(ProNewsReport Editorial):- London, United Kingdom Oct 26, 2021 (Issuewire.com)Publisher Discovery is excited to announce the launch of a brand newAI-driven platform incorporating advanced machine learning technology. The development of Publisher Discovery competitor analysis tools followed the initial trialling of the in-network application in conjunction with Affiliate Future, a leading UK affiliate network. This enabled their merchants access to the first application of AI and Machine Learning in the management and recruitment of new affiliates.

Results from the in-network technology have shown hugely increased user efficiency in internal affiliate recruitment saving hours of account management time each week. The effectiveness of the technology led to the award of Highly Commended in the Best Use of AI category at this years Performance Marketing Awards in London.

This really proved the power of AI in affiliate recruitment. Publisher Discovery CEO Tom Bourne explained The ability to use AI to analyse the network data and from that to match, the best affiliates to the right merchant programmes have proven its worth in recruitment time savings as well as in commercial terms.

John Vickers of Affiliate Future which trialled the initial installation of Publisher Discoverys Cloudfind app said This has been a great proof of concept for us and has helped our clients to achieve some impressive results really quickly. Weve been really looking forward to adding the new tools analysing the external competitors and initial tests have shown the same impressive results.

The new platform uses AI and machine learning to help advertisers to analyse their competitors affiliate programmes, understand more about the publishers and find their best affiliates to recruit. This will provide a much simplified and far more intuitive platform enabling quick searches to add to your recruitment process.

The new technology launch has been complemented by an upgraded UI in the platform and is reflected in the new branding and website launched just recently.

Publisher Discovery showed these new technologies at Affiliate Summit East in New York last week and will be giving live demonstrations on their stand at PerformanceIn.live later this year. You can read more on the website at publisherdiscovery.com.

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Publisher Discovery relaunches affiliate analysis tools with AI and machine learning - Pro News Report

Child psychiatric disorders, such as oppositional defiant disorder and attention-deficit/hyperactivity disorder (ADHD), can feature outbursts of anger and physical aggression. A better understanding of what drives these symptoms could help inform treatment strategies. Yale researchers have now used a machine learning-based approach to uncover disruptions of brain connectivity in children displaying aggression.

While previous research has focused on specific brain regions, the new study identifies patterns of neural connections across the entire brain that are linked to aggressive behavior in children. The findings, published in the journalMolecular Psychiatry, build on a novel model of brain functioning called the connectome that describes this pattern of brain-wide connections.Maladaptive aggression can result in harm to self or others. This challenging behavior is one of the main reasons for referrals to child mental health services, saidDenis Sukhodolsky, senior author and associate professor in the Yale Child Study Center. Connectome-based modeling offers a new account of brain networks involved in aggressive behavior.

For the study, which is the first of its kind, researchers collected fMRI (functional magnetic resonance imaging) data while children performed an emotional face perception task in which they observed faces making calm or fearful expressions. Seeing faces that express emotion can engage brain states relevant to emotion generation and regulation, both of which have been linked to aggressive behavior, researchers said. The scientists then applied machine learning analyses to identify neural connections that distinguished children with and without histories of aggressive behavior.

They found that patterns in brain networks involved in social and emotional processes such as feeling frustrated with homework or understanding why a friend is upset predicted aggressive behavior. To confirm these findings, the researchers then tested them in a separate dataset and found that the same brain networks predicted aggression. In particular, abnormal connectivity to the dorsolateral prefrontal cortex a key region involved in the regulation of emotions and higher cognitive functions like attention and decision-making emerged as a consistent predictor of aggression when tested in subgroups of children with aggressive behavior and disorders such as anxiety, ADHD, and autism.

These neural connections to the dorsolateral prefrontal cortex could represent a marker of aggression that is common across several childhood psychiatric disorders.This study suggests that the robustness of these large-scale brain networks and their connectivity with the prefrontal cortex may represent a neural marker of aggression that can be leveraged in clinical studies, saidKarim Ibrahim, associate research scientist at the Yale Child Study Center and first author of the paper. The human functional connectome describes the vast interconnectedness of the brain. Understanding the connectome is on the frontier of neuroscience because it can provide us with valuable information for developing brain biomarkers of psychiatric disorders.

