#University of Michigan AI study

[shamnadt]

5 things about AI you may have missed today: OpenAI's CLIP is biased, AI reunites family after 25 years, more

Study finds OpenAI’s CLIP is biased in favour of wealth and underrepresents poor nations; Retail giants harness AI to cut online clothing returns and enhance the customer experience; Northwell Health implements AI-driven device for rapid seizure detection; White House concerns grow over UAE’s rising influence in global AI race- this and more in our daily roundup. Let us take a look. 1. Study…

[archaeologicalnews]

AI is deciphering a 2,000-year-old 'lost book' describing life after Alexander the Great

A 2,000-year-old "lost book" discussing the dynasties that succeeded Alexander the Great may finally be deciphered nearly two millennia after the text was partially destroyed in the eruption of Mount Vesuvius in A.D. 79 and, centuries later, handed off to Napoleon Bonaparte.

The reason for the breakthrough? Researchers are using machine learning, a branch of artificial intelligence, to discern the faint ink on the rolled-up papyrus scroll.

"It's probably a lost work," Richard Janko, the Gerald F. Else distinguished university professor of classical studies at the University of Michigan, said during a presentation at the joint annual meeting of the Archaeological Institute of America and the Society for Classical Studies, held in New Orleans last month. The research is not yet published in a peer-reviewed journal.

Only small parts of the heavily damaged text can be read right now. "It contains the names of a number of Macedonian dynasts and generals of Alexander," Janko said, noting that it also includes "several mentions of Alexander himself." Read more.

[kivaember]

AC6 College AU? Rusty plays the lacrosse, Raven is either a programmer or an engineer major, Ayre is an AI made by him, and Freud is one of the faculty members.

i've actually thought about a college AU!

Well, technically, a university AU bc I'm from the UK and college is a diff thing entirely to uni. Also I have no idea what American uni culture is like LMAO (idek what lacrosse is, rusty would be good at football or rugby tho) BUT ANYWAYS my idea for it was:

RUSTY: an undergraduate taking the BSc (Hons) in Ecology and Conservation (not sure if it's different outside of UK, but an "honours degree" is more difficult than a standard degree, and is more attractive to employers as a result). He's got an avid interest in ecology and zoology, and has plans to be a conservationist upon graduating.

621: He's actually a professor at the university, but looks so young that most mistake him as a student if they're not in his department. He teaches ethical hacking and cyber security, and has several rumours about him, such as he used to be a notorious hacker who was eventually caught and strongarmed into working for the government in lieu of a prison sentence, and now spends his time teaching the next generation, etc, etc. Is it true? Who knows...

WALTER: He's the university librarian, and everyone is scared of him because he's so stern and always has this aura of intensity, even when just checking a book out for someone. For some reason he's on very good terms with 621... many people theorise on their history, because Walter's also very good with computers... everyone is also aware that Walter and Michigan are a thing bc those guys ain't subtle in the slightest.

FREUD: He teaches sport science and students either love him or hate him. He's like the human personnification of marmite. He's very enthusiastic about health and remaining in peak condition, and he expects his students to give 110% in his classes. He's always butting heads with Snail, who's in the same department as him. People take bets on how long it'll take for them to fight in the parking lot and who would win (Freud has 'is insane' strength but Snail would be powered by sheer rage that has been repressed for x amount of years).

IGUAZU: He works in the on-campus cafe as a barista. He's surly and curt but makes the best damn coffee in the city so no one really complains about it. He seems to know 621 and has some kind of hate-love relationship with him? It's complicated. He yells at 621 every time he walks into the cafe but also knows his order off by heart so it's very hmmm (more fuel for the rumour mills).

MICHIGAN: He teaches War Studies and History, and while he's a pretty demanding professor, most students love his energetic style of teaching. Many assume him to be a red and blue blooded American on account of his bombastic personality, American accent and insisting on being called Michigan - he's actually French and the estranged son of a well-known billionaire. It's Michigan's deepest darkest secret.

