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TheGlobal Machine Learning in Finance MarketResearch report provided by Market Expertz is a detailed study report of theGlobal Machine Learning in Finance Market, which covers all the necessary information required by a new market entrant as well as the existing players to gain a deeper understanding of the market. The Global Machine Learning in Finance Marketreport is segmented in terms of regions, product type, applications, key players, and several other essential factors. The report also covers the global market scenario, providing deep insights into the cost structure of the product, production, and manufacturing processes, and other essential factors.

The report also covers the global market scenario, highlighting the pricing of the product, production and consumption volume, cost analysis, industry value, barriers and growth drivers, dominant market players, demand and supply ratio of the market, the growth rate of the market and forecast till 2026.

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The report includes accurately drawn facts and figures, along with graphical representations of vital market data. The research report sheds light on the emerging market segments and significant factors influencing the growth of the industry to help investors capitalize on the existing growth opportunities.

In market segmentation by manufacturers, the report covers the following companies-

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinanceOthers

Get to know the business better:The global Machine Learning in Finance market research is carried out at the different stages of the business lifecycle from the production of a product, cost, launch, application, consumption volume and sale. The research offers valuable insights into the marketplace from the beginning including some sound business plans chalked out by prominent market leaders to establish a strong foothold and expand their products into one thats better than others.

In market segmentation by types of Machine Learning in Finance, the report covers-

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced LeaningOthers

In market segmentation by applications of the Machine Learning in Finance, the report covers the following uses-

BanksSecurities CompanyOthers

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A conscious effort is made by the subject matter experts to analyze how some business owners succeed in maintaining a competitive edge while the others fail to do so makes the research interesting. A quick review of the realistic competitors makes the overall study a lot more interesting. Opportunities that are helping product owners size up their business further add value to the overall study.

With this global Machine Learning in Finance market research report, all the manufacturers and vendors will be aware of the growth factors, shortcomings, opportunities, and threats that the market has to offer in the forecast period. The report also highlights the revenue, industry size, types, applications, players share, production volume, and consumption to gain a proper understanding of the demand and supply chain of the market.

Years that have been considered for the study of this report are as follows:

Major Geographies mentioned in this report are as follows:

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The complete downstream and upstream essentials and value chains are carefully studied in this report. Current trends that are impacting and controlling the global Machine Learning in Finance market growth like globalization, industrialization, regulations, and ecological concerns are mentioned extensively. The Global Machine Learning in Finance market research report also contains technical data, raw materials, volumes, and manufacturing analysis of Machine Learning in Finance. It explains which product has the highest penetration in which market, their profit margins, break-even analysis, and R&D status. The report makes future projections for the key opportunities based on the analysis of the segment of the market.

Key features of the report:

What does the report offer?

For more details on the Machine Learning in Finance Report, click here @ https://www.marketexpertz.com/industry-overview/machine-learning-in-finance-market

Well-versed in economics and mergers and acquisitions, Jashi writes about companies and their corporate stratagem. She has been recognized for her near-accurate predictions by the business world, garnering trust in her written word.

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Machine Learning in Finance Market Provides in-depth analysis of the Industry, with Current Trends and Future Estimations to Elucidate the Investment...

Roundup Hello Reg readers. Here's a quick roundup of bits and pieces from the worlds of machine learning and AI.

Are you in Clearview's database? Probably: Folks covered by the EUs GDPR, the California Consumer Privacy Act, and similar laws, can ask Clearview the controversial face-recognition startup that scraped three billion images of people from the internet to reveal what images it may have of you in its database and delete them.

Thats what Thomas Smith, co-founder and CEO of Gado Images, a computer vision startup, did for OneZero. As a resident of America's Golden State, Smith filled out a California Consumer Privacy Act (CCPA) form demanding Clearview send him the profile they had on him. He could see what images Clearview had managed to scrape from the internet, and where they got them from.

He had to provide Clearview with a picture of himself along with a copy of his drivers license. Clearview had collected 10 images of Smith; some were taken from social media, such as Facebook, but it also went as far as to download snaps from he and his wifes personal blog and a Python meetup group in San Francisco. One of the 10 images, however, looks like a case of mistaken identity.

