Category Archives: Machine Learning

Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers

To be in the race, it is important to evolve with time. Technology is booming and the new-age era has seen many changes. In the past, since the world met the internet, things changed and how. From the time of cellphones to smartphones, computers to portable laptops, things have seamlessly changed with social media taking over everyone. Earlier Facebook was considered only for chatting and now it has become a medium to make money by creating content. Besides this, there are many other platforms like YouTube, TikTok, and Instagram to earn in millions. One of the key social media players, Rashed Ali Almansoori is a digital genius with years of experience.

He is a tech blogger who believes to cope up with the latest trends. Being a digital creator, Rashed loves to create meaningful yet informative content about technology. Authenticity is the key to establish your target audience over the web, says the blogger. His other expertise includes web development, web designing, SEO building, and promoting brands over the digital domain. Rashed states that many businesses have taken the digital route considering the popular social media has given in the last decade. The coming decade will see many other innovations out of which Artificial Intelligence will be the main highlight among all.

The digital expert is currently learning the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). It would not be a surprise if machines perform tasks effectively than humans in the coming time. Upgrading yourself to stay in the game is the only solution, quoted Rashed. By learning the courses, he aims to integrate them into his works. Bringing novelty in his work is what the blogger is doing and it will benefit him in the future. The past year, the 29-year old techie built a strong image of himself on social media and his website is garnering millions of visitors from the Middle East and other countries.

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Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers - IBG NEWS

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Top Key Manufacturers of Machine Learning in Education industry Report:-

IBMMicrosoftGoogleAmazonCognizanPearsonBridge-UDreamBox LearningFishtreeJellynoteQuantum Adaptive Learning

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Cloud-BasedOn-Premise

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Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

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Global Machine Learning in Education market segmentation, by solution: Biological, Chemical, Mechanical, Global Machine Learning in Education market segmentation, by product: Stress Protection, Scarification, Pest Protection

Machine Learning in Education Market Regional Analysis:-North America(United States, Canada),Europe(Germany, Spain, France, UK, Russia, and Italy),Asia-Pacific(China, Japan, India, Australia, and South Korea),Latin America(Brazil, Mexico, etc.),The Middle East and Africa(GCC and South Africa).

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Report Table of Content Overview Gives Exact Idea About International Machine Learning in Education Market Report:

Chapter 1 describes Machine Learning in Education report important market inspection, product cost structure, and analysis, Machine Learning in Education market size and scope forecast from 2017 to 2026. Although, Machine Learning in Education market gesture, factors affecting the expansion of business also deep study of arise and existing market holders.

Chapter 2 display top manufacturers of Machine Learning in Education market with sales and revenue and market share. Furthermore, report analyses the import and export scenario of industry, demand and supply ratio, labor cost, raw material supply, production cost, marketing sources, and downstream consumers of market.

Chapter 3, 4, 5 analyses Machine Learning in Education report competitive analysis based on product type, their region wise depletion and import/export analysis, the composite annual growth rate of market and foretell study from 2017 to 2026.

Chapter 6 gives an in-depth study of Machine Learning in Education business channels, market sponsors, vendors, dispensers, merchants, market openings and risk.

Chapter 7 gives Machine Learning in Education market Research Discoveries and Conclusion

Chapter 8 gives Machine Learning in Education Appendix

To Analyze Details Of Table Of Content (TOC) of Machine Learning in Education Market Report, Visit Here: https://www.reportspedia.com/report/technology-and-media/global-machine-learning-in-education-market-2019-by-company,-regions,-type-and-application,-forecast-to-2024/30939 #table_of_contents

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Global Machine Learning in Education Market 2020 Study of Growing Trends, Future Scope, New investment, Regional Analysis, Upcoming Business...

Normally, supercomputers installed at academic and national laboratories get configured once, acquired as quickly as possible before the money runs out, installed and tested, qualified for use, and put to work for a four or five or possibly longer tour of duty. It is a rare machine that is upgraded even once, much less a few times.