Added Sukhodolsky: This connectome model of aggression could also help us develop clinical interventions that can improve the coordination among these brain networks and hubs like the prefrontal cortex. Such interventions could include teaching the emotion regulation skills necessary for modulating negative emotions such as frustration and anger.

Reference: Ibrahim K, Noble S, He G, et al. Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling. Mol Psychiatry. Published online October 25, 2021:1-15. doi:10.1038/s41380-021-01317-5

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Machine Learning Reveals Aggression Symptoms in Childhood ADHD - Technology Networks

As the self-driving car travels down a dark, rural road, a deer lingering among the trees up ahead looks poised to dart into the cars path. Will the vehicle know exactly what to do to keep everyone safe?

Some computer scientists and engineers arent so sure. But researchers at the University of Virginia School of Engineering and Applied Science are hard at work developing methods they hope will bring greater confidence to the machine-learning world not only for self-driving cars, but for planes that can land on their own and drones that can deliver your groceries.

The heart of the problem stems from the fact that key functions of the software that guides self-driving cars and other machines through their autonomous motions are not written by humans instead, those functions are the product of machine learning. Machine-learned functions are expressed in a form that makes it essentially impossible for humans to understand the rules and logic they encode, thereby making it very difficult to evaluate whether the software is safe and in the best interest of humanity.

Researchers in UVAs Leading Engineering for Safe Software Lab the LESS Lab, as its commonly known are working to develop the methods necessary to provide society with the confidence to trust emerging autonomous systems.

The teams researchers are Matthew B. Dwyer, Robert Thomson Distinguished Professor; Sebastian Elbaum, Anita Jones Faculty Fellow and Professor; Lu Feng, assistant professor; Yonghwi Kwon, John Knight Career Enhancement Assistant Professor; Mary Lou Soffa, Owens R. Cheatham Professor of Sciences; and Kevin Sullivan, associate professor. All hold appointments in the UVA Engineering Department of Computer Science. Feng holds a joint appointment in the Department of Engineering Systems and Environment.

Since its creation in 2018, the LESS Lab has rapidly grown to support more than 20 graduate students, publish more than 50 publications and obtain competitive external awards totaling more than $10 million in research funding. The awards have come from such agencies as the National Science Foundation, the Defense Advanced Research Projects Agency, the Air Force Office of Scientific Research and the Army Research Office.

The labs growth trajectory matches the scope and urgency of the problem these researchers are trying to solve.

An inflection point in the rapid rise of machine learning happened just a decade ago when computer vision researchers won the ImageNet Large Scale Visual Recognition Challenge to identify objects in photos using a machine-learning solution. Google took notice and quickly moved to capitalize on the use of data-driven algorithms.

Other tech companies followed suit, and public demand for machine-learning applications snowballed. Last year, Forbes estimated that the global machine-learning market grew at a compound annual growth rate of 44% and is on track to become a $21 billion market by 2024.

But as the technology ramped up, computer scientists started sounding the alarm that mathematical methods to validate and verify the software were lagging.

Government agencies like the Defense Advanced Research Projects Agency responded to the concerns. In 2017, DARPA launched the Assured Autonomy program to develop mathematically verifiable approaches for assuring an acceptable level of safety with data-driven, machine-learning algorithms.

UVA Engineerings Department of Computer Science also took action, extending its expertise with a strategic cluster of hires in software engineering, programming languages and cyber-physical systems. Experts combined efforts in cross-cutting research collaborations, including the LESS Lab, to focus on solving a global software problem in need of an urgent solution.

By this time, the self-driving car industry was firmly in the spotlight and the problems with machine learning were becoming more evident. A fatal Uber crash in 2018 was recorded as the first human death from an autonomous vehicle.

The progress toward practical autonomous driving was really fast and dramatic, shaped like a hockey stick curve, and much faster than the rate of growth for the techniques that can ensure their safety, Elbaum said.

This August, tech blog Engadget reported that one self-driving car company, Waymo, had put in 20 million miles of on-the-road testing. Yet, devastating failures continue to occur; as the software has gotten more and more complex, no amount of real-world testing would be enough to find all the bugs.

