AI is All Around Us – Sometimes Without Us Even Noticing It

In a recent Forbes article, I discussed the ubiquity of AI, and how it is seeping into just about every aspect of our lives – for the better.

AI has potential to change the world in major ways — helping to cure cancer, improve the environment and generally make our lives easier and safer. But more pragmatic uses of AI are already changing our lives today.

How is AI at work in our everyday lives? Below are a few highlights from the article:

  • Smart spam filters. What person on the planet doesn’t feel like he’s drowning in emails, but did you know that without AI you would be getting a whole lot more junk mail? Most Internet Service Providers (ISPs) use AI algorithms to filter them out before they even reach our inbox – by looking for patterns in the emails that might point to spam.
  • Chatbots and virtual assistants. This form of chatty AI is popping up everywhere, and many times we don’t even realize that we are interacting with it. While some chatbots are still working on perfecting speech recognition and have a ways to go, they are continually learning and getting smarter all the time.
  • Navigation systems. We rely daily on map and navigation apps to get us where we’re going, helping us re-route in real time if there’s a slow down or accident up ahead, or alerting us to speed traps when we’re driving too fast. These navigations systems use complex algorithms to do all of this, leveraging huge datasets that are constantly being updated.
  • Personalized marketing. Chances are you have received personalized recommendations based on your buying or viewing history. While this can be annoying at times, sometimes it’s nice to know that someone understands you.

The Forbes article only shares a few examples of pervasive AI, but the list could go on and on. Take a look around and you may be surprised at how many everyday activities and objects are powered by AI in some form — and it’s only the tip of the iceberg.

Healthcare and EDI: A Match Made in Data Heaven

Electronic Data Interchange (EDI), has been around for decades.  It is a process through which data is exchanged in a standardized and structured format so that different data systems can contain the same information almost instantly. A good comparison might be the way a person speaking English might communicate with a person speaking Russian. They often need a middleman to serve as a translator for both. That’s what EDI does for systems – serves as the middleman so that system A can synchronize data with system B seamlessly.

Several standards exist to format the interchangeable data between systems. Some major sets of EDI standards, include:

  • The UN-recommended UN/EDIFACT, the only international standard, predominant outside of North America
  • The US standard ANSI ASC X12 (X12), which is predominant in North America
  • GS1 – An EDI set of standards predominant in global supply chains
  • The TRADACOMS standard,developed by the ANA (Article Number Association now known as GS1 UK).  It is ominant in the UK retail industry
  • The ODETTE standard, used within the European automotive industry
  • The VDA standard, used within the European automotive industry mainly in Germany
  • The HL7, a semantic interoperability standard used for healthcare administrative data.

We use EDI in many industries where standard data exchange is needed, and a key industry is healthcare. It is critical for the healthcare industry to adopt EDI-enabled systems, such as ONC Certified EHR systems and HIPAA X12 compliant billing systems, since the exchange of data between providers, payers and patients is critical. The adoption of these systems has a direct impact on all parties involved.

Here is a simple example of how EDI can help your visit to the doctor be more rewarding:

Imagine if you went to the cardiologist for the first time because of a recently diagnosed condition – a condition for which you have visited your primary care provider a couple of times. Your social history, demographic and medical history data will be asked by the cardiologist.  However, that information already exists in the primary care provider’s Electronic Health Record (EHR) system.  Using EDI, your cardiologist could receive this information in just a few seconds, enabling him to better focus on your condition, instead of wasting time asking questions you already answered somewhere else. And, since a good doctor cannot make a good diagnosis without all the necessary facts, the doctor can rest assured that he is receiving accurate information

The main goal of EDI in healthcare is to provide up-to-date information on a patient’s condition across the healthcare ecosystem. It helps reduce common mistakes that often occur when handling patient information and helps reduce fraudulent behavior by both the providers and the payers.

As an EDI senior software developer with over 11 years of experience, I have seen first-hand the complexity that is involved with EDI and all the challenges it still has yet to address.  But what if Artificial Intelligence (AI) was included in the mix, helping EDI developers create better and more efficient systems?  This could lead to better and faster communication between systems and go beyond the boundaries of formats and standards.  AI could help improve the way healthcare systems communicate with each other and fill in the gaps when additional information may be required.

