Category Archives: Machine Learning

Companies across industries are exploring and implementingartificial intelligence(AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According toMcKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Withoutquality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems asrules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, amachine learning systemis adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, butsupervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve.What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

This article was originally published byElana Krasner on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published June 13, 2020 13:00 UTC

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The key differences between rule-based AI and machine learning - The Next Web

Applications are now open for the nanodegree program, which will help Udacity train developers on the Microsoft Azure cloud infrastructure.

Microsoft and Udacity are teaming together to invest $4 million in a machine learning (ML) training collaboration, which begins with the Machine Learning Scholarship Program for Microsoft Azure which starts today.

The program focuses on artificial intelligence, which is continuing to grow at a face pace. AI engineers are in high demand, particularly as enterprises build new cloud applications and move old ones to the cloud. The average AI salary in the US is $114,121 a year based on data from Glassdoor.

"AI is driving transformation across organizations and there is increased demand for data science skills," said Julia White, corporate vice president, Azure Marketing, Microsoft, in a Microsoft blog post. "Through our collaboration with Udacity to offer low-code and advanced courses on Azure Machine Learning, we hope to expand data science expertise as experienced professionals will truly be invaluable resources to solving business problems."

SEE: Building the bionic brain (free PDF) (TechRepublic)

The interactive scholarship courses begin with a two-month long course, "Introduction to machine learning on Azure with a low-code experience."

Students will work with live Azure environments directly within the Udacity classroom and build on these foundations with advanced techniques such as ensemble learning and deep learning.

To earn a spot in th foundations course, students will need to submit an application. According to the blog post, "Successful applicants will ideally have basic programming knowledge in any language, preferably Python, and be comfortable writing scripts and performing loop operations."

Udacity's nanodegrees have been growing in popularity. Monthly enrollment in Udacity's nanodegrees has increased by a factor of four since the beginning of the coronavirus lockdown. Among Udacity's consumer customers, in the three weeks starting March 9 the company saw a 56% jump in weekly active users and a 102% increase in new enrollments, and they've stayed at or just below those new levels since then, according to a Udacity spokesperson.

After students complete the foundations course, Udacity will select top performers to receive a scholarship to the new machine learning nanodegree program with Microsoft Azure.

This typically four-month nanodegree program will include:

Students who aren't selected for the scholarship will still be able to enroll in the nanodegree program when it is available to the general public.

Anyone interested in becoming an Azure Machine Learning engineer and learning from experts at the forefront of the field can apply for the scholarshiphere.Applications will be open from June 10 to June 30.

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Microsoft and Udacity partner in new $4 million machine-learning scholarship program for Microsoft Azure - TechRepublic

LOS ALAMITOS, Calif., June 12, 2020 /PRNewswire/ --The Computer Vision and Pattern Recognition (CVPR) Conference, one of the largest events exploring artificial intelligence, machine learning, computer vision, deep learning, and more, will take place 14-19 June as a fully virtual event. Over the course of six days, the event will feature 45 sessions delivered by 1467 leading authors, academics, and experts to more than 6500 attendees, who have already registered for the event.

"The excitement, enthusiasm, and support for CVPR from the global community has never been more apparent," said Professor of Computer Science at Cornell University and Co-Chair of the CVPR 2020 Committee Ramin Zabih. "With large attendance, state of the art research, and insights delivered by some of the leading authorities in computer vision, AI, and machine learning, our first-ever fully virtual event is shaping up to be an exciting experience for everyone involved."

As a fully virtual event, attendees will have access to all CVPR program components, including fireside chats, workshops, tutorials, and oral and poster presentations via a robust, fully searchable, password-protected portal. Credentials to access the portal are provided to attendees shortly upon registration.

CVPR fireside chats, workshops, and tutorials will be conducted via live video with live Q&A between presenters and participants. Oral and poster presentations, which will be repeated, will include a pre-recorded video from the presenter(s), followed by a live Q&A session. Attendees will also be able to access presentations/papers and the pre-recorded videos at their convenience to help ensure maximum access given the diverse time zones in which conference participants live. Additionally, CVPR participants can leverage complementary video chat features and threaded question and answer commenting associated with each session and each sponsor to support further knowledge sharing and understanding. Multiple online networking events with video and text chat elements are also included.

