China and the US are the two leading AI superpowers that have dominated research

Microsoft Corp co-founder Bill Gates spoke out against protectionism in technological research around topics like artificial intelligence, arguing that open systems will inevitably win out over closed ones.

In conversation with Bloomberg News editor-in-chief John Micklethwait at the New Economy Forum in Beijing on Thursday, Gates was skeptical about the idea that ongoing United States (US)-China trade tensions could ever lead to a bifurcated system of two internets and two mutually exclusive strands of tech research and development.

AI is very hard to put back in the bottle, Gates said, and whoever has an open system will get massively ahead by virtue of being able to integrate more insights from more sources. Citing Microsoft’s AI research in Beijing, Gates pondered the rhetorical question of whether it was producing Chinese AI or American AI. In the case of Microsoft’s UK research campus in Cambridge and the findings it produces, he said that almost every one of those papers is going to have some Chinese names on it, some European names on it and some Americans names on it.

China and the US are the two leading AI superpowers that have dominated research, however cooling political relations between them have slowed the international collaboration that underpins innovation. Huawei Technologies Co, Beijing’s tech champion, has been subject to a variety of sanctions from Washington, in part because Chinas rapid AI development is perceived as a rising threat.

Gates said he was more worried today than five years ago about the rise of nationalist and protectionist political tendencies across the globe, and that he now wonders whether that will prove a cyclical trend or a more permanent change. Still, as far as the US and China were concerned, he said hes even more passionate about the value of engagement than ever.
The other key takeaways from the talk

Gates said there is no doubt solar and wind are key parts of a new energy mix needed to battle climate change. “Quite a bit of nuclear may be required to fill in for fossil fuels as we move to zero carbon. But he doubts a carbon tax would be realistic in the US Republicans have largely sworn off the idea and, by and large, he said, Democrats are not pushing it as a key priority, either. The ability of political leaders to convince their electorates of the benefits and value of globalization has gone down,” said Gates.

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As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications such as online learning and incremental learning

In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. memory, CPU, energy, bandwidth).

Here are a few methods that are part of all compression techniques:

Parameter Pruning And Sharing

• Reducing redundant parameters which are not sensitive to the performance
• Robust to various settings
• Redundancies in the model parameters are explored and the uncritical yet redundant ones are removed

Low-Rank Factorisation

• Uses matrix decomposition to estimate the informative parameters of the deep convolutional neural networks

Transferred/Compact Convolutional Filters

• Special structural convolutional filters are designed to reduce the parameter space and save storage/computation

Knowledge Distillation

• A distilled model is used to train a more compact neural network to reproduce the output of a larger network

Now let’s take a look at a few papers that introduced novel compression models:

1.Deep Neural Network Compression with Single and Multiple Level Quantization

In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ).

The network quantization is considered from both width and depth level.

2.Efficient Neural Network Compression

In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration.

3.3LC: Lightweight and Effective Traffic Compression

3LC is a lossy compression scheme developed by the Google researchers that can be used for state change traffic in distributed machine learning (ML) that strikes a balance between multiple goals: traffic reduction, accuracy, computation overhead, and generality. It combines three techniques — value quantization with sparsity multiplication, base encoding, and zero-run encoding.SEE ALSO

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4.Universal Deep Neural Network Compression

This work for the first time, introduces universal DNN compression by universal vector quantization and universal source coding. In particular, this paper examines universal randomised lattice quantization of DNNs, which randomises DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution.

5.Compression using Transform Coding and Clustering

The compression (encoding) approach consists of transform and clustering with great encoding efficiency, which is expected to fulfill the requirements towards the future deep model communication and transmission standard. Overall, the framework works towards light weight model encoding pipeline with uniform quantization and clustering has yielded great compression performance, which can be further combined with existing deep model compression approaches towards light-weight models.

6.Weightless: Lossy Weight Encoding

The encoding is based on the Bloomier filter, a probabilistic data structure that saves space at the cost of introducing random errors. The results show that this technique can compress DNN weights by up to 496x; with the same model accuracy, this results in up to a 1.51x improvement over the state-of-the-art.

7.Adaptive Estimators Show Information Compression

The authors developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, they explored compression in networks with a range of different activation functions. 