And the software failures that have occurred have made many wary of on-the-road testing.

Even if a simulation works 100 times, would you jump into an autonomous car and let it drive you around? Probably not, Dwyer said. Probably you want some significant on-the-street testing to further increase your confidence. And at the same time, you dont want to be the one in the car when that test goes on.

And then there is the need to anticipate and test every single obstacle that might come up in the 4-million-mile public roads network.

Think about the complexities of a particular scenario, like driving on the highway with hauler trucks on either side so that an autonomous vehicle needs to navigate crosswinds at high speeds and around curves, Dwyer said. Getting a physical setup that matches that challenging scenario would be difficult to do in real-world testing. But in simulation that can get that much easier.

And thats one area where LESS Lab researchers are making real headway. They are developing sophisticated virtual reality simulations that can accurately create such difficult scenarios that might otherwise be impossible to test on the road.

There is a huge gap between testing in the real world versus testing in simulation, Elbaum said. We want to close that gap and create methods in which we can expose autonomous systems to the many complex scenarios they will face. This will substantially reduce testing time and cost, and it is a safer way to test.

Having simulations so accurate they can stand in for real-world driving would be the equivalent of rocket fuel for the huge amount of sophisticated testing it will take to fix the timeline and human cost problem. But it is just half of the solution.

The other half is developing mathematical guarantees that can prove the software will do what it is supposed to do all the time. And that will require a whole new set of mathematical frameworks.

Before machine learning, engineers would write explicit, step-by-step instructions for the computer to follow. The logic was deterministic and absolute, so humans could use formal mathematical rules that existed to test the code and guarantee it worked.

So if you really wanted a property that the autonomous system only makes sharp turns when there is an obstacle in front of it, before machine learning, mechanical engineers would say, I want this to be true, and software engineers would build that rule into the software, Dwyer said.

Today, the computer continuously improves a probability that an algorithm will produce an expected outcome by being fed images of examples to learn from. The process is no longer based on absolutes, following rules a human can understand.

With machine learning, you can give an autonomous system examples of sharp turns only with obstacles in front of it, Dwyer said. But that doesnt mean that the computer will learn and write a program that guarantees that is always true. Previously, humans checked the rules they codified into systems. Now we need a new way to check that a program the machine wrote observes the rules.

Elbaum stresses that there are still a lot of open-ended questions that need to be answered in the quest for concrete, tangible methods. We are playing catch-up to ensure these systems do what they are supposed to do, he said.

That is why the combined strength of the LESS Labs faculty and students is critically important for speeding up the discovery. Just as important is the commitment of the LESS Lab in working collectively and in concert with other UVA experts on machine learning and cyber-physical systems to enable a future where people can trust autonomous systems.

The labs mission could not be more relevant if we are ever to realize the promise of self-driving cars, much less eliminate the fears of a deeply skeptical public.

If your autonomous car is driving on a sunny day down the road with no other cars, it should work, Dwyer said. If its rainy and its at night and its crowded and there is a deer on the side of the road, it should also work, and in every other scenario.

We want to make it work all the time, for everybody, and in every context.

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Before Machines Can Be Autonomous, Humans Must Work to Ensure Their Safety - University of Virginia

In the past decade, machine learning has become a familiar technology for improving the efficiency and accuracy of processes like recommendations, supply chain forecasting, developing chatbots, image and text search, and automated customer service functions, to name a few. Machine learning today is becoming even more pervasive, impacting every market segment and industry, including manufacturing, SaaS platforms, health care, reservations and customer support routing, natural language processing (NLP) tasks such as intelligent document processing, and even food services.

Take the case of Dominos Pizza, which has been using machine learning tools created to improve efficiencies in pizza production. Dominos had a project called Project 3/10, which aimed to have a pizza ready for pickup within three minutes of an order, or have it delivered within 10 minutes of an order, says Dr. Bratin Saha, vice president and general manager of machine learning services for Amazon AI. If you want to hit those goals, you have to be able to predict when a pizza order will come in. They use predictive machine learning models to achieve that.

