AI in STEM: 4 Expert Perspectives on Jobs, Labs & Limits

Five Physics Forums advisors share contrasting views on artificial intelligence’s effect on STEM fields, jobs, and society. Consensus points: near-term AI will mostly automate rules-based tasks rather than achieve general intelligence, public trust and data bias remain major obstacles, and AI’s biggest impact may come from being invisible and embedded in everyday tools rather than humanlike.

Key Takeaways

• PF Advisor gleem reviewed over 900 job classifications on the Department of Labor’s CareerOneStop database and estimated roughly 40 million U.S. jobs, about 25% of full-time workers, could be affected by AI to some degree.
• Intel announced its Neural Network Processor (NNP), a dedicated AI chip, in late 2019, which advisor gleem cites as a turning point for AI hardware.
• MIT researchers built an artificial-synapse chip designed to mimic neuronal synapses and reduce computation and memory demands for AI workloads, according to a 2020 MIT News report.
• Microsoft’s chatbot Tay was manipulated by users shortly after release, and Microsoft’s separate move to replace 50 human editors with AI led to an error mixing up photos of the band Little Mix.
• A cited study found that each robot can replace approximately 3.3 workers in certain automation contexts.
• Advisor russ_watters argues AI’s real-world value comes from mundane, invisible applications like adaptive thermostats and cars rather than humanlike machines.

Overview: Where AI Stands Today, According to PF Advisor gleem

Advisor gleem, who tracks AI and robotics developments on a roughly weekly basis, argues that many occupations are ripe for AI disruption, particularly those built on standard procedures rather than delicate physical manipulation. Robots factor into this discussion because even when they lack onboard AI, they can be coordinated by external AI systems.

For readers wanting a technical grounding, gleem recommends DARPA’s official overview of AI development and capabilities, available at darpa.mil.

Technical Advances in AI Hardware

Experts remain divided on the near-term likelihood of artificial general intelligence (AGI), a hypothetical AI matching or exceeding human cognitive ability across all domains. Gleem does not expect AGI within the next ten to twenty years, noting that most AI development still relies on traditional computer hardware and software, which limits processing complexity.

Intel announced its Neural Network Processor (NNP), a chip purpose-built for AI workloads, in late 2019, a development gleem expects to meaningfully affect AI implementation going forward. Some estimates put the growth in AI network interconnectivity, comparable to synaptic complexity, at roughly one order of magnitude per year, according to a report from The Next Platform.

MIT News reported in 2020 that MIT researchers developed an artificial-synapse chip mimicking neuronal synapses, which reduces computation and memory requirements for certain AI tasks. Gleem suggests that if these hardware trends continue, AI systems could approach human-like complexity sooner than some predictions suggest. Read the original report at MIT News.

Limitations Slowing AI Adoption

Gleem identifies five recurring criticisms of current AI systems:

• Task specificity, meaning a system performs only one function and loses that ability when retrained for another.
• Lack of context sensitivity in language applications.
• Inadvertent bias introduced by biased training data.
• High power consumption.
• Opacity, meaning it is difficult to determine how an AI system reached a given conclusion.

Gleem argues the biggest obstacle to wider adoption is public acceptance, driven by concerns over bias, liability, misuse, and privacy, with privacy likely the top concern for most people.

AI’s Effect on STEM Data Analysis and Research Jobs

Modern scientific instruments generate data volumes too large for humans to analyze in a reasonable timeframe, according to gleem. AI has already been applied to astronomical data and to clearing the Large Hadron Collider’s (LHC) data-analysis backlog, which accelerates discovery but may reduce demand for human analysts, including some graduate-student roles.

Gleem notes that some signal types, such as frequency spectra, may still be best interpreted by humans, while other domains, such as reflected microwave spectral signatures, are reportedly now analyzed more effectively by AI than by people. AI systems that write code or discover algorithms are also beginning to produce usable and occasionally surprising results.

