Category Archives: Machine Learning
Impact of COVID-19 Outbreak on Artificial Intelligence and Machine Learning Market to Witness AIBrain, Amazon, Anki, CloudMinds – Cole of Duty
Artificial Intelligence and Machine Learning Market 2020
This report studies the Artificial Intelligence and Machine Learning Market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Artificial Intelligence and Machine Learning Market analysis segmented by companies, region, type and applications in the report.
The major players covered in Artificial Intelligence and Machine Learning AIBrain, Amazon, Anki, CloudMinds, Deepmind, Google, Facebook, IBM, Iris AI, Apple, and Luminoso
The final report will add the analysis of the Impact of Covid-19 in this report Artificial Intelligence and Machine Learning industry.
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Artificial Intelligence and Machine Learning Market continues to evolve and expand in terms of the number of companies, products, and applications that illustrates the growth perspectives. The report also covers the list of Product range and Applications with SWOT analysis, CAGR value, further adding the essential business analytics. Artificial Intelligence and Machine Learning Market research analysis identifies the latest trends and primary factors responsible for market growth enabling the Organizations to flourish with much exposure to the markets.
Market Segment by Regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia and Italy)
Asia-Pacific (China, Japan, Korea, India and Southeast Asia)
South America (Brazil, Argentina, Colombia etc.)
Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)
Research objectives:
To study and analyze the global Artificial Intelligence and Machine Learning market size by key regions/countries, product type and application, history data from 2013 to 2017, and forecast to 2026.
To understand the structure of Artificial Intelligence and Machine Learning market by identifying its various sub segments.
Focuses on the key global Artificial Intelligence and Machine Learning players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in next few years.
To analyze the Artificial Intelligence and Machine Learning with respect to individual growth trends, future prospects, and their contribution to the total market.
To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
To project the size of Artificial Intelligence and Machine Learning submarkets, with respect to key regions (along with their respective key countries).
To analyze competitive developments such as expansions, agreements, new product launches and acquisitions in the market.
To strategically profile the key players and comprehensively analyze their growth strategies.
The Artificial Intelligence and Machine Learning Market research report completely covers the vital statistics of the capacity, production, value, cost/profit, supply/demand import/export, further divided by company and country, and by application/type for best possible updated data representation in the figures, tables, pie chart, and graphs. These data representations provide predictive data regarding the future estimations for convincing market growth. The detailed and comprehensive knowledge about our publishers makes us out of the box in case of market analysis.
Table of Contents: Artificial Intelligence and Machine Learning Market
Chapter 1: Overview of Artificial Intelligence and Machine Learning Market
Chapter 2: Global Market Status and Forecast by Regions
Chapter 3: Global Market Status and Forecast by Types
Chapter 4: Global Market Status and Forecast by Downstream Industry
Chapter 5: Market Driving Factor Analysis
Chapter 6: Market Competition Status by Major Manufacturers
Chapter 7: Major Manufacturers Introduction and Market Data
Chapter 8: Upstream and Downstream Market Analysis
Chapter 9: Cost and Gross Margin Analysis
Chapter 10: Marketing Status Analysis
Chapter 11: Market Report Conclusion
Chapter 12: Research Methodology and Reference
Key questions answered in this report
What will the market size be in 2026 and what will the growth rate be?
What are the key market trends?
What is driving this market?
What are the challenges to market growth?
Who are the key vendors in this market space?
What are the market opportunities and threats faced by the key vendors?
What are the strengths and weaknesses of the key vendors?
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Machine Learning Market Projected to Register 43.5% CAGR to 2030 Intel, H2Oai – 3rd Watch News
A report Machine Learning has been recently published by Market Industry Reports (MIR). As per the report, the global machine learning market was estimated to be over ~US$ 2.7 billion in 2019. It is anticipated to grow at a CAGR of 43.5% from 2019 to 2030.