But that is not he case with the Corona system at Lawrence Livermore National Laboratory, which was commissioned in 2017 when North America had a total solar eclipse and hence its nickname. While this machine, procured under the Commodity Technology Systems (CTS-1) to not only do useful work, but to assess the CPU and GPU architectures provided by AMD, was not named after the coronavirus pandemic that is now spreading around the Earth, the machine is being upgraded one more time to be put into service as a weapon against the SARS-CoV-2 virus which caused the COVID-19 illness that has infected at least 2.75 million people (confirmed by test, with the number very likely being higher) and killed at least 193,000 people worldwide.

The Corona system was built by Penguin Computing, which has a long-standing relationship with Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Sandia National Laboratories the so-called Tri-Labs that are part of the US Department of Energy and that coordinate on their supercomputer procurements. The initial Corona machine installed in 2018 had 164 compute nodes, each equipped with a pair of Naples Epyc 7401 processors, which have 24 cores each running at 2 GHz with an all core turbo boost of 2.8 GHz. The Penguin Tundra Extreme servers that comprise this cluster have 256 GB of main memory and 1.6 TB of PCI-Express flash. When the machine was installed in November 2018, half of the nodes were equipped with four of AMDs Radeon Instinct MI25 GPU accelerators, which had 16 GB of HBM2 memory each and which had 768 gigaflops of FP64 performance, 12.29 teraflops of FP32 performance, and 24.6 teraflops of FP16 performance. The 7,872 CPU cores in the system delivered 126 teraflops at FP64 double precision all by themselves, and the Radeon Instinct MI25 GPU accelerators added another 251.9 teraflops at FP64 double precision. The single precision performance for the machine was obviously much higher, at 4.28 petaflops across both the CPUs and GPUs. Interestingly, this machine was equipped with 200 Gb/sec HDR InfiniBand switching from Mellanox Technologies, which was obviously one of the earliest installations of this switching speed.

In November last year, just before the coronavirus outbreak or, at least we think that was before the outbreak, that may turn out to not be the case AMD and Penguin worked out a deal to installed four of the much more powerful Radeon Instinct MI60 GPU accelerators, based on the 7 nanometer Vega GPUs, in the 82 nodes in the system that didnt already have GPU accelerators in them. The Radeon Instinct MI60 has 32 GB of HBM2 memory, and has 6.6 teraflops of FP64 performance, 13.3 teraflops of FP32 performance, and 26.5 teraflops of FP16 performance. Now the machine has 8.9 petaflops of FP32 performance and 2.54 petaflops of FP64 performance, and this is a much more balanced 64-bit to 32-bit performance, and it makes these nodes more useful for certain kinds of HPC and AI workloads. Which turns out to be very important to Lawrence Livermore in its fight against the COVID-19 disease.

To find out more about how the Corona system and others are being deployed in the fight against COVID-19, and how HPC and AI workloads are being intertwined in that fight, we talked to Jim Brase, deputy associate director for data science at Lawrence Livermore.

Timothy Prickett Morgan: It is kind of weird that this machine was called Corona. Foreshadowing is how you tell the good literature from the cheap stuff. The doubling of performance that just happened late last year for this machine could not have come at a better time.

Jim Brase: It pretty much doubles the overall floating point performance of the machine, which is great because what we are mainly running on Corona is both the molecular dynamics calculations of various viral and human protein components and then machine learning algorithms for both predictive models and design optimization.

TPM: Thats a lot more oomph. So what specifically are you doing with it in the fight against COVID-19?

Jim Brase: There are two basic things were doing as part of the COVID-19 response, and this machine is almost entirely dedicated to this although several of our other clusters at Lawrence Livermore are involved as well.

We have teams that are doing both antibody and vaccine design. They are mainly focused on therapeutic antibodies right now. They are basically designing proteins that will interact with the virus or with the way the virus interacts with human cells. That involves hypothesizing different protein structures and computing what those structures actually look like in detail, then computing using molecular dynamics the interaction between those protein structures and the viral proteins or the viral and human cell interactions.

