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Students in Texas taking their state-mandated exams this week are being used as guinea pigs for a new artificial intelligence-powered scoring system set to replace a majority of human graders in the region.

The Texas Tribune reports an automated scoring engine that utilizes natural language processing the technology that enables chatbots like OpenAIs ChatGPT to understand and communicate with users is being rolled out by the Texas Education Agency (TEA) to grade open-ended questions on the State of Texas Assessments of Academic Readiness (STAAR) exams. The agency is expecting the system to save $1520 million per year by reducing the need for temporary human scorers, with plans to hire under 2,000 graders this year compared to the 6,000 required in 2023.

We wanted to keep as many constructed open-ended responses as we can, but they take an incredible amount of time to score.

The STAAR exams, which test students between the third and eighth grades on their understanding of the core curriculum, were redesigned last year to include fewer multiple-choice questions. It now contains up to seven times more open-ended questions, with TEA director of student assessment Jose Rios saying the agency wanted to keep as many constructed open-ended responses as we can, but they take an incredible amount of time to score.

According to a slideshow hosted on TEAs website, the new scoring system was trained using 3,000 exam responses that had already received two rounds of human grading. Some safety nets have also been implemented a quarter of all the computer-graded results will be rescored by humans, for example, as will answers that confuse the AI system (including the use of slang or non-English responses).

While TEA is optimistic that AI will enable it to save buckets of cash, some educators arent so keen to see it implemented. Lewisville Independent School District superintendent Lori Rapp said her district saw a drastic increase in constructed responses receiving a zero score when the automated grading system was used on a limited basis in December 2023. At this time, we are unable to determine if there is something wrong with the test question or if it is the new automated scoring system, Rapp said.

AI essay-scoring engines are nothing new. A 2019 report from Motherboard found that they were being used in at least 21 states to varying degrees of success, though TEA seems determined to avoid the same reputation. Small print on TEAs slideshow also stresses that its new scoring engine is a closed system thats inherently different from AI, in that AI is a computer using progressive learning algorithms to adapt, allowing the data to do the programming and essentially teaching itself.

The attempt to draw a line between them isnt surprising theres no shortage of teachers despairing online about how generative AI services are being used to cheat on assignments and homework. The students being graded by this new scoring system may have a hard time accepting how they believe rules for thee and not for me are being applied here.

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Texas is replacing thousands of human exam graders with AI - The Verge

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Ukraines attacks on Russian oil refineries show the growing threat AI drones pose to energy markets  CNBC

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Ukraines attacks on Russian oil refineries show the growing threat AI drones pose to energy markets - CNBC

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Seven. Hundred. Dollars. After a year of asking questions about this much-hyped AI wearable, the Humane AI Pin is here, and, well, we still have lots of questions. Were also still trying to figure out how it all works and where it goes from here.

On this episode of The Vergecast, we dive deep into our review of the AI Pin and try to figure out what went wrong with this device and whether theres a real future for it or any other AI-powered gadget. The trouble, we discover, is that these devices are stacking new technology on top of new technology, and until it all works perfectly, none of it will work very well. Also, did we mention the AI Pin is seven hundred dollars?

After that, we talk about the growing rift between OpenAI and the rest of the internet after some very good reporting showed how many millions of YouTube videos the company transcribed and used to train its models. We also talk about how Taylor Swifts music came back to TikTok and whether there might be more to come.

Finally, we get a remarkably on-brand set of news in the lightning round, including E Ink screens, content regulation, and photo sharing. Sony also made a new party speaker, so of course, we spend an unreasonably long time on the party speaker. You have to look at those photos.

If you want to know more about everything we discuss in this episode, here are a few links to get you started, beginning with Humane:

And in the lightning round:

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Humane AI Pin review and OpenAIs YouTube project - The Verge

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News Release

Wednesday, April 10, 2024

NIH scientists use artificial intelligence called P-GAN to improve next-generation imaging of cells in the back of the eye.

Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is 100 times faster and improves image contrast 3.5-fold. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases.

Artificial intelligence helps overcome a key limitation of imaging cells in the retina, which is time, said Johnny Tam, Ph.D., who leads the Clinical and Translational Imaging Section at NIH's National Eye Institute.

