Modified Apr 18, 2023 23:57 IST

Amlan Borgohain was the hero of the Indian Grand Prix 4 at the Kanteerava track in Bengaluru after completing the sprint doubles, winning both the 100m and 200m. He did the same thing earlier this week at the Indian Grand Prix 3.

Another standout performance was Beranica Elangovan's spectacular personal best of 4.10m in the pole vault (only missing the national record after previously winning while trying 4.22m).

Jyothi Yarraji raced a personal best of 23.60 seconds in the 200m into a stiff headwind, winning a commanding victory over the incumbent national champion. Reliance Foundation athletes finished first, second, and third in the men's sprints, and first and second in the men's 200m.

There were 10 personal best results from seven Reliance Foundation competitors throughout both Indian Grand Prix events. Tejas Shirse (110m Hurdles in 13.81 seconds) and Ragul Kumar (400m in 48.48 seconds) set personal bests at the Indian Grand Prix 3.

Personal bests at Indian Grand Prix 4 included Baranica Elangovan (Women's Pole Vault with 4.10m, Jyothi Yarraji (200m in 23.60), Sapna Kumari (100m in 12.20s), Animesh Kujur (100m in 10.69), Ragul Kumar (100m in 10.77), Laxmipriya Kisan (400 m in 59.37s), Kishore Jena (Men's Javelin Throw with 77.22m) and Tejas Shirse (100m in 10.89 seconds) and (200m in 21.96s).

Tejas' 200m effort was his third personal best in three events, capping off an incredible week.

What particularly impressed James were the competitors who achieved personal bests amid significant headwinds. The -2.0m/s headwind in the Men's 100m Race A, for example, did not dissuade any of RF's competitors, with Amlan Borgohain, Ragul, and Tejas demonstrating their skill and tenacity despite the terrible circumstances.

Given that the Indian Grand Prix 3 and 4 were the athletics season openers, the coach is better informed of his game plan for the next months.

Coach James is sure that the athletes will do even better in the forthcoming tournaments after ironing out a few kinks and making some modifications.

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With outstanding performance and personal bests, Reliance Foundation athletes shine at Indian Grand Prix 4 - Sportskeeda

Asset allocation is like a full course meal full of different recipes, says Priyadarshini Moreshwar Mulye, a SEBI Registered Investment Advisor and Founder, ARTHA FinPlan.

In an interview with MintGenie, Mulye said that indexation benefit was one of the major distinguishing factors which debt funds used to have before changes in their tax implications.

To get better returns from the existing portfolio of mutual funds, one should first get the updated valuation. If the investment is made randomly and without any goal assigned, then please decide on the goal first. For short-term goals, one can assign short-term debt funds and for long-term goals, suitable equity funds.

Further, reduce the number of schemes which are repetitive from the same category and do not deliver returns at par with the respective benchmark for more than two years at least. This will help to curb overdiversification. Now you can clearly view your portfolio which will further help you to review regularly and make suitable changes along. While making any fresh investment too, decide on your basics, viz., future goals, investment tenure and risk appetite.

The Reserve Bank of India (RBI) has taken a Pause" for now with respect to repo rate changes. But it may change in the next policy meetings. When it comes to debt reduction", it gives us psychological relief and we can better utilize the money for other meaningful purposes. Considering this pause in rate hikes", if the loan borrower has surplus money, then as per applicable terms and conditions of the respective loan, one can pre-pay in the best possible capacity. However, he/she should not compromise on investments for future priority goals.

Balance is the Key!

Indexation benefit was one of the major distinguishing factors which debt funds used to have before changes in their tax implications. Now, as debt funds come with the same tax implications as fixed deposits, people have started considering them to invest. Along with them, hybrid funds are gaining attention too as they offer dual benefits and exposure to debt & equity asset classes.

However, one should invest in them if his/her goals & risk appetite fit for the same. Investors must invest in hybrid funds due to their features & suitability, not just because they offer tax advantage over Debt funds.

Asset allocation is like a full-course meal full of different recipes. We eat what we like & most importantly what suits our health. Likewise, asset allocation is selecting a suitable asset class in the right proportion to invest in suitable underlying investment options.

Now, to decide on asset allocation, investors should first decide on future goals & Investment tenure. This will help them to select broadly the allocation of asset classes, e.g., if short-term goals are more, then allocation to debt will be more in the portfolio.