With this machine, we do this iteratively to basically design a set of proteins. We have a bunch of metrics that we try to optimize on binding strength, the stability of the binding, stuff like that and then we do a detailed molecular dynamics calculations to figure out the effective energy of those binding events. These metrics determine the quality of the potential antibody or vaccine that we design.

TPM: To wildly oversimplify, this SARS-CoV-2 virus is a ball of fat with some spikes on it that wreaks havoc as it replicates using our cells as raw material. This is a fairly complicated molecule at some level. What are we trying to do? Stick goo to it to try to keep it from replicating or tear it apart or dissolve it?

Jim Brase: In the case of in the case of antibodies, which is what were mostly focusing on right now, we are actually designing a protein that will bind to some part of the virus, and because of that the virus then changes its shape, and the change in shape means it will not be able to function. These are little molecular machines that they depend on their shape to do things.

TPM: Theres not something that will physically go in and tear it apart like a white blood cell eats stuff.

Jim Brase: No. Thats generally done by biology, which comes in after this and cleans up. What we are trying to do is what we call neutralizing antibodies. They go in and bind and then the virus cant do its job anymore.

TPM: And just for a reference, what is the difference between a vaccine and an antibody?

Jim Brase: In some sense, they are the opposite of each other. With a vaccine, we are putting in a protein that actually looks like the virus but it doesnt make you sick. It stimulates the human immune system to create its own antibodies to combat that virus. And those antibodies produced by the body do exactly the same thing we were just talking about Producing antibodies directly is faster, but the effect doesnt last. So it is more of a medical treatment for somebody who is already sick.

TPM: I was alarmed to learn that for certain coronaviruses, immunity doesnt really last very long. With the common cold, the reason we get them is not just because they change every year, but because if you didnt have a bad version of it, you dont generate a lot of antibodies and therefore you are susceptible. If you have a very severe cold, you generate antibodies and they last for a year or two. But then youre done and your body stops looking for that fight.

Jim Brase: The immune system is very complicated and for some things it creates antibodies that remembers them for a long time. For others, its much shorter. Its sort of a combination of the of the what we call the antigen the thing about that, the virus or whatever that triggers it and then the immune system sort of memory function together, cause the immunity not to last as long. Its not well understood at this point.

TPM: What are the programs youre using to do the antibody and protein synthesis?

Jim Brase: We are using a variety of programs. We use GROMACS, we use NAMD, we use OpenMM stuff. And then we have some specialized homegrown codes that we use as well that operate on the data coming from these programs. But its mostly the general, open source molecular mechanics and molecular dynamics codes.

TPM: Lets contrast this COVID-19 effort with like something like SARS outbreak in 2003. Say you had the same problem. Could you have even done the things you are doing today with SARS-CoV-2 back then with SARS? Was it even possible to design proteins and do enough of them to actually have an impact to get the antibody therapy or develop the vaccine?

Jim Brase: A decade ago, we could do single calculations. We could do them one, two, three. But what we couldnt do was iterate it as a design optimization. Now we can run enough of these fast enough that we can make this part of an actual design process where we are computing these metrics, then adjusting the molecules. And we have machine learning approaches now that we didnt have ten years ago that allow us to hypothesize new molecules and then we run the detailed physics calculations against this, and we do that over and over and over.

TPM: So not only do you have a specialized homegrown code that takes the output of these molecular dynamics programs, but you are using machine learning as a front end as well.

Jim Brase: We use machine learning in two places. Even with these machines and we are using our whole spectrum of systems on this effort we still cant do enough molecular dynamics calculations, particularly the detailed molecular dynamics that we are talking about here. What does the new hardware allow us to do? It basically allows us to do a higher percentage of detailed molecular dynamics calculations, which give us better answers as opposed to more approximate calculations. So you can decrease the granularity size and we can compute whole molecular dynamics trajectories as opposed to approximate free energy calculations. It allows us to go deeper on the calculations, and do more of those. So ultimately, we get better answers.