Major Key Players of the Machine Learning Market are:Intel, H2O.ai, Amazon Web Services, Hewlett Packard Enterprise Development LP, IBM, Google LLC, Microsoft, SAS Institute Inc., SAP SE, and BigML, Inc., among others.
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There are various factors attributing to growth of the machine learning market including the availability of robust data sets and the adoption of machine learning techniques in modern applications such as self-driving cars, traffic alerts (Google Maps), product recommendations (Amazon), and transportation & commuting (Uber). Also, the adoption of machine learning across various industries, such as the finance industry, to minimize identity theft and detect fraud is adding to growth of the machine learning market.
Technologies powered by machine learning, capture and analyse data to improve marketing operations and enhance the customer experience. Moreover, the proliferation of large datasets, technological advancements, and techniques to provide a competitive edge in business operations are among major factors that will drive the machine learning market. Rapid urbanization, acceptance of machine learning in developed countries, rapid adoption of new technologies to minimize work and the presence of a large talent pool will push the machine learning market.
Major Applications of Machine Learning Market covered are:Healthcare & Life SciencesManufacturing, RetailTelecommunicationsGovernment and DefenseBFSI (Banking, financial services, and insurance)Energy and Utilities and Others
Research objectives:-
To study and analyze the global Machine Learning consumption (value & volume) by key regions/countries, product type and application, history data. To understand the structure of the Machine Learning market by identifying its various sub-segments. Focuses on the key global Machine Learning manufacturers, to define, describe and analyze the sales volume, value, market share, market competitive landscape, SWOT analysis, and development plans in the next few years. To analyze the Machine Learning with respect to individual growth trends, future prospects, and their contribution to the total market. To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
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Table of Content
1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered
2 Global Growth Trends2.1 Machine Learning Market Size2.2 Machine Learning Growth Trends by Regions2.3 Industry Trends
3 Market Share by Key Players3.1 Machine Learning Market Size by Manufacturers3.2 Machine Learning Key Players Head office and Area Served3.3 Key Players Machine Learning Product/Solution/Service3.4 Date of Enter into Machine Learning Market3.5 Mergers & Acquisitions, Expansion Plans
4 Breakdown Data by Product4.1 Global Machine Learning Sales by Product4.2 Global Machine Learning Revenue by Product4.3 Machine Learning Price by Product
5 Breakdown Data by End User5.1 Overview5.2 Global Machine Learning Breakdown Data by End User
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In the end, Machine Learning industry report specifics the major regions, market scenarios with the product price, volume, supply, revenue, production, and market growth rate, demand, forecast and so on. This report also presents SWOT analysis, investment feasibility analysis, and investment return analysis.
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Market Industry Reports is a global leader in market measurement & advisory services, Market Industry Reports is at the forefront of innovation to address the worldwide industry trends and opportunities. We identified the caliber of market dynamics & hence we excel in the areas of innovation and optimization, integrity, curiosity, customer and brand experience, and strategic business intelligence through our research.
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Machine Learning Market Projected to Register 43.5% CAGR to 2030 Intel, H2Oai - 3rd Watch News
Learn the business value of AI’s various techniques – TechTarget
As artificial technology gains traction in the enterprise, many on the business side remain fuzzy on AI techniques and how they can be applied to drive business value. Machine Learning and deep learning, for example, are two AI techniques that are often conflated. But machine learning can involve a wide variety of techniques for building analytics models or decision engines that don't involve neural networks, the mechanism for deep learning. And there is a whole range of AI techniques outside of machine learning as well that can be applied to solve business problems.
Business managers who recognize these distinctions will have a greater understanding of the business value of AI and be better prepared to have productive conversations with data scientists, data engineers, end users and executives about what's feasible and what's required. These distinctions can also guide discussions about the best way to implement AI applications.
Without a solid understanding of the various aspects and aims of AI techniques, businesses run the risk of not using AI to drive business value, experts in the field said.
