Things have become so convenient for us! We can sit down in front of our computers, click a button, and instantly order what we want or need. We do this so often, we develop patterns that reflect our gravitation toward a cadre of things we most commonly consume. This consistency is exactly what Max Bennett and his team at Bluecore want to see. As the Co-Founder of a “cross-channel Retail Marketing Platform” that specializes in email, Max believes it is possible to give customers what they need while making sure that companies maximize their revenue. Tracking spending habits and giving careful attention to the things that interest consumers the most is Bluecore’s specialty. Given that most of the emails people receive in their inboxes each day are quickly discarded, Max knows that in order to grasp and hold the attention of consumers, he has to filter in content that is most relevant. At Bluecore, Max opens the door to greater sales and increased customer loyalty by going straight to the “core”.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.
Max: Bluecore is one of the world’s largest consumer retail data companies. Our retail marketing platform, which specializes in email, is used by over 400 retail brands to better understand their customers, match them with the products they’re most likely to be interested in, and improve their experience with the brand (rather than annoy them with irrelevant communications).

We facilitate interactions with more than 400 million individual customers across a combined retailer catalog of over 125 million products, making the product set we manage larger than Walmart and second only to Amazon.

This is a ton of data — way too much to parse through, analyze and act on manually. That’s where our technology and AI come in.

In 2017, we introduced an AI layer that predicts with very high precision which customers will have a high affinity to specific products and/or categories, what customers are most likely to buy next based on what they’ve purchased or interacted with in the past, their lifetime value, and other actions they’re likely to take. It then gives the marketer the ability to surface those audience sets for one-to-one email communications to be deployed by Bluecore’s platform.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Max: In a few ways…
First, AI will evolve from being seen as some watershed moment to simply a continuation of a several-hundred-year-old trend of automating tasks away from people (think everything from weaving machines hundreds of years ago to ATMs, automated checkouts, factory robots, the list goes on). AI is different than past technological innovations in that it’s not just automating repetitive rules-based tasks, but also autonomously executing entire complex jobs. Because the public’s initial exposure to AI was largely through inconsequential events (a robot beating a human at Go; Watson dominating Jeopardy) and pop culture (Terminator), it still feels out of touch to many. But as it’s applied to more day-to-day business processes — and infiltrates the daily lives of the masses — it, like other seemingly exotic technologies before it, will become more familiar.

Second, unique data sets will become the currency of the future. In the AI industry, having differentiated access to nuanced data sets will be even more important than having the best algorithms. Algorithms are becoming public and open source, whereas unique data is more scarce. Ultimately, data is what fuels and enhances AI’s ability to make decisions and increases AI providers’ control.

Third, AI companies and products will be tailored to industries, not job functions. This will be hugely disruptive to existing software companies and business models, and could lead to a completely new landscape of leading tech companies. Instead of building AI around specific job functions (i.e. HR, accounting), it will be tailored to certain industries (i.e. retail, media). This is because an AI tuned to the specific goals, actions, data, and nuances of a specific industry, will always outperform one that is built generically. And in an AI-first world where the quality of an AI’s decisions is what differentiates products, verticalization will be necessary component of achieving this.

Fourth, we suspect that AI will lead to large scale adoption of performance-based business models . In the pre-AI world — where you use technology to augment and scale the work that you’re doing, it made sense to price software based on “usage” (i.e. number of “seats”, “API calls” or actions taken). But in an AI-first world, such pricing units are not a good proxy of value between customers and technology companies. Rather customers will opt to pay the AI for how well it does the job they asks of it. In other words, on performance.

Tamara: What is the biggest challenge facing the industry today in your opinion?
Max: In the retail industry, every retailer that’s not Amazon has to figure out how to survive in a world where a single dominant player is already winning at three of the four factors that influence customers’ buying decisions: How cheap it is, how convenient it is, and how much variety it offers. This leaves them hyper-focused on offering things that Amazon can’t offer — and competing on the one remaining factor: how unique the experience is.

Rather than a huge selection and five-minute delivery times, companies like Warby Parker are successfully competing on peripheral value adds, such as creating a brand that consumers want to align with, prioritizing the creation of its direct-to-consumer channels and relationships, and offering highly curated product selections. That’s just one retailer, though. The industry as a whole needs to determine what that experience piece is and tie it to their core competency

Retailers also need to decide what role their offline retail space will play in the future. There’s recently been a lot of talk about the death of retail in response to the widespread store closings we have been seeing over the last few months. We shouldn’t conflate retailers overinvestment in offline retail space with the idea that offline retail space is not a necessary and core part of the differentiated experience they will have to offer to survive. While it is true that retailers are largely over-invested in physical retail stores. For the stores they keep, they will have to figure out how to evolve them into vehicles for experience delivery, instead of just a glorified distribution center.

Tamara: How do you see your products/services evolving going forward?
Max: At Bluecore, we’re working to evolve our AI, which is currently focused on completing specific tasks, into a wholly autonomous email solution that’s programmed to act on the insights it uncovers in pursuit of very specific business outcomes. The way consumers will experience this is that all the annoying, irrelevant emails will go away. Right now, retailers don’t know what consumers want and their inboxes reflect that.

Bluecore will additionally continue to power a lot of the non-email content that consumers see when they shop online. The key differences will be that the ads they see will be based on driving future experiences rather than taking consumers back to past experiences or items they’ve interacted with.

Our vision is that our technology will move retailers away from websites that look like glorified catalogues and direct them toward discovery vehicles that driving unique experiences, personalized to the individual users.

Part of how we will achieve this is by giving customers far more sophisticated mechanisms of providing feedback on why they didn’t like a certain part of their experience. They’ll get better experiences as a result.

Service at Its Best: Getting Right to the “Core” of Customer Satisfaction was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In a hectic work environment, organization is the key both at home and in the office. Why feel grief when looking in multiple places for files when you can have them all in one location? Just ask Mika Javanainen, Vice President of Product Management at M-Files. He believes that getting work done should be as easy as clicking a button. M-Files unifies information across your business systems to give you immediate access to your information from anywhere. You can browse to find what you need and navigate through your folders. Whether it is using live enterprise search or focusing your search to see only the things that are important to you, M-Files can give you seamless instant access, “no migration required”. Tired of searching? M-Files can help you find it!

Tamara: Can you share a story that inspired you to get involved in AI?

Mika: I’ve been generally very impressed with the consumer-specific use cases for AI. It is fascinating how consumers have, in a relatively short period of time, adopted state-of-the-art technology in their personal lives, making their corporate IT services look almost embarrassing.

Consumer products have raised the bar of user expectations for technology. Great usability is not just nice to have but now a requirement. Think about digital assistants, for example. It is not that Apple’s Siri or Amazon’s Alexa would actually be smarter than their users and solve uber complex problems. Instead, they focus on eliminating routine work and adding comfort. Why would you need to pick up your phone, log into a music player, search for a playlist and hit the play button, if you can just say to Alexa what type of music you’d like to listen to now? Anyone can complete a manual task, but why spend time on it?

At the same time, I am seeing how so many businesses struggle with managing even the simplest information management routines. Email boxes are overflowing and users miss key messages, forget to reply, attach wrong files, save documents to wrong repositories so no one can find them, and accidentally share confidential files to externals. Digital workers are provided systems from the technology museum called the IT department, while users are struggling with the fact that the amount of digital information they need to deal with doubles every 18 months.

I see that AI can offer tremendous value for the enterprise in the area of information management. The solutions do not actually need to be complex. Actually, vice versa: Businesses should first and foremost focus on freeing their employees’ time from routine work so that they can leverage their brainpower for the most complex tasks and decisions. This can be done if you let computers manage the routines.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Mika: M-Files provides a next-generation intelligent information management platform that improves business performance by helping people find and use information more effectively. Unlike traditional enterprise content management (ECM) systems or content services platforms, M-Files unifies systems, data, and content across the organization without disturbing existing systems and processes or requiring data migration. M-Files breaks down silos by allowing users to access and leverage information that resides in any system and repository, including network folders, SharePoint, file sharing services, ECM systems, CRM, ERP and many others.

