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Healthcare professionals and AI experts alike are warning about some issues theyve identified in a machine learning model made for diagnosing the coronavirus.

The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia.

But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment.

More from a recentVentureBeat article:

Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data.

Frankenstein datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

In their current reported form, none of the machine learning models included in this review are likely candidates for clinical translation for the diagnosis/prognosis of COVID-19, the paper reads. Despite the huge efforts of researchers to develop machine learning models for COVID-19 diagnosis and prognosis, we found methodological flaws and many biases throughout the literature, leading to highly optimistic reported performance.

Publicly available datasets also commonly suffered from lower quality image formats and werent large enough to train reliable AI models.

How does that old expression go? the problem with computers is that they doexactlywhat you tell them to do.

I love that saying because, despite the fact that AI is growing the point of teaching itselfwithout as much human intervention, its still a glorified computer.

After all, a model is still only as good as the data being fed to it, and instances like this only underline just how much of a strict science machine learning is.

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Excerpt from:
There Are Issues with the COVID-19 Diagnosis Machine Learning Algorithms - Commercial Integrator

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Machine Learning and Analytics Made Easy - The Internet of Business

Highlights:

Cadence Litho Physical Analyzer with ML technology qualified for GF 12LP/12LP+ solutions, providing up to 33% greater detection efficiency with less than 10% runtime impact

Collaboration between Cadence and GF enables signoff engineers to speed production of chip designs on GFs 12LP and 12LP+ solutions for the AI, data center, hyperscale, aerospace and industrial markets

GLOBALFOUNDRIES (GF), the worlds leading specialty foundry, and Cadence Design Systems, Inc. (Nasdaq: CDNS) today announced a collaboration to facilitate design for manufacturing (DFM) signoff with machine learning (ML) prediction capabilities. As part of the collaboration, the Cadence Litho Physical Analyzer, a DFM pattern analysis tool integrated with GF-developed ML models, has been qualified for GFs 12LP and 12LP+ solutions.

The ML-enhanced Cadence Litho Physical Analyzer, optimized for GFs 12LP and 12LP+ solutions, provides customers with in-design automated DFM hotspot detection and fixing capabilities to speed implementation and time to market. The ML-based enhancement delivers up to 33% greater detection efficiency versus traditional pattern-matching checks with less than 10% runtime impact.

GF has released a corresponding ML-enhanced DFM kit as an update to its 12LP process design kits (PDKs), with the 12LP+ version scheduled for release in the second quarter of 2021, providing customers with a simple path to speed design and production of chips optimized for the artificial intelligence (AI), data center, internet of things (IoT) and other markets.

GFs most advanced FinFET solution, 12LP+ is optimized for AI training and inference applications and offers chip designers an efficient development experience and a fast time-to-market. 12LP+ builds upon GFs established 14nm/12LP platform, of which GF has shipped more than one million wafers. 12LP and 12LP+ deliver a superior combination of AI power, performance and area (PPA) benefits without the need to migrate to smaller and much costlier geometries.

Story continues

"By incorporating ML capabilities into the Cadence Litho Physical Analyzer, customers using GFs 12LP platform and 12LP+ solution can verify signoff quality during implementation, thereby achieving better silicon quality," said Jim Blatchford, vice president of technology enablement at GLOBALFOUNDRIES. "Our collaboration with Cadence also allowed the swift enablement and seamless integration with our customers design flows through the delivery of this new capability in our 12LP and 12LP+ PDKs."

The Cadence Litho Physical Analyzer, enhanced with ML, seamlessly integrates with the Cadence Innovus Implementation System and Cadence Virtuoso custom IC design platform, providing customers with a smoother design experience through a common, familiar interface. The Litho Physical Analyzer is part of the broader Cadence digital full flow, which supports Cadences Intelligent System Design strategy, enabling system-on-chip (SoC) design excellence. For more information on the Cadence advanced-node solutions, visit http://www.cadence.com/go/advnodesols.

"Not only does our Litho Physical Analyzer tool include ML capabilities, but the entire Cadence implementation platform has ML capabilities as well, which let us provide customers with a highly sophisticated solution," said Michael Jackson, corporate vice president, R&D in the Digital & Signoff Group at Cadence. "Through our latest collaboration with GF, customers can leverage our tools with the added boost of ML capabilities to achieve design success using GFs 12LP/12LP+ solutions. Furthermore, our Litho Physical Analyzer with ML capabilities enhances GFs DRC+ capabilities and improves yield based on the tools ability to effectively catch more hotspots and repair previously undetected hotspot patterns."

About GFGLOBALFOUNDRIES (GF) is the worlds leading specialty foundry. GF delivers differentiated feature-rich solutions that enable its customers to develop innovative products for high-growth market segments. GF provides a broad range of platforms and features with a unique mix of design, development and fabrication services. With an at-scale manufacturing footprint spanning the U.S., Europe and Asia, GF has the flexibility and agility to meet the dynamic needs of customers across the globe. GF is owned by Mubadala Investment Company. For more information, visit http://www.globalfoundries.com.

