Last week, we held our Bridging the AI Skills Gap webinar featuring Varun Ganapathi, head of our AI and Data Engineering and Mat Leonard, Product lead for our School of Artificial Intelligence.
The conversation centered on five key areas:
AI vs Machine learning vs Deep learning
How companies are using these technologies today?
Skills gap and talent shortage
Common use cases and outcomes
How to overcome the skills gap
AI, machine learning, and deep learning are easily confused and overlap with each other. The panel did a good job of breaking down the definitions:
AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
“AI is any technology that enables a system to demonstrate human-like intelligence,” explained Varun Ganapathi. “Machine Learning (ML) is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”
Today, different AI technologies are finding a place in various industries. For instance, Banking and Financial Services companies are using chatbots or virtual assistants to help customers with routine tasks like scheduling payments, automate most frequently asked questions. Predictive Analytics’ to reduce the risk of loan defaulters. Machine learning to identify patterns of transactions that might indicate fraudulent activity.
The expanding applications for AI continues to create a shortage of qualified workers in the field. AI is moving fast and enterprises need talent today. However, not just any talent. What once was a shortage of coding and software engineering expertise has now evolved into an overall shortage of skills in machine learning, robotics and algorithmic engineering.
“If you’re considering working in AI as a data scientist or machine learning engineer you need to find a good starting point, and it starts with knowing Python, C++, and learning mainstream deep learning libraries like TensorFlow or PyTorch,“ said Mat Leonard, Product Lead at Udacity’s School of Artificial Intelligence.
AI and machine learning are driving innovation and transformation. They are embedded in how we sift through large volumes of data and content and how we interact, connect, and buy today. They are the engines underlying many of our products and services.
Hear more from Varun and Mat about steps your organization can take to embrace AI and close the skills gap. Listen now.
Artificial intelligence (AI) enters our lives in many different ways. AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning. Deep learning is a branch of machine learning utilizing giant neural networks and massive data sets. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
Artificial intelligence is growing exponentially in all the major sectors, including health, social media analysis, self-driving cars, language processing and others. The AlphaGo victory is just one of the signs of amazing things to happen. The understanding of artificial intelligence opens lots of opportunities.
If you’re considering working in AI as a data scientist or machine learning engineer but need to find a good starting point, here are a few things to consider in your learning journey:
Get your mathematics strong. You should have some appreciation of the mathematical underpinnings, especially linear algebra and calculus. Specifically, you’ll need to be comfortable with matrix multiplication and partial derivatives.
To get a development role on an AI team, be sure to have at least one to two years of software development and machine learning experience under your belt. This can include building your own projects or working at a company driving key projects such as image or text classification. A great place to look for machine learning projects is arXiv where researchers often publish their papers. You can sharpen your skills by implementing models and systems from papers that capture your interest.
Learn Python.This handy programming language is the tool of choice for most machine learning engineers and data scientists. Python’s syntax is relatively easy to pick up and it has a vibrant and helpful community. The language also has excellent documentation and tons of training resources. With tools such as Jupyter notebooks and libraries like Numpy and Pandas, Python has become the first choice for developing machine learning and deep learning applications. Outside of machine learning, Python is useful for developing websites, videos games, and more. Udacity can get you coding in Python and building your first neural network in just three months!
Learn mainstream deep learning libraries like TensorFlow or PyTorch. Most deep learning systems are built in either TensorFlow or PyTorch, Python frameworks that provide APIs for defining and training deep learning models. You’ll want to be experienced with at least one of these frameworks as most AI teams are using them for research or product development. You should also consider joining the machine learning community.
The biggest area of AI research today seeks to enable computers to make inferences from complex data. Techniques to do this are termed machine learning (ML). AI and ML are large and rapidly-developing fields. While it’s impossible to capture their full potential in a this blog post, we’d like to invite you to Mat’s workshop on Natural Language Processing at the Global AI Conference on January 24th from 2pm – 6pm PST in Santa Clara, California.