M-Files also incorporates artificial intelligence (AI) technology to assist users by automatically analyzing, classifying and tagging documents and other information. By tagging multiple characteristics about documents and data, M-Files can establish relationships with objects in other systems, such as relating a document to a customer, project, or case managed in a CRM, ERP, or HR system. These relationships, along with other metadata, establish context that can vary by role, workflow, and even individual preferences to create a dynamic and personalized experience that lets users work the way they want without the organization giving up control.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Mika: We see AI as a key driver in the evolution of traditional enterprise content management (ECM) systems (records management, document management, etc.) to a smarter, more flexible and dynamic approach to managing information. Think of how music is managed on the iPhone: it just shows up how the user wants to listen to it, by artist, album, genre, playlist. The device intelligently knows it’s music and serves it up dynamically; the same content served up differently for different people. Now generalize that idea for the business environment. AI plays a critical role in automatically determining the metadata that makes the iPhone music behavior described above possible. This analogy is good for another important reason — a huge consumerization wave is driving change in all enterprise software markets, including ECM. People are expecting the simpler, more engaging and intelligent experiences they have with consumer services and apps in their business applications. This is part of the whole BYOD/BYOA trend, and it’s also is a big factor in why file sharing services are so popular. They appeal to people because they are simple, useful and let people work the way they want. We believe the future of content management is to deliver that simple, intuitive — even consumer-like — experience built on a foundation that delivers the control, security and automation that businesses require. AI is going to be a key part of making that happen and M-Files will continue to expand our investment in this area in the future.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Mika: AI is a developing technology that often requires a level of “training” to teach the software how to deliver optimal results in a given industry or business, but the key challenges are not really with the AI. It can be deployed now and will improve rapidly over time.

The bigger challenge comes with trying to integrate modern technologies like AI within existing, often entrenched and aging IT environments without “throwing the baby out with the bath water.” This requires acknowledging that the static and inflexible approaches of the past aren’t going to work, and embracing alternatives that can effectively integrate and maximize the investment in existing systems and processes. Often this means planning for a phased approach to modernization that minimizes the need for expensive migration projects. It also greatly simplifies change management by allowing innovation to start with smaller teams and departments, while not disturbing those who depend on existing systems and processes and aren’t quite ready to change.

Tamara: How do you see your products/services evolving going forward?

Mika: M-Files has an exciting vision to popularize a more modern and flexible approach that we call intelligent information management. We’re in the second phase of our strategy, and while this phase is exciting and we think a major innovation over current approaches, the third phase is even more exciting.

The first phase was all about information in M-Files, how we managed information uniquely by using a metadata-based approach. Leveraging metadata, users can organize content dynamically based on any attribute of the content: agreements can be viewed by expiry date, customer, owner, or agreement type, for example. M-Files leverages AI to automatically tag content with the key metadata so users do not even have to know where to save the content and what attributes to use.

The second phase is about the backend where we have made M-Files “repository neutral”. Now we really mean it when we say, “it’s not about where, but what”. Content and information can literally be anywhere and in any system, and we unify it based on context. The future is about freeing information from the constraints imposed by different systems and silos, user interfaces and idiosyncrasies. It’s truly about what the information is and why it is important to a certain individual at a certain company in a certain context.

The third phase is about the front end. What if everything just discussed could reveal itself wholly within other user interfaces and ecosystems? Think Office 365, G Suite, Salesforce, and even applications and ecosystems based around specialized solutions like the ArcGIS mapping platform from Esri. This third phase takes us to a place where Office 365 provides a user interface known by tens of millions to the M-Files intelligent information management platform that unifies and manages business content and information residing anywhere, in context.

Tamara: What is your favorite AI movie and why?

Mika: Terminator 2 is of course a true classic. I probably wasn’t old enough when I saw it the first time, but I remember I was very impressed with the visual effects. Was the plot so great? Maybe not. The Matrix had a great plot though.

Tamara: What type of advice would you give my readers about AI?

Mika: AI is of course a buzzword now as we know. While there are very wild visions on how to apply AI for different solutions, I think we often forget how the AI could just eliminate the simplest everyday routines in our lives. I would encourage readers to think how simple text analytics or computer vision could make your day better in the office. You will soon find that you probably repeat certain routines in your work and use too much time for routines that could be automated with AI.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Mika: In conjunction with Dimensional Research, M-Files conducted a global survey on the proliferation of information silos and the impacts they pose to organizations. Nearly half of respondents (48 percent) said they feel their company struggles with managing information silos, and 67 percent cited that navigating through different systems and locations to find and verify the most current versions of documents or files negatively affects their productivity.

M-Files delivers a simple, unified experience for intelligently finding, accessing, and managing information residing within any system without disturbing existing processes and the users that depend on them. This also eliminates the need for complex and expensive migration projects, two major stumbling blocks to innovation and business process improvement. In short, people can find what they need to do their jobs faster without worrying about where information is stored.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Mika: I’m not sure if this is funny, but I am amazed how high-tech companies have the brainpower to truly change the world with technological innovations, and yet we have to print instructions in our kitchen area to remind people to fill the fridge with new soda if you take the last can. Maybe we need an app for this? I am as guilty as my colleagues, by the way.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Mika:

1. I think the technology keeps evolving, but we have enough technology already to start applying it for automating routines.
2. I believe that competition in consumer goods and services will set the bar for AI development. Business-to-business providers will benefit from this phenomenon as the AI-based solutions and APIs become more accessible for everyone.
3. Especially in our industry, I believe that combining computer vision, location data, text analytics, knowledge graphs, and voice recognition will dramatically ease the lives of information workers. The systems become more proactive and will be able to provide the information that you need when you need it. Today, information management systems are more often passive data silos from which users have to search data. As an example, when you get an email where your colleague is asking for a copy of the slide deck that you showed last week, we have all the information to be able to auto-reply him with the right attachment. Now, we go and find the document and manually attach it and then hit the send button.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Mika:

1. I worry about the people that worry about AI. It is good to recognize the threats of the AI and how machines can develop their own mind in the future and become our greatest enemies, but realistically I think that the technology is not quite there, and we should focus our energy now to brainstorm helpful use cases for the current capabilities.
2. As the technology becomes more accessible for every user, the risk of making the wrong business decisions increases. I have started to see this for instance in many business intelligence applications where users are now able to create a self-service dashboard of their data and end up coming up with stunning-looking dashboards with completely wrong data. That then, of course, leads to wrong conclusions and business decisions.
3. Natural language understanding is an emerging technology in our field and often a superior way to extract key information and to make the right suggestions. However, this technology is very language-specific. There is a significant risk that these technologies become good enough for certain major languages and the citizens that speak less common languages are left a step behind.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Mika: I think AI can revolutionize the IT industry quite a bit as knowing the technical syntax of programming and query languages becomes less relevant for business users. We are already seeing signs of this in many business intelligence applications where traditional SQL queries are now being replaced by natural language queries. This will dramatically improve business persons’ access to the business-critical information. Companies that do not leverage these options are probably going to suffer against their smarter competitors.

The “Search” Has Ended: How AI Technology Brings Everything Together was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this economy, customers cannot afford to have to wait. They have multiple needs that must be met quickly and efficiently, which means it is critical for them to have products that they can trust. Not only do customers want to feel confident in what they use, they also want those products to be in real time. Just ask Jonathan Ellis, the Chief Technology Officer and Co-Founder of DataStax. He knows what it means for things to be instantaneous, individualized, continuous, and global. That is why he and the experts at DataStax are equipped to deliver Enterprise-Ready features in a Right Now Economy. Their technology guarantees quality and customer satisfaction in the here and now.

Tamara: Can you share a story that inspired you to get involved in AI?

Jonathan: I was studying computer science in college when the IBM Deep Blue defeated Garry Kasparov. It’s common knowledge that Deep Blue ran on massively parallel IBM RS-6000 servers, but most people don’t know that what made it unique was that these servers were paired with custom chess microprocessors. This let Deep Blue analyze ten times more moves per second than could be achieved in software alone. In a way, this was an early (and expensive) echo of what we see now, with deep learning models today taking advantage of GPU, FPGA, and custom silicon acceleration.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Jonathan: DataStax provides DataStax Enterprise (DSE), a distributed cloud database that is at the heart of many applications in IoT, fraud detection, and personalization use cases.

