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Machine Learning Has Value, but It’s Still Just a Tool – MedCity News

April 23, 2023 | Author: admin

Machine learning (ML) has exciting potential for a constellation of uses in clinical trials. But hype surrounding the term may build expectations that ML is not equipped to deliver. Ultimately, ML is a tool, and like any tool, its value will depend on how well users understand and manage its strengths and weaknesses. A hammer is an effective tool for pounding nails into boards, after all, but it is not the best option if you need to wash a window.

ML has some obvious benefits as a way to quickly evaluate large, complex datasets and give users a quick initial read. In some cases, ML models can even identify subtleties that humans might struggle to notice, and a stable ML model will consistently and reproducibly generate similar results, which can be both a strength and a weakness.

ML can also be remarkably accurate, assuming the data used to train the ML model was accurate and meaningful. Image recognition ML models are being widely used in radiology with excellent results, sometimes catching things missed by even the most highly trained human eye.

This doesnt mean ML is ready to replace clinicians judgment or take their jobs, but results so far offer compelling evidence that ML may have value as a tool to augment their clinical judgment.

A tool in the toolbox

That human factor will remain important, because even as they gain sophistication, ML models will lack the insight clinicians build up over years of experience. As a result, subtle differences in one variable may cause the model to miss something important (false negatives), or overstate something that is not important (false positives).

There is no way to program for every possible influence on the available data, and there will inevitably be a factor missing from the dataset. As a result, outside influences such as a person moving during ECG collection, suboptimal electrode connection, or ambient electrical interference may introduce variability that ML is not equipped to address. In addition, ML wont recognize if there is an error such as an end user entering an incorrect patient identifier, but because ECG readings are unique like fingerprints a skilled clinician might realize that the tracing they are looking at does not match what they have previously seen from the same patient, prompting questions about who the tracing actually belongs to.

In other words, machines are not always wrong, but they are also not always right. The best results come when clinicians use ML to complement, not supplant, their own efforts.

Maximizing ML

Clinicians who understand how to effectively implement ML in clinical trials can benefit from what it does well. For example:

The value of ML will continue to grow as algorithms improve and computing power increases, but there is little reason to believe it will ever replace human clinical oversight. Ultimately, ML provides objectivity and reproducibility in clinical trials, while humans provide subjectivity and can contribute knowledge about factors the program does not take into account. Both are needed. And while MLs ability to flag data inconsistencies may reduce some workload, those predictions still must be verified.

There is no doubt that ML has incredible potential for clinical trials. Its power to quickly manage and analyze large quantities of complex data will save study sponsors money and improve results. However, it is unlikely to completely replace human clinicians for evaluating clinical trial data because there are too many variables and potential unknowns. Instead, savvy clinicians will continue to contribute their expertise and experience to further develop ML platforms to reduce repetitive and tedious tasks with a high degree of reliability and a low degree of variability, which will allow users to focus on more complex tasks.

Photo: Gerd Altmann, Pixabay

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Machine Learning Has Value, but It's Still Just a Tool - MedCity News

Putting hydrogen on solid ground: Simulations with a machine … – Science Daily

April 23, 2023 | Author: admin

Hydrogen, the most abundant element in the universe, is found everywhere from the dust filling most of outer space to the cores of stars to many substances here on Earth. This would be reason enough to study hydrogen, but its individual atoms are also the simplest of any element with just one proton and one electron. For David Ceperley, a professor of physics at the University of Illinois Urbana-Champaign, this makes hydrogen the natural starting point for formulating and testing theories of matter.

Ceperley, also a member of the Illinois Quantum Information Science and Technology Center, uses computer simulations to study how hydrogen atoms interact and combine to form different phases of matter like solids, liquids, and gases. However, a true understanding of these phenomena requires quantum mechanics, and quantum mechanical simulations are costly. To simplify the task, Ceperley and his collaborators developed a machine learning technique that allows quantum mechanical simulations to be performed with an unprecedented number of atoms. They reported in Physical Review Letters that their method found a new kind of high-pressure solid hydrogen that past theory and experiments missed.

"Machine learning turned out to teach us a great deal," Ceperley said. "We had been seeing signs of new behavior in our previous simulations, but we didn't trust them because we could only accommodate small numbers of atoms. With our machine learning model, we could take full advantage of the most accurate methods and see what's really going on."

Hydrogen atoms form a quantum mechanical system, but capturing their full quantum behavior is very difficult even on computers. A state-of-the-art technique like quantum Monte Carlo (QMC) can feasibly simulate hundreds of atoms, while understanding large-scale phase behaviors requires simulating thousands of atoms over long periods of time.

To make QMC more versatile, two former graduate students, Hongwei Niu and Yubo Yang, developed a machine learning model trained with QMC simulations capable of accommodating many more atoms than QMC by itself. They then used the model with postdoctoral research associate Scott Jensen to study how the solid phase of hydrogen that forms at very high pressures melts.

The three of them were surveying different temperatures and pressures to form a complete picture when they noticed something unusual in the solid phase. While the molecules in solid hydrogen are normally close-to-spherical and form a configuration called hexagonal close packed -- Ceperley compared it to stacked oranges -- the researchers observed a phase where the molecules become oblong figures -- Ceperley described them as egg-like.

"We started with the not-too-ambitious goal of refining the theory of something we know about," Jensen recalled. "Unfortunately, or perhaps fortunately, it was more interesting than that. There was this new behavior showing up. In fact, it was the dominant behavior at high temperatures and pressures, something there was no hint of in older theory."

