Mediaboss Marketing

US-based Pranjali Awasthi, a child prodigy in the truest sense, is currently working on the overlap of neuroimaging and ML at the Neural Dynamics of Control Lab at Florida International University in Miami, Florida. At present, she is busy building a classifier for error detection in cognitive tasks using EEG imaging. This project has also received a grant from the New York Institute of Technology. The 15-year-old has also worked on an AI-based sign language detector, a mental health companion app, and an RNN-based diabetic retinopathy diagnostic tool.

Awasthi moved to the US from India with her parents when she was just 11. I grew up in an environment where learning and curiosity were encouraged. My parents are well-versed in academia, with my mother in humanities and my father in science fields. The importance of education has been stressed in the environment I have been growing in. I got into research because of my dad who was also pursuing research in the field of the computer-brain interface. Further, the factor of social impact was a big factor in my upbringing. I was always told that it is always how much impact you have made in your community at the end of the day, said Pranjali.

She is also an entrepreneur and has founded Indic Valley, an online store for underrepresented artists in India.

For the event, Pranjali spoke on the importance of introducing AI to children from a younger age. When I first started, I realised people dont take AI very seriously. It is also limiting the number of opportunities available for AI enthusiasts to connect and grow significantly, she said.

She feels that there are three main challenges that hinder AI learning among young students:

Pranjali believes that, despite the penetration of AI technology in almost every facet of our lives, the knowledge base is very concentrated in limited hands. Younger children especially are often left out from the conversation and discourse around it. This should not happen. Instead, there should be more assertiveness and programmes built specifically for young students to teach and practice AI, said Pranjali.

There are programmes for high schoolers, there are programmes even for middle schoolers, but I feel we need to start even more early and introduce AI as a core subject even in elementary school starting from basic projects to increase their knowledge base. Mandating AI learning and establishing teaching certifications should be considered, she added.

The average age in the US for a child to use social media is 10. Pranjali said this could be a good opportunity for introducing them to the algorithms running behind their favourite apps. She also believes children should be allowed to harness their creativity and translate that to learning and researching in AI.

Pranjali also spoke about the accessibility and availability aspect of AI. Learning AI in the current situation seems very out of reach for a lot of people. There are a lot of opportunities and resources available on the internet but they should be made available to all. Apart from making these resources available, attention should also be given to enforce and encourage AI learning. The focus should be on creating better innovators and making them excited to learn.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

Read more:
How This 15-Year-Old Created A Research Career In Machine Learning - Analytics India Magazine

This report titled as Global Machine Learning Operationalization Software Market, gives a brief about the comprehensive research and an outline of its growth in the market globally. It states about the significant market drivers, trends, limitations and opportunities to give a wide-ranging and precise data and also scrutinizes its growth in the overall markets development which is needed and expected.

The report also summarizes the various types of the Global Machine Learning Operationalization Software Market. Factors that influence the market growth of particular product category type and market status for it. A detailed study of the Global Machine Learning Operationalization Software Market has been done to understand the various applications of the products usage and features. Readers looking for scope of growth with respect to product categories can get all the desired information over here, along with supporting figures and facts.

Request Sample Copy of this Report: https://www.theresearchinsights.com/request_sample.php?id=35112&utm_source=blog&utm_medium=hbs

Market Segment as follows:

Global Machine Learning Operationalization Software: Regional Segment Analysis

North America

Europe

Asia Pacific

Middle East & Africa

South America

Companies Profiled in this report includes.

MathWorks

SAS

Microsoft

ParallelM

Algorithmia

H20.ai

TIBCO Software

SAP

IBM

Domino

Seldon

Datmo

Actico

RapidMiner

KNIME

Get Reasonable Discount on this Premium Report: https://www.theresearchinsights.com/ask_for_discount.php?id=35112&utm_source=blog&utm_medium=hbs

Key questions answered in the report include:

What will the market size and the growth rate be in 2026?

What are the key factors driving the Global Machine Learning Operationalization Software Market?

What are the key market trends impacting the growth of the global Machine Learning Operationalization Software Market?

What are the challenges to market growth?

Who are the key vendors in the Global Machine Learning Operationalization Software Market?

What are the market opportunities and threats faced by the vendors in the global Machine Learning Operationalization Software Market?

What are the trending factors influencing the market shares of the Americas, APAC, Europe, and MEA?