Sanmay Das and Nicholas Mattei, chair and vice chair respectively of the Association for Computing Machinery's Special Interest Group on Artificial Intelligence think one of the biggest blind spots is failing to see machine learning as one component of AI.
"Some can argue with this characterization, but we think that it loses sight of so much more that is encompassed in the goal of AI, which is to build intelligent agents," Das and Mattei told TechTarget.
Focusing only on the learning aspect of machine learning loses sight of how learning fits into a larger AI loop of perception, reasoning, planning and action. This larger framework can guide managers in understanding how all these areas can be mixed and combined to create intelligent applications.
Even when people are specifically talking about machine learning, they are typically describing supervised learning problems. Das, an associate professor of computer science and engineering at Washington University in St. Louis, and Mattei, an assistant professor of computer science at Tulane University, argued that this narrow view of machine learning techniques leaves out many advances in unsupervised machine learning and reinforcement learning problems that can drive business value.
Managers often discover machine learning as a byproduct of the success of deep learning. Juan Jos Lpez Murphy, an AI and big data tech director lead at Globant, an IT consultancy, said the positive side of this trend is that it opens people up to considering how they might apply machine learning to their business. "The money might not always be where the mouth is, but now there's an ear to that mouth," he said.
The downside is that people conflate neural networks with all of machine learning. As a result, he said he hears managers asking questions like "Which deep learning framework are you using?" which is never the relevant aspect of machine learning for a given application.
This confusion also tends to encourage people to focus on AI's "it" technologies, like computer vision and natural language processing. These kinds of applications, while advanced and exciting, are more complex to develop and may not provide as much immediate business value. In many cases, more classical machine learning approaches to tasks -- such as forecasts, churn prediction, risk scoring and optimization -- are better suited to solving business problems.
It is important for business managers to know which AI and machine learning techniques to deploy for which business problems.
For AI implementations requiring transparency and explainability, companies may want to stay away from deep learning techniques, which can result in so-called black box algorithms that are difficult for humans to understand. In these cases, Globant's Lopez Murphy finds clients turning to decision trees or logistic regression algorithms for explicitly reporting the impact of a variable.
Recommender engines, employed to great effect by online giants Netflix and Amazon, are used not only to sell the next item or recommend a movie, but also for internal applications and reports that people look at in their jobs. These applications and reports can be tackled with neural networks, but there are many more suitable approaches, Lopez Murphy said. Forecast models are used to derive confidence intervals that will enable short-term planning or to detect a sudden change in behavior, like outliers or changes to a trend.
"Many of these techniques [e.g., recommender systems and decision trees] have been available and used before deep learning, but are as relevant today, if not more so than they were before," Lopez Murphy said. These types of applications are also able to take advantage of data that is generally more available, curated and relevant than what is required to build deep learning applications.
Debu Chatterjee, senior director of platform AI engineering at ServiceNow, said the IT services software company regularly uses a variety of machine learning capabilities outside of deep learning to drive business value from AI, including classification, identifying similarity between things, clustering, forecasting and recommendations. For example, in service management, incoming tickets are initially read and routed by humans who decide which team is best suited to work on them. Machine learning models trained from these results can automatically route tickets to the best qualified groups for resolution without human intervention. This type of application uses classic supervised machine learning techniques like logistic regression to generate a working model that provides this decision support for optimized work routing.
ServiceNow also uses machine learning for pattern recognition. During a major event, many people call the service organization, but each IT fulfiller only sees one incident at a time, making it nearly impossible to manually recognize the overall pattern. Chatterjee said clustering techniques using machine learning can recognize the overall patterns to identify a major incident automatically, which can help to reduce the overall time to resolve incidents and events.
A wide variety of machine learning algorithms use unsupervised learning, an approach where the training data has not been labeled beforehand. Muddu Sudhakar, CEO of Aisera, a predictive AI service management provider, said that supervised learning models are highly accurate and trustworthy, but they require extensive datasets for training to achieve that high level of accuracy. Conversely, unsupervised learning models are less accurate and trustworthy, but learning takes place in real time without the need of any training data.