To verify their results, the researchers trained their machine learning model with data from density functional theory, a widely used technique that is less accurate than QMC but can accommodate many more atoms. They found that the simplified machine learning model perfectly reproduced the results of standard theory. The researchers concluded that their large-scale, machine learning-assisted QMC simulations can account for effects and make predictions that standard techniques cannot.

This work has started a conversation between Ceperley's collaborators and some experimentalists. High-pressure measurements of hydrogen are difficult to perform, so experimental results are limited. The new prediction has inspired some groups to revisit the problem and more carefully explore hydrogen's behavior under extreme conditions.

Ceperley noted that understanding hydrogen under high temperatures and pressures will enhance our understanding of Jupiter and Saturn, gaseous planets primarily made of hydrogen. Jensen added that hydrogen's "simplicity" makes the substance important to study. "We want to understand everything, so we should start with systems that we can attack," he said. "Hydrogen is simple, so it's worth knowing that we can deal with it."

This work was done in collaboration with Markus Holzmann of Univ. Grenoble Alpes and Carlo Pierleoni of the University of L'Aquila. Ceperley's research group is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Computational Materials Sciences program under Award DE-SC0020177.

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David Higginson of Phoenix Children’s Hospital on using machine … – Chief Healthcare Executive

April 23, 2023 | Author: admin

Chicago - David Higginson has some advice for hospitals and health systems looking to use machine learning.

"Get started," he says.

Higginson, the chief innovation officer of Phoenix Children's Hospital, offered a presentation on machine learning at the HIMSS Global Health Conference & Exhibition. He described how machine learning models helped identify children with malnutrition and people who would be willing to donate to the hospital's foundation.

After the session, he spoke with Chief Healthcare Executive and offered some guidance for health systems looking to do more with machine learning.

"I would say get started by thinking about how you going to use it first," Higginson says. "Don't get tricked into actually building the model."

"Think about the problem, frame it up as a prediction problem," he says, while adding that not all problems can be framed that way.

"But if you find one that is a really nice prediction problem, ask the operators, the people that will use it everyday: 'Tell me how you'd use this,'" Higginson says. "And work with them on their workflow and how it's going to change the way they do their job.

"And when they can see it and say, 'OK, I'm excited about that, I can see how it's going to make a difference,' then go and build it," he says. "You'll have more motivation to do it, you'll understand what the goal is. But when you finally do get it, you'll know it's going to be used."

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David Higginson of Phoenix Children's Hospital on using machine ... - Chief Healthcare Executive

Big data and machine learning can usher in a new era of policymaking – Harvard Kennedy School

April 23, 2023 | Author: admin

Q: What are the challenges to undertaking data analytical research? And where have these modes of analysis been successful?

The challenges are many, especially when you want to make a meaningful impact in one of the most complex sectorsthe health care sector. The health care sector involves a variety of stakeholders, especially in the United States, where health care is extremely decentralized yet highly regulated, for example in the areas of data collections and data use. Analytics-based solutions that can help one part of this sector might harm other parts, making finding globally optimal solutions in this sector extremely difficult. Therefore, finding data-driven approaches that can have public impact is not a walk in the park.

Then there are various challenges in implementation. In my lab, we can design advanced machine learning and AI algorithms that have outstanding performance. But if they are not implemented in practice, or if the recommendations they provide are not followed, they wont have any tangible impact.

In some of our recent experiments, for example, we found that the algorithms we had designed outperformed expert physicians in one of the leading U.S. hospitals. Interestingly, when we provided physicians with our algorithmic-based recommendations, they did not put much weight on the advice they got from the algorithms, and ignored it when treating patients, although they knew the algorithm most likely outperforms them.

We then studied ways of removing this obstacle. We found that combining human expertise with the recommendations provided by algorithms not only made it more likely for the physicians to put more weight on the algorithms advice, but also synthesized recommendations that are superior to both the best algorithms and the human experts.

We have also observed similar challenges at the policy level. For example, we have developed advanced algorithms trained on large-scale data that could help the Centers for Disease Control and Prevention improve its opioid-related policies. The opioid epidemic caused more than 556,000 deaths in the United States between 2000 and 2020, and yet the authorities still do not have a complete understanding of what can be done to effectively control this deadly epidemic. Our algorithms have produced recommendations we believe are superior to the CDCs. But, again, a significant challenge is to make sure CDC and other authorities listen to these superior recommendations.

I do not want to imply that policymakers or other authorities are always against these algorithm-driven solutionssome are more eager than othersbut I believe the helpfulness of algorithms is consistently underrated and often ignored in the practice.

Q: How do you think about the role of oversight and regulation in this field of new technologies and data analytical models?

Imposing appropriate regulations is important. There is, however, a fine line: while new tools and advancements should be guarded against misuses, the regulations should not block these tools from reaching their full potential.

As an example, in a paper that we published in the National Academy of Medicine in 2021, we discussed that the use of mobile health (mHealth) interventions (mainly enabled through advanced algorithms and smart devices) have been rapidly increasing worldwide as health care providers, industry, and governments seek more efficient ways of delivering health care. Despite the technological advances, increasingly widespread adoption, and endorsements from leading voices from the medical, government, financial, and technology sectors, these technologies have not reached their full potential.

Part of the reason is that there are scientific challenges that need to be addressed. For example, as we discuss in our paper, mHealth technologies need to make use of more advanced algorithms and statistical experimental designs in deciding how best to adapt the content and delivery timing of a treatment to the users current context.