Furthermore, investors must screen through suitable investment options under that asset class as per risk appetite, goal and tenure. Also, for short-term goals with fixed tenure, one can choose a bank fixed deposit, start a recurring deposit or post office investment.

For short-term goals with flexible tenure, liquid funds, money market funds or short-term debt funds can be suitable. For long-term goals, one can select suitable equity mutual funds. Apart, investors must read and understand the basics of the investment option before investing.

New fund offer these days come with different objectives and themes. Along with the positive scenario of the market, low NAV" is also the reason investors consider investing in the first place. However, in mutual funds, the growth potential of the fund irrespective of the NAV, and consistency in delivering returns at least at par with that of benchmark matter a lot along with its suitability to our future goals, risk appetite and tenure. So, be thoughtful while investing in the fund offer.

The sole purpose of 'investing' should be to achieve future goals. Many times, investors fail to achieve goals not only due to bad market conditions or changes in macroeconomic scenarios but also due to many behavioural aspects. Some of the reasons are-

So, though the reasons are many, one should invest with at least basic knowledge about the option. We must invest with discipline, have a proper review regularly and make suitable changes accordingly. Keep your portfolio simple and easy to understand.

Be thoughtful while investing!

Too many mutual funds

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Don't make hasty investment decisions when performance is negative, advises Priyadarshini Mulye of ARTHA FinPlan - MintGenie

Todd Central Greenhouse Offering Expanded Variety Of Plants | WKDZ Radio

The Todd County Central High School Greenhouse is offering an expanded selection of plants for sale to the public this spring.

Todd Central High School FFA Advisor and Horticulture Instructor Shayla Berry says the students in the greenhouse and horticulture classes she teaches have worked hard this year to offer a great selection for their customers.

click to download audioBerry says they are selling everything from ferns to succulents.

click to download audioShe adds the horticulture-related classes she teaches provide several important lessons for all students who take them.

click to download audioThe students spoke with News and Farm Director Alan Watts about their work in the greenhouse and through the horticulture classes.

Visit with Todd County Central Greenhouse Students

Visit with Horticulture Instructor and FFA Advisor Shayla Berry

Photos by Molly Skipworth and Susan Watts

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Todd Central Greenhouse Offering Expanded Variety Of Plants - wkdzradio.com

Taylor says that the course and Harnishs senior thesis, a play she wrote about the course material, This is Your Computer on Drugswhich she is also directing on April 29 and 30 at Columbiarepresent the culmination of their three-year collaborative relationship.

Harnish took her first class with Taylor, Philosophy of Religion, during the spring semester of her freshman year, after which she decided to become a religion major instead of the double major she had declared in philosophy and theater. This was also when COVID hit, right when Harnish was writing her midterm paper, so the course was completed over Zoom. She then enrolled in two more courses with Taylor during the fall 2020 semester, Theory and Recovering Place, because he had hinted at retirement. Both classes were conducted virtually.

It was the depths of the pandemic, and Harnish, who had returned to Indiana, where she grew up, was having a hard time. She was living alone in a government-subsidized apartment for artists in Indianapolis, working two jobs, taking 16 course credit hours, and trying to cope with life during COVID.

Come midterms, she emailed Taylor to alert him that she was planning on withdrawing from Columbia for the rest of the semester because of her difficulty managing everything. He offered to Zoom with her later that day.

He talked me into staying in school, said Harnish, and its a good thing he did, because my final project for Recovering Place was my first full-length play, The Foundation of Roses.

The 60-page script is a ghost story about her challenging childhood experiences, said Taylor. It was so remarkable that I nominated it for the Religion Departments Peter Awn Award, which is given annually to the most outstanding undergraduate paper or project in the department. My colleagues agreed with my assessment, and Alethea won the award in 2021.

Harnish has since written four more plays. One of them, Phantasmagoria, a one-person, autobiographical show, made its Off-Broadway debut in June 2022 when she performed it at the Downtown Urban Arts Festival, where it won second place for the Best Play Award. The work was about leaving her rural roots in Indiana to attend college in New York.

According to Harnish, she was the first person from her high school to get into an Ivy League university, and traveling halfway across the country to a big city was a culture shock. Meeting Taylor, who became a mentor, was very beneficial for her.

Over time, the relationship has morphed from a mentor-mentee one into something more reciprocal, said Harnish.