What are the key outcomes of the five forces analysis of the Global Machine Learning Operationalization Software Market?

This report provides pinpoint analysis for changing competitive dynamics. It offers a forward-looking perspective on different factors driving or limiting market growth. It provides a five-year forecast assessed on the basis of how the Global Machine Learning Operationalization Software Market is predicted to grow. It helps in understanding the key product segments and their future and helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments.

For More Information: https://www.theresearchinsights.com/enquiry_before_buying.php?id=35112&utm_source=blog&utm_medium=hbs

Contact us:

Robin

Sales manager

Contact number:

APAC +91-996-067-0000

UK +44-753-718-0101

USA +1-312-313-8080

sales@theresearchinsights.com

http://www.theresearchinsights.com

Follow this link:
Comprehensive Analysis of Global Machine Learning Operationalization Software Market with Current and Future Business Outlook | MathWorks, SAS,...

At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. Its a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesnt traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.

The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. Machine learning in the enterprise is in crisis, in my view, Craig Wiley, the director of product management for Google Clouds AI Platform, told me. As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.

Image Credits: Google

Wiley, who was also the general manager of AWSs SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, many of which were dead ends, according to him (including some of Googles own). Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models theyre building.

Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.

Image Credits: Google

The service is also integrated with Vizier, Googles AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.

Vertex also offers a Feature Store that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.

Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Clouds AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.

To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and and this may be a bit of a surprise BigQuery ML, Googles tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.

We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production, said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.

Continue reading here:
Google Cloud launches Vertex AI, a new managed machine learning platform - TechCrunch

Media and publishing used to be the domain of specialized companies who controlled the content. The internet broke that model, and today anyone can go online and publish a blog, a podcast, or star in their own video.

But the big tech companies want to take control, closing content into walled gardens. But thats not what the majority of publishers, big or small, want.

We get to hear the perspectives of the publishers at every scale, and they consistently tell us the same thing: They want to more directly connect to consumers; they dont want to be tied into these walled gardens which dictate how they must present their content and in some cases what content theyre allowed to present, said Dr. Peter Day (pictured, right), chief technology officer at Quantcast Corp.

Day and Shruti Koparkar (pictured, left), head of product marketing at Quantcast, spoke with John Furrier, host of theCUBE, SiliconANGLE Medias livestreaming studio, duringThe Cookie Conundrum: A Recipe for Success event. They discussed the importance of smart technology for the post-cookie future of digital marketing. (* Disclosure below.)

Quantcast has cast itself as a champion of the open internet as it sets out to find the middle ground between the ability to scale provided by walled gardens and access to individual-level user data. Urgency for the quest is provided by Goliath company Google, which announced it will no longer be supporting third-party cookies on its Chrome browser as of January 2022.

Our approach to a world without third-party cookies is grounded in three fundamental things, Koparkar stated. First is industry standards: We think its really important to participate and to work with organizations who are defining the standards that will guide the future of advertising, Koparkar said, naming IAB Technology Laboratorys Project Rearc and Prebid as open projects Quantcast is involved with.

The companys engineering team also participates in meetings with the World Wide Web Consortium (W3C) to keep on top of what is happening with web browsers and to monitor what Google is up to with its Federated Learning of Cohorts (FLoC) project.

The second fundamental principle to Quantcasts strategy is interoperability. With multiple identity solutions from Unified ID 2.0 to FLoC already existing, and more on the way, We think it is important to build a platform that can ingest all of these signals, and so thats what weve done, Koparkar said referring to the release of Quantcasts intelligent audience platform.

Innovation is the third principle. Being able to take in multiple signals, not only IDs and cohorts, but also contextual first-party consent, time, language, geolocation and many others is increasingly important, according to Kopackar.

All of these signals can help us understand user behavior, intent and interests in absence of third-party cookies, she said.

But these signals are raw, messy, complex and ever-changing. What you need is technology like AI and machine learning to bring all of these signals together, combine them statistically, and get an understanding of user behavior, intent and interest, and then act on it, Koparkar stated. And the only way to bring them all together to obtain coherent understanding is through intelligent technologies such as machine learning, she added.

The foundation of our platform has always been machine learning from before it was cool, Day said. Many of the core team members at Quantcast have doctorate degrees in statistics and ML, which means it drives the companys decision-making.

