SoftBank Group Corp, as part of its Academy of Artificial Intelligence (AI), announced on February 18 its support for Data Science for All / Empowerment (DS4A / Empowerment, for its acronym in English). This alliance aims to train and improve the skills of underrepresented communities seeking opportunities in the field of data science.

Developed by Correlation One, DS4A / Empowerment aims to train 10,000 people giving priority to Afro-descendants, Latinos, women, LGBTQ + and United States military veterans, over the next three years, providing new paths to economic opportunities in one of the fastest growing industries in the world.

The SoftBank AI Academy supports programs that complement the theoretical training of traditional technical education courses with practical lessons, including artificial intelligence and data management skills that can be immediately applied to business needs.

DS4A / Empowerment will provide training to employees of SoftBank Group International portfolio companies, including the Opportunity Fund and Latam Fund, as well as external candidates from the United States and Latin America, including Mexico.

The program is specifically designed to address gender equity and talent gaps in a field that has historically been inaccessible to many people, leading to a significant under -representation of women and Afro-descendants. Participants will work on real case studies that are expected to have a measurable impact on the operating performance of participating companies.

IDB Lab, the innovation laboratory of the Inter-American Development Bank Group, will join SoftBank and provide more than 10 full scholarships to underrepresented candidates in Latin America, while Beacon Council will offer 4 full scholarships for underrepresented candidates based in Miami.

Program participants will receive 13 weeks of data and analytics training (including optional Python training) while working on case studies and projects, including projects presented by SoftBank’s portfolio of companies. The initiative will also link participants with mentors who will provide career development and guidance. Upon completion of the program, external participants will be connected to employment opportunities at SoftBank and leading companies in the business, financial services, technology, healthcare, consulting and consumer sectors.

Program and enrollment details

DS4A Empowerment is an online program taught in English over a period of 13 weeks. Classes will be held on Saturdays from 10:00 am to 8:00 pm (Eastern Time, ET), beginning April 17, 2021.

The program registration period ends on March 7, 2021. Applicants who might consider applying include employees from the portfolio of companies affiliated with SoftBank in the region, as well as software engineers, technical product managers, technical marketers and anyone with a background in STEM who is interested in learning data analysis. To apply and learn more about the program, interested candidates can visit the official website of DS4A Empowerment .

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When Artificial Intelligence initiatives fall short, the blame is often placed on a skills gap. But there’s more to it. Does your organization prioritize these three foundational AI pillars?

Hiring the right technical talent remains a significant roadblock to Artificial Intelligence (AI) adoption for enterprise organizations. According to a recent O’Reilly survey, slightly more than one-sixth of respondents cited difficulty in hiring and retaining professionals with AI skills as a significant barrier to AI adoption in their organizations.

While the talent gap remains a large part of the dialog, this number has decreased from the previous year, signaling that other challenges are becoming top of mind for businesses exploring and deploying AI projects.

Still, the technical skills gap isn’t the biggest impediment to AI adoption, nor is it the reason so many AI projects fail. In fact, according to the same O’Reilly survey, respondents identified a lack of institutional support as the biggest problem, followed by difficulties in identifying appropriate business use cases.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Of course, this is a harder pill to swallow: It means the real challenge lies with us rather than with a limited number of professionals equipped to do the job.

3 pillars of AI project success

So how can organizations avoid the common pitfalls of AI projects? As with other technology implementations, it all comes down to proper company-wide training, the production environment, and having the right foundation in place. With these three pillars in place, you can start realizing the business value of AI earlier.

1. The right foundation

Successful AI projects require three things:

• Data scientists must be productively tooled, have domain expertise, and access to relevant data. While AI technology is becoming well-understood—from handling bias prevention, explainability, concept drift, and similar requirements—many teams still fall short here.
• Organizations must learn how to deploy and operate AI models in production. This requires DevOps, SecOps, and newly emerging AIOps tools and processes to be put in place so models continue working accurately in production over time.
• Product managers and business leaders must be involved from the beginning, in order to redesign new technical capabilities and decide how they will be applied to make customers happy.

