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NAIC releases highly-anticipated draft model bulletin on artificial … – Eversheds Sutherland(US) LLP

On July 17, 2023, the Innovation, Cybersecurity and Technology (H) Committee of the National Association of Insurance Commissioners (NAIC) released for comment a highly anticipated model bulletin (Model Bulletin) on regulatory expectations for the use of artificial intelligence1systems (AI Systems)2by insurers. The Model Bulletin encourages insurers to implement and maintain a board-approved written AI Systems Program (AIS Program) that addresses governance, risk management controls, internal audit functions and third-party AI systems.The goal of the AIS Program is to mitigate the risks of harm to consumers thru decisions made or supported by AI Systems, including third-party AI systems, that are arbitrary or capricious, unfairly discriminatory, or otherwise violate unfair trade practice laws or other legal standards, or that include data vulnerabilities.

The Bulletin also advisesinsurers of the information and documentation that insurance regulators may request during exams and investigations of the insurers AI Systems, including third-party AI Systems.

The Model Bulletin recognizes the Principles of Artificial Intelligence (Principles) adopted by the NAIC in 2020 as an important source of guidance for insurers to use in their continuing development of an AIS Program.It also explains how the regulatory expectations outlined in the Model Bulletin are rooted in existing law, including model laws on unfair practices, corporate governance, market conduct and property and casualty ratings.

Under the Model Bulletin, insurers are encouraged to maintain a written AIS Program that governs the use of AI Systems in order to mitigate the risk that use of such AI Systems, when making or supporting decisions that impact consumers, will result in decisions that are arbitrary or capricious, unfairly discriminatory, or otherwise violate unfair trade practice laws or other applicable law.

With regarding to governance, the AIS Program should:

With regard to risk management and internal controls, the AIS Program should document and address:

Each AIS Program should address the insurers standards for acquiring, use and reliance on AI Systems developed or deployed by a third- party, including:

Under the Model Bulletin, the applicable state regulator has the authority to request from the insurer information and documentation relating to the insurers AI Systems (as well as information and documentation developed by third parties that are relied upon by the insurer or its agent) as part of their market conduct examinations and as otherwise necessary to monitor the insurers compliance with the law.Such information and documentation include:

Issues related to the draft Model Bulletin include the definitions of AI Systems and algorithms; the extent of the governance and risk management controls expected; and the expectation that third-party vendors of AI Systems will agree to be subject to inspection and inquiry by 50 state insurance departments, including market conduct exams.

Comments on the Model Bulletin are due to the NAICs (H) Committee by September 5, 2023 and should be submitted to Miguel Romero (maromero@naic.org). The (H) Committee will hear comments from in-person attendees at the NAICs Summer National Meeting on Sunday, August 13, 2023 at 2:00 p.m.

________

1Artificial Intelligence is defined as machine-based systems designed to simulate human intelligence to perform tasks, such as analysis and decision-making, given a set of human-defined objectives. This definition treats machine learning as a subset of artificial intelligence.

2AI Systems are defined as an umbrella term describing artificial intelligence and big data related resources utilized by insurers.

3Algorithm is defined as a computation or machine learning process that augments or replaces human decision-making in insurance operations that impact customers.

If you have any questions about this Legal Alert, please feel free to contact any of the attorneys listed or the Eversheds Sutherland attorney with whom you regularly work.

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Flaws and Challenges in Artificial Intelligence (AI) – Fagen wasanni

Artificial intelligence (AI) is often hailed as a perfect technology that can analyze vast amounts of data and provide accurate solutions in an instant. However, the reality is that AI is wrong more often than it is right. The failure rate of AI projects is estimated to be around 80%, according to AI research firm Cognilytica.

Many of these failures can be attributed to flaws in design and methodology, rather than shortcomings of AI itself. There is a fundamental misunderstanding of how AI differs from traditional app development projects. While traditional applications are built around functionality, AI projects are built around data. This means that AI needs to analyze available data to gain insights before taking any action, instead of relying on pre-defined functions.

Even with a proper understanding of AI development, AI models still often fail to meet their objectives. This can be attributed to commission (doing something that shouldnt have been done) or omission (not doing something that should have been done) during the training process.

To improve the success rate of AI, it is crucial to identify and correct these deficiencies. This includes addressing issues in data inputs, processing logic, and available actions. As experience with AI grows, industry standards for design, development, and deployment will improve, leading to greater success with AI technologies.

