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The Major Manufacturers Covered in this Report @:

Dataiku, Baidu, Inc., Angoss Software Corporation, SAS Institute Inc., Intel Corporation, TrademarkVision, Siemens, Hewlett Packard Enterprise Development LP, SAP SE, Bosch, Domino Data Lab, Inc., Microsoft Corporation, Fair Isaac Corporation, GE, BigML, Inc., KNIME.com AG, NVIDIA, Amazon Web Services Inc., Funac, Kuka, Google, Inc., Teradata, Dell Inc., Oracle Corporation, Fractal Analytics Inc., Luminoso Technologies, Inc., IBM Corporation, Alpine Data, RapidMiner, Inc., TIBCO Software Inc..

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Machine Learning in Manufacturing Market to Witness Robust Expansion by 2029 | Dataiku, Baidu, Inc. The Daily Vale - The Daily Vale

Department of Computer Science assistant professor Chris Heckman and CIRES research hydrologist Toby Minear have been awarded a Grand Challenge Research & Innovation Seed Grant to create an instrument that could revolutionize our understanding of the amount of water in our rivers, lakes, wetlands and coastal areas by greatly increasing the places where we measure it.

The new low-cost instrument would use radar and machine learning to quickly and safely measure water levels in a variety of scenarios.

This work could prove vital as the USDA recently proclaimed the entire state of Colorado to be a "primary natural disaster area" due to an ongoing drought that has made the American West potentially the driest it has been in over a millennium. Other climate records across the globe also continue to be broken, year after year. Our understanding of the changing water cycle has never been more essential at a local, national and global level.

A fundamental part to developing this understanding is knowing changes in the surface height of bodies of water. Currently, measuring changing water surface levels involves high-cost sensors that are easily damaged by floods, difficult to install and time consuming to maintain.

"One of the big issues is that we have limited locations where we take measurements of surface water heights," Minear said.

Heckman and Minear are aiming to change this by building a low-cost instrument that doesn't need to be in a body of water to read its average water surface level. It can instead be placed several meters away safely elevated from floods.

The instrument, roughly the size of two credit-cards stacked on one another, relies on high-frequency radio waves, often referred to as "millimeter wave", which have only been made commercially accessible in the last decade.

Through radar, these short waves can be used to measure the distance between the sensor and the surface of a body of water with great specificity. As the water's surface level increases or decreases over time, the distance between the sensor and the water's surface level changes.

The instrument's small form-factor and potential off-the-shelf usability separate it from previous efforts to identify water through radar.

It also streamlines data transmitted over often limited and expensive cellular and satellite networks, lowering the cost.

In addition, the instrument will use machine learning to determine whether a change in measurements could be a temporary outlier, like a bird swimming by, and whether or not a surface is liquid water.

Machine learning is a form of data analysis that seeks to identify patterns from data to make decisions with little human intervention.

While traditionally radar has been used to detect solid objects, liquids require different considerations to avoid being misidentified. Heckman believes that traditional ways of processing radar may not be enough to measure liquid surfaces at such close proximity.

"We're considering moving further up the radar processing chain and reconsidering how some of these algorithms have been developed in light of new techniques in this kind of signal processing," Heckman said.

In addition to possible fundamental shifts in radar processing, the project could empower communities of citizen scientists, according to Minear.

"Right now, many of the systems that we use need an expert installer. Our idea is to internalize some of those expert decisions, which takes out a lot of the cost and makes this instrument more friendly to a citizen science approach," he said.

By lowering the barrier of entry to water surface level measurement through low-cost devices with smaller data requirements, the researchers broaden opportunities for communities, even in areas with limited cellular networks, to measure their own water sources.

The team is also committing to open-source principles to ensure that anyone can use and build on the technology, allowing for new innovations to happen more quickly and democratically.

Minear, who is a Science Team and Cal/Val Team member for the upcoming NASA Surface Water and Ocean Topography (SWOT) Mission, also hopes that the new instrument could help check the accuracy of water surface level measurements made by satellites.

