentiment and emotion intensity. And interestingly, they found more than 75% of the systems they analyzed were predicting higher intensity scores to a specific gender or race. 

As AI adoption increases rapidly across industries, there is a growing concern about fairness and how human biases and prejudices are incorporated into these applications. And as we’ve shown here, this is a crucial topic that is receiving more and more traction in both scientific Literature and across industries. And understanding the human biases that percolate into our AI systems is vital to ensuring positive change in the coming years.    

If you’re interested in learning more about fairness in AI, here are some other interesting references:

https://fairmlbook.org/ 
https://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf
https://papers.nips.cc/paper/6316-satisfying-real-world-goals-with-dataset-constraints.pdf 

How AI can help to understand the customer

Ahead of us is a significant change in the way brands use customer experience (CX).  We are already starting to see the switch from companies competing on price and product to competing on CX. But what exactly do we mean by CX? Gartner defines CX as a customer’s perceptions and feelings caused by the one-off and cumulative effect of interactions with a supplier’s employees, systems, channels or products.   

Previously, the communication flow between customers and companies was either in person, writing or via a telephone call to the support line. Now, there are increasingly more ways customers can interact with brands, and when they do, they expect a high-quality experience “on demand.” 81% of marketing leaders were expected to mostly or completely compete based on customer experience by 2019, as revealed in the 2017 Gartner Customer Experience in Marketing Survey.  

There are many tools already giving insight to CX, such as NPS and Customer Success Scores. However, when companies need to make quick decisions, real-time insights are what’s helping decision makers. Technologies such as AI are now gathering these insights by allowing companies to organize and categorize data based on business needs, helping to make sense of all these interactions.  

To understand the customer from a CX perspective, and give some real-world examples, we can filter down a myriad of AI technologies and categorize them into three buckets: 

  • Speech Analytics: understanding, interpreting and analyzing voice conversations. Example: understand sentiment, IVR systems.
  • Image: capturing, processing and analyzing images, photos and video. Example: customer patterns, social media image analysis. 
  • Natural Language Processing: analyzing human expression and emotion. Example: text, chatbot, email analysis.  

The below table shows CX use cases and examples of these AI technologies in action:  

Source: Gartner 2019

Are data scientists the only ones needing to understand these technologies? No, it’s extremely valuable to both marketing and CX teams to gain an understanding of these tools. Every company has unique needs depending on CX goals and business objectives. Teams need to make a well-informed decision and understand which tools are most useful to their business, which will essentially lead to more accurate decision-making and a customer-first approach.     

Now, are people rushing to adopt these new AI technologies for CX? In Gartner´s 2018 Enterprise AI survey, it was revealed that of businesses that are already deploying AI, 26% are implementing it to improve customer experience. Although it may not seem urgent to start implementing these technologies right away, it’s important that businesses are aware and start to familiarize themselves with these AI applications.  

A good place to start is mapping out a customer journey and finding the ‘dark spots’. These are the areas that could benefit from deeper real-time insights, such as understanding the mood of a customer when they are talking with a chatbot. Having these insights will allow you to hand over the conversation to a human based on the customer’s emotion.  

Companies are dealing with an increasing number of interactions happening across multiple channels and devices. With customer expectations at an all-time high, it’s not easy to connect all these touch points and deliver an excellent customer experience. AI can help provide rich insights allowing you to get faster, real-time understandings, and optimize the overall customer journey. 

Recapping the week at MWC19

Mobile World Congress (MWC) 2019, the world’s largest exhibition for the mobile industry, welcomed leaders from mobile operators, device manufacturers, technology providers, vendors and more.  

This year’s event saw a focus on two core concepts: 5G and Artificial Intelligence. It was said to be one of the most important events in recent times for the mobile industry. In the days leading up to the show, a warm buzz of anticipation filled the air as attendees were eager to hear about the new groundbreaking technologies. We were excited to be surrounded by leaders in the field and pleased to be a co-exhibitor for the Washington State Delegation of Commerce. 