In addition to these challenges, AI suffers from ingrained flaws that produce incorrect or nonsensible results. AI models are often too eager to please, providing responses without thoroughly considering the query. Furthermore, while AI models have access to massive amounts of data, much of it is outdated and lacks context. Bias is also a concern when AI models are not exposed to properly vetted data.

The misconception that AI is infallible can lead to frustration and mistrust when it fails to meet human expectations. It is important to understand and accept the fallibility of AI to accurately assess its strengths and weaknesses.

Training humans to understand AI is just as crucial as training AI to understand humans. Despite its flaws, AI will play a significant role in shaping the future. Acknowledging its limitations and working towards improving its reliability will help harness its potential and avoid the negative consequences of AI failures.

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Artificial Intelligence Project Imagines Children of Disappeared … – Fagen wasanni

A project created by Argentine artist Santiago Barros utilizes artificial intelligence to envision what the children of those who disappeared during the countrys military dictatorship from 1976 to 1983 would look like today. Barros, who shares the photos from his project @IAbuelas on Instagram, believes that seeing these imagined children forces people to confront the ongoing horror and the fact that the real identities of many individuals are still unknown.

Approximately 30,000 people, including 500 children and babies, were killed or disappeared during Argentinas military coup. The Abuelas of Plaza de Mayo, a group comprised of mothers and grandmothers of the victims, has been tirelessly working to seek answers about their loved ones. While the group appreciates the artists initiative, they emphasize that DNA testing remains the only foolproof method of identification.

Barros uses the MidJourney app to combine photos of the disappeared parents, sourced from the public archive of the Abuelas website, to visualize the likely appearances of their offspring as adults today. Each combination produced by the app presents a female and a male possibility. Barros wanted to portray these grandchildren with wrinkles and gray hair, highlighting their potential as mature adults.

While the initiative has garnered attention, it should be noted that it is an artistic and playful endeavor, not a scientific one. Esteban Herrera, a member of Abuelas who is actively searching for a half-brother born in captivity, clarifies that the artist was collaborating from an artistic perspective and not attempting to replace the organizations DNA samples or other established methods of investigation.

Barross project aims to support the mission of the Abuelas by using AI as a means for younger generations to engage with the historical atrocities committed during the military dictatorship. However, it is important to recognize that the project does not substitute the crucial work carried out by the Abuelas in investigating illegal adoptions and collecting DNA samples.

(Note: The title of the article has been changed to match the provided heading.)

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Convergence of brain-inspired AI and AGI: exploring the path to intelligent synergy – EurekAlert

image:The development of AGI has been greatly inspired by the study of human intelligence (HI). In turn, AGI has the potential to benefit human intelligence. view more

Credit: The authors

With over 86 billion neurons, each having the ability to form up to 10,000 synapses with other neurons, the human brain gives rise to an exceptionally complex network of connections that underlie the proliferation of intelligence.

There has been a long-standing pursuit of humanity centered around artificial general intelligence (AGI) systems capable of achieving human-level intelligence or even surpassing itenabling AGI to undertake a wide range of intellectual tasks, including reasoning, problem-solving and creativity.

Brain-inspired artificial intelligence is a field that has emerged from this endeavor, integrating knowledge from neuroscience, psychology, and computer science to create AI systems that are not only more efficient but also more powerful. In a new study published in the KeAi journal Meta-Radiology, a team of researchers examined the core elements shared between human intelligence and AGI, with particular emphasis on scale, multimodality, alignment, and reasoning.

Notably, recent advancements in large language models (LLMs) have showcased impressive few-shot and zero-shot capabilities, mimicking human-like rapid learning by capitalizing on existing knowledge, shared Lin Zhao, co-first author of the study. In particular, in-context learning and prompt tuning play pivotal roles in presenting LLMs with exemplars to adeptly tackle novel challenges.

Moreover, the study delved into the evolutionary trajectory of AGI systems, examining both algorithmic and infrastructural perspectives. Through a comprehensive analysis of the limitations and future prospects of AGI, the researchers gained invaluable insights into the potential advancements that lie ahead within the field.

Our study highlights the significance of investigating the human brain and creating AI systems that emulate its structure and functioning, bringing us closer to the ambitious objective of developing AGI that rivals human intelligence, said corresponding author Tianming Liu. AGI, in turn, has the potential to enhance human intelligence and deepen our understanding of cognition. As we progress in both realms of human intelligence and AGI, they synergize to unlock new possibilities.