However, various regulatory challenges remainsuch as how best to protect user data. The Food and Drug Administration in a 2019 statement encouraged the development of mobile medical apps (MMAs) that improve health care but also emphasized its public health responsibility to oversee the safety and effectiveness of medical devicesincluding mobile medical apps. Balancing between encouraging new developments and ensuring that such developments abide by the well-known principle of do no harm is not an easy regulatory task.

At the end, what is needed are two-fold: (a) advancements in the underlying science, and (b) appropriately balanced regulations. If these are met, the possibilities for using advanced analytics science methods in solving our lingering societal problems are endless.

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Big data and machine learning can usher in a new era of policymaking - Harvard Kennedy School

Machine Learning Can Help to Flag Risky Messages on Instagram … – Drexel University

April 23, 2023 | Author: admin

As regulators and providers grapple with the dual challenges of protecting younger social media users from harassment and bullying, while also taking steps to safeguard their privacy, a team of researchers from four leading universities has proposed a way to use machine learning technology to flag risky conversations on Instagram without having to eavesdrop on them. The discovery could open opportunities for platforms and parents to protect vulnerable, younger users, while preserving their privacy.

The team, led by researchers from Drexel University, Boston University, Georgia Institute of Technology and Vanderbilt University recently published its timely work an investigation to understand what type of data input, such as metadata, text, and image features could be most useful for machine learning models to identify risky conversations in the Proceedings of the Association for Computing Machinerys Conference on Human-Computer Interaction. Their findings suggest that risky conversations can be detected by metadata characteristics, such as conversation length and how engaged the participants are.

Their efforts address a growing problem on the most popular social media platform among 13-to-21-year-olds in America. Recent studies have shown that harassment on Instagram is leading to a dramatic uptick of depression among its youngest users, particularly a rise in mental health and eating disorders among teenage girls.

The popularity of a platform like Instagram among young people, precisely because of how it makes its users feel safe enough to connect with others in a very open way, is very concerning in light of what we now know about the prevalence of harassment, abuse, and bullying by malicious users, said Afsaneh Razi, PhD, an assistant professor in Drexels College of Computing & Informatics, who was a co-author of the research.

At the same time, platforms are under increasing pressure to protect their users privacy, in the aftermath of the Cambridge Analytica scandal and the European Unions precedent-setting privacy protection laws. As a result, Meta, the company behind Facebook and Instagram, is rolling out end-to-end encryption of all messages on its platforms. This means that the content of the messages is technologically secured and can only be accessed by the people in the conversation.

But this added level of security also makes it more difficult for the platforms to employ automated technology to detect and prevent online risks which is why the groups system could play an important role in protecting users.

One way to address this surge in bad actors, at a scale that can protect vulnerable users, is automated risk-detection programs, Razi said. But the challenge is designing them in an ethical way that enables them to be accurate, but also non-privacy invasive. It is important to put younger generations safety and privacy as a priority when implementing security features such as end-to-end encryption in communication platforms.

The system developed by Razi and her colleagues uses machine learning algorithms in a layered approach that creates a metadata profile of a risky conversation its likely to be short and one-sided, for example combined with context clues, such as whether images or links are sent. In their testing, the program was 87% accurate at identifying risky conversations using just these sparse and anonymous details.

To train and test the system, the researchers collected and analyzed more than 17,000 private chats from 172 Instagram users ages 13-21 who volunteered their conversations more than 4 million messages in all to assist with the research. The participants were asked to review their conversations and label each one as safe or unsafe. About 3,300 of the conversations were flagged as unsafe and additionally categorized in one of five risk categories: harassment, sexual message/solicitation, nudity/porn, hate speech and sale or promotion of illegal activities.

Using a random sampling of conversations from each category, the team used several machine learning models to extract a set of metadata features things like average length of conversation, number of users involved, number of messages sent, response time, number of images sent, and whether or not participants were connected or mutually connected to others on Instagram most closely associated with risky conversations.

This data enabled the team to create a program that can operate using only metadata, some of which would be available if Instagram conversations were end-to-end encrypted.

Overall, our findings open up interesting opportunities for future research and implications forthe industry as a whole, the team reported. First, performing risk detection based on metadata features alone allows for lightweight detection methods that do not require the expensive computation involved in analyzing text and images. Second, developing systems that do not analyze content eases some of the privacy and ethical issues that arise in this space, ensuring user protection.

To improve upon it making a program that could be even more effective and able to identify the specific risk type, if users or parents opt into sharing additional details of the conversations for security purposes the team performed a similar machine learning analysis of linguistic cues and image features using the same dataset.

In this instance advanced machine learning programs combed through the text of the conversations and, knowing which contact the users had identified as unsafe, pinpointed the words and combinations of words that are prevalent enough in risky conversations that they could be used to trigger a flag.

For analysis of the images and videos which are central to communication on Instagram the team used a set of programs, one that that can identify and extract text on top of images and videos, and another that can look at and generate a caption for each image. Then, using a similar textual analysis the machine learning programs again created a profile of words indicative of images and videos shared in a risky conversation.

Trained with these risky conversation characteristics, the machine learning system was put to the test by analyzing a random sampling of conversations from the larger dataset that had not been used in the profile-generation or training process. Through a combination of analyses of both metadata traits, as well as linguistic cues and image features the program was able to identify risky conversations with accuracy as high as 85%.

Metadata can provide high-level cues about conversations that are unsafe for youth; however, the detection and response to the specific type of risk require the use of linguistic cues and image data, they report. This finding raises important philosophical and ethical questions in light of Metas recent push towards end-to-end encryption as such contextual cues would be useful for well-designed risk mitigation systems that leverage AI.

