Both weather and climate models have improved drastically in recent years, as advances in one field have tended to benefit the other. But there is still significant uncertainty in model outputs that are not quantified accurately. Thats because the processes that drive climate and weather are chaotic, complex, and interconnected in ways that researchers have yet to describe in the complex equations that power numerical models.

Historically, researchers have used approximations called parameterizations to model the relationships underlying small-scale atmospheric processes and their interactions with large-scale atmospheric processes. Stochastic parameterizations have become increasingly common for representing the uncertainty in subgrid-scale processes, and they are capable of producing fairly accurate weather forecasts and climate projections. But its still a mathematically challenging method. Now researchers are turning to machine learning to provide more efficiency to mathematical models.

Here Gagne et al. evaluate the use of a class of machine learning networks known as generative adversarial networks (GANs) with a toy model of the extratropical atmospherea model first presented by Edward Lorenz in 1996 and thus known as the L96 system that has been frequently used as a test bed for stochastic parameterization schemes. The researchers trained 20 GANs, with varied noise magnitudes, and identified a set that outperformed a hand-tuned parameterization in L96. The authors found that the success of the GANs in providing accurate weather forecasts was predictive of their performance in climate simulations: The GANs that provided the most accurate weather forecasts also performed best for climate simulations, but they did not perform as well in offline evaluations.

The study provides one of the first practically relevant evaluations for machine learning for uncertain parameterizations. The authors conclude that GANs are a promising approach for the parameterization of small-scale but uncertain processes in weather and climate models. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001896, 2020)

Kate Wheeling, Science Writer

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Machine Learning Improves Weather and Climate Models - Eos

Despite the huge contributions of deep learning to the field of artificial intelligence, theres something very wrong with it: It requires huge amounts of data. This is one thing that boththe pioneersandcritics of deep learningagree on. In fact, deep learning didnt emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.

Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

In hiskeynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for self-supervised learning, his roadmap to solve deep learnings data problem. LeCun is one of thegodfathers of deep learningand the inventor ofconvolutional neural networks (CNN), one of the key elements that have spurred a revolution in artificial intelligence in the past decade.

Self-supervised learning is one of several plans to create data-efficient artificial intelligence systems. At this point, its really hard to predict which technique will succeed in creating the next AI revolution (or if well end up adopting a totally different strategy). But heres what we know about LeCuns masterplan.

First, LeCun clarified that what is often referred to as the limitations of deep learning is, in fact, a limit ofsupervised learning. Supervised learning is the category of machine learning algorithms that require annotated training data. For instance, if you want to create an image classification model, you must train it on a vast number of images that have been labeled with their proper class.

[Deep learning] is not supervised learning. Its not justneural networks. Its basically the idea of building a system by assembling parameterized modules into a computation graph, LeCun said in his AAAI speech. You dont directly program the system. You define the architecture and you adjust those parameters. There can be billions.

Deep learning can be applied to different learning paradigms, LeCun added, including supervised learning,reinforcement learning, as well as unsupervised or self-supervised learning.

But the confusion surrounding deep learning and supervised learning is not without reason. For the moment, the majority of deep learning algorithms that have found their way into practical applications are based on supervised learning models, which says a lot aboutthe current shortcomings of AI systems. Image classifiers, facial recognition systems, speech recognition systems, and many of the other AI applications we use every day have been trained on millions of labeled examples.

Reinforcement learning and unsupervised learning, the other categories of learning algorithms, have so far found very limited applications.

Supervised deep learning has given us plenty of very useful applications, especially in fields such ascomputer visionand some areas of natural language processing. Deep learning is playing an increasingly important role in sensitive applications, such as cancer detection. It is also proving to be extremely useful in areas where the scale of the problem is beyond being addressed with human efforts, such aswith some caveatsreviewing the huge amount of content being posted on social media every day.

If you take deep learning from Facebook, Instagram, YouTube, etc., those companies crumble, LeCun says. They are completely built around it.

But as mentioned, supervised learning is only applicable where theres enough quality data and the data can capture the entirety of possible scenarios. As soon as trained deep learning models face novel examples that differ from their training examples, they start to behave in unpredictable ways. In some cases,showing an object from a slightly different anglemight be enough to confound a neural network into mistaking it with something else.

ImageNet vs reality: In ImageNet (left column) objects are neatly positioned, in ideal background and lighting conditions. In the real world, things are messier (source: objectnet.dev)

Deep reinforcement learning has shownremarkable results in games and simulation. In the past few years, reinforcement learning has conquered many games that were previously thought to off-limits for artificial intelligence. AI programs have already decimated human world champions atStarCraft 2, Dota, and the ancient Chinese board game Go.

But the way these AI programs learn to solve problems is drastically different from that of humans. Basically, a reinforcement learning agent starts with a blank slate and is only provided with a basic set of actions it can perform in its environment. The AI is then left on its own to learn through trial-and-error how to generate the most rewards (e.g., win more games).

This model works when the problem space is simple and you have enough compute power to run as many trial-and-error sessions as possible. In most cases, reinforcement learning agents take an insane amount of sessions to master games. The huge costs have limited reinforcement learning research to research labsowned or funded by wealthy tech companies.

Reinforcement learning agents must be trained on hundreds of years worth of session to master games, much more than humans can play in a lifetime (source: Yann LeCun).

Reinforcement learning systems are very bad attransfer learning. A bot that plays StarCraft 2 at grandmaster level needs to be trained from scratch if it wants to play Warcraft 3. In fact, even small changes to the StarCraft game environment can immensely degrade the performance of the AI. In contrast, humans are very good at extracting abstract concepts from one game and transferring it to another game.

Reinforcement learning really shows its limits when it wants to learn to solve real-world problems that cant be simulated accurately. What if you want to train a car to drive itself? And its very hard to simulate this accurately, LeCun said, adding that if we wanted to do it in real life, we would have to destroy many cars. And unlike simulated environments, real life doesnt allow you to run experiments in fast forward, and parallel experiments, when possible, would result in even greater costs.

LeCun breaks down the challenges of deep learning into three areas.

First, we need to develop AI systems that learn with fewer samples or fewer trials. My suggestion is to use unsupervised learning, or I prefer to call it self-supervised learning because the algorithms we use are really akin to supervised learning, which is basically learning to fill in the blanks, LeCun says. Basically, its the idea of learning to represent the world before learning a task. This is what babies and animals do. We run about the world, we learn how it works before we learn any task. Once we have good representations of the world, learning a task requires few trials and few samples.

Babies develop concepts of gravity, dimensions, and object persistence in the first few months after their birth. While theres debate on how much of these capabilities are hardwired into the brain and how much of it is learned, what is for sure is that we develop many of our abilities simply by observing the world around us.

The second challenge is creating deep learning systems that can reason. Current deep learning systems are notoriously bad at reasoning and abstraction, which is why they need huge amounts of data to learn simple tasks.