###

Contact the author: Tianming Liu, School of Computing, University of Georgia, tianming.liu@gmail.com

The publisher KeAi was established by Elsevier andChina Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 100 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

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When Brain-inspired AI Meets AGI.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Red Bull Investigates the Use of Artificial Intelligence in Formula 1 – Fagen wasanni

Artificial intelligence (AI) is not only a popular topic in Hollywood but also in the world of Formula 1, with Red Bull Racing exploring its potential for enhancing performance in their cars. According to Craig Skinner, the teams chief designer, AI has already been utilized in driver training through the use of models and simulations, allowing drivers to learn the race tracks and improve their skills. Now, Red Bull is looking to harness AI in their design departments as well.

Skinner acknowledges that to effectively utilize AI, the team needs to have a comprehensive understanding of what makes a car fast. Teaching AI requires teaching it what to look for, and this knowledge comes from having an in-house team with expertise in car performance, aerodynamics, and vehicle dynamics. While Red Bull is currently investigating and incorporating AI, Skinner emphasizes the importance of truly understanding the problem before implementing these technologies.

A significant part of Red Bulls success in understanding car performance is attributed to their design guru, Adrian Newey. With Neweys never compromise philosophy, the team prioritizes car performance above all else. Skinner describes joining the team and witnessing Neweys unwavering dedication to adding performance, which revolutionized their approach to designing Formula 1 cars.

According to Skinner, Red Bull is willing to sacrifice aesthetics for speed. They prioritize performance over looks and are willing to put an ugly car on the grid if it proves to be faster. This mindset highlights their commitment to pushing boundaries and maximizing performance in their pursuit of success.

As Red Bull continues to explore the potential of AI in Formula 1, their focus remains on enhancing their understanding of car performance and leveraging technology to gain a competitive edge.

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Forget human extinction these are the real risks posed by AI today – New Scientist

Erik McGregor/LightRocket via Getty Images

The alarm bells have been rung. In May, computer scientist Geoffrey Hinton, known as the godfather of AI, quit his role at Google to warn of the existential threat posed by artificial intelligence. The Center for AI Safety followed up with an open letter, signed by Hinton and hundreds of others, warning that advanced AI could destroy humanity. Mitigating the risk of extinction from AI should be a global priority, read the statement.

This sudden surge of concern seems to have been motivated by the rapid advance of AI-powered chatbots like ChatGPT and the race to build more powerful systems. The fear is that the tech industry is recklessly accelerating the escalation of AIs capabilities. All of which sounds scary.

But the warnings are also suspiciously vague. When you scrutinise the scenarios typically put forward for precisely how AI could wipe out humans, it is hard to avoid the conclusion that such fears arent well-founded. Many experts are instead warning that fretting over long-term doomsday scenarios is a distraction from the immediate risks posed by existing AIs.

Generally, the people who talk about existential risks reckon that we are on a trajectory towards artificial general intelligence (AGI), roughly defined as machines that can out-think humans. They predict that people will invest advanced AIs with more autonomy, giving them access to vital infrastructure, such as the power grid or financial markets, or even putting them at the forefront of warfare at which point they could go rogue or otherwise resist our attempts to control them.

But it remains to be seen if AIs will ever reach the kind of super-intelligence

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Convergence of brain-inspired AI and AGI: Exploring the path to intelligent synergy – Tech Xplore

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

by KeAi Communications Co.

The development of AGI has been greatly inspired by the study of human intelligence (HI). In turn, AGI has the potential to benefit human intelligence. Credit: The authors

With over 86 billion neurons, each having the ability to form up to 10,000 synapses with other neurons, the human brain gives rise to an exceptionally complex network of connections that underlie the proliferation of intelligence.

There has been a long-standing pursuit of humanity centered around artificial general intelligence (AGI) systems capable of achieving human-level intelligence or even surpassing itenabling AGI to undertake a wide range of intellectual tasks, including reasoning, problem-solving and creativity.

Brain-inspired artificial intelligence is a field that has emerged from this endeavor, integrating knowledge from neuroscience, psychology, and computer science to create AI systems that are not only more efficient but also more powerful. In a new study published in Meta-Radiology, a team of researchers examined the core elements shared between human intelligence and AGI, with particular emphasis on scale, multimodality, alignment, and reasoning.

"Notably, recent advancements in large language models (LLMs) have showcased impressive few-shot and zero-shot capabilities, mimicking human-like rapid learning by capitalizing on existing knowledge," shared Lin Zhao, co-first author of the study. "In particular, in-context learning and prompt tuning play pivotal roles in presenting LLMs with exemplars to adeptly tackle novel challenges."