The researchers acknowledge that there are limitations to their research because it only looked at messages on Instagram though the system could be adapted to analyze messages on other platforms that are subject to end-to-end encryption. They also note that the program could become even more accurate if its training were to continue with a larger sampling of messages.

But they note that this proves that this work shows that effective automated risk detection is possible, and while protecting privacy is a valid concern, there are ways to making progress and these steps should be pursued in order to protect the most vulnerable users of these popular platforms.

Our analysis provides an important first step to enable automated (machine learning based)

detection of online risk behavior going forward, they write. Our system is based on reactive characteristics of the conversation however our research also paves the way for more proactive approaches to risk detection which are likely to be more translatable in the real world given their rich ecological validity.

This research was funded by the U.S. National Science Foundation and the William T. Grant Foundation.

Shiza Ali, Chen Ling and Gianlucca Stringhini, from Boston University; Seunghyun Kim and Munmun De Choudhury, from Georgia Institute of Technology; and Ashwaq Alsoubai and Pamela J. Wisniewski, from Vanderbilt University, contributed to this research.

Read the full paper here: https://dl.acm.org/doi/10.1145/3579608

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Machine Learning Can Help to Flag Risky Messages on Instagram ... - Drexel University

What is one downside to deep learning? – Rebellion Research

April 23, 2023 | Author: admin

What is one downside to deep learning?

Deep learning is a subset of machine learning that involves training artificial neural networks to recognize patterns in data. While deep learning has shown remarkable success in recent years, enabling breakthroughs in fields such as computer vision, natural language processing, and robotics, it is not without its flaws. One of the major challenges facing deep learning is its slow adaptability to changing environments and new data.

Deep learning algorithms typically train on large datasets. To recognize patterns in the data. These patterns can become used to make predictions or classify new data. That the model has not seen before. However, the performance of deep learning models usually deteriorate sover time. As the data trained on becomes outdated. Or no longer reflects the real-world conditions. Known as the problem of concept drift. Where the statistical properties of the data change over time. As a result, leading to degraded performance of the model.

There are several techniques that have become proposed to address the problem of concept drift in deep learning. One approach uses a continuous learning framework. Where the model becomes updated over time with new data to prevent the accumulation of errors due to concept drift. Another approach uses transfer learning. Where a pre-trained model fine-tuned on new data to adapt to the changing environment.

Despite these approaches, deep learning models still struggle with slow adaptability to new data and changing environments. Due in part to the fact that deep learning models highly parameterized and require large amounts of data to learn complex representations of the input data. As a result, updating the model with new data can be computationally expensive and time-consuming, making it difficult to adapt quickly to changing conditions.

In conclusion, the slow adaptability of deep learning models to changing environments. And new data becomes a major flaw. Moreover, one that needs to be addressed to enable their wider adoption in real-world applications. While techniques such as continuous learning and transfer learning show promise. More research becomes needed to develop more efficient and effective approaches to address this challenge. By addressing this flaw, deep learning can continue to revolutionize fields ranging from healthcare to finance to transportation, enabling new breakthroughs and transforming our world.

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What is one downside to deep learning? - Rebellion Research

Data Sharing That Safeguards Patient Privacy is Crucial for Future of … – Yale School of Medicine

April 23, 2023 | Author: admin

It is the responsibility of researchers to tell the truest truth, says Daniel Boffa, MD, professor of surgery (thoracic). The accuracy and precision of medical research depend on the data used.

The medical community has increasingly recognized the importance of data sharing. The 21st Century Cures Act, passed in 2016 to accelerate medical discovery, encourages scientists to share data more openly so that other investigators may build upon it. More recently, editors of JAMA announced that researchers must include a data sharing plan in their manuscripts. The compilation of greater data sets allows investigators to generate a more complete picture of what is happening in patients. Furthermore, it helps them understand differences among various groups of patients, which will ultimately lead to providing more personalized medicine. However, data sharing also comes with the risk of loss of patient privacy, Boffa warns in a commentary published April 18 in JAMA.

Weve never had a better opportunity to leverage patient information to make powerful changes, says Boffa. But it has to be done in a way in which patient privacy is securewhich is challenging, but possible.

Boffa, a thoracic surgeon who specializes in cancer, is engaged in an initiative to compile all cancer data for patients in the United States through the National Cancer Database.

When a patient is diagnosed with cancer, the hospital generates a record with patient data. However, patients commonly receive care from more than one institution, resulting in a single patients data becoming scattered among various locations.

Weve never had a better opportunity to leverage patient information to make powerful changes, but it has to be done in a way in which patient privacy is securewhich is challenging, but possible.

Then, hospitals will share their records to various cancer databases without the identifying information. Because the data is anonymous, researchers are left with an incomplete picturedata collected for one patient related to testing, treatment, cancer stage, or patient attributes in one database is often missed by another. All of these databases have unique, incompletely overlapping pictures of each cancer patient, says Boffa.

To address this, he and his colleagues are trying to create a national cancer identifier. We basically take the identifying information and use advanced cryptography to turn it into an encrypted identifier that cannot be reversed to reveal the patients identify, he says. This new identifier is like a tag that can be used to tie all a patients data together in the national database. This new tool, he says, will be incredibly powerful.

When you have the data for every single cancer patient at your fingertips, the number of discoveries we will be able to make will be mind-blowing, he says. You may one day be able to use artificial intelligence to ask and answer cancer questions within a massive pool of patient information, similar to how platforms like ChatGPT use internet data.

This task, however, presents a significant challenge: protecting patient privacy. Because of advances in computer technology, the theoretical risk of reidentification of anonymous data is very high. Anonymous data is not private, says Boffa. If you put all of this information together, even if no name is included, a patient can still be identified. In collaboration with Yale computer scientists, Boffas team has poured massive time and energy into ensuring their project protects patient privacy.