Two things make DSE uniquely well suited for modern real-time AI applications. First, it offers a unified platform including both Spark Streaming and Graph capabilities. This simplifies building complex machine-learning applications like Deloitte MissionGraph.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Jonathan: One of the big challenges today is updating models in real-time in response to new information without doing a whole training cycle offline. I expect to see a lot of effort towards creating new training methods that offer reusable ways to accomplish this.

Tamara: How do you see your products/services evolving going forward?

Jonathan: It’s still too hard to build applications in general on a distributed database. This is a problem across the industry: everyone has a pretty good handle on the relational model at this point, but relational doesn’t scale once you need to partition it across multiple machines. So, we need a way to make data models scalable and partitionable automatically or semi-automatically.

Tamara: What is your favorite AI movie and why?

Jonathan: I’m not much of a moviegoer, but my favorite AI book might be Isaac Asimov’s I, Robot. Asimov’s robots were intelligent humanoids and this is the book where Isaac Asimov introduced his Three Laws of Robotics. Unfortunately, I understand that the 2004 movie did not actually have much in common with this.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Jonathan: Neither my co-founder Matt Pfeil or I had worked for an enterprise software company before, and we were completely naive about what that model entailed. We thought we could just let the world know that we were building the world’s best distributed database for hybrid cloud and that would be sufficient. Fortunately, we had advisors, including our early investors that clued us in to what building a sales force looked like.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Jonathan: AI is to workers in the 21st century what automation was in the 20th, and while there’s both positive and negative connotations there I see the positive dominating. Yes, people lost their jobs when assembly lines replaced blacksmiths. But, for example, washing machines took something that took basically an entire day each week and turned it into almost an afterthought. Washing machines probably cost some jobs, but most people couldn’t afford to hire a laundry service; they did it themselves. So, automation made their lives immensely better. And I think that’s going to be the dominant effect as intelligent assistants mature — taking some of the drudgery out of knowledge work, making lives better a little at a time.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Jonathan: The skills gap for machine learning itself will narrow, but companies will still struggle in applying AI to business problems. I don’t think we’ll see many turnkey, AI-based solutions in the next three years; it will stay a Wild West of bespoke efforts at least that long.

Right Here, Right Now: “Making Empowering Moments a Reality” was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

We live in a time when it is of utmost importance to protect our personal information from those who seek to take advantage of us. Our day to day interactions and even our spending habits have to be guarded with care. Moreover, we have to function in such a way that we can feel a sense of security in all that we do. Dr. Swatee Singh understands this and appreciates the opportunity to provide that security. In the Technology Division at American Express, Swatee is part of a team that anticipates the needs of their customers before they can even verbalize them. Her background in Machine Learning and Electrical Engineering has sharpened her skillset in such a way that she is uniquely equipped to deliver on her promises to customers with precision. By developing a line of products that fit the emerging needs of customers both domestically and internationally, American Express demonstrates how much they truly value their customers.

Tamara: Can you share a story that inspired you to get involved in AI?

Swatee: “The Terminator”! I have been fascinated by robots powered by AI to protect and serve us ever since I saw the movie as a young child. A young female can face shocking negation from friends, family and even her teachers when she expresses her desire to work in such an area, and I certainly faced my fair share. I began my journey in this space looking to use AI to detect breast cancer. It made me realize the power of what AI can do to improve care and extend the lives of people. We literally saved women’s lives!! Their gratitude and stories of how the impact reverberated for not just them, but also their families and communities, continues to be my inspiration for the faith I have in the power of AI and its ability to transform our world.

Since then, I have used my transferable AI skill set to better service our customers by anticipating their needs and delighting them!

In addition, I have been fortunate to have had multiple female mentors who have shown me how to be a successful as a female in traditionally male-dominated fields like AI, data science, and engineering. I strive to “pay it forward” in kind and do my part to balance the representation of women who build and engineer systems for customers, many of whom are women themselves.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Swatee: American Express works to develop products and services to meet the emerging needs of our customers, in an effort to provide the world’s best customer experience every day. We treat our customers’ data as sacrosanct and look to find ways to become an essential part of their digital lives while preserving their privacy and the security of their data.

AI pervades several aspects of our business today, with several external customer-facing touchpoints and internal process-driven initiatives benefiting from AI.

Customers directly experience the difference with American Experience from the first time they come to our website. Through AI, we can predict their needs and enhance their customer experience by making it easier for them to choose the right card product. Another example is in how we service existing customers through chatbots that can anticipate customer needs and concerns and quickly address them through digital channels.

Internally we have sought to combine robotics with AI to make our processes more efficient. As an example, we have seen a 600% efficiency improvement in back-office card processing, which is significantly improving the customer experience by helping our Analysts to assist customers with changes to their American Express products more efficiently.

These are just a few examples of how we are changing the way we do business and differentiate ourselves from the competition via the use of big data and AI.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Swatee: Big data and machine learning has the potential to revolutionize every aspect of our lives by making them simpler and more connected. Cheap computing power in the Cloud, along with fundamental shifts in paradigms such as those offered by blockchain and other technologies, will fuel this growth exponentially. Personalized services that make our experiences seamless and efficient are already here and will only expand.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Swatee: I see the biggest challenge as a two-pronged problem: how we regulate AI and how it is executed.

A deep ethical, regulatory and legal discussion on how AI should be integrated in our lives is long overdue. For example, how much does a personal digital assistant truly “hear,” and how long does it retain the data? How do companies ensure that meaningful checks and balances are in place to identify data that has been mistakenly collected and delete it?

Additionally, most large corporations struggle with legacy systems that sit in silos. Realizing the dream of AI-powered apps is often tempered with reality on the ground. For example, the inability to link data across data stores within the same company, and lack of the right engineering and data science talent often slow the enthusiasm of companies. While daunting, the winds of change are definitely sweeping across most of corporate America as companies look to improvise and adapt.

Tamara: How do you see your products/services evolving going forward?

Swatee: We are focused on improving every aspect of the customer experience through AI. The size and scale of American Express’ systems and applications, and especially our industry-leading position in big data, have allowed us to get to the market earlier than most financial services companies with AI- and robotics-enabled solutions for customers. We continue to actively pursue new use cases and implement new capabilities leveraging these technologies, to ensure we bring continuous innovations to our existing offerings, as well as develop new products and services to meet the emerging needs of our customers, merchants and partners. Our focus is on applications across a range of functions, including acquisitions, commercial payments, marketing, loyalty, credit, fraud, and merchant services.

Tamara: What is your favorite AI movie and why?

Swatee: Just one? And I can pick only movies, not TV shows (what about “Altered Carbon”)?? “The Terminator,” for sure, as it’s the first AI movie I personally ever watched and fell in love with. More recently I have enjoyed “I, Robot” because it brings to light the morality and ethics we might want to impart to these bots and how they may evolve on their own. Or even “Ex Machina” because it features a “female” version of AI, and wouldn’t it be cool to have her walking amongst us? (As a side note, art seems to imitate life — even in the representation of female characters in tech-related movies. Male characters tend to outnumber females, and women are more likely to play supporting, rather than leading roles. As a woman in tech, I hope this changes soon … both in art and in life.)

Most AI movies depict anthropomorphized bots, but I feel it’s a lot more likely that what we actually will experience is along the lines of a smarter washing machine, smarter homes or even smarter water utilization, as water becomes an increasingly precious resource.

Tamara: What type of advice would you give my readers about AI?

Swatee: I have determined that AI isn’t a job killer or, for that matter, what will bring about humanity’s end. AI is the glue that will make our experiences better and cut out inefficiencies. AI-powered self-driving cars, smarter power grids and smart homes are already becoming a reality, and their role in our lives should be embraced and not feared. AI will help us move toward a Knowledge Society — one that promotes equal, inclusive, and universal access to all knowledge creation. Such a society creates a level playing field, as the ability to create knowledge isn’t bound by gender or other social constraints. When combined with world-class education that inspires innovation, it can open new opportunities for all of us.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Swatee: The very core of American Express is a commitment to service, and everything AI touches in this company directly improves the lives of our customers. Here is an example — we use deep learning to detect and predict fraudulent activity. This saves our customers time and money and allows them the freedom to use their American Express products without limits on out-of-pattern spend.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Swatee: As we look to use more and more open source tech stacks to power our AI revolution, the most fun we have had is in creating chatbots from scratch for internal use. The complexity of human language, sarcasm and its ability to confound algorithms never ceases to amaze us, as we saw some of our early bots stumped by common idioms, which led to them returning nonsensical, sometimes funny answers.