Moreover, the study delved into the evolutionary trajectory of AGI systems, examining both algorithmic and infrastructural perspectives. Through a comprehensive analysis of the limitations and future prospects of AGI, the researchers gained invaluable insights into the potential advancements that lie ahead within the field.

"Our study highlights the significance of investigating the human brain and creating AI systems that emulate its structure and functioning, bringing us closer to the ambitious objective of developing AGI that rivals human intelligence," said corresponding author Tianming Liu. "AGI, in turn, has the potential to enhance human intelligence and deepen our understanding of cognition. As we progress in both realms of human intelligence and AGI, they synergize to unlock new possibilities."

More information: Lin Zhao et al, When Brain-inspired AI Meets AGI, Meta-Radiology (2023). DOI: 10.1016/j.metrad.2023.100005

Provided by KeAi Communications Co.

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AWS announces generative A.I. tool to save doctors time on paperwork – CNBC

Attendees walk through an expo hall during Amazon Web Services' Reinvent conference at the Venetian in Las Vegas on Nov. 29, 2022.

Noah Berger | Getty Images Entertainment | Getty Images

AmazonWebServices on Wednesday announced a new service for health-care software providers called AWS HealthScribe, which uses generative artificial intelligence and speech recognition to automatically draft clinical documentation.

The service aims to save health-care workers time using AI-generated transcripts and summaries of patient visits, which can then be entered into the electronic health record system. AWS HealthScribe can also extract notable medical terms, medications and other key details, according to the company, and physicians can double-check each line of generated text with the original transcript.

Clinical documentation is a major pain point for doctors and nurses. A study funded by theAmerican Medical Associationin 2016 found that for every hour a physician spent with a patient, they spent an additional two hours on administrative work. The study said physicians also tend to spend an additional one to two hours doing clerical work outside of working hours, which many in the industry refer to as "pajama time."

As a result, several companies like Microsoft's Nuance Communications, and now AWS, have been working to build solutions to reduce this administrative burden.

"It is clear that generative AI has the power to transform the health-care and life sciences industry in many ways," Swami Sivasubramanian, AWS's vice president of database, analytics and machine-learning servicessaid at during a keynote speech at AWS Summit New York Wednesday.

Microsoft's Nuance announced its generative clinical notes application, DAX Express, in March. Similar to AWS HealthScribe, Dax Expressautomatically generates a draft of a clinical note within seconds after a patient visit. It can record a conversation between a doctor and a patient in real time and create a note using a combination of existing AI and OpenAI's newest model,GPT-4.

With both services, physicians can review the AI-generated notes before entering them into the electronic health record system.

AWS HealthScribe is powered by Amazon Bedrock, which is the company's service for building generative AI applications. AWS said Wednesday that AWS HealthScribe is compliant with HIPAA and does not retain any customer information. Customers can also choose where they would like to store their clinical documentation.

The cost of the service will vary, as AWS HealthScribe is available on a pay-as-you-go basis, according to a company blog post. Customers will be charged based on the seconds of audio processed per month.

Several organizations are already using HealthScribe, according to AWS, including the software company 3M Health Information Systems. Detlef Koll, 3M's vice president of global R&D, said the company has been working with AWS since this past fall to introduce the technology responsibly, ideally without causing disruptions in documentation quality.

Koll said the technology is "excellent," in his experience, and that the tool will not function as decision support or change the care that patients receive.

"Technology is an enabler for a solution, it's not the solution," Koll told CNBC in an interview Wednesday.

The initial use cases for AWS HealthScribe were geared toward general medicine and orthopedics specialties, according to an AWS company release. The technology is available in a limited private preview capacity starting Wednesday, and Tehsin Syed, general manager of health AI at AWS, said the company plans to work closely with its customers to determine plans for expanding access.

"We update the underlying technology based on the feedback that we're getting," Syed told CNBC. He added,"From an adoption perspective, I think there's a lot of interest, and we want to be very careful about making sure it's going to work at scale in the right way."

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Which US workers are exposed to AI in their jobs? – Pew Research Center

A radiologist at Unfallkrankenhaus Berlin uses anAI-based app to look at a patients brain scan images on a tablet. (Monika Skolimowska/picture alliance via Getty Images)

Pew Research Center conducted this study to understand how American workers may be exposed to artificial intelligence (AI) at their jobs. The study emphasizes the impact of AI on different groups of workers, such as men and women and racial and ethnic groups, and it includes new survey findings on how American adults think AI will impact them personally, setting it apart from preceding analyses.