Boffa is excited about new data sharing policies such as JAMAs but is concerned that they come with little guidance for doing so in a safe and secure way.

Making patient data anonymous is an important first step, says Boffa, but researchers also need to share data in a way that is trackable and accountable. In other words, they should know of everyone who has access to it and understand the security of the computing environmentsuch as whether the servers are secure and passwords are encrypted at the secondary institutions.

Furthermore, researchers should avoid downstream sharing and exchanging information in nonsecure ways, he says. This includes not emailing anonymous datasets or leaving them on unencrypted laptops. I would treat anonymous data the same way I would treat data that has identifying information, says Boffa. It should be treated as sensitive and as potentially harmful as data that has a patients social security number.

Although data sharing presents these complicated challenges, overcoming them will be critical for the future of personalized medicine. Right now, researchers are accomplishing so much with incomplete information, says Boffa. But conducting research at scale that includes many different variables will open the door to many more discoveries. There are so many more knowable pieces to the puzzle now, he says. By tying all of this together, that is the most credible way of determine for every single patient, what is the best, safest, and most effective treatment for them.

Other leaders at Yale are also dedicated to meeting these challenges and making data more accessible. A little over a decade ago, Harlan Krumholz, MD, Harold H. Hines, Jr. Professor of Medicine (Cardiology) and Joseph Ross, MD, professor of medicine (general medicine) and of public health (health policy and management), co-founded the Yale Open Data Access Project (YODA) with a goal to make data more widely available and to promote open science. The data sits within a repository so that researchers can work on it in a private, safe space, says Krumholz. It has guardrails up so that it can be both high ethics and high science.

As a result of the project, over 100 manuscripts have been published that would not have been possible without the sharing of data. Were leaving an era where most investigators had the perception that they had no ethical responsibility to ensure that the most that can come of it occurs, says Krumholz. Were trying to promote this idea that ethically, for the money, time, and willingness of people to be part of studies, we ought to be working hard to figure out how we safely and securely leverage data thats generated for the greatest amount of public good possible.

In his JAMA commentary, Boffa commends YODA as an accountable and transparent repository and distributor of clinical trials data that successfully pursues the goal of sharing patient information in a more secure manner. He writes that YODA shows how sound techniques developed at the federal level can also be embraced by individual institutions and organizations.

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Microsoft Readies AI Chip as Machine Learning Costs Surge – Slashdot

April 23, 2023 | Author: admin

After placing an early bet on OpenAI, the creator of ChatGPT, Microsoft has another secret weapon in its arsenal: its own artificial intelligence chip for powering the large-language models responsible for understanding and generating humanlike language. The Information: The software giant has been developing the chip, internally code-named Athena, since as early as 2019, according to two people with direct knowledge of the project. The chips are already available to a small group of Microsoft and OpenAI employees, who are testing the technology, one of them said. Microsoft is hoping the chip will perform better than what it currently buys from other vendors, saving it time and money on its costly AI efforts. Other prominent tech companies, including Amazon, Google and Facebook, also make their own in-house chips for AI. The chips -- which are designed for training software such as large-language models, along with supporting inference, when the models use the intelligence they acquire in training to respond to new data -- could also relieve a shortage of the specialized computers that can handle the processing needed for AI software. That shortage, reflecting the fact that primarily just one company, Nvidia, makes such chips, is felt across tech. It has forced Microsoft to ration its computers for some internal teams, The Information has reported.

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Microsoft Readies AI Chip as Machine Learning Costs Surge - Slashdot

206 MSU graduates receive Board of Trustees’ Award for earning a … – MSUToday

April 23, 2023 | Author: admin

This semester, a record-breaking 206 graduating students were recognized by the Michigan State University Board of Trustees for achieving the highest scholastic average a 4.0 GPA.

Each semester, students graduating with a 4.0 GPA are presented with the Board of Trustees Award for their academic excellence. Additionally, each awardee will be acknowledged during their individual commencement ceremonies in May and will receive $1,000 from the university in recognition of their accomplishment.

MSU Board of Trustees Chair Rema Vassar, Ph.D., recognized these students in todays board meeting.

It is an honor to present these students with an award that embodies the culmination of all the hard work and dedication they have demonstrated, Vassar said. We are proud of them, and optimistic they will continue as lifelong learners who make a difference in their communities.

Interim President Teresa K. Woodruff, Ph.D., also recognized the excellence of the awardees.

These awards are an embodiment of our students resilience and continuous commitment to their education during their years at MSU, said Woodruff. I am confident their continued excellence will permeate every aspect of their future, and I look forward to the impact they will have in Michigan and in the world.

Students receiving this award include:

Alexander S. Adamopoulos: Communication, College of Communication Arts and Sciences. Adamopoulos is from Grand Rapids, Michigan, and attended East Grand Rapids High School.

Hemkesh Agrawal: Computer Science, College of Engineering and a member of the Honors College. Agrawal is from Delhi, India, and attended Ramjas School.

Hana A. Al Aifan: Criminal Justice, College of Social Science. Al Aifan is from Riyadh, Saudi Arabia, and attended the American International School of Bucharest.

Kameron L. Alcantara: Social Work, College of Social Science. Alcantara is from Glendale Heights, Illinois, and attended Glenbard East High School.

Zeeba Ali: Neuroscience, Lyman Briggs College and a member of the Honors College. Ali is from Rochester Hills, Michigan, and attended Rochester High School.