The annual AI Frontiers Conference is a three-day conference designed to deliver the latest breakthroughs, trends and prediction in AI to practitioners, academics, businesses and startups. The conference recently took place at the San Jose Convention Center from Nov. 9 to 11, 2018, bringing together experts from AI giants such as Google, Facebook, Microsoft, and AI rising stars like OpenAI, Uber, and Udacity. We had an opportunity to present one of the AI workshops on natural language processing, as well as attend the conference, and wanted to share our top three takeaways from the conference.
1.) AI is prevalent across industries
AI no longer refers to theoretical research at academic institutions or R&D labs; instead, it is a foundational technology that is disrupting society and driving innovations in key industries. From the way we get to work, to how doctors identify and treat diseases, AI is poised to forge a future of endless new possibilities. Some key industries taking advantage of AI include healthcare and finance. For example, in healthcare AI is used to predict diseases, identify high-risk patient groups, automate diagnostic tests and to increase speed and accuracy of treatment. It can also be used to improve drug formulations, predictive care, and DNA analysis that can positively impact quality of healthcare and affect human lives. Another key industry is Finance. Banks are already using AI to streamline their formerly manual processes for tracking data, saving time and maximizing cost benefits. The new horizon? Leveraging AI beyond internal processes to inform consumer interaction. As the finance world grows and develops with this technology, the next step is machine learning that changes and adapts to improve fraud detection and provides smarter customer service by conversing with users every day. By using AI to inform both consumer-facing and internal processes, the potential return on investment can be huge.
2.) AI redefines what it means to be human
Dr. Kai-Fu Lee has been at the center of the AI revolution for more than 30 years. For his Ph.D. thesis at Carnegie Mellon University, Dr. Lee developed the world’s first speaker-independent, continuous speech recognition system. Today, he is Chairman and Chief Executive Officer of Sinovation Ventures, as well as President of its Artificial Intelligence Institute. He spoke at the conference and his message was that AI is giving our society a wake-up call. Currently, so much of our time is spent on busywork and repetitive work, which will largely be automated in the age of AI. Yet,key attributes such as creativity and empathy cannot be substituted by machines or data. Organizations in the world of AI will require people to excel at connecting with others and gaining people’s trust. Many transactions are already occurring online, but high-end corporate sales will require the ability to build long-term customer relationships. “My advice to employees would be to become lifelong learners, always looking for the next skill and never believing that the next 10 years will be like the previous decade,” he stressed. There’s a widening skills gap between traditional and machine-augmented work, but it also creates a real need for new training, new types of experts, and, ultimately, a shift from the workforce we know to a workforce open to new possibilities, as far as new skills, productivity, and contributions by humans made hand-in-hand with machines.
3.) AI goes mainstream faster than imagined
Some of the biggest brands on the planet are placing huge bets on artificial intelligence, betting on everything from face-scanning smartphones and consumer gadgets to computerized health care and self-driving cars. It’s also worth noting that AI has quickly gone mainstream in popular consumer devices such as Apple’s Siri, Amazon’s Alexa, and Google’s Home Assistant. And this trend is happening faster than many could have imagined just a few years ago. As companies embrace the transformative potential of AI, they have been snapping up all the available talent from the relatively small pool of scientists and technicians trained in artificial intelligence, and its sub-disciplines, machine learning and deep learning. As the scarcity of people with the requisite knowledge and abilities has deepened, those companies have been cultivating efforts to up-skill AI skills across their workforce. Making a success of AI in an organization ultimately rests upon diversity: diversity of thinking, of personnel, and of skillsets. On-boarding team members from across the organization, maintaining a critical and inclusive hiring policy, and implementing a cohesive workforce transformation initiative to up-skill and re-skill personnel are vital to bridge the skills gap.
The conference provided a front-row seat of the frontiers of AI and machine learning and highlighted some extraordinary breakthroughs in various industries. As AI technologies become a reality, companies and their workforce must keep up –
And they must do so quickly.
Find out how Udacity Enterprise is helping companies transform their workforce to remain competitive.
A recent article from the University of California’s Chief Innovation Officer, about the impact of disruptive technologies on jobs and skills, poses critical questions about how we connect learning to jobs—today, and in the future.