COVID-19’s Effect on Automation Incentives

Gleem argues that COVID-19 gave businesses new incentive to reduce dependence on human labor, since human resources represent one of the largest costs and management challenges for companies. The FDIC proposed a revamped quarterly reporting system to replace manual data-entry processes exposed as bottlenecks during the pandemic, a change gleem says AI could help automate, according to a report from ScienceDaily.

A cited estimate found that each robot can replace approximately 3.3 workers in certain automation contexts, and many bureaucratic document-processing roles are well suited to this kind of automation.

Healthcare, Communication, and Retail Automation

Gleem admits to previously believing healthcare workers would be among the least affected by AI, but now expects COVID-19 to accelerate telemedicine, robotic delivery of supplies and medications, and AI-driven scheduling and triage systems that reduce direct patient-provider contact. Gleem reviewed over 900 job classifications listed on the Department of Labor’s CareerOneStop database and estimated that roughly 40 million jobs, about 25% of full-time U.S. workers, could be affected by AI to some degree, particularly in communication-heavy occupations. That database is available at CareerOneStop.

Gleem also points to cash transactions disappearing, Amazon’s experiments with unattended stores, and large retailers testing automated stocking and floor-cleaning robots as evidence of accelerating retail automation.

Broader Social Concerns and Notable AI Failures

A Brookings Institution report indicates that AI is likely to substantially affect higher-end white-collar work in addition to the blue-collar and low-end white-collar jobs historically disrupted by mechanical automation and computerization, though the report notes significant uncertainty in its predictions. Read the full report at Brookings.

Microsoft’s experimental chatbot Tay, designed to learn from interactions on the web, was manipulated by users shortly after its release. Separately, Microsoft replaced 50 human editors with an AI system to select featured articles, and the system mistakenly mixed up photographs of members of the band Little Mix, creating public backlash. Facial-recognition systems have also shown well-documented difficulty accurately identifying people of color.

Gleem concludes that AI will rarely appear as humanlike robots but will become pervasive across business, science, and conflict, while remaining imperfectly reliable, much like humans. Gleem also expresses skepticism that nefarious AI applications can be effectively controlled, since developers may deploy an advantageous AI despite legal or ethical constraints, just as illegal hacking persists despite existing laws.

Perspective: AI as a Tool, According to PF Advisor Astronuc

Astronuc frames the discussion around definitions, pointing readers to overviews from Accenture, IBM, and IBM’s LinuxONE platform.

Pacific Northwest National Laboratory (PNNL) uses AI across both large- and small-scale applications, with data analytics, or “big data,” as a key area. Astronuc notes that AI results are only as reliable as the underlying data and the rules or engine driving the analysis. AI is useful for predictive analysis of networks and systems, but Astronuc warns that a wrong prediction can destabilize a system; the consequences range from trivial in benign cases to catastrophic where injuries or deaths could result.

In science and engineering, Astronuc points to AI’s use in optimizing complex alloy compositions, such as stainless steels involving iron, chromium, nickel, molybdenum, manganese, carbon, and nitrogen (Fe–Cr–Ni–Mo–Mn–(C,N) systems). Computational chemistry tools such as CALPHAD, along with complementary software, model thermophysical and mechanical properties, corrosion, and creep behavior in these alloys, with complexity scaling quickly as more elements are added.

Simulating these alloys in radiation environments, such as nuclear reactors, adds further complexity, since neutron flux causes atomic displacements, transmutation, and radiation-induced chemistry at the atomic level. Astronuc concludes that AI can be beneficial when used correctly, such as with accurate, factual inputs, but dangerous when misused, citing AI-driven misinformation and inappropriate health recommendations as examples. In Astronuc’s view, AI’s impact ultimately depends on the motivations of its users.

Perspective: AI’s Strength Is Its Mundanity, According to PF Advisor russ_watters

Russ_watters argues that “AI” is poorly defined and often portrayed like science fiction in ways that overstate its value, calling its mundanity its actual strength. He uses the Star Trek character Data to illustrate the problem: a machine can exceed humans in many capabilities while still lacking basic human emotion and irrationality, raising the question of why a machine should aim to pass for human at all.