These sensors could also give local, regional and national communities more insight into their water usage and supply over time and could be used to help make evidence-informed policy decisions about water rights and usage.

"I'm very excited about the opportunities that are presented by getting data in places that we don't currently get it. I anticipate that this could give us better insight into what is happening with our water sources, even in our backyard," said Heckman.

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Keeping water on the radar: Machine learning to aid in essential water cycle measurement - CU Boulder Today

In our latest Innovation Spotlight, Cognex introduces a vision system that automates error detection in minutes without PC or programming skills.

Increasingly, packaging products require their own custom inspection systems to perfect quality, eliminate false rejects, improve throughput, and eliminate the risk of a recall. Some of the foundational machine vision applications along a packaging line include verifying that a label on a package is present, correct, straight, and readable. Other simple packaging inspections involve presence, position, quality, and readability on a label.

But packaging like bottles, cans, cases, and boxes cant always be accurately inspected by traditional machine vision. For applications that present variable, unpredictable defects on confusing surfaces such as those that are highly patterned or suffer from specular glare, manufacturers have typically relied on the flexibility and judgment-based decision-making of human inspectors. Yet human inspectors have some very large tradeoffs for the modern consumer packaged goods industry: they arent necessarily scalable.

Deep Learning expands the range of possible inspection applications

For applications which resist automation yet demand high quality and throughput, Deep Learning technology is a flexible tool that application engineers can have confidence in as their packaging needs grow and change.

Deep Learning technology from Cognex can handle all different types of packaging surfaces, including paper, glass, plastics, and ceramics, as well as their labels. Be it a specific defect on a printed label or the cutting zone for a piece of packaging, Deep Learning solutions can identify all these regions of interest simply by learning the varying appearance of the targeted zone.

Using an array of tools, Deep Learning can then locate and count complex objects or features, detect anomalies, and classify said objects or even entire scenes. And finally, it can recognize and verify alphanumeric characters using a pre-trained font library.

A simple solution, even for complex tasks

While manufacturers recognize the importance of digitalizing their processes using artificial intelligence, many are still hesitant to invest in them because of a lack of resources. Yet the combination of machine vision and Deep Learning is the on-ramp for companies to adopt smarter technologies that will give them the scale, precision, efficiency, and financial growth for the next generation.

A new, full-featured vision system now puts the power of Deep Learning-based image analysis into an easy-to-use package that gets error-proofing applications running in minutes.

The In-Sight 2800 system can be trained with just a few images to automate everything from simple pass/fail inspections to advanced classification and sorting - no PC or programming is needed. The interface guides users through the application development process step-by-step, making it simple for even new vision users to set up any job.

Changes in products and materials or line speed? Not a problem!

The combination of Deep Learning and traditional vision tools gives users the flexibility to solve a broad range of inspection applications. Tools can be used individually for simple jobs or chained together for more complex logic sequences. A powerful classifying tool can be trained using as few as five images to identify and sort defects into different categories and correctly identify parts with variation.

The new In-Sight 2800 system also offers a wide variety of accessories and field-changeable components to help users adapt quickly to changes such as new parts, faster line speeds and higher quality standards.

Watch this video to see why In-Sight 2800 is the easy choice for your next machine vision deployment and enter for your chance to win an In-Sight 2800:

https://connect.cognex.com/IS2800-Giveaway-LP-EN?src=7014W000000urCPQAY

This content was sponsored by Cognex.

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Leveraging the benefits of deep learning in the smart factory - Packaging Europe

The $20 million National Science Foundation award will help UAB and eight other Alabama-based universities build research infrastructure. UABs share will be about $2 million.

Yogesh Vohra Yogesh Vohra, Ph.D., is a co-principal investigator on a National Science Foundation award that will bring the University of Alabama at Birmingham about $2 million over five years.