The question is, how do we go beyond feed-forward computation and system 1? How do we make reasoning compatible with gradient-based learning? How do we make reasoning differentiable? Thats the bottom line, LeCun said.

System 1 is the kind of learning tasks that dont require active thinking, such as navigating a known area or making small calculations. System 2 is the more active kind of thinking, which requires reasoning.Symbolic artificial intelligence, the classic approach to AI, has proven to be much better at reasoning and abstraction.

But LeCun doesnt suggest returning to symbolic AI or tohybrid artificial intelligence systems, as other scientists have suggested. His vision for the future of AI is much more in line with that of Yoshua Bengio, another deep learning pioneer, who introduced the concept ofsystem 2 deep learningat NeurIPS 2019 and further discussed it at AAAI 2020. LeCun, however, did admit that nobody has a completely good answer to which approach will enable deep learning systems to reason.

The third challenge is to create deep learning systems that can lean and plan complex action sequences, and decompose tasks into subtasks. Deep learning systems are good at providing end-to-end solutions to problems but very bad at breaking them down into specific interpretable and modifiable steps. There have been advances in creatinglearning-based AI systems that can decompose images, speech, and text. Capsule networks, invented by Geoffry Hinton, address some of these challenges.

But learning to reason about complex tasks is beyond todays AI. We have no idea how to do this, LeCun admits.

The idea behind self-supervised learning is to develop a deep learning system that can learn to fill in the blanks.

You show a system a piece of input, a text, a video, even an image, you suppress a piece of it, mask it, and you train a neural net or your favorite class or model to predict the piece thats missing. It could be the future of a video or the words missing in a text, LeCun says.

The closest we have to self-supervised learning systems are Transformers, an architecture that has proven very successful innatural language processing. Transformers dont require labeled data. They are trained on large corpora of unstructured text such as Wikipedia articles. And theyve proven to be much better than their predecessors at generating text, engaging in conversation, and answering questions. (But they are stillvery far from really understanding human language.)

Transformers have become very popular and are the underlying technology for nearly all state-of-the-art language models, including Googles BERT, Facebooks RoBERTa,OpenAIs GPT2, and GooglesMeena chatbot.

More recently, AI researchers have proven thattransformers can perform integration and solve differential equations, problems that require symbol manipulation. This might be a hint that the evolution of transformers might enable neural networks to move beyond pattern recognition and statistical approximation tasks.

So far, transformers have proven their worth in dealing with discreet data such as words and mathematical symbols. Its easy to train a system like this because there is some uncertainty about which word could be missing but we can represent this uncertainty with a giant vector of probabilities over the entire dictionary, and so its not a problem, LeCun says.

But the success of Transformers has not transferred to the domain of visual data. It turns out to be much more difficult to represent uncertainty and prediction in images and video than it is in text because its not discrete. We can produce distributions over all the words in the dictionary. We dont know how to represent distributions over all possible video frames, LeCun says.

For each video segment, there are countless possible futures. This makes it very hard for an AI system to predict a single outcome, say the next few frames in a video. The neural network ends up calculating the average of possible outcomes, which results in blurry output.

This is the main technical problem we have to solve if we want to apply self-supervised learning to a wide variety of modalities like video, LeCun says.

LeCuns favored method to approach supervised learning is what he calls latent variable energy-based models. The key idea is to introduce a latent variable Z which computes the compatibility between a variable X (the current frame in a video) and a prediction Y (the future of the video) and selects the outcome with the best compatibility score. In his speech, LeCun further elaborates on energy-based models and other approaches to self-supervised learning.

Energy-based models use a latent variable Z to compute the compatibility between a variable X and a prediction Y and select the outcome with the best compatibility score (image credit: Yann LeCun).

I think self-supervised learning is the future. This is whats going to allow to our AI systems, deep learning system to go to the next level, perhaps learn enough background knowledge about the world by observation, so that some sort of common sense may emerge, LeCun said in his speech at the AAAI Conference.

One of the key benefits of self-supervised learning is the immense gain in the amount of information outputted by the AI. In reinforcement learning, training the AI system is performed at scalar level; the model receives a single numerical value as reward or punishment for its actions. In supervised learning, the AI system predicts a category or a numerical value for each input.

In self-supervised learning, the output improves to a whole image or set of images. Its a lot more information. To learn the same amount of knowledge about the world, you will require fewer samples, LeCun says.

We must still figure out how the uncertainty problem works, but when the solution emerges, we will have unlocked a key component of the future of AI.

If artificial intelligence is a cake, self-supervised learning is the bulk of the cake, LeCun says. The next revolution in AI will not be supervised, nor purely reinforced.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published April 5, 2020 05:00 UTC

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Self-supervised learning is the future of AI - The Next Web

Google has rolled out a new technology to improve audio quality in Duo calls when the service cant maintain a steady connection called WaveNetEQ. Its based on technology from Googles DeepMind division that aims to replace audio jitter with artificial noise that sounds just like human speech, generated using machine learning.

If youve ever made a call over the internet, chances are youve experienced audio jitter. It happens when packets of audio data sent as part of the call get lost along the way or otherwise arrive late or in the wrong order. Google says that 99 percent of Duo calls experience packet loss: 20 percent of these lose over 3 percent of their audio, and 10 percent lose over 8 percent. Thats a lot of audio to replace.

Every calling app has to deal with this packet loss somehow, but Google says that these packet loss concealment (PLC) processes can struggle to fill gaps of 60ms or more without sounding robotic or repetitive. WaveNetEQs solution is based on DeepMinds neural network technology, and it has been trained on data from over 100 speakers in 48 different languages.

Here are a few audio samples from Google comparing WaveNetEQ against NetEQ, a commonly used PLC technology. Heres how it sounds when its trying to replace 60ms of packet loss:

Heres a comparison when a call is experiencing packet loss of 120ms:

Theres a limit to how much audio the system can replace, though. Googles tech is designed to replace short sounds, rather than whole words. So after 120ms, it fades out and produces silence. Google says it evaluated the system to make sure it wasnt introducing any significant new sounds. Plus, all of the processing also needs to happen on-device since Google Duo calls are end-to-end encrypted by default. Once the calls real audio resumes, WaveNetEQ will seamlessly fade back to reality.

Its a neat little bit of technology that should make calls that much bit easier to understand when the internet fails them. The technology is already available for Duo calls made on Pixel 4 phones, thanks to the handsets December feature drop, and Google says its in the process of rolling it out to other unnamed handsets.

Link:
Google is using machine learning to improve the quality of Duo calls - The Verge

We share relevant third party stories on our website. This release was written and issued by Agxio.

A revolutionary new machine learning platform built entirely by the brilliance of AI could prove to be a vital weapon in the fight against coronavirus.

Apollo is a pioneering system to deliver a fully automated, AI-driven machine learning engine and is already being hailed as a game-changer.

Created by Cambridge and Aberystwyth-based applied AI innovation company, Agxio, Apollo operates beyond-human-scale performance, enabling the robotic platform to evaluate critical data to produce predictive models to solve real world problems. It then optimises these to look for patterns or configurations of parameters that human modellers may not even consider or have the patience to develop. And in a matter of hours.