While education and tooling have improved significantly over the last several years, there’s still much room for improvement in actually operating AI models in production. In that vein, product management and user interaction design are becoming common hurdles in AI success.

These problems can be addressed by investing in hands-on education. Outside the classroom and conference halls, professionals from all across your organization must get experience actually working on AI projects, understanding what they can do and how the technology can push your business forward.

2. Company-wide collaboration and training

Certainly, talent is part of the problem, but it’s not just data science talent that’s needed. The root of the problem usually lies within business and product expertise. As important as technical talent is, understanding how AI will work within a product and how it translates to better customer experience and new revenue is just as critical – and that responsibility doesn’t fall solely on the R&D team.

For example, we have algorithms that can read X-rays as accurately as humans, but we’re just now beginning to integrate this capability into the clinical workflow. If doctors and nurses aren’t trained on how to use this technology to streamline their workflow, it holds no value for them or their patients.

Being able to train and deploy accurate AI models doesn’t address the question of how to most effectively use them to help your customers. Doing this requires educating all organizational disciplines – sales, marketing, product, design, legal, customer success, finance – on why the technology is useful and how it will impact their job function.

Done well, new AI-enabled capabilities empower product teams to completely rethink the user experience. It’s the difference between Netflix or Spotify adding recommendations as a side feature versus designing their user interface around content discovery. It makes a big difference, but it also takes a village to achieve. That’s why company-wide buy-in spearheaded by the executive team is vital to AI success.

3. Proper production environment

Not all production environments are the same, so not all outcomes will be the same. It’s important to understand the limitations of AI projects based on the talent, infrastructure, and data you have and to set clear expectations from the get-go.

For example, a recent research paper (done for the ACM Conference on Human Factors in Computing Systems (CHI) series of academic conferences) explored a new deep-learning model used to detect diabetic retinopathy from images of patients’ eyes. Scientists trained a deep-learning model to identify early stages of diabetic retinopathy in patients from pictures of corneas from eye exams over the past several years. The goal was to reduce blindness, a symptom of the disease when left untreated.

The paper describes what happened when the same accurate, effective model was used in clinics in rural Thailand: The machines used to take images of patients’ eyes were not as sophisticated as the ones used for training the model. The exam rooms used were not completely dark, as the trained model assumed. For some patients, taking another day off for follow-ups or additional testing wasn’t a viable option. To boot, not all doctors and nurses were trained to explain why this new test was necessary.

The lack of proper infrastructure and cohesive education for hospital staff, coupled with an understanding of practical limitations, is a prime example of why AI projects fail.

The AI talent gap will remain a challenge for the next few years, as education catches up to industry. But in the meantime, there are steps organizations can take to ensure their AI projects prevail.

It’s not enough to just train your models – train your organization, too. Take the time to educate every facet of your business on why you’re tackling a certain AI project, how it will impact their role and the customer experience, and what the expectations are.

The right talent will come – will your organization be ready to use it?

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Data Scientist’ was recently named the most sought-after role from a recruitment perspective in the US according to figures from Glassdoor, and it’s been at the top spot for the last four years. Businesses are increasingly looking at those in this role to deliver business growth, and, in turn they are seen as imperative to the future of the organisation.

But what can Data Scientists offer, and why are they now so important to an enterprise? And how can the profession be formalised for those within the role as well as those looking to recruit them? Will bringing in an industry-approved certification programme bring the credibility that it needs and give Data Scientists a holistic view of their own professional development?

The growing need for data scientists

• Are data scientists going out of style?

In recent years, Data Scientists have predominantly come from academic background – having studied quantitative disciplines such as economics, machine learning, statistics or operations research, to name a few. Yet as the profession has developed, many universities have founded specialist degree programmes in Data Science.

In a Data Scientist role, professionals are expected to work with business leaders and key decision makers in order to solve business problems. This is typically executed by harvesting, analysing and then understanding data, all to provide insight and recommendations that help the organisation to move towards whatever goal it is trying to achieve.