The total NSF EPSCoR Research Infrastructure Improvement Program award of $20 million with its principal investigator Gary Zank, Ph.D., based at the University of Alabama in Huntsville will help strengthen research infrastructure at UAB, UAH, Auburn University, Tuskegee University, the University of South Alabama, Alabama A&M University, Alabama State University, Oakwood University, and the University of Alabama.

The award, Future technologies and enabling plasma processes, or FTPP, aims to develop new technologies using plasma in hard and soft biomaterials, food safety and sterilization, and space weather prediction. This project will build plasma expertise, research and industrial capacity, as well as a highly trained and capable plasma science and engineering workforce, across Alabama.

Unlike solids, liquids and gas, plasma the fourth state of matter does not exist naturally on Earth. This ionized gaseous substance can be made by heating neutral gases. At UAB, Vohra, a professor and university scholar in the UAB Department of Physics, has employed microwave-generated plasmas to create thin diamond films that have many potential uses, including super-hard coatings and diamond-encapsulated sensors for extreme environments. This new FTPP grant will support research into plasma synthesis of materials that maintain their strength at high temperatures, superconducting thin films and developing plasma surface modifications that incorporate antimicrobial materials in biomedical implants.

Vohra says the UAB Department of Physics will mostly use its share of the award to support faculty in the UAB Center for Nanoscale Materials and Biointegration and two full-time postdoctoral scholars, and support hiring of a new faculty member in computational physics with a background in machine-learning. The machine-learning predictions using the existing databases on materials properties will enable our research team to reduce the time from materials discovery to actual deployment in real-world applications, Vohra said.

The NSF EPSCoR Research Infrastructure Improvement Program helps establish partnerships among academic institutions to make sustainable improvements in research infrastructure, and research and development capacity. EPSCoR is the acronym for Established Program to Stimulate Competitive Research, an effort to level the playing field for states, territories and a commonwealth that historically have received lesser amounts of federal research and development funding.

Jurisdictions can compete for NSF EPSCoR awards if their five-year level of total NSF funding is less than 0.75 percent of the total NSF budget. Current qualifiers include Alabama, 22 other states, and Guam, the U.S. Virgin Islands and Puerto Rico.

Besides Alabama, the other four 2022 EPSCoR Research Infrastructure Improvement Program awardees are Hawaii, Kansas, Nevada and Wyoming.

In 2017, UAB was part of another five-year, $20 million NSF EPSCoR award to Alabama universities.

The Department of Physics is part of the UAB College of Arts and Sciences.

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NSF award will boost UAB research in machine-learning-enabled plasma synthesis of novel materials - University of Alabama at Birmingham

The future of high-performance computing will be virtualized, VMware's Uday Kurkure has told The Register.

Kurkure, the lead engineer for VMware's performance engineering team, has spent the past five years working on ways to virtualize machine-learning workloads running on accelerators. Earlier this month his team reported "near or better than bare-metal performance" for Bidirectional Encoder Representations from Transformers (BERT) and Mask R-CNN two popular machine-learning workloads running on virtualized GPUs (vGPU) connected using Nvidia's NVLink interconnect.

NVLink enables compute and memory resources to be shared across up to four GPUs over a high-bandwidth mesh fabric operating at 6.25GB/s per lane compared to PCIe 4.0's 2.5GB/s. The interconnect enabled Kurkure's team to pool 160GB of GPU memory from the Dell PowerEdge system's four 40GB Nvidia A100 SXM GPUs.

"As the machine learning models get bigger and bigger, they don't fit into the graphics memory of a single chip, so you need to use multiple GPUs," he explained.

Support for NVLink in VMware's vSphere is a relatively new addition. By toggling NVLink on and off in vSphere between tests, Kurkure was able to determine how large of an impact the interconnect had on performance.

And in what should be a surprise to no one, the large ML workloads ran faster, scaling linearly with additional GPUs, when NVLink was enabled.

