What the H-1B Visa Issue Means for Tech Advancement

New changes to the H-1B visa program this year may be spurring renewed concern about exactly how the U.S. can fill a critical skills gap.

On one hand, given the president’s Executive Order, “Buy American, Hire American,” the government is reforming the H-1B program to, among other things, give priority to only the most highly skilled and highest paid H-1B candidates. While bringing in qualified and experienced professionals may seem like a good thing, it’s still not enough to fill critical positions in the tech industry to meet the growing demand for advanced software. And, the government has added restrictions to the H-1B application process that make it harder for all skilled workers from other countries to obtain visas to work in the U.S.

Unfortunately, the tech industry is headed for a perfect storm when it comes to recruiting skilled tech professionals. Many foreign workers are not able to work for U.S. firms, despite the fact that the country is already facing a critical shortage of qualified workers.

One key area where demand for skilled workers just can’t be met is data science. Highly-skilled data scientists are needed in droves to develop and continually maintain algorithms that teach AI programs to recognize patterns and predict behaviors and other outcomes.

Part of the problem stems from the fact that there are just not enough programs to educate data scientists. According to the University of California, Riverside, only one-third of U.S. universities offer data scientist programs and most of them are offered to graduate students; only six offer these programs to undergraduates. It is estimated that by 2020 there will be 490,000 data scientist positions but only 200,000 skilled professionals to fill the openings.

Recent H-1B visa developments

The H-1B visa could be one way to help fill the skilled tech worker gap, yet recent regulations have added restrictions to the application process. The government is taking longer to approve the visas and denying more workers. The application acceptance rate has also been impacted, dropping 13 percent from the previous year (74 percent vs 87 percent), making it the lowest percentage in 10 years.

Other changes include a new Labor Condition Application (LCA) form that employers of H-1B applicants need to submit. Additionally, earlier last fall, the government had suspended premium processing for certain H-1B petitions which has been used to expedite processing when employees move to a different location in the same company or to a new company, but premium processing has since been reinstated.
All of these changes are creating an air of uncertainty and, for the most part, are making it harder for companies to get the specialized tech expertise they need.

The answer is close by: nearshoring

To address the worker shortage caused by a lack of skilled IT professionals – from the U.S. and overseas – many companies are turning to nearshore providers. Nearshorers offer the next best option to having employees on staff – close proximity to clients which facilitates strong communication and collaboration. The ability to work closely with their clients, and share a common language enables these providers to become immersed in and fully understand a client’s business environment – which is critical to developing and maintaining advanced IT solutions.

Most nearshore firms share the same work ethic and high industry standards as their mainland U.S. counterparts, and hire very skilled data scientists, many of whom have attended U.S. universities.

A great combination: H-1B visa program and nearshoring

Experienced IT professionals will continue to be in demand by all types of companies in all industries, as, for example, advances in AI enables them to gain critical insight, improve customer experience and outcomes and make better decisions. Yet, as the demand for data scientists (and other tech experts) continues to grow and greatly outpace supply, companies need to find innovative ways to meet their staffing needs. While the H-1B visas offer a good solution by bringing diverse perspectives to software development, companies need to supplement in-house talent with other approaches as well, given the limited numbers of visas that are being issued.

Nearshoring provides an ideal way to fill this need; it offers the best of both worlds – providing the geographic and cultural closeness that supports the in-depth communication and collaboration needed for high-level software development, while also offering broader perspectives from professionals from other geographical areas. By combining the H-1B visa program with services from a nearshore provider, companies will have a winning talent recruitment strategy that puts them on the fast track to success.

Caribbean American Heritage Month Recognizes Value of Diversity

This month marks the 13 Anniversary of National Caribbean American Heritage month and this year’s theme, Contributing to the American Landscape, couldn’t be more fitting.

Today, Caribbean Americans, as well as U.S. territories located in the Caribbean, are contributing greatly to the U.S. in many ways, through tourism, cultural diversity and increasingly through business expertise.  And, despite set-backs from the hurricane, Puerto Rico is no exception.

