Career Paths to AI Include a Non-Technical Start; and an ML Course for a Congressman
The AI strategist is among new titles with non-technical roots; a 72-year-old US Congressman studies ML; a US-EU report advises on AI and the workforce
By John P. Desmond, Editor, AI in Busines
Career paths in AI are not confined to programming and developer jobs. The impact of AI on an organization is such that it requires some thinking about strategy, how the AI can augment the existing way the organization conducts its business, or lead to new opportunities not tried before.
In this way, the title of AI Strategist enters the domain, someone whose job it is to figure out how to apply AI to move the organization forward.
A recent account from Emerj included The AI Strategist among a list of non-technical career paths to help drive AI in the enterprise. Also on the list:
AI Opportunity Spotter
Functional Subject Matter Expert
AI Project Manager
AI Educator
AI Trainer
AI Training Orchestrator
“These are typically leaders and other subject-matter experts who are unfamiliar with code,” stated Matthew DeMello, senior editor, author of the account. “However, they must be familiar enough with true AI capabilities in fundamental capacities.”
For mid-career or later professionals with little exposure to AI or technical coursework, it may be that it's never too late. The example of 72-year-old Congressman Don Beyer (D-Va.), who is working towards a master's in machine learning at George Mason University, is instructive. As recently reported in The Washington Post , he’s a studious guy, an economist, who likes to stay busy. He got exposed to AI through his Congressional work and got interested in learning more.
Now he’s in the Congressional AI caucus, co-led by Rep. Ray Obernolte (R-Calif.), which is in the mix on having an impact on where AI regulation goes in the US. “We want to make sure that we approach the regulation of AI in a way that’s thoughtful, that does the protection of consumers and privacy that needs to be done, but also doesn’t stifle the innovation and entrepreneurialism that has characterized the last 50 years of the technology industry in America,” Rep. Obernolte stated about Congressional consideration of AI.
Rep. Beyer is interested in the use of AI in the mental health field, in part due to the tragic suicide of a young member of his Washington staff. He sees AI as being potentially useful in spotting warning signs.
“There must be another thousand markers, many of which may be subtle,” Beyer stated about factors that could be part of a risk profile. “But if you put them all together, you can use machine learning to say, ‘What do these 47,000 people,’ or over the course of 10 years, ‘What do these 500,000 people have in common’ that may give you the ability to interrupt that path” for someone else?”
The Impact of AI on the Future of Workforces Studied by US-EU
A recent report issued for the US-European Union Trade and Technology Council US-EU-TTC) on the impact of AI on the future of workforces in the EU and the US, found that policymakers in government are at a critical juncture. “The challenge for policymakers is to foster progress and innovation in AI while shielding workers and consumers from potential types of harm that could arise,” stated the authors, who were not specifically identified.
However, the leadership of the US-EU-TTC is specified. For the US, the TTC is co-chaired by US Trade Representative Katherine Tai, Secretary of Commerce Gina Raimondo, and Secretary of State Anthony Blinken. For the EU, the TTC is co-chaired by European Commission Executive Vice Presidents Valdis Dombrovskis and Margrethe Vestager.
.In its conclusion, the report suggests governments have a “clear agenda” to guide AI development in a positive direction. The most important agenda points
Invest in training and job transition services;
Encourage development and adoption of AI that is beneficial for labor markets;
Invest in the capacity of regulatory agencies to ensure that AI systems are transparent and fair for workers.
Fleshing this out, the authors state that investment in AI “can direct firms away from a socially optimal split between automation and augmentation of worker tasks.” To counteract that, the authors suggest that public funds be used to “encourage and stimulate AI research that augments instead of automates work.”
Further, support for academic research is needed to study “how AI can exacerbate existing biases that are present in society, resulting in discrimination along racial, gender, and economic lines.”
The study included some revealing case studies, including one on a warehouse operation. ‘The case study of warehousing …illustrates that workplace algorithms are inherently opaque, are shrouded in industrial secrecy, and are protected by nondisclosure agreements with workers.”
Furthermore, “Even warehouse workers themselves are not always made aware of the software that manages them … workers can only guess the procedures of data extraction and analytics that organize and surveil their activities.”
Governments are moving towards more effective regulation of the impact of AI. In October 2022 Spain launched a pilot regulatory sandbox on AI, a way to connect policymakers with AI developers and adopters. The hope is that it will generate best practice guidelines for companies, including small and medium-sized businesses, to reduce barriers to the adoption of AI while staying consistent with future AI regulations in Europe.
Drishti Engineering Head Advises on Non-Technical Skills Developers Need
Non-technical skills will help even those with a bit of a technical background find a career path in AI, suggests Arvind Saraf, head of engineering for Drishti, writing in February 2022 in Analytics Insight. Drishti offers streams of data collected from manual activities on an assembly line.
“To ensure success in a deep tech career, coding alone isn’t enough,” Saraf stated. He recommends working on the following skills:
Creativity;
Communications;
Problem-solving ability;
Time management, and
Big picture curiosity
“Understanding the larger product and how the users can use it dictates a better understanding of AI problems to solve, which may not even be the hardest engineering problems,” Saraf stated. “Luckily, it is a skill that can be taught.”
Read the source articles and information in Emerj, in The Washington Post,in a recent report issued for the US-European Union Trade and Technology Council and from Analytics Insight.
(Write to the editor here.)