By exposure to AI, we refer to the likelihood that the activities workers perform on their jobs may be replaced or aided by artificial intelligence. We make no determination as to whether workers may lose their jobs as a result or gain new jobs, and we also do not consider the role of robots.

Most of the analysis is based on data from the Occupational Information Network (O*NET), Version 27.3. O*NET analysts rate the importance of 41 work activities related to job performance in individual occupations. We grouped these activities into three categories based on their likely exposure to AI: low exposure, medium exposure and high exposure. Occupations were ranked by the relative importance of low- or high-exposure activities. Those in the top quarter of each ranking are the least or most exposed occupations. The remaining occupations have medium exposure.

Data on the employment and earnings of workers in individual occupations and their demographic profiles are from the Current Population Survey (IPUMS). Monthly files from January to December 2022 were combined to form an annual file. Earnings data is available for a quarter of this sample. The AI-exposure rankings of 873 detailed occupations from the O*NET data are matched to 485 broader occupations listed in the CPS.

A part of the analysis is based on a Pew Research Center survey of 11,004 U.S. adults conducted from Dec. 12 to 18, 2022. Everyone who took part in the survey is a member of the Centers American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATPs methodology.

Here are the questions used for this report, along with responses, and its methodology.

Artificial intelligence (AI): Broadly speaking, AI refers to a range of applications of machine learning, computer vision and natural language processing that allow computers or machines to perform tasks autonomously. AI can substitute for or complement a variety of human tasks, such as writing, drawing, providing customer service, reading radiology scans, driving cars and more. ChatGPT and Dall-E are examples of AI-driven technologies.

Low or high exposure to AI: We rated a set of 41 job-related work activities common to all occupations as having low, medium or high exposure to AI. How much an occupation is exposed to AI depends on which of these work activities is more important in that particular job. The importance scale runs from one (not important) to five (extremely important). In this report, we focus on the importance of low- and high-exposure activities. In one step of analysis, occupations are ranked by the relative importance of high-exposure activities in them; the jobs ranking in the top 25% are occupations that we rate as most exposed to AI. In another step, occupations are ranked by the relative importance of low-exposure activities in them. Those ranking in the top 25% are occupations that are least exposed to AI. A full description of the ranking process is in the methodology.

The terms occupation and job are often used interchangeably in the report, as are the terms earnings and wages.

White, Black, Asian and American Indian or Pacific Islander workers include those who report being only one race and are not Hispanic. Hispanics are of any race.

High school graduate refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). Adults with some college include those with an associate degree and those who attended college but did not obtain a degree.

U.S. born refers to individuals who are U.S. citizens at birth, including people born in the 50 U.S. states, the District of Columbia, Puerto Rico or other U.S. territories, as well as those born elsewhere to at least one parent who is a U.S. citizen. The terms foreign born and immigrant are used interchangeably in this report. They refer to people who are not U.S. citizens at birth.

Artificial intelligence (AI) recently gained new attention with the release of ChatGPT and Dall-E. These tools and the broader array of AI-driven business applications represent a new reality for workers.

Historically, changes in technology have often automated physical tasks, such as those performed on factory floors. But AI performs more like human brainpower and, as its reach grows, that has raised questions about its impact on professional and other office jobs questions that Pew Research Center seeks to address in a new analysis of government data.

Which jobs are more exposed to AI? Work-related tasks vary in their exposure to AI. Some activities, such as repairing equipment, may have low exposure to AI, while others may have a medium or a high degree of exposure. Also, activities with different levels of exposure may be equally important within many jobs.

In our analysis, jobs are considered more exposed to artificial intelligence if AI can either perform their most important activities entirely or help with them.

For example, AI could replace, at least to a degree, the tasks getting information and analyzing data or information, or it could help with working with computers. These are also among the key tasks for judicial law clerks and web developers, and they are more exposed to AI than other workers. However, AI alone cannot assist and care for others or perform general physical activities. Thus, nannies for whom these are essential activities are less exposed to AI.

In our analysis, jobs that placed in the top 25% when ranked by the importance of work activities with high exposure to AI were judged to be the most exposed. Jobs that placed in the top 25% when ranked by the importance of work activities with low exposure to AI are the least exposed. The remaining jobs, such as chief executives, are likely to see a medium level of exposure to AI. (Refer to the appendix for an extended list of examples of occupations in each group.)