About CadenceCadence is a pivotal leader in electronic design, building upon more than 30 years of computational software expertise. The company applies its underlying Intelligent System Design strategy to deliver software, hardware and IP that turn design concepts into reality. Cadence customers are the worlds most innovative companies, delivering extraordinary electronic products from chips to boards to systems for the most dynamic market applications, including consumer, hyperscale computing, 5G communications, automotive, mobile, aerospace, industrial and healthcare. For six years in a row, Fortune magazine has named Cadence one of the 100 Best Companies to Work For. Learn more at cadence.com.

2021 Cadence Design Systems, Inc. All rights reserved worldwide. Cadence, the Cadence logo and the other Cadence marks found at http://www.cadence.com/go/trademarks are trademarks or registered trademarks of Cadence Design Systems, Inc. All other trademarks are the property of their respective owners.

View source version on businesswire.com: https://www.businesswire.com/news/home/20210323005177/en/

Contacts

Michael MullaneyGLOBALFOUNDRIES518-305-1597michael.mullaney@globalfoundries.com

Cadence Newsroom408-944-7039newsroom@cadence.com

Read more:
GLOBALFOUNDRIES and Cadence Add Machine Learning Capabilities to DFM Signoff for GFs Most Advanced FinFET Solutions - Yahoo Finance

With hundreds, if not thousands, of road accidents occurring within the time that it takes you to finish reading this article, the importance of dash cameras is ever essential. Its a wonder that more modern-day cars dont come with DVRs fitted as standard. Regardless, many have resorted to the perfectly capable aftermarket camera as a simple precaution in case of an unfortunate mishap.

While these dash cams have provided a lot of helpful evidence since their introduction (not to mention plenty of content for the likes of us to work with), their limitations mean that sometimes crucial info is missed. Enter the dash cam from Israeli start-up, Nexar. The company has taken the simple concept of a regular camera mounted to the windshield and reinvented it to collect data on the spot and piece together the moments leading up to the incident.

The Drive reports that, in the event of a collision, Nexars connected dash cam will locate any vehicles that have been involved in the accident and retraces their respective paths leading up to the impact using data gathered from its GPS and motion sensors. It then displays this, along with camera footage, providing a comprehensive report on a virtual dashboard. It effectively allows you or the authorities to reconstruct the event. The data is shown side-by-side with actual footage, with information such as vehicle speed and G-force.

Nexar claims their dash cams will automatically detect 90% of accidents to create a report. The data and metrics collected by the camera and sensors can be assembled into an easy-to-view report that can then be shared with insurers and authorities as evidence.

Japanese auto insurer Mitsui Sumitomo Insurance has shown interest in the idea. It has already helped deploy the cameras, stating that its helping hundreds of thousands of Drivers who have added Nexars dash cam into their vehicles.

Go here to see the original:
Recreate Your Very Own Seconds From Disaster Episode With This Machine Learning Dash Cam - CarScoops

Dive Brief:

This partnership suggests the chain is relying more heavily on AI and machine learning systems to improve its system.

"Machine learning models can provide significant value to an organization in several business applications, but without a solid [machine learning]operations pipeline, that value cannot be truly realized," Zack Fragoso, manager, data science and AI at Domino's, said in the release.

The chain began testing AI in 2018 with an Alexa-like voice recognition application called DOM. In 2019, Domino's began piloting Dragontail Systems' AI technology at its Australian and New Zealand locations to scan each pizza and ensure they measured up to quality standards. The pilot was expanded after improving quality scores by 15% in its first month.

The chain also deployed AI technology across its Malaysia and Singapore division to automate delivery operations and consolidate operations. Further,during its most recent Q4 earnings call, CEO Ritch Allison said the chain has added AI-enabled forecasting alongside GPS driver tracking, enhanced makeline and cutting technologies to "better match demand with capacity in our stores."

"These innovations are all designed to increase speed, accuracy and efficiency, allowing us to continue to better serve our customers,"he said.

As all these technologies are deployed disparately across a system as big as Domino's, it's important to streamline them to optimize management weigh viability to scale. Such a streamline is particularly critical now. While Domino's has been ahead of the curve on this technology, its rivals are catching up. Pizza Hut parent Yum Brands recently announced the acquisition of AI company Kvantumto improve consumer insights, for example. Pizza Hut U.K.also started using an AI-powered data analytics solution in 2020.

Meanwhile, Papa John's in November added kea's Indecisive AI to its system, which helps the chain better manage phone calls, easing some labor pressure.

On Domino's Q4 call, Allison said the company will continue to invest in technology to both drive efficiencies in store and improve its corporate team's ability to support the business. The company will likely be able to maintain a technology advantage through both those investments and through its fledgling Innovation Garage, which is focused specifically on advantageous technology innovations.

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Domino's teams with Datatron to streamline AI and machine learning tech - Restaurant Dive