Is Your AI Program Emotionally Intelligent?

There is no doubt about it — it seems like we’re interacting with AI apps all day long in our business and our personal lives. This is spurring the growing demand for AI to “understand” how we are feeling and to be able to respond to us appropriately. A recent article I wrote for Forbes discusses this industry focus known as emotion AI, or “affective computing,” in which AI programs can recognize, interpret, process and simulate human emotions. In the article I address the possibilities that Emotion AI can deliver and the limitations that must be overcome in order to develop these types of smart – and emotionally intelligent — programs.

We can see the beginnings of Emotion AI in action with chatbots, which are based on Natural Language Processing (NLP). Some are trained to hear the emotion in a customer’s voice inflections to determine, for example, if he/she is angry or frustrated, as well as by the choice of words that are used. But since there are many ways to express anger and other emotions, a very large dataset is needed to train AI programs to accomplish this.

There are many exciting possibilities for Emotion AI on the horizon. Imagine if an AI healthcare app can identify mental or physical illness based on the way a patient looks or sounds, a marketer can judge a person’s reactions to an ad, or a contact center AI app can route a call to a supervisor if the customer sounds annoyed to avoid further frustration. Emotion AI can provide a critical business edge and a much better customer experience.

While there’s still more work to be done, the possibilities are endless. We just might find ourselves someday having a heart-to-heart with a robot.

Wovenware Expands Operations; Moves Headquarters to New San Juan Location

SAN JUAN, Puerto Rico, Sept. 24, 2019 –Wovenware, a U.S. nearshore provider of Artificial Intelligence (AI) and software development services and solutions, today announced that it has tripled the size of its corporate headquarters, moving the company’s operations to a 13,000-square foot facility on Calle Los Angeles in San Juan, Puerto Rico, only a half-mile from its previous location. The move was made to accommodate the company’s expanding staff and client base, as well as its growing portfolio of services and solutions.

“We are committed to expanding our presence in Puerto Rico and across the U.S., as we deliver on our promise to provide customers with advanced AI-based solutions as well as other innovative digital transformation services,” said Christian Gonzalez, CEO, Wovenware. “Our new, modern offices provide a collaborative and dynamic work environment for our employees, where they can be comfortable to innovate together and bring solutions to life for clients.”

New Headquarters Keeps Pace with Company Growth

The new corporate headquarters, located in the neighborhood of Santurce, now houses the company’s expanding staff of more than a 100 team members including software engineers, data scientists, and data specialists, as well as all administrative, business development and marketing operations. The modern facilities include more than 150 development positions and feature seven conference rooms, two stand up areas, private phone booths, a quiet lounge, a maternity private area, a wellness room, lockers and a kitchen, among other additions that improve the employee experience.

Since the beginning of 2019, Wovenware has experienced significant growth in its nearshore delivery model for software engineering and AI services and solutions, bringing on strategic new customers in areas, such as insurance, healthcare, telecommunications and defense.

To accommodate this growth, the company has added more than 48 employees, since January alone, in software development and data services. It also continues to hire at all levels for a variety of roles.

Wovenware and its executives, also have received industry recognition for their expertise in AI and other disruptive technologies. In June 2019, the company was recognized by industry-leading research firm, Forrester, in its study, All Enterprises Need (Computer) Vision. In the report, Forrester describes the four most established use cases for computer vision. Wovenware was listed along with only one other provider for creating labeled training datasets for computer vision solutions.

Wovenware and its employees also remain committed to giving back to the community, with leadership on the board of Abre Puerto Rico, an organization that works to enable greater government transparency across Puerto Rico; active involvement with ConPRmetidos, a community organization committed to rebuilding Puerto Rico; and Grupo Guayacan, which works to support Puerto Rico’s entrepreneurs.

Putting the Spotlight on Environmental AI During the O’Reilly AI Conference

I recently had the opportunity to speak at the O’Reilly Artificial Intelligence Conference in Silicon Valley, sharing my experiences with environmental AI, and more specifically, how we’re applying it to help wipe out mosquito-borne diseases. I was honored to join the ranks of speakers from world-class organizations, such as Google, UC Berkley, Bloomberg and others, to discuss how AI is really changing the world.

During my presentation, I discussed our work for the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika, Dengue and Chikungunya.