With the appropriate data, Apollo and the power of machine learning can be used to analyse and predict the efficacy of potential vaccine combinations, outbreak trends, behavioural nudge factors, early warning indicators, medical images against risk indicators, and isolation rate projections, for example. The range of use cases for automated machine learning is however endless.

Importantly, the fully automated AI-driven engine doesnt require the user to be a programming expert or data scientist specialist enabling an expert in a non-data science or machine learning field to be able to study ideas or data that would otherwise take years of experience to be able to apply.

Agxio, which is already backed by the Welsh Government through the Development Bank of Wales, is now offering free use of the platform, together with its technical support team, to all credible researchers, practitioners and government bodies working to defeat COVID-19 for the duration of the pandemic.

Agxio CEO and co-founder, Dr Stephen Christie says: Whats different about Apollo is that this is AI built by AI - artificially intelligent machine learning. Its the machine building the machines, a series of robots building the best brains to answer targeted questions. Apollo is designed to focus on problems that are beyond human scale in dimension or complexity and is, without doubt, the most advanced approach of its kind.

What would take a human literally weeks and months to do, Apollo can generate in minutes and hours. Machine learning is one of the most important tools and defining technologies of our generation, and Apollo is a complete game-changer in terms of accelerating the building of machine learning and solutions.

While humans naturally tend to have biases, Apollo doesnt have any and is additionally data-agnostic. Most importantly, Apollo has speed and accuracy - and, right now, we need both to be really responsive to the situation. Accurate evaluation of data is vital in the governments planning of next-step measures. And I think it is critical for the government to be using the best tools and techniques we have available at this time.

To that end, the Agxio team has additionally created a single COVID-19 data portal for the global community. Coviddata.io is open to any parties for augmentation as cases, data and innovations evolve.

Dr Christie who was awarded Tech CEO of the Year 2019 and 2020 (Innovation & Excellence Awards) and has additionally won Life Sciences Awards (EBA) two years running - explains: If you are going to do anything around research and machine learning, data is critical - as is the sharing and pooling of that data in a properly trusted and curated form, and making the data accessible and available to researchers.

When making projections on isolation rates and strategies, you need real data and an engine that is able to crunch that data in a structured way, which is Apollo. Secondly, you need the data to be carefully curated and comprehensive. If you dont have either of those, youre going to struggle to come up with the correct answer.

Agxio secured investment from the Development Bank of Wales in January 2020. Andrew Critchley is an Investment Executive with the Development Bank of Wales. He adds: As backers of Agxio, we are delighted to see the company offering free use of their Apollo platform and expertise to help with the fight against Covid19.

Weve got to work together to beat this pandemic. Agxios cutting edge technology has the potential to help save lives, the impact could be global.

Apollo was originally developed as an expert system to enable arable farmers to analyse traditional and advanced IoT data to address the growing populations needs for improved yields and disease resistance. However, it has since proved to be a powerful tool for a number of different applications including fraud analytics, disease detection, economic anomalies, and bio-sequencing applications - automating the role of the data scientist to build optimal machine learning models against a target prediction. Data-agnostic, it can operate on numerical, textual and image data, both on and off premises.

Agxio is keen to hear from any data scientists and Python machine learning programmers who would like to volunteer support to researchers projects. If you would like to put your COVID-19 initiative forward for access to the Apollo platform, or volunteer your technical expertise to projects, please contact Covid-19@agxio.com

For more information please visit http://www.agxio.com.

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Agxio offers AI-built-by-AI fully-automated machine learning platform free in global fight against COVID-19 - Development Bank of Wales

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

MONROVIA, Calif., April 7, 2020 /PRNewswire/ --Parasoft, a global software testing automation leader for over 30 years, received the VDC Research Embedded Award for 2020. The technology research and consulting firm yearly recognizes cutting-edge Software and Hardware Technologies in the embedded industry. This year, Parasoft C/C++test, aunified development testing solution forsafety and securityof embedded C and C++ applications, was recognized for its new, innovative approach that expedites the adoption of software code analysis, increasing developer productivity and simplifying compliance with industry standards such as CERT C/C++, MISRA C 2012 and AUTOSAR C++14. To learn more about Parasoft C/C++test, please visit: https://www.parasoft.com/products/ctest.

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

"Parasoft has continued its investment in the embedded market, adding new products and personnel to boost its market presence. In addition to highlighting expanded partnerships and coding-standard support, the company announced the integration of AI capabilities into its static analysis engine. While defect prioritization systems have been part of static analysis solutions for well over ten years, Parasoft's solution takes the idea a step further. Their solution now effectively learns from past interactions with identified defects and the codebase to better help users triage new findings," states Chris Rommel, EVP, VDC Research Group.

Parasoft's latest innovation applies AI/Machine Learning to the process of reviewing static analysis findings. Static analysis is a foundational part of the quality process, especially in safety-critical development (e.g., ISO26262, IEC61508), and is an effective first step to establish secure development practices. A common challenge when deploying static analysis tools is dealing with the multitude of reported findings. Scans can produce tens of thousands of findings, and teams of highly qualified resources need to go through a time-consuming process of reviewing and identifying high-priority findings. This process leads to finding and reviewing critical issues late in the cycle, delaying the delivery, and worse, allowing insecure/unsafe code to become embedded into the codebase.

Parasoft leaps forwardbeyond the rest of the competitive market by having AI/ML take into account the context of both historical interactions with the code base and prior static analysis findings to predict relevance and prioritize new findings. This innovation helps organizations achieve compliance with industry standards and offers a unique application of AI/ML in helping organizations with the adoption of Static Analysis. This innovative technology builds on Parasoft's previous AI/ML innovations in the areas of Web UI, API, and Unit testing - https://blog.parasoft.com/what-is-artificial-intelligence-in-software-testing.

"We are extremely honored to have received this award, particularly in light of the competition, VDC's expertise and knowledge of the embedded market," said Mark Lambert, VP of Products at Parasoft. "We have always been committed to innovation led by listening to our customers and leveraging capabilities that will help drive them forward. This creativity has always driven Parasoft's development and is something that has been in the company's DNA from its founding."

Story continues

About Parasoft (www.parasoft.com):Parasoft, the global leader in software testing automation, has been reducing the time, effort, and cost of delivering high-quality software to the market for the last 30+ years. Parasoft's tools support the entire software development process, from when the developer writes the first line of code all the way through unit and functional testing, to performance and security testing, leveraging simulated test environments along the way. Parasoft's unique analytics platform aggregates data from across all testing practices, providing insights up and down the testing pyramid to enable organizations to succeed in today's most strategic development initiatives, including Agile/DevOps, Continuous Testing, and the complexities of IoT.