8.MLPrune: Multi-Layer Pruning For Neural Network Compression

It is computationally expensive to manually set the compression ratio of each layer to find the sweet spot between size and accuracy of the model. So,in this paper, the authors propose a Multi-Layer Pruning method (MLPrune), which can automatically decide appropriate compression ratios for all layers.

Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments. The above-discussed techniques achieve not only higher model compression but also reduce the compute resources required during inferencing. This enables model deployment in mobile phones, IoT edge devices as well as “inferencing as a service” environments on the cloud.

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Machine learning is one of the most promising technologies today that makes it possible for machines to learn how the human brain works and replicate this learning to analyze varied data types and deduce meaningful insights.

Machine learning models

At the core of machine learning are three models that help machines unearth insights and patterns. These are:

• Supervised models: These are used with historical data where the output is pre-defined. For instance, when you speak, Alexa can recognize the words and sentences she has been trained on and respond appropriately.
• Unsupervised models: These are used on transactional data to identify patterns. Based on your interaction with Alexa, she can identify the patterns to suggest topics you may be interested in.
• Reinforcement learning: It is a technique where machines learn to respond to situations on their own, without instructions. For every mistake (a negative outcome) that Alexa makes, she ‘learns’ from it to become smarter and refine the response next time.

FIs can benefit the most from machine learning

Businesses are increasingly leaning on machine learning, as volumes of data are exploding and they need actionable insights to fuel business growth. Given the benefits it promises, numerous industries—manufacturing, energy, healthcare, cyber defense, financial institutions—are making significant investments in machine learning. In fact, financial institutions (FIs) stand to benefit the most from machine learning, according to a PwC report.

Money-rich FIs, especially banks, have always been a favorite target for criminals. And, today’s technological advancements have provided cyber criminals with sophisticated techniques—data breach, phishing, malware, sweatshops, and so forth—to break into business systems and cause losses.

Machine learning, with its innate ability to monitor millions of online transactions in real-time, can help financial institutions in a myriad of ways.

• Document interpretation: Machine learning helps financial institutions interpret financial and legal documents—bank statements, tax statements, contracts, etc—across a wide range of parameters that help gain in-depth insights into customers’ financial health.
• Risk management: Financial institutions can accurately assess the credit-worthiness of a customer—whether an individual or a company—and make informed lending decisions for improved risk management.
• Additional revenue: Using analytics to understand customer preferences and inclination to spend, financial institutions can harness these insights to pitch other products and services to increase their revenue.
• Customer service: Applying behavioral analytics, banks and financial institutions can better understand the financial needs of their customers and offer more relevant services. This enables financial institutions to strengthen customer relationships and earn their trust.
• Channel-agnostic access: Leveraging customer data to anticipate customers’ channel preferences, financial institutions can provide seamless user experience to their customers across devices and locations.
• Process automation: Machine learning helps financial institutions make automated decisions in real-time that reduces the response time. According to Accenture, FIs can reduce costs incurred on middle and back offices across infrastructure, maintenance, and operations by 20-25%.
• Security: With fraud on the rise, financial institutions are obliged to ensure online security of their customers. Customer security is the most important area where machine learning has proved immensely helpful in fighting fraud by accurately identifying fraudsters from a group of authentic customers. Real-time analysis of digital intelligence enables financial institutions to prevent fraud from poisoning their business ecosystem, thereby providing customers with a safe and secure online journey.

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This report focuses on the Global Machine Learning and Location Data Applications Market landscape, future outlook, growth opportunities, and key and key contacts. The research objective is to present the development of the market in the US, Europe and Others. In addition, industry development trends and marketing channels are analyzed. The industry analysis have also been done to examine the impact of various factors and understand the overall attractiveness of the industry.

Machine Learning and Location Data Applications Market scenario, many emerging cities across the globe are struggling for traffic congestion, increased fuel consumption, and deteriorated air quality. Increasing fuel consumption has secured a long-term energy security of several countries, making them increasingly susceptible to global oil supply fluctuations. Bike-sharing services majorly minimize the long streaks of traffic, moreover, allowing countries to manage environmental challenges and energy dependency effectively.

