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The impact of introducing artificial intelligence technology to architecture and its leverage on the concept of design automation

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Thesis: A strategic perspective on the commercialization of artificial intelligence

Submitted by Siddhartha Ray Barua.

Abstract: Many companies are increasing their focus on Artificial Intelligence as they incorporate Machine Learning and Cognitive technologies into their current offerings. Industries ranging from healthcare, pharmaceuticals, finance, automotive, retail, manufacturing and so many others are all trying to deploy and scale enterprise Al systems while reducing their risk. Companies regularly struggle with finding appropriate and applicable use cases around Artificial Intelligence and Machine Learning projects. The field of Artificial Intelligence has a rich set of literature for modeling of technical systems that implement Machine Learning and Deep Learning methods. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of Artificial Intelligence systems. The aim of this research is to identify high and low value market segments and use cases within the industries, prognosticate the evolution of different Al technologies and begin to outline the implications of commercialization of such technologies for various stakeholders. This thesis also provides a framework to better prepare business owners to commercialize Artificial Intelligence technologies to satisfy their strategic goals.

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Impact.AI: Democratizing AI through K-12 Artificial Intelligence Education

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Randi Williams

Feb. 15, 2023

• Randi Williams Former Research Assistant
• Cynthia Breazeal Professor of Media Arts and Sciences; MIT Dean for Digital Learning
• Hal Abelson Former Professor
• Impact.AI: K-12 AI Literacy

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Williams, Randi. Impact.AI: democratizing AI through K-12 artificial intelligence education. Dissertation Proposal. Massachusetts Institute of Technology, 2023.

Today’s artificial intelligence (AI) technologies are changing how many people play, work, learn, and govern themselves. This rapid technological change is already having significant effects on individuals’ lives and opportunities. Thus, researchers, educators, and government leaders must consider how to prepare a diverse citizenry to thrive in the emerging age of AI, for example through outreach initiatives like grade school AI curricula. We propose to develop AI curricula and educational platforms that support K-12 students in fostering identities as technosocial change agents while they learn about AI. First, we introduce a new AI literacy framework, Impact.AI, that covers the AI concepts, practices, and perspectives that align with a technosocial change agent identity. This framework will inform the development of middle school AI curricula that empower students to become conscious consumers, ethical engineers, and informed advocates of AI. Next, we will develop AI education tools that facilitate students’ learning about AI as they work on AI projects. These tools will provide technical scaffolding, encourage creativity, and guide reflection. Finally, we will bring together these two strands of work to evaluate how well these artifacts improve learning outcomes and students’ attitudes toward AI. As AI becomes increasingly prevalent in everyday life, it is important that all people have the opportunity to both understand and shape the technology.

Williams, R. (2024) Impact.AI: Democratizing AI through K-12 artificial intelligence education. [Doctoral dissertation, Massachusetts Institute of Technology].

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Williams, R.; and Breazeal, C. 2023. Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students. In Proceedings of the 13th Symposium on Education Advances in Artificial Intelligence (EAAI ’24). AAAI, Menlo Park, CA, USA.

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Artificial Intelligence

Leading the AI-driven organization

Beth Stackpole

Apr 3, 2024

Artificial intelligence technology is evolving quickly, and most organizations are struggling to keep up. Experimentation and pilot projects are well underway as companies try to create a framework for capitalizing on AI.

To achieve desired outcomes, leaders need to be equipped to lead AI initiatives, according to MIT Sloan senior lecturer Paul McDonagh-Smith, the faculty director of a new MIT Sloan Executive Education course about leading AI-driven organizations . The course looks at how leaders can apply AI technologies to optimize value in business operations, guide organizations beyond exploration to implementation, and ensure the responsible application of AI, which is crucial as expanded use cases increase enterprise risk.

“There’s currently a real delta between the numerous proofs of concept and full-blown AI strategies,” McDonagh-Smith said. To close that gap, leaders need to create a plan for using AI that takes into account key business priorities, the organization’s data strategy, and employee skills.

Throughout, leaders need to remember that the key to AI is “not humans plus machines, but humans multiplied by machines, augmenting and assisting our capabilities across the existing and emergent tasks performed in our organizations,” he said.