The most popular applications of unsupervised learning techniques cluster data into self-organizing maps. Another family of popular unsupervised techniques helps to discover the relationships among objects extracted from the data. Sudhakar said these techniques are popular for market-basket or associative data analysis personalization (i.e., users who buy X and Y products are more likely to buy Z) and recommendation systems for browsing webpages.
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Learn the business value of AI's various techniques - TechTarget
Machine Learning As A Service In Manufacturing Market Augmented Expansion to Be Registered by 2018-2023 – 3rd Watch News
Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.
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Market Analysis
According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.
Segmentation by Components
The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.
Segmentation by End-users
The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.
Segmentation by Deployment Mode
The market has been analyzed and segmented by the following deployment mode, namely public and private.
Regional Analysis
The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.
Vendor Analysis
Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.
Benefits
The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
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Machine Learning As A Service In Manufacturing Market Augmented Expansion to Be Registered by 2018-2023 - 3rd Watch News
COVID 19 Impact on Machine Learning in Medicine Market Outlook 2020 Industry Size, Top Key Manufacturers, Growth Insights, Demand Analysis and…
Machine Learning in Medicine Market 2020 Industry provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains.
Machine Learning in Medicine Industry Report help to understand the market scenario, comprehensive analysis, development policies and manufacturing process.
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Development policies and plans are discussed as well as growth rate, manufacturing processes, economic growth are analyzed. This research report also states import or export data, industry supply and consumption figures as well as cost structure, price, industry revenue (Million USD) and gross margin Machine Learning in Medicine by regions like North America, Europe, Japan, China and other countries.
Deep analysis about market status, enterprise competition pattern, advantages and disadvantages of enterprise products, industry development trends (2019-2024), regional industrial layout characteristics and macroeconomic policies, industrial policy has also be included.
Major Players in Machine Learning in Medicine market are:
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Most important types of Machine Learning in Medicine products covered in this report are:
Most widely used downstream fields of Machine Learning in Medicine market covered in this report are:
Facets of the Market Report:-
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Major Regions that plays a vital role in Machine Learning in Medicine market are: North America, Europe, China, Japan, Middle East & Africa, India, South America, Others.
There are 13 Chapters to thoroughly display the Machine Learning in Medicine market:-
Chapter 1: Machine Learning in Medicine Market Overview, Product Overview, Market Segmentation, Market Overview of Regions, Market Dynamics, Limitations, Opportunities and Industry News and Policies.
Chapter 2: Machine Learning in Medicine Industry Chain Analysis, Upstream Raw Material Suppliers, Major Players, Production Process Analysis, Cost Analysis, Market Channels and Major Downstream Buyers.
Chapter 3: Value Analysis, Production, Growth Rate and Price Analysis by Type of Machine Learning in Medicine., Chapter 4: Downstream Characteristics, Consumption and Market Share by Application of Machine Learning in Medicine.
Chapter 5: Production Volume, Price, Gross Margin, and Revenue ($) of Machine Learning in Medicine by Regions (2014-2019).
Chapter 6: Machine Learning in Medicine Production, Consumption, Export and Import by Regions (2014-2019).
Chapter 7: Machine Learning in Medicine Market Status and SWOT Analysis by Regions.
Chapter 8: Competitive Landscape, Product Introduction, Company Profiles, Market Distribution Status by Players of Machine Learning in Medicine.
Chapter 9: Machine Learning in Medicine Market Analysis and Forecast by Type and Application (2019-2024).
Chapter 10: Market Analysis and Forecast by Regions (2019-2024).
Chapter 11: Industry Characteristics, Key Factors, New Entrants SWOT Analysis, Investment Feasibility Analysis.
Chapter 12: Market Conclusion of the Whole Report.