The report also provides an analysis of the market competitive landscape and offers information on several companies including Microsoft Corporation, SAP SE,Sas Institute Inc., Amazon Web Services, Inc., Bigml, Inc., Google Inc., Fair Isaac Corporation, Baidu, Inc., Hewlett Packard Enterprise Development Lp, Intel Corporation

The report provides a comprehensive assessment of the market. We do this through in-depth qualitative insights, historical data and verifiable prospects for market size. The outlook presented in the report was derived using proven methodology and assumptions. Through this, the research report serves as a repository for analysis and information on all aspects of the market, including, but not limited to, local markets, technologies, types and applications.

The detailed qualitative and quantitative analysis of the market is also included in the report, with the information collected from market participants operating in the main areas of the value-added series of markets. A separate analysis of macro-and micro-economic aspects, rules and trends that affect the overall development of the market has also been included in the report.

Following are the List of Chapter Covers in the Machine Learning and Location Data Applications Market:

1. Machine Learning and Location Data Applications Market Overview
2. Global Economic Impact on Industry
3. Global Market Competition by Manufacturers
4. Global Market Analysis by Application
5. Marketing Strategy Analysis, Distributors/Traders
6. Market Effect Factors Analysis
7. Global Machine Learning and Location Data Applications Market Forecast

About Us

We at, QYReports, a leading market research report published accommodate more than 4,000 celebrated clients worldwide putting them at advantage in today’s competitive world with our understanding of research. Our list of customers includes prestigious Chinese companies, multinational companies, SME’s and private equity firms whom we have helped grow and sustain with our fact-based research. Our business study covers a market size of over 30 industries offering unfailing insights into the analysis to reimagine your business. We specialize in forecasts needed for investing in a new project, to revolutionize your business, to become more customer centric and improve the quality of output.

The post Know How Machine Learning and Location Data Applications Market Is Thriving Continuously By Top Key Players SAP SE, Sas Institute Inc., Bigml, Inc., Google Inc., Baidu, Inc appeared first on Artificial Intelligence.

Thanks to the technological advancements in the field of data analytics, the global market for neutral network software is witnessing an exponential rise in its size and revenue. Since neutral network software is highly effective in reducing the cost and operational time in a number of enterprises, its usage in business application, such as such as fraud detection and risk assessment, is increased by leaps and bounds.

The neural network software market is majorly driven by the remarkable rise in the demand for data archiving tools, used for organizing a massive amount of unorganized data created by various end users. Additionally, the high adoption rate of digital technologies and the increasing demand for predicting solutions are likely to boost this market in the near future. However, the slow digitization rate across emerging markets, dearth of technical expertise, and various other operational challenges may hinder the neural network software market’ growth over the forthcoming years.

Analytical software, data mining and archiving software, and optimization software are the key products available in this market. Currently, the demand for analytical software is higher than other neutral network software. However, the data mining and archiving software is expected to witness a high-paced demand growth over the next few years, thanks to the rising need for the classification and clustering of unorganized data. The significant areas where neural network software find application is financial operations, trading, business analytics, and product maintenance.

Global Neural Network Software Market: Overview

Large-scale digitization and seamless connectivity of a vast variety of electronic end-points and sensors are two important aspects common to all enterprises that call themselves technologically advanced and digitally competent. To be able to make use of the vast volumes of data generated from interactions between the connected entities and apply it for the benefit of the business, effective analytical, predictive tools are required. Artificial neural networks, the computational devices, which could be either an algorithm or an actual hardware, are modeled after the operations and structure of neural network of living beings.

Owing to their ability to learn from the inputs provided, much as their biological counterparts, artificial neural networks are considered to be the future of data analytics. A neural network software simulates an artificial neural network algorithm for use in a computer system and is used to apply the concepts of artificial neural networks to input data.

This report on the global neural network software market presents a detailed overview of the present growth dynamics of the market and its key segments. The report includes several forward-looking quantitative and qualitative projections about aspects such as market valuation, overall sales, demand and supply statistics in key regional markets, and overall future growth prospects. The neural network software market report also presents a detailed overview of the factors expected to have a notable impact on the overall development of the market in the next few years, including growth drivers, challenges, regulatory aspects across key regional markets, opportunities, and level of competition.

