Mila and Bell last Friday announced an 18-month collaborative project to apply deep learning neural network algorithms to Bell's systems and data. Bell has made significant investments to develop extensive data analytics capabilities and AI applications in multiple areas of its operations, and this collaboration is the latest step in advancing its AI expertise.

Mila researchers will work alongside Bell's Machine Learning and AI teams to build on those investments by using cutting-edge deep learning neural network techniques to identify opportunities for improving business performance and customer experience.

These neural network deep learning models, inspired by the human brain, teach computers to recognize complex patterns in pictures, text, sounds and other data to produce accurate insights and predictions.

By advancing its understanding of deep learning AI techniques, Bell will continue to enhance its customer experience and accelerate its transition from a traditional telecommunications company to a technology services leader. As part of the collaboration, Bell and Mila will write a paper highlighting their technical findings in support of global AI advancement.

Stphane Ltourneau, Executive Vice President of Mila

Mila is very pleased to work with Bell and apply its renowned expertise in deep learning to the telecommunications sector. Through this collaboration, we look forward to combining Mila's research capabilities with Bell's extensive industry knowledge in order to highlight and harness AI's potential in this evolving field.

Michel Richer, SVP, Enterprise Solutions, Data Engineering & AI, Bell Canada

Becoming an AI leader is key to our transition from a traditional telco to a tech services leader. Working with a global leader like Mila right here in Montral is a great opportunity for Bell to benefit from leading-edge researchers, advance our AI and Cloud expertise, further improve the customer experience and establish our role as a technology services leader.

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Bell to Deploy Deep Learning AI on Systems and Data in 18-Month Partnership With Mila - The Fast Mode

MLOps Pipeline

MLOps, or Machine Learning Operations, brings together processes, best practices, and technologies to manage putting machine learning models into production environments at scale. It fills a major gap enterprises face in getting return from AI and analytics investments.

Research shows only 15% of major companies have widespread machine learning applications running across their business. So the majority of expensive modeling work stays stuck in labs and pilot projects. MLOps fixes this bottleneck by automating the steps needed to deploy, monitor, and update models in reliable pipelines.

Key business benefits MLOps delivers includes:

Without MLOps, models degrade, data science productivity drops, and adoption stalls. Adding MLOps boosts ROI on analytics spending by maintaining model performance post-deployment.

MLOps engineers build the continuous development and deployment capabilities for machine learning models to run successfully as applications. Their expertise combines software engineering, data engineering, and DevOps skills tailored for operationalizing analytics.

Their key responsibilities include:

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Operationalizing Machine Learning to Drive Business Value | by dparente | Daniel Parente | Feb, 2024 - Medium

Winterberry Group

-Generative AI and machine learning are rapidly changing how brands bring content, particularly video content, to a variety of platforms and audiences -- upending traditional creative, strategy and media silos, and offering new efficiencies amid risks-

Global Video and Digital Content Production Spend, 2020 - 2024 Estimated

AI Use Cases for Creative Production and Content Creation

NEW YORK, Feb. 07, 2024 (GLOBE NEWSWIRE) -- New research released today from Winterberry Group, a strategic advisory consultancy, documents the far-reaching effects that artificial intelligence specifically generative AI and machine learning are having on creative ideation and production, as well as moving the considerations for audience and channel selection earlier into the process, as global spending on video and digital content production is projected to top $121 billion globally this year 47 percent of it accountable to the United States.

As brands seek to reduce not just cost but also productivity from partners creating content, the approach to managing and measuring the value of content, taking a sustainable path to creation, on-the-fly versioning and video and content creative optimization, are some of the ramifications of these investments. Through the course of the interviews, it has become increasingly clear that the people and process requires an aligned integration of creative, strategy and media teams according to Winterberry Group -- in the new study The New Creative Paradigm: How AI is Transforming Video and Content Production (February 2024).

The research is sponsored by APR [Advertising Production Resources], a Denver, CO-based creative production consultancy.

As consumers have dramatically changed how they consume media, brands and agencies have altered their media spend amongst linear and across digital channels to stay connected to them, said Bruce Biegel, senior managing partner, Winterberry Group. During the past 10 years, consumers have flipped the time spent per day on digital versus traditional media today they spend eight hours a day with digital.

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Source: Winterberry Group, The New Creative Paradigm How AI Is Transforming Video and Content Production, February 2024.

Thats 60 percent more than they spend with traditional media, he said. And theyre consuming video media in a variety of lengths and formats, with a mix of long-form, short-form and branded content. Brands are having to re-think how they bring digital content to consumers and how they produce it. Generative AI and machine learning, collectively AI are now transforming how we plan, develop, measure and value content created.

For close to a decade, according to the report, machine learning has been relied upon by marketers to drive targeting, attribution and measurement the algorithm. Now it is being teamed with generative AI the next innovation frontier to assist brands and agencies with research distillation, concept ideation, content production, versioning and testing.

