Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It’s resource intensive and requires specialized expertise.
At the heart of any successful machine learning/artificial intelligence (ML/AI) initiative is a commitment to high-quality training data and a pathway to quality data that is proven and well-defined. Without this quality data pipeline, the initiative is doomed to fail.
Computer vision or data science teams often turn to external partners to develop their data training pipeline, and these partnerships drive model performance.
There is no one definition of quality: “quality data” is completely contingent on the specific computer vision or machine learning project. However, there is a general process all teams can follow when working with an external partner, and this path to quality data can be broken down into four prioritized phases.
nnotation criteria and quality requirements
Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML/AI use case.
The computer vision team needs to establish an unambiguous set of rules that describe what quality means in the context of their project. Annotation criteria are the collection of rules that define which objects to annotate, how to annotate them correctly, and what the quality targets are.
Accuracy or quality targets define the lowest acceptable result for evaluation metrics like accuracy, recall, precision, F1 score, et cetera. Typically, a computer vision team will have quality targets for how accurately objects of interest were classified, how accurately objects were localized, and how accurately relationships between objects were identified.
Workforce training and platform configuration
Platform configuration. Task design and workflow setup require time and expertise, and accurate annotation requires task-specific tools. At this stage, data science teams need a partner with expertise to help them determine how best to configure labeling tools, classification taxonomies, and annotation interfaces for accuracy and throughput.
Worker testing and scoring. To accurately label data, annotators need a well-designed
By: Ben Schneider
Title: Computer vision in AI: The data needed to succeed
Sourced From: www.technologyreview.com/2021/04/29/1023746/computer-vision-in-ai-the-data-needed-to-succeed/
Published Date: Thu, 29 Apr 2021 14:00:00 +0000
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Cryptocurrency Payments for Insurance: Are Insurance Companies Really Embracing Bitcoin and Altcoins?
It is no longer unusual to hear that a bank accepts savings in Bitcoin, Ethereum, and the like. Or that a loan company helps businesses with crypto. After all, the traditional financial and insurance industries were among the first to adopt cryptocurrencies. The latter ones have found more than one way to incorporate these means of payment into their business. This approach proved useful not only for companies but also for policyholders.
The above claim was confirmed by several recent surveys, including that of Goldman Sachs, which showed that 6% of respondents (over 300 financial executives in the insurance sector) verified that their companies invest in crypto.
Benefits for Policyholders and Insurance Companies
Several things make cryptocurrencies attractive, not only for insurance companies but also for policyholders. Some of them are beneficial to both parties, and some are specific.
So, when it comes to policyholders, they can expect several advantages of using crypto. One of the most notable is the opportunity for diversification. Thanks to crypto, they can get another asset (on top of the traditional ones) to add to their diversification strategy. By doing this, they can spread risk and keep their funds protected.
Also, policyholders can count on speedy transactions because crypto transactions are usually processed much faster than wire transfers. Receiving claim payouts on time in urgent situations is possible thanks to cryptocurrency.
We should also note that they get more privacy because they can stay pseudonymous.
On the other hand, insurance companies benefit from reduced transaction costs, faster settlements, improved security, and a few other things.
It’s one thing to discuss things in theory and another to see how they work in real life. Fortunately, there are many successful examples of insurance companies accepting crypto as a payment plan.
INGUARD is one of the leading digital insurance companies based in the U.S. It provides its services in all 50 U.S. States. What makes INGUARD truly special is that they were the first insurance companies in North America to accept Bitcoin payments in 2013.
Interestingly, this brand is partnered with numerous tech companies who share their vision for insurance, including Fitbit and Michelin.
Some insurance companies rely on the blockchain. Lemonade is an excellent example of this. This brand throws blockchain technology and artificial intelligence into the mix or provides pet, car, home, and other types of insurance. It goes without saying that policyholders can use cryptocurrency as a payment plan.