As an example, take the interoperability standard, HL7, which contains two different versions that both can be used at the same time.  The two versions allow for the transmission of different types of data.  With the proper AI implementation, a developer could quickly close the gap between both formats by relying on the AI capabilities to determine the required output regardless of the versions and then just validating that the output is the expected one.  This would significantly reduce development time along the way.

EDI has transformed the way data is shared in healthcare for improved patient outcomes, streamlined processes and reduced healthcare costs. Making it smarter through the use of AI can go a long way to putting these benefits into overdrive.

Can Artificial Intelligence (AI) Really Be Used to Help Heal the Planet?

An article I wrote recently for Forbes discusses the transition of Artificial Intelligence (AI) from an enterprise app to one being used in the field — quite literally.

AI has been known for helping businesses make better decisions, automate core tasks and other activities, but it also is playing a critical role helping to protect our planet, preserving wildlife, preventing forest fires, protecting marine life, and in many other ways that may not get the same attention as business apps.

As I’ve discussed recently, here in Puerto Rico Wovenware has been fortunate to participate in an important environmental and health project to help prevent mosquito-borne illnesses.

It’s very rewarding to apply our AI expertise to help address issues impacting the general population and environment. But the mosquito project is just one example of environmental AI; there are so many more.

Machine learning is being used to monitor wildlife and determine the population of snow leopards. It’s also being used to predict where forest fires might erupt, by using historical meteorological data and an artificial neural network; helping to combat wildlife poachers; and using satellite imagery to track coral bleaching in the oceans and look for marine life diseases and pollution.

As the Forbes article explains, by augmenting the time-consuming, manual counting and categorizing activities typically done by humans to create massive datasets, AI is letting us go to places that were previously impossible. AI is no longer about helping businesses succeed, but healing our common planet as well.

How else do you see AI being applied out in the world?

Computer Vision and Deep Learning Apps Are Taking Center Stage — Quite Literally

When you’re a superstar like Taylor Swift, security can be a major concern. Now there are new tools that can help celebrities stay safe: AI combined with image detection. A recent article in The Verge reported that at a May 2018 Taylor Swift concert, her security team employed facial recognition to identify potential stalkers. Images from a facial recognition camera were cross-referenced with a database of hundreds of the pop star’s known stalkers to see if there was a match. When you think of deep learning and computer vision, this type of use case might not be the first thing that comes to mind, but it just goes to show that its possibilities are endless.

Computer vision captures, processes and analyzes real world images and videos to provide meaningful information, and we’re just beginning to harness its real capabilities. According to research firm, Tractica it’s expected to soar from an estimated $1.1 billion in 2016 to $26.2 billion by 2025.

Deep learning is giving a major boost to the capabilities of computer vision and sparking renewed interest in its capabilities. As a form of AI that enables algorithms to learn by example, deep learning uses learning data representations, as opposed to task-specific algorithms to derive deeper and more independent insights than other forms of machine learning.

Consider the following examples: of deep learning-enabled computer vision in action:

  • Ensuring safety in autonomous cars. It’s being applied in autonomous driving to navigate roads and make quick decisions in real time, such as identifying an oncoming vehicle or slowing down on icy pavement.
  • Designing better ads. Companies, such as Gannett, are turning to deep learning and computer vision to design better online ads, determining which colors, images and fonts work best. The company says that this has boosted click-through rates across different news sites.
  • Improving patient outcomes. It can help physicians diagnose diseases, among other applications. For example, a physician or radiologist can use it to review brain scans to determine healthy or not so healthy areas of the brain.
  • Improving urban planning. Computer vision and deep learning solutions can detect the number of buses and cars on busy highways and side roads to more effectively manage traffic.
  • Gauging emotions. In areas like education or retail, it can be used to determine the emotions of consumers or students and their reactions to the classroom or in-store experience. In education in particular, surveillance cameras can determine if classroom instruction is interesting by how engaged students appear to be.

Sending Computer Vision and Deep Learning to the Moon

When computer vision and deep learning solution are deployed in satellites, the possibilities are extended even further. Satellite imagery gives us an elevated look at massive amounts of images for applications such as:

  • Tackling deforestation. Computer vision and deep learning can help detect the number or specific species of trees in certain forests and parks to determine their growth or risk, and if deforestation is occurring, it can help to address the specific factors that could be causing it.
  • Tracking economic growth. By monitoring the numbers of cars, electric lights in the night sky, or construction activities, we can track the development and economic growth of countries around the world.
  • Responding to world crises. In situations such as a refugee crisis or war, it can help provide valuable information that can be used to plan for the supply of life-sustaining resources like food and shelter materials.