Taylor, who started teaching at Williams College in 1973, and arrived full-time at Columbia in 2007, said that early on he detected something very special about Alethea. It was not just her exceptional intelligence, interest, maturity, and determination, but also a rare imaginative creativity.

Once campus came back to life in fall 2021, at the start of Harnishs junior year, the two continued their conversations in person, and Harnish started sending Taylor examples of her writing. They met regularly during Taylors office hours to discuss her work. One day, she asked him what he was working on for his next book. Hegel and quantum mechanics, he said.

In one of those strange moments the theoretical physicist Wolfgang Pauli and the psychologist Carl Jung labeled synchronicity, said Taylor, Alethea said, Thats weird because I want to write and produce a play for my senior thesis about quantum physics and New Age spirituality.

Out of that convergence came the course theyre now co-teaching. They started by delving deeper into their shared interest in the material through reading and further discussion. Few people realize that personal computers, the Internet, the World Wide Web, and the Metaverse all trace their origins to hippies and the drug culture of the 1960s, said Taylor.

The more I thought about it, the clearer it became that this would be the perfect subject for my last course, he continued. My professional career spanned precisely the half-century from the 1960s to the present.

When Taylor asked her to co-teach the course, Harnish was initially terrified. We had spent almost two years in conversation by that point, and I knew that this would be the opportunity of a lifetime, she said. His insisting that he was also learning from me gave me the confidence to take on such a role.

Although Harnish has fully embraced her leadership role with the course this semester, she is not sure if she will pursue a career in higher education. Her immediate plans after graduation are to travel to Greece this summer with a Brooklyn-based theater company, providing administrative support for its apprentice program. She then wants to spend a year in New York, completing the applications for various playwriting fellowships and other writing programs.

Back in the classroom, the next time Hippie Physics meets, Harnish, dressed in a jean shirt, long, pleated skirt, and cowboy boots, leads the discussion on the assigned readings from The Book by Alan Watts and Zen Mind, Beginners Mind by Shunryu Suzuki. One of her touches has been to start every session spending a few moments listening to one of the eras classic rock songs, and then opening the floor to a parsing of the songs meaning. Todays selection is Led Zeppelins Stairway to Heaven.

After she stops the music, she says, What is the implication philosophically of there being a stairway to heaven for us? Were down here, and we have to get up there.

As he watches her effortlessly command the classroom, Taylor says, Strangely, the success of this course makes it both easier and more difficult for me to stop teaching. We hear much, perhaps too much, today about the problems with higher education, and especially with the humanities. But as I watch Alethea teach and her fellow undergraduates respond to her, I have hope for the future.

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A Student Graduates, a Professor Retires, but They Will Stay in Touch - Columbia University

Watch out for the man with the silicon chipHold on to your job with a good firm gripCause if you dont youll have had your chipsThe same as my old man

Scottish revival singer-songwriter Ewan MacColls 1986 track My Old Man was an ode to his father, an iron-moulder who faced an existential threat to his job because of the advent of technology. The lyrics could finds some resonance nearly four decades on, as industry leaders and tech stalwarts predict the advancement in large language models such as OpenAIs GPT-4 and their ability to write essays, code, and do maths with greater accuracy and consistency, heralding a fundamental tech shift; almost as significant as the creation of the integrated circuit, the personal computer, the web browser or the smartphone. But there still are question marks over how advanced chatbots could impact the job market. And if the blue collar work was the focus of MacColls ballad, artificial intelligence (AI) models of the generative pretrained transformer type signify a greater threat for white collar workers, as more powerful word-predicting neural networks that manage to carry out a series of operations on arrays of inputs end up producing output that is significantly humanlike. So, will this latest wave impact the current level of employment?

According to Goldman Sachs economists Joseph Briggs and Devesh Kodnani, the answer is a resounding yes, and they predict that as many as 300 million full-time jobs around the world are set to get automated, with workers replaced by machines or AI systems. What lends credence to this stark prediction is the new wave of AI, especially large language models that include neural networks such as Microsoft-backed OPenAIs ChatGPT.

The Goldman Sachs economists predict that such technology could bring significant disruption to the labour market, with lawyers, economists, writers, and administrative staff among those projected to be at greatest risk of becoming redundant. In a new report, The Potentially Large Effects of Artificial Intelligence on Economic Growth, they calculate that approximately two-thirds of jobs in the US and Europe are set to be exposed to AI automation, to various degrees.

