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AI revolution: How artificial intelligence is reshaping education and jobs in America

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Artificial intelligence has rapidly become a part of American’s lives. What once was a fringe concept a few years ago is now an everyday tool.

Its expansive reach affects what and how students study, as well as the job sector, prompting some to question how students and higher education at large should respond.

The best way an undergrad can prepare for an AI-altered workforce is to develop human qualities that machines cannot replicate, such as critical thinking, creativity, and social intelligence, some experts told The College Fix.

While the value of specific majors may diminish, careers in mental health, healthcare, and fields requiring high-level decision-making and management will remain viable, they said.

But make no mistake, the role of humans will increasingly center on collaboration with AI.

AI will be a job killer. It will also be a job creator.

While some jobs will be eliminated, others will be created.

“The amount of work that’s being created and the opportunities to both create and contribute are going to be expanded exponentially,” said corporate advisor Jack Myers, a University of Arizona lecturer in its School of Information Science.

Forecasts predicting the coming obsolescence of countless careers should be viewed “through the prism of not only what’s going to be eliminated, but what’s going to be created,” he told The College Fix in a telephone interview.

Jobs in coding, basic processing, routine bookkeeping, low-complexity customer service and translation will all soon be eliminated, Myers said.

But the opportunities ushered in by AI are going to be exceptional, said Myers, author of the book “The Tao of Leadership: Harmonizing Technological Innovation and Human Creativity in the AI Era.”

“If you look at almost any area of human creation,” Myers said, “it will be enhanced through the same type of collaborative partnership as if the creator was hiring an expert to assist and support in the process.”

Joey Kim, chair of the Department of Engineering and Computer Science at Master’s University, described AI as “simply a tool.”

“With the advent of new tools, careers do disappear,” Kim said in a telephone interview with The Fix. “There’s also careers that get modified…. It’s not simply binary and careers [either] remain unaffected or [become] obsolete. There is a spectrum.”

But like it or not, AI will be part of many jobs, said Michael Pavlin, an associate professor in the School of Business and Economics at Wilfrid Laurier University, who has been involved in AI research since the early 2000s and serves as the chair of his school’s management analytics program.

“It’s hard to imagine a white collar job where you’re not going to be interacting with AI at some level,” he said in a telephone interview.

However, despite recent AI advances, he said he remains “more on the skeptical side,” later adding he believes “we’re being a little bit oversold.”

Reva Freedman, an associate professor of computer science at Northern Illinois University with expertise in computational linguistics, said AI “is going to have a huge impact on the job market, but not different in kind to the effect that computerization had with the invention of the PC in 1983.”

“In offices, [b]efore the invention of the PC, lots of people had jobs as secretaries and clerks. Secretaries typed memos that other people wrote. Those jobs have been largely replaced by people using word processors themselves,” she said via email. “Clerks did a variety of jobs that have been automated by use of Excel and other software.”

The jobs that will survive require high-level thinking, management skills, or require hands-on work, such as medicine, Freedman said.

Gary Clemenceau, a “deep geek” turned chaplain and author, who claims 30 years of experience in tech, agrees. He told The Fix that “mental health and healthcare jobs, and anything that requires dealing with humans and higher-order thinking, will still be viable.”

AI and the dumbing-down of higher education

But will there be any higher-order thinking left?

“For teachers, it’s absolutely impossible to give a writing [assignment] today that students can’t cheat on,” Freedman said. “Even for an in-class assignment, you can now get glasses that allow you to look up stuff on the web during an exam.”

Kim said the misuse of AI in the classroom devalues a degree’s representation of how well one has been trained in a program and successfully met its requirements.

Freedman also expressed concerns over the misuse of AI in other segments of society, citing allegations it was used to write a recent MAHA report said to contain made-up citations.

Pavlin told The Fix he is more concerned about less obvious errors that require a greater level of expertise to detect. For example, when querying AI about esoteric subjects related to his research, he tends to find deeper ways in which AI makes mistakes than he would if he similarly asked AI a question about general relativity.

In that sense, AI is not bulletproof. Kim echoed similar sentiments: “When big important decisions must be made where it’s either life-or-death or costing millions and millions of dollars, you’re going to need something more than ChatGPT.”