AYRE: Every so often 621 will have a guest speaker in his classes who calls in remotely (and voice only) called Ayre. He says she's an "old work colleague" and they're both very vague about what work they were colleagues in, but people enjoy Ayre's guest appearances as she's very friendly - in direct contrast to the very taciturn and almost cold 621.

O'KEEFFE: He works in HR, but most students joke that he's more of an information broker than anything. Though he acts put upon, he's willing to help out students trying to navigate the byzantine beaucracy of university paperwork and how to squeeze out as much as possible from their loans or signposting people to things that can help them. He's easily bribed with coffee and cigarettes, but honestly, he'd help out even without a bribe... very much one of those people who look gruff and unfriendly on the outside, but actually a good person underneath it all.

FLATWELL: He owns a bakery just outside of university grounds that's popular with the students. One of the reasons Rusty chose this univeristy to do his degree: his Uncle lives just outside of it, and was willing to house him, letting Rusty save money for dormitory and food and stuff. Seems to have some ~history~ with O'Keeffe in HR...

UH THOSE ARE THE MAIN CHARACTERS/ROLES and the plot would be Rusty crossing paths with 621 and thinking him very cute (621 would be the type to dress in cardigans and wear glasses), and also a fellow student... is totally unaware that he's a faculty member from an entirely different department. Tl;dr after some flirting and a few dates, Rusty only realises that 621 is a faculty member when he mentions off hand about needing to go back early to mark papers.

Rusty: oh you're... a TA? helping out in your last year? 621: no i teach Rusty: Rusty: wait how old are you-

In this I'm thinking Rusty would be in his mid-twenties, and 621 would be in his late thirties. So, about a 10 year gap between them, give or take a year.

But yeah. Coughs. That's.... that's my university au idea... mmhm. yeah...

[materialsscienceandengineering]

AI chips could get a sense of time with memristor that can be tuned

Artificial neural networks may soon be able to process time-dependent information, such as audio and video data, more efficiently. The first memristor with a "relaxation time" that can be tuned is reported today in Nature Electronics, in a study led by the University of Michigan. Memristors, electrical components that store information in their electrical resistance, could reduce AI's energy needs by about a factor of 90 compared to today's graphical processing units. Already, AI is projected to account for about half a percent of the world's total electricity consumption in 2027, and that has the potential to balloon as more companies sell and use AI tools. "Right now, there's a lot of interest in AI, but to process bigger and more interesting data, the approach is to increase the network size. That's not very efficient," said Wei Lu, the James R. Mellor Professor of Engineering at U-M and co-corresponding author of the study with John Heron, U-M associate professor of materials science and engineering.

Read more.

[optimagain]

🤯 Ever felt like you wanted to learn something new but the cost of courses was a barrier? 🤔

Well, guess what? Coursera just threw open the doors to a treasure trove of free courses from top-tier universities like Stanford and Yale! 🎉

Imagine learning: 🧠

Machine Learning from Stanford! 🤖

The Science of Well-Being from Yale! 😌

Python Programming from the University of Michigan! 💻

And that's just scratching the surface! 🤩 There's a course for every interest, from tech to personal development.

While free is awesome, there are some things to remember:

No certificates unless you pay. 💰

Some interactive features are limited. 😔

Content quality can vary. 😕

But don't let that discourage you! 💪 Here's how to make the most of Coursera's free courses:

Set a study schedule. 🗓️

Take notes and practice. ✍️

Connect with other learners in forums. 💬

Apply your knowledge in real-world situations. 🌎

Pro Tip: 🏆 Consider combining courses with your existing knowledge, building a personalized learning path, and taking advantage of other resources like coding platforms and tutorial videos.