Related: How we determined the degree to which jobs are exposed to artificial intelligence

Will exposure to AI lead to job losses? The answer to this is unclear. Because AI could be used either to replace or complement what workers do, it is not known exactly which or how many jobs are in peril. For this reason, our study focuses on the level of exposure jobs have to AI. It sets aside the question of whether this exposure will lead to jobs lost or jobs gained.

Consider customer service agents. Evidence shows that AI could either replace them with more powerful chatbots or it could enhance their productivity. AI may also create new types of jobs for more skilled workers much as the internet age generated new classes of jobs such as web developers. Another way AI-related developments might increase employment levels is by giving a boost to the economy by elevating productivity and creating more jobs overall.

Overall, AI is designed to mimic cognitive functions, and it is likely that higher-paying, white-collar jobs will see a fair amount of exposure to the technology. But our analysis doesnt consider the role of AI-enabled machines or robots that may perform mechanical or physical tasks. Recent evidence suggests that industrial robots may reduce both employment and wages. Moreover, jobs held by low-wage workers, those without a high school diploma, and younger men are more exposed to the effects of industrial robots.

What data did we use? This analysis rests on data on the importance of 41 essential work activities in 873 occupations from the U.S. Department of Labors Occupational Information Network (O*NET, Version 27.3). We used our judgment to determine which of these activities have low, medium or high exposure to AI, but focus on the importance of low- and high-exposure activities. For additional analysis, the 873 occupations were further grouped to a total of 485 for which government data on employment and earnings of workers were available. That allowed us to analyze the potential impact of AI on different groups of workers. Other findings about how workers feel about AI come from a Center survey of 11,004 U.S. adults conducted between Dec. 12 and 18, 2022. (Refer to the text boxes and methodology for more details.)

In our analysis, we considered two major questions when assessing the exposure of jobs to AI:

The O*NET database lists a set of 41 work activities in common across all occupations. Examples of these activities are getting information, selling or influencing others, and handling and moving objects (refer to the methodology for the complete list). We used our collective judgment to designate each activity as having high, medium or low exposure to AI. Consensus on some activities, such as performing general physical activities or processing information, was reached quickly. The former is judged as having low exposure to AI and the latter is judged as having high exposure.

In other instances, we used additional details on a work activity to reach consensus. The question we asked ourselves at this stage was the following:

Are most of the detailed tasks that comprise a work activity exposed to AI?

For example, the job activity performing for or working directly with the public is ambiguous on the surface. But consider the list of detailed tasks that comprise this broad activity:

The consensus we reached was that most of these detailed tasks, such as interfacing with customers or creating music, had a high degree of exposure to AI. Only a few tasks auditioning, comedic or dramatic performances and dancing were considered to have relatively low exposure to AI. For that reason, the broad activity performing for or working directly with the public is deemed to have high exposure to AI.

At the other end of the exposure scale is the work activity coaching and developing others, entailing:

The focus of most of these detailed tasks involves personal interaction. So, we judged that the activity coaching and developing others has low exposure to AI.

Overall, 16 work activities were assessed to have high exposure to AI, 16 more were judged to have medium exposure, and nine were deemed to have low exposure. (Refer to the methodology for where each activity was classified.)

The 41 work activities listed in O*NET are spread across all occupations in the O*NET database. That is to say, each occupation is a mix of low, medium and high AI-exposure activities. The question then is:

Which work activities are relatively more important in a job? Are high- or low-exposure activities more important than other activities?

To answer this, we first estimated the averages of the importance ratings for high-, medium- and low-exposure activities in each job, where the rating of each activity within a category is taken from the O*NET database. The rating for each activity ranges from one (not important) to five (extremely important).

Overall, among the 873 occupations we looked at, high-exposure activities were rated as being important to extremely important in 77% of occupations, and medium-exposure activities were similarly important in 72% of occupations. Low-exposure activities were rated as important in 39% of occupations. This suggests that high, medium and low exposure could simultaneously be important in a job.

The final step was to estimate the relative importance of high-, medium- or low-exposure activities in each job that is, to determine which tasks are more important than the others in any given job. This procedure is described in the methodology. Occupations were then ranked two ways, once by the relative importance of high-exposure work activities and again by the relative importance of low-exposure work activities.