Yet, as some of the scholars interviewed by The Fix noted, the increasing overuse of AI by students may lead to the attrition of capacities beyond their proficiency at using ChatGPT.

“I think it’s impacting their learning,” Pavlin said. “Not all students, but [there is] definitely a subset of students where I’m concerned about their critical thinking skills.”

AI and the college student

When asked how students could best prepare for the careers that await them in an AI-altered job market, most of the scholars interviewed recommended they develop their more uniquely human attributes.

“The machines are already smarter than the human brain in many instances,” Myers told The Fix. “[They have] been for a while and that’s just going to continue to become increasingly the norm.”

“So where does the human come in?” Myers asked rhetorically, answering that humans enter through the “collaborative process” and “the unique human qualities of the human brain.” These he said are developed in the social sciences and humanistic majors.

Clemenceau said students must develop their human qualities.

“Students need to put down their phones and THINK,” Clemenceau wrote in an email to The College Fix. “AI is not very good at being creative.”

Whether majoring in computer science and learning to code is still a wise choice was a point of some disagreement.

“Coding will be irrelevant as a tool or resource to bring to the table,” Myers said. “The AI is doing its own coding going forward. It doesn’t need the human coders anymore.”

In contrast, Freedman noted that people “have been saying ever since I was a beginning programmer (in the 70’s) that programs that can write programs were coming.”

“Is it more true now? Probably. Does that mean the [number] of programmers needed will go down? T[h]at’s a much harder question to answer.”

“I think there will always be room for people who care about the quality of their work, understand the business needs, and can communicate with non-programmers,” she said.

As for choosing a major, though, she added: “I don’t think students’ majors have a lot to do with their success in the work world; their personal qualities are a lot more important. So I don’t think we can tell students what majors will be more useful.”

Kim expressed similar sentiments, saying “I personally believe that with any major, if you’re going to be using your tools to your advantage, and if you’re really going to be motivated enough to not just follow the crowd, you will have a job.”

Clemenceau said the future may be bleaker than his optimistic peers.

“I see two roads,” he said via email. “A small percentage of people will reject AI as inhuman and soulless and empty, and take the ‘human road’ as much as possible, living more spiritual lives.”

However, he added, a “larger percentage of people will fully embrace AI and (sadly) sacrifice part of their humanity, becoming less creative, less able to think critically – and more easily manipulated.”

MORE: Using AI to write essays can impair brain function: MIT study

IMAGE CAPTION AND CREDIT: A graphic showing a laptop user employing AI / Supatman, CanvaPro

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Artificial intelligence helps break barriers for Hispanic homeownership | Business

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Artificial intelligence helps break barriers for Hispanic homeownership | Business | journalgazette.net


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UW lab spinoff focused on AI-enabled protein design cancer treatments

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A Seattle startup company has inked a deal with Eli Lilly to develop AI powered cancer treatments. The team at Lila Biologics says they’re pioneering the translation of AI design proteins for therapeutic applications. Anindya Roy is the company’s co-founder and chief scientist. He told KUOW’s Paige Browning about their work.

This interview has been edited for clarity.

Paige Browning: Tell us about Lila Biologics. You spun out of UW Professor David Baker’s protein design lab. What’s Lila’s origin story?

Anindya Roy: I moved to David Baker’s group as a postdoctoral scientist, where I was working on some of the molecules that we are currently developing at Lila. It is an absolutely fantastic place to work. It was one of the coolest experiences of my career.

The Institute for Protein Design has a program called the Translational Investigator Program, which incubates promising technologies before it spins them out. I was part of that program for four or five years where I was generating some of the translational data. I met Jake Kraft, the CEO of Lila Biologics, at IPD, and we decided to team up in 2023 to spin out Lila.

You got a huge boost recently, a collaboration with Eli Lilly, one of the world’s largest pharmaceutical companies. What are you hoping to achieve together, and what’s your timeline?

The current collaboration is one year, and then there are other targets that we can work on. We are really excited to be partnering with Lilly, mainly because, as you mentioned, it is one of the top pharma companies in the US. We are excited to learn from each other, as well as leverage their amazing clinical developmental team to actually develop medicine for patients who don’t have that many options currently.