Jesse W. Amburgey: Studio Art, College of Arts and Letters. Amburgey is from Lansing, Michigan, and attended Laingsburg High School.

Jasmine A. Amine: Psychology, College of Social Science and a member of the Honors College. Amine is from Westland, Michigan, and attended Canton High School.

Alexandra L. Anderson: Kinesiology, College of Education. Anderson is from Danville, Kentucky, and attended Boyle County High School.

Anthony R. Arapaj: Supply Chain Management, Eli Broad College of Business. Arapaj is from Macomb, Michigan, and attended Chippewa Valley High School.

Nicholas F. Balesky: Finance, Eli Broad College of Business and a member of the Honors College. Balesky is from East Lansing, Michigan, and attended Okemos High School.

Brooke R. Bannon: Packaging, College of Agriculture and Natural Resources. Bannon is from Fenton, Michigan, and attended Hartland High School.

Brenden D. Barnes: Interdisciplinary Studies in Social Science: Social Science Education, College of Social Science. Barnes is from Battle Creek, Michigan, and attended Lakeview High School.

Matthew J. Baylis: Marketing, Eli Broad College of Business. Baylis is from Apex, North Carolina, and attended Green Hope High School.

Reid Becker: Human Biology, College of Natural Science. Becker is from Holland, Michigan, and attended Holland Christian High School.

Drew A. Beckman: Genomics and Molecular Genetics, Lyman Briggs College and a member of the Honors College. Beckman is from Sterling Heights, Michigan and attended Adlai E. Stevenson High School.

Olivia J. Beebe: Advertising Management, College of Communication Arts and Sciences. Beebe is from Ionia, Michigan, and attended Ionia High School.

Alec J. Bensman: Computer Science, College of Engineering and a member of the Honors College. Bensman is from Cincinnati, Ohio, and attended Walnut Hills High School.

Grace A. Bonnema: Human Biology, Lyman Briggs College and a member of the Honors College. Bonnema is from Kalamazoo, Michigan, and attended Mattawan High School.

Samantha N. Bourgeois: Construction Management, College of Agriculture and Natural Resources; English, College of Arts and Letters and a member of the Honors College. Bourgeois is from Berkley, Michigan, and attended Berkley High School.

Bailey A. Bowcutt: Microbiology, Lyman Briggs College and a member of the Honors College. Bowcutt is from Cheyenne, Wyoming, and attended Cheyenne Central High School.

Charlotte A. Bridges: Political - Science Prelaw, College of Social Science and a member of the Honors College. Bridges is from Livonia, Michigan, and attended Mercy High School.

Devin J. Brust: Accounting, Eli Broad College of Business and a member of the Honors College. Brust is from Troy, Michigan, and attended Athens High School.

Thomas F. Burgess III: Mechanical Engineering, College of Engineering and a member of the Honors College. Burgess is from Honor, Michigan, and attended Lake Orion High School.

Calista Busch: Genomics and Molecular Genetics, Lyman Briggs College and a member of the Honors College. Busch is from Mason, Ohio, and attended William Mason High School.

Ian R. Byram: Computer Science, College of Engineering. Byram is from Grand Blanc, Michigan, and attended Grand Blanc Community School.

Jim M. Camilleri: Human Biology, College of Natural Science and a member of the Honors College. Camilleri is from Grosse Ile, Michigan, and attended Grosse Ile High School.

Chad M. Casey: Supply Chain Management, Eli Broad College of Business. Casey is from Grosse Pointe Farms, Michigan, and attended Dwight D. Eisenhower High School.

Drishti Chauhan: Human Biology, Lyman Briggs College and a member of the Honors College. Chauhan is from East Lansing, Michigan, and attended International Academy Okma.

Xinjia (Jocelyn) Chen: Accounting, Eli Broad College of Business and a member of the Honors College. Chen is from South Lyon, Michigan, and attended South Lyon High School.

Ryan M. Christian: Human Biology, College of Natural Science. Christian is from Waterford, Michigan, and attended Waterford Mott High School.

Alexis Y. Chuong: Chemical Engineering, College of Engineering and a member of the Honors College. Chuong is from Livonia, Michigan, and attended Winston Churchill High School.

Maximo E. Clark: Genomics and Molecular Genetics, Lyman Briggs College. Clark is from Ann Arbor, Michigan, and attended Pioneer High School.

Kaedon D. Cleland-Host: Physics, Mathematics, College of Natural Science and a member of the Honors College. Cleland-Host is from Lake Orion, Michigan, and attended Herbert Henry Dow High School.

Abigail V. Comar: Fisheries and Wildlife, College of Agriculture and Natural Resources; Journalism, College of Communication Arts and Sciences and a member of the Honors College. Comar is from Green Bay, Wisconsin, and attended Notre Dame Academy.

Maura A. Culler: Social work, College of Social Science and a member of the Honors College. Culler is from Louisville, Kentucky, and attended Atherton High School.

Jessica M. Culver: Criminal Justice, College of Social Science. Culver is from Clarkston, Michigan, and attended Clarkston Senior High School.

Trevor L. Dalrymple: Biochemistry/Biotechnology, Lyman Briggs College and a member of the Honors College. Dalrymple is from Rockford, Michigan, and attended Grand Rapids Christian High School.

Riley O. Damore: Marketing, Eli Broad College of Business and a member of the Honors College. Damore is from Battle Creek, Michigan, and attended Lakeview High School.

Ryan J. Danaj: Biosystems Engineering, College of Engineering. Danaj is from Washington, Michigan, and attended Romeo High School.

Natalie P. Daube: Finance, Eli Broad College of Business and a member of the Honors College. Daube is from Canonsburg, Pennsylvania, and attended Peters Township High School.