The images in Smiths profile are accompanied by URLs pointing to where each photo was nabbed. By clicking through these links, a Clearview customer typically the police running a search using Smith's photo would be able to figure out personal details like where he works, where he went to university, whom hes married to, and who some of his friends are. That means things like stills from CCTV could be used to pull up the entire life of those pictured in the image.

The app has been served cease-and-desist letters from Google, YouTube, Twitter, and Facebook to stop lifting images from their platforms, and to delete any existing ones it has in its database.

If you want to get your data from Clearview, and are eligible under CCPA or GDPR, Smith recommends sending Clearview an email to privacy@clearview.ai to request your profile. Follow any instructions you receive, he said.

Expect your request to take up to two months to process. Be persistent in following up. And remember that once you receive your data, you have the option to demand that Clearview delete it or amend it if youd like them to do so.

But if you dont live in California or in the European Union, or somewhere with similar laws, the best thing to do to prevent startups like Clearview from snaffling your data is to make your social media profiles private. Dont post snaps of your mug anywhere on the internet that is available for anyone to see.

This isn't totally avoidable, however. If your friends upload pictures of you, Clearview can still scrape them as long as theyre public.

Hey AI, is it going to rain today? Training machine learning models to predict whether it's going to rain or not by looking at the movement of clouds gathered by weather stations or satellites is all the rage at the moment.

Researchers over at Google have developed MetNet, a deep neural network that can forecast where its going to rain in the US up to eight hours before it happens. The team claims that its system was more accurate than the predictive tools employed by the National Oceanic and Atmospheric Administration (NOAA) a US federal scientific agency that monitors the weather, oceans, and the atmosphere on Earth when it comes to forecasting rain.

MetNet inspects data recorded by the radar stations in the Multi-Radar/Multi-Sensor System (MRMS) and the Geostationary Operational Environmental Satellite system, both operated by the NOAA. Images of a top down view of clouds, and atmospheric measurements are given as inputs and MetNet spits out a probability distribution of precipitation over an area spanning 64 square kilometers, covering the entire US at one kilometer resolution.

There are advantages and disadvantages to using neural networks like MetNet to forecast the weather. Although machine learning models provide a cheap alternative to supercomputers, which have to carry out complex calculations, they are generally less accurate and dont deal well with freak weather events that they havent been trained on.

We are actively researching how to improve global weather forecasting, especially in regions where the impacts of rapid climate change are most profound, the researchers said.

While we demonstrate the present MetNet model for the continental US, it could be extended to cover any region for which adequate radar and optical satellite data are available.

You can read more about how MetNet works here.

Star Trek Voyager and Deep Space Nine get an AI makeover: Heres something that will please Star Trek fans: you can now watch clips from Star Trek Voyager and Deep Space Nine in much better quality now that theyve been revamped with the help of AI algorithms.

A YouTube user, going by the name Billy Reichard, has posted a series of videos for Trekkies to watch. Old clips taken from both TV series have been run through Gigapixel AI, a commercial AI tool developed by Topaz Labs, a computer vision company based in Texas, to increase the quality. This is necessary because, it appears, portions of the Voyager and DS9 archives are NTSC-grade and it would be too much faff to restore them in full high definition.

Reichard explained his work on Reddit's r/StarTrek group and compared the AI-generated quality to 4K. He said he planned to play around with the Gigapixel AI software more and will be producing more Star Trek clips for people to enjoy.

Heres one from Voyager...

Youtube Video

And one from Deep Space Nine. Enjoy

Youtube Video

Sponsored: Webcast: Why you need managed detection and response

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Call for netizens to demand scraped pics from Clearview, ML weather forecasts, and Star Trek goes high def with AI - The Register

Machine Learning as a Service Market report provide pin-point analysis of theMachine Learning as a Service industry: Capacity, Production, Value, Consumption and Status(2014-2019) and Six- Year Forecast (2020-2026). BedsidesMachine Learning as a Service marketresearch report enriched on worldwide competition by topmost prime manufactures (Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround) which providing information such asCompany Profiles, Product Picture and Specification, Product Details, Capacity, Price, Cost, Gross Consumption, Revenue and contact information is provided for better understanding. In addition, this report discusses the key drivers influencing Market Growth, Opportunities, The Challenges and the Risks faced by key manufacturers and the market as a whole.