Everyone from politicians to policy makers, utopianists to university professors, innovators to investors, is talking about the future of work, the fourth industrial revolution, and the automation age. It’s hard to avoid these topics, and if you’re between the ages of, say, 16 and 80, you probably shouldn’t avoid them.
Our work lives are changing, and depending on how we manage the transition, this could either be a new golden age, or a serious shock to the system.
At Udacity, we’re engaged in helping lifelong learners across the globe empower themselves through learning, in order to build rewarding lives and careers. As such, we’re acutely aware of the looming changes—the theories around how it’s going to happen, and what it’s all going to mean.
We engage every day with innovators, educators, students, employees and thought leaders, to better understand what education needs to do, be, and represent as we move forward. We work with recruiters, hiring managers, entrepreneurs, and executives, to better forecast what skills will be needed, where the demand will be, and what career advancement will look like in the days, years, and decades to come. We collaborate with individuals, startups, and global corporations, to better understand how and where the work of the future will happen. In short, we spend a vast amount of time learning from anyone and everyone about what the future holds, and how we can best prepare our students to succeed.
We listen, we talk, we watch, we ask, and we read.
One article that recently impressed us for its ambitious scope, rich degree of insight, and clear-eyed understanding of where the world is heading, is a post by Christine Gulbranson, the Chief Innovation Officer for the University of California System. The article is entitled The Future of Work: The Impact of Disruptive Technologies on Jobs and Skills. Here is a sample of the wisdom Gulbranson shares in this provocative and timely piece:
“It’s not difficult to make some basic calculations about what skill sets will be needed in the future: automate predictable manual labor jobs and the skills demanded for such jobs decreases. More automated factories will increase the demand for hard skills in mechanical engineering, software architecture, coding, algorithms, data structures, data analysis/data science, and machine architecture/design. Increasing gene editing and robotic surgery will increase the demand for software engineers and mechanical engineers who also have medical skills. Move to IoT cities and policy makers and lawyers will need to understand coding, software architecture, economics, and more, on top of what they’re expected to know today.
Clearly with a rise of connected devices and infrastructure, machines, AI, spatial computing, blockchain, and autonomous vehicles, there comes an increase in demand for STEAM skills. However, sitting on top of hard skills is a deep and strong layer for cognitive, analytical, and soft skills. Employers won’t be looking for a degree that signifies what a candidate knows: they will be looking for someone who can learn, combine and analyze, problem-solve, create, and adjust.”
It’s that last sentence that especially resonated with us, because this echoes exactly what we hear directly from employers every single day. The pace of modern business and the rapid advance of technology have significantly altered the hiring landscape in such a way that characteristics such as agility, growth mindset, adaptability, creativity, and grit have emerged as the most important factors in predicting a successful hire.
That’s not to say that acquired skills don’t matter—they do!—but the ability to learn new skills and apply them has become just as important as the skills you already possess.
This is also not to say that educational pedigree doesn’t have a place any longer—it does—but what constitutes credible pedigree is changing rapidly. As we’ve learned in the years since first launching our Nanodegree programs, a Nanodegree credential fulfills a dual role. In addition to affirming your skills acquisition, earning a Nanodegree credential stands as evidence that you are a self-motivated problem-solver who possesses grit and determination.
Gulbranson’s article concludes on a sobering note of caution:
“Finally, as we already know today, if education can’t keep up with changing industry, then the skills gap will hinder technological advancement and adoption.”
She goes on to ask some powerful questions, such as:
Are students learning how to learn, handle high complexity, and be flexible?
Are they learning how to make the invisible visible, and how to make good decisions using data and analysis?
Are there solutions that don’t cost an arm and a leg and last four years when the industry needs a software engineer who is also a psychologist to create a product that detects the mood of drivers and auto-shuts off the car appropriately?
We’re proud to be part of a new generation of learning providers offering opportunities that represent a “yes” answer to all the above, and we’re grateful to innovators like Christine Gulbranson who are out there asking the hard questions, and providing the right answers.
Through your commitment to lifelong learning at your organization, you are helping build rewarding careers for employees, while creating an environment for innovation.
Visit udacity.com/enterprise to discover how we can help your organization successfully navigate workforce transformation!