Testing showed Mask R-CNN training running 15 percent faster in a twin GPU, NVLink configuration, and 18 percent faster when using all four A100s. The performance delta was even greater in the BERT natural language processing model, where the NVLink-enabled system performed 243 percent faster when running on all four GPUs.

What's more, Kurkure says the virtualized GPUs were able to achieve the same or better performance compared to running the same workloads on bare metal.

"Now with NVLink being supported in vSphere, customers have the flexibility where they can combine multiple GPUs on the same host using NVLink so they can support bigger models, without a significant communication overhead," Kurkure said.

Based on the results of these tests, Kurkure expects most HPC workloads will be virtualized moving forward. The HPC community is always running into performance bottlenecks that leaves systems underutilized, he added, arguing that virtualization enables users to make much more efficient use of their systems.

Kurkure's team was able to achieve performance comparable to bare metal while using just a fraction of the dual-socket system's CPU resources.

"We were only using 16 logical cores out of 128 available," he said. "You could use that CPU resources for other jobs without affecting your machine-learning intensive graphics modules. This is going to improve your utilization, and bring down the cost of your datacenter."

By toggling on and off NVLink between GPUs, additional platform flexibility can be achieved by enabling multiple isolated AI/ML workloads to be spread across the GPUs simultaneously.

"One of the key takeaways of this testing was that because of the improved utilization offered by vGPUs connected over a NVLink mesh network, VMware was able to achieve bare-metal-like performance while freeing idle resources for other workloads," Kurkure said.

VMWare expects these results to improve resource utilization in several applications, including investment banking, pharmaceutical research, 3D CAD, and auto manufacturing. 3D CAD is a particularly high-demand area for HPC virtualization, according to Kurkure, who cited several customers looking to implement machine learning to assist with the design process.

And while it's possible to run many of these workloads on GPUs in the cloud, he argued that cost and/or intellectual property rules may prevent them from doing so.

An important note is VMware's tests were conducted using Nvidia's vGPU Manager in vSphere as opposed to the hardware-level partitioning offered by multi-instance GPU (MIG) on the A100. MIG essentially allows the A100 to behave like up to seven less-powerful GPUs.

By comparison, vGPUs are defined in the hypervisor and are time-sliced. You can think of this as multitasking where the GPU rapidly cycles through each vGPU workload until they're completed.

The benefit of vGPUs is users can scale well beyond seven GPU instances at the cost of potential overheads associated with rapid context switching, Kurkure explained. However, at least in his testing, the use of vGPUs didn't appear to have a negative impact on performance compared to running on bare metal with the GPUs passed through to the VM.

Whether MIG would change this dynamic remains to be seen and is the subject of another ongoing investigation by Kurkure's team. "It's not clear when you should be using vGPU and when we should be running in MIG mode," he said.

With vGPU with NVLink validated for scale-up workloads, VMware is now exploringoptions such as how these workloads scale across multiple systems and racks over RDMA over converged Ethernet (RoCE). Here, he says, networking becomes a major consideration.

"The natural extension of this is scale out," he said. "So, we'll have a number of hosted connected by RoCE."

VMware is also investing how virtualized GPUs perform with even larger AI/ML models,

Kurkure's team is also investigating how these architectures scale with even larger AI/ML, like GPT-3, as well as how they can be applied to telco workloads running at the edge.

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VMware claims 'bare-metal' performance on virtualized GPUs - The Register

LONDON, May 24, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the artificial intelligence (AI) in drug discovery market, the rising adoption of cloud-based applications and services by pharmaceutical companies will contribute to the growth of AI in the drug discovery market. Among the various end-users of cloud-based drug discovery platforms, pharmaceutical vendors are likely to be major stakeholders, holding a high-value share of the global cloud-based drug discovery platform market. An opportunity analysis of the global market reveals that leading software vendors have already adopted cloud-based drug discovery platforms to facilitate seamless research and development processes. Moreover, the cloud-based drug discovery platform revolution will witness significant growth in the coming years, thereby creating better opportunities for software vendors for growth and expansion. For example, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, announced in December 2021 that it is collaborating with Pfizer to develop innovative, cloud-based solutions that have the potential to improve how new medicines are developed, manufactured, and distributed for clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. Thus, the increasing adoption of cloud-based applications and services by pharmaceutical companies will contribute positively to the AI drug discovery market size.