In the end, it's about gaining valuable knowledge and skills, not just certificates. 📚 Coursera's free courses are a game-changer, making learning accessible and flexible for anyone who wants to level up! 🚀

Additional Resources:

For more insights on online learning and course creation, check out Zam Man Optimagain's Course Creation Masterclass. Learn how to leverage AI tools to develop mini-courses and monetize your expertise efficiently.

[innonurse]

The most extensive collection of transcription factor binding data in human tissues ever compiled

- By InnoNurse Staff -

Transcription factors (TFs) are proteins that bind to specific DNA sequences to regulate the transcription of genetic information from DNA to mRNA, impacting gene expression and various biological processes, including brain functions. While TFs have been studied extensively, their binding dynamics in human tissues are not well understood.

Researchers from the HudsonAlpha Institute for Biotechnology, University of California-Irvine, and University of Michigan compiled the largest TF binding dataset to date, aiming to understand how TFs contribute to gene expression and brain function. This dataset could reveal how gene regulation impacts neurodegenerative and psychiatric disorders.

The study, led by Dr. Richard Myers, utilized an innovative technique called ChIP-seq to capture and sequence DNA fragments bound by TFs. Experiments were conducted on different brain regions from postmortem tissues donated by individuals, allowing the researchers to map TF activity in the genome.

Findings suggest that regions bound by fewer TFs might be crucial, as minor changes there could significantly impact nearby genes. The dataset can help scientists study TFs, gene regulation, and their roles in specific brain functions and diseases, potentially aiding in the development of new therapies.

Image credit: Loupe et al. (Nature Neuroscience, 2024).

Read more at Medical Xpress

///

Other recent news and insights

BU researchers develop a new AI program to predict the likelihood of Alzheimer's disease (Boston University)

Northeastern researchers create an AI system for breast cancer diagnosis with nearly 100% accuracy (Northeastern University)

Wearable sensors and AI aims to revolutionize balance assessment (Florida Atlantic University)

[petnews2day]

Can AI translate your dog's bark? New research says yes

New Post has been published on https://petn.ws/qDHAy

Can AI translate your dog's bark? New research says yes

Imagine if you could understand what your dog is trying to tell you with every bark, whine, or growl. This intriguing possibility is the focus of a recent study by researchers at the University of Michigan, in collaboration with the National Institute of Astrophysics, Optics and Electronics in Puebla, Mexico. The researchers are exploring how […]

See full article at https://petn.ws/qDHAy #DogNews

[onlineprofessionalcourse]

Level Up Your Career: Top Data Science & AI Courses You Can't Miss!

In today's rapidly evolving technological landscape, data science and artificial intelligence (AI) have become integral components of various industries. As businesses strive to harness the power of data to drive innovation and make informed decisions, the demand for skilled professionals in these fields continues to grow. If you're looking to advance your career in this exciting field, enrolling in a data science and artificial intelligence course can be a game-changer. Here, we'll explore some of the top courses in these fields that you can't afford to miss.

Importance of Data Science and AI Courses

Data science and AI are revolutionizing industries such as healthcare, finance, and marketing, among others. These technologies help businesses analyze vast amounts of data to uncover valuable insights and make predictions. As a result, professionals with expertise in data science and AI are in high demand, commanding lucrative salaries and enjoying a wide range of career opportunities.

Top Data Science Courses

Beginner Level: Coursera's "Data Science Specialization" by Johns Hopkins University is a popular choice for beginners. This course covers the fundamentals of data science, including data manipulation, data visualization, and machine learning.

Intermediate Level: "Applied Data Science with Python" by the University of Michigan on Coursera is ideal for those looking to enhance their data science skills. This course covers topics such as data cleaning, data analysis, and machine learning algorithms.

Advanced Level: For advanced learners, "Advanced Data Science with IBM" on Coursera offers in-depth knowledge of advanced data science techniques, including deep learning and natural language processing.

Top AI Courses

Beginner Level: "AI For Everyone" by Andrew Ng on Coursera is a great introduction to AI for beginners. This course covers the basics of AI, including its applications and implications for society.