Example: One of the documents in our search data included some ratings (0 to 5 stars) with guidelines on how to read the document. In response to the question, “How many stars are there in the universe?” the bot responded, “Users were limited to 5 stars.”

We came to respect the field of natural language processing a lot more deeply after that experiment!

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Swatee: There are so many different things that excite me when it comes to AI!

• I’m excited about the potential to expand the definition of “customer” from consumers and merchants to include things. Autonomous machines that are powered by AI will become another customer for financial services companies like ours, and this will transform and bring new forms of value and interaction to this industry.
• AI combined with Internet of Things (IoT) and blockchain also has the potential to revolutionize this space as it enters new markets with eye scans for payments, for example, or a digital identity authorizing payments on our behalf while ensuring privacy and protection of identity in this digital space.
• Making payments easier and making financial services less prone to fraud will open up the gates to a more interconnected world, which has the potential to amplify global commerce to bring prosperity and services to the whole world! The potential for peer-to-peer value exchange services powered by self-determining, intelligent and distributed AI will revolutionize this space.
• The opportunity to continue expanding the presence of women and diverse talent in technical roles through the fast-growing AI space. The field offers lots of career options and opportunity for advancement. By encouraging and mentoring top tech talent at all levels to enter the field — from elementary school through the university level, and in entry-level roles to executives in the C-suite — we can help to change the face of technology and bring more diverse perspectives to bear. This will be great for the industry because it will increase innovation to help address the increasingly diverse needs of consumers.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Swatee: In this case, there are a few things that worry me.

• Invasion of privacy: I worry companies will misuse Know Your Customer (KYC) to collect more and more data that they don’t really need, or link data from various sources all in the name of better “servicing” that violates the privacy of the population.
• Bias: Underwriting and decisions on loans are now increasingly made using automated AI-powered algorithms. The potential for bias in a non-detectable manner can’t be ignored. This becomes especially true if algorithms are trained on datasets that carry inherent human biases on such characteristics as gender, race, and social class. Fortunately, we have controls as an industry against them, but enforcing these controls may become a herculean task in an AI reality.
• Lack of cohesive laws and regulatory-legal preparedness: From a regulatory perspective, our archaic laws and methods of enforcement haven’t yet caught up with the potential for the misuse of data in an AI world.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Swatee: Specifically within financial services, I expect customer servicing and customer experiences to vastly improve. Several financial products should become cheaper as we drive down costs and realize increased internal efficiencies. More broadly, seamless payments and the transfer of payments between peers or machines and humans will change the way we do commerce. In this ever increasingly connected world, AI has the potential to uplift the lives of millions of people across the globe.

I also expect to see more women in AI roles and leadership. Many companies are joining trailblazers like American Express in increasing their focus on females and other diverse talent for tech roles at all levels. By offering affinity groups, communities of technical practice, mentorship and advocacy programs, not to mention flexible work environments and family-friendly benefits, employers are helping to revolutionize the industry. By volunteering for programs that promote STEM careers, leading diversity and inclusion efforts in our workplaces, as well as mentoring younger colleagues, those of us already working in the field can help advance the revolution on an individual level.

At the Core of Commitment: How American Expresses Utilizes AI to Prioritize Customer Care was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

We use our smart devices so frequently that we often to do not give much attention to what keeps them running. When we open an app, we simply trust that it will function without an issue. Part of that confidence lies in how much we rely on Wi-Fi. In the coffee shop, in the mall, and waiting at the doctor’s office, Wi-Fi is readily available to us. Bob Friday and his team at Mist Systems are well aware of how critical it is for us to have the technology we need, when and where we need it. That is why as the CTO and Co-Founder of Mist, Bob has established an AI-driven wireless network that is it at the top of its game in wireless, cloud, and data science. Thanks to Bob, our wireless systems and Wi-Fi operations will continue to be dependable, just the way we like them.

Tamara: Can you share a story that inspired you to get involved in AI?

Bob: In 2011, I saw the Watson for Jeopardy episode, and thought, ‘If they can build software that plays Jeopardy, we can build a virtual IT assistant that answers questions on par with a wireless expert.’ This was one of the inspirations for Mist. The other inspiration was my co-founder Sujai Hajela and I saw a need from enterprises putting business critical and customer engagement services on their wireless networks for AI-driven IT, where software could proactively help them with the visibility needed on their users connectivity.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Bob: Mist’s goal is to be the leader in AI for IT. Today, that manifests itself in the form of an AI-driven wireless network that automates Wi-Fi operations, lowers operational costs, and enables new value added mobile services using location. As part of our journey, Mist built the first virtual network assistant (VNA), which uses AI to answer questions on par with a human Wi-Fi domain expert. In addition, we are the only vendor leveraging AI to virtualize Bluetooth LE, bringing the convenience, accuracy and scale of indoor location on par with GPS.

AIOps (AI for IT operations) is a burgeoning market with huge potential. We are proud to be leading the charge from a technology standpoint, all the while helping businesses and end users have amazing wireless experiences.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Bob: Ultimately, we will see AI evolve to the point that IT infrastructures are totally self-healing. Today, we have done a great job collecting data, classifying it with domain knowledge, and then using machine learning and other techniques to turn this information into actionable insight. However, the next step is still ahead of us — where the network proactively and automatically corrects on its own.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Bob: The biggest challenge facing the industry today is the gap that exists between data scientists, domain experts, and cloud. On the one hand, you can find great mathematicians with PhDs. On the other, you can find RF experts with years of experience working with Wi-Fi and other wireless technologies. The trick is bridging the gap between the two so the respective sets of expertise work in harmony with one another. In my opinion, this is why small companies (i.e. startups), have an advantage over big established companies when it comes to leveraging AI. In addition to less baggage from legacy solutions, startups have less organizational silos. This leads to better collaboration and faster innovation, which is key when bringing new AI technology to market.

Tamara: How do you see your products/services evolving going forward?

Bob: We brought the first AI-powered virtual network assistant to the industry in February 2018, which we plan to constantly evolve going forward. It is amazing to see the Mist VNA getting smarter every day and answering more and more Wi-Fi questions with stunning accuracy. Eventually, we will reach the ultimate goal of using VNA to build a completely self-healing network. In addition, we plan to broaden our VNA services beyond wireless to bring similar AI expertise to other strategic IT domains, such as security and analytics. We have just scratched the surface on what AI can do for IT. The next few years will be very exciting!

Tamara: What is your favorite AI movie and why?

Bob: I wouldn’t be a real CTO at an AI company if I didn’t say “2001: A Space Odyssey”. It is obviously a classic and one of the first to breach the notion of an AI-driven assistant. Beyond that, I enjoy “The Matrix” series as they broach some very interesting ideas, particularly around humanity and simulation theories. Finally, “Iron Man” is a favorite of mine. Who doesn’t want their own Jarvis?

Tamara: What type of advice would you give my readers about AI?

Bob: No one can or should ignore AI. It will be relevant to all industries and impact all of our lives. If you are in the business of AI, you will need the right talent, so start early and seek out the best.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Bob: Our product makes Wi-Fi more predictable and reliable. If you ask any teenager, there are few things in life more important than great Wi-Fi! More seriously, we are also working hard to bring new indoor location services to market that provide real value to many people. For example, the Mist platform can help companies locate employees in an emergency. It can help a sick patient find the closest nurse. It can prevent kindergartners from wandering off site without adult supervision. The real-world applications of AI-driven wireless are quite numerous, and still evolving every day.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Bob: It’s been interesting to see our company — a small startup that has been shipping product for under two years — become the default wireless standard for some of the biggest companies on Earth. This is a real testimony to the benefit of AI and the demand for new automated wireless networks. On the more humorous side, a partner and I got lost in a large hospital where we were installing location. We felt like mice in the maze trying to find the cheese.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Bob: There are lots of things that excite me about AI in the IT industry. One is the need for the technology. Traditional wireless networks, for example, are too manual and outdated, making the market ripe for innovation. Second are the results. AI-driven wireless really works, with Mist’s VNA being a prime example. Lastly, it is the promise of the technology. There is so much more to come.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Bob: The only thing that worries me is the misperception that AI will replace IT professionals. A virtual network assistant is exactly what the name suggests — an assistant. It won’t eliminate the need for people; it just makes them faster, smarter, and more cost effective.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Bob: AI is here to stay — it’s not a passing fad. It’s like internet and TV, and the adoption rate will be on par with cell phones and radio. In the future, it will be so embedded you probably won’t even know it is there.