Request for a sample of the global artificial intelligence (AI) in drug discovery market report

The global artificial intelligence in drug discovery market size is expected to grow from $0.79 billion in 2021 to $1.04 billion in 2022 at a compound annual growth rate (CAGR) of 31.6%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The AI in drug discovery market is expected to reach $2.99 billion in 2026 at a CAGR of 30.2%.

Use of AI through Machine Learning (ML) is a trend in assessing pre-clinical studies during the drug development process. Pre-clinical studies are non-clinical studies for novel drug substances to establish clinical efficacy and safety in a controlled environment before testing with a final target population. ML modelling pharmacokinetic (PK) and pharmacodynamic (PD) methodologies are applied in in-vitro and preclinical PK studies to successfully anticipate the dose concentration response relationship of pipeline assets. In addition, deep learning methodologies are employed as In-Silico methods for successfully predicting the therapeutic/pharmacological properties of novel molecules by utilizing transcriptomic data, which includes various biological systems and controlled conditions. Besides the drug discovery market, machine learning technology finds its application in the AI in medical diagnostics market as well as AI in medical imaging market.

Major players in the artificial intelligence for drug discovery and development market are IBM Corporation, Microsoft, Atomwise Inc., Deep Genomics, Cloud Pharmaceuticals, Insilico Medicine, Benevolent AI, Exscientia, Cyclica, and BIOAGE.

The global artificial intelligence in drug discovery market report is segmented by technology into deep learning, machine learning; by drug type into small molecule, large molecules; by disease type into metabolic disease, cardiovascular disease, oncology, neurodegenerative diseases, others; by end-users into pharmaceutical companies, biopharmaceutical companies, academic and research institutes, others.

In 2021, North America was the largest region in the artificial intelligence (AI) in drug discovery market. It was followed by the Asia-Pacific, Western Europe, and then the other regions. The regions covered in the AI in drug discovery market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, and Africa.

Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide artificial intelligence (AI) in drug discovery market overviews, artificial intelligence (AI) in drug discovery market analyze and forecast market size and growth for the whole market, artificial intelligence (AI) in drug discovery market segments and geographies, artificial intelligence (AI) in drug discovery market trends, artificial intelligence (AI) in drug discovery market drivers, artificial intelligence (AI) in drug discovery market restraints, artificial intelligence (AI) in drug discovery market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

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Artificial Intelligence (AI) In Drug Discovery Market Growth Is Driven At A 30% Rate With Increasing Adoption Of Cloud-Based Applications And Services...

Adam Oliner, co-founder and CEO of Graft used to run machine learning at Slack, where he helped build the companys internal artificial intelligence infrastructure. Slack lacked the resources of a company like Meta or Google, but it still had tons of data to sift through and it was his job to build something on a smaller scale to help put AI to work on the dataset.

With a small team, he could only build what he called a miniature solution in comparison to the web scale counterparts. After he and his team built it, however, he realized that it was broadly applicable and could help other smaller organizations tap into AI and machine learning without huge resources.

We built a sort of mini Graft at Slack for driving semantic search and recommendations throughout the product. And it was hugely effective And that was when we said, this is so useful, and so powerful if we can get this into the hands of most organizations, we think we could really change the way people interact with their data and interact with AI, Oliner told me.

Last year he decided to leave Slack and go out on his own and started Graft to solve the problem for many companies. He says the beauty of the solution is that it provides everything you need to get started. Its not a slice of a solution or one that requires plug-ins to complete. He says it works for companies right out of the box.

The point of Graft is to make the AI of the 1% accessible to the 99%. he said. What he means by that is giving smaller companies the ability to access and put to use modern AI, and in particular pre-trained models for certain specific tasks, something he says offers a tremendous advantage.