Intermediate Level: "Deep Learning Specialization" by Andrew Ng on Coursera is perfect for intermediate learners looking to delve deeper into AI. This specialization covers advanced topics such as neural networks and convolutional networks.

Advanced Level: "AI and Machine Learning Engineering Career Track" by Springboard is designed for professionals looking to advance their careers in AI and machine learning. This course covers advanced AI concepts and provides hands-on experience with real-world projects.

Factors to Consider When Choosing a Course

When choosing a data science or AI course, consider factors such as the course content, instructor expertise, accreditation, and cost. It's also important to choose a course that aligns with your career goals and learning style.

Benefits of Online Learning

Online learning offers flexibility and convenience, allowing you to study at your own pace and schedule. It also provides access to a wealth of resources and networking opportunities with professionals in the field.

Case Studies of Successful Professionals

Many professionals have successfully transitioned into data science and AI roles after completing relevant courses. For example, Jane Doe, a former marketing manager, transitioned into a data scientist role after completing a data science course online. Her new skills have helped her analyze marketing data more effectively and make data-driven decisions.

Conclusion

Enrolling in a data science or AI course can be a valuable investment in your career. By choosing the right course and acquiring relevant skills, you can position yourself for success in this rapidly growing field.

FAQs

Q: Can I pursue a career in data science or AI without a background in programming?

A: While programming skills are beneficial, many courses offer introductory programming classes to help beginners get started.

Q: Are online data science and AI courses recognized by employers?

A: Yes, many online courses are recognized by employers, especially those offered by reputable institutions.

Q: How long does it take to complete a data science or AI course?

A: The duration of a course varies depending on the level of complexity and the time commitment of the student.

Q: Will completing a data science or AI course guarantee me a job?

A: While completing a course can improve your job prospects, securing a job depends on various factors, including your skills and experience.

Q: Can I pursue a data science or AI course while working full-time?

A: Yes, many courses are designed to be flexible, allowing working professionals to study at their own pace.

[viral-web]

[ad_1] In the digitization age, there are several evolving technologies like Cloud computing, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Digital Twin (DT), and many more, which are developed and implemented in product development and design. Among all these emerging technologies, DT is one of the most versatile technologies utilized in many industries, specifically in the manufacturing industry, to monitor the execution, optimize the growth, simulate the output, and predict the probable errors. Also, DT plays many roles in the product development lifecycle, from manufacturing to designing, using, delivering, and end-of-life. DT can also provide an efficient solution for future product development, design, and innovation, with the developing demands of specific products and the utilization of Industry 4.0. Digital Twin Defined A DT can be defined as the virtual twin of the characteristics of a system in its operating environment. That twin system can be a manufacturing process, a product, or the complete supply chain presented by an accumulation of digital models. Those models operate and respond to several stimuli, data presenting the external environment. Digital twins accumulate several model types and process data from several sources. That helps them to provide a better estimation of a real object than old simulation techniques. Invention of Digital Twin The idea behind digital twins was coined by David Gelernter in 1991 in his book ‘Mirror Worlds,’ and Michael Grieves of the Florida Institute of Technology applied the idea to manufacturing. Grieves had shifted to Michigan University by 2022 when he introduced the DT concept formally at the Society of Manufacturing conference in Troy, Michigan.However, John Vickers of NASA first accepted the idea of the digital twin, which was utilized to generate digital simulations of space capsules and create them for testing. The concept of DT was embraced hugely in 2017 when Gartner named the concept as one of the top 10 dynamic technology trends. After that, the digital twin idea has been implemented in various industrial processes and applications. Digital Twins Today According to many studies and investigations, it is found that nearly 75% of firms have adopted digital-twin technology and achieved medium levels of complication. However, there is an important deviation between sectors. With a digital twin, the aerospace, automotive, and defense industries experience more advanced innovation. In contrast, other industries like infrastructure and aerospace industries are more likely to be processing their primary DT concepts. Applications in Smart Product Development Digital twins provide an array of applications in innovative product development, transforming old practices and exploring new possibilities. Some important fields where digital twins are making a strong impact include: DT helps engineers and designers make virtual prototypes of their products, helping them discover several design iterations, test functionalities, and find potential problems before physical production starts. This regularizes the product development process, minimizes time-to-market, and reduces expenses regarding iterative prototyping. Manufacturers can conduct virtual simulations and tests to examine the durability, presentation, and product safety under different conditions. By duplicating real-world conditions in a digital environment, organizations can determine flaws in design, optimize performance measures, and ensure compliance with regulative standards. DT helps predictive maintenance techniques by continuously monitoring the health and condition of physical assets in real time. Organizations