"The CVPR Committee has gone to great lengths to deliver a first-in-class virtual conference experience that all attendees can enjoy," said IEEE Computer Society Executive Director Melissa Russell, co-sponsor of the event. "We are thrilled to be part of this endeavor and are excited to deliver and witness in the coming days the 'what's next' in AI, computer vision and machine learning."

Details on the full virtual CVPR 2020 schedule can be found on the conference website at http://cvpr2020.thecvf.com/program. All times are Pacific Daylight Time (Seattle Time).

Interested individuals can still register for CVPR at http://cvpr2020.thecvf.com/attend/registration. Accredited members of the media can register for the CVPR virtual conference by emailing [emailprotected].

About CVPR 2020CVPR is the premier annual computer vision and pattern recognition conference. With first-in-class technical content, a main program, tutorials, workshops, a leading-edge expo, and attended by more than 9,000 people annually, CVPR creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation. CVPR 2020, originally scheduled to take place 14-19 June 2020 at the Washington State Convention Center in Seattle Washington, will now be a fully virtual event. Authors and presenters will virtually deliver presentations and engage in live Q&A with attendees. For more information about CVPR 2020, the program, and how to participate virtually, visit http://cvpr2020.thecvf.com/.

About the Computer Vision FoundationThe Computer Vision Foundation is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV).

About the IEEE Computer SocietyThe IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit http://www.computer.org for more information.

SOURCE IEEE Computer Society

http://www.computer.org

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CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday - PRNewswire

As companies step up the use of machine learning-enabled systems in their day-to-day operations, they become increasingly reliant on those systems to help them make critical business decisions. In some cases, the machine learning systems operate autonomously, making it especially important that the automated decision-making works as intended.

However, machine learning-based systems are only as good as the data that's used to train them. If there are inherent biases in the data used to feed a machine learning algorithm, the result could be systems that are untrustworthy and potentially harmful.

In this article, you'll learn why bias in AI systems is a cause for concern, how to identify different types of biases and six effective methods for reducing bias in machine learning.

The power of machine learning comes from its ability to learn from data and apply that learning experience to new data the systems have never seen before. However, one of the challenges data scientists have is ensuring that the data that's fed into machine learning algorithms is not only clean, accurate and -- in the case of supervised learning, well-labeled -- but also free of any inherently biased data that can skew machine learning results.

The power of supervised learning, one of the core approaches to machine learning, in particular depends heavily on the quality of the training data. So it should be no surprise that when biased training data is used to teach these systems, the results are biased AI systems. Biased AI systems that are put into implementation can cause problems, especially when used in automated decision-making systems, autonomous operation, or facial recognition software that makes predictions or renders judgment on individuals.

Some notable examples of the bad outcomes caused by algorithmic bias include: a Google image recognition system that misidentified images of minorities in an offensive way; automated credit applications from Goldman Sachs that have sparked an investigation into gender bias; and a racially biased AI program used to sentence criminals. Enterprises must be hyper-vigilant about machine learning bias: Any value delivered by AI and machine learning systems in terms of efficiency or productivity will be wiped out if the algorithms discriminate against individuals and subsets of the population.

However, AI bias is not only limited to discrimination against individuals. Biased data sets can jeopardize business processes when applied to objects and data of all types. For example, take a machine learning model that was trained to recognize wedding dresses. If the model was trained using Western data, then wedding dresses would be categorized primarily by identifying shades of white. This model would fail in non-Western countries where colorful wedding dresses are more commonly accepted. Errors also abound where data sets have bias in terms of the time of day when data was collected, the condition of the data and other factors.

All of the examples described above represent some sort of bias that was introduced by humans as part of their data selection and identification methods for training the machine learning model. Because the systems technologists build are necessarily colored by their own experiences, they must be very aware that their individual biases can jeopardize the quality of the training data. Individual bias, in turn, can easily become a systemic bias as bad predictions and unfair outcomes are automated.

Part of the challenge of identifying bias is due to the difficulty of seeing how some machine learning algorithms generalize their learning from the training data. In particular, deep learning algorithms have proven to be remarkably powerful in their capabilities. This approach to neural networks leverages large quantities of data, high performance compute power and a sophisticated approach to efficiency, resulting in machine learning models with profound abilities.