In the “Mist” of Technological Greatness: A Pioneer in the Industry was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is just something about seeing the light bulb go off in a child’s mind when that child truly understands something. Since Math tends to be the subject that many children fear, it is even more encouraging for teachers when they find programs that make their students excited about learning. That is what happens with Happy Numbers! Through AI technology, Happy Numbers can individualize instruction and ongoing learning for students. Thanks to Evgeny Milyutin, CEO of Happy Numbers, and his group of experts, this program has taken the “sadness” out of numbers so that there is nothing left but joy. Is it working? Well, you do the Math!

Tamara: Can you share a story that inspired you to get involved in AI?

Evgeny: When I was tutoring, I saw so many opportunities to make my life easier by giving part of the job to the technology. There was also a certain feeling that we underestimate what technology can do in terms of teaching, so we’ve started to experiment/prototyping.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Evgeny: Happy Numbers is all about blending technology and pedagogy, resulting in individualized math instruction for students. Using a combination of visual modeling, concrete manipulatives, and other traditional approaches alongside cutting-edge technology allows students to become more independent and engaged in their learning. Some of the features include: immediate feedback, adaptive scoring, and sound support for non-readers and auditory learners. Best of all, Happy Numbers is compatible with a variety of operating systems and technologies, including Apple products (iPad and Mac), Chromebooks, PC, and Android devices.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Evgeny: As technology evolves, so will teaching. These changes will help teachers make better decisions when it comes to individualizing instruction. Different students learn in different ways, and technology will help ensure that students get the attention they need, through the method of instruction they understand best.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Evgeny: Being a teacher is stressful, and it’s no secret that teachers are overworked. One of the biggest challenges we face is being able to make our teachers’ lives easier, less stressful, and more efficient. Burned out and overstressed teachers leave their jobs and we don’t want that. So we need to give them the tools that will help them get the job done well.

Tamara: How do you see your products/services evolving going forward?

Evgeny: Our services are going to advance more and more in terms of individualized instruction. Students will get access to more precise and “smart” resources through HappyNumbers, and these will provide actionable insights to teachers.

Tamara: What is your favorite AI movie and why?

Evgeny: Terminator. It made me appreciate what can happen when technology goes wrong.

Tamara: What type of advice would you give my readers about AI?

Evgeny: Don’t be afraid about AI/technology — there are lots of polemics going on about AI stealing our jobs. One should see it in a different way: AI will make our jobs easier and more efficient, and we need to adapt ourselves to this new reality and embrace the new tools that are available to us.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Evgeny: Happy Numbers is like a superpower; it helps build up classrooms, providing what they need for better instruction. We aim for teaching math in fun, engaging, and easy-to-understand ways; we don’t want “hating math” to be an option.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Evgeny: We were asked how many dinosaurs work at Happy Numbers, since that is our business mascot. It was a young child that uses the product that thought that dinosaurs existed in the product!

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Evgeny:

1. I’m excited that schools will embrace technology early enough — starting pre-k or even earlier. For subjects like math, it is vital to build the foundation early before it’s too late.

2. That there is more and more attention being given to training teachers to become tech-savvy experts!

3. I’m excited to see how the VR/AR industry is developing, which will definitely find its place in the classrooms. I expect it will make wonders, once paired with the right AI technology.

4. Schools are becoming increasingly data-driven, and those interested in the technology could help in gathering and interpreting this data.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Evgeny: I don’t have those worries, really. We are not in the “military drones” industry, where the tools you develop could be used for good and bad. Educational products based on AI wouldn’t hurt anyone, but would revolutionize the way we teach and learn, which is something we definitely need.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Evgeny: Using AI/technology for instruction will no longer be an option; it will be a must. Data gathered by AI about the students will become a vital key for instruction, particularly individualized instruction. Teachers will also use AI-based virtual teaching assistants to get actionable insights based on this data.

Problem Solved! Technology that Individualizes Learning for All Students was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Cyber attacks are at an all time high! Each day, hundreds of people fall victim to a breach in security that causes their personal information to be compromised. Not only is it inconvenient, it is nerve-wrecking, given that you can never be quite sure who has access to your private data. Thanks to Dr. Salvatore Stolfo and the team at Allure Security, it is possible to be several steps ahead of people who seek to ruin your day. Using AI technology to be proactive rather than reactive and create decoys that confuse hackers, Allure can potentially prevent an attack before it even occurs. A tenured professor at Columbia University with over 47 patents and ongoing research using AI to detect fraud and prevent data loss, Salvatore is helping customers around the world to rest easy.

Tamara: Can you share a story that inspired you to get involved in AI?

Salvatore: When I was just a kid, it was natural to be enthralled with the Apollo missions and space flight. When looking at star maps, it was natural as well to wonder where all those points of light came from. How the heck did our minds get here, and how could the universe ask questions about itself? In college, I learned about a conference held in Dartmouth well over a decade earlier where scientists were talking about “Artificial Intelligence.” The luminaries then, McCarthy, Newell, Simon, Minsky, were true inspirations. When I arrived at NYU Courant Institute, I spent considerable time learning about and doing research in Mathematical Logic, thinking that was the answer to the question of “What is Thinking?” I’ve been pondering AI ever since. In the mid-1990’s, however, I also studied security problems including transaction fraud and was amazed at how clever thieves were at stealing money. I then realized that machines had to be a lot smarter at defending and protecting themselves. I pitched the idea to DARPA to fund my lab to work on behavior-based security, which in essence was Machine Learning for computer security. My IDS lab at Columbia has focused on applying AI to security ever since.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Salvatore: Allure Security has developed products that detect and respond to advanced cyber threats, malware, and malicious insider activity before sensitive data is stolen. Much of the security technologies available today have focused on prevention. Those products that focus on detection do so by analyzing network, endpoint, application or user behavior. This is only natural and has been my focus for quite some time as well in my early research. Some years ago, I realized that the true answer to the problem of stopping data loss needed to focus on the data, the quarry attackers are after. Data and data flows also have behaviors that can be modeled by AI methods. Allure’s Data-loss Detection and Response (DDR) platform accomplishes this by using patented beaconing and decoy technologies to track and protect documents inside and outside enterprise walls. Organizations can deploy Allure DDR in hours, accurately detect breaches that bypass other security controls, and reduce the cost and time of post-incident investigations. We use AI to create authentic-looking, highly convincing decoy documents that make a would-be adversary believe they have stolen something of value. We also use machine learning that determines what is normal behavior for a company’s documents: who typically accesses them, where in the world they travel, and in particular document flows that are clearly malicious.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Salvatore: The success of AI driven applications is dependent upon experts who are knowledgeable and capable of developing AI infrastructures, but also have deep expertise in the application domain where hard problems haven’t been solved in scale. There will undoubtedly be numerous “general purpose infrastructures” that support AI applications, but these will only be successfully applied when human subject matter experts engineer solutions to problems they understand deeply. One can expect an ever increasing supply of such general purpose tools and systems that handle huge amounts of data streaming from arbitrary sources, and process that data intelligently and quickly to solve problems that no human has ever tackled.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Salvatore: There are a number of challenges: probably “brittleness” and “robustness” of the AI systems. These issues are affected by the quality and breath of data available. There is a growing need to prepare data to be used and modeled in AI ensuring that any missed “corner” cases doing have an adverse impact on the outcome of the analytics. While there are plenty of off-the-shelf products and services that include AI or ML functionality, or will soon do so, some of the best results will be at organizations that use their own data to train their own AI systems, or augment third-party ones that ensures not only accurate results in the lab, but accurate results in the real work context in which they are deployed. Mitigating error is an open issue. Real-world data is dirty, especially when multiple disparate sources are culled together. Linking data together about the same entities will get more complicated with more sources and larger stores of information will have to be processed. Probably the most difficult problem to solve is defining the high value use cases for the analyses. For this, AI expertise is necessary to do the job effectively, but business savvy and industry knowledge are also required to get the best value from the high cost of the analytics.

Tamara: How do you see your products/services evolving going forward?

