How To Boost Artificial Intelligence Education In Your Company
The impact of automation in the workforce extends far beyond any individual employee, organization or even industry. As artificial intelligence advances and companies adopt this technology, we’re beginning to see an economic shift.
In 2017, JPMorgan estimated that automation has the potential to increase global gross domestic product by more than$1.1 trillionover the next 10 years. Meanwhile, according to Bloomberg (paywall), PwC predicted that AI could add up to$15.7 trillionto the worldwide economy by 2030. While there’s a huge discrepancy between these projections, I believe the underlying message is clear: Automation is a major factor in the future of global economic growth.
And yet, some experts are anticipating an “AI winter,” a period when investments and developments in AI begin to slow down or stall. Although I’ve observed some success in funding AI in various ways — such as coding boot camps and computer science degrees in an effort to close the skills gap — we haven’t seen the complete payoff we’ve expected. AI still has bias problems; deep learning still gets stumped by simple, if unexpected, variables; neural networks are far from the point where they can be consistently leveraged strategically for business.
As a serial AI entrepreneur, I’ve made it my goal to dedicate enormous resources to research and development for machine learning and AI, and I’ve been able to build tech companies that do the same. Although some do not agree we are approaching an AI winter, from my perspective, as the economy comes to increasingly rely on more sophisticated machines, we can confront a potential plateau of AI development by training machines as an industry and providing more education to those creating the technology.
In my experience, most of today’s AI developers are constrained by the mandate to automate. As a result, machines can become products of what I call “teaching to the test.” They can do singular tasks incredibly well, given incredibly narrow conditions.
When AI has to go off-script, so to speak, it struggles. For example, tech company DeepMind was working to develop AI that would function as a world-class Breakout player. But according toWired, whatthey foundin this initial brush with gaming AI was that the “seemingly supersmart AI could play only the exact style of Breakout it had spent hundreds of games mastering.” Even the smallest change to the game mechanics or layout caused the whole thing to break.
My point is that the majority of AI today doesn’t have critical thinking skills or common sense. Instead, it’s been designed to succeed through sheer rote memorization. It’s processing information to give an answer or action, but it’s not actually understanding what’s happening.
To address this, I believe tech companies need a playbook for research and development to prepare themselves for the next wave of innovation. And although there’s no canonical curriculum for training AI, there are a few key steps executives can take to ensure they are investing in AI education the right way:
Perhaps most importantly, it’s crucial that organizations hire the right people. Staffing for AI isn’t easy. Ensuring your recruits have the right technical chops can be expensive, but it’s worth it. Look for employees who have a history of dedicated curiosity. Have they published research? Are they interested in continued education? I believe AI developers who are equal parts programmers and researchers are the most valuable asset a tech company can have. Organizations that can recruit and keep this talent will find that they are suddenly the owners of valuable IP because their new AI hires are able to devote themselves to experiments and training the tech, rather than learning the skills for the job.
Enable your entire team to become more AI-literate.