Compiling a list of insurance companies accepting crypto without mentioning
Title: Cryptocurrency Payments for Insurance: Are Insurance Companies Really Embracing Bitcoin and Altcoins?
Sourced From: www.cryptoninjas.net/2023/11/20/cryptocurrency-payments-for-insurance-are-insurance-companies-really-embracing-bitcoin-and-altcoins/
Published Date: Mon, 20 Nov 2023 06:07:04 +0000
Did you miss our previous article…
Inside the hunt for new physics at the world’s largest particle collider
In 1977, Ray and Charles Eames released a remarkable film that, over the course of just nine minutes, spanned the limits of human knowledge. Powers of Ten begins with an overhead shot of a man on a picnic blanket inside a one-square-meter frame. The camera pans out: 10, then 100 meters, then a kilometer, and eventually all the way to the then-known edges of the observable universe—1024 meters. There, at the farthest vantage, it reverses. The camera zooms back in, flying through galaxies to arrive at the picnic scene, where it plunges into the man’s skin, digging down through successively smaller scales: tissues, cells, DNA, molecules, atoms, and eventually atomic nuclei—10-14 meters. The narrator’s smooth voice-over ends the journey: “As a single proton fills our scene, we reach the edge of present understanding.”
During the intervening half-century, particle physicists have been exploring the subatomic landscape where Powers of Ten left off. Today, much of this global effort centers on CERN’s Large Hadron Collider (LHC), an underground ring 17 miles (27 kilometers) around that straddles the border between Switzerland and France. There, powerful magnets guide hundreds of trillions of protons as they do laps at nearly the speed of light underneath the countryside. When a proton headed clockwise plows into a proton headed counterclockwise, the churn of matter into energy transmutes the protons into debris: electrons, photons, and more exotic subatomic bric-a-brac. The newly created particles explode radially outward, where they are picked up by detectors.
In 2012, using data from the LHC, researchers discovered a particle called the Higgs boson. In the process, they answered a nagging question: Where do fundamental particles, such as the ones that make up all the protons and neutrons in our bodies, get their mass? A half-century earlier, theorists had cautiously dreamed the Higgs boson up, along with an accompanying field that would invisibly suffuse space and provide mass to particles that interact with it. When the particle was finally found, scientists celebrated with champagne. A Nobel for two of the physicists who predicted the Higgs boson soon followed.
But now, more than a decade after the excitement of finding the Higgs, there is a sense of unease, because there are still unanswered questions about the fundamental constituents of the universe.
Perhaps the most persistent of these questions is the identity of dark matter, a mysterious substance that binds galaxies together and makes up 27% of the cosmos’s mass. We know dark matter must exist because we have astronomical observations of its gravitational effects. But since the discovery of the Higgs, the LHC has seen no new particles—of dark matter or anything else—despite nearly doubling its collision energy and quintupling the amount of data it can collect. Some physicists have said that particle physics is in a “crisis,” but there is disagreement even on that characterization: another camp insists the field is fine and still others say that there is indeed a crisis, but that crisis is good. “I think the community of particle phenomenologists is in a deep crisis, and I think people are afraid to say those words,” says Yoni Kahn, a theorist at the University of Illinois Urbana-Champaign.
The anxieties of particle physicists may, at first blush, seem like inside baseball. In reality, they concern the universe, and how we can continue to study it—of interest if you care about that sort of thing. The past 50 years of research have given us a spectacularly granular view of nature’s laws, each successive particle discovery clarifying how things really work at the bottom. But now, in the post-Higgs era, particle physicists have reached an impasse in their quest to discover, produce, and study new particles at colliders. “We do not have a strong beacon telling us where to look for new physics,” Kahn says.