The recent rise of machine learning across diverse industries has been driven by improvements in other technological areas, says Sahanot the least of which is the increasing compute power in cloud data centers.

Over the last few years, explains Saha, the amount of total compute that can be thrown at machine learning problems has been doubling almost every four months. That's 5 to 6 times more than Moore's Law. As a result, a lot of functions that once could only be done by humansthings like detecting an object or understanding speechare being performed by computers and machine learning models.

At AWS, everything we do works back from the customer and figuring out how we reduce their pain points and how we make it easier for them to do machine learning. At the bottom of the stack of machine learning services, we are innovating on the machine learning infrastructure so that we can make it cheaper for customers to do machine learning and faster for customers to do machine learning. There we have two AWS innovations. One is Inferentia and the other is Trainium.

The current machine learning use cases that help companies optimize the value of their data to perform tasks and improve products is just the beginning, Saha says.

Machine learning is just going to get more pervasive. Companies will see that they're able to fundamentally transform the way they do business. Theyll see they are fundamentally transforming the customer experience, and they will embrace machine learning.

AWS Machine Learning Infrastructure

Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma. This is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic today is machine learning in the cloud. Across all industries, the exponential increase of data collection demands faster and novel ways to analyze data, but also learn from it to make better business decisions. This is how machine learning in the cloud helps fuel innovation for enterprises, from startups to legacy players.

Two words for you: data innovation. My guest is Dr. Bratin Saha, vice president and general manager of machine learning services for Amazon AI. He has held executive roles at NVIDIA and Intel. This episode of Business Lab is produced in association with AWS. Welcome, Bratin.

Dr. Bratin Saha: Thank you for having me, Laurel. It's great to be here.

Laurel: Off the top, could you give some examples of how AWS customers are using machine learning to solve their business problems?

Bratin: Let's start with the definition of what we mean by machine learning. Machine learning is a process where a computer and an algorithm can use data, usually historical data, to understand patterns, and then use that information to make predictions about the future. Businesses have been using machine learning to do a variety of things, like personalizing recommendations, improving supply chain forecasting, making chatbots, using it in health care, and so on.

For example, Autodesk was able to use the machine learning infrastructure we have for their chatbots to improve their ability to handle requests by almost five times. They were able to use the improved chatbots to address more than 100,000 customer questions per month.

Then there's Nerd Wallet. Nerd Wallet is a personal finance startup that did not personalize the recommendations they were giving to customers based on the customer's preferences. Theyre now using AWS machine learning services to tailor the recommendations to what a person actually wants to see, which has significantly improved their business.

Then we have customers like Thomson Reuters. Thomson Reuters is one of the world's most trusted providers of answers, with teams of experts. They use machine learning to mine data to connect and organize information to make it easier for them to provide answers to questions.

In the financial sector, we have seen a lot of uptake in machine learning applications. One company, for example, is a payment service provider, was able to build a fraud detection model in just 30 minutes.

The reason Im giving you so many examples is to show how machine learning is becoming pervasive. It's going across geos, going across market segments, and being used by companies of all kinds. I have a few other examples I want to share to show how machine learning is also touching industries like manufacturing, food delivery, and so on.

Domino's Pizza, for example, had a project called Project 3/10, where they wanted to have a pizza ready for pickup within three minutes of an order, or have it delivered within 10 minutes of an order. If you want to hit those goals, you have to be able to predict when a pizza order will come in. They use machine learning models to look at the history of orders. Then they use the machine learning model that was trained on that order history. They were then able to use that to predict when an order would come in, and they were able to deploy this to many stores, and they were able to hit the targets.

Machine learning has become pervasive in how our customers are doing business. It's starting to be adopted in virtually every industry. We have more than several hundred thousand customers using our machine learning services. One of our machine learning services, Amazon SageMaker, has been one of the fastest growing services in AWS history.

Laurel: Just to recap, customers can use machine learning services to solve a number of problems. Some of the high-level problems would be a recommendation engine, image search, text search, and customer service, but then, also, to improve the quality of the product itself.