In our analysis, jobs that are most exposed to AI are in the top 25% of occupations ranked by the relative importance of high-exposure activities. Jobs that are least exposed to AI are in the top 25% of occupations ranked by the relative importance of low-exposure work activities. The other jobs may be thought of as having a medium level of exposure to AI. (Refer to the appendix for examples of occupations that are among the most or least exposed or have a medium level of exposure.)

To take an example, consider mechanical drafters, who prepare detailed working diagrams of machinery and mechanical devices. Mechanical drafters are among the workers most exposed to AI. For them, high-exposure activities have an average rating of 3.28 but low-exposure activities have an average rating of 2.36, where a rating of 3 means an activity is important.

For nannies, among the least exposed workers, high-exposure activities have an average rating of 2.36 but low-exposure activities have a rating of 3.03.

Our analysis follows in the footsteps of other researchers who have recently examined the impact of AI on the workplace. Eloundou, Manning, Mishkin and Rock (March 2023) conclude that about one-in-five U.S. workers may see an impact on half or more of their job tasks. Felten, Raj and Seamans (April 2021) find that white-collar occupations requiring advanced degrees are most exposed to AI, as are industries providing financial or legal services. Webb (January 2020) reports that high-skill occupations, highly educated and older workers will be more impacted by AI, but he does not draw conclusions about the nature or the extent of the impact on workers. Our findings are broadly consistent with the results of these analyses.

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Will architects really lose their jobs to AI? – Dezeen

As part of our AItopia series exploring how AI will impact architecture and design, Dezeen examines whether the technology will end up taking architects' jobs.

In 2019, New York-based designer Sebastian Errazuriz caused a stir with his claim that 90 per cent of architects could lose their jobs to machines.

Four years on, following the emergence of several generative-AI models such as Midjourney and ChatGPT, Errazuriz is writing a book about AI's impact on society and told Dezeen his opinion has not changed.

"It's an enormous issue that we need to try and deal with," he said."People always say, 'but isn't AI just another tool?' Right now it looks like a tool, but the tool is getting really good, really fast and the purpose of this tool is to think for itself."

He claims that well-known architects who mocked his warnings in 2019 have recently conceded privately that he was right.

Architecture at high risk of automation

Investment bank Goldman Sachs made headlines in March with its prediction that AI could replace the equivalent of 300 million jobs globally across all industries.The researchers estimated that 37 per cent of architecture and engineering work tasks "could be automated by AI", placing it among the most-exposed industries.

And this month, the Organisation for Economic Co-operation and Development echoed the warnings about job losses in skilled professions.

A survey conducted by design technology firm RevitGods found that 55 per cent of US architects are "moderately concerned" about being replaced by AI in the future, with a further 20 per cent "very concerned".

But not everyone shares Errazuriz's pessimism. Among them is Phillip Bernstein, associate dean and professor adjunct at the Yale School of Architecture who previously held senior roles at software firm Autodesk and Pelli Clarke Pelli Architects and has authored a book on architecture and AI.

"I have been around long enough to see multiple waves of technological change in the industry and this argument happens every single time," Bernstein told Dezeen.

"It happened during CAD [computer-aided design], it happened during BIM [building information modelling], and now it's happening with AI," he added. "We somehow always seem to survive these things."

The Dezeen guide to AI

Bernstein argues that even the best AIs are still nowhere near the competence of a qualified architect.

"I happen to think that what we do as architects is pretty complicated," he said."At it's best, it's about managing a complex, multi-variable problem and making a series of ambiguous judgements that require trade-offs, and I don't believe we will get to a model that can do even 10 per cent of that in the foreseeable future."

"If we're not even at the point where a car can drive itself, I think we're a long way from the point where an algorithm can be a professional architect," he continued.

"Our current traditional methodology will radically change"

The possible emergence of artificial general intelligence in the future a hotly debated topic would likely upend all intellectual industries and much of our current society.

For now, most people's idea of AI architecture is likely the dreamlike, eerily real-looking visualisations created with text-to-image models such as Midjourney.However, AI tools created specifically for use in architecture and design are beginning to enter the market.

Combined, the most advanced of these can generate massing images, floor plans, cost analyses, material specifications and technical drawings though none can yet do it all.

In a recent interview with Dezeen, the co-founder and CEO of one of the leading systems, LookX, dismissed the idea that it could replace architects.

Instead, Wanyu He argued, AI "will liberate us from repetition and allow us to concentrate on things with added value to society".