As companies continue to try to innovate, digitize and transform their operations, the demand for technology talent has never been higher. Training talent for the future and building a stronger workforce, in many cases, requires traditional businesses to think and act more like a nimble startup. Companies today need to reskill the workforce, inject new talent, and enable them a new way of working. Without skilled staff, there can be no digital transformation.
The reality is business has transformed and evident all around us including small changes in everything from how food is made and delivered, to how financial transactions are conducted, to how products are made, operated, and sold result in fundamental changes to how we live and work. Artificial intelligence (AI) and machine learning technologies are poised for a monumental impact.
The New York Times estimates that there are only 10,000 people in the world right now with “the education, experience and talent needed” to develop the AI technologies that businesses are betting on to create a host of new economic opportunities. Speculative figures indicate that there are around 300,000 AI practitioners globally, but millions more roles available for people with these qualifications.
The critical issue for companies lies in the fact that AI expertise comes at a price—meaning that only those organizations with the necessary resources and clout are able to attract machine learning talent. This is reflected in booming annual salaries and startling industry recruitment efforts. There is still a pronounced shortage of AI talent. In fact, it is getting worse as more and more enterprises form their own AI groups and make AI part of their corporate strategy,” argues Gary Kazantsev, Bloomberg’s Head of Machine Learning. It’s clear that recruiting one or two AI experts—a challenge in itself—won’t be enough to make the technology an actionable success in 2018.
While skills and training initiatives play catch-up, ballooning salaries, scarce talent, and an aggressively competitive hiring landscape means that the race is already on between those who stand to gain the most from AI through the ability to adopt early on, and those who will be trailing behind in their dust. This is what the AI skills gap looks like—and right now, it’s a gap that is only widening. The growing disparity between the hiring power of companies and the present scarcity of AI talent has big implications, not only for determining the winners and losers of the AI revolution, but for the future of the workforce itself. This is no longer a ‘simple’ question of technology, but of skills, personnel, and strategy. As AI technologies become a reality, companies and their workforce must keep up—and they must do so quickly.
Read our whitepaper and find out how your company can bridge the AI talent gap. Download here.
Audi recently published a blog post discussing its online learning initiative and partnership with Udacity
Artificial intelligence (AI) promises to revolutionize the automotive industry and, more importantly, the automobile. It’s no surprise that Audi has invested in its employee “data camp” training focused on big data and artificial intelligence. Intelligent robots, digital mobility services, and autonomous cars will all rely on these skills, so Audi is staying one step ahead. The company has partnered with Udacity to help accelerate its transformation into a digital car company.
Today’s businesses are undergoing a digital transformation. The Internet of Things (IoT) is making smart homes, smart factories, and smart cities possible. Autonomous vehicles are changing the transportation industry. Artificial intelligence and machine learning are enabling predictive approaches to decision making and driving business insights.
This digital transformation that is sweeping industries by storm would not be possible without data. Data is the enabler of new technologies and solutions. Data is where important and actionable business insights are derived. In a recent Udacity webinar titled “Shaping the Future of the Workforce,” the discussion centered on how Artificial and data science are the building blocks of digital transformation and there is a massive skills gap and substantial competition for talent surrounding those skill sets.
Regardless of the industry, companies are struggling to find qualified and experienced talent to not only help make sense of all the data but to use the data to be competitive. “How is your company going to deal with all this new information – as quick as your competition? There are key data job openings you need to fill and time is not on your side,” said Andrew Cartwright, Enterprise Sales Lead at Udacity.
Breakthroughs in machine learning, supported by the huge explosion of data are fueling the rapid rate of growth and development of artificial intelligence (AI) regardless of the industry. AI is at the forefront of a tidal wave of disruption. Employees today not only lack the right set of skills, but the ones they currently have are becoming obsolete over time. And, companies want to integrate AI strategies, but the don’t have the right talent with the right skills. In fact, there are less than 10,000 professionals in the world with the skills necessary to tackle AI. “Yet, we know the talent need for AI is over one million and we currently have over 100,000 students studying AI related fields. So, one of the biggest roadblocks in the active adoption of AI across industries is the sheer scarcity of appropriately skilled professionals,” Andrew Cartwright reiterated.