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Parasoft wins 2020 VDC Research Embeddy Award for Its Artificial Intelligence (AI) and Machine Learning (ML) Innovation - Yahoo Finance

New Jersey, United States: Market Research Intellect has added a new research report titled, AI/Machine Learning Market Professional Survey Report 2020 to its vast collection of research reports. The AI/Machine Learning market is expected to grow positively for the next five years 2020-2026.

The AI/Machine Learning market report studies past factors that helped the market to grow as well as, the ones hampering the market potential. This report also presents facts on historical data from 2011 to 2019 and forecasts until 2026, which makes it a valuable source of information for all the individuals and industries around the world. This report gives relevant market information in readily accessible documents with clearly presented graphs and statistics. This report also includes views of various industry executives, analysts, consultants, and marketing, sales, and product managers.

Market Segment as follows:

The global AI/Machine Learning Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading AI/Machine Learning company.

AI/Machine Learning Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the AI/Machine Learning market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for AI/Machine Learning .

The AI/Machine Learning Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting AI/Machine Learning market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global AI/Machine Learning market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the AI/Machine Learning market have also been included in the study.

Ask For Discount (Special Offer: Get 25% discount on this report) @ https://www.marketresearchintellect.com/ask-for-discount/?rid=193669&utm_source=SI&utm_medium=888

Table of Content

1 Introduction of AI/Machine Learning Market1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 AI/Machine Learning Market Outlook4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 AI/Machine Learning Market, By Deployment Model5.1 Overview

6 AI/Machine Learning Market, By Solution6.1 Overview

7 AI/Machine Learning Market, By Vertical7.1 Overview

8 AI/Machine Learning Market, By Geography8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 AI/Machine Learning Market Competitive Landscape9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix11.1 Related Research

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AI/Machine Learning Market Size Analysis, Top Manufacturers, Shares, Growth Opportunities and Forecast to 2026 - Science In Me

New Jersey, United States: Market Research Intellect has added a new research report titled, Machine Learning as a Service Market Professional Survey Report 2020 to its vast collection of research reports. The Machine Learning as a Service market is expected to grow positively for the next five years 2020-2026.

The Machine Learning as a Service market report studies past factors that helped the market to grow as well as, the ones hampering the market potential. This report also presents facts on historical data from 2011 to 2019 and forecasts until 2026, which makes it a valuable source of information for all the individuals and industries around the world. This report gives relevant market information in readily accessible documents with clearly presented graphs and statistics. This report also includes views of various industry executives, analysts, consultants, and marketing, sales, and product managers.

Key Players Mentioned in the Machine Learning as a Service Market Research Report:

Market Segment as follows:

The global Machine Learning as a Service Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading Machine Learning as a Service company.

Machine Learning as a Service Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the Machine Learning as a Service market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for Machine Learning as a Service .

The Machine Learning as a Service Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting Machine Learning as a Service market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global Machine Learning as a Service market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the Machine Learning as a Service market have also been included in the study.

Ask For Discount (Special Offer: Get 25% discount on this report) @ https://www.marketresearchintellect.com/ask-for-discount/?rid=195381&utm_source=SI&utm_medium=888

Table of Content

1 Introduction of Machine Learning as a Service Market1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Machine Learning as a Service Market Outlook4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Machine Learning as a Service Market, By Deployment Model5.1 Overview

6 Machine Learning as a Service Market, By Solution6.1 Overview

7 Machine Learning as a Service Market, By Vertical7.1 Overview

8 Machine Learning as a Service Market, By Geography8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Machine Learning as a Service Market Competitive Landscape9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix11.1 Related Research

Complete Report is Available @ https://www.marketresearchintellect.com/product/global-machine-learning-as-a-service-market-size-and-forecast/?utm_source=SI&utm_medium=888

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Awarded to recognize Google Cloud partners who have made a positive impact on the world

Quantiphi, an award-winning applied artificial intelligence and data science software and services company, announced today that it has been named 2019 Social Impact Partner of the Year by Google Cloud. Quantiphi was recognized for its achievements for working with nonprofits, research institutions, and healthcare providers, to leverage AI for Social Good.

We are believers in the power of human acumen and technology to solve the worlds toughest challenges. This award is a recognition of our mission driven culture and our passion to apply AI for social good, said Asif Hasan, Co-founder, Quantiphi. Partnering with Google Cloud has given us the opportunity to work with the worlds leading nonprofit, healthcare and research institutions and we are truly humbled by this recognition.

Recommended AI News:Opinion: Young Jamaicans Invention Could Help Tackle Spread of Viruses Like COVID-19

Were delighted to recognize Quantiphis commitment to social impact, said Carolee Gearhart, Vice President, Worldwide Channel Sales at Google Cloud. By applying its capabilities in AI and ML to important causes, Quantiphi has demonstrated how Google Cloud partners are contributing to positive change in the world.

A few initiatives that helped Quantiphi earn this recognition:

Recommended AI News:Automation Provides A Content Lifeline For Remote Work

Quantiphi previously earned the Google Cloud Machine Learning Partner of the Year twice in a row for 2018 and 2017 and is a premier partner for Google Cloud and holds Specializations in machine learning, data analytics and marketing analytics.

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When Machines Design: Artificial Intelligence and the Future of Aesthetics

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Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

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When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

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New Jersey, United States: Market Research Intellect has added a new research report titled, Data Science and Machine-Learning Platforms Market Professional Survey Report 2020 to its vast collection of research reports. The Data Science and Machine-Learning Platforms market is expected to grow positively for the next five years 2020-2026.

The Data Science and Machine-Learning Platforms market report studies past factors that helped the market to grow as well as, the ones hampering the market potential. This report also presents facts on historical data from 2011 to 2019 and forecasts until 2026, which makes it a valuable source of information for all the individuals and industries around the world. This report gives relevant market information in readily accessible documents with clearly presented graphs and statistics. This report also includes views of various industry executives, analysts, consultants, and marketing, sales, and product managers.

Key Players Mentioned in the Data Science and Machine-Learning Platforms Market Research Report:

Market Segment as follows:

The global Data Science and Machine-Learning Platforms Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading Data Science and Machine-Learning Platforms company.

Data Science and Machine-Learning Platforms Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the Data Science and Machine-Learning Platforms market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for Data Science and Machine-Learning Platforms .

The Data Science and Machine-Learning Platforms Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting Data Science and Machine-Learning Platforms market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global Data Science and Machine-Learning Platforms market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the Data Science and Machine-Learning Platforms market have also been included in the study.