Tam is developing a technology called adaptive optics (AO) to improve imaging devices based on optical coherence tomography (OCT). Like ultrasound, OCT is noninvasive, quick, painless, and standard equipment in most eye clinics.

Adaptive optics takes OCT-based imaging to the next level, said Tam. Its like moving from a balcony seat to a front row seat to image the retina. With AO, we can reveal 3D retinal structures at cellular-scale resolution, enabling us to zoom in on very early signs of

While adding AO to OCT provides a much better view of cells, processing AO-OCT images after theyve been captured takes much longer than OCT without AO.

Tams latest work targets the retinal pigment epithelium (RPE), a layer of tissue behind the light-sensing retina that supports the metabolically active retinal neurons, including the photoreceptors. The retina lines the back of the eye and captures, processes, and converts the light that enters the front of the eye into signals that it then transmits through the optic nerve to the brain. Scientists are interested in the RPE because many diseases of the retina occur when the RPE breaks down.

Imaging RPE cells with AO-OCT comes with new challenges, including a phenomenon called speckle. Speckle interferes with AO-OCT the way clouds interfere with aerial photography. At any given moment, parts of the image may be obscured. Managing speckle is somewhat similar to managing cloud cover. Researchers repeatedly image cells over a long period of time. As time passes, the speckle shifts, which allows different parts of the cells to become visible. The scientists then undertake the laborious and time-consuming task of piecing together many images to create an image of the RPE cells that's speckle-free.

Tam and his team developed a novel AI-based method called parallel discriminator generative adversarialnetwork (P-GAN)a deep learning algorithm. By feeding the P-GAN network nearly 6,000 manually analyzed AO-OCT-acquired images of human RPE, each paired with its corresponding speckled original, the team trained the network to identify and recover speckle-obscured cellular features.

When tested on new images, P-GAN successfully de-speckled the RPE images, recovering cellular details. With one image capture, it generated results comparable to the manual method, which required the acquisition and averaging of 120 images. With a variety of objective performance metrics that assess things like cell shape and structure, P-GAN outperformed other AI techniques. Vineeta Das, Ph.D., a postdoctoral fellow in the Clinical and Translational Imaging Section at NEI, estimates that P-GAN reduced imaging acquisition and processing time by about 100-fold. P-GAN also yielded greater contrast, about 3.5 greater than before.

By integrating AI with AO-OCT, Tam believes that a major obstacle for routine clinical imaging using AO-OCT has been overcome, especially for diseases that affect the RPE, which has traditionally been difficult to image.

Our results suggest that AI can fundamentally change how images are captured, said Tam. Our P-GAN artificial intelligence will make AO imaging more accessible for routine clinical applications and for studies aimed at understanding the structure, function, and pathophysiology of blinding retinal diseases. Thinking about AI as a part of the overall imaging system, as opposed to a tool that is only applied after images have been captured, is a paradigm shift for the field of AI.

More news from the NEI Clinical and Translational Imaging Section.

This press release describes a basic research finding. Basic research increases our understanding of human behavior and biology, which is foundational to advancing new and better ways to prevent, diagnose, and treat disease. Science is an unpredictable and incremental process each research advance builds on past discoveries, often in unexpected ways. Most clinical advances would not be possible without the knowledge of fundamental basic research. To learn more about basic research, visit https://www.nih.gov/news-events/basic-research-digital-media-kit.

NEIleads the federal governments effortsto eliminate vision loss and improve quality of life through vision researchdriving innovation, fostering collaboration, expanding the vision workforce, and educating the public and key stakeholders.NEI supports basic and clinical science programs to develop sight-saving treatments and to broaden opportunities for people with vision impairment.For more information, visithttps://www.nei.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

Vineeta Das, Furu Zhang, Andrew Bower, et al. Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography.Communications Medicine. April 10, 2024,https://doi.org/10.1038/s43856-024-00483-1.

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AI makes retinal imaging 100 times faster, compared to manual method - National Institutes of Health (NIH) (.gov)

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$10 Billion Productivity Startup Notion Wants To Build Your AI Everything App  Forbes

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$10 Billion Productivity Startup Notion Wants To Build Your AI Everything App - Forbes

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