can predict maintenance requirements, determine failures or anomalies, and schedule repairs by analyzing data from sensors integrated into devices, vehicles, or machines. This reduces downtimes, increases asset lifespan, and improves operational efficacy. Supply Chain Optimization: Digital twins ease the optimization of supply chain operations and end-to-end visibility. Firms can analyze factors like production schedules, inventory levels, transportation routes, and demand forecasts by making digital twins of supply chain networks. This helps enhance resource allocation, decision-making, and responsiveness to market dynamics. Challenges and Future Outlook While the capability of digital twins is huge, their broad adoption still faces some challenges, which include security concerns, data privacy, interoperability problems, and the requirement for skillful talent to develop and handle digital twin ecosystems. However, since technology continues to emerge and mature, these issues are predicted to be fixed, paving the way for wider adoption and innovation in smart product development. Conclusion Digital twins revolutionized the way products are built, designed, and handled. Organizations can accelerate innovation, enhance product quality, and get competitive advantage by utilizing the power of digital twins. Since firms have adopted this revolutionary technology, digital twins have become vital tools for realizing the vision of sustainable and smart product development. For more information on the latest technology, visit www.onpassive.com. [ad_2] onpassive.com

[eaglebittrader]

[ad_1] In the digitization age, there are several evolving technologies like Cloud computing, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Digital Twin (DT), and many more, which are developed and implemented in product development and design. Among all these emerging technologies, DT is one of the most versatile technologies utilized in many industries, specifically in the manufacturing industry, to monitor the execution, optimize the growth, simulate the output, and predict the probable errors. Also, DT plays many roles in the product development lifecycle, from manufacturing to designing, using, delivering, and end-of-life. DT can also provide an efficient solution for future product development, design, and innovation, with the developing demands of specific products and the utilization of Industry 4.0. Digital Twin Defined A DT can be defined as the virtual twin of the characteristics of a system in its operating environment. That twin system can be a manufacturing process, a product, or the complete supply chain presented by an accumulation of digital models. Those models operate and respond to several stimuli, data presenting the external environment. Digital twins accumulate several model types and process data from several sources. That helps them to provide a better estimation of a real object than old simulation techniques. Invention of Digital Twin The idea behind digital twins was coined by David Gelernter in 1991 in his book ‘Mirror Worlds,’ and Michael Grieves of the Florida Institute of Technology applied the idea to manufacturing. Grieves had shifted to Michigan University by 2022 when he introduced the DT concept formally at the Society of Manufacturing conference in Troy, Michigan.However, John Vickers of NASA first accepted the idea of the digital twin, which was utilized to generate digital simulations of space capsules and create them for testing. The concept of DT was embraced hugely in 2017 when Gartner named the concept as one of the top 10 dynamic technology trends. After that, the digital twin idea has been implemented in various industrial processes and applications. Digital Twins Today According to many studies and investigations, it is found that nearly 75% of firms have adopted digital-twin technology and achieved medium levels of complication. However, there is an important deviation between sectors. With a digital twin, the aerospace, automotive, and defense industries experience more advanced innovation. In contrast, other industries like infrastructure and aerospace industries are more likely to be processing their primary DT concepts. Applications in Smart Product Development Digital twins provide an array of applications in innovative product development, transforming old practices and exploring new possibilities. Some important fields where digital twins are making a strong impact include: DT helps engineers and designers make virtual prototypes of their products, helping them discover several design iterations, test functionalities, and find potential problems before physical production starts. This regularizes the product development process, minimizes time-to-market, and reduces expenses regarding iterative prototyping. Manufacturers can conduct virtual simulations and tests to examine the durability, presentation, and product safety under different conditions. By duplicating real-world conditions in a digital environment, organizations can determine flaws in design, optimize performance measures, and ensure compliance with regulative standards. DT helps predictive maintenance techniques by continuously monitoring the health and condition of physical assets in real time. Organizations