Salvatore: In the mid-1980’s, a fast growing AI field was quickly and prematurely commercialized. These “Expert Systems” encapsulated domain expertise intended, as standalone applications provided machine experts. There were many artifacts of the Knowledge-Based Expert Systems technology of the day including “Automated Cable Expertise,” a system used by the telephone company to predict local failures in the local phone loop. There were infectious disease diagnosis and treatment to automate services one would expect of their internist, and various applications too numerous to enumerate. These failed in the marketplace because they were expensive to update and grow their “expertise” requiring programming and teaching by human experts. These products and their underlying technology weren’t capable of sustaining any real businesses. Machine Learning technology has changed everything, making AI practical, scalable, and effective largely due to the enormous data resources that are now available from other technical developments of the past decade.

Tamara: What is your favorite AI movie and why?

Salvatore: 2001: A Space Odyssey, of course! For its time, it was amazingly accurate in what the future could be (most having occurred by now), but also because Hal’s inconsistent theory (remember I was driven to Mathematical Logic) led to plausible outcomes. Talk about brittleness.

Tamara: What type of advice would you give my readers about AI?

Salvatore: Do not confuse “Hollywood AI”, with “Computer Science (real-world) AI”. There is as large a gulf between the two as there is between “Hollywood Physics” and “Physics Physics”.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Salvatore: Everyone’s lives are directly impacted by the internet. There is nothing surprising about that statement. However, with the huge advantage the internet provides all of us, it is also not a surprise that this technology comes along with dangers we all know too well. It is hard to know anyone whose personal data hasn’t been lost by some large enterprise. There are too many examples to enumerate. Adversaries attacking our computer systems have always had the advantage over defenders, and I want to change that by employing AI machine learning techniques to change this asymmetry in favor of the defender. The core of our technology is to detect when adversaries have penetrated and bypassed deployed defenses, and when they do, to confound and confuse them with bogus data they may think has value, but doesn’t. This “knowledge attacks”, an active defense hack back, now for the first time makes the adversary pay a cost for their attacks and exfiltration. They must expend time and effort to figure out what is real and what is fake, making their effort no longer free. Creating highly believable decoys and deceptive material is now achievable with AI Machine Learning techniques.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Salvatore: When the company grew its sales and marketing team, we had a company outing to an F-1 racing car track. I made it clear my driving skills were learned in the mean streets of Brooklyn, New York, not Brookline, MA. Unbeknownst to me and the rest of the team, one of our sales engineers was a semi-pro F-1 driver who knew how to clear the road for himself. I didn’t flip my car, but I did flip at his car as it routinely blew past me.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Salvatore: CyberSecurity based upon AI will change the battlefield in favor of defenders. I am looking forward to this new challenge to see how adversaries will react. I am certain they will attempt to use AI against AI, but that too will come as a cost to them.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Salvatore: First, I worry about the brittleness of deployments that don’t account for “corner cases” since there was insufficient training data could lead to significant negative consequences. I also worry about product developers who deploy in critical applications who do not account that the lack of robustness of these systems could be responsible for harm.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Salvatore: Soon, very sophisticated (probably nation-state) adversaries will employ AI methods to attack our computer systems in very sophisticated ways.

Rest Assured; It’s Under Control!: Using AI Technology to Protect Your Personal Information was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

When we make a call for help during critical situations, we trust that our request for assistance will be answered rapidly and with accuracy. There is no room for error! That is why the expert team at Corti has utilized AI technology to create a system that analyzes voice patterns over the phone to help emergency personnel recognize typical patient symptoms. Corti CEO, Andreas Cleve, knows that every moment is crucial, so precision is key. Andreas and his team are taking the guesswork out of patient care just in the nick of time.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Andreas: Corti is a Copenhagen-based artificial intelligence (AI) platform that uses machine learning to provide emergency medical personnel with life-saving diagnostic assistance.

By applying speech recognition and analyzing audio, language, dialect, and background noise during a live emergency 911 call, Corti’s AI technology detects critical illnesses — such as cardiac arrests — in real-time, accelerating the diagnostic process and response strategies for emergency dispatchers.

How Corti Works:

• During an emergency call, data is processed through Corti’s deep neural networks in real-time. Corti analyzes symptom descriptions, tone of voice, breathing patterns, and everything a patient says and shares, and finds important patterns by comparing it to the millions of emergency calls that the platform has already analyzed, meaning it continually learns over time
• Corti acts as a digital assistant for the dispatcher handling the call, providing them with clear-cut assistance, suggesting the best questions to ask, and making recommendations for triaging patients
• Corti increases efficiency and accuracy, allowing emergency departments to elevate their call-handling and achieve better results

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Andreas: We hope to see some big applied AI use cases for core AI / machine learning companies, that move research from academia to a concrete, widespread use case.

Tamara: How do you see your products/services evolving going forward?

Andreas: Corti is in ongoing collaborations with other potential partners, with expansion into new markets to be announced later this year.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Andreas: With state-of-art machine learning technology, which enhances over time, Corti wants to advance the healthcare sector — and support emergency service call centers — by improving the speed and accuracy of diagnostics. This is achieved even when there is background noise, and regardless of whether the call is made by the patient themselves or another individual close to them.

The issue has previously been handled by emergency dispatchers on the phone, where they have to rely only on their own knowledge, handling more or less the worst days of people’s lives with no tools to do so. This is often made even more difficult when having to make sense of symptoms relayed by a panicked friend or relative. Emergency dispatchers have a tough job assuring callers while also trying to ask questions that could save the patient’s life. On top of this, they must remember to ask whoever is on the phone for the address of the incident to ensure the ambulance en route is headed to the right place.

Copenhagen EMS (Emergency Medical Services), which provides immediate care to people with acute illness or injury, has implemented Corti on the official emergency number 112, utilizing its AI to detect cardiac arrest cases faster and more accurately. Previously, dispatchers could only recognize cardiac arrest from descriptions over the phone around 73% of the time but, in an early, small-scale study, the machine learning model knew the calls were reporting cardiac arrest 95% of the time.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Andreas: We are most excited about the possibilities for voice integrations in the emergency medical space. According to research, less than 50% of verbal information delivered at handoffs by ambulance crews was accurately retained by emergency dispatch staff. Voice-activated AI assistants can help staff retain information to get patients into the right treatment, faster, and alleviate the pitfalls of misdiagnosis early on.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Andreas: We look forward to seeing the amazing applications of AI augmentation done in real-time, to assist people as they perform a task.

In the Nick of Time: How AI is Transforming Emergency Response Systems to Help Save Lives was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

What if you could change the way you do things just with the click of a button? Imagine that button allows you to create apps that are specifically tailored to your learning and behavioral needs. That seems ideal, especially if you want to be in control. Thanks to Founding CEO Leon Young and the cadre of experts at Cogniss, you now have the option to “build learning” and have fun in the process. The only limit is your imagination!

Tamara: Can you share a story that inspired you to get involved in AI?

Leon: Having worked on the front-line of digital education for the past 14 years, I’ve been largely underwhelmed by the application of technology to learning. So much of it has been about content distribution and process management, rather than using technology to deliver the best possible educational outcomes. Around a decade ago, adaptive learning systems started to emerge, but it quickly became apparent that adaptive learning was merely a simple way to personalize the consumption of traditional curricula. That’s useful for improving engagement amongst students with differing abilities, but on its own does nothing to improve the quality of knowledge acquisition, retention and application.

I was inspired to get involved because AI, combined with our emerging understanding of how the brain actually functions, creates a model for systems that engage and educate on a deeper, more impactful level. The integration of AI into education will form the foundation for edtech to finally begin delivering on its promises.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Leon: Cogniss is an app-building platform that allows people to develop high quality learning solutions at much lower costs. It democratizes sophisticated app creation; anyone can use Cogniss to create their own AI-backed 2D, 3D, virtual reality or augmented reality app for education through a simple point-and-click interface. Following a successful beta testing phase, we will be launching version 2.0 of the platform in Q2 2018.

Powering Cogniss is a suite of AI engines grounded in cognitive science principles to ensure app users achieve optimal outcomes. They include the Cognission Engine (a machine learning algorithm that monitors user performance and perfectly levels the delivery of new information) and the Behavior Engine (which personalizes behavioral incentives and activity triggers).