I like the Domino's Pizza example. Everyone understands how a pizza business may work. But if the goal is to turn pizzas around as quickly as possible, to increase that customer satisfaction, Domino's had to be in a place to collect data, be able to analyze that historic data on when orders came in, how quickly they turned around those orders, how often people ordered what they ordered, et cetera. That was what the prediction model was based on, correct?

Bratin: Yes. You asked a question about how we think about machine learning services. If you look at the AWS machine learning stack, we think about it as a three-layered service. The bottom layer is the machine learning infrastructure.

What I mean by this is when you have a model, you are training the model to predict something. Then the predictions are where you do this thing called inference. At the bottom layer, we provide the most optimized infrastructure, so customers can build their own machine learning systems.

Then there's a layer on top of that, where customers come and tell us, "You know what? I just want to be focused on the machine learning. I don't want to build a machine learning infrastructure." This is where Amazon SageMaker comes in.

Then there's a layer on top of that, which is what we call AI services, where we have pre-trained models that can be used for many use cases.

So, we look at machine learning as three layers. Different customers use services at different layers, based on what they want, based on the kind of data science expertise they have, and based on the kind of investments they want to make.

The other part of our view goes back to what you mentioned at the beginning, which is data and innovation. Machine learning is fundamentally about gaining insights from data, and using those insights to make predictions about the future. Then you use those predictions to derive business value.

In the case of Domino's Pizza, there is data around historical order patterns that can be used to predict future order patterns. The business value there is improving customer service by getting orders ready in time. Another example is Freddy's Frozen Custard, which used machine learning to customize menus. As a result of that, they were able to get a double-digit increase in sales. So, it's really about having data, and then using machine learning to gain insights from that data. Once you've gained insights from that data, you use those insights to drive better business outcomes. This goes back what you mentioned at the beginning: you start with data and then you use machine learning to innovate on top of it.

Laurel: What are some of the challenges organizations have as they start their machine learning journeys?

Bratin: The first thing is to collect data and make sure it is structured wellclean datathat doesn't have a lot of anomalies. Then, because machine learning models typically get better if you can train them with more and more data, you need to continue collecting vast amounts of data. We often see customers create data lakes in the cloud, like on Amazon S3, for example. So, the first step is getting your data in order and then potentially creating data lakes in the cloud that you can use to feed your data-based innovation.

The next step is to get the right infrastructure in place. That is where some customers say, "Look, I want to just build the whole infrastructure myself," but the vast majority of customers say, "Look, I just want to be able to use a managed service because I don't want to have to invest in building the infrastructure and maintaining the infrastructure, and so on.

The next is to choose a business case. If you haven't done machine learning before, then you want to get started with a business case that leads to a good business outcome. Often what can happen with machine learning is to see it's cool, do some really cool demos, but those dont translate into business outcomes, so you start experiments and you don't really get the support that you need.

Finally, you need commitment because machine learning is a very iterative process. You're training a model. The first model you train may not get you the results you desire. There's a process of experimentation and iteration that you have to go through, and it can take you a few months to get results. So, putting together a team and giving them the support they need is the final part.

If I had to put this in terms of a sequence of steps, it's important to have data and a data culture. Its important in most cases for customers to choose to use a managed service to build and train their models in the cloud, simply because you get storage a lot easier and you get compute a lot easier. The third is to choose a use case that is going to have business value, so that your company knows this is something that you want to deploy at scale. And then, finally, be patient and be willing to experiment and iterate, because it often takes a little bit of time to get the data you need to train the models well and actually get the business value.

Laurel: Right, because it's not something that happens overnight.

Bratin: It does not happen overnight.

Laurel: How do companies prepare to take advantage of data? Because, like you said, this is a four-step process, but you still have to have patience at the end to be iterative and experimental. For example, do you have ideas on how companies can think about their data in ways that makes them better prepared to see success, perhaps with their first experiment, and then perhaps be a little bit more adventurous as they try other data sets or other ways of approaching the data?

Bratin: Yes. Companies usually start with a use case where they have a history of having good data. What I mean by a history of having good data is that they have a record of transactions that have been made, and most of the records are accurate. For example, you don't have a lot of empty record transactions.

