Expo Puerto Rico 2019 — Delivering Insights to Help Entrepreneurs Grow

Recently I had the pleasure of participating in Expo Puerto Rico 2019, a great event that allows entrepreneurs to network and learn valuable information about exporting their services outside of Puerto Rico.

During the event I was part of a presentation track: Exporting Services: Results and Challenges, and I shared background on Wovenware and our successes exporting our AI and software engineering services across the U.S., Europe, Canada and the Caribbean. Joining me in the session track was the esteemed Manuel Laboy Rivera, Secretary of Economic Development & Commerce; and fellow Puerto Rico businessmen, Francisco Martinez of CDM; and Franco Mondo of the BMA Group.

During his presentation, Manuel Laboy Rivera shared information on Act 20, a major incentive for Puerto Rican businesses to export services. Also known as the “Export Services Act,” it provides great incentives to ensure Puerto Rico’s competitiveness in attracting investments and establishes a legal framework to stimulate the establishment and development of a wide array of ventures, such as manufacturing, social media, other internet-based operations, commercial businesses, and the export of services. Key among the incentives is a tax break, which taxes companies on only 4% of their annual profits from exports. This represent a major cut from the typical 20-to-30 percent that is normally taxed on overall profits.

What struck me most throughout the event was the sense of enthusiasm and drive shared among the entrepreneurs in attendance; along with the diversity of talent and professional services offered across the island. From presentations from the likes of Google and Shopify, and successful local entrepreneurs, business growth is alive and well in Puerto Rico, and opportunities abound as the growth of complex technologies, such as e-commerce and AI require specialized services that we can offer.

Expo Puerto 2019 reinforced that Puerto Rico entrepreneurs have not only major incentives and the cooperation of government to grow their businesses through the export of their services, but they have the support of each other –  a sure way to bring business to the next level.

Augmented Analytics – The Next Evolution in AI

I recently wrote about augmented analytics for a Forbes Technology Council column, after reading an interesting article on the subject in The Wall Street Journal. As the news article mentioned, while AI is one of the most important technology advances of our era, it still has a ways to go before it can identify cause and effect relationships. To do this requires massive data-sets, that will be more readily available through augmented analytics, or AI-as-a-Service. Once it accomplishes this feat, however, it’s set to bring AI to the masses and seriously up the game for what AI can accomplish.

According to The Wall Street Journal article, determining causal relationships requires the ability to sift through tons of “large and noisy data-sets to detect faint signals, or the proverbial needle in a haystack.” It’s one thing to use AI to find patterns that can indicate growing traffic issues in a community, for example, and quite another to determine what is causing the back-ups. That level of insight needs massive and more granular amounts of data, and that’s where augmented analytics comes into play.

As the article explains augmented analytics will be delivered via pre-defined AI as a service, or pre-populated data that can supplement the limited data-sets data scientists may already have.

Earlier this year, Gartner identified this as an emerging role for 2019 – The Augmented Developer, who will build advanced algorithms based on augmented analytics. Gartner also identified Augmented Analytics as a Top Ten Strategic Tech Trend for 2019.

Yet, I mentioned in the Forbes article that while it may be considered a top tech trend for this year, the notion of AI as a Service, providing augmented data and analytics, won’t be realized for some time – perhaps not for at least three years or more, since there are key business challenges that must be overcome. There needs to be greater domain knowledge in the industry in order to create pre-defined data-sets; and then there’s the challenge of enabling generic data to meet unique business needs.

While the lure of working with providers that claim to already offer pre-defined AI as Service is enticing, buyers need to be informed. They need to ask the hard-hitting questions, such as:

  • Do you have domain knowledge? How much do you know about my specific industry to provide me with the data to address my business problem?
  • How easily can I integrate my own data with the pre-defined data-sets?
  • How can I measure the algorithm’s performance?
  • Will you remain a long-term partner that can work with me to continuously augment and train my data-sets?