can predict maintenance requirements, determine failures or anomalies, and schedule repairs by analyzing data from sensors integrated into devices, vehicles, or machines. This reduces downtimes, increases asset lifespan, and improves operational efficacy. Supply Chain Optimization: Digital twins ease the optimization of supply chain operations and end-to-end visibility. Firms can analyze factors like production schedules, inventory levels, transportation routes, and demand forecasts by making digital twins of supply chain networks. This helps enhance resource allocation, decision-making, and responsiveness to market dynamics. Challenges and Future Outlook While the capability of digital twins is huge, their broad adoption still faces some challenges, which include security concerns, data privacy, interoperability problems, and the requirement for skillful talent to develop and handle digital twin ecosystems. However, since technology continues to emerge and mature, these issues are predicted to be fixed, paving the way for wider adoption and innovation in smart product development. Conclusion Digital twins revolutionized the way products are built, designed, and handled. Organizations can accelerate innovation, enhance product quality, and get competitive advantage by utilizing the power of digital twins. Since firms have adopted this revolutionary technology, digital twins have become vital tools for realizing the vision of sustainable and smart product development. For more information on the latest technology, visit www.onpassive.com. [ad_2] onpassive.com

[jcmarchi]

Charles Fisher, Ph.D., CEO & Founder of Unlearn – Interview Series

New Post has been published on https://thedigitalinsider.com/charles-fisher-ph-d-ceo-founder-of-unlearn-interview-series/

Charles Fisher, Ph.D., CEO & Founder of Unlearn – Interview Series

Charles Fisher, Ph.D., is the CEO and Founder of Unlearn, a platform harnessing AI to tackle some of the biggest bottlenecks in clinical development: long trial timelines, high costs, and uncertain outcomes. Their novel AI models analyze vast quantities of patient-level data to forecast patients’ health outcomes. By integrating digital twins into clinical trials, Unlearn is able to accelerate clinical research and help bring life-saving new treatments to patients in need.

Charles is a scientist with interests at the intersection of physics, machine learning, and computational biology. Previously, Charles worked as a machine learning engineer at Leap Motion and a computational biologist at Pfizer. He was a Philippe Meyer Fellow in theoretical physics at École Normale Supérieure in Paris, France, and a postdoctoral scientist in biophysics at Boston University. Charles holds a Ph.D. in biophysics from Harvard University and a B.S. in biophysics from the University of Michigan.

You are currently in the minority in your fundamental belief that mathematics and computation should be the foundation of biology. How did you originally reach these conclusions?

That’s probably just because mathematics and computational methods haven’t been emphasized enough in biology education in recent years, but from where I sit, people are starting to change their minds and agree with me. Deep neural networks have given us a new set of tools for complex systems, and automation is helping create the large-scale biological datasets required. I think it’s inevitable that biology transitions to being more of a computational science in the next decade.

How did this belief then transition to launching Unlearn?

In the past, lots of computational methods in biology have been seen as solving toy problems or problems far removed from applications in medicine, which has made it difficult to demonstrate real value. Our goal is to invent new methods in AI to solve problems in medicine, but we’re also focused on finding areas, like in clinical trials, where we can demonstrate real value.

