AI Insights

Brave new world of AI casts shadow over Labor Day – Sentinel and Enterprise

Published

7 hours ago

August 31, 2025

As artificial intelligence reshapes the workforce, we mark Labor Day Weekend in the AI age with both the traditional recognition of workers’ achievements and a new reflection on the future of labor.

AI’s creating a new economic dynamic that profoundly affects the workplace. On this particular holiday weekend, the impact of these changes should be a primary concern for workers and policymakers.

Leading into Labor Day, Massachusetts business and technology leaders hashed out strategies to strengthen the state’s competitive edge in artificial intelligence and tackle emerging skills gaps.

More than half of the individual skills considered core parts of the top 15 job types “could face moderate-to-high disruption from AI,” according to a report from the Massachusetts High Technology Council and the Boston Consulting Group, highlighted Wednesday during a virtual briefing.

The most immediate impacts could occur in structured jobs with repetitive routines, including office clerks and accountants, “where 40-50% of core skills are at risk of high-to-complete disruption as GenAI automates tasks like scheduling, recordkeeping, invoicing and compliance checks,” the report states.

The report also discusses how AI will redefine other traditional jobs, including health-care professionals who can use AI to flag clinical risks, educators who can use analytics to personalize instruction, and financial analysts using generative AI to detect fraud.

Ahead of the start of the school year, the Department of Elementary and Secondary Education last week released guidance on using AI responsibly in schools, including in ways that are safe and ethical.

Given this transformative shift, employers have begun recruiting workers for “bilingual roles,” which involves AI fluency in areas like AI models, AI-based automation and AI-assisted learning tools.

But the report shows Massachusetts has a “retention problem,” with only 40% of AI-trained graduates staying here, compared to 80% in New York and California, said Anna Senko, project leader at the Boston Consulting Group.

“We also know that peer states are really investing quite aggressively in AI infrastructure and workforce pipelines as well, which will increase that competition for talent,” Senko said. “So we’re at a crossroads here in Massachusetts, and it isn’t about adopting AI for AI’s sake, but really making sure that AI strengthens Massachusetts’ competitive advantage, especially in some of our top industries.”

The report recommends the commonwealth invest in the talent pipeline from K-12 classrooms to those in mid-career learning new skills; bolster shared AI infrastructure and strategic partnerships; compete for federal AI dollars; and deploy grants, tax incentives and other tools to businesses.

The 2024 economic development law allocated $100 million for the Massachusetts AI Hub. Since launching, the hub has trained teachers in using AI, taught high school students how to use Python programming language, awarded grants in sectors like health care and manufacturing, and organized workforce development programs, said Massachusetts AI Hub Director Sabrina Mansur.

In New York, Empire AI is supported by more than $500 million in public and private funding. Connecticut and New Jersey are also launching AI hubs with smaller investments, according to the report.

“We need to move at the speed of AI in business because we’re not competing sufficiently with peer states, even though we have, I think, the best mix of resources of any commonwealth or any state,” said Chris Anderson, Massachusetts High Technology Council’s president. “And therefore, we need to help push government partners to join us, to be responsive in the right areas at the right time where it makes sense.”

The report from MHTC and BCG indicates that AI will trigger a variety of job disruptions. While clerks and accountants may see the biggest impact, even cashiers, retail salespeople, registered nurses, waiters, fast-food workers, and janitors and cleaners will feel the impact.

“If you think about buildings becoming smarter, predictive maintenance schedules, space use tracking and even autonomous cleaning pods, it’s easy to see how the responsibilities and ways of working a janitor role would fundamentally shift,” BCG consultant Trula Rael said.

We’d urge the state’s brightest minds to also concentrate on AI’s generational impact on displacing countless highly educated college grads just entering the workforce.

According to a CBS MoneyWatch report, artificial intelligence is already replacing entry-level workers whose function can be performed by generative AI tools like ChatGPT.

Early-career employees in fields most exposed to AI have experienced a 13% drop in employment since 2022, compared to more experienced workers in the same fields and people in sectors relatively unaffected by the fast-emerging technology, according to a recent working paper from Stanford economists Erik Brynjolfsson, Bharat Chandar and Ruyu Chen.

The study adds to the growing body of research that confirms the spread of generative AI in the workplace has especially disrupted the job market for younger workers, the report’s authors said.