Chapter 13: Appendix Such as Methodology and Data Resources of This Research
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COVID 19 Impact on Machine Learning in Medicine Market Outlook 2020 Industry Size, Top Key Manufacturers, Growth Insights, Demand Analysis and...
Machine learning algorithm from RaySearch enhances workflow at Swedish radiation therapy clinic – DOTmed HealthCare Business News
RaySearch Laboratories AB (publ) has announced that by using a machine learning algorithm in treatment planning RayStation*, Mlar Hospital in Eskilstuna, Sweden, has made significant time savings in dose planning for radiation therapy. The algorithm in question is a deep learning method for contouring the patients organs. The decision to implement this advanced technology was made to save time, thereby alleviating the prevailing shortage of doctors specialized in radiation therapy at the hospital which was also exacerbated by the COVID-19 situation.
When creating a plan for radiation treatment of cancer, it is critical to carefully define the tumor volume. In order to avoid unwanted side-effects, it is also necessary to identify different organs in the tumors environment, so-called organs at risk. This process is called contouring and is usually performed using manual or semi-automatic tools.
The deep learning contouring feature in RayStation uses machine learning models that have been trained and evaluated on previous clinical cases to create contours of the patients organs automatically and quickly. Healthcare staff can review and, if necessary, adjust the contours. The final result is reached much faster than with other methods.
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Johan Lf, founder and CEO, RaySearch, says: Mlar Hospital was very quick to implement RayStation in 2015 and now it has shown again how quickly new technology can be adopted and brought into clinical use. The fact that this helps to resolve a situation where hospital resources are unusually strained is of course also very positive.
About RaySearchRaySearch is a medical technology company that develops innovative software solutions to improve cancer care. The company markets worldwide its treatment planning system RayStation and next-generation oncology information system RayCare. Over 2,600 clinics in more than 65 countries use RaySearch software to improve life and outcomes for patients. The company was founded in 2000 and the share has been listed on Nasdaq Stockholm since 2003.
About RayStationRayStation is a flexible, innovative treatment planning system, chosen by many of the leading cancer centers worldwide. It combines unique features such as unmatched adaptive therapy capabilities, multi-criteria optimization, market-leading algorithms for IMRT and VMAT optimization with highly accurate dose engines for photon, electron, proton and carbon ion therapy. RayStation supports a wide range of treatment machines, providing one control center for all treatment planning needs and ensuring centers get greater value from existing equipment. RayStation also seamlessly integrates with RayCare, the next-generation oncology information system. By harmonizing the treatment planning, we enable better care for cancer patients worldwide.
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Machine learning algorithm from RaySearch enhances workflow at Swedish radiation therapy clinic - DOTmed HealthCare Business News
AI and Machine Learning Are Changing Everything. Here’s How You Can Get In On The Fun – ExtremeTech
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There isnt a new story every week about an interesting new application of artificial intelligence and machine learning happening out there somewhere. There are actually at least five of those stories. Maybe 10. Sometimes, even more.
Like how UK officials are using AI tospot invasive plant species and stop thembefore they cause expensive damage to roads. Or how artificial intelligence is playing a key role inthe fight against COVID-19. Or even in the ultimate in mind-bending Black Mirror-type ideas, how AI is actually being used to help tobuild and manageother AIs.
Scariness aside, the power of artificial intelligence and machine learning to revolutionize the planet is taking hold in virtually every industry imaginable. With implications like that, it isnt hard to understand how a computer science type trained in AI practices can become a key member of any business witha paycheck to match.
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AI and Machine Learning Are Changing Everything. Here's How You Can Get In On The Fun - ExtremeTech
What a machine learning tool that turns Obama white can (and cant) tell us about AI bias – The Verge
Its a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.