These are sometimes called trunk models or foundation models, a term that a group at Stanford is trying to coin. These are essentially very large pre-trained models that encode a lot of semantic and structural knowledge about a domain of data. And this is useful because you dont have to start from scratch on every new problem, he said.

The company is still a work in progress, working with beta customers to refine the solution, but expects to launch a product later this year. For now they have a team of 11 people, and Oliner says that its never too early to think about building a diverse team.

When he decided to start the company, the first person he sought out was Maria Kazandjieva, former head of engineering at Netflix. I have been working at building the rest of the founding team and also hiring others with an eye toward diversity and inclusion. So, you know, just [the other day], we were talking with recruiting communities that are focused on women and people of color, partly because we feel like investments now in building diverse team will just make it so much easier later on, he said.

As the journey begins for Graft, the company announced what it is calling a pre-seed investment of $4.5 million led by GV with help from NEA, Essence VC, Formulate Ventures and SV Angel.

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Slacks former head of machine learning wants to put AI in reach of every company - TechCrunch

This September 13-15, at the Santa Clara Marriott, CA, 800+ attendees will attend the co-located AI Hardware Summit and Edge AI Summit.

SANTA CLARA, Calif., May 10, 2022--(BUSINESS WIRE)--Metas VP of Infrastructure Hardware, Alexis Black Bjorlin, will open the flagship AI Hardware Summit with a keynote, while her colleague Vikas Chandra, Metas Director of AI Research will open Edge AI Summit. Other notable keynotes include Microsoft Azures CTO, Mark Russinovich, plus Wells Fargos EVP of Model Risk, Agus Sudjianto; Synopsys President & COO, Sassine Ghazi; Cadences Executive Chairman, Lip-Bu Tan; and Siemens EVP, IC EDA, Joseph Sawicki, among many others

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220510005105/en/

Machine learning and deep learning are fast becoming major line items on agendas in board rooms in every organization across the globe. The technology stack needed to support these workloads, and to execute them quickly, efficiently, and affordably, is fast developing in both the datacenter and in client systems at the edge.

In 2018, a new Silicon Valley event called the AI Hardware Summit launched to provide a platform to discuss innovations in hardware necessary for supporting machine learning both at the very large scale, and in small resource-constrained environments. The event attracted enormous interest from the semiconductor and systems sectors, welcomed Habana Labs into the industry in its inaugural year, and subsequently hosted Alphabet Inc.s Chairman and Turing Award Winner, John L. Hennessy, as a keynote speaker in 2019. Shortly after, the Edge AI Summit was launched to focus specifically on deploying machine learning in commercial use cases in client systems.

Hennessy said of the AI Hardware Summit: "Its a great place where lots of people interested in AI Hardware are coming together and exchanging ideas, and together we make the technology better. Theres a synergistic effect at these summits which is really amazing and powers the entire industry."

Story continues

Fast forward a few years of virtual shows and the events are back in-person with a fresh angle. An all-star cast of tech visionary speakers will address optimizing and accelerating machine learning hardware and software, focusing on the intersection between systems design and ML development. Developer workshops with HuggingFace are a new feature this year focused on helping bring new hardware innovation into leading enterprises.

The co-location of the two industry-leading summits combines the proposition to focus on building, optimizing and unifying software-defined ML platforms across the cloud-edge continuum. Attendees of the AI Hardware Summit can expect content spanning from hardware and infrastructure up to models/applications, whereas the Edge AI Summit has a much tighter focus on case studies of ML in enterprise.

This years audience will consist of machine learning practitioners and technology builders from various engineering disciplines, discussing topics such as systems-first ML, AI acceleration as a full-stack endeavour, software defined systems co-design, boosting developer efficiency, optimizing applications across diverse ML platforms and bringing state of the art production performance into the enterprise.