This is a position which is very much in demand when today’s broader business context is brought into view. IDC has reported that the global volume of data generated is set to increase tenfold – to 175 zettabytes (ZB) a year by 2025. Sixty per cent of this data will be produced and managed by organisations, and so they must employ those who have the skills to work with it, spot trends that may emerge and therefore advise the business on how to act upon the stories that the data tells.

In order to do so, Data Scientists use a variety of data platforms and programing languages. In fact, modern applications of Data Science range from traditional transactional data analytics, right through to natural language processing, machine learning and computer vision. Data scientists are working across many industries, driving insights and outcomes. In the healthcare sector Data Scientists are using cognitive computing technologies in order to deliver personalised and precision medicine to patients, as just one example.

It’s important that Data Scientists develop a good level of business acumen in order to fulfil the full potential of their role. Only then will be able to identify a business-specific problem, work up a hypothesis, test conclusions and determine appropriate methods to influence strategic choices through data. Furthermore, to then take this insight into the boardroom and effectively relay the findings and deliver solid consultancy requires strong communication and visualisation skills.

Data Scientists driving business value

In plain terms, those enterprises that invest in the people with the skillsets and tools to understand the data and provide this level of insight now, will position themselves for growth going forwards. And the demand for these professionals will only continue to increase with the growing amount of data being produced.

Data Science is still classed as a new profession and it’s one that’s becoming more accessible to a range of those either already in a technical field, or those just entering the world of work. Individuals with relevant experience can reskill and move into the Data Science field, whilst those studying can opt for a degree to take them into this career – both are attracted to the role due to its growing importance for business growth and the ability to impact results, as well as the lucrative salaries.

However, due to the rapid rate at which the Data Science landscape is evolving, fuelled by the demand for these professionals from the corporate world, we are now seeing a talent shortage emerge. Therefore, having several possible routes for individuals to enter the profession is helpful but doesn’t necessarily help businesses to identify, train and retain these employees. This is only amplified by the disparity in the experience and skills that individuals can have across the profession, which leaves employers struggling.

• Machine learning & data science: what it means and what businesses are doing to stay ahead

The requirement for a certification programme

In order to combat this, through a certification programme, Data Scientists can not only differentiate themselves in the labour market, but can also give themselves enhanced visibility to recruiters and employers, clearly stating their experience and the value they can bring to a business. These organisations can then easily identify the best candidates for new roles, but also ensure that current or prospective employees are working to the required high standards that the job demands.

To ensure consistency, compliance, and service quality across the board, a global certification programme is essential for the Data Scientist profession. This would give employers the tools to not only identify potential talent for their workforce via an objective and reliable framework, but would inform the wider industry on how a certification can be utilised by some of the world’s largest organisations.

The recent ‘Facing the storm: Navigating the global skills crisis’ report further emphasises the value of these guidelines, with certification programmes having the third highest impact in terms of the development of policies that bolster labour market competitiveness. However, there is still a long way to go to tackle the skills shortage as adoption of is still very low, at just 24 per cent.

Winning the war for talent

• Business intelligence, advanced analytics & data science: Make the right choice

Over time, existing professions have realised the value of certification. For instance, Business, Enterprise and Solution Architects can attain a specific, peer-reviewed, vendor-neutral and globally recognised credential through The Open Group OpenCA program. Following in the footsteps of the architect’s programmes, there is now a dedicated framework for Data Scientists. Working alongside IBM, The Open Group and its members have developed the Open Data Scientist certification programme which is already available to professionals and employers in this field.

As AI marches on to infiltrate all areas of a business, the demand for these skills and therefore an accompanying framework will continue to grow. In order to start making insightful sense from their rapidly growing data sets and to support the business by facilitating data-driven decisions, organisations can look to a Data Science certification programme for the relevant tools. This will arm organisations to win the fight for the best talent. But, importantly, it will also make sure that Data Scientists are professionally empowered with the skills and experience required to fulfil this crucial role, that will drive the businesses of tomorrow.