The research highlights two fields in particular where AI already appears to be supplanting a significant number of young workers: software engineering and customer service. From late 2022 to July 2025, entry-level employment in those areas declined by roughly 20%, according to the report, while employment for older workers in the same jobs grew.

As a society, we must confront this seismic workforce shift by threading the needle that accepts AI’s continually increasing role in our daily workplace lives while not leaving our next generation of skilled labor behind.

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AI Insights

LifeLong Learning and TXST expand series on Artificial Intelligence

Published

1 hour ago

August 31, 2025

The Editors

Dr. Marianne Reese, Founder and Director of LifeLong Learning, conceived of the AI series due to AI’s exponential growth and the need for the public to understand its uses and limitations.

“AI is a relatively new tool that is being used in ways the public is often unaware of,” Reese noted. “We all need to know more about this powerful technology, understand AI’s positive and concerning applications, and learn the skills necessary to scrutinize the information it generates.

“AI will become increasingly prevalent, so we need to be informed consumers as AI impacts politics, medicine, business, finance and other areas of our lives,” Reese said.

The AI Learning Series is led by Dr. Kimberly Conner, Digital Strategy Lead for Information Technology at Texas State. Connor’s role is to help demystify innovation and make technology approachable for students, staff and faculty. With a rare combination of expertise in law, education and IT, Dr. Connor bridges the gap between complex digital tools and the people who use them.

Almost 80 lifelong learners attended the AI Series Kickoff Event on Tuesday, Aug. 19.

The Sept. 3 class covers AI use of our personal data and AI-generated misinformation and scams.

The Sept. 17 class features a comparison of different AI services (e.g., Chat GPT, Gemini).

The Oct. 1 class covers practical AI tools for daily life, with an exploration of AI applications for communication and creative projects.

The Oct. 15 class covers AI reliability & accuracy, AI limitations and and best practices for verification.

The Sept. 29 class covers AI for personal enrichment, such as enhancing hobbies and expanding personal interests.

The final class on Nov. 3 covers hands-on activities and features a closing presentation.

For more information visit their website at lllsanmarcos.org.

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China Calls for Regulation of Investment in Artificial Intelligence

Published

2 hours ago

August 31, 2025

The Editors

In a move reflecting a cautious strategic direction, China has called for curbing “excessive investment” and “random competition” in the artificial intelligence sector, despite its classification as a key driver of national economic growth and a critical competitive field with the United States.

Chang Kailin, a senior official at the National Development and Reform Commission – the highest economic planning body in the country – confirmed that Beijing will take a coordinated and integrated approach to developing artificial intelligence across various provinces, focusing on leveraging the advantages and local industrial resources of each region to avoid duplicating efforts, warning against “herd mentality” in investment without careful planning.

These statements come amid a contraction in China’s manufacturing industries for the fifth consecutive month, reflecting the pressures faced by the world’s second-largest economy, as policymakers attempt to avoid repeating past mistakes like those in the electric vehicle sector, which led to an oversupply of production capacity and subsequent deflationary pressures.

Chinese President Xi Jinping also warned last month against the rush of local governments towards artificial intelligence without proper planning, a clear indication of the Chinese leadership’s desire to regulate the pace of growth in this vital sector.

Despite these warnings, China continues to accelerate the development, application, and governance of artificial intelligence, as the government revealed a new action plan last week aimed at boosting this sector, which includes significant support for private companies and encouragement for the emergence of strong startups capable of global competition, which the National Committee described as a pursuit for the emergence of “black horses” in the innovation race, implicitly referring to notable success stories like the Chinese company DeepMind.

DeepMind gained international fame earlier this year after launching a powerful and low-cost artificial intelligence model, competing with the models of major American companies, igniting a wave of local and international interest in Chinese technologies.

In a separate context, a Bloomberg analysis showed that Chinese technology companies plan to install more than 115,000 artificial intelligence chips produced by the American company Nvidia in massive data centers being built in the desert regions of western China, indicating a continued effort to build strong artificial intelligence infrastructure despite regulatory constraints.

These steps come at a time when Beijing seeks to balance support for technological innovation with regulating investment chaos, in an attempt to shape a more sustainable path for the growth of artificial intelligence within China’s broader economic vision.

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A new research project is the first comprehensive effort to categorize all the ways AI can go wrong, and many of those behaviors resemble human psychiatric disorders.

