What’s It Like Being an Entrepreneur in Emerging Markets? Inventure$ Conference Sheds Light

Last week Christian and I had the opportunity to speak at the annual Inventure$ conference in Calgary Canada, addressing an audience of fellow entrepreneurs, venture capitalists, angel investors, startups and thought leaders about my entrepreneurial journey and some best practices I’ve learned along the way.

The key takeway? Organizational change is not linear, but requires many different pathways, recalculations and stops and starts along the way. For those companies however, willing to take the risk, the effort is well worth it.

And, the risk takers, are also often called early adopters – those entrepreneurs who are willing to come to the game early and adopt the new technologies that will give them competitive advantage – even if all the kinks aren’t worked out yet.

Especially with today’s data-driven AI and predictive analytics, there’s really no time to wait. As I explained to conference attendees, your entrepreneurial data – your customer profiles, historical information, product data – is really the lifeblood of your organization. Data-driven ones, that recognize the value of their data and its role in today’s early AI solutions, are the ones who will come out ahead of the pack.

The late adopters, or technology laggards, will be at a distinct disadvantage when it comes to AI adoption, because it’s not something that generates benefits overnight. AI requires very unique data – and lots of it – in order to train algorithms to solve the business problem you’re applying it to. For this reason, laggards are not only late to the game, but they will constantly be scrambling to catch up.

Being an entrepreneur always involves risk — and it comes from all sides. Taking a risk on new technologies, like AI and predictive analytics, however, can go a long way to actually mitigating risk and arming entrepreneurs with data-driven insights to make better decisions and to ultimately take their business to the next level.

Bringing it Down to Earth: Four Ways Pragmatic AI is Being Used Today

I recently wrote an article for Forbes.com as part of my role on the Forbes Technology Council, exploring the ways Pragmatic AI is being used today.

When many people think of AI they automatically think of something straight out of science fiction, robots that are smarter than the humans who created them. But, as I mentioned in the article, a different form of AI – Pragmatic AI – is being applied successfully today.

Without even knowing it, we are interacting with Pragmatic AI applications day in and day out. They are the automated chatbots that answer our calls and questions, the customer service rep that texts with us on a retail site, providing a better and faster customer experience.

As the article goes on to explain, four key categories of Pragmatic AI, include speech recognition and Natural Language Processing through virtual assistants; predictive analytics that identify historical patterns to predict future outcomes; image recognition; and self-driving cars.

Despite the fact that Pragmatic AI is alive and well today, and the future holds even more applications of AI that rival our wildest imagination, the need for human intelligence isn’t going away anytime soon. As the article concludes, “Humans and their ability to reason must always remain a part of the AI workflow, since software can never know as much as humans – today or in the future.”

Celebrating National Caribbean-American Heritage Month and Recognizing Contributions to U.S. Business Growth

National Caribbean-American Heritage Month was adopted by the U.S. House of Representatives in 2005 “to recognize the significance of Caribbean people and their descendants in the history and culture of the United States.” It acknowledges the many ways Caribbean-Americans contribute to the fabric of life in the U.S., including valuable business and industry support.

As famous Caribbean immigrants, such as founding father Alexander Hamilton and journalist Malcolm Gladwell have proven, the impact of diversity on our society, as well as the business world is immense.

And, today it’s not just Caribbean transplants to the mainland that are having an impact. Businesses are springing up all across the U.S.-owned Caribbean islands, with many of them in the technology sector. These businesses are not only bringing new ideas and innovation to U.S. industry, but they’re also playing a critical role in helping to fill a much-needed tech talent gap.

One area where this is particularly evident is in the shortage of data scientists. The growth of Artificial Intelligence (AI) – in areas ranging from customer service chatbots to predictive analytics – is making it hard for companies to find the AI talent they need to develop and train algorithms. Because of this, many mainland companies are turning to U.S.-based nearshore service providers to meet the talent need, as well as to lower the costs of building their own expensive infrastructure to support the ongoing development of AI solutions.

A pipeline of highly educated, qualified talent

With the University of Puerto Rico and similar schools graduating highly educated and technically trained students each year, as well as students graduating from mainland universities who are bringing their skills back to Puerto Rico, there is a steady stream of new qualified technology professionals to supplement the growing needs of U.S. industry.

Nearshoring firms that specialize in AI, as well as other types of technology and business expertise, are hiring these professionals and helping U.S. firms gain the insight and competitive advantage that they need. Nearshorers are able to provide the same services and capabilities as U.S. mainland firms, but at a lower cost. The fees of software developers in Puerto Rico, for example, are typically 30-50 percent lower than their mainland U.S. counterparts, even though they offer the same quality.

Caribbean-Americans have contributed greatly to U.S. innovation, culture, history, cuisine and the arts – and the business realm is no exception.