But even with these new machines, we still cant do enough. If you think about the design space on, say, a protein that is a few hundred amino acids in length, and at each of those positions you can put in 20 different amino acids, you on the order of 20200 in the brute force with the possible number of proteins you could evaluate. You cant do that.

So we try to be smart about how we select where those simulations are done in that space, based on what we are seeing. And then we use the molecular dynamics to generate datasets that we then train machine learning models on so that we are basically doing very smart interpolation in those datasets. We are combining the best of both worlds and using the physics-based molecular dynamics to generate data that we use to train these machine learning algorithms, which allows us to then fill in a lot of the rest of the space because those can run very, very fast.

TPM: You couldnt do all of that stuff ten years ago? And SARS did not create the same level of outbreak that SARS-CoV-2 has done.

Jim Brase: No, these are all fairly new early new ideas.

TPM: So, in a sense, we are lucky. We have the resources at a time when we need them most. Did you have the code all ready to go for this? Were you already working on this kind of stuff and then COVID-19 happened or did you guys just whip up these programs?

Jim Brase: No, no, no, no. Weve been working on this kind of stuff for her for a few years.

TPM: Well, thank you. Id like to personally thank you.

Jim Brase: It has been an interesting development. Its both been both in the biology space and the physics space, and those two groups have set up a feedback loop back and forth. I have been running a consortium called Advanced Therapeutic Opportunities in Medicine, or ATOM for short, to do just this kind of stuff for the last four years. It started up as part of the Cancer Moonshot in 2016 and focused on accelerating cancer therapeutics using the same kinds of ideas, where we are using machine learning models to predict the properties, using both mechanistic simulations like molecular dynamics, but all that combined with data, but then also using it other the other way around. We also use machine learning to actually hypothesize new molecules given a set of molecules that we have right now and that we have computed properties on them that arent quite what we want, how do we just tweak those molecules a little bit to adjust their properties in the directions that we want?

The problem with this approach is scale. Molecules are atoms that are bonded with each other. You could just take out an atom, add another atom, change a bond type, or something. The problem with that is that every time you do that randomly, you almost always get an illegal molecule. So we train these machine learning algorithms these are generative models to actually be able to generate legal molecules that are close to a set of molecules that we have but a little bit different and with properties that are probably a little bit closer to what we what we want. And so that allows us to smoothly adjust the molecular designs to move towards the optimization targets that we want. If you think about optimization, what you want are things with smooth derivatives. And if you do this in sort of the discrete atom bond space, you dont have smooth derivatives. But if you do it in these, these are what we call learned latent spaces that we get from generative models, then you can actually have a smooth response in terms of the molecular properties. And thats what we want for optimization.

The other part of the machine learning story here is these new types of generative models. So variational autoencoders, generative adversarial models the things you hear about that generate fake data and so on. Were actually using those very productively to imagine new types of molecules with the kinds of properties that we want for this. And so thats something we were absolutely doing before COVID-19 hit. We have taken these projects like ATOM cancer project and other work weve been doing with DARPA and other places focused on different diseases and refocused those on COVID-19.

One other thing I wanted to mention is that we havent just been applying biology. A lot of these ideas are coming out of physics applications. One of our big things at Lawrence Livermore is laser fusion. We have 192 huge lasers at the National Ignition Facility to try to create fusion in a small hydrogen deuterium target. There are a lot of design parameters that go into that. The targets are really complex. We are using the same approach. Were running mechanistic simulations of the performance of those targets, we are then improving those with real data using machine learning. So now we now have a hybrid model that has physics in it and machine learning data models, and using that to optimize the designs of the laser fusion target. So thats led us to a whole new set of approaches to fusion energy.