In particular, he claims it will dramatically speed up the feasibility-testing process, freeing up time to spend on the more creatively rewarding aspects of architecture.

Few have spent more time experimenting with AI-architecture tools than Harvard Graduate School of Design research associate and ArchiTAG co-founder George Guida.He agrees that the technology will not replace architects anytime soon.

"Our current traditional methodology will radically change, but not be substituted," said Guida. "I do think that architects still will need to stay at the centre of being the driver within that process."

"So I think productivity will increase let's say we'll have more time and space to design. I think that the role of the architect will simply have to evolve, but it won't be replaced," he continued.

"It will give smaller firms a stronger edge"

But even if AI is not capable of usurping the human architect outright, could architecture jobs still be lost as the technology is adopted?

"I do think there are large swathes of the work that are potentially automatable by AI," said Bernstein. "So the question is what happens to that additional capacity. Do we use it to do our jobs better, or do we eliminate some of those jobs?"

While both believe that AI could eventually be used by developers to design the simplest, most generic projects in-house, Bernstein and Guida are optimistic that the technology presents an opportunity for architecture studios to command higher fees.

"If I can say to a client that I'm using this tech to do a better job, then maybe I can charge more for my time," said Bernstein.

That could give smaller studios that are quick to adopt AI a chance to compete with bigger firms, argues Guida.

"Increasing productivity gives a great opportunity for emerging practices to bring a competitive edge," he said. "So in the short term it won't remove jobs if anything, it will give smaller firms a stronger edge."

Tim Fu uses "Midjourney for architecture" to transform crumpled paper into starchitect buildings

But Errazuriz remains unconvinced. He thinks the major impact will be at bigger firms, where, he points out, only a small handful of employed architects spend a significant portion of their time on creative work.

He argues the idea that AI will enable all these architects to remain in their jobs and simply spend more time exploring their imaginations is "wishful thinking".

"What will most probably happen is that you just start reducing the size of those architecture studios," he said."Depending on how really good this software gets, in the worst-case scenario they would continue to decrease enormously over a 10-year period."

His comments appear to chime with remarks made earlier this year by Morphosis founder Thom Mayne that AI will lead to a decrease in the number of architects in individual studios to a "more intimate" level.

Wider factors at play

An alternative outcome is that the productivity gains afforded by AI could lead to studios and therefore their employees taking on more projects at once.

Architecture critic Kate Wagner has argued this was the main upshot of CAD, lengthening working hours despite similar hopes in the technology's early days that it could free up time for creativity.

Bernstein hopes that the adoption of AI in architecture won't see history repeat itself.

"It's true that CAD didn't enhance the value proposition of architecture, but it was happening alongside a period of buildings getting much more complex and the design and construction industry getting riskier," said Bernstein.

"So architects were drawing more defensively, and CAD made doing that easier. Now, as we are starting to leverage data about buildings to much greater effect, there is a potential value proposition there."

Saudi-based architect Reem Mosleh, who has made a name for herself as a leading voice on AI design, agrees that other factors will influence how the technology ends up impacting the architecture profession.

For instance, she believes it could reduce overtime in combination with a wider cultural shift away from working long hours.

"After Covid, people's priorities in life have changed drastically," she said. "So I really hope that with AI we could actually have this opportunity to live with better balance."

"You can't run away from it"

Regardless of their opinion on the threat that AI poses to jobs, everyone Dezeen spoke to agreed that architects should be proactively getting to grips with the technology.

"If I were a young practitioner I would be playing with this stuff so I understand it, and if I were a practice I would be giving practitioners time to try it out," said Bernstein.

"You can't run away from it, you need to run towards it," said Errazuriz. "Otherwise you'll be like those people that refuse to have a cell phone. You need to stay up-to-date, checking the latest things that come out and incorporating it into the flow or your team or your own work."

"For now, we're the ones giving the prompts," he added. "And so we need to sort of dig deep into our own creativity, into our storytelling, into why we're doing something."

Mosleh is hopeful that any disruption to architecture jobs will be outweighed by new opportunities opened up by AI.

"Architects who decide not to go beyond their normal practice will definitely be at risk," she said. "If you don't evolve you get replaced, it's nature."

"But at the same time I'm sure that those who are seizing the moment, and taking the opportunity will actually have better jobs and more opportunities."

The main image was created using Dall-E 2.

AItopia

This article is part of Dezeen'sAItopiaseries, which explores the impact of artificial intelligence (AI) on design, architecture and humanity, both now and in the future.

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