Typically, we have seen that the level of data maturity varies between different parts of a company. You start with the part of a company where the data culture is a lot more prevalent. You start from there so that you have a record of historical transactions that you stored. You really want to have fairly dense data to use to train your models.

Laurel: Why is now the right time for companies to start thinking about deploying machine learning in the cloud?

Bratin: I think there is a confluence of factors happening now. One is that machine learning over the last five years has really taken off. That is because the amount of compute available has been increasing at a very fast rate. If you go back to the IT revolution, the IT revolution was driven by Moore's Law. Under Moore's Law, compute doubled every 18 months.

Over the last few years, the amount of total compute has been doubling almost every four months. That's five times more than Moore's Law. The amount of progress we have seen in the last four to five years has been really amazing. As a result, a lot of functions that once could only be done by humanslike detecting an object or understanding speechare being performed by computers and machine learning models. As a result of that, a lot of capabilities are getting unleashed. That is what has led to this enormous increase in the applicability of machine learningyou can use it for personalization, you can use it in health care and finance, you can use it for tasks like churn prediction, fraud detection, and so on.

One reason that now is a good time to get started on machine learning in the cloud is just the enormous amount of progress in the last few years that is unleashing these new capabilities that were previously not possible.

The second reason is that a lot of the machine learning services being built in the cloud are making machine learning accessible to a lot more people. Even if you look at four to five years ago, machine learning was something that only very expert practitioners could do and only a handful of companies were able to do because they had expert practitioners. Today, we have more than a hundred thousand customers using our machine learning services. That tells you that machine learning has been democratized to a large extent, so that many more companies can start using machine learning and transforming their business.

Then comes the third reason, which is that you have amazing capabilities that are now possible, and you have cloud-based tools that are democratizing these capabilities. The easiest way to get access to these tools and these capabilities is through the cloud because, first, it provides the foundation of compute and data. Machine learning is, at its core, about throwing a lot of compute on data. In the cloud, you get access to the latest compute. You pay as you go, and you don't have to make upfront huge investments to set up compute farms. You also get all the storage and the security and privacy and encryption, and so onall of that core infrastructure that is needed to get machine learning going.

Laurel: So Bratin, how does AWS innovate to help organizations with machine learning, model training, and inference?

Bratin: At AWS, everything we do works back from the customer and figuring out how we reduce their pain points and how we make it easier for them to do machine learning. At the bottom of the stack of machine learning services, we are innovating on the machine learning infrastructure so that we can make it cheaper for customers to do machine learning and faster for customers to do machine learning. There we have two AWS innovations. One is Inferentia and the other is Trainium. These are custom chips that we designed at AWS that are purpose-built for inference, which is the process of making machine learning predictions, and for training. Inferentia today provides the lowest cost inference instances in the cloud. And Trainium, when it becomes available later this year, will be providing the most powerful and the most cost-effective training instances in the cloud.

We have a number of customers using Inferentia today. Autodesk uses Inferentia to host their chatbot models, and they were able to improve the cost and latencies by almost five times. Airbnb has over four million hosts who welcome more than 900 million guests in almost every country. Airbnb saw a two-times improvement in throughput by using the Inferentia instances, which means that they were able to serve almost twice as many requests for customer support than they would otherwise have been able to do. Another company called Sprinklr develops a SaaS customer experience platform, and they have an AI-driven unified customer experience management platform. They were able to deploy the natural language processing models in Inferentia, and they saw significant performance improvements as well.

Even internally, our Alexa team was able to move their inferences over from GPUs to Inferentia-based systems, and they saw more than a 50% improvement in cost due to these Inferentia-based systems. So, we have that at the lowest layer of the infrastructure. On top of that, we have the managed services, where we are innovating so that customers become a lot more productive. That is where we have SageMaker Studio, which is the world's first IDE, that offers tools like debuggers and profilers and explainability, and a host of other toolslike a visual data preparation toolthat make customers a lot more productive. At the top of it, we have AI services where we provide pre-trained models for use cases like search and document processingKendra for search, Textract for document processing, image and video recognitionwhere we are innovating to make it easier for customers to address these use cases right out of the box.