While the AI Hardware Summit has broadened its scope beyond focusing purely on hardware, there will still be plenty for hardware-focused attendees to explore. The event website, http://www.aihardwaresummit.com, gives accessible information on why a software-focused or hardware-focused attendee should register.

The Edge AI Summit features more end user use cases than any other event of its kind, and is a must attend for anyone moving ML workloads to the edge. The event website, http://www.edgeaisummit.com, gives more information.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220510005105/en/

Contacts

If you would like to contact the organizers or get involved in either show, please contact Priya Khosla, Head of Marketing at Priya.Khosla@kisacoresearch.com

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Tech Visionaries to Address Accelerating Machine Learning, Unifying AI Platforms and Taking Intelligence to the Edge, at the Fifth Annual AI Hardware...

2022 MAY 09 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on artificial intelligence are discussed in a new report. According to news reporting out of the University of California Irvine by NewsRx editors, research stated, This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS).

Financial supporters for this research include Casualty Actuarial Society Award: NA.

Our news correspondents obtained a quote from the research from University of California Irvine: These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a four-year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. Data were provided by a private insurance carrier to the CAS after anonymizing the dataset. These data are available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public.

According to the news reporters, the research concluded: Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

For more information on this research see: Machine Learning in Ratemaking, an Application in Commercial Auto Insurance. Risks, 2022,10(80):80. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks10040080.

Our news editors report that more information may be obtained by contacting Spencer Matthews, Department of Statistics, Donald Bren School of Information and Computer Science, University of California Irvine, Irvine, CA 92697, USA. Additional authors for this research include Brian Hartman.

(Our reports deliver fact-based news of research and discoveries from around the world.)

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Researchers From University Of California Irvine Publish Research In Machine Learning (Machine Learning In Ratemaking, An Application In Commercial...

Overfitting is a basic problem in supervised machine learning where the model shows well generalisation capabilities on seen data but poorly performs on unseen data. Overfitting occurs as a result of the existence of noise, the small size of the training set, and the complexity involved in algorithms. In this article, we will be discussing different strategies to overcome the overfitting of machine learners while at the training stage. Following are the topics to be covered.

Lets start with the overview of overfitting in the machine learning model.

Model is overfitting data when it memorises all the specific details of the training data and fails to generalise. It is a statistical error caused by poor statistical judgments. Because it is too closely tied to the data set, it adds bias to the model. Overfitting limits the models relevance to its data set and renders it irrelevant to other data sets.

Definition according to statistics

In the presence of a hypothesis space, a hypothesis is said to overfit the training data if there exists some alternative hypothesis with a smaller error than the hypothesis over the training examples, but the alternative hypothesis with a smaller overall error than the entire distribution of instances.

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Detecting overfitting is almost impossible before you test the data. During the training, there are two errors: training error and validation error when the training is constantly decreasing but the validation error decreases for a period and then starts to increase but meanwhile the training error is still decreasing. This kind of scenario is overfitting.

Lets understand the mitigation strategies for this statistical problem.

There are different stages in a machine learning project where different mitigation techniques could be applied to mitigate the overfitting.

High dimensional data lead to model overfitting because in these data the number of observations is much less than the number of features. This will result in indeterministic answers to the problem.

Ways to mitigate

During the process of data wrangling, one can face the problem of outliers in the data. As these outliers increase the variance in the dataset and due to this the model will train itself to these outliers and will result in an output which has high variance and low bias. Hence the bias-variance tradeoff is disturbed.

Ways to mitigate

They either require particular attention or should be utterly ignored, depending on the circumstances. If the data set contains a significant number of outliers, it is critical to utilise a modelling approach that is resistant to outliers or to filter out the outliers.

Cross-validation is a resampling technique used to assess machine learning models on a small sample of data. Cross-validation is primarily used in applied machine learning to estimate a machine learning models skill on unseen data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the models training.

Evaluation Procedure using K-fold cross-validation

The above is the process of K fold when k is 5 this is known as 5 folds.