With a large number of keynote presentations, panel discussion and exhibitors, there were many outtakes from the event. A hot topic that continued to emerge was AI bias. On day two I was able to discuss this topic with other like-minded people: Elena Fersman (Ericsson), Beena Ammanath (HPE), Beth Smith (IBM) and Kriti Sharma (Sage), who are all working towards an unbiased future for AI.  

We discussed ‘Democratizing AI and Attacking Algorithmic Bias’. The discussion of bias in AI continued throughout the event as many people came to speak with us about how to overcome this problem. If you missed this talk and want to hear more, see an edited version here.  

We also attended the Applied AI Forum: an exclusive conference that brought together telecom leaders, AI specialists, start-ups and academics, with an aim to spur debate and discussion on the practice of AI across the digital economy. Google and IBM Watson held an interesting panel discussion that explored ‘Applied AI: new trends and strategies’. In this forum, we were able to share lessons learned and discuss recent breakthroughs with both data scientists and global leads from several large enterprise companies.  

Another key highlight of MWC was our exciting hiring announcement! On the second day, we released our plans for the year: to double the size of our company by the end of 2019. With the rapid growth of AI applications seen across all industries, there is an increasing demand for high-quality data. And with this, our company is growing faster than ever. We are looking for more talent to join our team in Portugal, Japan, and the United States. See our careers page for more information.  

What a big week it was as we move into a new era of Intelligent Connectivity. A huge thanks to GSMA, a body representing the interests of mobile networks globally, and everyone we met at the event.  

We’d love to continue the discussions we had, especially around the topic on bias in AI. Reach out at pr@definedcrowd.com, we’d be glad to hear from you. We are already thinking about what next year might hold.   

Job description: talent for a smarter AI

We’re in a huge growth stage and are looking for talent to join our global team in Portugal, Japan, and the United States. Check out our careers page for current openings.

We accelerate the evolution of Artificial Intelligence initiatives by delivering high-quality training data to enterprise companies. We are investing heavily in our business and the people to make this happen. Over the coming months, we’ll be searching for professionals who are looking to embark on an exciting career while making a difference in AI.  

So, who are we? We’re an 80-employee startup based in Seattle, with offices in Lisbon, Porto, and Tokyo. Our CEO, Daniela Braga founded DefinedCrowd in 2015 to fill a gap in the market by offering high-quality training data to help machine learning products reach the market at optimal quality and speed. And with the rapid growth of AI applications and the high demand for this data, our business is growing so quickly that we are looking to nearly double our team by the end of 2019.

“We have a very ambitious goal –  to be the number one provider of data for AI in the world. This year will be crucial to achieving this goal, as we mature our product, grow our client base, and increase our partnerships with the companies that are leading the AI revolution.”

Founder and CEO, Dr. Daniela Braga

We are currently hiring positions in the following departments: Development, Product, Marketing, and Operations, with several openings for Software Developers (Frontend, Backend, and Full Stack), QA Automation Engineers, and Machine Learning Engineers. These positions are available within our four offices and will have an important role in the expansion of the company’s product: an all-in-one data platform.  

Earlier this year, DefinedCrowd was selected as one of CB Insights top 100 AI startups. Our client list includes many Fortune 500 companies including BMW, Mastercard, Nuance, and Yahoo Japan. We also have partnerships with IBM, Microsoft, and Amazon.  

“We are looking for the best talent to join our team in this exciting moment, and to be part of the construction of a smarter AI”

Founder and CEO, Dr. Daniela Braga

For anyone interested, make sure you keep an eye out on our careers page https://careers.definedcrowd.com, where there will be new jobs added throughout the year.  

5 trends in AI for 2019

It´s not easy to project trends in a market evolving as rapidly as AI. However, through analysis of cross-industry data and experience with a diverse client-base, we’re willing to make some bets. From automating mundane daily tasks to leveraging computer vision for more accurate medical diagnoses, here are 5 trends in AI we expect to emerge in 2019. 

TREND 1: “EDGY” AI 

Edge AI refers to processing AI algorithms locally instead of relying on cloud services or data centers. 

