Are we doing enough to get everyone -- IT and business professional alike -- geared up for the data analytics-driven economy ahead of us? No way. However, the Covid-19 crisis, with its massive pressure on organizations to digitally transform overnight, has caused business leaders to step up their training in critical technology areas.
That's the key takeaway from a survey of 937 professionals, released by Netwrix, which tracked changes in IT priorities as a result of the crisis. The study, conducted in June 2020, showed that the IT skills shortage requires organizations to prioritize investment in IT staff education. The share of CIOs and IT directors that plan to invest in IT education has almost doubled since the onset of the crisis, from 20% in late 2019 to 38% now. In addition, with hiring freezes in place at many organizations, 31% of respondents see educating their current IT pros as a great option for addressing the skills shortage.
Still, we're a long way off from investing in or supporting the development of AI skills employers need for the years ahead. The Bipartisan Policy Center, a research arm of the U.S. Congress, just issued a report that stresses the need for greater AI education and training so public and private-sector organizations can stay ahead in the global economy. "Closing the AI talent gap requires a targeted approach to training, recruiting, and retaining skilled workers. This AI talent should ideally have a multidisciplinary skill set that includes ethics."
Competition for AI-skilled professionals is not limited to just technology companies, the report adds. It's "spanning almost all industries as businesses seek to leverage the strengths of AI. For instance, in the race to manufacture self-driving cars, the auto industry competes with Silicon Valley for the same experts."
The good news is that those professionals seeking to build up their AI resumes need not wait for corporate and government actions. There are many free or low-cost resources available from the world's leading universities that cover many key aspects of AI, machine learning, deep learning and natural language processing.
Lukas Spranger, a data scientist and software engineer, has been compiling a growing list of online courses over at the KDNuggets site. These include the following:
- Machine learning (Stanford University): Introductory course covers classic techniques.
- Introduction to Artificial Intelligence (University of California Berkeley): Covers everything pertaining to understanding AI.
- Natural Language Processing with Deep Learning (Stanford): Modern NLP techniques.
- Emerging Challenges in Deep Learning (Simons Institute): A series covering topics such as "efficient deep learning with humans in the loop," "aligning ML objectives with human values," "is deeper better when shallow is good?"
- ECE AI Seminar Series (New York University): A series covering topics such as "obstacles to progress in deep learning and AI," "The AI trinity: data + infrastructure + algorithms"
Interestingly, along with the courses mentioned above, there is also a no-code way to deploy an AI model -- that is, without writing a single line of code by employing open-source and cloud-based resources. In a recent post, Francesco Palma walks through a process for classifying pictures of flowers, using Giotto, a no-code AI platform.
The bottom line is that demand for advanced technology skills will keep growing as organizations increasingly rely on technology and digital resources to compete in an incredibly fierce global economy. Bachelors and masters degrees earned in one's twenties will only go so far -- what is happening is people will be assembling their own skillsets through ongoing learning, available through an abundance of online courses and skills refreshes.