McDonagh-Smith had the following advice for leaders who are guiding AI initiatives:

Create an AI playbook

There is no singular route to large-scale AI deployment. Rather, McDonagh-Smith advises leaders to use their answers to these three sets of questions to map out their organization’s journey:

What are the organization’s critical business problems and strategies, and can AI be used to deliver upon on those strategies? 

AI use isn’t about staying ahead of the curve or being part of a trend. Organizations need to clearly define the business problem they are trying to solve and fully understand the breadth of available AI technologies and techniques available to them so they can match a solution to the problem. One way to facilitate this exercise is to deconstruct a business challenge into a set of subproblems. “In the field of AI, you determine which technologies and techniques are going to be helpful for these subproblems,” McDonagh-Smith said. “You also need to abstract and separate the signal from the noise.”

Is the organization’s data AI-ready (that is, are the datasets that are being trained suitably complete and managed with the proper governance)?

A key ingredient to successful AI is having the right datasets for exploring, analyzing, and recognizing patterns for each use case and business problem. Organizations need to elevate their data game by launching data cleansing and data management initiatives, establishing data governance, and aligning with the right third-party data partners where and when it makes sense.

What is the AI maturity level of organization employees, and where are there key skills gaps? What kind of programs should be in place to upskill employees on critical new competencies?

AI skills are not overly abundant, so organizations need programs to upskill and train employees on technical skills and technical decision-making to fully leverage AI’s capabilities.

Culture is a big part of the equation: Organizations will need to create silo-busting cross-functional teams, make failure permissible to encourage creativity, and encourage innovative ways to combine human and machine capabilities in complementary systems.

Given the current frenzy around AI, level-setting expectations is a crucial element of enterprise-scale AI success. One way to achieve this is to recognize the key duality of AI: —Organizations need to adopt a fast-and-slow, two-tier approach to their enterprise AI strategies, McDonagh-Smith said. “Fast experiments and proofs of concept are fed into the creation of slower, longer-term strategy,” he said, pointing to the idea about two modes of thinking — one fast and intuitive and the other slow and analytical — that Nobel Prize-winning behavioral economist Daniel Kahneman  examined in his work.

“By applying Kahneman’s ‘thinking fast and slow’ [concept] to how we use AI today, we can enhance organizational capabilities and competitiveness tomorrow,” McDonagh-Smith said. In addition, “we’ve got to be careful that the enthusiasm and optimism doesn’t get waylaid and that we’re able to have a practical, reasonable strategy for how we’re going to integrate AI into our businesses.”

Embrace experimentation and responsible AI 

With those questions answered, organizations have a strong foundation for moving forward with AI initiatives. When doing so, McDonagh-Smith said, leaders should remember the following:   

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Embrace experimentation while setting strategy. With AI, organizations need to continuously experiment, build proofs of concept, and promote sandbox activities. At the same time, they need to capture the lessons and data from these experiments — both successes and failures — and use them to inform medium-to-longer-term AI strategies. Most organizations lean too far in one direction or the other, McDonagh-Smith said.

“We might be experimenting but not envisioning what this means for a medium-to-longer-term strategy,” he said. “But if all we’re thinking about is medium-to-longer-term enterprise strategies, we’re never going to be able to fine tune enough of the proofs of concept to create value for our businesses.”

Ensure responsible AI use. Without the proper safeguards, AI opens the door to significant enterprise risks, including potential brand damage, privacy infractions, and the spread of dangerous misinformation. Companies need to identify and communicate their ethical values, create accountability through organizational structures, and take continuous steps to detect and remediate bias.

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How One Tech Skeptic Decided A.I. Might Benefit the Middle Class

David Autor, an M.I.T. economist and tech contrarian, argues that A.I. is fundamentally different from past waves of computerization.

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By Steve Lohr

David Autor seems an unlikely A.I. optimist. The labor economist at the Massachusetts Institute of Technology is best known for his in-depth studies showing how much technology and trade have eroded the incomes of millions of American workers over the years.