An unknown author once wrote, “Diversity is the one true thing we all have in common. Celebrate it every day.” National Caribbean-American Month recognizes the strength the U.S. possesses because of this diversity, as well as our common commitment to innovation, leadership and the entrepreneurial spirit.

Top Technology Innovations of Last Three Years: Predictive Analytics is High on the List

Forbes Technology Council members were recently asked what they consider to be the top technology innovations of the past three years. This was a great question that got me thinking. Technology has been moving at the speed of sound, and there have been so many innovations that come to mind. Yet, perhaps the biggest one yet is predictive analytics using AI-based deep learning.

As I say in the Forbes article, the ability of a computer to learn by just analyzing data without having to let the algorithm know what variables are important is unprecedented. This form of unsupervised learning is drastically changing the role of technology.

My Forbes Technology Council peers had some great responses as well, such as augmented reality; inexpensive, fast storage; real-time language translation; chatbots; brain-computer interfaces; and the cloud.

There’s been a whole lot of innovation in the tech space – from all of those mentioned in the article, and more that are evolving almost daily. Tech innovation is alive and well and changing the way we live, work, interact and evolve. It will be great to see what the next three years has in store.

Facebook Data Privacy Issue Puts Spotlight on How Data is Collected

The recent Facebook privacy issue, which affected more than 50 million Facebook users, has been front and center in the news of late, and has people everywhere questioning their social media footprints and who has access to them. It also will affect how companies collect and use private data and spur new regulatory controls.

Forbes Technology Council members were recently asked to weigh in on what the Facebook investigation might mean for all the parties involved. Some members speculated that it will require a complete overhaul of how data is collected; that in the future social media giants will need to pay users for their data; there will be more and more disclosures on sites like Facebook; and in some cases, it may impact revenue from advertisers.

While all of the above is true. I also think that the collection of this data points to the fact that data is becoming as valuable as gold. In the end, we are more alike than we think – and advancements in deep learning algorithms are enabling specific patterns to become easier to spot and target. As I mentioned in the article, despite this, the future of personality targeting must include regulatory oversight in order to protect consumers and ensure that computers don’t make decisions for us.

AI and its ability to sort through tons of data is solving all kinds of problems that would not have been possible in the past; its allowing a whole new level of personalization and targeted marketing. The key is in recognizing that behind that data is real live people who deserve to know how their data is being used.

Do you agree?

The Critical Steps to Effective Machine Learning Predictive Algorithms

On my last blog post, Getting on the Right Path for a Machine Learning Career, I talked about the skills, courses and requirements needed to pursue a career in data science. As I mentioned, artificial intelligence (AI) offers many specialized areas and it’s important to select the field that best fits with your skills and interests.

But aside from the educational component, a good data scientist needs to be a good story teller capable of unraveling and effectively communicating the story behind the petabytes of data that when analyzed reveal a story that leads us to answer questions that would have been impossible without data science.

But to reach this story teller status, a good data scientist needs to be adept at completing key machine learning steps, including exploratory analysis, data preparation, statistical analysis, programming, algorithm implementation, research, visualization and writing. Often, all of these steps are done by one person or in teams.

While each step is important to the full machine learning process, data preparation is perhaps the most essential element to effective algorithms, yet the most time-consuming task. In fact, a recent article points to the 80/20 rule, which states that most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data.

Recently, as participants in the xView Challenge Wovenware addressed all of these critical stages of the machine learning life cycle while creating our object detection model based on deep learning. With this event – and each time we start a new project – we learn new things about team work, our expertise and how to best leverage it.

But from the xView Challenge project, here are the two key steps we took and lessons learned:

Data Preparation

We spent a good amount of time preparing the dataset and creating experiments. performing tasks such as, image chipping, data augmentation and exploratory analysis, in order to create clusters of related patterns. And interestingly most of the work was done by software developers following the lead of data scientists, with little knowledge of the predictive models to be created.

Experiment Creation

Once the data was ready and we completed exploratory analysis, it was time to start experimenting. This is another task that takes time as you iterate over and over with different parameters. In the past, we created four or five experiments and from the results, decided on a final approach. For this challenge, we went crazy and defined dozens. So, we took a step back and decided to automate our experiments creation and from this, our Wovenware Experiments Factory was born. In a matter of days, we created the beta version where we could input different parameters, such as architectures, processor, augmentation methods, resolutions and lists of patterns. Based on this, in a matter of minutes we can configure our factory to run hundreds of experiments by reading recipes from our warehouse — just like a baker baking using his time-tested recipes from a little wooden box.

These two steps in the deep learning flow, are examples of how software developers can start contributing to a team of data scientists and incrementally gain the skills while studying to become a master.

In a follow-up post I will talk about the tools and techniques for data cleansing and preparation. Until then, I would love to hear your thoughts and questions.