While tourism continues to be a major part of our economy, what is happening in the business offices, manufacturing plants, and board rooms across the island is a silent surge in business as the mainland increasingly realizes the expertise, efficiency and cost-savings that can be gained by working in and with Puerto Rico.

As mainland firms increasingly nearshore work – from IT development to manufacturing – they are greatly contributing to the growth of Puerto Rican firms, while filling much-needed roles caused by a growing shortage of tech talent on the mainland.

And, more than five million Puerto Ricans live on the mainland and continue to be proud of their heritage. Many successful state-side Puerto Ricans are committed to serving as mentors to Puerto Rico-based businesses and willing to invest in the island, as well as in its start-ups and firmly established businesses.

As we mark Caribbean American Heritage month, it’s important to note how far Puerto Rico has come in becoming  a prime destination for entrepreneurial businesses, providing talent, creativity and diversity to businesses – both on the island and the mainland. Through continued investment by mainland businesses, collaborative initiatives like nearshoring, and support of government in the form of tax incentives and other programs that spur growth, we can ensure that this Caribbean territory becomes a key contributor to industrial growth in the U.S.

Teaching Chatbots to Do the Right Thing

Along with the great opportunity that AI provides comes great responsibility. As we develop and train algorithms, Natural Language Processing and other forms of AI that chatbots and other applications use to augment – or in some instances, replace – the role of humans, we have a responsibility to make sure that we are creating ethical human stand-ins and that we are not inadvertently imbuing them with our own biases.

This was a topic I wrote about in a recent article on Forbes.com, “Expanding on Asimov’s Laws to Create Responsible Chatbots.” Creating ethical, responsible chatbots and other smart apps requires taking a very thoughtful approach to AI development. Part of that includes hiring a diverse team of experts involved in the development, training and maintenance of these programs, enabling these programs to be more sensitive to – and reflect and be respectful of – different cultures and ways of thinking. In addition to data scientists and data engineers, the team should include natural language experts, who can provide chatbot responses that are polite, correct and appropriate, and reflect the normal flow of conversation. In order for chatbots to respond accurately, programs must be built from the ground up in the language that they will be used. This will avoid any inadvertent errors or insensitivities that could result from poor translations. Additionally, companies need to continually monitor these programs to make sure they are not learning inappropriate language and other wrong things in the course of conversing with humans.

It’s critical that organizations are transparent when it comes to AI programs. While it may not always be obvious if someone is chatting with a bot online, it’s the company’s responsibility to inform users that they are conversing with software and not a human. And, companies should recognize that it’s not always appropriate to use chatbots. There are sensitive conversations, for example, such as discussing medical diagnoses, when people should speak with humans instead of chatbots.

Chatbots enable companies to the increase customer experience by being more attentive and responsive to their customers’ needs. By being respectful of these customers, treating them ethically and creating chatbots that will add to rather than detract from their experience, companies will be able to effectively harness the power and value that these AI programs offer.

Artificial Intelligence (AI) Glossary Of Terms

Artificial Intelligence (AI) is increasingly being used in a variety of industries to solve critical business challenges, but despite its growth, it’s still a nascent technology that continues to evolve. As such, there can be some confusion about AI terminology. To provide greater clarity, the following glossary, while not scientific or authoritative, provides a basic understanding of widely used terms and their definitions.

One term absent in this glossary is Robotic Process Automation (RPA) because it is not a type of AI, even though some people mistakenly categorize it as AI. RPA is a software robot that automates rote, repeatable tasks, such as insurance claims processing. The software is not learning, but rather repeating the same process over and over.

The AI Litmus Test

In order to be classified as AI, the software needs to incorporate some type of “intelligence” that is not explicitly programmed and have the capability to continually learn by recognizing patterns in the data.

The Glossary

AI Algorithm – A set of mathematical instructions that are able to learn from data. Data is run through the AI algorithm to provide the AI solution with the intelligence to look for and identify patterns to answer business questions. Data scientists train AI algorithms to predict specific outcomes.

Chatbot or Bot – A type of AI program that can mimic human communication in speech or text and is based on an area of AI called Natural Language Processing (NLP). Chatbots are often used in call centers or for customer service support on websites to answer basic questions or engage with customers.