Deep learning, however, is a "black box." It's not clear how an individual decision was arrived at by the neural network predictive model. You can't simply query the system and determine with precision which inputs resulted in which outputs. This makes it hard to spot and eliminate potential biases when they arise in the results. Researchers are increasingly turning their focus on adding explainability to neural networks. Verification is the process of proving the properties of neural networks. However, because of the size of neural networks, it can be hard to check them for bias.

Until we have truly explainable systems, we must understand how to recognize and measure AI bias in machine learning models. Some of the biases in the data sets arise from the selection of training data sets. The model needs to represent the data as it exists in the real world. If your data set is artificially constrained to a subset of the population, you will get skewed results in the real world, even if it performs very well against training data. Likewise, data scientists must take care in how they select which data to include in a training data set and which features or dimensions are included in the data for machine learning training.

Companies are combating inherent data bias by implementing programs to not only broaden the diversity of their data sets, but also the diversity of their teams. More diversity on teams means that people of many perspectives and varied experiences are feeding systems the data points to learn from. Unfortunately, the tech industry today is very homogeneous; there are not many women or people of color in the field. Efforts to diversify teams should also have a positive impact on the machine learning models produced, since data science teams will be better able to understand the requirements for more representative data sets.

There are a few sources for the bias that can have an adverse impact on machine learning models. Some of these are represented in the data that is collected and others in the methods used to sample, aggregate, filter and enhance that data.

There are no doubt other types of bias that might be represented in the data set than just the ones listed above, and all those forms should be identified early in the machine learning project.

1. Identify potential sources of bias. Using the above sources of bias as a guide, one way to address and mitigate bias is to examine the data and see how the different forms of bias could impact the data being used to train the machine learning model. Have you selected the data without bias? Have you made sure there isn't any bias arising from errors in data capture or observation? Are you making sure not to use an historic data set tainted with prejudice or confirmation bias? By asking these questions you can help to identify and potentially eliminate that bias.

2. Set guidelines and rules for eliminating bias and procedures. To keep bias in check, organizations should set guidelines, rules and procedures for identifying, communicating and mitigating potential data set bias. Forward-thinking organizations are documenting cases of bias as they occur, outlining the steps taken to identify bias, and explaining the efforts taken to mitigate bias. By establishing these rules and communicating them in an open, transparent manner, organizations can put the right foot forward to address issues of machine learning model bias.

3. Identify accurate representative data. Prior to collecting and aggregating data for machine learning model training, organizations should first try to understand what a representative data set should look like. Data scientists should use their data analysis skills to understand the nature of the population that is to be modeled along with the characteristics of the data used to create the machine learning model. These two things should match in order to build a data set with as little bias as possible.

4. Document and share how data is selected and cleansed. Many forms of bias occur when selecting data from among large data sets and during data cleansing operations. In order to make sure few bias-inducing mistakes are made, organizations should document their methods of data selection and cleansing and allow others to examine when and if the models exhibit any form of bias. Transparency allows for root-cause analysis of sources of bias to be eliminated in future model iterations.

5. Evaluate model for performance and select least-biased, in addition to performance. Machine learning models are often evaluated prior to being placed into operation. Most of the time these evaluation steps focus on aspects of model accuracy and precision. Organizations should also add measures of bias detection in their model evaluation steps. Even if the model performs with certain levels of accuracy and precision for particular tasks, it could fail on measures of bias, which might point to issues with the training data.

6. Monitor and review models in operation. Finally, there is a difference between how the machine learning model performs in training and how it performs in the real world. Organizations should provide methods to monitor and continuously review the models as they perform in operation. If there are signs that certain forms of bias are showing up in the results, then the organization can take action before the bias causes irreparable harm.

When bias becomes embedded in machine learning models, it can have an adverse impact on our daily lives. The bias is exhibited in the form of exclusion, such as certain groups being denied loans or not being able to use the technology, or in the technology not working the same for everyone. As AI continues to become more a part of our lives, the risks from bias only grow larger. Companies, researchers and developers have a responsibility to minimize bias in AI systems. A lot of it comes down to ensuring that the data sets are representative and that the interpretation of data sets is correctly understood. However, just making sure that the data sets aren't biased won't actually remove bias, so having diverse teams of people working toward the development of AI remains an important goal for enterprises.