Once pre-defined AI as a Service takes hold it will change the role of AI as we know it, but it’s going to take some time. In the meantime, customized AI solutions are available today that are bringing the level of personalization and unique business understanding to help you solve key business challenges. There’s really no need to wait for what comes next.

Beyond Alexander Graham Bell’s Wildest Dreams – A Telecom Industry Powered by AI

In July 1876, Alexander Graham Bell introduced the telephone, to the world at the Centennial Exhibition in Philadelphia. While initially only used in business, by the early 1900s the telephone became a staple in most homes. Little did he know that his invention would spark the telecommunications industry, enabling communication across wire-based, wireless, Internet and other devices across the world – even between humans and robots.

Innovations such as the Internet of Things (IoT), smart phones, mobile apps and live streaming services are drastically transforming the way we communicate, conduct business and live. But such disruptive innovation doesn’t come without challenges for telecommunications providers.

Consumers are demanding a positive user experience and, to remain competitive, it’s critical that you deliver it – all the time. They want to enjoy continuously enhanced entertainment options on multiple devices, and expect to be connected at all times – with no disruption to network performance or dropped calls.

The challenge you face as a telecom provider is to create a strong, scalable telecom infrastructure enabling constant connection through a variety of devices, and at the same time, provide data and voice services that are consistently high quality, reliable, and affordable – all without missing a beat.

Moreover, telecom providers must transform to keep pace with an industry poised to grow increasingly more sophisticated and demanding thanks to key market drivers, such as:

  • The transition to 5G. Representing the next evolution of mobile communications, 5G is expected to enable even greater connectivity, speed, bandwidth and other capabilities than ever before, supporting applications such as augmented reality and virtual reality, and enabling communication among billions of sensors and devices.
  • Content is becoming king. As telecom providers work to compete against cable and Internet service providers, they are looking to offer content from the likes of Netflix, Hulu, Amazon and others to differentiate their offerings and win more customers in an increasingly competitive and demanding marketplace. Partnerships with these and other companies are becoming a key goal.
  • Focus on customer experience. With more and more options, today’s consumers and business users alike know what they want, and that means connectivity at the speed of light, quick resolution to connectivity issues and a delightful user experience. Telecom providers are tasked with providing these services, while keeping costs down.

AI Emerges as the Solution

To meet these and other challenges, the telecom industry is turning to AI to help improve the user experience, operate more efficiently and reduce costs. According to a report from Market Research News, 40 percent of surveyed industry executives revealed that their companies plan to invest more than- $1 million in AI during 2018-2020. And further, 68 percent of respondents operating in large companies identified customer experience enhancement as a key driver of their AI investments.

But what exactly are the types of AI initiatives underway in the telecom industry?

  • Predictive Analytics. Telecom providers are leveraging the vast amounts of data collected over the years in order to extract actionable insights to improve the customer experience, become more efficient and increase revenue. This data is being culled from devices, networks, mobile applications and geolocations, as well as usage and billing data from customer databases and services.

    AI-driven predictive analytics is helping telecoms provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. For example, it is helping them monitor the state of equipment, predict failure based on patterns, and proactively fix problems with communications hardware or set-top boxes in customers’ homes.

    Historical data is also helping them predict the likelihood of customer churn, identify customers who would be receptive to an upsell of new services and see where they can streamline resources.
  • Machine Learning. This algorithm-driven form of AI is improving network optimization, helping telecom providers build self-optimizing networks (SONs), to automatically improve network quality based on traffic information by region and time zone. Advanced algorithms allow them to look for patterns within data, to detect and predict network anomalies, and proactively fix problems before customers are negatively impacted. As operators transition their network architectures with software-defined networking and virtualization technologies that enable automation, AI will leverage these capabilities to self-diagnose, self-heal and self-orchestrate the network.
  • Chatbots. Conversational AI platforms — known as chatbots or virtual assistants — are improving the customer experience and taking the burden off of customer service agents for routine customer inquiries, such as support requests for installation, set up, troubleshooting and maintenance, which often overwhelm customer support centers. Chatbots are significantly reducing customer hold times and driving improved customer experience.

