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.

Wovenware Included in Landscape Overview of Computer Vision Providers by Independent Research Firm

SAN JUAN, Puerto Rico, Aug. 26, 2019 –Wovenware, a U.S. nearshore provider of Artificial Intelligence (AI) and software development services and solutions, today announced that it has been included in a report from leading global research and advisory firm, Forrester Research, Inc.  The report, New Tech: Enterprise Computer Vision Solutions, Q3 2019, Forrester Research Inc., August 9, 2019 (access requires subscription or payment) provides a landscape overview of 33 providers of computer vision solutions and services. Wovenware was included as an Early Stage provider of computer vision professional services.

“We’re honored to be recognized by Forrester as one of today’s Early Stage computer vision providers, and we remain committed to providing advanced computer vision solutions, private crowd and data labelling services, and the ongoing data training to help customers derive actionable insights from their solutions,” said Carlos Meléndez, COO, Wovenware.

The report was developed by Forrester to help application development and delivery (AD&D) professionals understand the landscape of computer vision vendors and identify the right ones to help them realize their computer vision goals. The firm divided the 33 computer vision software vendors it examined into four market segments: computer vision platforms, professional services firms, specialty solutions, and annotation solutions. It also categorized them according to Late-Stage, Growth Stage and Early Stage status. Wovenware was listed as an Early Stage, Professional Services provider. The Forrester report also reported the state of the computer vision marketplace, key trends driving its growth and considerations for companies looking to adopt it.

“Custom computer vision solutions provide the deep-dive analysis that comes from understanding and honing in on unique business needs and specific customer behaviors. They require a deep understanding of those customer behaviors and challenges, as well as experience into what resonates in certain markets, and how to transfer that knowledge to machines,” added Meléndez. “Computer vision is gaining major traction in a variety of industries helping to identify vulnerabilities to safety, anomalies in medical images and to improve customer experience. Yet, companies need to take a strategic approach to its implementation, mapping out the most direct and efficient route to making the promise of computer vision a reality.”

In addition to this report, Wovenware also was cited in a June 14 research report from Forrester, entitled, All Enterprises Need (Computer) Vision, (access requires subscription or payment). In the report, Forrester describes the four most established use cases for computer vision, while also sharing the key questions to ask when planning your computer vision strategy.

About Wovenware

Wovenware delivers customized, smart applications that create measurable value for customers. Through its nearshore capabilities, the company has become the partner of choice for organizations needing to re-engineer their systems and processes to increase profitability, realize efficiencies and seize new market opportunities. Wovenware’s team of expert software engineers and data scientists understand the unique business needs of customers to leverage complex technologies, such as AI, chatbots and cloud-based solutions, and it works closely with them to develop and manage solutions that align with their business goals. Headquartered in Puerto Rico, Wovenware works with customers across North America and around the world. Visit us at www.wovenware.com, or connect with us on Twitter, Facebook, or LinkedIn.

Putting Computer Vision into Focus

Just a few short years ago, computer vision was seen as something that held great potential but which wasn’t quite there yet. Today, thanks to advances in AI, more affordable GPU capacity and an accumulation of data and the means to train it, it’s becoming a strategic technology asset for companies in a variety of industries. In fact, according to a Forrester blog post , 58% of senior business purchase influencers said that their firms are implementing, planning to implement, or interested in implementing computer vision in the coming year.

This thinking was recently outlined in a Forrester research report entitled, All Enterprises Need (Computer) Vision, June 14, 2019 (access requires subscription or payment). In the report, Forrester describes the four most established use cases for computer vision, while also sharing the key questions to ask when planning your computer vision strategy. We at Wovenware were honored that we were cited as an example of a provider that creates labeled training datasets, which we consider the secret sauce to effective computer vision solutions.

While the common strategy of computer vision is to capture, process and analyze real world images and videos to uncover meaningful information, there are currently different ways to get there. Automated machine learning tools for creating computer vision apps are available that provide plug-and-play capabilities that make it possible for a basic programmer to pretty easily build a basic model. We’ve found, however, that these types of machine learning solutions might be a good way to begin, yet companies serious about leveraging computer vision for competitive advantage need custom solutions built from the ground up, around real-world images that are identified and labeled from scratch.

Custom deep learning-based computer vision solutions provide the deep-dive analysis that comes from understanding and honing in on unique business needs and specific customer behaviors. They leverage a deep understanding of customer behaviors and challenges – within specific industries – as well as experience into what resonates in certain markets, and how to transfer that knowledge to machines.

What’s important as well, for companies looking into implementing computer vision, is to consider that in order to be effective, it requires constant care and feeding – in the form of new data, images, video and other content. Computer vision can never be a one and done proposition.

Yet, as Forrester eloquently describes in the report, computer vision is allowing companies to collect unprecedented intelligence about the most important aspects of their businesses. It is enabling a whole new level of awareness, understanding and insights that can improve lives, making people safer, cities more efficient and health diagnoses more accurate. Consider the following examples of computer vision at work today.

  • Transforming advertising. 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. Computer vision can help physicians diagnose diseases, among other applications. For example, a physician or radiologist can use it to review brain scans and determine healthy or not so healthy areas of the brain.
  • Enabling safer autonomous driving. Deep learning-enabled computer vision is 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.
  • Making shopping easier. In one example, cameras are being placed in the ceiling above aisles and on shelves in a brick & mortar retail location, and using computer vision technology these cameras can determine when an object is taken from a shelf and who has taken it. If an item is returned to the shelf, the system is also able to remove that item from a customer’s virtual basket. The network of cameras allows the app to track people in the store at all times, ensuring it bills the right items to the right shopper when they walk out, without having to use facial recognition.

Satellites Bring Computer Vision to a Whole New Level

When computer vision is deployed in satellites, its possibilities are boundless. Consider the following:

  • 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, construction, 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, satellite imagery can help provide valuable information that can be used to plan for the supply of life-sustaining resources like food and shelter materials.

Computer vision is gaining major traction in a variety of industries, providing an extra set of really smart eyes that can identify vulnerabilities to safety, identify anomalies in medical images and improve customer experience. Yet, companies need to take a strategic approach to its implementation, mapping out the most direct and efficient route to making the promise of computer vision a reality.