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

In the age of technological and digital disruption, AI is showing rapid advancements across almost every industry. And with this, real-world business solutions are being vigorously adopted especially in the banking sector with one-third of banking CIO’s actively planning an AI project, highest among all industries.   

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.   

DefinedCrowd JapanがHmcommとのパートナーシップを発表

English version available here

当社は、日本における音声認識技術のリーダーであり、ソニーの出資先スタートアップ企業でもあるHmcomm株式会社とのパートナーシップを発表できることをたいへん光栄に思います。

この協業により、ビジネスと次世代AI向け技術開発の両面において大きな可能性を得たことを確信しています。

Hmcommの名前は「human-machine communication(ヒトとマシンのコミュニケーション)」に由来しており、ASR(自動音声認識)分野における革新的なAIソリューションを開発しています。DefinedCrowdの高品質なトレーニングデータを活用することにより、HmcommのAIは次なるレベルへと発展を遂げるでしょう。

当社ファウンダー兼CEOであるダニエラ・ブラガは次のように語ります。

「Hmcommとのパートナーシップ合意をたいへん光栄に思います。ソニーの出資先かつ、日本における音声認識技術のリーダーを担う企業とのコラボレーションは、私たちにとってまたとない機会です。DefinedCrowdの高品質なトレーニングデータによって、HmcommのASRモデル強化のお手伝いができることをたいへん嬉しく思います。」

ソニー株式会社のイノベーションファンド室にてシニアインベストメントマネジャーを務める北川 純氏から、次のようにお言葉をいただきました。

「ソニーグループの投資先であるスタートアップ2社間での戦略的パートナーシップ締結の基本合意について、心よりお喜び申し上げます。Machine Learningを活用した音声認識精度最大化の両輪となる『アルゴリズム』、『トレーニングデータ』それぞれの領域において、数多くの導入実績と顧客数を持つ両社が連携することにより、新技術のさらなる社会実装が推進され、画期的な顧客体験がもたらされることを期待しております。」

国立研究開発法人産業技術総合研究所(AIST)発のスタートアップであるHmcommは、辞書ベースのモデル学習と感情アノテーションから成るソリューション提供のパイオニアとして発展を遂げてきました。DefinedCrowdによる音声アノテーションは、マシンラーニングと人による作業とを組み合わせることで、効率的かつ信頼できる品質を実現し、Hmcommのモデルに新たな価値を加えることができる存在であると確信しています。

今回の業務提携に関して、Hmcomm株式会社代表取締役CEOの三本幸司氏は下記のようにコメントしています。
「私たちと同じソニーグループの出資先であるDefinedCrowdとパートナーシップ合意に至り、たいへんうれしく思います。AIの社会実装を高速に推進するためには、質の良い収集基盤が不可欠であり、この分野において、グローバル・トップレベルのDefinedCrowdとの協業スキームは、私たちのソリューションの質を短期的に、飛躍的に向上することができると期待しています」

言語学のエキスパートによって創設され、品質とグローバルな規模というコアバリューを備えた企業として、DefinedCrowdはこのパートナーシップ合意を通し、Hmcommの価値をさらに国際的なレベルへと押し上げる力となれれば幸いです。日本の新たなパートナーとの協業によって大きく広がる可能性に、私たちの期待は高まるばかりです。

DefinedCrowd Japan announces strategic partnership with Hmcomm

日本語版はこちら

We are delighted to announce our partnership with Hmcomm Inc, a leader in speech recognition technology in Japan and a fellow Sony-backed startup. A strategic move for both businesses, our joining poses great potential for the development of next-generation solutions in AI.

Hmcomm – whose name is derived from human-machine communication – develops innovative AI solutions in the field of automatic speech recognition (ASR). DefinedCrowd will be providing them with high-quality training data to take their AI initiatives to the next level. Our Founder and CEO expressed her enthusiasm about the joint venture:

“We are thrilled to partner with Hmcomm. It’s an exciting opportunity to collaborate with a fellow Sony-backed company that is a leader in speech recognition technology in Japan. We look forward to helping improve Hmcomm’s ASR model with high quality training data.”

Dr. Daniela Braga, Founder and CEO, DefinedCrowd

An AI startup spun out of the National Institute of Advanced Industrial Science and Technology (AIST), Hmcomm has developed ground-breaking solutions in their field, combining both dictionary model learning and emotional annotation processes. DefinedCrowd’s ability to provide efficient and reliable speech annotation through our unique combination of machine learning technology and human intelligence, means we’re perfectly positioned to deliver added value to Hmcomm’s work.

“We’re happy to announce our partnership with DefinedCrowd, a Sony investee company like us. To enable the social implementation of AI, access to a data collection platform with high-quality is key. I’m excited to collaborate with a global leader in AI data like DefinedCrowd that elevates our solution dramatically and rapidly.”

Koji Mitsumoto, CEO, Hmcomm

Our partnership is further endorsed by Mr. Jun Kitagawa, Senior Investment Manager of Sony’s Innovation Fund: “We sincerely look forward to the formation of a strategic partnership between the two startups that Sony has chosen to support. As experts in algorithms and training data technology, two fundamental pillars in the maximization of speech recognition accuracy through Machine Learning, the collaboration between these companies holds great potential. Both have shown rapid customer growth with outstanding results and we look forward to further promoting their work as they bring about revolutionary customer experiences through new and innovative technologies.”

Founded by a linguistics expert and with quality and global scale as two of our core values, this partnership is a perfect example of DefinedCrowd’s commitment to raising the bar on an international level. We couldn’t be more excited to see the results of our joint efforts with Hmcomm and look forward to working closely with our new partners in Japan.

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.”

ChimamandaNgozi 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/ 
http://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 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 are at an all-time high, it’s not easy to connect all these touch points and deliver an excellent customer. 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.