The telecommunications industry has changed in many ways, and we are at the tipping point of a new transformation in how services are delivered, measured and improved. Today, if Alexander Graham Bell articulated his famous command over the telephone for the first time, “Mr. Watson come here, I want to see you,” he may have been met with the response, “Mr. Bell, can you text me your reason for calling? I’m live-streaming Game of Thrones right now.” Or, then again, thanks to AI, he may have already asked Alexa to record it for him.

The Prescription for Effective AI in Healthcare is Quality Data

Nowhere is Artificial Intelligence (AI) more promising than in the healthcare arena. The potential of using AI to help find cures to diseases or more effectively bring treatment into underserved, hard-to-reach areas is huge. Although some developments may be many years away, AI is currently being used in many areas of healthcare, from prevention and diagnosis, to treatment and monitoring, and more.

AI is enabling this by crunching huge quantities of data, detecting patterns in images, behaviors and symptoms and becoming smarter by teaching itself in real time in order to address some of the most pressing healthcare issues today. But to get the most value from AI, healthcare organizations – and the industry as a whole – must overcome some key challenges.

Capturing the right data

There’s no doubt that more data is being collected today and that trend is expected to continue. Some sources estimate that healthcare is currently responsible for 30 percent or more of all data and an IDC study predicts that this will increase by 36 percent compounded annually by 2025. This will outpace the growth rate of the manufacturing, financial services and media and entertainment industries. Part of this growth will come from data culled from “smart” diagnostic equipment and devices, including wearables, that can be uploaded for analysis.

While there’s a lot of healthcare data and more to come, it’s important that we’re collecting the right type of data. There are several key principles data scientists follow to develop effective AI algorithms, and one of the most important ones is that the more data, the better. When a data scientist develops algorithms to try to test a hypothesis or solve a business problem, he/she uses data as the fuel for the AI engine. The more data that is available, the more accurate a picture the program can develop of what’s going on.

At the same time, the quality of the data is essential. That requires data to be relevant, accurate and properly prepared, e.g., coded, cleaned, etc. In addition, it’s important that a variety of data is collected. If you only provide information from one source, it’s too limited. It may not include the accurate answer to the problem you are trying to solve or to accurately predict outcomes. Without the right data, a clinician could be missing medical conditions or misdiagnosing them.

Data challenges in healthcare

There are several data challenges inherent in the healthcare industry that must be overcome in order to develop sound AI programs that provide value, including:

  • The focus has been elsewhere. For the past several years, the major tech investment and focus in healthcare has been on converting paper-based medical records to electronic health records (EHRs) to comply with government regulations, gain incentives and avoid penalties. While that’s clearly important, the potential for investment in AI to develop better diagnostic, research and treatment options shouldn’t be overlooked.
  • We’re not aggregating as many different data types as we should. While the industry is furiously working to collect data, a lot of it is coming from medical records and other text-based data sources. But to get a more complete picture, consider all the sources, including audio, imaging and video. For example, why can’t a device be placed on a stethoscope to capture the audio of a heart murmur, arrhythmia or other heart-related ailment and then upload it to a centralized database? It can not only be used to monitor the progression of the condition for that particular patient, but also by AI programs for training data to help diagnose the condition in others. Or based on how that patient responds to a particular treatment (how the heart rhythm has changed), it can be used to provide data for predictive analytics programs. In a similar way, images can be captured of skin conditions or other problems, and videos can be taken of a person’s gait or other conditions. Since AI programs are able to easily work with videos, images and other types of unstructured data, it makes sense to make that data available for analysis.
  • Healthcare data is siloed. Many departments, from pharmacy to radiology, still have data trapped in siloed systems that they are not sharing. In addition to investing in integration, healthcare providers need to offer incentives to get these different departments to share the information that they have. Organizations need to determine who the owners are and communicate clear financial rewards and benefits for tearing down the siloes and sharing data.