Global Neural Network Software Market: Geographical Dynamics

For the study, the global market for neural network software has been segmented in terms of geography into regions such as North America, Europe, Asia Pacific, and Middle East and Africa. Of these, North America is presently the leading market in terms of revenue contribution to the global market as well as technological advancements in the field of neural network. The region leads owing to the presence of a large number of technology companies excelling in the field of neural networks, large number of enterprises with highly digitized and technologically advanced ecosystems who could be potential buyers of neural network software.

In the next few years, however, regions such as Asia Pacific and Middle East and Africa are expected to emerge as the ones with the most promising growth prospects. Rising investment in smart cities, focus on digitization of processes and operations across industrial, commercial, and public sectors, and an increasing number of enterprises adopting technological implementation would foster the growth prospects of the neural network software market in these regions.

Global Neural Network Software Market: Competitive Landscape

Some of the world’s leading tech giants such as Google Inc., Microsoft Corporation, IBM, Intel Corporation, Qualcomm Technologies Inc., and Oracle are investing vast capital and human resources towards the development of neural networks that most closely resemble and work like the highly complex biological neural network. The market is also witnessing the entry of a large number of small- and medium-sized companies, which are helping the market gain strength through innovative neural network software solutions and systems for a vast range of applications.

Other than the technology companies mentioned above, some more of the neural network software market’s most notable vendors are GMDH, Llc, Neural Technologies Limited, Afiniti, SAP SE, Ward Systems Group, Inc., Alyuda Research, Llc., Slagkryssaren Ab, Starmind International Ag, Neuralware, Slagkryssaren AB, Swiftkey, and Starmind International AG.

About TMR Research:

TMR Research is a premier provider of customized market research and consulting services to business entities keen on succeeding in today’s supercharged economic climate. Armed with an experienced, dedicated, and dynamic team of analysts, we are redefining the way our clients’ conduct business by providing them with authoritative and trusted research studies in tune with the latest methodologies and market trends.

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Companies across the globe are quickly realizing that big data and analytics can help their operational effectiveness. Companies are trying to integrate new technologies into their current business plans and even going to lengths of to improve the efficiency of their operations.

Many companies have been asking the time-honored questions, how do we improve quality; how do we keep production at sustainable levels; how do we improve our current business model? Big data is playing a major role in this change, and these companies are embracing the fact that they have to use this data purposely.

Today, we will explore in more depth about the new era of big data and its potential untapped multi-billion dollar market.

Big Data Beginnings

Originally big data started off as only a term to describe a dataset that was beyond its scope of the average database. Thanks to the rise of technology over the last decade the term stuck and big data expanded giving birth to a whole new meaning. Big data expands upon being massive in its data set as well as being a collective set of technologies that manage, capture, and store this data in order that it may solve complex problems.

Dynamics of Big Data and Software Companies

Currently, many of the bridges between big data and software lie in the Internet of Things (IoT) connecting and tying everything together with the help of major Internet service providers. However, looking at this from the big data side many companies want to have all the answers with analytics and insight. On the other side of the coin, you have the software supplier wanting to provide a front end to their customers to be able to explain what’s going on.

The IoT has been providing a bridge to tie these ideas together in order to evolve the transition. The value behind this entire process lies in scaling solutions for different companies based on solutions that they need. By finding solutions in small percentages it could save corporations millions if not billions in efficiency gains. This is being accomplished by examining data from a variety of sources in the rapidly developing market.

Risks Involved For Companies Looking For Big Data Solutions

Right now, there are smaller big data companies in the marketplace who have one-stop fixes for a few scaling solutions. However, many companies are looking for a distinctive new technology that is different from what the average company in the marketplace is offering as it has to cater to their needs in terms of big data. After all, every business has different business functions that make them distinct from their competitors.

Final Thoughts

Right now we are in the birthing period of big data in the digital frontier, and it will be an exciting time to use the bridge of the IoT to plug in missing gaps. In the next couple years, there will be a real opportunity to lead the industry in a new direction by using big data to escalate technology into the edge of the digital spectrum.

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