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Table of Content

1 Introduction of Data Science and Machine-Learning Platforms Market1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Data Science and Machine-Learning Platforms Market Outlook4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Data Science and Machine-Learning Platforms Market, By Deployment Model5.1 Overview

6 Data Science and Machine-Learning Platforms Market, By Solution6.1 Overview

7 Data Science and Machine-Learning Platforms Market, By Vertical7.1 Overview

8 Data Science and Machine-Learning Platforms Market, By Geography8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Data Science and Machine-Learning Platforms Market Competitive Landscape9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix11.1 Related Research

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Tags: Data Science and Machine-Learning Platforms Market Size, Data Science and Machine-Learning Platforms Market Growth, Data Science and Machine-Learning Platforms Market Forecast, Data Science and Machine-Learning Platforms Market Analysis, Data Science and Machine-Learning Platforms Market Trends, Data Science and Machine-Learning Platforms Market

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NEW YORK, March 30, 2020 /PRNewswire/ --

Global Machine Learning Market 2020-2024 The analyst has been monitoring the global machine learning market and it is poised to grow by USD 11.16 bn during 2020-2024, progressing at a CAGR of 39% during the forecast period. Our reports on global machine learning market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.

Read the full report: https://www.reportlinker.com/p05082022/?utm_source=PRN

The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by increasing adoption of cloud-based offerings. In addition, increasing use of machine learning in customer experience management is anticipated to boost the growth of the global machine learning market as well.

Market Segmentation The global machine learning market is segmented as below: End-User: BFSI Retail Telecommunications Healthcare Others

Geographic Segmentation: APAC Europe MEA North America South America

Key Trends for global machine learning market growth This study identifies increasing use of machine learning in customer experience management as the prime reasons driving the global machine learning market growth during the next few years.

Prominent vendors in global machine learning market We provide a detailed analysis of around 25 vendors operating in the global machine learning market 2020-2024, including some of the vendors such as Alibaba Group Holding Ltd., Alphabet Inc., Amazon.com Inc., Cisco Systems Inc., Hewlett Packard Enterprise Development LP, International Business Machines Corp., Microsoft Corp., Salesforce.com Inc., SAP SE and SAS Institute Inc. . The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.

Read the full report: https://www.reportlinker.com/p05082022/?utm_source=PRN

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The Global Machine Learning Market is expected to grow by USD 11.16 bn during 2020-2024, progressing at a CAGR of 39% during the forecast period -...

The chassis the new model will boast has been improved as well. The new G80's rear-drive platform is lower, which allows for more interior space and better handling, and crucially, isn't shared with any lowly Hyundais or Kias. Nineteen percent of the G80's body is now aluminum, resulting in a car that's 243 pounds lighter than the model it replaces. It's apparently quieter, too, thanks to improved door seals, new engine compartment sound insulation, and sound-reducing wheels. Electronically Controlled Suspension with Road Preview uses the front camera to anticipate bumps, potholes, and rough surfaces just like the Audi A8.

Its luxurious interior is equipped with a 12.3-inch digital instrument cluster and an ultra-wide 14.5-inch infotainment screen with Apple CarPlay, Android Auto, and the ability to receive over-the-air navigation updates. Genesis' latest active safety and assisted driving systems are all accounted for as well, including Highway Driving Assist that can now change lanes at the flick of the turn signal and Smart Cruise Control with Machine Learning that intelligently adapts to its owner's driving style.

So, presumably, if you drive like an idiot, your G80 will drive like one too. Although we don't think local law enforcement will take too kindly to that excuse when they catch your Genesis autonomously cutting somebody off a little too aggressively.

Official pricing has yet to be announced but we expect it to start somewhere in the $50,000 ballpark just like the BMW 5 Series and Mercedes-Benz E-Class.

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The 2021 Genesis G80 Packs 'Machine Learning Cruise Control' to Go With Stunning Looks - The Drive

DMway analytics, leading provider of machine learning automation platforms, announced it was offering its predictive analytics and automated ML platform to every Ministry of Health department globally. In the USAthis includes all State level authorities and elsewhere the equivalent.

The DMway Auto-ML platform is developed by leading Ph.D.s in the field of auto-machine learning and data science, and can transform non-scientists (analysts, BIand data experts) into capable, insightful Data Science Citizens. In practical terms this means that the analysis of Covid-19 data that can currently be carried out by very few people, can now be carried out by many.We are weaponizing Data Science automation to fight back against the virus.

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Machine learning and predictive analytics aregoingto be key in winning the Covid-19 battle. That much is clear. Analyzing data isessential in being able to understand thespreadand treatment effectiveness. The world needs many more people analyzing the data. The insight from global information on the spread of the virus and itsbehaviorwill bekey inminimizing the damage. The ability to empower thousands of citizen data scientists could potentiallyrevolutionizethe speed at which we can react to data as it is made available.

Recommended AI News:ResoluteAI Partners With FinTech Studios to Integrate News Database Into Foundation Research Platform

Gil Nizri, DMway analytics CEO, said:The time is right for technology leaders to donate as much as they can to help the world in confronting this invisible and brutal enemy.We have invested millions of dollars in our tool, but free access at this critical time is essential. Machine learning will be a key tool in dealing with Covid-19. We cannot see the use ofmachinelearningrestricted to a few individuals with access and knowledge of machinelearningtools. We hope the DMway tool will be used to empower thousands of relevantpeopleto analyze Covid-19 related data. It is simple to learn, and we will train people en-masse of up to 100 at atimevia video link from our HQ here inIsrael.Nizri added,Our machinelearningfor Covid-19 course has been especiallycartedthis past two weeks and adjusted to biologists, epidemic analysts and healthcare data experts.Covid-19 is the enemy. Let us fight this together.A global team against thiskiller.

Recommended AI News:Blackboard Ally Integrates BeeLine Reader to Improve Accessibility of Digital Learning Content for All Students

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DMway Analytics Offers Its AUTO-ML Platform Free of Charge to Every Ministry of Health Department and Covid-19 Research Center Globally - AiThority

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.

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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...

COVID-19 will change how most of us live and work, at any rate temporarily. Its additionally making a test for tech organizations, for example, Facebook, Twitter, and Google, that usually depend on parcels and heaps of personal work to direct substance. Are AI furthermore, AI propelled enough to enable these organizations to deal with the interruption?

Its essential that, even though Facebook has initiated ageneral work-from-home strategy to ensure its laborers (alongside Google and arising number of different firms), it at first required its contractual workerswho moderate substance to keep on coming into the workplace. That circumstancejust changed after fights, as per The Intercept.

Presently, Facebook is paying those contractual workers. At thesame time, they sit at home since the idea of their work (examining peoplegroups posts for content that damages Facebooks terms of administration) isamazingly security delicate. Heres Facebooks announcement:

For both our full-time representatives and agreementworkforce, there is some work that is impossible from home because ofwellbeing, security, and legitimate reasons. We have played it safe to secureour laborers by chopping down the number of individuals in some random office,executing prescribed work from home all-inclusive, truly spreading individualsout at some random office, and doing extra cleaning. Given the quicklydeveloping general wellbeing concerns, we are finding a way to ensure ourgroups. We will be working with our accomplices throughout this week to sendall contractors who perform content survey home, until further notification.Well guarantee the payment of all employees during this time.

Facebook, Twitter, Reddit, and different organizations are inthe equivalent world-renowned pontoon: Theres an expanding need to politicizetheir stages, just to take out counterfeit news about COVID-19. Yetthe volunteers who handle such assignments cant do as such from home,particularly on their workstations. The potential arrangement? Human-madereasoning (AI) and AI calculations intended to examine the flawed substance andsettle on a choice about whether to dispense with it.

