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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:

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Personal Business

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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

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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

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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...

COVID-19 will change how the majority of us live and work, at least in the short term. Its also creating a challenge for tech companies such as Facebook, Twitter and Google that ordinarily rely on lots and lots of human labor to moderate content. Are A.I. and machine learning advanced enough to help these firms handle the disruption?

First, its worth noting that, although Facebook has instituted a sweeping work-from-home policy in order to protect its workers (along with Googleand a rising number of other firms), it initially required its contractors who moderate content to continue to come into the office. That situation only changed after protests,according toThe Intercept.

Now, Facebook is paying those contractors while they sit at home, since the nature of their work (scanning peoples posts for content that violates Facebooks terms of service) is extremely privacy-sensitive. Heres Facebooks statement:

For both our full-time employees and contract workforce there is some work that cannot be done from home due to safety, privacy and legal reasons. We have taken precautions to protect our workers by cutting down the number of people in any given office, implementing recommended work from home globally, physically spreading people out at any given office and doing additional cleaning. Given the rapidly evolving public health concerns, we are taking additional steps to protect our teams and will be working with our partners over the course of this week to send all contract workers who perform content review home, until further notice. Well ensure that all workers are paid during this time.

Facebook, Twitter, Reddit, and other companies are in the same proverbial boat: Theres an increasing need to police their respective platforms, if only to eliminate fake news about COVID-19, but the workers who handle such tasks cant necessarily do so from home, especially on their personal laptops. The potential solution? Artificial intelligence (A.I.) and machine-learning algorithms meant to scan questionable content and make a decision about whether to eliminate it.

HeresGoogles statement on the matter, via its YouTube Creator Blog.

Our Community Guidelines enforcement today is based on a combination of people and technology: Machine learning helps detect potentially harmful content and then sends it to human reviewers for assessment. As a result of the new measures were taking, we will temporarily start relying more on technology to help with some of the work normally done by reviewers. This means automated systems will start removing some content without human review, so we can continue to act quickly to remove violative content and protect our ecosystem, while we have workplace protections in place.

To be fair, the tech industry has been heading in this direction for some time. Relying on armies of human beings to read through every piece of content on the web is expensive, time-consuming, and prone to error. But A.I. and machine learning are still nascent, despite the hype. Google itself, in the aforementioned blog posting, pointed out how its automated systems may flag the wrong videos. Facebook is also receiving criticism that its automated anti-spam system is whacking the wrong posts, including those thatoffer vital information on the spread of COVID-19.

If the COVID-19 crisis drags on, though, more companies will no doubt turn to automation as a potential solution to disruptions in their workflow and other processes. That will force a steep learning curve; again and again, the rollout of A.I. platforms has demonstrated that, while the potential of the technology is there, implementation is often a rough and expensive processjust look at Google Duplex.

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Nonetheless, an aggressive embrace of A.I. will also create more opportunities for those technologists who have mastered A.I. and machine-learning skills of any sort; these folks may find themselves tasked with figuring out how to automate core processes in order to keep businesses running.

Before the virus emerged, BurningGlass (which analyzes millions of job postings from across the U.S.), estimated that jobs that involve A.I. would grow 40.1 percent over the next decade. That percentage could rise even higher if the crisis fundamentally alters how people across the world live and work. (The median salary for these positions is $105,007; for those with a PhD, it drifts up to $112,300.)

If youre trapped at home and have some time to learn a little bit more about A.I., it could be worth your time to explore online learning resources. For instance, theres aGooglecrash coursein machine learning. Hacker Noonalso offers an interesting breakdown ofmachine learningandartificial intelligence.Then theres Bloombergs Foundations of Machine Learning,a free online coursethat teaches advanced concepts such as optimization and kernel methods.

Originally posted here:
Will COVID-19 Create a Big Moment for AI and Machine Learning? - Dice Insights

Roundup Let's get cracking with some machine-learning news.

Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds.

CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: Supervised machine learning doesnt live up to the hype, he declared. It isnt actual artificial intelligence akin to C-3PO, its a sophisticated pattern-matching tool.

Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples. But driving is unpredictable, and the same route can differ day to day, depending on the weather or traffic conditions. Trying to model every scenario is not only impossible but expensive.

In fact, the better your model, the harder it is to find robust data sets of novel edge cases. Additionally, the better your model, the more accurate the data you need to improve it, Seltz-Axmacher said.

More time and money is needed to provide increasingly incremental improvements. Over time, only the most well funded startups can afford to stay in the game, he said.

Whenever someone says autonomy is ten years away thats almost certainly what their thought is. There arent many startups that can survive ten years without shipping, which means that almost no current autonomous team will ever ship AI decision makers if this is the case, he warned.

If Seltz-Axmacher is right, then we should start seeing smaller autonomous driving startups shutting down in the near future too. Watch this space.

Waymo to pause testing during Bay Area lockdown: Waymo, Googles self-driving car stablemate, announced it was pausing its operations in California to abide by the lockdown orders in place in Bay Area counties, including San Francisco, Santa Clara, San Mateo, Marin, Contra Costa and Alameda. Businesses deemed non-essential were advised to close and residents were told to stay at home, only popping out for things like buying groceries.

It will, however, continue to perform rides for deliveries and trucking services for its riders and partners in Phoenix, Arizona. These drives will be entirely driverless, however, to minimise the chance of spreading COVID-19.

Waymo also launched its Open Dataset Challenge. Developers can take part in a contest that looks for solutions to these problems:

Cash prizes are up for grabs too. The winner can expect to pocket $15,000, second place will get you $5,000, while third is $2,000.

You can find out more details on the rules of the competition and how to enter here. The challenge is open until 31 May.

More free resources to fight COVID-19 with AI: Tech companies are trying to chip in and do what they can to help quell the coronavirus pandemic. Nvidia and Scale AI both offered free resources to help developers using machine learning to further COVID-19 research.

Nvidia is providing a free 90-day license to Parabricks, a software package that speeds up the process of analyzing genome sequences using GPUs. The rush is on to analyze the genetic information of people that have been infected with COVID-19 to find out how the disease spreads and which communities are most at risk. Sequencing genomes requires a lot of number crunching, Parabricks slashes the time needed to complete the task.

Given the unprecedented spread of the pandemic, getting results in hours versus days could have an extraordinary impact on understanding the viruss evolution and the development of vaccines, it said this week.

Interested customers who have access to Nvidias GPUs should fill out a form requesting access to Parabricks.

Nvidia is inviting our family of partners to join us in matching this urgent effort to assist the research community. Were in discussions with cloud service providers and supercomputing centers to provide compute resources and access to Parabricks on their platforms.

Next up is Scale AI, the San Francisco based startup focused on annotating data for machine learning models. It is offering its labeling services for free to any researcher working on a potential vaccine, or on tracking, containing, or diagnosing COVID-19.

Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus, it said in a statement.

Researchers have already shown how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is so little existing data to go on.

In those situations, the role of well-annotated data to train models o diagnostic tools is even more critical. If you have a lot of data to analyse and think Scale AI could help then apply for their help here.

PyTorch users, AWS has finally integrated the framework: Amazon has finally integrated PyTorch support into Amazon Elastic Inference, its service that allows users to select the right amount of GPU resources on top of CPUs rented out in its cloud services Amazon SageMaker and Amazon EC2, in order to run inference operations on machine learning models.

Amazon Elastic Inference works like this: instead of paying for expensive GPUs, users select the right amount of GPU-powered inference acceleration on top of cheaper CPUs to zip through the inference process.

In order to use the service, however, users will have to convert their PyTorch code into TorchScript, another framework. You can run your models in any production environment by converting PyTorch models into TorchScript, Amazon said this week. That code is then processed by an API in order to use Amazon Elastic Inference.

The instructions to convert PyTorch models into the right format for the service have been described here.

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Originally posted here:
Self-driving truck boss: 'Supervised machine learning doesnt live up to the hype. It isnt C-3PO, its sophisticated pattern matching' - The Register