In order for organizations to bridge the talent gap the webinar stressed four key areas:
establish continuous workforce training,
derive proficiency in real-world skills beyond videos and online tests,
establish ongoing workforce assessment and calibration,
generate access to top-tier talent pool, internal and external
Academic institutions, companies, and online education providers are combining their efforts to find and foster talent. Organizations can enrich their staff through internal training, while at the same time creating the right conditions to accumulate and retain new talent.
The concept of lifelong learning is accordingly transforming from a discretionary aspiration to a career necessity. No longer is it a supplemental luxury to learn new skills, and no longer is learning new skills something you do only when you’re pursuing a significant career change. Being relevant, competitive, and in-demand in today’s fast-moving world requires an ongoing commitment to lifelong learning regardless of your role or career path.
At Udacity, we are committed to very similar objectives and strategies. Our industry partnerships are critical to the success of our approach, both in terms of establishing “a true 21st century curriculum,” and for developing a “clearer view on future skills and employee needs.” Our emphasis on learn-by-doing is fueled by our desire to help see every employee we teach be in-demand.
The following quote has been variously attributed to everyone from Lao Tzu to Maimonides to Anne Isabella Thackeray Ritchie:
“Give someone a fish, and you feed them for a day. Teach someone to fish, and you feed them for a lifetime.”
Given its ubiquity throughout modern history, it’s clearly a resonant message, and part of its appeal has to do with its broad applicability—it’s germane to so many different use cases.
The quote is generally interpreted as a lesson about self-sufficiency, but it’s also sage advice when thinking about short-term “band-aids” vs. long-term solutions. Why solve something for a day, only to have the same problem again tomorrow? Why not embrace a long-term solution that eliminates the problem once and for all?
Hiring managers and recruiters confront this issue every day. After all, hiring is essentially an act of problem-solving—a company has a need, and the right hiring decision will solve for it. But what IS the right hiring decision? If you’re a company in need of talent, the solution is often right in front of you!
Let’s take the example of a company website.
Company X is a small company. They have a website, but it’s not very good, and it’s becoming a problem. They need a new site, but no one internally has the skills to do the work. What should Company X do? One solution is to hire someone from outside their organization to do the work. In theory, this makes sense, because professionals will know what to do, and how to do it. The challenges with this approach, however, are multi-fold. One obvious issue, is that there’s no real way to know whether the outside entity will do a good job. But the bigger question is, how can you know whether they’ll “get” you? A website isn’t just functional. It’s a symbol of brand identity. It communicates values as much as it provides services. So you want to work with someone who understands who you are as a company. Finding an outside entity that is both reliable, and that understands your brand, is difficult, and even if you DO find someone, they’re not yours for keeps. They do the work, then they’re off to the next client.
Hiring an outside entity often results in a “fed for a day” solution. If all goes well, you’ll get your new site, but as your company expands and evolves, you’ll be hungry again soon.
So what’s the alternative?
If you’re in a Company X kind of a situation, take a moment to look around you. What do you see? Chances are, what you see are dedicated, reliable, hardworking individuals who are committed to your company, and who most definitely “get” you. But at first glance, you might not be seeing the people who can build your new site.
Or are you?
Here at Udacity, we think you are! We think there are people at your company right now, who are just a Nanodegree program away from giving you exactly what you need. Don’t believe us? Poll your employees today. Find out whether someone at your company harbors an interest in web development. Chances are, there’s someone who’d jump at this kind of opportunity.
So, here’s a suggestion for companies in need of talent. Instead of investing in a one-time, short-term approach, invest in a Nanodegree program on behalf of one or more of your employees instead, and give your company the gift of a long-term solution to your talent needs.
Employees, this is an action item for you as well. If you’ve got a passion for something, and you think pursuing your passion can help your company, speak up! That’s what Kat Halo did. Her company hired someone else to do their marketing, but Kat knew she could do a better job. She took it upon herself to learn digital marketing with Udacity, and now, she’s doing marketing for her company!