Heres Googles announcement on the issue, using its YouTube Creator Blog.

Our Community Guidelines requirement today depends on ablend of individuals and innovation: Machine learning recognizes possiblydestructive substance and afterward sends it to human analysts for evaluation.Because of the new estimates were taking, we will incidentally begin dependingmore on innovation to help with a portion of the work regularly done bycommentators. This implies computerized frameworks will begin evacuating somesubstance without human audit, so we can keep on acting rapidly to expelviolative substances and ensure our environment. At the same time, we have aworking environment assurances set up.

Also, the tech business has been traveling right now sometime.Depending on the multitudes of individuals to peruse each bit of substance onthe web is costly, tedious, and inclined to mistake. Be that as it may, AI,whats more, AI is as yet early, despite the promotion. Google itself, in thepreviously mentioned blog posting, brought up how its computerized frameworksmay hail inappropriate recordings. Facebook is additionally getting analysisthat its robotized against spam framework is whacking inappropriate posts,remembering those that offer essential data for the spread of COVID-19.

In the case of the COVID-19 emergency delay, more organizationswill not surely turn to machine learning as a potential answer forinterruptions in their work process and different procedures. That will drive aprecarious expectation to absorb information; over and over, the rollout of AIstages has exhibited that, while the capability of the innovation is there,execution is regularly an unpleasant and costly proceduresimply see GoogleDuplex.

In any case, a forceful grasp of AI will likewise make more opendoors for those technologists who have aced AI, whats more, AI aptitudes ofany kind; these people may wind up entrusted with making sense of how tomechanize center procedures to keep organizations running.

Before the infection developed, Burning Glass (which breaks downa great many activity postings from over the US), evaluated that employmentsthat include AI would grow 40.1 percent throughout the following decade. Thatrate could increase considerably higher if the emergency on a fundamental levelchanges how individuals over the world live and work. (The average compensationfor these positions is $105,007; for those with a Ph.D., it floats up to$112,300.)

With regards to irresistible illnesses, counteraction, surveillance,and fast reaction endeavors can go far toward easing back or slowing downflare-ups. At the point when a pandemic, for example, the ongoing coronavirusepisode occurs, it can make enormous difficulties for the administration andgeneral wellbeing authorities to accumulate data rapidly and facilitate areaction.

In such a circumstance, machine learning can assume an immensejob in foreseeing a flare-up and limiting or slowing down its spread.

Human-made intelligence calculations can help mine through newsreports and online substances from around the globe, assisting specialists inperceiving oddities even before it arrives at pestilence extents. The crownepisode itself is an extraordinary model where specialists applied AI toexamine flight voyager information to anticipate where the novel coronaviruscould spring up straightaway. A National Geographic report shows how checkingthe web or online life can help identify the beginning periods.

Practical usage of prescient demonstrating could speak to asignificant jump forward in the battle to free the universe of probably themost irresistible maladies. Substantial information examination can enablede-to to concentrate the procedure and empower the convenient investigation offar-reaching informational collections created through the Internet of Things(IoT) and cell phones progressively.

Artificial intelligence and colossal information examination have a significant task to carry out in current genome sequencing techniques. High.

As of late, weve all observed great pictures of medicinalservices experts over the globe working vigorously to treat COVID-19 patients,frequently putting their own lives in danger. Computer-based intelligence couldassume a critical job in relieving their burden while guaranteeing that thenature of care doesnt endure. For example, the Tampa General Hospital inFlorida is utilizing AI to recognize fever in guests with a primary facialoutput. Human-made intelligence is additionally helping specialists at theSheba Medical Center.

The job of AI and massive information in treating worldwidepandemics and other social insurance challenges is just set to develop. Hence,it does not shock anyone that interest for experts with AI aptitudes hasdramatically increased in recent years. Experts working in social insuranceinnovations, getting taught on the uses of AI in medicinal services, andbuilding the correct ranges of abilities will end up being critical.

As AI rapidly becomes standard, medicinal services isundoubtedly a territory where it will assume a significant job in keeping usmore secure and more advantageous.

The subject of how machine learning can add to controlling theCOVID-19 pandemic is being presented to specialists in human-made consciousness(AI) everywhere throughout the world.

Artificial intelligence instruments can help from multiplepoints of view. They are being utilized to foresee the spread of thecoronavirus, map its hereditary advancement as it transmits from human tohuman, accelerate analysis, and in the improvement of potential medications,while additionally helping policymakers adapt to related issues, for example,the effect on transport, nourishment supplies, and travel.

In any case, in every one of these cases, AI is just potent onthe off chance that it has adequate guides. As COVID-19 has brought the worldinto the unchartered domain, the profound learning frameworks,which PCs use to obtain new capacities, dont have the information they have todeliver helpful yields.

Machine leaning is acceptable at anticipating nonexclusiveconduct, yet isnt truly adept at extrapolating that to an emergencycircumstance when nearly everything that happens is new, alerts LeoKrkkinen, a teacher at the Department of Electrical Engineering andAutomation in Aalto University, Helsinki and an individual with Nokias BellLabs. On the off chance that individuals respond in new manners, at thatpoint AI cant foresee it. Until you have seen it, you cant gain fromit.

Regardless of this clause, Krkkinen says powerful AI-basednumerical models are assuming a significant job in helping policymakers see howCOVID-19 is spreading and when the pace of diseases is set to top. Bydrawing on information from the field, for example, the number of passings, AImodels can assist with identifying what number of contaminations areuninformed, he includes, alluding to undetected cases that are as yetirresistible. That information would then be able to be utilized to advise thefoundation regarding isolate zones and other social removing measures.

It is likewise the situation that AI-based diagnostics that arebeing applied in related zones can rapidly be repurposed for diagnosingCOVID-19 contaminations. Behold.ai, which has a calculation for consequentlyrecognizing both malignant lung growth and fallen lungs from X-beams, provideddetails regarding Monday that the count can rapidly distinguish chest X-beamsfrom COVID-19 patients as unusual. Right now, triage might accelerate findingand guarantee assets are dispensed appropriately.

The dire need to comprehend what sorts of approach intercessionsare powerful against COVID-19 has driven different governments to grant awardsto outfit AI rapidly. One beneficiary is David Buckeridge, a teacher in theDepartment of Epidemiology, Biostatistics and Occupational Health at McGillUniversity in Montreal. Equipped with an award of C$500,000 (323,000), hisgroup is joining ordinary language preparing innovation with AI devices, forexample, neural systems (a lot of calculations intended to perceive designs),to break down more than 2,000,000 customary media and internet-based lifereports regarding the spread of the coronavirus from everywhere throughout theworld. This is unstructured free content traditional techniques cantmanage it, Buckeridge said. We need to remove a timetable fromonline media, that shows whats working where, accurately.