Data is only useful if you can make sense of it, if you can organize it, and if you can take action on it, Day said. And to do that at this kind of scale its absolutely necessary to use machine learning technology.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of The Cookie Conundrum: A Recipe for Success event. (* Disclosure: TheCUBE is a paid media partner for The Cookie Conundrum: A Recipe for Success event. Neither Quantcast Corp., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Show your support for our mission with our one-click subscription to our YouTube channel (below). The more subscribers we have, the more YouTube will suggest relevant enterprise and emerging technology content to you. Thanks!

Support our mission: >>>>>> SUBSCRIBE NOW >>>>>> to our YouTube channel.

Wed also like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we dont have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary onSiliconANGLE along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams attheCUBE take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

If you like the reporting, video interviews and other ad-free content here,please take a moment to check out a sample of the video content supported by our sponsors,tweet your support, and keep coming back toSiliconANGLE.

See the article here:
Quantcast uses machine learning and AI to take on walled garden giants in the fight for the open internet - SiliconANGLE News

A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. Credit: Los Alamos National Laboratory

A black hole permanently scrambles information that cant be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment.

A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling.

Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes, said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters.

Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results dont condemn quantum machine learning, but rather highlight the importance of understanding its limits, Holmes said.

In the classic Hayden-Preskill thought experiment, a fictitious Alice tosses information such as a book into a black hole that scrambles the text. Her companion, Bob, can still retrieve it using entanglement, a unique feature of quantum physics. However, the new work proves that fundamental constraints on Bobs ability to learn the particulars of a given black holes physics means that reconstructing the information in the book is going to be very difficult or even impossible.

Any information run through an information scrambler such as a black hole will reach a point where the machine learning algorithm stalls out on a barren plateau and thus becomes untrainable. That means the algorithm cant learn scrambling processes, said Andrew Sornborger a computer scientist at Los Alamos and coauthor of the paper. Sornborger is Director of Quantum Science Center at Los Alamos and leader of the Centers algorithms and simulation thrust. The Center is a multi-institutional collaboration led by Oak Ridge National Laboratory.

Barren plateaus are regions in the mathematical space of optimization algorithms where the ability to solve the problem becomes exponentially harder as the size of the system being studied increases. This phenomenon, which severely limits the trainability of large scale quantum neural networks, was described in a recent paper by a related Los Alamos team.

Recent work has identified the potential for quantum machine learning to be a formidable tool in our attempts to understand complex systems, said Andreas Albrecht, a co-author of the research. Albrecht is Director of the Center for Quantum Mathematics and Physics (QMAP) and Distinguished Professor, Department of Physics and Astronomy, at UC Davis. Our work points out fundamental considerations that limit the capabilities of this tool.

In the Hayden-Preskill thought experiment, Alice attempts to destroy a secret, encoded in a quantum state, by throwing it into natures fastest scrambler, a black hole. Bob and Alice are the fictitious quantum dynamic duo typically used by physicists to represent agents in a thought experiment.

You might think that this would make Alices secret pretty safe, Holmes said, but Hayden and Preskill argued that if Bob knows the unitary dynamics implemented by the black hole, and share a maximally entangled state with the black hole, it is possible to decode Alices secret by collecting a few additional photons emitted from the black hole. But this prompts the question, how could Bob learn the dynamics implemented by the black hole? Well, not by using quantum machine learning, according to our findings.

A key piece of the new theorem developed by Holmes and her coauthors assumes no prior knowledge of the quantum scrambler, a situation unlikely to occur in real-world science.

Our work draws attention to the tremendous leverage even small amounts of prior information may play in our ability to extract information from complex systems and potentially reduce the power of our theorem, Albrecht said. Our ability to do this can vary greatly among different situations (as we scan from theoretical consideration of black holes to concrete situations controlled by humans here on earth). Future research is likely to turn up interesting examples, both of situations where our theorem remains fully in force, and others where it can be evaded.

Reference: Barren Plateaus Preclude Learning Scramblers by Zo Holmes, Andrew Arrasmith, Bin Yan, Patrick J. Coles, Andreas Albrecht and Andrew T. Sornborger, 12 May 2021, Physical Review Letters.DOI: 10.1103/PhysRevLett.126.190501

Funding: U.S. Department of Energy, Office of Science

Read more from the original source:
Quantum Machine Learning Hits a Limit: A Black Hole Permanently Scrambles Information That Can't Be Recovered - SciTechDaily