You are using artificial intelligence and machine learning to create cancer treatments. What exactly are you developing?

Lila Biologics is a pre-clinical stage company. We use machine learning to design novel drugs. We have mainly two different interests. One is to develop targeted radiotherapy to treat solid tumors, and the second is developing long acting injectables for lung and heart diseases. What I mean by long acting injectables is something that you take every three or six months.

Tell me a little bit more about the type of tumors that you are focusing on.

We have a wide variety of solid tumors that we are going for, lung cancer, ovarian cancer, and pancreatic cancer. That’s something that we are really focused on.

And tell me a little bit about the partnership you have with Eli Lilly. What are you creating there when it comes to cancers?

The collaboration is mainly centered around targeted radiotherapy for treating solid tumors, and it’s a multi-target research collaboration. Lila Biologics is responsible for giving Lilly a development candidate, which is basically an optimized drug molecule that is ready for FDA filing. Lilly will take over after we give them the optimized molecule for the clinical development and taking those molecules through clinical trials.

Why use AI for this? What edge is that giving you, or what opportunities does it have that human intelligence can’t accomplish?

In the last couple of years, artificial intelligence has fundamentally changed how we actually design proteins. For example, in last five years, the success rate of designing protein in the computer has gone from around one to 2% to 10% or more. With that unprecedented success rate, we do believe we can bring a lot of drugs needed for the patients, especially for cancer and cardiovascular diseases.

In general, drug design is a very, very difficult problem, and it has really, really high failure rates. So, for example, 90% of the drugs that actually enter the clinic actually fail, mainly due to you cannot make them in scale, or some toxicity issues. When we first started Lila, we thought we can take a holistic approach, where we can actually include some of this downstream risk in the computational design part. So, we asked, can machine learning help us designing proteins that scale well? Meaning, can we make them in large scale, or they’re stable on the benchtop for months, so we don’t face those costly downstream failures? And so far, it’s looking really promising.

When did you realize you might be able to use machine learning and AI to treat cancer?

When we actually looked at this problem, we were thinking whether we can actually increase the clinical success rate. That has been one of the main bottlenecks of drug design. As I mentioned before, 90% of the drugs that actually enter the clinic fail. So, we are really hoping we can actually change that in next five to 10 years, where you can actually confidently predict the clinical properties of a molecule. In other words, what I’m trying to say is that can you predict how a molecule will behave in a living system. And if we can do that confidently, that will increase the success rate of drug development. And we are really optimistic, and we’ll see how it turns out in the next five to 10 years.

Beyond treating hard to tackle tumors at Lila, are there other challenges you hope to take on in the future?

Yeah. It is a really difficult problem to predict how a molecule will behave in a living system. Meaning, we are really good at designing molecules that behave in a certain way, bind to a protein in a certain way, but the moment you try to put that molecule in a human, it’s really hard to predict how that molecule will behave, or whether the molecule is going to the place of the disease, or the tissue of the disease. And that is one of the main reasons there is a 90% failure in drug development.

I think the whole field is moving towards this predictability of biological properties of a molecule, where you can actually predict how this molecule will behave in a human system, or how long it will stay in the body. I think when the computational tools become good enough, when we can predict these properties really well, I think that’s where the fun begins, and we can actually generate molecules with a really high success rate in a really short period of time.

Listen to the interview by clicking the play button above.



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California governor facing balancing act as AI bills head to his desk | MLex

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By Amy Miller ( September 13, 2025, 00:43 GMT | Comment) — California Gov. Gavin Newsom is facing a balancing act as more than a dozen bills aimed at regulating artificial intelligence tools in a wide range of settings head to his desk for approval. He could approve bills to push back on the Trump administration’s industry-friendly avoidance of AI regulation and make California a model for other states — or he could nix bills to please wealthy Silicon Valley companies and their lobbyists.California Gov. Gavin Newsom is facing a balancing act as more than a dozen bills aimed at regulating artificial intelligence tools in a wide range of settings head to his desk for approval….

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