So, crisis or no crisis, researchers are trying something new. They are repurposing detectors to search for unusual-looking particles, squeezing what they can out of the data with machine learning, and planning for entirely new kinds of colliders. The hidden particles that physicists are looking for have proved more elusive than many expected, but the search is not over—nature has just forced them to get more creative.
n almost-complete theory
As the Eameses were finishing Powers of Ten in the late ’70s, particle physicists were bringing order to a “zoo” of particles that had been discovered in the preceding decades. Somewhat drily, they called this framework, which enumerated the kinds of particles and their dynamics, the Standard Model.
Roughly speaking, the Standard Model separates fundamental particles into two types: fermions and bosons. Fermions are the bricks of matter—two kinds of fermions called up and down quarks, for example, are bound
By: Dan Garisto
Title: Inside the hunt for new physics at the world’s largest particle collider
Sourced From: www.technologyreview.com/2024/02/20/1088002/higgs-boson-physics-particle-collider-large-hadron-collider/
Published Date: Tue, 20 Feb 2024 10:00:00 +0000
Transforming document understanding and insights with generative AI
At some point over the last two decades, productivity applications enabled humans (and machines!) to create information at the speed of digital—faster than any person could possibly consume or understand it. Modern inboxes and document folders are filled with information: digital haystacks with needles of insight that too often remain undiscovered.
Generative AI is an incredibly exciting technology that’s already delivering tremendous value to our customers across creative and experience-building applications. Now Adobe is embarking on our next chapter of innovation by introducing our first generative AI capabilities for digital documents and bringing the new technology to the masses.
AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents.
ccelerating productivity across popular document formats
As the creator of PDF, the world’s most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.
With AI Assistant in Acrobat, project managers can scan, summarize, and distribute meeting highlights in seconds, and sales teams can quickly personalize pitch decks and respond to client requests. Students can shorten the time they spend hunting through research and spend more time on analysis and understanding, while social media and marketing teams can quickly surface top trends and issues into daily updates for stakeholders. AI Assistant can also streamline the time it takes to compose an email or scan a contract of any kind, enhancing productivity for knowledge workers and consumers globally.
Innovating with AI—responsibly
Adobe has continued to evolve the digital document category for over 30 years. We invented the PDF format and open-sourced it to the world. And we brought Adobe’s decade-long legacy of AI innovation to digital documents, including the award-winning Liquid Mode, which allows Acrobat to dynamically reflow document content and make it readable on smaller screens. The experience we’ve gained by building Liquid Mode and then learning how customers get value from it is foundational to what we’ve delivered in AI Assistant.
Today, PDF is the number-one business file format stored in the cloud, and PDFs are where individuals and organizations keep, share, and collaborate on their most important information. Adobe remains committed to secure and responsible AI innovation for digital documents, and AI Assistant in Acrobat has guardrails in place so that all customers—from individuals to the largest enterprises—can use the new features with confidence.
Like other Adobe AI features, AI Assistant in Acrobat has been developed and deployed in alignment with Adobe’s AI principles and is governed by secure data protocols. Adobe has taken a model-agnostic approach to developing AI Assistant, curating best-in-class technologies to provide customers with the value they need. When working with third-party large language models (LLMs), Adobe contractually obligates them to employ confidentiality and security protocols that match our own high standards, and we specifically prohibit third-party LLMs from manually reviewing or training their models on Adobe customer data.
The future of intelligent document experiences
Today’s beta features are part of a larger Adobe vision to transform digital document experiences with generative AI. Our vision for what’s next includes the following:
Insights across multiple documents and document types: AI Assistant will work across multiple documents, document types, and sources, instantly surfacing the most important information from everywhere.AI-powered authoring, editing, and formatting: Last year, customers edited tens of billions of documents in Acrobat. AI Assistant will make it simple to quickly generate first drafts, as well as
By: Deepak Bharadwaj
Title: Transforming document understanding and insights with generative AI
Sourced From: www.technologyreview.com/2024/02/20/1088584/transforming-document-understanding-and-insights-with-generative-ai/
Published Date: Tue, 20 Feb 2024 16:08:01 +0000
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