Those same methods actually are the things were also applying to molecular design for medicines. And the two actually go back and forth and sort of feed on each other and support each other. In the last few weeks, some of the teams that have been working on the physics applications have actually jumped over onto the biology side and are using some of the same sort of complex workflows that were using on these big parallel machines that theyve developed for physics and applying those to some of the biology applications and helping to speed up the applications on these on this new hardware thats coming in. So it is a really nice synergy going back and forth.

TPM: I realize that machine learning software uses the GPUs for training and inference, but is the molecular dynamics software using the GPUs, too?

Jim Brase: All of the molecular dynamics software has been set up to use GPUs. The code actually maps pretty naturally onto the GPU.

TPM: Are you using the CUDA variants of the molecular dynamics software, and I presume that it is using the Radeon Open Compute, or ROCm, stack from AMD to translate that code so it can run on the Radeon Instinct accelerators?

Jim Brase: There has been some work to do, but it works. Its getting its getting to be pretty solid now, thats one of the reasons we wanted to jump into the AMD technology pretty early, because you know, any time you do first-in-kind machines its not always completely smooth sailing all the way.

TPM: Its not like Lawrence Livermore has a history of using novel designs for supercomputers. [Laughter]

Jim Brase: We seldom work with machines that are not Serial 00001 or Serial 00002.

TPM: Whats the machine learning stack you use? I presume it is TensorFlow.

Jim Brase: We use TensorFlow extensively. We use PyTorch extensively. We work with the DeepChem group at Stanford University that does an open chemistry package built on TensorFlow as well.

TPM: If you could fire up an exascale machine today, how much would it help in the fight against COVID-19?

Jim Brase: It would help a lot. Theres so much to do.

I think we need we need to show the benefits of computing for drug design and we are concretely doing that now. Four years ago, when we started up ATOM, everybody thought this was nuts, the general idea that we could lead with computing rather than experiment and do the experiments to focus on validating the computational models rather than the other way around. Everybody thought we were nuts. As you know, with the growth of data, the growth of machine learning capabilities, more accessibility to sophisticated molecular dynamics, and so on its much more accepted that computing is a big part of this. But we still have a long way to go on this.

The fact is, machine learning is not magic. Its a fancy interpolator. You dont get anything new out of it. With the physics codes, you actually get something new out of it. So the physics codes are really the foundation of this. You supplement them with experimental data because theyre not right necessarily, either. And then you use the machine learning on top of all that to fill in the gaps because you havent been able to sample that huge chemical and protein space adequately to really understand everything at either the data level or the mechanistic level.

So thats how I think of it. Data is truth sort of and what you also learn about data is that it is not always the same as you go through this. But data is the foundation. Mechanistic modeling allows us to fill in where we just cant measure enough data it is too expensive, it takes too long, and so on. We fill in with mechanistic modeling and then above that we fill in that then with machine learning. We have this stack of experimental truth, you know, mechanistic simulation that incorporates all the physics and chemistry we can, and then we use machine learning to interpolate in those spaces to support the design operation.

For COVID-19, there are there are a lot of groups doing vaccine designs. Some of them are using traditional experimental approaches and they are making progress. Some of them are doing computational designs, and that includes the national labs. Weve got 35 designs done and we are experimentally validating those now and seeing where we are with them. It will generally take two to three iterations of design, then experiment, and then adjust the designs back and forth. And were in the first round of that right now.

One thing were all doing, at least on the public side of this, is we are putting all this data out there openly. So the molecular designs that weve proposed are openly released. Then the validation data that we are getting on those will be openly released. This is so our group working with other lab groups, working with university groups, and some of the companies doing this COVID-19 research can contribute. We are hoping that by being able to look at all the data that all these groups are doing, we can learn faster on how to sort of narrow in on the on the vaccine designs and the antibody designs that will ultimately work.

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One Supercomputers HPC And AI Battle Against The Coronavirus - The Next Platform

Physicians are prematurely relying on Epic's deterioration index, saying they're unable to wait for a validation process that can take months to years. The artificial intelligence gives them a snapshot of a patient's illness and helps them determine who needs more careful monitoring. News on technology is from Verily, Google, MIT, Livongo and more, as well.