The struggle to address surging demand for scaled, contextually relevant content is not new, said Jamie Posnanski, former global head of content for Accenture Song. But this research highlights why many of us who have been helping brands solve for these challenges are truly excited. The rapid evolution of AI in this space is presenting new, transformational opportunities to accelerate content creation and dramatically improve consumptionwhile overcoming legacy processes and fragmented technology stacks to unify media, data and content in ways that were never before possible.

Such application is also adding complexity with over 80 percent of marketers working with two or more production partners, and nearly 13 percent working with six or more today. Even though more than six in ten marketers are currently leveraging generative AI to support creative development and more than nine in ten are leveraging or planning to leverage creative intelligence from AI to support content development and optimization, the market is still in its early stage of evolution.

Our research, based on a survey of 250 decision-makers at brands across the United States and United Kingdom, as well as in-depth interviews with dozens of industry experts, takes a deep dive into the lifecycle of creative production and content creation from planning, to process management and governance, to measurement, said Charles Ping, managing director, Winterberry Group. We identify how functional roles at brands and agencies are changing as a result raising the profile for holistic planning among creatives, strategists, media planners, data scientists and the vitality of the data and intelligence they leverage.

Source: Winterberry Group, The New Creative Paradigm How AI Is Transforming Video and Content Production, February 2024.

Such deployments are not without significant challenges, among them regulatory uncertainty, lack of clarity in costs and resource intensiveness of AI, bias elimination, limited trust in AI-generated content, and variability in quality and consistency, according to the research. Brands also have invested heavily in sustainability and diversity-equity-inclusion initiatives and it is unclear what impact AI may have on these business objectives, the report states. Human command and oversight are critical for all these reasons.

We believe brands and agencies will benefit from this research generative AI has everyones attention, and its testing and deployments, when successful, bring efficiencies and quantities in content, Kate Briganti, chief strategy officer, APR. Adherence to brand standards, consumer acceptance, engagement, and a scalable process for production must be achieved in tandem. We are excited to bring this discussion to the forefront in this white paper.

Results of the full study, as a PDF, are available for download from Winterberry Group.

ABOUT WINTERBERRY GROUP

Winterberry Group is a strategic consultancy specializing in the intersecting disciplines of advertising, marketing, data, technology and commerce. We collaborate with stakeholders across the advertising and marketing ecosystemservice providers, technology developers, media companies, brands and investor groupsto identify and activate growth opportunities that drive the creation of real and lasting value. We bring decades of experience and deep industry, operational and M&A expertise that bridges strategic development and tactical executiondriving unprecedented speed-to-action. And through our highly collaborative approach, we enable knowledge transfer and actionability, giving our clients a competitive edge and powering growth in performance, team engagement and shareholder value. Learn more at https://winterberrygroup.com.

[Editors Note: Editorial members of the media may request a full copy of the research by contacting media contacts Bruce Biegel or Charles Ping. Selected charts, findings and figures from the study may be published, sourcing Winterberry Group, The New Creative Paradigm How AI Is Transforming Video and Content Production, February 2024.

Media Contacts: USA: Bruce Biegel Senior Managing Partner Winterberry Group bruce@winterberrygroup.com

UK and Europe: Charles Ping Managing Director Winterberry Group cping@winterberrygroup.com

Photos accompanying this announcementare available at https://www.globenewswire.com/NewsRoom/AttachmentNg/7af6537d-84e8-4bfc-adc2-a08a29ff1dd8 https://www.globenewswire.com/NewsRoom/AttachmentNg/3c51e88e-1919-4daa-876f-5314690e0bbc

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New Research From Winterberry Group Explores Artificial Intelligence and its Role in Transforming Video and Content ... - Yahoo Finance

Summary

Machine Learning (ML) is a transformative force in todays technology landscape, influencing a wide array of sectors and daily activities. At its core, ML utilizes algorithms and statistical models to allow computers to perform tasks without explicit instructions, learning from data. The field encompasses various learning types, including supervised, unsupervised, and reinforcement learning, each catering to different applications and challenges.

From enhancing personal convenience to revolutionizing industries, machine learning applications are vast and varied. This article explores ten everyday use cases of machine learning, showcasing its importance and ubiquity in our daily activities.

Virtual Personal Assistants (VPAs) represent a major leap in how individuals interact with digital devices. By leveraging advanced natural language processing (NLP) and machine learning algorithms, these assistants can understand and execute a wide array of tasks based on verbal or written instructions. Their evolution began with simple pattern-matching programs like ELIZA in the 1960s, and has since progressed to sophisticated systems capable of engaging in natural conversation, learning from interactions, and even performing tasks without explicit commands. Todays VPAs, integrated into smartphones, smart speakers, and various online platforms, offer unprecedented convenience and efficiency in managing daily routines, accessing information, and controlling smart home devices.

Recommendation systems are integral to enhancing user experience on digital platforms by suggesting products, services, or content based on user preferences and behavior. These systems utilize complex algorithms analyzing past behavior, similarities between users, and item attributes to predict and present the most relevant suggestions. Their application spans various sectors, including e-commerce, streaming services, and social media, significantly impacting decision-making and discovery processes.