Computer Vision – A field of AI that strives to gather insight from images or videos. It can be used for image classification, object detection and object tracking, among other applications.

Crowdsourcing Services – These services distribute, among many people, the work involved in collecting and preparing data, including image recognition, data normalization, and algorithm training for machine learning, among other tasks.

Data Engineers – Professionals who are trained to collect, prepare and manage the big data that will be used by AI programs.

Data Scientists – These highly skilled professionals design and continually train mathematical algorithms to be able to answer business questions or help predict outcomes. They follow the scientific method, formulating hypotheses and carrying out experiments to prove their assumptions, leading to new discoveries and insights.

Deep Learning – This highly cognitive field of AI encompasses the next generation of machine learning, where machines can teach themselves by making connections or comparisons in the data that are often overlooked or unknown. Deep learning software requires a huge amount of data and advanced processors to get accurate outcomes.

Image Recognition – The process of detecting and identifying a variety of objects in images and videos using computer vision.

Insights as a Service – A cloud-delivered, pre-built approach to AI, which allows a company to purchase previously sourced data — without owning the data or the AI solution – to help support a business case it is trying to solve. This approach is helpful if a company is looking for insights into a universal question that does not require personalized data.

Machine Learning – This is a form of AI that is capable of learning by recognizing patterns in identified variables in the training data.

Model as a Service – In this model companies collect their own data, but turn to a cloud-based provider to develop, re-train, maintain and operate the AI algorithm. This approach particularly makes sense if a company has very customized or proprietary information, but not the in-house expertise or resources to build or manage an AI solution. A churn model predicting which customers might leave a company is an example of this.

Natural Language Processing – An AI discipline that enables computers to process and understand human language.

Artificial Neural Networks – This is a component of an AI solution that is similar to biological neural networks. These networks use stages of learning to give AI the ability to solve complex problems by breaking them down into smaller problems.

Pragmatic AI – This is the type of AI that is most prevalent today. It applies AI principles to help answer business questions and predict outcomes for tasks, such as predicting the stock market or customer churn.

Pure AI – This type of AI is futuristic, with the ambitious goal of building human-like AI programs that have the same – or even superior – intelligence and capabilities as humans. For instance, this could refer to human-like robots that can make better decisions than humans and pass the Turing test.

Turing Test – A test developed by Alan Turing, to determine a machine’s ability to exhibit intelligent behavior equivalent or indistinguishable from a human.

Predictive Analytics – Based on a large amount of historical data, these AI programs are designed to predict future outcomes and behaviors based on past data.

Pre-packaged Data – Pre-packaged data sets that can be purchased. They offer a quick way to gather training data for AI algorithms.

Private Crowd – A private group of data specialists, usually under NDAs and personally known to the employer, who help with data collection, identification, labeling and preparation of training data or images. Private crowds provide greater accuracy and business-specific knowledge than more generic public crowds (or crowd sourcing).

Public Crowd – A group of non-professionals who can help collect, identify and label large data sets for AI training, but don’t have any knowledge of the company’s specific business. These people are usually not under NDA and not personally known to the employer.

Microsoft Girls Steam Challenge Shows The Future of Tech is in Good Hands

On May 23, I had the honor and privilege to be part of the judging panel for the first-ever Microsoft Girls Steam Challenge in Puerto Rico. At a time when it’s more critical than ever for women to enter the technology field and take on positions that have been traditionally dominated by men, the event served to spark interest and enthusiasm among girls in middle- and high-school, and show the world that woman can code with the best of them.

The Microsoft Puerto Rico Girls STEAM Challenge 2019 brought together girls from public and private schools in grades six to twelve, across the island, to develop projects in the areas of STEAM (Science, Technology, Engineering, Arts, and Mathematics). The goal was to help them recognize the impact they can have on society by being part of the new generation of female technology innovators.

Using Microsoft technologies exclusively, the girls were asked to create a two-minute video and a presentation on Microsoft Sway. The 10 finalist projects presented their projects, and the five winners of the Superior category have the opportunity to participate in the Innovation Learning Week, a week of technical training. The global winners received a Microsoft Surface computer.