Stat:AI Used To Predict Covid-19 Patients' Decline Before Proven To WorkDozens of hospitals across the country are using an artificial intelligence system created by Epic, the big electronic health record vendor, to predict which Covid-19 patients will become critically ill, even as many are struggling to validate the tools effectiveness on those with the new disease. The rapid uptake of Epics deterioration index is a sign of the challenges imposed by the pandemic: Normally hospitals would take time to test the tool on hundreds of patients, refine the algorithm underlying it, and then adjust care practices to implement it in their clinics. Covid-19 is not giving them that luxury. (Ross, 4/24)

Modern Healthcare:Verily, Google Cloud Develop COVID-19 Chatbot For HospitalsGoogle's sister company Verily Life Sciences has joined the mix of companies offering COVID-19 screening tools that hospitals can add to their websites. The screener, called the COVID-19 Pathfinder, takes the form of a chatbot or voicebotessentially personified computer programs that can instant-message or speak to human users in plain English. (Cohen, 4/23)

Boston Globe:Tech From MIT May Allow Caregivers To Monitor Coronavirus Patients From A DistanceA product developed at the Massachusetts Institute of Technology is being used to remotely monitor patients with COVID-19, using wireless signals to detect breathing patterns of people who do not require hospitalization but who must be watched closely to ensure their conditions remain stable. The device, developed at MITs Computer Science and Artificial Intelligence Laboratory by professor Dina Katabi and her colleagues, could in some situations lower the risk of caregivers becoming infected while treating patients with the coronavirus. (Rosen, 4/23)

Stat:A Gulf Emerges In Health Tech: Some Companies Surge, Others Have LayoffsYou might expect them to be pandemic-proof: Theyre the companies offering glimpses of the future in which you dont have to go to the doctors office, ones that would seem to be insulated from a crisis in which people arent leaving their homes. Yet theres a stark divide emerging among the companies providing high-demand virtual health care, triage, and testing services. While some are hiring up and seeing their stock prices soar, others are furloughing and laying off their workers. (Robbins and Brodwin, 4/24)

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'Err On The Side Of Patient Care': Doctors Turn To Untested Machine Learning To Monitor Virus - Kaiser Health News

How organizations are leveraging AI to track a fraudulent activity (for example, in the financial industry) and what tools are available to the enterprises right now?

Machine Learning is not new in the world of payment fraud. In fact, one of the pioneers of Machine Learning is Professor Leon Cooper, Director of Brown Universitys Centre for Neural Science. The Centre was founded in 1973 to study animal nervous systems and the human brain. However, if you follow his career, Dr. Coopers machine learning technology was adapted for spotting fraud on credit cards and is still used today for identifying payment fraud within many financial institutions around the world.

Firms Need to be Secure to the Core Before Considering Digital Transformation

Machine learning technologies improve when they are presented with more and more data. Since there is a lot of payment data around today, payment fraud prevention has become an excellent use case for AI. To date, machine learning technologies have been used mainly by banks. Still, today more and more merchants are taking advantage of this technology to help automate fraud detection, including many retailers and telecommunications companies.

What are the interesting developments in this space for enterprises?

There is a lot of information on how machine learning is helping to understand human behavior and, more specifically, false/positive detection. However, it is our view that there is not enough focus on how automation could benefit the whole, end to end process, particularly within day to day fraud management business processes.

Until now, the fraud detection industry has focused on detecting fraud reactively; but it has not focussed on proactively evaluating the impact of automation on the whole end to end fraud management process. Clearly, the interdependencies on these two activity streams are significant, so the question remains why fraud prevention suppliers arent considering both.

Automation Is Booming Robots Are Taking Over Amid Lock-downs

Fraud is increasing, so at what point do we recognize that the approach of throwing budget and increasing the number of analysts in our teams is not working and that we need to consider automating more of the process? Machines dont steal data, so why are the manual processes/interventions not attracting more attention?