There are a great many tangible benefits to hiring from within. A recent CareerBuilder article affirms that you’ll save money and see better performance, and Adam Foroughi, writing for Entrepreneur, notes the following:
Motivated employees work harder.
Opportunity, happy people = higher retention.
Internal hires adapt better to new roles.
And finally, as noted in a recent article from Inc., “Wharton research shows that external hires cost18 to 20 percent more than those promoted from within.”
In a world marked by rapid technological advancement, more and more companies all across the hiring landscape are embracing digital transformation initiatives, and this is leading them to look anew at the talent within their own ranks. At Udacity, our Enterprise team works directly with hundreds of different companies who are investing in their employees by proactively offering opportunities to reskill and upskill through our Nanodegree programs. If you’re not yet investing in the talent you already have, now’s a really good time to consider doing so!
Let’s now return to our quote:
“Give someone a fish, and you feed them for a day. Teach someone to fish, and you feed them for a lifetime.”
The key lesson here lies in the distinction between “a day” and “a lifetime.” As a company, when it comes to making hiring decisions, you want to invest in a long-term solution that works for the long term, and that’s what investing in the development of existing employees is all about. When you need talent, you often need look no further than the people right in front of you.
Solving problems and answering questions through data analysis is quickly becoming the norm in today’s data-driven world. As real-world experiments become ubiquitous in modern business, data scientists have become the beating heart of the big data economy. It’s not just that they are designing new systems; they are going to bat for new sources of data and new ways to use that data.
Yet, with the ever-increasing demand for skills, the talent gap has widened. In a recent annual survey of employers, Deloitte and the International Society of Certified Employee Benefits Specialists (ISCEBS) remarked, “The shortage of qualified talent and the skills gap has emerged as the biggest challenge facing employers over the next three years.”
This skills gap continues to widen, despite the available pool of domestic and H1B job applicants. How can employers expect to fill their needs for such capabilities in emerging technologies?
More and more smart companies are training their existing employees to acquire the skills they need in the technologies and disciplines that are critical to their evolving business objectives.
Use training strategically to fill skills gaps
As technologies rapidly evolve and corporate initiatives change, talent development is proving to be a faster and more cost-effective solution than talent acquisition. “Upskilling”—investing in the skills of front-line workers—has upfront costs, but it can save employers time and money in the long run. When employees are always learning, it has the effect of reducing turnover and improving employee retention, helping a company keep pace with or outdo its competition. Upskilling also allows companies to retain employees who fit within the company culture; it’s much less risky than bringing in someone new.
And while upskilling can be used in many circumstances, it can have big returns if a company:
Is looking to find more efficient processes;
Finds that machine learning, big data, and data science are playing an increasingly important role in the workplace;
Is in an industry that is under tough competitive pressure or that evolves quickly;
Wants to find new ways of doing business;
Is looking to offer new products and services
Re-Skilling Existing Employees
Here at Udacity, we’ve developed a solution for Enterprises that enables them to assess their workforce, understand their skills gaps and deploy transformational, hands-on and cutting edge curriculum personalized to their employees. The costs of re-skilling employees far outweigh recruiting, training and ramping a new employee.
In addition to saving time and money, reskilling employees maintains established corporate culture, enables uninterrupted employee productivity, and provides other benefits to your organization, including:
Improved employee engagement and retention by building self-esteem with a culture of higher promotion potential and refreshingly novel or challenging responsibilities.
Better company brand reputation (on employer review sites like Glassdoor.com) as a place for career development and longevity for future candidates.
Reduced dependency on outside consultants or vendors for necessary skill sets.
As Henry Ford, founder of Ford Motor Company, professed, “The only thing worse than training your employees and having them leave is not training them and having them stay.”
Find out how much your organization can save using Udacity for Enterprise to transform your workforce.
Emerging areas, such as machine learning, artificial intelligence (AI) and big data, require special skill sets in high demand. Beyond traditional four-year degrees and time-intensive training programs, the alternative paths to developing those skills are limited. The learning required is not something that can be accomplished through a Netflix or YouTube-style exploration of a catalog of videos. Training in machine learning or AI requires deeper, more structured learning and commitment.