While AI in healthcare is still in the early stages, the possibilities it offers in prevention, diagnosis, treatment and monitoring – just to name a few – are immense and exciting. The first step is to get our data houses in order so we can take advantage of the greenfield opportunities that are available to help clinicians make better, more informed decisions, get better outcomes, and – even get further along the path of curing cancer and other diseases.

What you need to know about AI Computing at the Edge

I recently wrote about AI and edge computing in an article for Forbes. Today, more and more data is being collected by a growing number of IoT devices and there is growing interest in edge computing. What does that mean for the enterprise, and what does the future hold for AI at the edge?

There’s room for both the cloud and the edge

In the article, I discussed how edge computing will continue to co-exist with cloud computing. The fact is, cloud computing is not going away anytime soon and there’s a time and place for both computing in the cloud and at the edge. Cloud computing has become the gold standard for enterprise computing today because it solves the problem of storing and managing all types of applications, as well as the huge volumes of data required to develop and maintain algorithms for AI apps.

At the same time, there is a growing interest in enterprise edge computing, since it makes sense to develop AI functionality closer to a data source, especially when you may need to know, and possibly act on, information in real time – and when a situation may change rapidly. For example, consider a video surveillance application where you need to be able to identify if someone is acting suspiciously or aggressively, or predict dangerous behavior based on a person’s actions.

The challenges of AI at the edge

The article also explored the challenges involved in developing AI algorithms for edge computing. In addition to the typical issues of the shortage of data scientists to program the apps and the specialized GPU servers needed to crunch the data, the limitations of edge computing make it even more difficult for AI development. IoT and other edge devices currently lack the processing power and bandwidth that is needed for this process.

So, while there may be interest in AI at the edge, there is still not a feasible way to develop it – right now, that is. Many companies are busy working on this problem and testing new solutions that are on the horizon. Yet there’s no need to wait and put your AI plans on hold — there are a lot of business problems that you can solve right now using cloud AI to move your business forward.

For additional AI insights from Wovenware, you can read: Are You Prepared for AI in 2019? Key Trends Driving its Growth and AI is All Around Us – Sometimes Without Us Even Noticing It.

What you need to know about AI Computing at the Edge

I recently wrote about Artificial Intelligence (AI) and edge computing in an article for Forbes. Today, more and more data is being collected by a growing number of IoT devices and there is growing interest in edge computing. What does that mean for the enterprise, and what does the future hold for AI at the edge?

There’s room for both the cloud and the edge

In the article, I discussed how edge computing will continue to co-exist with cloud computing. The fact is, cloud computing is not going away anytime soon and there’s a time and place for both computing in the cloud and at the edge. Cloud computing has become the gold standard for enterprise computing today because it solves the problem of storing and managing all types of applications, as well as the huge volumes of data required to develop and maintain algorithms for AI apps.

At the same time, there is a growing interest in enterprise edge computing, since it makes sense to develop AI functionality closer to a data source, especially when you may need to know, and possibly act on, information in real time – and when a situation may change rapidly. For example, consider a video surveillance application where you need to be able to identify if someone is acting suspiciously or aggressively, or predict dangerous behavior based on a person’s actions.

The challenges of AI at the edge

The article also explored the challenges involved in developing AI algorithms for edge computing. In addition to the typical issues of the shortage of data scientists to program the apps and the specialized GPU servers needed to crunch the data, the limitations of edge computing make it even more difficult for AI development. IoT and other edge devices currently lack the processing power and bandwidth that is needed for this process.

So, while there may be interest in AI at the edge, there is still not a feasible way to develop it – right now, that is. Many companies are busy working on this problem and testing new solutions that are on the horizon. Yet there’s no need to wait and put your AI plans on hold — there are a lot of business problems that you can solve right now using cloud AI to move your business forward.

For additional AI insights from Wovenware, you can read: Are You Prepared for AI in 2019? Key Trends Driving its Growth and AI is All Around Us – Sometimes Without Us Even Noticing It.