This method is used to prevent the learning speed slow-down problem. Because of noise learning, the accuracy of algorithms stops improving beyond a certain point or even worsens.

The green line represents the training error, and the red line represents the validation error, as illustrated in the picture, where the horizontal axis is an epoch and the vertical axis is an error. If the model continues to learn after the point, the validation error will rise while the training error will fall. So the goal is to pinpoint the precise time at which to discontinue training. As a result, we achieved an ideal fit between under-fitting and overfitting.

Way to achieve the ideal fit

To compute the accuracy after each epoch and stop training when the accuracy of test data stops improving, and then use the validation set to figure out a perfect set of values for the hyper-parameters, and then use the test set to complete the final accuracy evaluation. When compared to directly using test data to determine hyper-parameter values, this method ensures a better level of generality. This method assures that, at each stage of an iterative algorithm, bias is reduced while variance is increased.

Noise reduction, naturally, becomes one study path for overfitting inhibition. Pruning is recommended to lower the size of final classifiers in relational learning, particularly in decision tree learning, based on this concept. Pruning is an important principle that is used to minimise classification complexity by removing less useful or irrelevant data, and then to prevent overfitting and increase classification accuracy. There are two types of pruning.

In many circumstances, the amount and quality of training datasets may have a considerable impact on machine learning performance, particularly in the domain of supervised learning. The model requires enough data for learning to modify parameters. The sample count is proportional to the number of parameters.

In other words, an extended dataset can significantly enhance prediction accuracy, particularly in complex models. Existing data can be changed to produce new data. In summary, there are four basic techniques for increasing the training set.

When creating a predictive model, feature selection is the process of minimising the number of input variables. It is preferable to limit the number of input variables to lower the computational cost of modelling and, in some situations, to increase the models performance.

The following are some prominent feature selection strategies in machine learning:

Regularisation is a strategy for preventing our network from learning an overly complicated model and hence overfitting. The model grows more sophisticated as the number of features rises.

An overfitting model takes all characteristics into account, even if some of them have a negligible influence on the final result. Worse, some of them are simply noise that has no bearing on the output. There are two types of strategies to restrict these cases:

In other words, the impact of such ineffective characteristics must be restricted. However, there is uncertainty in the unnecessary characteristics, so minimise them altogether by reducing the models cost function. To do this, include a penalty word called regularizer into the cost function. There are three popular regularisation techniques.

Instead of discarding those less valuable qualities, it assigns lower weights to them. As a result, it can gather as much information as possible. Large weights can only be assigned to attributes that improve the baseline cost function significantly.

Hyperparameters are selection or configuration points that allow a machine learning model to be tailored to a given task or dataset. To optimise them is known as hyperparameter tuning. These characteristics cannot be learnt directly from the standard training procedure.

They are generally resolved before the start of the training procedure. These parameters indicate crucial model aspects such as the models complexity or how quickly it should learn. Models can contain a large number of hyperparameters, and determining the optimal combination of parameters can be thought of as a search issue.

GridSearchCV and RandomizedSearchCV are the two most effective Hyperparameter tuning algorithms.

GridSearchCV

In the GridSearchCV technique, a search space is defined as a grid of hyperparameter values, and each point in the grid is evaluated.

GridSearchCV has the disadvantage of going through all intermediate combinations of hyperparameters, which makes grid search computationally highly costly.

Random Search CV

The Random Search CV technique defines a search space as a bounded domain of hyperparameter values that are randomly sampled. This method eliminates needless calculation.

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Overfitting is a general problem in supervised machine learning that cannot be avoided entirely. It occurs as a result of either the limitations of training data, which might be restricted in size or comprise a large amount of data, or noises, or the restrictions of algorithms that are too sophisticated and need an excessive number of parameters. With this article, we could understand the concept of overfitting in machine learning and the ways it could be mitigated at different stages of the machine learning project.

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Steps to perform when your machine learning model overfits in training - Analytics India Magazine