But Mr. Autor is now making the case that the new wave of technology — generative artificial intelligence, which can produce hyper-realistic images and video and convincingly imitate humans’ voices and writing — could reverse that trend.

“A.I., if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization,” Mr. Autor wrote in a paper that Noema Magazine published in February.

Mr. Autor’s stance on A.I. looks like a stunning conversion for a longtime expert on technology’s work force casualties. But he said the facts had changed and so had his thinking.

Modern A.I., Mr. Autor said, is a fundamentally different technology, opening the door to new possibilities. It can, he continued, change the economics of high-stakes decision-making so more people can take on some of the work that is now the province of elite, and expensive, experts like doctors, lawyers, software engineers and college professors. And if more people, including those without college degrees, can do more valuable work, they should be paid more, lifting more workers into the middle class.

The researcher, whom The Economist once called “the academic voice of the American worker,” started his career as a software developer and a leader of a computer-education nonprofit before switching to economics — and spending decades examining the impact of technology and globalization on workers and wages.

Mr. Autor, 59, was an author of an influential study in 2003 that concluded that 60 percent of the shift in demand favoring college-educated workers over the previous three decades was attributable to computerization. Later research examined the role of technology in wage polarization and in skewing employment growth toward low-wage service jobs .

Other economists view Mr. Autor’s latest treatise as a stimulating, though speculative, thought exercise.

“I’m a great admirer of David Autor’s work, but his hypothesis is only one possible scenario,” said Laura Tyson, a professor at the Haas School of Business at the University of California, Berkeley, who was chair of the Council of Economic Advisers during the Clinton administration. “There is broad agreement that A.I. will produce a productivity benefit, but how that translates into wages and employment is very uncertain.”

That uncertainty usually veers toward pessimism. Not just Silicon Valley doomsayers, but mainstream economists predict that many jobs, from call center workers to software developers, are at risk. In a report last year , Goldman Sachs concluded that generative A.I. could automate activities equivalent to 300 million full-time jobs globally.

In Mr. Autor’s latest report, which was also published in the National Bureau of Economic Research, he discounts the likelihood that A.I. can replace human judgment entirely. And he sees the demand for health care, software, education and legal advice as almost limitless, so that lowering costs should expand those fields as their products and services become more widely affordable.

It’s “not a forecast but an argument” for an alternative path ahead, very different from the jobs apocalypse foreseen by Elon Musk, among others, he said.

Until now, Mr. Autor said, computers were programmed to follow rules. They relentlessly got better, faster and cheaper. And routine tasks, in an office or a factory, could be reduced to a series of step-by-step rules that have increasingly been automated. Those jobs were typically done by middle-skill workers without four-year college degrees.

A.I., by contrast, is trained on vast troves of data — virtually all the text, images and software code on the internet. When prompted, powerful A.I. chatbots like Open AI’s ChatGPT and Google’s Gemini can generate reports and computer programs or answer questions.

“It doesn’t know rules,” Mr. Autor said. “It learns by absorbing lots and lots of examples. It’s completely different from what we had in computing.”

An A.I. helper, he said, equipped with a storehouse of learned examples can offer “guidance” (in health care, did you consider this diagnosis?) and “guardrails” (don’t prescribe these two drugs together).

In that way, Mr. Autor said, A.I. becomes not a job killer but a “worker complementary technology,” which enables someone without as much expertise to do more valuable work.

Early studies of generative A.I. in the workplace point to the potential. One research project by two M.I.T. graduate students , whom Mr. Autor advised, assigned tasks like writing short reports or news releases to office professionals. A.I. increased the productivity of all workers, but the less skilled and experienced benefited the most. Later research with call center workers and computer programmers found a similar pattern.

But even if A.I. delivers the largest productivity gains to less-experienced workers, that does not mean they will reap the rewards of higher pay and better career paths. That will also depend on corporate behavior, worker bargaining power and policy incentives.

Daron Acemoglu, an M.I.T. economist and occasional collaborator of Mr. Autor’s, said his colleague’s vision is one possible path ahead, but not necessarily the most likely one. History, Mr. Acemoglu said, is not with the lift-all-boats optimists.