Also Read: Deep Learning vs Machine Learning vs Artificial Intelligence: A Beginners Guide

Email filtering and spam detection technologies are essential for maintaining the integrity and usability of email communication. By utilizing machine learning and NLP, these systems can identify and segregate unsolicited or harmful content from legitimate messages. This not only protects users from potential threats like phishing and malware but also improves productivity by reducing clutter in the inbox.

Social media platforms use machine learning algorithms to curate and prioritize content in a users feed, aiming to enhance engagement by displaying posts, news, and advertisements likely to be of interest. These recommendation systems analyze user interactions, relationships, and content preferences to create a personalized experience, keeping users informed and engaged with relevant content.

The banking sector has significantly benefited from machine learning in detecting and preventing fraudulent transactions. By analyzing patterns of behavior and transaction data, algorithms can identify anomalies that may indicate fraud, reducing financial losses and safeguard customer assets. These systems can quickly adapt to new fraudulent techniques, ensuring banks and their customers are always a step ahead of potential threats.

Smart home devices leverage machine learning algorithms to enhance the convenience, efficiency, and security of living spaces. These intelligent systems learn from users behaviors and preferences to automate tasks such as lighting, temperature control, and security monitoring. By analyzing data collected from interactions and sensors, machine learning enables these devices to predict users needs and adjust settings accordingly, providing a personalized and adaptive home environment.

Also Read: Top 10 Machine Learning Projects In 2024

Traffic and navigation systems powered by machine learning significantly improve travel efficiency and safety. These systems analyze vast amounts of data from various sources, including GPS devices, sensors, and historical traffic patterns, to provide real-time traffic updates, optimal routing, and predictive traffic flow models. Machine learning algorithms can identify patterns and predict potential bottlenecks, suggesting alternative routes to minimize travel time and avoid congestion.

Language translation services have been revolutionized by machine learning, breaking down language barriers and facilitating global communication. These services use neural machine translation (NMT) techniques that learn from vast datasets of translated texts to produce more accurate and contextually relevant translations. Machine learning enables these systems to understand nuances, idioms, and cultural specificities, providing translations that are not just literal but also contextually appropriate.

Health and fitness trackers use machine learning to provide insights into users physical well-being and activity levels, promoting healthier lifestyles. By analyzing data from sensors tracking heart rate, steps, sleep patterns, and more, these devices offer personalized recommendations, activity tracking, and health monitoring. Machine learning algorithms process the collected data to identify trends, set goals, and even detect potential health issues early, encouraging proactive health management.

Autonomous vehicles represent a pinnacle of machine learning application, combining sensors, data, and advanced algorithms to navigate safely without human intervention. These vehicles analyze real-time data from LIDAR, radar, cameras, and GPS to understand their environment, make decisions, and learn from encounters. Machine learning enables these systems to recognize traffic signs, avoid obstacles, predict the actions of other road users, and continuously improve driving strategies through experience.

Also Read: Top 10 Must-Have Machine Learning Skills

Looking ahead, the integration of ML with emerging technologies like quantum computing, augmented reality, and personalized medicine promises to unlock even more profound changes in how we live and work. Ethical AI, federated learning, and enhanced natural language processing are just a few areas where MLs future developments hold exciting potential.

In conclusion, ML is not just a technological advancement but a facilitator of future innovations and improvements in various aspects of life. Its ability to learn and adapt makes it a pivotal element in the ongoing evolution of technology, offering endless possibilities for enhancing efficiency, understanding, and human capabilities.

The journey into the world of Machine Learning is ongoing, and its continuous evolution promises to bring further advancements and opportunities for innovation. As we delve deeper into ML, we are not just observers but active participants in shaping a future where technology enhances every aspect of our lives.

What is Machine Learning?

Where is Machine Learning Used?

How Does Machine Learning Enhance Security?

What Role Does Machine Learning Play in Transportation?

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10 Everyday Use Cases of Machine Learning - Blockchain Council

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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Editors Highlights are summaries of recent papers by AGUs journal editors.Source: Journal of Advances in Modeling Earth Systems

Atmospheric models must represent processes on spatial scales spanning many orders of magnitude. Although small-scale processes such as thunderstorms and turbulence are critical to the atmosphere, most global models cannot explicitly resolve them due to computational expense. In conventional models, heuristic estimates of the effect of these processes, known as parameterizations, are designed by experts. A recent line of research uses machine learning to create data-driven parameterizations directly from very high-resolution simulations that require fewer assumptions.

Yuval and OGorman [2023] provide the first such example of a neural network parameterization of the effects of subgrid processes on the vertical transport of momentum in the atmosphere. A careful approach is taken to generate a training dataset, accounting for subtle issues in the horizontal grid of the high-resolution model. The new parameterization generally improves the simulation of winds in a coarse-resolution model, but also over-corrects and leads to larger biases in one configuration. The study serves as a complete and clear example for researchers interested in the application of machine learning for parameterization.

Citation: Yuval, J., & OGorman, P. A. (2023). Neural-network parameterization of subgrid momentum transport in the atmosphere. Journal of Advances in Modeling Earth Systems, 15, e2023MS003606. https://doi.org/10.1029/2023MS003606

Oliver Watt-Meyer, Associate Editor, JAMES

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