The group at McGill is utilizing a blend of managed and solo AI techniques to distill the key snippets of data from the online media reports. Directed learning includes taking care of a neural system with information that has been commented on, though solo adapting just utilizes crude information. We need a structure for predisposition various media sources have an alternate point of view, and there are distinctive government controls, says Buckeridge. People are acceptable at recognizing that, yet it should be incorporated with the AI models.

The data obtained from the news reports will be joined withother information, for example, COVID-19 case answers, to give policymakers andwellbeing specialists a significantly more complete image of how and why theinfection is spreading distinctively in various nations. This is appliedresearch in which we will hope to find significant solutions quick,Buckeridge noted. We ought to have a few consequences of significance togeneral wellbeing in April.

Simulated intelligence can likewise be utilized to helprecognize people who may be accidentally tainted with COVID-19. Chinese techorganization Baidu says its new AI-empowered infrared sensor framework canscreen the temperature of individuals in the nearness and rapidly decide ifthey may have a fever, one of the indications of the coronavirus. In an 11March article in the MIT Technology Review, Baidu said the innovation is beingutilized in Beijings Qinghe Railway Station to recognize travelers who areconceivably contaminated, where it can look at up to 200 individuals in asingle moment without upsetting traveler stream. A report given out fromthe World Health Organization on how China has reacted to the coronavirus saysthe nation has additionally utilized essential information and AI to reinforcecontact following and the administration of need populaces.

Human-made intelligence apparatuses are additionally being sent to all the more likely comprehend the science and science of the coronavirus and prepare for the advancement of viable medicines and an immunization. For instance, fire up Benevolent AI says its man-made intelligence determined information diagram of organized clinical data has empowered the recognizable proof of a potential restorative. In a letter to The Lancet, the organization depicted how its calculations questioned this chart to recognize a gathering of affirmed sedates that could restrain the viral disease of cells. Generous AI inferred that the medication baricitinib, which is endorsed for the treatment of rheumatoid joint inflammation, could be useful in countering COVID-19 diseases, subject to fitting clinical testing.

So also, US biotech Insilico Medicine is utilizing AI calculations to structure new particles that could restrict COVID-19s capacity to duplicate in cells. In a paper distributed in February, the organization says it has exploited late advances in profound figuring out how to expel the need to physically configuration includes and learn nonlinear mappings between sub-atomic structures and their natural and pharmacological properties. An aggregate of 28 AI models created atomic structures and upgraded them with fortification getting the hang of utilizing a scoring framework that mirrored the ideal attributes, the analysts said.

A portion of the worlds best-resourced programmingorganizations is likewise thinking about this test. DeepMind, the London-basedAI pro possessed by Googles parent organization Alphabet, accepts its neuralsystems that can accelerate the regularly painful procedure of settling thestructures of viral proteins. It has created two strategies for preparingneural networks to foresee the properties of a protein from its hereditaryarrangement. We would like to add to the logical exertion bydischarging structure forecasts of a few under-contemplated proteins related toSARS-CoV-2, the infection that causes COVID-19, the organization said.These can assist scientists with building comprehension of how the infectioncapacities and be utilized in medicate revelation.

The pandemic has driven endeavor programming organizationSalesforce to differentiate into life sciences, in an investigation showingthat AI models can gain proficiency with the language of science, similarly asthey can do discourse and picture acknowledgment. The thought is that the AIframework will, at that point, have the option to plan proteins, or recognizecomplex proteins, that have specific properties, which could be utilized totreat COVID-19.

Salesforce took care of the corrosive amino arrangements ofproteins and their related metadata into its ProGen AI framework. The frameworktakes each preparation test and details a game where it attempts to foresee thefollowing amino corrosive in succession.

Before the finish of preparing, ProGen has gotten aspecialist at foreseeing the following amino corrosive by playing this gameroughly one trillion times, said Ali Madani, an analyst at Salesforce.ProGen would then be able to be utilized practically speaking for proteinage by iteratively anticipating the following doubtlessly amino corrosive andproducing new proteins it has never observed. Salesforce is presentlylooking to collaborate with scholars to apply the innovation.

As governments and wellbeing associations scramble to containthe spread of coronavirus, they need all the assistance they with canning get,including from machine learning. Even though present AI innovations are a longway from recreating human knowledge, they are ending up being useful infollowing the episode, diagnosing patients, sanitizing regions, andaccelerating the way toward finding a remedy for COVID-19.

Information science and AI maybe two of the best weapons we havein the battle against the coronavirus episode.

Not long before the turn of the year, BlueDot, a human-madeconsciousness stage that tracks irresistible illnesses around the globe, haileda group of bizarre pneumonia cases occurring around a market inWuhan, China. After nine days, the World Health Organization (WHO) dischargedan announcement proclaiming the disclosure of a novel coronavirusin a hospitalized individual with pneumonia in Wuhan.

BlueDot utilizes everyday language preparation and AIcalculations to scrutinize data from many hotspots for early indications ofirresistible pestilences. The AI takes a gander at articulations from wellbeingassociations, business flights, animal wellbeing reports, atmosphere informationfrom satellites, and news reports. With so much information being created oncoronavirus consistently, the AI calculations can help home in on the bits thatcan give appropriate data on the spread of the infection. It can likewisediscover significant connections betweens information focuses, for example,the development examples of the individuals who are living in the zonesgenerally influenced by the infection.

The organization additionally utilizes many specialists who havesome expertise in the scope of orders, including geographic data frameworks,spatial examination, information perception, PC sciences, just as clinicalspecialists in irresistible clinical ailments, travel and tropical medication,and general wellbeing. The specialists audit the data that has been hailed bythe AI and convey writes about their discoveries.

Joined with the help of human specialists, BlueDots AI cananticipate the beginning of a pandemic, yet additionally, conjecture how itwill spread. On account of COVID-19, the AI effectively recognized the urbancommunities where the infection would be moved to after it surfaced in Wuhan.AI calculations considering make a trip design had the option to foresee wherethe individuals who had contracted coronavirus were probably going to travel.

Presently, AI calculations can play out the equivalenteverywhere scale. An AI framework created by Chinese tech monster Baiduutilizes cameras furnished with PC vision and infrared sensors to foreseeindividuals temperatures in open territories. The frame can screen up to 200individuals for every moment and distinguish their temperature inside the scopeof 0.5 degrees Celsius. The AI banners any individual who has a temperatureabove 37.3 degrees. The innovation is currently being used in Beijings QingheRailway Station.

Alibaba, another Chinese tech monster, has built up an AI framework that can recognize coronavirus in chest CT filters. As indicated by the analysts who built up the structure, the AI has a 96-percent exactness. The AI was prepared on information from 5,000 coronavirus cases and can play out the test in 20 seconds instead of the 15 minutes it takes a human master to analyze patients. It can likewise differentiate among coronavirus and common viral pneumonia. The calculation can give a lift to the clinical focuses that are as of now under a ton of strain to screen patients for COVID-19 disease. The framework is supposedly being embraced in 100 clinics in China.