In general white-collar workers, and workers in advanced economies in general, are projected to be at a greater risk than blue collar workers in developing countries. The combination of significant labour cost savings, new job creation, and a productivity boost for non-displaced workers raises the possibility of a labour productivity boom like those that followed the emergence of earlier general-purpose technologies like the electric motor and personal computer, the report said.

And OpenAI itself predicts that a vast majority of workers will have at least part of their jobs automated by GPT models. In a study published on the arXiv preprint server, researchers from OpenAI and the University of Pennsylvania said that 80 percent of the US workforce could have at least 10 percent of their tasks affected by the introduction of GPTs.

Central to these predictions is the way models such as ChatGPT get better with more usage GPT stands for Generative Pre-trained Transformer and is a marker for how the platform works; being pre-trained by human developers initially and then primed to learn for itself as more and more queries are posed by users to it. The OpenAI study also said that around 19 per cent of US workers will see at least 50 per cent of their tasks impacted, with the qualifier that GPT exposure is likely greater for higher-income jobs, but spans across almost all industries. These models, the OpenAI study said, will end up as general-purpose technologies like the steam engine or the printing press.

A January 2023 paper, by Anuj Kapoor of the Indian Institute of Management Ahmedabad and his co-authors, explored the question of whether AI tools or humans were more effective at helping people lose weight. The authors conducted the first causal evaluation of the effectiveness of human vs. AI tools in helping consumers achieve their health outcomes in a real-world setting by comparing the weight loss outcomes achieved by users of a mobile app, some of whom used only an AI coach while others used a human coach as well.

Interestingly, while human coaches scored higher broadly, users with a higher BMI did not fare as well with a human coach as those who weighed less.

The results of our analysis can extend beyond the narrow domain of weight loss apps to that of healthcare domains more generally. We document that human coaches do better than AI coaches in helping consumers achieve their weight loss goals. Importantly, there are significant differences in this effect across different consumer groups. This suggests that a one-size-fits-all approach might not be most effective Kapoor told The Indian Express.

The findings: Human coaches help consumers achieve their goals better than AI coaches for consumers below the median BMI relative to consumers who have above-median BMI. Human coaches help consumers achieve their goals better than AI coaches for consumers below the median age relative to consumers who have above-median age.

Human coaches help consumers achieve their goals better than AI coaches for consumers below the median time in a spell relative to consumers who spent above-median time in a spell. Further, human coaches help consumers achieve their goals better than AI coaches for female consumers relative to male consumers.

While Kapoor said the paper did not go deeper into the why of the effectiveness of AI+Human plans for low BMI individuals over high BMI individuals, he speculated on what could be the reasons for that trend: Humans can feel emotions like shame and guilt while dealing with other humans. This is not always true, but in general and theres ample evidence to suggest this research has shown that individuals feel shameful while purchasing contraceptives and also while consuming high-calorie indulgent food items. Therefore, high BMI individuals might find it difficult to interact with other human coaches. This doesnt mean that health tech platforms shouldnt suggest human plans for high BMI individuals. Instead, they can focus on (1) Training their coaches well to make the high BMI individuals feel comfortable and heard and (2) deciding the optimal mix of the AI and Human components of the guidance for weight loss, he added.

Similarly, the female consumers responding well to the human coaches can be attributed to the recent advancements in the literature on Human AI interaction, which suggests that the adoption of AI is different for females/males and also theres differential adoption across ages, Kapoor said, adding that this can be a potential reason for the differential impact of human coaches for females over males.

An earlier OECD paper on AI and employment titled New Evidence from Occupations most exposed to AI asserted that the impact of these tools would be skewed in favour of high-skilled, white-collar ones, including: business professionals; managers; science and engineering professionals; and legal, social and cultural professionals.

This contrasts with the impact of previous automating technologies, which have tended to take over primarily routine tasks performed by lower-skilled workers. The 2021 study noted that higher exposure to AI may be a good thing for workers, as long as they have the skills to use these technologies effectively. The research found that over the period 2012-19, greater exposure to AI was associated with higher employment in occupations where computer use is high, suggesting that workers who have strong digital skills may have a greater ability to adapt to and use AI at work and, hence, to reap the benefits that these technologies bring. By contrast, there is some indication that higher exposure to AI is associated with lower growth in average hours worked in occupations where computer use is low. On the whole, the study findings suggested that the adoption of AI may increase labour market disparities between workers who have the skills to use AI effectively and those who do not. Making sure that workers have the right skills to work with new technologies is therefore a key policy challenge, which policymakers will increasingly have to grapple with.