“We’ve been here before with other digital technologies, and it hasn’t happened,” he said.

Mr. Autor acknowledges the challenges. “But I do think there is value in imagining a positive outcome, encouraging debate and preparing for a better future,” he said. “This technology is a tool, and how we decide to use it is up to us.”

Steve Lohr writes about technology and its impact on the economy, jobs and the workplace. More about Steve Lohr

Explore Our Coverage of Artificial Intelligence

News  and Analysis

U.S. clinics are starting to offer patients a new service: having their mammograms read not just by a radiologist, but also by an A.I. model .

OpenAI unveiled Voice Engine , an A.I. technology that can recreate a person’s voice from a 15-second recording.

Amazon said it had added $2.75 billion to its investment in Anthropic , an A.I. start-up that competes with companies like OpenAI and Google.

The Age of A.I.

A.I. tools can replace much of Wall Street’s entry-level white-collar work , raising tough questions about the future of finance.

The boom in A.I. technology has put a more sophisticated spin on a kind of gig work that doesn’t require leaving the house: training A.I, models .

Teen girls are confronting an epidemic of deepfake nudes in schools  across the United States, as middle and high school students have used A.I. to fabricate explicit images of female classmates.

A.I. is peering into restaurant garbage pails  and crunching grocery-store data to try to figure out how to send less uneaten food into dumpsters.

David Autor, an M.I.T. economist and tech skeptic, argues that A.I. is fundamentally different  from past waves of computerization.

Economists doubt that A.I. is already visible in productivity data . Big companies, however, talk often about adopting it to improve efficiency.

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Most work is new work, long-term study of U.S. census data shows

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This is part 1 of a two-part MIT News feature examining new job creation in the U.S. since 1940, based on new research from Ford Professor of Economics David Autor. Part 2 is available here .

In 1900, Orville and Wilbur Wright listed their occupations as “Merchant, bicycle” on the U.S. census form. Three years later, they made their famous first airplane flight in Kitty Hawk, North Carolina. So, on the next U.S. census, in 1910, the brothers each called themselves “Inventor, aeroplane.” There weren’t too many of those around at the time, however, and it wasn’t until 1950 that “Airplane designer” became a recognized census category.

Distinctive as their case may be, the story of the Wright brothers tells us something important about employment in the U.S. today. Most work in the U.S. is new work, as U.S. census forms reveal. That is, a majority of jobs are in occupations that have only emerged widely since 1940, according to a major new study of U.S. jobs led by MIT economist David Autor.

“We estimate that about six out of 10 jobs people are doing at present didn’t exist in 1940,” says Autor, co-author of a newly published paper detailing the results. “A lot of the things that we do today, no one was doing at that point. Most contemporary jobs require expertise that didn’t exist back then, and was not relevant at that time.”

This finding, covering the period 1940 to 2018, yields some larger implications. For one thing, many new jobs are created by technology. But not all: Some come from consumer demand, such as health care services jobs for an aging population.

On another front, the research shows a notable divide in recent new-job creation: During the first 40 years of the 1940-2018 period, many new jobs were middle-class manufacturing and clerical jobs, but in the last 40 years, new job creation often involves either highly paid professional work or lower-wage service work.

Finally, the study brings novel data to a tricky question: To what extent does technology create new jobs, and to what extent does it replace jobs?

The paper, “ New Frontiers: The Origins and Content of New Work, 1940-2018 ,” appears in the Quarterly Journal of Economics . The co-authors are Autor, the Ford Professor of Economics at MIT; Caroline Chin, a PhD student in economics at MIT; Anna Salomons, a professor in the School of Economics at Utrecht University; and Bryan Seegmiller SM ’20, PhD ’22, an assistant professor at the Kellogg School of Northwestern University.

“This is the hardest, most in-depth project I’ve ever done in my research career,” Autor adds. “I feel we’ve made progress on things we didn’t know we could make progress on.”

