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Angle down icon An icon in the shape of an angle pointing down. "I started applying in July, and soon I hit 200 applications," Oliver Wu, a computer science major at the University of Michigan, told Newsweek. alvarez via Getty Images

Computer science major Oliver Wu says he pulled out all the stops in his quest for a summer internship.

"456 applications, 56 interviews, and 0 sleep in 4 months, all for 1 internship," the University of Michigan junior wrote in a TikTok post published on January 11.

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Wu, whose post has been viewed more than 2.9 million times, told Newsweek that he started applying even before classes resumed in the fall.

"I started applying in July, and soon I hit 200 applications," Wu said, adding that he was making 15 to 20 applications a day before the school term began.

Wu told Newsweek that he did feel "burned out" from the long-drawn search, which saw him receive "hundreds of rejections."

"The hardest part was staying positive and working hard," Wu said.

But Wu remained unfazed and pressed on with his search.

"I did not want to feel regret that I could have tried harder, so I made up my mind to pursue this with everything I had," Wu told the outlet.

Wu's efforts eventually paid off. He secured three offers and will join Ford as an enterprise technology intern this summer.

"I was in class at the time, and I remember stepping out, going into the hallway, and jumping up and down while silently screaming in excitement for around 10 minutes," Wu recounted to Newsweek.

Wu isn't the only one who has had to struggle with a slowing job market for tech.

The industry-wide layoffs, which began in end-2022, don't seem to have abated. Tech companies have continued to ax staff to streamline their operations.

Last week, Google CEO Sundar Pichai told staff to brace for more job cuts this year. Pichai said the layoffs were about "removing layers to simplify execution and drive velocity in some areas."

Wu did not immediately respond to a request for comment from Business Insider sent outside regular business hours.

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Computer science major applied to 456 internships, got three offers - Business Insider

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A research team from Purdue University's Department of Computer Science and Institute for Digital Forestry, with collaborator Sren Pirk at Kiel University in Germany, has discovered that artificial intelligence can simulate tree growth and shape.

The DNA molecule encodes both tree shape and environmental response in one tiny, subcellular package. In work inspired by DNA, Bedrich Benes, professor of computer science, and his associates developed novel AI models that compress the information required for encoding tree form into a megabyte-sized neural model.

After training, the AI models encode the local development of trees that can be used to generate complex tree models of several gigabytes of detailed geometry as an output.

In two papers, one published in ACM Transactions on Graphics and the other in IEEE Transactions on Visualizations and Computer Graphics, Benes and his co-authors describe how they created their tree-simulation AI models.

"The AI models learn from large data sets to mimic the intrinsic discovered behavior," Benes said.

Non-AI-based digital tree models are quite complicated, involving simulation algorithms that consider many mutually affecting nonlinear factors. Such models are needed in endeavors such as architecture and urban planning, as well as in the gaming and entertainment industries, to make designs more realistically appealing to their potential clients and audiences.

After working with AI models for nearly 10 years, Benes expected them to be able to significantly improve the existing methods for digital tree twins. The size of the models was surprising, however. "It's complex behavior, but it has been compressed to rather a small amount of data," he said.

Co-authors of the ACM Transactions on Graphics paper were Jae Joong Lee and Bosheng Li, Purdue graduate students in computer science. Co-authors of the IEEE Transactions on Visualization and Computer Graphics paper were Li and Xiaochen Zhou, also a Purdue graduate student in computer science; Songlin Fei, the Dean's Chair in Remote Sensing and director of the Institute for Digital Forestry; and Sren Pirk of Kiel University, Germany.

The researchers used deep learning, a branch of machine learning within AI, to generate growth models for maple, oak, pine, walnut and other tree species, both with and without leaves. Deep learning involves developing software that trains AI models to perform specified tasks through linked neural networks that attempt to mimic certain functionalities of the human brain.

"Although AI has become seemingly pervasive, thus far it has mostly proved highly successful in modeling 3D geometries unrelated to nature," Benes said. These include endeavors related to computer-aided design and improving algorithms for digital manufacturing.

"Getting a 3D geometry vegetation model has been an open problem in computer graphics for decades," stated Benes and his co-authors in their ACM Transactions paper. Although some approaches to simulating biological behaviors are improving, they noted, "simple methods that would quickly provide many 3D models of real trees are not readily available."

Experts with biological expertise have traditionally developed tree-growth simulations. They understand how trees interact with environmental conditions. Understanding these complicated interactions depends upon characteristics bestowed upon the tree by its DNA. These include branching angles, which are much larger for pines than for oaks, for example. The environment, meanwhile, dictates other characteristics that can result in the same type of tree grown under two different conditions displaying completely different shapes.

"Decoupling the tree's intrinsic properties and its environmental response is extremely complicated," Benes said. "We looked at thousands of trees, and we thought, 'Hey, let AI learn it.' And maybe we can then learn the essence of tree form with AI."