It isnt a stretch to imagine most of the fraud risk strategy process becoming automated. Instead of the expanding teams of today performing the same manual task continually, those same staff members could be used to spot enhancements in customer insight. This would enable analysts to thoroughly investigate complex fraud patterns, which a machine has not identified, or to assist in other tasks outside of risk management, which provide added business value.

Process automation is continuing to innovate and provide increased efficiency and profit gains in the places its implemented. The automation revolution isnt coming; its here.

What are the major concerns?

One major concern is the lack of urgency in adopting new ways of working there is a need to be more agile and innovative to stop the fraudsters continuing to win. We need to act fast and innovate, but many organizations are struggling to keep up, and the fraudsters are winning.

The use cases are well defined for the use of machine learning and AI, with big data sets, etc. but machine learning will not fix poor data management processes alone. Machines dont steal data. People do.

With the number of digital payments being made across the globe increasing dramatically, how can organizations ensure maximum sales conversion and payment acceptance, whilst mitigating any risk exposure?

Strategy alignment for taking digital payments is critical. The more organizations can operate holistically and not get caught out by silos and operational gaps, the better. Put simply; if key stakeholders in both the sales and marketing and risk teams are working to the same set of key performance indicators (KPIs), then mistakes will be mitigated. Many issues arise due to operational gaps, and those gaps will be exploited by the highly sophisticated and technically advanced modern-day fraudster.

Artificial Intelligence Infused with Big Data Creating a Tech-driven World

The reality is that technology is accelerating the convergence business activities. Managing that convergence and adapting your organization to ensure it remains competitive becomes more and more important. Successful organizations with a competitive future will continue to ensure maximum sales conversions and payment acceptance, whilst mitigating any risk exposure, by exploiting best of breed technology as much as possible.

Excerpt from:
Automation, AI, and ML The Heroes in the World of Payment Fraud Detection - EnterpriseTalk

Principled Technologies (PT) ran analytics and synthetic, containerized workloads on a ~$40K Dell EMC PowerEdge R7525 and a similarly priced HPE ProLiant DL380 Gen10 to gauge performance and performance/cost ratio.

To explore the performance on certain machine learning tasks of a ~$40K Dell EMC PowerEdge R7525 server powered by AMD EPYC 7502 processors, the experts at PT set up two testbeds and compared its performance results to those of a similarly priced HPE ProLiant DL380 Gen10 powered by Intel Xeon Gold 6240 processors.

The first study, Finish machine learning preparation tasks on Kubernetes containers in less time with the Dell EMC PowerEdge R7525, utilizes a workload that emulates simple image processing tasks that a company might run in the preparation phase of machine learning.

According to the first study, we found that the Dell EMC server: Processed 3.3 million images in 55.8% less time Processed 2.26x the images each second Had 2.32x the value in terms of image processing rate vs. hardware cost.

The second study, Get better k-means analytics workload performance for your money with the Dell EMC PowerEdge R7525, utilizes a learning algorithm used to mimic data mining that a company might use to improve the customer experience or prevent fraud.

According to the second study, we found that the Dell EMC solution: Completed a k-means clustering workload in 40 percent less time Processed 67 percent more data per second Carried a 74 percent better performance/cost ratio in terms of data processing performance vs. hardware price.

To explore the results PT found when comparing the two current-gen ~$40K server solutions, read the Kubernetes study here facts.pt/rfcwex2 and the k-means study here facts.pt/0jyo64h.

About Principled Technologies, Inc.Principled Technologies, Inc. is the leading provider of technology marketing and learning & development services.

Principled Technologies, Inc. is located in Durham, North Carolina, USA. For more information, please visit http://www.principledtechnologies.com.

Company ContactPrincipled Technologies, Inc.1007 Slater Road, Suite #300Durham, NC 27703press@principledtechnologies.com

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The Dell EMC PowerEdge R7525 Saved Time During Machine Learning Preparation Tasks and Achieved Faster Image Processing Than a HPE ProLiant DL380...