When I was invited in March to be one of the judges, I didn’t know what to expect, but was ready and willing to contribute and give back, since promoting technology education is part of Wovenware’s Corporate Social Responsibility initiative. In addition, when I was younger, I had the opportunity to participate in similar programs at the university level and I was curious to hear what was on the minds of young girls in a time overloaded with information and distractions. Little did I know how inspiring these insights would be.

After attending an earlier meeting with the other judges to go over the rules, I received the summary of the eight projects I was assigned to review. The projects needed to include at least two disciplines covered in STEAM, one of them being technology, and when reviewing them I had to take into consideration key attributes, such as innovation, methodology, results and social impact. The themes presented were: health, economy, education, citizen safety and environmental sustainability.

I was pleasantly surprised by the depth of understanding and creativity in each project I reviewed. I received them on a Saturday afternoon and quickly became absorbed in them for the day. Some of the topics included: a homemade water filter and purifier using aloe vera, inspired by the challenges of hurricane Maria; seismic prediction models using oceanic and seismic events data; and stroke detection using facial recognition and machine learning. I got the opportunity to see the semifinalists present live, Their understanding of the subject matter, energy and engagement skills were on point and quite impressive. These girls, many only 14 or 15-years-old, made it perfectly clear that the next generation of innovators has great ideas and they deserve to be heard.

At the award ceremony I was able to review other finalists’ projects and talk to these amazing Puerto Rican young women who are defining our future. Once the official Microsoft winners were announced I had the opportunity to go on stage and present a special recognition from Wovenware to the girls that demonstrated unparalleled passion, perseverance, discipline and the curiosity factor that pushes you to a higher level.

While this opportunity reminded me of my university experiences with technology competitions, it also demonstrated just how far we have come. In my experience years ago, the room was filled with young men, and only a small handful of young women. It seems we are certainly coming full circle and the future of technology will be led by both men and women.

I’m extremely honored to have been invited by Keren Henriquez, Director- Education & Corporate Social Responsibility at Microsoft to be part of this important event; and I’m hopeful that the next generation of technology innovators — comprised of really smart women and men – is in good hands.

RPA and AI: A Marriage Made in Heaven

Companies have been diving in head first to take advantage of the benefits that RPA and AI offer, albeit for very different purposes. RPA, which automates tedious, repeatable tasks, is enabling companies to increase productivity, and reduce costs and errors, while freeing up staff to focus on more meaningful activities. Companies on the other hand, are using the cognitive functionality of AI to recognize patterns in data, gain business insights and predict behaviors and outcomes.

But it doesn’t have to be an either/or situation. In fact, RPA can be used to augment AI and vice versa. I recently wrote about this topic in an SD Times article, “Is Smarter RPA on the Horizon?” For example, by integrating RPA with an AI system, it is able to conduct higher-level tasks. If a caller dials into an automated system to retrieve an account balance, an RPA system can provide that information to the customer by “reading” the data. Now, imagine if it can connect with an AI system which tells it to extend a specific offer to the customer based on his/her interests, past history and other key customer-specific information. Thanks to AI, the RPA system is able to offer a higher-level response based on intelligence.

At the same time, RPA can provide value to an AI system. AI requires lots and lots of data, the more the better to achieve the most accurate outcomes. And RPA can be a huge help in this area. By conducting screen scraping to aggregate website data, RPA systems can automate and help expedite the data collection process needed to feed the AI algorithms. This enables data scientists – who are in short supply – to focus on the key tasks of writing the algorithms and training the AI programs.

When developing RPA and AI programs that are able to work together, companies should make sure they have a very collaborative development process. For example, they should log all actions and share that information with all teams. It’s also important to have close oversight of RPA development because its fast processing speeds can easily allow a mistake to cause a lot of problems in a short period of time.

The RPA/AI partnership represents the next level of maturity in AI development, and we expect to see more companies combining these technologies for mutual benefit. That way it can make AI development faster and RPA programs “smarter.”