Scientists typically build models based on hypotheses and observations of nature. As models created by humans, they have reasoning behind them. The researchers' models generalize behavior from several thousand trees' worth of input data that became encoded within the AI. Then the researchers validate that the models behave the way the input data behave.

The AI tree models' weakness is that they lack training data that describes real-world 3D tree geometry.

"In our methods, we needed to generate the data. So our AI models are not simulating nature. They are simulating tree developmental algorithms," Benes said. He aspires to reconstruct 3D geometry data from real trees inside a computer.

"You take your cellphone, take a picture of a tree, and you get a 3D geometry inside the computer. It could be rotated. Zoom in. Zoom out," he said. "This is next. And it's perfectly aligned with the mission of digital forestry."

More information: Jae Joong Lee et al, Latent L-systems: Transformer-based Tree Generator, ACM Transactions on Graphics (2023). DOI: 10.1145/3627101

Xiaochen Zhou et al, DeepTree: Modeling Trees with Situated Latents, IEEE Transactions on Visualization and Computer Graphics (2023). DOI: 10.1109/TVCG.2023.3307887

Journal information: ACM Transactions on Graphics

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AI learns to simulate how trees grow and shape in response to their environments - Tech Xplore

The National Academy of Sciences (NAS) has selected Eshan Chattopadhyay, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, as the 2024 co-recipient of the Michael and Sheila Held Prize, along with collaborator David Zuckerman, professor of computer science at the University of Texas at Austin, for their novel work on randomized algorithms.

Given annually since 2018, the Held Prize honors "outstanding, innovative, creative, and influential research in the areas of combinatorial and discrete optimization, or related parts of computer science." The pair will receive the award, along with a $100,000 prize, at the NAS 161st Annual Meeting on April 28.

"I am deeply honored to receive this award and to have my work with David on randomness extractors recognized by the larger scientific community," Chattopadhyay said.

Read the entire story on the Cornell Bowers CIS website.

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Chattopadhyay receives the NAS Michael and Sheila Held Prize | Cornell Chronicle - Cornell Chronicle

In a paper that has been submitted to the journal Management Science, Thompsons team concluded that its not enough for AI systems to be good at tasks now performed by people. The system must be good enough to justify the cost of installing it and redesigning the way the job is done. Thompson said that for now, there are a lot of places where ... humans are a more cost-efficient way to do that.

The researchers focused on AI applications that use machine vision, the ability of computers to understand visual data such as pictures and video. For instance, many smartphones have a machine vision feature that can identify objects that have been photographed by the phones camera.

The researchers chose machine vision because its a well-established technology thats already used for many tasks, like reading labels or inspecting manufactured goods for defects.

Over a three-year period beginning in 2020, the researchers identified 420 commercial and industrial tasks where an AI system with machine vision might be capable of supplanting human workers. They conducted online surveys of people with expert knowledge of how each task was performed, to figure out how much a business might lower its costs by substituting machine vision for human labor.

The researchers found that in most cases, the visual aspect of a job was just a small part of a workers responsibilities. For instance, bakers must visually inspect the loaves of bread theyre producing. But bakers spend only about 6 percent of their time doing this. Shifting this job to AI might enable the bakery to get by with fewer workers. But the cost of todays machine vision systems is much too high to justify cutting labor costs by just 6 percent. Only if AI becomes much cheaper would the tradeoff make sense.

Applying the same measurements to all 420 tasks, Thompson and his colleagues found that installing machine vision in place of human labor would lead to lower overall costs only 23 percent of the time. In the great majority of cases, human workers were cheaper.

Of course, these results apply only to the current state of the art. Thompson noted that AI systems keep getting better and cheaper. Over time, theyll be cost-effective at a much larger number of jobs.

In addition, the study only looks at machine vision applications. It doesnt include research into the impacts of the latest generative AI systems, such as ChatGPT. Thompson said his group is now working on ways to assess the potential impact of these systems. But he noted that this might take quite a while, because figuring out the potential cost savings for thousands of specific tasks requires a great deal of research.

While the new research paper has yet to go through peer review, other academics think Thompsons team is on the right track.

Christoph Riedl, associate professor of supply chain and information management systems at Northeastern University, called the study absolutely plausible. Riedl noted that it fits the pattern of other major innovations that only achieved their full potential many years after they were first invented. When we started using electricity to run factories instead of steam engines, switching to electricity didnt make factories more productive, said Riedl. We had to learn how to use this new technology to gain any benefits from it.

Joseph Fuller, a professor at Harvard Business School, where he studies the future of work, said that the latest AI innovations will have dramatic impacts on some jobs. For instance, he said generative AI is already so good at creating computer software that it will reduce demand for human programmers. Its relatively easy to teach an AI how to write software just feed it lots of examples created by humans.

But teaching an AI how to manufacture a complex object is far more difficult. I cant unleash it on some database on the accuracy of manufacturing processes, said Fuller. There is no such data, so I have nothing to train it on.

Hiawatha Bray can be reached at hiawatha.bray@globe.com. Follow him @GlobeTechLab.

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AI not yet ready to replace human workers, says MIT report - The Boston Globe