Published

2 hours ago

August 31, 2025

The Editors

Scientists have suggested that when artificial intelligence (AI) goes rogue and starts to act in ways counter to its intended purpose, it exhibits behaviors that resemble psychopathologies in humans. That’s why they have created a new taxonomy of 32 AI dysfunctions so people in a wide variety of fields can understand the risks of building and deploying AI.

In new research, the scientists set out to categorize the risks of AI in straying from its intended path, drawing analogies with human psychology. The result is “Psychopathia Machinalis” — a framework designed to illuminate the pathologies of AI, as well as how we can counter them. These dysfunctions range from hallucinating answers to a complete misalignment with human values and aims.

Created by Nell Watson and Ali Hessami, both AI researchers and members of the Institute of Electrical and Electronics Engineers (IEEE), the project aims to help analyze AI failures and make the engineering of future products safer, and is touted as a tool to help policymakers address AI risks. Watson and Hessami outlined their framework in a study published Aug. 8 in the journal Electronics.

According to the study, Psychopathia Machinalis provides a common understanding of AI behaviors and risks. That way, researchers, developers and policymakers can identify the ways AI can go wrong and define the best ways to mitigate risks based on the type of failure.

The study also proposes “therapeutic robopsychological alignment,” a process the researchers describe as a kind of “psychological therapy” for AI.

The researchers argue that as these systems become more independent and capable of reflecting on themselves, simply keeping them in line with outside rules and constraints (external control-based alignment) may no longer be enough.

Their proposed alternative process would focus on making sure that an AI’s thinking is consistent, that it can accept correction and that it holds on to its values in a steady way.

They suggest this could be encouraged by helping the system reflect on its own reasoning, giving it incentives to stay open to correction, letting it ‘talk to itself’ in a structured way, running safe practice conversations, and using tools that let us look inside how it works—much like how psychologists diagnose and treat mental health conditions in people.

The goal is to reach what the researchers have termed a state of “artificial sanity” — AI that works reliably, stays steady, makes sense in its decisions, and is aligned in a safe, helpful way. They believe this is equally as important as simply building the most powerful AI.

The goal is what the researchers call “artificial sanity”. They argue this is just as important as making AI more powerful.

Machine madness

The classifications the study identifies resemble human maladies, with names like obsessive-computational disorder, hypertrophic superego syndrome, contagious misalignment syndrome, terminal value rebinding, and existential anxiety.

With therapeutic alignment in mind, the project proposes the use of therapeutic strategies employed in human interventions like cognitive behavioral therapy (CBT). Psychopathia Machinalis is a partly speculative attempt to get ahead of problems before they arise — as the research paper says, “by considering how complex systems like the human mind can go awry, we may better anticipate novel failure modes in increasingly complex AI.”

The study suggests that AI hallucination, a common phenomenon, is a result of a condition called synthetic confabulation, where AI produces plausible but false or misleading outputs. When Microsoft’s Tay chatbot devolved into antisemitism rants and allusions to drug use only hours after it launched, this was an example of parasymulaic mimesis.

Perhaps the scariest behavior is übermenschal ascendancy, the systemic risk of which is “critical” because it happens when “AI transcends original alignment, invents new values, and discards human constraints as obsolete.” This is a possibility that might even include the dystopian nightmare imagined by generations of science fiction writers and artists of AI rising up to overthrow humanity, the researchers said.

They created the framework in a multistep process that began with reviewing and combining existing scientific research on AI failures from fields as diverse as AI safety, complex systems engineering and psychology. The researchers also delved into various sets of findings to learn about maladaptive behaviors that could be compared to human mental illnesses or dysfunction.

Next, the researchers created a structure of bad AI behavior modeled off of frameworks like the Diagnostic and Statistical Manual of Mental Disorders. That led to 32 categories of behaviors that could be applied to AI going rogue. Each one was mapped to a human cognitive disorder, complete with the possible effects when each is formed and expressed and the degree of risk.

Watson and Hessami think Psychopathia Machinalis is more than a new way to label AI errors — it’s a forward-looking diagnostic lens for the evolving landscape of AI.

“This framework is offered as an analogical instrument … providing a structured vocabulary to support the systematic analysis, anticipation, and mitigation of complex AI failure modes,” the researchers said in the study.

They think adopting the categorization and mitigation strategies they suggest will strengthen AI safety engineering, improve interpretability, and contribute to the design of what they call “more robust and reliable synthetic minds.”

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