“Technician, fingernail”

To conduct the study, the scholars dug deeply into government data about jobs and patents, using natural language processing techniques that identified related descriptions in patent and census data to link innovations and subsequent job creation. The U.S. Census Bureau tracks the emerging job descriptions that respondents provide — like the ones the Wright brothers wrote down. Each decade’s jobs index lists about 35,000 occupations and 15,000 specialized variants of them.

Many new occupations are straightforwardly the result of new technologies creating new forms of work. For instance, “Engineers of computer applications” was first codified in 1970, “Circuit layout designers” in 1990, and “Solar photovoltaic electrician” made its debut in 2018.

“Many, many forms of expertise are really specific to a technology or a service,” Autor says. “This is quantitatively a big deal.”

He adds: “When we rebuild the electrical grid, we’re going to create new occupations — not just electricians, but the solar equivalent, i.e., solar electricians. Eventually that becomes a specialty. The first objective of our study is to measure [this kind of process]; the second is to show what it responds to and how it occurs; and the third is to show what effect automation has on employment.”

On the second point, however, innovations are not the only way new jobs emerge. The wants and needs of consumers also generate new vocations. As the paper notes, “Tattooers” became a U.S. census job category in 1950, “Hypnotherapists” was codified in 1980, and “Conference planners” in 1990. Also, the date of U.S. Census Bureau codification is not the first time anyone worked in those roles; it is the point at which enough people had those jobs that the bureau recognized the work as a substantial employment category. For instance, “Technician, fingernail” became a category in 2000.

“It’s not just technology that creates new work, it’s new demand,” Autor says. An aging population of baby boomers may be creating new roles for personal health care aides that are only now emerging as plausible job categories.

All told, among “professionals,” essentially specialized white-collar workers, about 74 percent of jobs in the area have been created since 1940. In the category of “health services” — the personal service side of health care, including general health aides, occupational therapy aides, and more — about 85 percent of jobs have emerged in the same time. By contrast, in the realm of manufacturing, that figure is just 46 percent.

Differences by degree

The fact that some areas of employment feature relatively more new jobs than others is one of the major features of the U.S. jobs landscape over the last 80 years. And one of the most striking things about that time period, in terms of jobs, is that it consists of two fairly distinct 40-year periods.

In the first 40 years, from 1940 to about 1980, the U.S. became a singular postwar manufacturing powerhouse, production jobs grew, and middle-income clerical and other office jobs grew up around those industries.

But in the last four decades, manufacturing started receding in the U.S., and automation started eliminating clerical work. From 1980 to the present, there have been two major tracks for new jobs: high-end and specialized professional work, and lower-paying service-sector jobs, of many types. As the authors write in the paper, the U.S. has seen an “overall polarization of occupational structure.”

That corresponds with levels of education. The study finds that employees with at least some college experience are about 25 percent more likely to be working in new occupations than those who possess less than a high school diploma.

“The real concern is for whom the new work has been created,” Autor says. “In the first period, from 1940 to 1980, there’s a lot of work being created for people without college degrees, a lot of clerical work and production work, middle-skill work. In the latter period, it’s bifurcated, with new work for college graduates being more and more in the professions, and new work for noncollege graduates being more and more in services.”

Still, Autor adds, “This could change a lot. We’re in a period of potentially consequential technology transition.”

At the moment, it remains unclear how, and to what extent, evolving technologies such as artificial intelligence will affect the workplace. However, this is also a major issue addressed in the current research study: How much does new technology augment employment, by creating new work and viable jobs, and how much does new technology replace existing jobs, through automation? In their paper, Autor and his colleagues have produced new findings on that topic, which are outlined in part 2 of this MIT News series.

Support for the research was provided, in part, by the Carnegie Corporation; Google; Instituut Gak; the MIT Work of the Future Task Force; Schmidt Futures; the Smith Richardson Foundation; and the Washington Center for Equitable Growth.

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Prof. David Autor speaks with PBS host Walter Isaacson about the fear  surrounding AI’s impact in the workforce and his view that AI could provide new opportunities for middle class workers. “Most of the time, technology is good for the elite and not so good for everybody else,” says Autor. “[AI] is a case where the technology might compete a little bit more with the elite and enable more people to do valuable work,” resulting in higher wages and more job opportunity for the middle class. 

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