Laurel: So, there are some benefits, for sure, for machine learning services in the cloudlike improved customer service, improved quality, and, hopefully, increased profit, but what key performance indicators are important for the success of machine learning projects, and why are these particular indicators so important?

Bratin: We are working back from the customer, working back from the pain points based on what customers tell us, and inventing on behalf of the customers to see how we can innovate to make it easier for them to do machine learning. One part of machine learning, as I mentioned, is predictions. Often, the big cost in machine learning in terms of infrastructure is in the inference. That is why we came out with Inferentia, which are today the most cost-effective machine learning instances in the cloud. So, we are innovating at the hardware level.

We also announced Tranium. That will be the most powerful and the most cost-effective training instances in the cloud. So, we are first innovating at the infrastructure layer so that we can provide customers with the most cost-effective compute.

Next, we have been looking at the pain points of what it takes to build an ML service. You need data collection services, you need a way to set up a distributed infrastructure, you need a way to set up an inference system and be able to auto scale it, and so on. We have been thinking a lot about how to build this infrastructure and innovation around the customers.

Then we have been looking at some of the use cases. So, for a lot of these use cases, whether it be search, or object recognition and detection, or intelligent document processing, we have services that customers can directly use. And we continue to innovate on behalf of them. I'm sure we'll come up with a lot more features this year and next to see how we can make it easier for our customers to use machine learning.

Laurel: What key performance indicators are important for the success of machine learning projects? We talked a little bit about how you like to improve customer service and quality, and of course increase profit, but to assign a KPI to a machine learning model, that's something a bit different. And why are they so important?

Bratin: To assign the KPIs, you need to work back from your use case. So, let's say you want to use machine learning to reduce fraud. Your overall KPI is, what was the reduction in fraud detection? Or let's say you want to use it for churn reduction. You are running a business, your customers are coming, but a certain number of them are churning off. You want to then start with, how do I reduce my customer churn by some percent? So, you start with the top-level KPI, which is a business outcome that you want to achieve, and how to get an improvement in that business outcome.

Lets take the churn prediction example. At the end of the day, what is happening is you have a machine learning model that is using data and the amount of training it had to make certain predictions around which customer is going to churn. That boils down, then, to the accuracy of the model. If the model is saying 100 people are going to churn, how many of them actually churn? So, that becomes a question of accuracy. And then you also want to look at how well the machine learning model detected all the cases.

So, there are two aspects of quality that you're looking for. One is, of the things that the model predicted, how many of them actually happened? Let's say this model predicted these 100 customers are going to churn. How many of them actually churn? And let's just say 95 of them actually churn. So, you have a 95% precision there. The other aspect is, suppose you're running this business and you have 1,000 customers. And let's say in a particular year, 200 of them churned. How many of those 200 did the model predict would actually churn? That is called recall, which is, given the total set, how much is the machine learning model able to predict? So, fundamentally, you start from this business metric, which is what is the outcome I want to get, and then you can convert this down into model accuracy metrics in terms of precision, which is how accurate was the model in predicting certain things, and then recall, which is how exhaustive or how comprehensive was the model in detecting all situations.

So, at a high level, these are the things you're looking for. And then you'll go down to lower-level metrics. The models are running on certain instances on certain pieces of compute: what was the infrastructure cost and how do I reduce those costs? These services, for example, are being used to handle surges during Prime Day or Black Friday, and so on. So, then you get to those lower-level metrics, which is, am I able to handle surges in traffic? Its really a hierarchical set of KPIs. Start with the business metric, get down to the model metrics, and then get down to the infrastructure metrics.

Laurel: When you think about machine learning in the cloud in the next three to five years, what are you seeing? What are you thinking about? What can companies do now to prepare for what will come?

Bratin: I think what will happen is that machine learning will get more pervasive. Because what will happen is customers will see that they're able to fundamentally transform the way to do business. Companies will see that they fundamentally are transforming the customer experience, and they will embrace machine learning. We have seen that at Amazon as wellwe have a long history of investing in machine learning. We have been doing this for more than 20 years, and we have changed how we serve customers with amazon.com or Alexa or Amazon Go, Prime. And now with AWS, where we have taken this knowledge that we have gained over the past two decades of deploying machine learning at scale and are making it available to our customers now. So, I do think we will see a much more rapid uptake of machine learning.