A different AI created by specialists from Renmin Hospital ofWuhan University, Wuhan EndoAngel Medical Technology Company, and the ChinaUniversity of Geosciences purportedly shows 95-percent precision ondistinguishing COVID-19 in chest CT checks. The framework is a profoundlearning calculation prepared on 45,000 anonymized CT checks. As per a preprintpaper distributed on medRxiv, the AIs exhibition is practically identical tomaster radiologists.

One of the fundamental approaches to forestall the spread of thenovel coronavirus is to decrease contact between tainted patients andindividuals who have not gotten the infection. To this end, a few organizationsand associations have occupied with endeavors to robotize a portion of themethods that recently required wellbeing laborers and clinical staff tocooperate with patients.

Chinese firms are utilizing automatons and robots to performcontactless conveyance and to splash disinfectants in open zones to limit thedanger of cross-contamination. Different robots are checking individuals forfever and other COVID-19 manifestations and administering free hand sanitizerfoam and gel.

Inside emergency clinics, robots are conveying nourishment andmedication to patients and purifying their rooms to hinder the requirement forthe nearness of attendants. Different robots are caught up with cooking ricewithout human supervision, decreasing the quantity of staff required to run theoffice.

In Seattle, specialists utilized a robot to speak with and treatpatients remotely to limit the introduction of clinical staff to contaminatedindividuals.

By the days end, the war on the novel coronavirus isnt overuntil we build up an immunization that can vaccinate everybody against theinfection. Be that as it may, growing new medications and medication is anexceptionally protracted and expensive procedure. It can cost more than abillion dollars and take as long as 12 years. That is the sort of period wedont have as the infection keeps on spreading at a quickening pace.

Luckily, AI can assist speed with increasing the procedure.DeepMind, the AI investigate lab procured by Google in 2014, as of lateannounced that it has utilized profound figuring out how to discover new dataabout the structure of proteins related to COVID-19. This is a procedure thatcould have taken a lot more months.

Understanding protein structures can give significant insightsinto the coronavirus immunization recipe. DeepMind is one of a few associationsthat are occupied with the race to open the coronavirus immunization. It hasutilized the consequence of many years of AI progress, just as research onprotein collapsing.

Its imperative to take note of that our structureforecast framework is still being developed, and we cant be sure of theprecision of the structures we are giving, even though we are sure that theframework is more exact than our prior CASP13 framework, DeepMindsscientists composed on the AI labs site. We affirmed that our frameworkgave an exact forecast to the tentatively decided SARS-CoV-2 spike proteinstructure partook in the Protein Data Bank, and this gave us the certainty thatour model expectations on different proteins might be valuable.

Even though it might be too soon to tell whether were going thecorrect way, the endeavors are excellent. Consistently spared in finding thecoronavirus antibody can save hundredsor thousandsof lives.

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What Researches says on Machine learning with COVID-19 - Techiexpert.com - TechiExpert.com

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

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the companys business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence.

10 Business Functions That Are Ready To Use Artificial Intelligence

Marketing

If your company isnt using artificial intelligence in marketing, it's already behind. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it. Another way AI is used in marketing is through chatbots. These bots can help solve problems, suggest products or services, and support sales. Artificial intelligence also supports marketers by analyzing data on consumer behavior faster and more accurately than humans. These insights can help businesses make adjustments to marketing campaigns to make them more effective or plan better for the future.

Sales

There is definitely a side of selling products and services that is uniquely human, but artificial intelligence can arm sales professionals with insights that can improve the sales function. AI helps improve sales forecasting, predict customer needs, and improve communication. And intelligent machines can help sales professionals manage their time and identify who they need to follow-up with and when as well as what customers might be ready to convert.

Research and Development (R&D)

What about artificial intelligence as a tool of innovation? It can help us build a deeper understanding in nearly any industry, including healthcare and pharmaceuticals, financial, automotive, and more, while collecting and analyzing tremendous amounts of information efficiently and accurately. This and machine learning can help us research problems and develop solutions that weve never thought of before. AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.

IT Operations

Also called AIOps, AI for IT operations is often the first experience many organizations have with implementing artificial intelligence internally. Gartner defines the term AIOps as the application of machine learning and data science to IT operations problems. AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes. It can help identify issues so the IT team can proactively fix them before any IT systems go down. As the IT systems to support our businesses become more complex, AIOps helps the IT improve system performance and services.

Human Resources

In a business function with human in the name, is there a place for machines? Yes! Artificial intelligence really has the potential to transform many human resources activities from recruitment to talent management. AI can certainly help improve efficiency and save money by automating repetitive tasks, but it can do much more. PepsiCo used a robot, Robot Vera, to phone and interview candidates for open sales positions. Talent is going to expect a personalized experience from their employer just as they have been accustomed to when shopping and for their entertainment. Machine learning and AI solutions can help provide that. In addition, AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.

Contact Centers

The contact center of an organization is another business area where artificial intelligence is already in use. Organizations that use AI technology to enhance rather than replace humans with these tasks are the ones that are incorporating artificial intelligence in the right way. These centers collect a tremendous amount of data that can be used to learn more about customers, predict customer intent, and improve the "next best action" for the customer for better customer engagement. The unstructured data collected from contact centers can also be analyzed by machine learning to uncover customer trends and then improve products and services.

Building Maintenance

Another way AI is already at work in businesses today is helping facilities managers optimize energy use and the comfort of occupants. Building automation, the use of artificial intelligence to help manage buildings and control lighting and heating/cooling systems, uses internet-of-things devices and sensors as well as computer vision to monitor buildings. Based upon the data that is collected, the AI system can adjust the building's systems to accommodate for the number of occupants, time of day, and more. AI helps facilities managers improve energy efficiency of the building. An additional component of many of these systems is building security as well.

Manufacturing

Heineken, along with many other companies, uses data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt. Machine vision can also support the quality control process at manufacturing facilities.

Accounting and Finance

Many organizations are finding the promise of cost reductions and more efficient operations the major appeal for artificial intelligence in the workplace, and according to Accenture Consulting, robotic process automation can produce amazing results in these areas for the accounting and finance industry and departments. Human finance professionals will be freed-up from repetitive tasks to be able to focus on higher-level activities while the use of AI in accounting will reduce errors. AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.

Customer Experience

Another way artificial intelligence technology and big data are used in business today is to improve the customer experience. Luxury fashion brand Burberry uses big data and AI to enhance sales and customer relationships. The company gathers shopper's data through loyalty and reward programs that they then use to offer tailored recommendations whether customers are shopping online or in brick-and-mortar stores. Innovative uses of chatbots during industry events are another way to provide a stellar customer experience.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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10 Business Functions That Are Ready To Use Artificial Intelligence - Forbes

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.

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

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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 Automobile Market Research Provides an In-Depth Analysis on the Future Growth Prospects and Industry Trends Adopted by the...