How Do These Applications Become Reality? It’s Not that Simple.

While there are clear benefits to the use of deep learning-based computer vision solutions the question of the hour is, how do companies get there? The democratization of this type of AI, through pre-packaged apps such as SalesForce.com’s Einstein, or Google AI tout the availability of “AI for everyone,” yet, when everyone has access to the same benefits, no one can really stand apart.

The democratization of AI has made it easy to create plug-and-play models, but it is still hard to create good models. The plethora of code and tutorials make it possible for a basic programmer to pretty easily build a basic model, but there’s a huge gap between a basic AI solution to answer rudimentary questions and one with the deep understanding of a specific business. When it comes to deep learning, skill is less important than experience in knowing which parameters to choose for each dataset and business problem.

While starting with ready-made deep learning solutions might be a good way to get your feet wet, companies serious about leveraging deep learning-enabled computer vision for competitive advantage eventually need to step away from the quick fix and develop custom applications from scratch. Custom solutions provide not only the deep dive analysis that comes from understanding your unique business needs and specific customer behaviors, but it uses these data-and image-driven insights to solve your business challenges and drive your specific growth path. Ready-made solutions simply do not have the robustness and experience required to do the job.

The problem is that true deep learning-enabled computer vision requires a very specific and highly honed expertise that can be hard to find. Data scientists spend years understanding not only the tech involved, but specific industry customer behaviors and challenges. They learn to understand what resonates in certain markets and they know how to transfer their understanding to machines. In addition, even with out-of-the-box solutions, really beneficial AI requires constant care and feeding. Deep learning solutions need to be constantly fed new data, images, video and other content to be accurate and up-to-date. Very few organizations have the resources to accomplish this in-house. So, while it may be okay to dabble with some of the out-of-the-box AI solutions, customer-developed solutions built from scratch are unmatched in providing true business advantage.

Powerful deep learning insights derived from computer vision technologies are enabling a whole new level of awareness, understanding and insights, improving lives, making people safer, cities more efficient and health diagnoses more accurate. These benefits require a deep commitment among the organizations that deploy them and a trust in what they can really accomplish. As Taylor Swift asks in the title of one of her top songs, “Are you Ready for It?”

Are you prepared for AI in 2019? Key Trends Driving its Growth

AI is continuing to make great strides. Industries as diverse as telecommunications, insurance, financial services and education are embracing new applications – and this growth is only expected to accelerate in 2019 despite challenges such as the shortage of data scientists. AI will not only continue to make inroads across sectors but it will become a pervasive technology because it enables companies to make better decisions and handle processes more effectively and efficiently. The companies that adopt the technology sooner rather than later, will be in the best position to leverage AI for competitive advantage in the coming year and beyond.

With all the benefits that AI offers, it’s no wonder that it’s taking off. From machine learning, which automates routine tasks; to chatbots, which simulate human communication; deep learning, which is fast resembling the thinking of humans; and predictive analytics, which uses historical data to predict future outcomes – the benefits of AI are very attractive.

AI is already having a major impact on the way business is being conducted today. One implication is that BI, as we know it, will soon disappear. The term, Business Intelligence, which has been around since 1958 (long before it could be traced as a search term), will no longer be a viable approach. In 2019 the term will morph instead into Business Insights, marked by a reduced focus on dashboards and reports, and instead an increased focus on outcome-driven, value-based analytics. This will be driven by the emphasis on data, as well as the ability of AI-enabled apps to capture as well as predict outcomes based on this data.

While there are two main thrusts in AI today: pragmatic AI, which has immediate applications today, and pure or open AI, which is focused on simulating humans, in the coming year and foreseeable future, the focus will be on pragmatic AI. Companies are using these smart apps to solve actual business and real-world problems, ranging from automatically detecting parasites in blood smears, helping security personnel at airports improve safety by avoiding false alarms, and enabling financial services firms to provide personalized customer advice through chatbots to increase loyalty and satisfaction.