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Machine learning: As AI tools gain heft, the jobs that could be at stake - The Indian Express

Phases of solid hydrogen. The left is the well-studied hexagonal close packed phase, while the right is the new phase predicted by the authors machine learning-informed simulations. Image by Wesley Moore. Credit: The Grainger College of Engineering at the University of Illinois Urbana-Champaign

Putting hydrogen on solid ground: simulations with a machine learning model predict a new phase of solid hydrogen.

A machine-learning technique developed by University of Illinois Urbana-Champaign researchers has revealed a previously undiscovered high-pressure solid hydrogen phase, offering insights into hydrogens behavior under extreme conditions and the composition of gaseous planets like Jupiter and Saturn.

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 didnt 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 whats 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 packedCeperley compared it to stacked orangesthe researchers observed a phase where the molecules become oblong figuresCeperley 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 Ceperleys 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 hydrogens 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 hydrogens 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 its worth knowing that we can deal with it.

Reference: Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo by Hongwei Niu, Yubo Yang, Scott Jensen, Markus Holzmann, Carlo Pierleoni and David M. Ceperley, 17 February 2023, Physical Review Letters.DOI: 10.1103/PhysRevLett.130.076102

This work was done in collaboration with Markus Holzmann of Univ. Grenoble Alpes and Carlo Pierleoni of the University of LAquila. Ceperleys 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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Hydrogen's Hidden Phase: Machine Learning Unlocks the Secrets of the Universe's Most Abundant Element - SciTechDaily

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Credit: Pixabay/CC0 Public Domain

A new machine-learning method could help us gauge the time of our internal body clock, helping us all make better health decisions, including when and how long to sleep.

The research, which has been conducted by the University of Surrey and the University of Groningen, used a machine learning program to analyze metabolites in blood to predict the time of our internal circadian timing system. The study is published in the journal Proceedings of the National Academy of Sciences.

To date the standard method to determine the timing of the circadian system is to measure the timing of our natural melatonin rhythm, specifically when we start producing melatonin, known as dim light melatonin onset (DLMO).

Professor Debra Skene, co-author of the study from the University of Surrey, said, "After taking two blood samples from our participants, our method was able to predict the DLMO of individuals with an accuracy comparable or better than previous, more intrusive estimation methods."

The research team collected a time-series of blood samples from 24 individuals12 men and 12 women. All participants were healthy, did not smoke and had regular sleeping schedules seven days before they visited the University clinical research facility. The research team then measured over 130 metabolite rhythms using a targeted metabolomics approach. This metabolite data was then used in a machine learning program to predict circadian timing.

Professor Skene stated, "We are excited but cautious about our new approach to predicting DLMOas it is more convenient and requires less sampling than the tools currently available. While our approach needs to be validated in different populations, it could pave the way to optimize treatments for circadian rhythm sleep disorders and injury recovery.

"Smart devices and wearables offer helpful guidance on sleep patternsbut our research opens the way to truly personalized sleep and meal plans, aligned to our personal biology, with the potential to optimize health and reduce the risks of serious illness associated with poor sleep and mistimed eating."

Professor Roelof Hut, co-author of the study from University of Groningen, said, "Our results could help to develop an affordable way to estimate our own circadian rhythms that will optimize the timing of behaviors, diagnostic sampling, and treatment."

More information: Woelders, Tom et al, Machine learning estimation of human body time using metabolomic profiling, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2212685120

Journal information: Proceedings of the National Academy of Sciences

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New machine-learning method predicts body clock timing to improve sleep and health decisions - Medical Xpress

Machine learning startup Wallaroo Labs Inc., better known as Wallaroo.ai, said today its partnering with the virtualization software giant VMware Inc. to create a unified edge machine learning and artificial intelligence deployment and operations platform thats aimed at communications service providers.

Wallaroo.ai is the creator of a unified platform for easily deploying, observing and optimizing machine learning in production, on any cloud, on-premises or at the network edge. The company says its joining with VMware to help CSPs better make money from their networks by supporting them with scalable machine learning at the edge.