Machine Learning as a Service Market Major Factors: Machine Learning as a Service Market Overview, Machine Learning as a Service Market Analysis by Application, Economic Impact on Market, Market Competition, Industrial Chain, Sourcing Strategy and Downstream Buyers, Machine Learning as a Service Market Effect, Factors, Analysis, Machine Learning as a Service Market Forecast, Marketing Strategy Analysis, Distributors/Traders.

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Summation of Machine Learning as a Service Market:Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

Based onProduct Type, Machine Learning as a Service market report displays the manufacture, profits, value, and market segment and growth rate of each type, covers:

Private clouds Public clouds Hybrid cloud

Based onend users/applications, Machine Learning as a Service market report focuses on the status and outlook for major applications/end users, sales volume, market share and growth rate for each application, this can be divided into:

Personal Business

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The report offers in-depth assessment of the growth and other aspects of the Machine Learning as a Service market in important countries (regions), including:

The key insights of the Machine Learning as a Service Market report:

The report provides Key Statistics on the Market Status of the Machine Learning as a Service market manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.

The Machine Learning as a Service market report provides a basic overview of the industry including its definition, applications and manufacturing technology.

The report presents the Company Profile, Product Specifications, Capacity, Production Value, and 2013-2020 market shares for key vendors.

The total Machine Learning as a Service market is further divided By Company, By Country, And By Application/Type for the competitive landscape analysis.

The report estimates 2020-2026 market Development Trends of Machine Learning as a Service industry.

Analysis of Upstream Raw Materials, Downstream Demand, And Current Market Dynamics is also carried out

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Machine Learning as a Service Market 2020 Size, Share, Technological Innovations & Growth Forecast To 2026 - Daily Science

A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, werent very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs.

Heres a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate, said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. These results show us that machine learning isnt magic; there are clearly other factors at play when it comes to predicting the life course.

The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data. Over 457 groups applied, of which 160 were selected to participate, and their predictions were evaluated with an error metric that assessed their ability to predict held-out data (i.e., data held by the organizer and not available to the participants).

The Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study) based at Princeton, Columbia University, and the University of Michigan, which has been studying a cohort of about 5,000 children born in 20 large American cities between 1998 and 2000. Its designed to oversample births to unmarried couples in those cities, and to address four questions of interest to researchers and policymakers:

When we began, I really didnt know what a mass collaboration was, but I knew it would be a good idea to introduce our data to a new group of researchers: data scientists, said Sara McLanahan, the William S. Tod Professor of Sociology and Public Affairs at Princeton. The results were eye-opening.

The Fragile Families Study data set consists of modules, each of which is made up of roughly 10 sections, where each section includes questions about a topic asked of the childrens parents, caregivers, teachers, and the children themselves. For example, a mother who recently gave birth might be asked about relationships with extended kin, government programs, and marriage attitudes, while a 9-year-old child might be asked about parental supervision, sibling relationships, and school. In addition to the surveys, the corpus contains the results of in-home assessments, including psychometric testing, biometric measurements, and observations of neighborhoods and homes.

The goal of the Challenge was to predict the social outcomes of children aged 15 years, which encompasses 1,617 variables. From the variables, six were selected to be the focus:

Contributing researchers were provided anonymized background data from 4,242 families and 12,942 variables about each family, as well as training data incorporating the six outcomes for half of the families. Once the Challenge was completed, all 160 submissions were scored using the holdout data.

In the end, even the best of the over 3,000 models submitted which often used complex AI methods and had access to thousands of predictor variables werent spot on. In fact, they were only marginally better than linear regression and logistic regression, which dont rely on any form of machine learning.

Either luck plays a major role in peoples lives, or our theories as social scientists are missing some important variable, added McLanahan. Its too early at this point to know for sure.

Measured by the coefficient of determination, or the correlation of the best models predictions with the ground truth data, material hardship i.e., whether 15-year-old childrens parents suffered financial issues was .23, or 23% accuracy. GPA predictions were 0.19 (19%), while grit, eviction, job training, and layoffs were 0.06 (6%), 0.05 (5%), and 0.03 (3%), respectively.