Challenges to growth

Even in a year of growth, there are several challenges to AI adoption in the year ahead:

  • Data is needed to drive AI adoption. In order to automate processes, predict outcomes or communicate with humans, AI requires a huge quantity of data. The more data you can provide, the better – and the more accurate the outcome will be. Also, the data needs to be in good shape for the predictions and results to be valid. With data trapped in different silos, many organizations don’t know what data they have, where it is, or how to clean it up. Getting their data houses in order will be a first step to AI adoption for many companies in the new year.
  • The shortage of data scientists will not abate. According to LinkedIn’s Workforce Report for August 2018, there is a shortage of 151,717 people with data science skills, and IBM estimates that by 2020, the number of jobs for U.S. data professionals will increase to around 2.7 million. While there are just not enough qualified data scientists to go around, one thing is certain: the solution does not lie in using citizen app developers to create algorithms with pre-defined, as-a-service apps. Creating algorithms, teaching AI programs – as well as providing the constant refinement and training that is needed – is a complex, specialized process that requires the skills of qualified data scientists.
  • Heavy-duty computing power is required. The number of simultaneous calculations needed to create algorithms, as well as the cost and complexity of the advanced GPU servers needed to process them, makes it difficult for most companies to develop AI programs in-house. However, companies will increasingly turn to service providers to provide the specialized servers and skills needed to jumpstart their AI initiatives.

Growth areas ahead

Despite the road bumps that these challenges present, AI will be moving full speed ahead. Here are two new areas of growth we anticipate in 2019

  • Data will move to the edge. The challenge of having good, sound data to fuel AI apps will give rise to a new approach to how data is captured, stored, curated and delivered. The focus will be on ensuring data is clean at the edge –where it enters (not in the back-end system). To turn edge data into insight for real-time action, it must be processed close to its source to avoid the latency, bandwidth and cost issues of sending data to a cloud-based data center. There will be huge market opportunities for companies that build tools to help enter and validate data at the edge.
  • 2019 will be the year of computer vision. Another area of growth will be in virtual reality (VR), augmented reality, (AR) image detection and facial recognition. The use of AI to find patterns and insights from images and video will become as popular as data analytics. AI will involve not only image detection, but also movement and activity, enabling organizations to predict certain behaviors, for example, if a fight is about to erupt in a crowded group of people.

 

AI has established strong footholds across industries and applications and proven its value in improving the customer experience, offloading repetitive manual processes, and predicting future outcomes, among other activities. In 2019, organizations will continue to make progress in overcoming challenges and embrace AI at a faster clip as they reach for the brass ring of business insight and agility.

One of The Best Entrepreneurial Companies in America – Yep, That’s Wovenware

Entrepreneur Magazine just published its Entrepreneur360™ ranking of the best entrepreneurial companies in America. We’re quite honored and pleased to rank number 88 out of the 360 companies included – that’s the top quarter of the list.

Entrepreneur magazine started the E360 awards to identify 360 small businesses each year that are mastering the art and science of growing a business. We, along with other firms, were evaluated based on five metrics: impact, innovation, growth, leadership and business valuation.

According to Jason Feifer, editor in chief of Entrepreneur magazine, “These companies are deemed successful not only by revenue numbers, but by how well-rounded they are. The companies that make the list have pushed boundaries with their innovative ideas, fostered strong company cultures, impacted their communities for the better, and increased their brand awareness.”

It’s thrilling to be included on the A list and ranked according to an unbiased, scientific process from experts in the field, and we’ve worked hard to earn this recognition.

We’re very proud of our accomplishments over the past year, bringing advanced AI solutions to an ever-widening base of customers and helping them to solve real world business challenges. We’ve expanded the company, bringing on new staff, new business partners and branching into new areas of innovation. But maybe what we’re most proud of is how we’ve built a resilient company that remains committed to giving back to our local community, as well as the larger U.S. technology community. We’ve worked hard to help rebuild parts of Puerto Rico still ravaged by the hurricanes; and we continue to foster the next generation of software engineers and data scientists, sharing our expertise and excitement about all that AI has to offer.

We look forward to continuing to reach new standards of success in 2019. I have to say, this inclusion among the top entrepreneurs helps give us a proud send-off to 2018, and provides icing on the cake for a year well-served.