Its aiming to solve the problem of managing edge machine learning through easier deployment, more efficient inference and continuous optimization of models at 5G edge locations and in distributed networks. CSPs will also benefit from a unified operations center that allows them to observe, manage and scale up edge machine learning deployments from one place.

More specifically, Wallaroo.ai said, its new offering will make it simple to deploy AI models trained in one environment in multiple resource-constrained edge endpoints, while providing tools to help test and continuously optimize those models in production. Benefits include automated observability and drift detection, so users will know if their models start to generate inaccurate responses or predictions. It also offers integration with popular ML development environments, such as Databricks, and cloud platforms such as Microsoft Azure.

Wallaroo.ai co-founder and Chief Executive Vid Jain told SiliconANGLE that CSPs are specifically looking for help in deploying machine learning models fortasks such as monitoring network health, network optimization, predictive maintenance and security. Doing so is difficult, he says, because the models have a number of requirements, including the need for very efficient compute at the edge.

At present, most edge locations are constrained by low-powered compute resources, low memory and low-latency. In addition, CSPs need the ability to deploy the models at many edge endpoints simultaneously, and they also need a way to monitor those endpoints.

We offer CSPs a highly efficient, trust-based inference server that is ideally suited for fast edge inferencing, together with a single unified operations center, Jain explained. We are also working on integrating orchestration software such as VMware that allows for monitoring, updating and management of all the edge locations running AI. The Wallaroo.AI server and models can be deployed into telcos 5G infrastructure and bring back any monitoring data to a central hub.

Stephen Spellicy, vice president of service provider marketing, enablement and business development at VMware, said the partnership is all about helping telecommunications companies put machine learning to work in distributed environments more easily. Machine learning at the edge has multiple use cases, he explained, such as better securing and optimizing distributed networks and providing low-latency services to businesses and consumers.

Wallaroo.ai said its platform will be able to operate across multiple clouds, radio access networks and edge environments, which it believes will become the primary elements of a future, low-latency and highly distributed internet.

TheCUBEis an important partner to the industry, you know,you guys really are a part of our events and we really appreciate you coming and I know people appreciate thecontent you create as well Andy Jassy

THANK YOU

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Wallaroo.ai partners with VMware on machine learning at the edge - SiliconANGLE News

In a first-of-its-kind effort, social media researchers from Drexel University, Vanderbilt University, Georgia Institute of Technology and Boston University are turning to young social media users to help build a machine learning program that can spot unwanted sexual advances on Instagram. Trained on data from more than 5 million direct messages annotated and contributed by 150 adolescents who had experienced conversations that made them feel sexually uncomfortable or unsafe the technology can quickly and accurately flag risky DMs.

The project, which was recently published by the Association for Computing Machinery in its Proceedings of the ACM on Human-Computer Interaction, is intended to address concerns that an increase of teens using social media, particularly during the pandemic, is contributing to rising trends of child sexual exploitation.

In the year 2020 alone, the National Center for Missing and Exploited Children received more than 21.7 million reports of child sexual exploitation which was a 97% increase over the year prior. This is a very real and terrifying problem, said Afsaneh Razi, PhD, an assistant professor in Drexels College of Computing & Informatics, who was a leader of the research.

Social media companies are rolling out new technology that can flag and remove sexually exploitative images and helps users to more quickly report these illegal posts. But advocates are calling for greater protection for young users that could identify and curtail these risky interactions sooner.

The groups efforts are part of a growing field of research looking at how machine learning and artificial intelligence be integrated into platforms to help keep young people safe on social media, while also ensuring their privacy. Its most recent project stands apart for its collection of a trove of private direct messages from young users, which the team used to train a machine learning-based program that is 89% accurate at detecting sexually unsafe conversations among teens on Instagram.

Most of the research in this area uses public datasets which are not representative of real-word interactions that happen in private, Razi said. Research has shown that machine learning models based on the perspectives of those who experienced the risks, such as cyberbullying, provide higher performance in terms of recall. So, it is important to include the experiences of victims when trying to detect the risks.

Each of the 150 participants who range in age from 13- to 21-years-old had used Instagram for at least three months between the ages of 13 and 17, exchanged direct messages with at least 15 people during that time, and had at least two direct messages that made them or someone else feel uncomfortable or unsafe. They contributed their Instagram data more than 15,000 private conversations through a secure online portal designed by the team. And were then asked to review their messages and label each conversation, as safe or unsafe, according to how it made them feel.