It probably came as a big surprise to many of the session attendees that the world’s most deadly animal is the mosquito – and, they’ve killed 32 times as many people as every war in human history combined. They also may not have known that the Zika virus is behind an unprecedented rise in the number of children being born with microcephaly, or that there are 390 million Dengue infections each year.

As we know all too well here in Puerto Rico thanks to Hurricanes Maria and Irma, stagnant water and weather events can make the mosquito population explode. During the conference I shared how we at Wovenware are working to apply AI to automate much of the work involved in classifying mosquitoes, so entomologists can concentrate on research such as why mosquitoes have become immune to insecticides.

Wovenware has developed an RPA solution composed of deep learning models and other processes that’s designed to automate the identification and classification of mosquito species, reducing the amount of manual processes previously required of entomologists. This automation is making it faster to count, classify and identify the species and gender of Aedes Aegypti captured mosquitoes.
I shared with the group the architectures and technologies we’ve employed to build the neural networks, including RetinaNet, ResNet and TensorFlow.

As I mentioned to attendees, we’re still in the early stages of the project, but future plans call for us to integrate to other related projects, analyze time series and remotely monitor and classify species.
As we continue to refine and evolve our solution, we hope to take our work to other countries and geographies where mosquitoes are a major issue.

In addition to the incredible response I received from my presentation, the O’Reilly Artificial Intelligence Conference truly was inspirational. Listening to other presentations and speaking with data scientists, as well as academics and business leaders was a confirmation that amazing discoveries, technologies and use cases continue to evolve around AI, and we’re really just at the tip of the iceberg in terms of what it is capable of achieving. It’s clear that AI is moving beyond the business world and beginning to solve environmental problems, while making the world a safer place.

Digital Transformation Begins with Company Culture

Search the key words, digital transformation, and you’re bound to come up with hundreds of articles and insights. The fact is, digital transformation is on every company’s priority list. But as I mentioned in a recent blog post for Forbes, what truly drives a culture of digital transformation requires something less tangible than software, artificial intelligence or other innovative technologies. It really revolves around a shared, customer-centric mindset that permeates from a company’s leadership – a company culture that embraces change, and a willingness to take carefully controlled risk.

Among the statistics I shared in the article, there is one that clearly supports this view. According to a McKinsey report, one third of key decision makers state that culture is the most significant barrier to digital effectiveness followed by a lack of understanding of digital trends (25%). Further, a joint analysis from MIT Sloan and Deloitte reveals that companies failing to transform digitally generally fell short of their expectations because they “didn’t change mindsets and processes or build cultures that fostered change.”

Despite the complexity and techie-“ness” of digital transformation, it really boils down to corporate culture. It essentially revolves around the following three requirements in order to be successful:

  • It must begin at the top. An organization must have leadership alignment in order to create the cultural shift required to truly become data-driven. This means leaders should not only approve digital transformation initiatives, but become active participants in them.
  • It needs a designated internal team. It’s important to assign a specific team dedicated to the task and clearly communicate who is on this team to the entire organization. It’s important to not only focus on those people with specific technical skills, but also employees who understand the overall corporate goals and business challenges and how to best roll out change to the organization.
  • The customer must be the key focus. Nothing drives a culture of digital disruption more than the customer. Customers increasingly expect companies to respond swiftly to their needs and understand their specific business and how it ticks. Today it’s understood that the best way to accomplish these mandates is through digitalization — it’s the fastest and surest route to success.

Digital transformation is not always easy, nor does it happen overnight, but companies that adopt the right mind-set and embrace change enterprise-wide will find that the efforts are not only well- worth it, but a prerequisite to today’s business success.

Checking in on 2019 Predictions – It’s Still All About AI

With half of the year in the rear-view mirror, it’s a good time to re-examine the annual predictions we made back in January to see where we stand, which ones are on track and what new ones may be emerging.

There’s one thing that we know for sure when it comes to the tech marketplace – the focus on AI has been growing and there’s no sign of it letting up. From 2018 to 2019 alone, the use of AI in corporations has tripled according to Gartner. IDC expects investment in AI solutions to increase 44 percent globally this year over last, with retail and banking leading the way.