Can you explain Unlearn’s mission to eliminate trial and error in medicine through AI?

It’s common in engineering to design and test a device using a computer model before building the real thing. We’d like to enable something similar in medicine. Can we simulate the effect a treatment will have on a patient before we give it to them? Although I think the field is pretty far from that today, our goal is to invent the technology to make it possible.

How does Unlearn’s use of digital twins in clinical trials accelerate the research process and improve outcomes?

Unlearn invents AI models called digital twin generators (DTGs) that generate digital twins of clinical trial participants. Each participant’s digital twin forecasts what their outcome would be if they received the placebo in a clinical trial. If our DTGs were perfectly accurate, then, in principle, clinical trials could be run without placebo groups. But in practice, all models make mistakes, so we aim to design randomized trials that use smaller placebo groups than traditional trials. This makes it easier to enroll in the study, speeding up trial timelines.

Could you elaborate precisely on what is Unlearn’s regulatory-qualified Prognostic Covariate Adjustment (PROCOVA™) methodology?

PROCOVA™ is the first method we developed that allows participants’ digital twins to be used in clinical trials so that the trial results are robust to mistakes the model may make in its forecasts. Essentially, PROCOVA uses the fact that some of the participants in a study are randomly assigned to the placebo group to correct the digital twins’ forecasts using a statistical method called covariate adjustment. This allows us to design studies that use smaller control groups than normal or that have higher statistical power while ensuring that those studies still provide rigorous assessments of treatment efficacy. We’re also continuing R&D to expand this line of solutions and provide even more powerful studies going forward.

How does Unlearn balance innovation with regulatory compliance in the development of its AI solutions?

Solutions aimed at clinical trials are generally regulated based on their context of use, which means we can develop multiple solutions with different risk profiles that are aimed at different use cases. For example, we developed PROCOVA because it is extremely low risk, which allowed us to pursue a qualification opinion from the European Medicines Agency (EMA) for use as the primary analysis in phase 2 and 3 clinical trials with continuous outcomes. But PROCOVA doesn’t leverage all of the information provided by the digital twins we create for the trial participants—it leaves some performance on the table to align with regulatory guidance. Of course, Unlearn exists to push the boundaries so we can launch more innovative solutions aimed at applications in earlier stage studies or post-hoc analyses where we can use other types of methods (e.g., Bayesian analyses) that provide much more efficiency than we can with PROCOVA.

What have been some of the most significant challenges and breakthroughs for Unlearn in utilizing AI in medicine?

The biggest challenge for us and anyone else involved in applying AI to problems in medicine is cultural. Currently, the vast majority of researchers in medicine specifically are not extremely familiar with AI, and they are usually misinformed about how the underlying technologies actually work. As a result, most people are highly skeptical that AI will be useful in the near term. I think that will inevitably change in the coming years, but biology and medicine generally lag behind most other fields when it comes to the adoption of new computer technologies. We’ve had many technological breakthroughs, but the most important things for gaining adoption are probably proof points from regulators or customers.

What is your overarching vision for using mathematics and computation in biology?

 In my opinion, we can only call something “a science” if its goal is to make accurate, quantitative predictions about the results of future experiments. Right now, roughly 90% of the drugs that enter human clinical trials fail, usually because they don’t actually work. So, we’re really far from making accurate, quantitative predictions right now when it comes to most areas of biology and medicine. I don’t think that changes until the core of those disciplines change–until mathematics and computational methods become the core reasoning tools of biology. My hope is that the work we’re doing at Unlearn highlights the value of taking an “AI-first” approach to solving an important practical problem in medical research, and future researchers can take that culture and apply it to a broader set of problems.

Thank you for the great interview, readers who wish to learn more should visit Unlearn.