Collecting this dataset was very challenging due to sensitivity of the topic and because the data is being contributed by minors in some cases, Razi said. Because of this, we drastically increased the precautions we took to preserve confidentiality and privacy of the participants and to ensure that the data collection met high legal and ethical standards, including reporting child abuse and the possibility of uploads of potentially illegal artifacts, such as child abuse material.

The participants flagged 326 conversations as unsafe and, in each case, they were asked to identify what type of risk it presented nudity/porn, sexual messages, harassment, hate speech, violence/threat, sale or promotion of illegal activities, or self-injury and the level of risk they felt either high, medium or low.

This level of user-generated assessment provided valuable guidance when it came to preparing the machine learning programs. Razi noted that most social media interaction datasets are collected from publicly available conversations, which are much different than those held in private. And they are typically labeled by people who were not involved with the conversation, so it can be difficult for them to accurately assess the level of risk the participants felt.

With self-reported labels from participants, we not only detect sexual predators but also assessed the survivors perspectives of the sexual risk experience, the authors wrote. This is a significantly different goal than attempting to identify sexual predators. Built upon this real-user dataset and labels, this paper also incorporates human-centered features in developing an automated sexual risk detection system.

Specific combinations of conversation and message features were used as the input of the machine learning models. These included contextual features, like age, gender and relationship of the participants; linguistic features, such as wordcount, the focus of questions, or topics of the conversation; whether it was positive, negative or neutral; how often certain terms were used; and whether or not a set of 98 pre-identified sexual-related words were used.

This allowed the machine learning programs to designate a set of attributes of risky conversations, and thanks to the participants assessments of their own conversations, the program could also rank the relative level of risk.

The team put its model to the test against a large set of public sample conversations created specifically for sexual predation risk-detection research. The best performance came from its Random Forest classifier program, which can rapidly assign features to sample conversations and compare them to known sets that have reached a risk threshold. The classifier accurately identified 92% of unsafe sexual conversations from the set. It was also 84% accurate at flagging individual risky messages.

By incorporating its user-labeled risk assessment training, the models were also able to tease out the most relevant characteristics for identifying an unsafe conversation. Contextual features, such as age, gender and relationship type, as well as linguistic inquiry and wordcount contributed the most to identifying conversations that made young users feel unsafe, they wrote.

This means that a program like this could be used to automatically warn users, in real-time, when a conversation has become problematic, as well as to collect data after the fact. Both of these applications could be tremendously helpful in risk prevention and the prosecution of crimes, but the authors caution that their integration into social media platforms must preserve the trust and privacy of the users.

Social service providers find value in the potential use of AI as an early detection system for risks, because they currently rely heavily on youth self-reports after a formal investigation had occurred, Razi said. But these methods must be implemented in a privacy-preserving matter to not harm the trust and relationship of the teens with adults. Many parental monitoring apps are privacy invasive since they share most of the teen's information with parents, and these machine learning detection systems can help with minimal sharing of information and guidelines to resources when it is needed.

They suggest that if the program is deployed as a real-time intervention, then young users should be provided with a suggestion rather than an alert or automatic report and they should be able to provide feedback to the model and make the final decision.

While the groundbreaking nature of its training data makes this work a valuable contribution to the field of computational risk detection and adolescent online safety research, the team notes that it could be improved by expanding the size of the sample and looking at users of different social media platforms. The training annotations for the machine learning models could also be revised to allow outside experts to rate the risk of each conversation.

The group plans to continue its work and to further refine its risk detection models. It has also created an open-source community to safely share the data with other researchers in the field recognizing how important it could be for the protection of this vulnerable population of social media users.

The core contribution of this work is that our findings are grounded in the voices of youth who experienced online sexual risks and were brave enough to share these experiences with us, they wrote. To the best of our knowledge, this is the first work that analyzes machine learning approaches on private social media conversations of youth to detect unsafe sexual conversations.

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

In addition to Razi, Ashwaq Alsoubai and Pamela J. Wisniewski, from Vanderbilt University; Seunghyun Kim and Munmun De Choudhury, from Georgia Institute of Technology; and Shiza Ali and Gianluca Stringhini, from Boston University, contributed to the research.

Read the full paper here: https://dl.acm.org/doi/10.1145/3579522

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Sliding Out of My DMs: Young Social Media Users Help Train ... - Drexel University

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