Cogniss brings together complexity of function and simplicity of design, amounting to a Swiss Army knife for educational app creation. In addition to the visual app maker, the platform offers a single-identity social network where users can create profiles, browse the universe of available Cogniss apps, join teams, issue challenges and share milestones. It also features Cogniss Insights, a flexible data analytics tool that tracks and manages users’ learning or behavior change journeys.

Eventually, as the pool of data grows, Cogniss will apply deep learning to a broad range of data types, equipping educators, parents, administrators, and researchers with a new level of intelligence that can, for instance, help diagnose learning, cognitive and health issues early, test the efficacy of educational interventions, and enhance human decision-making generally.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Leon: Convergences of AI with new fields of knowledge and technologies will drive revolutionary shifts in how we perceive and deliver education and health interventions.

First is the convergence of AI with cognitive science. Algorithms anchored in research around how the brain processes, stores, and recalls information is making it possible for software to map individual learners’ cognitive data. It can also determine what we should be learning, and when, in order to optimize long-term knowledge or behavioral retention.

Secondly, the convergence of AI with innovative low/no-code development platforms is removing the barriers to more widespread creation and adoption of effective educational apps. Educational apps are typically bespoke solutions for a target learning behavior, and app development is a cost-intensive process, especially if the app has to compete with high quality, professional studio-made games. Now, through AI-powered low- or no-code platforms like Cogniss, anyone with minimal or even no coding knowledge can build complex educational software using a simple graphical user interface at a fraction of the cost typically required to employ custom developers.

Finally, the convergence of AI with big data analytics is powering a new generation of platforms and applications capable of processing billion of data points to help detect, track, predict and manage a dizzying variety of learning and health issues. We’re only just beginning to scratch the surface of the valuable insights that deep learning can extract from large, unstructured datasets.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Leon: The biggest challenge currently facing the AI and data analytics industry is how to properly grapple with and prevent the misuse of personal data. Recent high-profile data breaches have exacerbated people’s concerns over how data is being used to shape and impact their lives. There’s a clear expectation — imperative, even — for companies to embrace an approach to data privacy that’s radically user-centered, ethical and transparent. This has become a major point of differentiation in the industry.

Part of such an approach will involve formalizing an ethical framework that puts consumers in full control of what personal data gets shared, and recognizes that an AI-driven system is also only as reliable as the data it is given to work with. There needs to be concentrated efforts to control for potential hidden biases, a constant reviewing of the data being collected, and limits on how it should be used. How we mitigate the risks of AI should also be guided by human input from teachers, health professionals, ethicists, sociologists, and others.

Tamara: How do you see your products/services evolving going forward?

Leon: Cogniss is about much more than making sophisticated app creation accessible to everyone. It’s also designed to elucidate valuable learning, health, and behavioral insights by applying deep learning technology to a broad spectrum of datasets.

As more users engage with Cogniss apps, the platform learns to personalize each individual experience, recommending additional activities that will help them achieve their desired learning outcomes. And the more user-app interactions Cogniss captures, the more the platform’s AI can facilitate research into a backlog of critical questions around education and health that require datasets too complex for humans to process.

On the whole, the industry is shifting from the narrow, bespoke AI focused on executing a single, specific task, to broad AI, or systems that exhibit intelligent behavior across a range of purposes. Cogniss falls firmly into the second camp, built as it is to address virtually any learning or behavioral challenge using the same underlying software. With increasing amounts of data, from student performance levels, to physical and mental health indicators, to publicly available information such as population demographics, Cogniss will gradually evolve to fulfill its broader purpose as an intelligent predictive system capable of diagnosing learning problems before they become noticeable.

Tamara: What is your favorite AI movie and why?

Leon: 2001: A Space Odyssey. Seeing it as a child was a revelation. I was too young to understand the bigger questions posed by the movie (and perhaps still don’t), but the anthropomorphic depiction of HAL enthralled me. It still holds up after all this time because it encapsulates both the promise and the danger of AI.

Tamara: What type of advice would you give my readers about AI?

Leon: Invest the time into really understanding what AI, machine learning and deep learning are. They are conceptually complex and I find most people who don’t work in the area, who think they understand what these things are, really don’t. A conceptual understanding is necessary to fully appreciate the depth and breadth of the effects AI is starting to have on every facet of our day-to-day lives — in particular, how it will affect the job market as well as the broader economic and socio-political landscape of the future.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Leon: The convergence of AI with new technologies is revolutionizing the way we live our lives. Cogniss embraces this convergence by integrating AI, mobile, social networking, big data analytics and games into a single, affordable app-building platform. It empowers people and organizations wanting to innovate in education or health by letting them create their own AI-powered app solutions for a low monthly subscription fee, rather than fork out tens or hundreds of thousands of dollars to a custom developer.

People can count on Cogniss apps to help them effectively achieve learning outcomes because the platform is, at its core, a product of applied research. Its technology is informed by a panel of six leading academics and researchers specializing in a range of areas, from serious game design to sociology, psychology, education, virtual reality and cognitive neuroscience.

We’ve found many different applications for Cogniss, all of which help improve people’s quality of life in some way. Apps built on Cogniss are helping organizations grow emotional intelligence in children, improve cognition and slow brain ageing in the elderly, engage otherwise disengaged high school students, boost corporate sustainability, manage the adverse effects of problem gambling, improve compliance with medications, and collect data for clinical trials. That’s on top of the more obvious applications, such as STEM and literacy learning.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Leon: When I first conceptualized Cogniss, it was, as the name suggests, focused on creating educational apps around purely cognitive activities, like learning mathematics or languages. But once we got the model right, we found it worked for a far greater range of cases than we had imagined. Early after the release of our platform, we had customers using Cogniss to build solutions for things as diverse as brain training, to slowing the onset of dementia, to business simulation games.

What really amazed me was when I received a call from a young man by the name of Bart Atherinos. Bart wanted to build an app for young soccer players to foster mentorship and mindset development, and to deliver very specialised skills training. When he showed me his designs, it was like a light went on — I saw it fit the Cogniss model perfectly. That was a real turning point in realizing just how broad the application of this technology could be.

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Leon: Three things that most excite me about AI in the education industry:

1. AI offers myriad ways to provide instant feedback to students and teachers. Formative assessments now take the form of non-intrusive online quizzes, interactive videos, simulations, games and educational apps. When allied with a powerful AI-driven data system, these assessments can collect detailed insights into students’ concept mastery and problem-solving sequences, meaning key decisions about learning can be properly informed by continuous data. AI makes visible actionable data that previously would have gone unnoticed.
2. With data being collected on multiple fronts, AI can detect and analyze patterns across education, health and other indicators. For example, educators can access real-time information showing whether a student’s performance is being affected by inadequate sleep or emotional stress. Researchers can draw from an increasing volume of anonymized and public data to gauge the impact of certain interventions on certain populations. The more AI is applied, the more we will be able to address the nuanced learning conditions, preferences, weaknesses or backgrounds of individual students.
3. AI can assist learning institutions by automating a range of tasks, from record keeping, to lesson planning, to teaching, grading, and recommending activities. The time-saving nature of that is significant — AI can act as an extra pair of hands, freeing up educators to give individualized attention to students and field tough questions.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Leon: Three things that worry me about AI in the education industry:

1. The increased use of AI in education raises the need to guard against misuse of student data. This will become more significant as AI becomes more widespread, and more people without a deep technical understanding are put in place to implement it. With data privacy becoming a universal concern, precautions like giving individuals effective controls over their own data will be crucial to building trust in organizations with access to that data.
2. There’s a danger that using AI to customize learning for individual students could exacerbate existing learning inequalities. In contrast to an education system that prioritizes standardization, AI-driven learning systems often tailor learning paths to students’ particular interests and aptitudes. However, AI can make mistakes — there’s a constant need to control for potential biases in the system that might say, pigeonhole students too early on, or not push students to do the things that will offer them the greatest long-term benefits.
3. The idea that AI will completely replace human educators can be counterproductive. With the unpredictability of technological advance, who’s to say AI will or won’t be able to perfectly emulate the interpersonal skills that teachers bring to the classroom, or how long that will take? Rather than think in terms of either/or, it’s more useful to think of AI as playing a key collaborative role alongside humans in the constant striving for our education systems to deliver the best possible outcomes.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Leon: The only thing we can expect for sure is the unexpected. The convergence of AI with other fast evolving technologies is going to have an exponential effect on the rate of change and the disruption of society at every level. What we really need to see in the next three years is a deeper understanding of the opportunities and risks presented by AI, and the development of models that facilitate progress in a way that serves humanity as whole rather than the interests of the few.