Smartphones, cars, and wearable devices are examples of devices that need to make faster and more accurate real-time decisions. Autonomous vehicles, for instance, need to make hundreds of decisions per second – brake, accelerate, turn on lights, identify and interpret traffic patterns, signals, and speed limits – all while simultaneously responding to the driver’s voice commands. These decisions must take place in a fraction of a second, and they need to be independent of the connectivity issues that come with cloud computing.  This means that autonomous vehicles need powerful chips to process all this information rapidly and accurately.  

Tech leaders like Nvidia, Qualcomm, Apple, AMD, and ARM are investing in developing and delivering chips that can handle these kinds of workloads. 

In 2019 we’ll see more models being deployed at the edge as well as specialized chips allowing AI models to operate independently from the centralized cloud, or on the “edge” if you prefer.

TREND 2: AI IN HEALTHCARE 

 Last year the FDA (U.S. Food and Drug Administration) approved IDx-DR, an AI-enabled software that can independently diagnose diabetic retinopathy before severe complications (such as blindness) emerge.  

The FDA also cleared Dip.io, a product developed by startup Healthy.io, as a class II medical device. This diagnostic tool can monitor urinary tract infections and track pregnancy-related complications by analyzing photos of dipstick urine tests. It’s as simple as uploading a photo, the model takes it from there.   

2019 will be a remarkable year for AI in healthcare. 

TREND 3: PREDICTIVE MAINTENANCE 

Equipment failure is one of the main causes of production downtime, a huge line-item for any asset-intensive business. However, today maintenance teams spend 80% of their time collecting data but only 20% analyzing it.  

Factory and field equipment generate mountains of unleveraged data that could go a long way to solving these issues. Alongside cameras and sensors, ML-driven algorithms can learn to check assets’ “vital signs,” catch small irregularities (a loose screw) before they turn into larger ones (a damaged turbine) and provide productivity predictions, allowing firms to plan accordingly.      

With sensors becoming more affordable, and edge computing gaining momentum, machine learning will become even more heavily incorporated in industrial processes in 2019. 

TREND 4: CONVERSATIONAL AI 

We say conversational AI, what pops into your head?  If it’s chatbots, you’re not alone. While that’s certainly a huge part, the technology is much broader as it is integrated across messaging apps and voice-enabled virtual assistants who go far beyond the scope of chatbots.    

In 2019 we can expect to see even more AI deployed to handle routine customer service interactions. Whether you’re booking a flight, searching for a new restaurant or requesting the arrival date of your next purchase, AI can assist you.   

Research from eMarketer shows that this year 66.6 million Americans are expected to use speech or voice recognition technology. Banking and retail are great examples of industries already using conversational AI initiatives, and as the technology continues to mature in 2019, we expect to see even more use cases in even more industries.

TREND 5: RPA / BACK OFFICE AUTOMATION 

RPA (Robot Process Automation) covers a variety of back-office tasks that can be automated by bots. It’s not a new concept, nor is it AI. But here are some interesting facts:   

  • According to McKinsey, RPA will have an economic impact of around $6.7 trillion by 2025.   
  • Forrester Research also mentioned that RPA market is estimated to grow to $2.1 billion by 2021.  

Although RPA is not considered AI – since it’s rule-based and can’t learn anything on its own – there’s been a collaboration between RPA and AI.  Due to its capacity of automating repetitive and time-consuming tasks, RPA can save employees tons of time, at the same time it can ensure processes are running smoothly and precisely. On the other hand, AI can enhance RPA.  

For instance, take a bank that’s onboarding a new client and needs to adhere to Know Your Customer/Anti Money Laundering Compliance Regulation. RPA is great for doing a lot of manual work. What AI can do is analyze the data the RPA’s pull in a more sophisticated manner, and arm a Compliance officer with more useful information.  

Whether there is a need to automate processes or implement solutions in this field, RPA has been mainly leveraged by large companies – until now. In 2019 we can expect to see small and medium-size businesses starting to adopt RPA, due to its clear benefits and increased popularity.  

100 most promising AI startups

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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. 

Source: CB Insights

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.