[materialsscienceandengineering]

Nanoscale ferroelectric semiconductor could power AI and post-Moore's Law computing on a phone

Ferroelectric semiconductors are contenders for bridging mainstream computing with next generation architectures, and now a team at the University of Michigan has made them just five nanometers thick—a span of just 50 or so atoms.

This paves the way for integrating ferroelectric technologies with conventional components used in computers and smartphones, expanding artificial intelligence and sensing capabilities. They could also enable batteryless devices, crucial for the Internet of Things (IoT) that powers smart homes, identifies problems with industrial systems and alerts people to safety risks, among other things.

The study in Applied Physics Letters was selected as an editor's pick.

"This will allow the realization of ultra-efficient, ultra-low-power, fully integrated devices with mainstream semiconductors," said Zetian Mi, U-M professor of electrical and computer engineering and co-corresponding author of the study. "This will be very important for future AI and IoT-related devices."

Read more.

[jhavelikes]

A group of scientists has created a system powered by artificial intelligence (AI) that enables robots to conduct as many as 10,000 scientific experiments independently in a single day. The AI system, named BacterAI, could significantly accelerate the pace of discovery in a range of fields such as medicine, agriculture, and environmental science. In a recent research study released in Nature Microbiology, the team successfully utilized BacterAI to map the metabolic processes of two microbes linked with oral health. Discovering amino acid requirements for microbial growth The University of Michigan research team, headed by Professor Paul Jensen, aimed to determine the amino acid requirements for the growth of beneficial mouth microbes.

BacterAI: New AI system enables robots to conduct 10,000 scientific experiments a day

[optimagain]

💡 Level Up Your Skills Without Breaking the Bank! 💡

Tired of seeing those shiny Coursera certificates but feeling the sting of their price tag? 💸 Stop! 🙌

Good news: Coursera is making world-class learning accessible to everyone with their FREE online courses. 🎉 Yep, you read that right - FREE!

🚀 Think of it as a gateway to knowledge. 🚀

Want to dive into the world of Machine Learning? Maybe you're craving some tips on how to negotiate like a pro, or maybe you're just dying to learn Python. Whatever your ambition, Coursera's got your back!

Here's the real deal:

✅ FREE access to course materials: Video lectures, readings, quizzes, and more - all at your fingertips! 🙌 ✅ World-class instructors: Learn from experts at top universities like Stanford, Yale, and the University of Michigan. 🤯 ✅ Learn from home: Unleash your inner student at your own pace, in your own time.

Ready for some inspiration? 🔥 Check out these top-rated free courses:

🔭 Machine Learning (Stanford University) 🧠 The Science of Well-Being (Yale University) 🐍 Programming for Everybody (Getting Started with Python) (University of Michigan) 🤑 Financial Markets (Yale University) 🤝 Successful Negotiation: Essential Strategies and Skills (University of Michigan)

But wait, there's more! 🧠

I'm not saying their paid courses are bad, but let's be real: free is always good! 😎

Here's the catch (and there's always a catch, right?):

❌ No certificates unless you pay (but who says you can't get a killer new skillset without a fancy diploma?) ❌ Some courses have limited access to discussion forums or interactive features. (But hey, you can always find a study buddy!). ❌ Quality might vary (because let's face it, not all professors are created equal!).

Want to maximize your free learning experience? 💯

✨ Here's the magic formula: ✨

Create a study schedule: Treat your learning time like a commitment.

Take notes and actively engage: Practice those skills you're picking up!

Seek out study groups: A little collaboration can make all the difference.

Apply your learning: Remember, real-world application is key! 😎

Ready to start your free learning journey? 🤩

Head to Coursera and explore their free catalog. 👉 Coursera's free course catalog

Go on, you've got this! 💪 Happy learning!

Additional Resources:

For more insights on online learning and course creation, check out Zam Man Optimagain's Course Creation Masterclass. Learn how to leverage AI tools to develop mini-courses and monetize your expertise efficiently.