Then we'll see a lot of broad use cases like intelligent document processing, a lot of paper-based processing, will become automated because a machine learning model is now able to scan those documents and infer information from theminfer semantic information, not just the syntax. If you think of paper-based processes, whether it's loan processing and mortgage processing, a lot of that will get automated. Then, we are also seeing businesses get a lot more efficient in terms of personalization like forecasting, supply chain forecasting, demand forecasting, and so on.

We are seeing a lot of uptake of machine learning in health. We have customers, GE for example, uses a machine learning service for radiology. They use machine learning to scan radiology images to determine which ones are more serious, and therefore, you want to get the patients in early. We are also seeing potential and opportunity for using machine learning in genomics for precision medicine. So, I do think a lot of innovation is going to happen with machine learning in health care.

We'll see a lot of machine learning in manufacturing. A lot of manufacturing processes will become more efficient, get automated, and become safer because of machine learning.

So, I see in the next five to 10 years, pick any domainlike sports, NFL, NASCAR, Bundesliga, they're all using our machine learning services. NFL uses Amazon SageMaker to give their fans a more immersive experience through Next Gen Stats. Bundesliga uses our machine learning services to make a range of predictions and provide a much more immersive experience. Same with NASCAR. NASCAR has a lot of data history from their races, and they're using that to train models to provide a much more immersive experience to their viewers because they can predict much more easily what's going to happen. So, sports, entertainment, financial services, health care, manufacturingI think we'll see a lot more uptake of machine learning and making the world a smarter, healthier, and safer place.

Laurel: What a great conversation. Thank you very much, Bratin for joining us on Business Lab.

Bratin: Thank you. Thank you for having me. It was really nice talking to you.

Laurel: That was Dr. Bratin Saha, Vice President and General Manager of Machine Learning Services for Amazon AI, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles river. That's it for this episode of Business Law. I'm your host, Laurel Ruma. I'm the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can also find us in prints on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com. This show is available wherever you get your podcasts. If you enjoy this episode, we hope you'll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening.

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Machine learning in the cloud is helping businesses innovate - MIT Technology Review

Owen Dugan

Owen Dugan awarded $10,000 as a 2021 Davidson Fellow Scholarship Winner

The Davidson Fellows Scholarship Program has announced the 2021 scholarship winners. Among the honorees is 18-year-old Owen Dugan of Sleepy Hollow. Only twenty students across the country are recognized as scholarship winners each year.

I am honored to be a Davidson Fellow, to have my work nationally recognized, and to join the Davidson Fellows community, said Dugan.

For his project, Dugan developed several new techniques to improve and expand the scope of OccamNet, a new interpretable neural network architecture, with the goal of increasing adoption of interpretable and reliable machine learning techniques.

The 2021 Davidson Fellows Scholarship recipients have risen to the challenges of a global pandemic to complete significant projects within their fields of study, said Bob Davidson, founder of the Davidson Institute. To be awarded this recognition, these students have shown immense skill and work ethic, and they should be commended as they continue their educational and research journeys while continuing to work to solve some of the worlds most vexing problems.

The 2021 Davidson Fellows were honored during a virtual ceremony in September 2021. The ceremony can be viewed online at https://www.davidsongifted.org/gifted-programs/fellows-scholarship/fellows/fellows-ceremony/.

The Davidson Fellows Scholarship program offers $50,000, $25,000, and $10,000 college scholarships to students 18 or younger, who have completed significant projects that have the potential to benefit society in the fields of science, technology, engineering, mathematics, literature, and music. The Davidson Fellows Scholarship has provided more than $8.6 million in scholarship funds to 386 students since its inception in 2001, and has been named one of the most prestigious undergraduate scholarships by U.S. News & World Report. It is a program of the Davidson Institute, a national nonprofit organization headquartered in Reno, Nev. that supports profoundly gifted youth.

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Sleepy Hollow Teen Receives National Scholarship for Development of New Machine Learning Techniques - River Journal Staff