The results raise questions about the relative performance of complex machine-learning models compared with simple benchmark models. In the Challenge, the simple benchmark model with only a few predictors was only slightly worse than the most accurate submission, and it actually outperformed many of the submissions, concluded the studys coauthors. Therefore, before using complex predictive models, we recommend that policymakers determine whether the achievable level of predictive accuracy is appropriate for the setting where the predictions will be used, whether complex models are more accurate than simple models or domain experts in their setting, and whether possible improvement in predictive performance is worth the additional costs to create, test, and understand the more complex model.

The research team is currently applying for grants to continue studies in this area, and theyve also published 12 of the teams results in a special issue of a journal called Socius, a new open-access journal from the American Sociological Association. In order to support additional research, all the submissions to the Challenge including the code, predictions, and narrative explanations will be made publicly available.

The Challenge isnt the first to expose the predictive shortcomings of AI and machine learning models. The Partnership on AI, a nonprofit coalition committed to the responsible use of AI, concluded in its first-ever report last year that algorithms are unfit to automate the pre-trial bail process or label some people as high-risk and detain them. The use of algorithms in decision making for judges has been known to produce race-based unfair results that are more likely to label African-American inmates as at risk of recidivism.

Its well-understood that AI has a bias problem. For instance, word embedding, a common algorithmic training technique that involves linking words to vectors, unavoidably picks up and at worst amplifies prejudices implicit in source text and dialogue. A recent study by the National Institute of Standards and Technology (NIST) found that many facial recognition systems misidentify people of color more often than Caucasian faces. And Amazons internal recruitment tool which was trained on resumes submitted over a 10-year period was reportedly scrapped because it showed bias against women.

A number of solutions have been proposed, from algorithmic tools to services that detect bias by crowdsourcing large training data sets.

In June 2019, working with experts in AI fairness, Microsoft revised and expanded the data sets it uses to train Face API, a Microsoft Azure API that provides algorithms for detecting, recognizing, and analyzing human faces in images. Last May, Facebook announced Fairness Flow, which automatically sends a warning if an algorithm is making an unfair judgment about a person based on their race, gender, or age. Google recently released the What-If Tool, a bias-detecting feature of the TensorBoard web dashboard for its TensorFlow machine learning framework. Not to be outdone, IBM last fall released AI Fairness 360, a cloud-based, fully automated suite that continually provides [insights] into how AI systems are making their decisions and recommends adjustments such as algorithmic tweaks or counterbalancing data that might lessen the impact of prejudice.

Continued here:
Researchers find AI is bad at predicting GPA, grit, eviction, job training, layoffs, and material hardship - VentureBeat

QualcomPage No-158

The scope of the Global Artificial Intelligence and Machine Learning Report:

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Market segmentation, by product types:Deep LearningNatural Language ProcessingMachine VisionOthersMarket segmentation, by applications:HealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturing

Important Aspects of Artificial Intelligence and Machine Learning Report:

Why To Select This Report:

Complete analysis on market dynamics, market status and competitive Artificial Intelligence and Machine Learning view is offered.

Forecast Global Artificial Intelligence and Machine Learning Industry trends will present the market drivers, constraints and growth opportunities.

The five-year forecast view shows how the market is expected to grow in coming years.

All vital Global Artificial Intelligence and Machine Learning Industry verticals are presented in this study like Product Type, Applications and Geographical Regions.

Table of Contents

Part 1 Market Overview

Part 2 Global Market Status and Future Forecast

Part 3 Asia-Pacific Market Status and Future Forecast

Part 4 Asia-Pacific Market by Geography

Part 5 Europe Market Status and Future Forecast

Part 6 Europe Market by Geography

Part 7 North America Market Status and Future Forecast

Part 8 North America Market by Geography

Part 9 South America Market Status and Future Forecast

Part 10 South America Market by Geography

Part 11 Middle East & Africa Market Status and Future Forecast

Part 12 Middle East & Africa Market by Geography

Part 13 Key Companies

Part 14 Conclusion

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Artificial Intelligence and Machine Learning Market 2020 Industry Share, Size, Technology, Application, Revenue, Top Companies Analysis and 2025...