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6 ways to reduce different types of bias in machine learning - TechTarget

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Los Angeles, United State: QY Research recently published a research report titled, Global Machine Learning Chips Market Research Report 2020-2026. The research report attempts to give a holistic overview of the Machine Learning Chips market by keeping the information simple, relevant, accurate, and to the point. The researchers have explained each aspect of the market thoroughmeticulous research and undivided attention to every topic. They have also provided data in statistical data to help readers understand the whole market. The Machine Learning Chips Market report further provides historic and forecast data generated through primary and secondary research of the region and their respective manufacturers.

Get Full PDF Sample Copy of Report: (Including Full TOC, List of Tables & Figures, Chart) https://www.qyresearch.com/sample-form/form/1839774/global-machine-learning-chips-market

Global Machine Learning Chips Market report section gives special attention to the manufacturers in different regions that are expected to show a considerable expansion in their market share. Additionally, it underlines all the current and future trends that are being adopted by these manufacturers to boost their current market shares. This Machine Learning Chips Market report Understanding the various strategies being carried out by various manufacturers will help reader make right business decisions.

Key Players Mentioned in the Global Machine Learning Chips Market Research Report: Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing Machine Learning Chips

Global Machine Learning Chips Market Segmentation by Product: Neuromorphic Chip, Graphics Processing Unit (GPU) Chip, Flash Based Chip, Field Programmable Gate Array (FPGA) Chip, Other Machine Learning Chips

Global Machine Learning Chips Market Segmentation by Application: , Robotics Industry, Consumer Electronics, Automotive, Healthcare, Other

The Machine Learning Chips market is divided into the two important segments, product type segment and end user segment. In the product type segment it lists down all the products currently manufactured by the companies and their economic role in the Machine Learning Chips market. It also reports the new products that are currently being developed and their scope. Further, it presents a detailed understanding of the end users that are a governing force of the Machine Learning Chips market.

In this chapter of the Machine Learning Chips Market report, the researchers have explored the various regions that are expected to witness fruitful developments and make serious contributions to the markets burgeoning growth. Along with general statistical information, the Machine Learning Chips Market report has provided data of each region with respect to its revenue, productions, and presence of major manufacturers. The major regions which are covered in the Machine Learning Chips Market report includes North America, Europe, Central and South America, Asia Pacific, South Asia, the Middle East and Africa, GCC countries, and others.

Key questions answered in the report:

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Table of Content

1 Study Coverage1.1 Machine Learning Chips Product Introduction1.2 Key Market Segments in This Study1.3 Key Manufacturers Covered: Ranking of Global Top Machine Learning Chips Manufacturers by Revenue in 20191.4 Market by Type1.4.1 Global Machine Learning Chips Market Size Growth Rate by Type1.4.2 Neuromorphic Chip1.4.3 Graphics Processing Unit (GPU) Chip1.4.4 Flash Based Chip1.4.5 Field Programmable Gate Array (FPGA) Chip1.4.6 Other1.5 Market by Application1.5.1 Global Machine Learning Chips Market Size Growth Rate by Application1.5.2 Robotics Industry1.5.3 Consumer Electronics1.5.4 Automotive1.5.5 Healthcare1.5.6 Other1.6 Study Objectives1.7 Years Considered 2 Executive Summary2.1 Global Machine Learning Chips Market Size, Estimates and Forecasts2.1.1 Global Machine Learning Chips Revenue Estimates and Forecasts 2015-20262.1.2 Global Machine Learning Chips Production Capacity Estimates and Forecasts 2015-20262.1.3 Global Machine Learning Chips Production Estimates and Forecasts 2015-20262.2 Global Machine Learning Chips, Market Size by Producing Regions: 2015 VS 2020 VS 20262.3 Analysis of Competitive Landscape2.3.1 Manufacturers Market Concentration Ratio (CR5 and HHI)2.3.2 Global Machine Learning Chips Market Share by Company Type (Tier 1, Tier 2 and Tier 3)2.3.3 Global Machine Learning Chips Manufacturers Geographical Distribution2.4 Key Trends for Machine Learning Chips Markets & Products2.5 Primary Interviews with Key Machine Learning Chips Players (Opinion Leaders) 3 Market Size by Manufacturers3.1 Global Top Machine Learning Chips Manufacturers by Production Capacity3.1.1 Global Top Machine Learning Chips Manufacturers by Production Capacity (2015-2020)3.1.2 Global Top Machine Learning Chips Manufacturers by Production (2015-2020)3.1.3 Global Top Machine Learning Chips Manufacturers Market Share by Production3.2 Global Top Machine Learning Chips Manufacturers by Revenue3.2.1 Global Top Machine Learning Chips Manufacturers by Revenue (2015-2020)3.2.2 Global Top Machine Learning Chips Manufacturers Market Share by Revenue (2015-2020)3.2.3 Global Top 10 and Top 5 Companies by Machine Learning Chips Revenue in 20193.3 Global Machine Learning Chips Price by Manufacturers3.4 Mergers & Acquisitions, Expansion Plans 4 Machine Learning Chips Production by Regions4.1 Global Machine Learning Chips Historic Market Facts & Figures by Regions4.1.1 Global Top Machine Learning Chips Regions by Production (2015-2020)4.1.2 Global Top Machine Learning Chips Regions by Revenue (2015-2020)4.2 North America4.2.1 North America Machine Learning Chips Production (2015-2020)4.2.2 North America Machine Learning Chips Revenue (2015-2020)4.2.3 Key Players in North America4.2.4 North America Machine Learning Chips Import & Export (2015-2020)4.3 Europe4.3.1 Europe Machine Learning Chips Production (2015-2020)4.3.2 Europe Machine Learning Chips Revenue (2015-2020)4.3.3 Key Players in Europe4.3.4 Europe Machine Learning Chips Import & Export (2015-2020)4.4 China4.4.1 China Machine Learning Chips Production (2015-2020)4.4.2 China Machine Learning Chips Revenue (2015-2020)4.4.3 Key Players in China4.4.4 China Machine Learning Chips Import & Export (2015-2020)4.5 Japan4.5.1 Japan Machine Learning Chips Production (2015-2020)4.5.2 Japan Machine Learning Chips Revenue (2015-2020)4.5.3 Key Players in Japan4.5.4 Japan Machine Learning Chips Import & Export (2015-2020)4.6 South Korea4.6.1 South Korea Machine Learning Chips Production (2015-2020)4.6.2 South Korea Machine Learning Chips Revenue (2015-2020)4.6.3 Key Players in South Korea4.6.4 South Korea Machine Learning Chips Import & Export (2015-2020) 5 Machine Learning Chips Consumption by Region5.1 Global Top Machine Learning Chips Regions by Consumption5.1.1 Global Top Machine Learning Chips Regions by Consumption (2015-2020)5.1.2 Global Top Machine Learning Chips Regions Market Share by Consumption (2015-2020)5.2 North America5.2.1 North America Machine Learning Chips Consumption by Application5.2.2 North America Machine Learning Chips Consumption by Countries5.2.3 U.S.5.2.4 Canada5.3 Europe5.3.1 Europe Machine Learning Chips Consumption by Application5.3.2 Europe Machine Learning Chips Consumption by Countries5.3.3 Germany5.3.4 France5.3.5 U.K.5.3.6 Italy5.3.7 Russia5.4 Asia Pacific5.4.1 Asia Pacific Machine Learning Chips Consumption by Application5.4.2 Asia Pacific Machine Learning Chips Consumption by Regions5.4.3 China5.4.4 Japan5.4.5 South Korea5.4.6 India5.4.7 Australia5.4.8 Taiwan5.4.9 Indonesia5.4.10 Thailand5.4.11 Malaysia5.4.12 Philippines5.4.13 Vietnam5.5 Central & South America5.5.1 Central & South America Machine Learning Chips Consumption by Application5.5.2 Central & South America Machine Learning Chips Consumption by Country5.5.3 Mexico5.5.3 Brazil5.5.3 