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Karen Roby talks with Columbia professor Sameer Maskey about the lack of trained AI talent and why he believes underserved communities may be the solution to the problem.

Nearly every industry is using artificial intelligence in one way or another to improve business outcomes. AI holds great promise as new and exciting applications are discovered, but there is a catch. There aren’t enough trained AI engineers capable of carrying out the work. Karen Roby talks with Sameer Maskey, a professor of AI at Columbia University and founder of Fusemachines, about the shortage and what can be done. The following is an edited transcript of the interview.

Sameer: The requirement on the number of engineers from the AI perspective is in the millions, and what you have is a couple hundred thousand,  some say it’s 300,000 some say it’s 600,000, but what’s required is multiple millions of numbers, engineers who know AI and who can build AI products, build algorithms, implement algorithms and so forth. So that’s the general numbers that are out there talking about shortages.

What you can see is the foundations of AI is a lot of machine learning. So a lot of the AI products that get built these days, they are built using machine learning algorithms, and machine learning is a lot of math. It’s a lot of parameters, statistics, calculus, linear algebra, and so forth. And unlike learning a new programming language like Python, which you can sort of pick up in a couple of months, at least the basic versions of it, there’s no shortcut of learning math, right?

And especially in undergrad computer science especially, it has been the case that a lot of focus is in programming algorithms and so forth, but less so in math. It’s starting to change as there are more and more computer science students who want to learn AI and they realize there’s a lot of math that you need to know before you start initial learning. So there’s this catch up across the whole globe around all the computer science engineers on the knowledge and math to be able to do machine learning. And I think that’s part of the reason this lack of talent and machine learning engineering because they need to know math.

Karen: So how do we get more people trained in this special field?

Sameer: There are a couple of things that can be done and is being done as well. It’s from the education perspective. For example, Columbia has started data science institute where the mix of program math and applications to build products is being trained to students and more and more universities who are opening these data science programs and masters of data science and so forth. So one of the things that can be done and is being done is having more and more of these data science and AI programs with this very specific focus on training engineers, our students, on these.

The other portion that we are starting to see is these sort of mini-programs where it might not be a two year master’s program but a one year program to sort of do hyper-focused courses on machine learning, deep learning, and computer vision, natural language processing, just focused on these courses for them to learn here. So this kind of a mix of full-on master’s programs and data science, these probably one-year programs, educational programs for training the talent up would be good to create more talent, to build more AI applications.

Karen: AI engineers can make a really nice living while working in a really exciting field. So are the students that you teach, do they seem really enthusiastic about their futures?

Sameer: Students are super excited about learning AI because they see a lot of opportunities right now, and as you pointed out, there’s tremendous growth. There was a PWC report recently where the AI contribution to our GDP is going to be in trillions of dollars over the next decade and so forth. So everybody can see that pretty much every company is starting to think about either how to build something with the AI or how to apply AI in their products or how to use AI to improve internal processes and so forth. So to do any of this, you need this skill set. And everybody also knows there are not enough engineers, so the demand for AI engineers is through the roof.

And because of the supply and demand not matching this, the engineers are also getting paid quite a bit. And so the students, they see their fellow students making quite a bit of money on salaries after they get trained up on AI, so everybody’s really excited to, “I want to learn more on machine learning and I want to get into the workforce and build AI products.” So they’re super excited.

Karen: At Fusemachines, you guys feel that the answer to the talent shortage is through democratizing AI training. So talk a little bit about why you’re looking to the underserved communities.

Sameer: There’s quite a bit of talent in underserved communities in the US and around the world who may not have gotten the right kind of exposure or right kind of experience or that kind of learning environment to really flourish. And we are very keen on finding those talents in like poor counties in the US, poor cities in the US, in the inner cities of the US, and underserved communities around the world, from the developing world and so forth. And you really find these gems of the talent and give them an opportunity to learn AI with the right kind of framework, right kind of learning tool and right kind of teachers to enable them to get to a point where they’re equally good as other engineers from the best schools. And if we do that, then I think we’ll be able to scale faster on being able to train engineers in the volumes that we require.

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