Adam C. Dec: Computational Data Science, College of Engineering. Dec is from Holt, Michigan, and attended Lansing Catholic High School.

Joseph C. Dec: Civil Engineering, College of Engineering. Dec is from Holt, Michigan, and attended Lansing Catholic High School.

Madeline M. Deeb: Human Biology, Lyman Briggs College and a member of the Honors College. Deeb is from Canton, Michigan, and attended Salem High School.

Brendan R. Doane: Mechanical Engineering, College of Engineering and a member of the Honors College. Doane is from Jackson, Michigan, and attended Lumen Christi High School.

Michael R. Dodde: Agribusiness Management, College of Agriculture and Natural Resources and a member of the Honors College. Dodde is from Conklin, Michigan, and attended Coopersville High School.

Nicklaus J. Donovan: Finance, Eli Broad College of Business and a member of the Honors College. Donovan is from Dewitt, Michigan, and attended Haslett High School.

Rachel E. Drobnak: Crop and Soil Science, College of Agriculture and Natural Resources and a member of the Honors College. Drobnak is from Olmsted Township, Ohio, and attended Olmsted Falls High School.

Jon P. Droste: Mechanical Engineering, College of Engineering and a member of the Honors College. Droste is from Dewitt, Michigan, and attended Dewitt High School.

Zoe C. Dunnum: Psychology, College of Social Science and a member of the Honors College. Dunnum is from Rockford, Michigan, and attended Rockford Senior High School.

Kiet V. Duong: Mechanical Engineering, College of Engineering and a member of the Honors College. Duong is from Ho Chi Minh City, Vietnam, and attended Vietnam National University High School for the Gifted.

Greyson J. Dwyer: Supply Chain Management, Eli Broad College of Business and a member of the Honors College. Dwyer is from East Lansing, Michigan, and attended Haslett High School.

James H. Eagle: Music Performance, College of Music and a member of the Honors College. Eagle is from Mason, Michigan, and attended Okemos High School.

Carlos M. Enriquez: Marketing, Eli Broad College of Business. Enriquez is from Novi, Michigan, and attended South Lyon East High School.

Malavika P. Eswaran: Neuroscience, College of Natural Science and a member of the Honors College. Eswaran is from Canton, Michigan, and attended Suzhou Singapore International School.

Leslie E. Ewalt: Human Biology, College of Natural Science. Ewalt is from Muskegon, Michigan, and attended Mona Shores High School.

Caleb B. Fisher: Biochemistry and Molecular Biology, College of Natural Science. Fisher is from Laingsburg, Michigan, and attended Laingsburg High School.

Mackenzie R. Fitzgerald: Human Biology, Chemistry, Lyman Briggs College and a member of the Honors College. Fitzgerald is from Clarkston, Michigan, and attended North Farmington High School.

Sarah M. Foreman: Psychology, College of Social Science. Foreman is from Belmont, Michigan, and attended Comstock Park High School.

Grace S. Foster: Actuarial Science, College of Natural Science. Foster is from Grosse Pointe, Michigan, and attended Grosse Pointe South High School.

Alexander T. Frischmuth: Finance, Eli Broad College of Business. Frischmuth is from Plymouth, Michigan, and attended Canton High School.

Brian E. George: Supply Chain Management, Eli Broad College of Business. George is from Farmington Hills, Michigan, and attended North Farmington High School.

Anthony P. Giordano: Supply Chain Management, Eli Broad College of Business. Giordano is from New Baltimore, Michigan, and attended De La Salle Collegiate High School.

Garrett M. Gleason: Political Science-Prelaw, College of Social Science. Gleason is from Flint, Michigan, and attended Carman-Ainsworth High School.

Krishna S. Gogineni: Microbiology, Human Biology, Lyman Briggs College and a member of the Honors College. Gogineni is from Van Buren Township, Michigan, and attended International Academy Okma.

Shannon K. Good: Animal Science, College of Agriculture and Natural Resources. Good is from Caledonia, Michigan, and attended Caledonia High School.

Caroline G. Gormely: Computer Science, College of Engineering and a member of the Honors College. Gormely is from Grosse Pointe, Michigan, and attended Grosse Pointe South High School.

Devin K. Granzo: Finance, Eli Broad College of Business. Granzo is from Midland, Michigan, and attended Midland High School.

Lauren E. Grasso: Biology, Lyman Briggs College and a member of the Honors College. Grasso is from Holt, Michigan, and attended Holt High School.

Jessica J. Greatorex: Psychology, College of Social Science. Greatorex is from Clarkston, Michigan, and attended Clarkston High School.

Thaddaeus A. Greiner: Computer Science, College of Engineering and a member of the Honors College. Greiner is from Plymouth, Michigan, and attended Plymouth High School.

Tristyn I. Griffin: Psychology, Political Science-Prelaw, College of Social Science. Griffin is from Trenton, Michigan, and attended Plymouth High School.

Evan K. Griffis: Fisheries and Wildlife, College of Agriculture and Natural Resources and a member of the Honors College. Griffis is from Newberry, Michigan, and attended Newberry High School.

Rebecca E. Grodsky: Veterinary Nursing, College of Veterinary Medicine. Grodsky is from Farmington, Michigan, and attended Farmington High School.

Sohan Gupta: Mechanical Engineering, College of Engineering and a member of the Honors College. Gupta is from Richmond, Kentucky, and attended the Delhi Public School Ruby Park Kolkata.

Siddarth Guruvi: Supply Chain Management, Eli Broad College of Business. Guruvi is from West Bloomfield, Michigan, and attended American Embassy School.