Argentina5.6 Middle East and Africa5.6.1 Middle East and Africa Machine Learning Chips Consumption by Application5.6.2 Middle East and Africa Machine Learning Chips Consumption by Countries5.6.3 Turkey5.6.4 Saudi Arabia5.6.5 U.A.E 6 Market Size by Type (2015-2026)6.1 Global Machine Learning Chips Market Size by Type (2015-2020)6.1.1 Global Machine Learning Chips Production by Type (2015-2020)6.1.2 Global Machine Learning Chips Revenue by Type (2015-2020)6.1.3 Machine Learning Chips Price by Type (2015-2020)6.2 Global Machine Learning Chips Market Forecast by Type (2021-2026)6.2.1 Global Machine Learning Chips Production Forecast by Type (2021-2026)6.2.2 Global Machine Learning Chips Revenue Forecast by Type (2021-2026)6.2.3 Global Machine Learning Chips Price Forecast by Type (2021-2026)6.3 Global Machine Learning Chips Market Share by Price Tier (2015-2020): Low-End, Mid-Range and High-End 7 Market Size by Application (2015-2026)7.2.1 Global Machine Learning Chips Consumption Historic Breakdown by Application (2015-2020)7.2.2 Global Machine Learning Chips Consumption Forecast by Application (2021-2026) 8 Corporate Profiles8.1 Wave Computing8.1.1 Wave Computing Corporation Information8.1.2 Wave Computing Overview8.1.3 Wave Computing Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.1.4 Wave Computing Product Description8.1.5 Wave Computing Related Developments8.2 Graphcore8.2.1 Graphcore Corporation Information8.2.2 Graphcore Overview8.2.3 Graphcore Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.2.4 Graphcore Product Description8.2.5 Graphcore Related Developments8.3 Google Inc8.3.1 Google Inc Corporation Information8.3.2 Google Inc Overview8.3.3 Google Inc Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.3.4 Google Inc Product Description8.3.5 Google Inc Related Developments8.4 Intel Corporation8.4.1 Intel Corporation Corporation Information8.4.2 Intel Corporation Overview8.4.3 Intel Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.4.4 Intel Corporation Product Description8.4.5 Intel Corporation Related Developments8.5 IBM Corporation8.5.1 IBM Corporation Corporation Information8.5.2 IBM Corporation Overview8.5.3 IBM Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.5.4 IBM Corporation Product Description8.5.5 IBM Corporation Related Developments8.6 Nvidia Corporation8.6.1 Nvidia Corporation Corporation Information8.6.2 Nvidia Corporation Overview8.6.3 Nvidia Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.6.4 Nvidia Corporation Product Description8.6.5 Nvidia Corporation Related Developments8.7 Qualcomm8.7.1 Qualcomm Corporation Information8.7.2 Qualcomm Overview8.7.3 Qualcomm Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.7.4 Qualcomm Product Description8.7.5 Qualcomm Related Developments8.8 Taiwan Semiconductor Manufacturing8.8.1 Taiwan Semiconductor Manufacturing Corporation Information8.8.2 Taiwan Semiconductor Manufacturing Overview8.8.3 Taiwan Semiconductor Manufacturing Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.8.4 Taiwan Semiconductor Manufacturing Product Description8.8.5 Taiwan Semiconductor Manufacturing Related Developments 9 Machine Learning Chips Production Forecast by Regions9.1 Global Top Machine Learning Chips Regions Forecast by Revenue (2021-2026)9.2 Global Top Machine Learning Chips Regions Forecast by Production (2021-2026)9.3 Key Machine Learning Chips Production Regions Forecast9.3.1 North America9.3.2 Europe9.3.3 China9.3.4 Japan9.3.5 South Korea 10 Machine Learning Chips Consumption Forecast by Region10.1 Global Machine Learning Chips Consumption Forecast by Region (2021-2026)10.2 North America Machine Learning Chips Consumption Forecast by Region (2021-2026)10.3 Europe Machine Learning Chips Consumption Forecast by Region (2021-2026)10.4 Asia Pacific Machine Learning Chips Consumption Forecast by Region (2021-2026)10.5 Latin America Machine Learning Chips Consumption Forecast by Region (2021-2026)10.6 Middle East and Africa Machine Learning Chips Consumption Forecast by Region (2021-2026) 11 Value Chain and Sales Channels Analysis11.1 Value Chain Analysis11.2 Sales Channels Analysis11.2.1 Machine Learning Chips Sales Channels11.2.2 Machine Learning Chips Distributors11.3 Machine Learning Chips Customers 12 Market Opportunities & Challenges, Risks and Influences Factors Analysis12.1 Machine Learning Chips Industry12.2 Market Trends12.3 Market Opportunities and Drivers12.4 Market Challenges12.5 Machine Learning Chips Market Risks/Restraints12.6 Porters Five Forces Analysis 13 Key Finding in The Global Machine Learning Chips Study 14 Appendix14.1 Research Methodology14.1.1 Methodology/Research Approach14.1.2 Data Source14.2 Author Details14.3 Disclaimer

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Machine Learning Chips Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - 3rd Watch News