Russ_watters cites the “AI effect,” a pattern in which any unsolved computing problem gets labeled AI until it is solved, at which point it becomes just a tool, as with handwriting recognition and speech recognition. He argues the important question is not whether a machine sounds human, but whether it performs useful functions quickly and accurately.

He lists several examples of AI becoming impactful precisely by being ubiquitous and largely invisible:

• Thermostats that learn household preferences and optimize energy use.
• Cars that adapt shift behavior to match an individual driver’s style.
• TV and DVR systems that recommend and record shows a viewer didn’t know they wanted.
• Smart refrigerators that reorder staples automatically.
• Health signals inferred from behavioral changes, such as reduced movement, that suggest illness.
• Social platforms that infer preferences and surface tailored ads or content.

While acknowledging some of these applications feel intrusive, russ_watters believes the upside of the “internet of things” and pervasive intelligence is significant and often underappreciated.

Perspective: AI Hype Is Overblown, According to PF Advisor jambaugh

Jambaugh argues that recent media coverage of AI is overblown, since current systems remain far from matching human conceptual understanding, even as neural-network models have made genuine practical advances in pattern recognition and classification.

In education, jambaugh sees potential for automated learning but criticizes current trends for forcing learners to adapt to computerized instruction rather than tailoring instruction to individual learners. He argues AI research should focus on automating a teacher’s ability to diagnose why a student made a specific mistake and adapting instruction to correct conceptual misunderstandings.

Jambaugh notes that neural networks, including recurrent ones, are deterministic once trained, meaning their outputs can in principle be encoded as direct algorithms. Training itself can be automated, but the resulting code is often opaque even to the programmer who built it, creating risk of unexpected negative consequences from over-trusting hidden algorithms.

Voice assistants such as Siri and Alexa illustrate the current state of the field, according to jambaugh: they encode aggregate behavior on centralized servers and cannot fully adapt to individual users beyond a limited set of customization options. He predicts a period of modest disappointment in AI’s promise over coming decades, until another paradigm shift occurs.

Frequently Asked Questions

Will artificial general intelligence (AGI) arrive soon?

PF Advisor gleem does not expect AGI, meaning AI matching human cognitive ability across all domains, within the next ten to twenty years. Most near-term AI development is expected to focus on automating rules-based tasks rather than achieving general reasoning comparable to humans.

How many jobs could AI affect?

Advisor gleem reviewed over 900 job classifications on the Department of Labor’s CareerOneStop database and estimated that roughly 40 million U.S. jobs, about 25% of full-time workers, could be affected by AI to some degree, with communication-heavy occupations particularly exposed.

What is the “AI effect”?

The AI effect, as described by advisor russ_watters, is the pattern of labeling any unsolved computing problem as artificial intelligence, only to reclassify it as an ordinary tool once it is solved. Handwriting recognition and speech recognition are cited as past examples.

What are the biggest obstacles to AI adoption?

Advisor gleem cites public acceptance as the biggest obstacle, driven by concerns over bias, liability, misuse, and especially privacy. Technical limitations such as task specificity, lack of context sensitivity, biased training data, high power consumption, and opaque decision-making also slow adoption.

What went wrong with Microsoft’s AI chatbot Tay?

Microsoft’s chatbot Tay was designed to learn from interactions on the web but was manipulated by users shortly after its release. Separately, Microsoft replaced 50 human editors with an AI system to select featured articles, and that system mistakenly mixed up photographs of members of the band Little Mix.

Where is AI already making a practical difference in STEM?

AI is already applied to astronomical data analysis and clearing the Large Hadron Collider’s data-analysis backlog, according to advisor gleem. Advisor Astronuc adds that AI assists in optimizing complex alloy compositions and modeling material behavior under radiation, using tools such as CALPHAD.

Why does russ_watters think invisible AI matters more than humanlike AI?

Russ_watters argues that AI has the biggest real-world impact when it is ubiquitous and unnoticed, embedded in devices like adaptive thermostats, cars that learn driving styles, and recommendation systems, rather than when it attempts to mimic human behavior or emotion.