We hate to say we told you so, but it’s true. We predicted that AI would become more pervasive in all types of organizations in 2019 and that trend is sticking, with new use cases, technologies and players entering the fray almost daily.

Yet, as we mentioned in our January predictions, while AI use is growing, the task is still rather difficult for many companies for a variety of reasons. Consider the following:

  • Data is needed to drive AI adoption – Having an AI solution without good data is like having a car with no gasoline – you’re not going to get very far. This has been and continues to be a critical challenge to organizations of all types and sizes. Data is the fuel that runs AI programs and the more and better data that organizations have, the more accurate the AI program will be. Organizations want to set the world on fire with AI, but they’re realizing that it’s much more difficult than it sounds. Because of the data problem and the demand for AI programs to address critical business needs, data scientists, as well as data engineers, are taking on the added responsibility of making sure that the data is clean and in good shape before it is fed into AI algorithms. There has also been a growth in data labeling and data cleansing services to accomplish that.
  • The shortage of data scientist continues – While more courses, certificates and university degrees in data science are becoming available than even a few months ago, the need for data scientists still outstrips the demand and the gap continues to grow. Some data scientists are dealing with data issues, as mentioned above, instead of spending their quality time experimenting and training and fine-tuning algorithms. And, the need doesn’t end with data scientists, but extends to data engineers as well. Many companies are turning to nearshorers and other outsourcers to address their AI needs in the midst of the shortage of these experts.
  • Heavy-duty computing power is required – Some companies are using laptops as a learning tool for AI development, but when it comes to developing robust AI programs at scale, there is still no substitute for using advanced GPU processors which are capable of crunching volumes of data and handling simultaneous calculations quickly. Given the cost of these GPUs, and the shortage of in-house data scientist and data engineering expertise, the trend of outsourcing AI projects continues to grow; it’s a lot cheaper than investing in the infrastructure for many companies in the long run. One interesting new offshoot, however, is that I suspect the cost of GPU computing may begin to come down as new GPU providers enter the mainstream.

Earlier this year, we also looked ahead at specific areas of growth, and predicted:

  • Data will move to the edge – With the increased focus on video surveillance and security applications, the need to process and analyze the data at the edge, where it is captured, is continuing to increase and AI is stepping up to the plate. A first key step is to ensure that the data captured at the edge is clean, so companies can not only avoid the latency, cost and bandwidth issues of sending it to the cloud first, but even more importantly, so it can be accessed quickly for real-time insight. Keeping the data local also helps to ensure greater security and privacy. According to VentureBeat, looking ahead, there will not only be a focus on data at the edge, but AI at the edge too, as 5G enables greater connectivity with very fast speeds and extremely low latency.
  • 2019 will be the year of computer vision – According to Forrester, computer vision is really taking off, with lots of investment and hundreds of startups. It’s being used to monitor people’s behaviors for security or for diagnosing certain conditions in medical imaging, among other applications. Forrester notes that most companies do not have the in-house expertise to undertake these programs themselves.

It’s hard to believe it’s almost here, but as we sprint to the finish line in 2019, we can expect more AI developments and use cases, as well as solutions to the challenges of AI adoption. What lies beyond that is anyone’s guess, but based on our track record to date, AI should remain the focus for quite some time.

The Telecom Industry Gets By with a Little Help from Its AI Friends

In an article I recently shared with Telecommunications magazine, I discussed how things like IoT, mobile and live streaming services are fast becoming a way of life for people everywhere. Today’s consumers and businesses are demanding great experiences from their telecom providers simply because they can. They know that there are many options available to them and they’ll go with the ones that provide the most content, the best seamless experience and fastest service. But, such disruptive innovation doesn’t come without challenges for telecommunications providers.

As we transition to 5G, content continues to be king amidst a barrage of new program options and the availability of lightning fast service. And, as consumers become more informed and demanding of great Internet service, telecom providers are turning to AI to help them meet expectations.

As the article states, things like predictive analytics, machine-and deep learning and chatbots are stepping up to the plate to improve service offerings, better meet demand, improve network quality and interact with customers on a more personal level.

The telecommunications industry is indeed going through enormous transition as it works to keep pace in a competitive marketplace for streaming services, lightning-fast connections and always-on service quality. Thanks to AI, they’re meeting the need head-on, armed with the insights to take on whatever comes next in voice, data or video.