Don’t Just Play the Game, Make the Game: Taking Apps to the Next Level was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Companies have many things on which they must focus each day. From staying on top of the latest financial trends, to workforce scheduling, to ensuring that they have products of the highest quality, every element is crucial. At the top of the list, though, is customer satisfaction. Keith Strier at EY Global understands that it is critical for companies to be “ethical” while keeping their customers first. That is why Keith and his team make it a priority to provide AI solutions that meet their clients’ needs, no matter how complex. With their expert advice, Keith and EY Global are clearing the paths for “purpose-driven services” to help their clients reach their goals.

Tamara: Can you share a story that inspired you to get involved in AI?

Keith: In the summer of 2016, I was in a shopping mall, looking around for one of those wall maps so I could locate a store, but I had no luck after wandering for ten minutes. Then I saw a poster that advertised, “Can’t find a store? Text us and our bot will help.” I had nothing to lose, so I stopped walking in circles, and texted the bot, “Where’s the pharmacy?” The response back was fast, and personalized (to my location), providing perfect directions. I followed up with a second question, “What are the options in the food court?” and was again quickly rewarded with a list of dining choices. I was hooked. I knew right then that AI, the ability to talk with machines, to ask questions in a natural way, was the future. I also thought it would be cool to end the tyranny of mall maps.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Keith: EY is a global professional services firm that assists clients in the public and private sectors along the entire AI journey with services that include implementing foundational data management programs, developing enterprise AI strategies, prototyping AI use-cases and designing anticipatory user experiences as well as the hands-on coding, training, scaling and continuous monitoring of algorithms and models. EY also helps clients apply AI across the value chain in every domain, from cognitive tools that automate complex procurement processes to predictive models that optimize public transportation systems, from media and mining companies to law enforcement agencies and water authorities.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Keith: AI will become increasingly more democratized and accessible through innovative interfaces and cloud services. The first wave will be force multipliers for data scientists, tools that make their jobs easier, automating menial tasks, like data cleansing and feature extraction. The second wave will be more disruptive, expanding use-cases and the user-base by making data science possible without a data science degree. This will ignite widespread adoption at the business level, outside of IT and the Analytics team. We already saw this play out over the last decade with mobile apps. Initially, building an app was a real chore, requiring a team of highly trained developers, architects, designers, and testers. Today, a 12 year old can assemble a high-functioning mobile app and submit it to the app store in minutes, using any number of DIY “app builder” platforms. This has already started to happen, in fact, with the rise of DIY chatbot platforms that require little to no coding.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Keith: This is a complex space with many challenges, but there is perhaps one fundamental challenge — widespread misunderstanding around the nature of AI. AI is not implemented like a mature technology; rather, AI is emergent and must be applied, leveraging a spectrum of methods, tools and knowledge domains. In this nuanced description lies a world of implications for how AI — apart from other technologies — should be evaluated, developed, prototyped, tested, integrated, scaled, governed and monitored over time to ensure performance, reliability, accountability and explainability. Many companies do not have this understanding and approach AI with a more classic IT mindset that sets the stage for unmet expectations and poor outcomes. However, this is an addressable issue and the collective responsibility of advisory firms, systems integrators and service providers. As well, industry and professional associations have this responsibility along with the need to clarify and forge a common view and vocabulary.

Tamara: How do you see your products/services evolving going forward?

Keith: EY’s end-to-end AI advisory services must stay current with a dynamically evolving space, which requires a relentless commitment to scanning, learning and experimenting. Moreover, this isn’t “one space”, but an amalgam of landscapes. It includes the actions of business leaders, data scientists, service providers and entrepreneurs working across adjacent fields (robotic, intelligent and autonomous systems). I expect that EY’s portfolio of AI capabilities and solutions will keep evolving in lock-step with academic, commercial and regulatory advancements.

Tamara: What is your favorite AI movie and why?

Keith: I don’t have a favorite AI movie, but I have a favorite AI scene. It’s from the animated show, Rick and Morty, in which Rick is this super-genius who frequently designs robots to serve his every need. In one scene, Rick is eating dinner, and there is a small robot on the table serving him butter. This tiny robot starts to share his dreams about doing more, revealing his hope for a bigger purpose. However, Rick shuts him down: “Your purpose is to serve me butter. That’s it. You will always serve me butter.” At that moment, this tiny robot realizes the narrow, pointless nature of its existence, saying with despair: “OMG.” For most normal people, it was just a funny scene about a robot that’s really upset that its life revolves around butter. For me, the scene cleverly reveals one of the more fundamental challenges with AI, which will be understanding how best to apply it. Of course, learning that it was and will always be a butter-serving robot was devastating for the little machine, but in the real world, it will be essential to design AI systems with a clear and focused intent. Perhaps not so much fun for them, but the highest value machines will have the most tightly scoped purpose.

Tamara: What type of advice would you give my readers about AI?

Keith: Don’t fear the rise of AI and smart machines; take the time to understand the full narrative. Despite the movies, the most likely scenarios are much less cinematic. Stunning improvements in diagnostic accuracy, energy efficiency, manufacturing quality and workplace productivity will materialize, as human and machine capabilities are more deeply integrated and scaled in every context. For sure, mistakes will be made along the way, some AI systems will not perform as engineered. For this reason, we also need to understand not only the potential value but also the potential risks, and to design these systems to be ethically-aligned, transparent, accountable and explainable. Ultimately, however, I believe humanity will be greatly improved through the increased reliance of algorithms in every field from medicine to transportation to public safety.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Keith: EY advises the world’s leading private sector companies as well as many governments around the world on how to apply AI, and when doing so, we educate our clients on the importance of anchoring to key design principles that promote a human-centered and ethical approach. As with sustainability, diversity and inclusiveness, promoting responsible uses of AI is consistent with EY’s purpose-driven culture. We aspire to help large enterprises and public agencies achieve their objectives, but also to help them stay informed of the potential disruptiveness of these technologies and to plan appropriately for their integration into a human workforce. In this way, EY helps to build a better working world through purpose-driven AI services.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Keith: The most interesting story in this area was learning that EY was not only building up capabilities in this space, but was already one of the largest consumers of intelligent automation within its core operations and business. That is, EY has built and deployed over 600 software robots, saving millions of hours of work for thousands of EY employees, enabling them to do their jobs better as they run our day to day operations in HR, finance, procurement and tax. EY is in fact one of the most automated enterprises in the world, among the top ten earliest adopters of robotics and AI at scale, which is a surprising revelation to those who see EY as just an “accounting firm.”

Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)

Keith:

• AI will accelerate scientific discovery by orders of magnitude, enabling us to discover new galaxies and cancer treatments.
• AI will create unprecedented levels of public safety, enabling models to predict natural disasters much earlier and to respond to emergencies much faster.
• AI will transform the boundaries of human abilities, equipping those without sight with AR/AI systems that see for them, and enabling machine-to-brain interfaces that power a new generation of sensory-rich prosthetics.

Tamara: What are the 3–5 things worry you about AI? Why? (industry specific)

Keith:

• AI is just math at enormous scale, and its potency as a tool for dramatic change in every facet of our lives is not a function of some unseen, uncontrolled, mythical force; it depends entirely on our decisions and actions. Nothing worries me about AI specifically, only what we do, or don’t do, with it. This includes:
• Lack of planning that leads to failed AI programs and disillusioned executives, delaying the realization of value for shareholders and customers
• Poorly designed AI systems, governance models and training approaches that lead to biased and/or unreliable outcomes
• Failure to passionately pursue AI use-cases that could meaningfully improve the human condition out of fear and misunderstanding.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Keith: The inevitable integration of AI into everything — within three years, we will be gesturing to televisions, talking to toasters and accessing financial data with a facial expression. However, it is likely that we’ll still be sitting in traffic and shuttling our kids all weekend between friends, the mall, the field, among others things. All the while, we will be counting the days to the arrival of autonomous, if not aerial vehicles.

Finding Your Purpose: Providing Professional Advice to Navigate AI was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.