Laura A. Hall: Accounting, Eli Broad College of Business. Hall is from Howell, Michigan, and attended Fenton High School.

Shems Hamdan: Human Biology, Lyman Briggs College and a member of the Honors College. Hamdan is from East Lansing, Michigan, and attended East Lansing High School.

Jackson A. Haugen: Computer Science, College of Engineering; Mathematics, College of Natural Science; and a member of the Honors College. Haugen is from Boyne Falls, Michigan, and attended Petoskey High School.

DeLaney M. Heckman: Kinesiology, College of Education. Heckman is from Allegan, Michigan, and attended Allegan High School.

John J. Henige: Biochemistry and Molecular Biology, College of Natural Science. Henige is from Franklin, Michigan, and attended University Detroit Jesuit High School.

Justin P. Henkelman: Computer Science, College of Engineering. Henkelman is from Hudsonville, Michigan, and attended Hudsonville High School.

Benjamin C. Henley: Human Biology, Lyman Briggs College and a member of the Honors College. Henley is from Fenton, Michigan, and attended Fenton High School.

Anne N. Henseler: Education, College from Education and a member of the Honors College. Henseler is from Ypsilanti, Michigan, and attended Father Gabriel Richard High School.

Sylvia E. Hodges: English, College of Arts and Letters and a member of the Honors College. Hodges is from Grosse Pointe Park, Michigan, and attended Grosse Pointe South High School.

Derik M. Holmberg: Human Biology, Lyman Briggs College and a member of the Honors College. Holmberg is from Greenville, Michigan, and attended Greenville High School.

Dana L. Holt: Graphic Design, College of Arts and Letters and a member of the Honors College. Holt is from Rochester Hills, Michigan, and attended Avondale High School.

Taylor L. Hori: Entomology, College of Agriculture and Natural Resources. Hori is from San Diego, California, and attended Rancho Buena Vista High School.

Trent C. Hughes: Finance, Eli Broad College of Business. Hughes is from Wolverine Lake, Michigan, and attended Walled Lake Central High School.

Sam H. Huller: Special Education-Learning Disabilities, College of Education and a member of the Honors College. Huller is from Oxford, Michigan, and attended Oxford High School.

Jayla J. Irons: Political Science, College of Social Science and a member of the Honors College. Irons is from Chicago, Illinois, and attended Whitney M. Young Magnet High School.

Neha A. Iska: Neuroscience, Lyman Briggs College and a member of the Honors College. Iska is from Grand Blanc, Michigan, and attended Powers Catholic High School.

Charlotte G. Jansky: Music Performance, College of Music and a member of the Honors College. Jansky is from Little Silver, New Jersey, and attended Red Bank Regional High School.

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206 MSU graduates receive Board of Trustees' Award for earning a ... - MSUToday

Data Science Salon Brings the New York City AI and Machine … – GlobeNewswire

April 23, 2023 | Author: admin

NEW YORK, April 19, 2023 (GLOBE NEWSWIRE) -- Data Science Salon, the most diverse data science community in the US, is excited to announce two all-day events in NYC, NY on June 7th and 8th, 2023 focusing on state-of-the-art AI and machine learning applications.

Only six months after the last Data Science Salon (DSS) in the Big Apple, DSS will be back in New York City with two events in the first week of June. The event on June 7th will be held at the S&P Global Ratings Headquarters in Manhattan and bring together local industry leaders from finance and technology data science fields. The second event on June 8th will focus on AI and machine learning applications in media and advertising and takes place at Blender Workspace in the heart of NoMad.

Both events include a combination of talks, panel conversations, lots of time for networking, and an optional expo in a casual environment. The two days bring together industry leaders and specialists face-to-face to share actionable insights and educate each other about innovative solutions in artificial intelligence, machine learning, predictive analytics and acceptance around best practices. Data Science Salon attendees are executives, senior data science practitioners, data science managers, analysts, and engineering professionals. 150 attendees are expected to attend each event day and over one thousand people will tune in virtually.

The event lineup features 20 speakers per day, including data leaders from Morgan Stanley, The Federal Reserve Bank of New York, S&P Global, T. Rowe Price, Freddie Mac, Barclays Investment Bank on June 7th and experts from Penguin Random House, BuzzFeed, Meta, Moet & Hennessy, and Parrot Analytics, and many more on June 8th.

Some topics covered at Data Science Salon NYC include:

Over the years Data Science Salon has grown into an amazing community of likeminded practitioners across multiple domains. We learn from each other different applications and techniques that normally we would not have seen within our own industry. Such a strong community of smart applied data scientists within an open and collaborative setting! Moody Hadi, Head of Credit Analytics New Product Development, S&P Global

Visit the Data Science Salon NYC website to view the complete conference agenda and register for one or both events.

The Data Science Salon (DSS) is a unique vertical focused conference which grew into a diverse community of senior data science, machine learning and other technical specialists. The community gathers face-to-face and virtually to educate each other, illuminate best practices and innovate new solutions in a casual atmosphere; you can also tune into the DSS webinars, Meetups, and podcast episodes. Learn more about Data Science Salon on the DSS website.

Contacts:For media inquiries:Esther Rietmann+1 305-215-4527esther@formulatedby.com

For sponsorship inquiries:Anna Anisin+1 305-215-4527anna@formulatedby.com

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Data Science Salon Brings the New York City AI and Machine ... - GlobeNewswire

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206 MSU graduates receive Board of Trustees’ Award for earning a … – MSUToday

Data Science Salon Brings the New York City AI and Machine … – GlobeNewswire

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