Its not just Obama, either. Get the same algorithm to generate high-resolution images of actress Lucy Liu or congresswoman Alexandria Ocasio-Cortez from low-resolution inputs, and the resulting faces look distinctly white. As one popular tweet quoting the Obama example put it: This image speaks volumes about the dangers of bias in AI.
But whats causing these outputs and what do they really tell us about AI bias?
First, we need to know a little a bit about the technology being used here. The program generating these images is an algorithm called PULSE, which uses a technique known as upscaling to process visual data. Upscaling is like the zoom and enhance tropes you see in TV and film, but, unlike in Hollywood, real software cant just generate new data from nothing. In order to turn a low-resolution image into a high-resolution one, the software has to fill in the blanks using machine learning.
In the case of PULSE, the algorithm doing this work is StyleGAN, which was created by researchers from NVIDIA. Although you might not have heard of StyleGAN before, youre probably familiar with its work. Its the algorithm responsible for making those eerily realistic human faces that you can see on websites like ThisPersonDoesNotExist.com; faces so realistic theyre often used to generate fake social media profiles.
What PULSE does is use StyleGAN to imagine the high-res version of pixelated inputs. It does this not by enhancing the original low-res image, but by generating a completely new high-res face that, when pixelated, looks the same as the one inputted by the user.
This means each depixelated image can be upscaled in a variety of ways, the same way a single set of ingredients makes different dishes. Its also why you can use PULSE to see what Doom guy, or the hero of Wolfenstein 3D, or even the crying emoji look like at high resolution. Its not that the algorithm is finding new detail in the image as in the zoom and enhance trope; its instead inventing new faces that revert to the input data.
This sort of work has been theoretically possible for a few years now, but, as is often the case in the AI world, it reached a larger audience when an easy-to-run version of the code was shared online this weekend. Thats when the racial disparities started to leap out.
PULSEs creators say the trend is clear: when using the algorithm to scale up pixelated images, the algorithm more often generates faces with Caucasian features.
It does appear that PULSE is producing white faces much more frequently than faces of people of color, wrote the algorithms creators on Github. This bias is likely inherited from the dataset StyleGAN was trained on [...] though there could be other factors that we are unaware of.
In other words, because of the data StyleGAN was trained on, when its trying to come up with a face that looks like the pixelated input image, it defaults to white features.
This problem is extremely common in machine learning, and its one of the reasons facial recognition algorithms perform worse on non-white and female faces. Data used to train AI is often skewed toward a single demographic, white men, and when a program sees data not in that demographic it performs poorly. Not coincidentally, its white men who dominate AI research.
But exactly what the Obama example reveals about bias and how the problems it represents might be fixed are complicated questions. Indeed, theyre so complicated that this single image has sparked heated disagreement among AI academics, engineers, and researchers.
On a technical level, some experts arent sure this is even an example of dataset bias. The AI artist Mario Klingemann suggests that the PULSE selection algorithm itself, rather than the data, is to blame. Klingemann notes that he was able to use StyleGAN to generate more non-white outputs from the same pixelated Obama image, as shown below:
These faces were generated using the same concept and the same StyleGAN model but different search methods to Pulse, says Klingemann, who says we cant really judge an algorithm from just a few samples. There are probably millions of possible faces that will all reduce to the same pixel pattern and all of them are equally correct, he told The Verge.
(Incidentally, this is also the reason why tools like this are unlikely to be of use for surveillance purposes. The faces created by these processes are imaginary and, as the above examples show, have little relation to the ground truth of the input. However, its not like huge technical flaws have stopped police from adopting technology in the past.)
But regardless of the cause, the outputs of the algorithm seem biased something that the researchers didnt notice before the tool became widely accessible. This speaks to a different and more pervasive sort of bias: one that operates on a social level.
Deborah Raji, a researcher in AI accountability, tells The Verge that this sort of bias is all too typical in the AI world. Given the basic existence of people of color, the negligence of not testing for this situation is astounding, and likely reflects the lack of diversity we continue to see with respect to who gets to build such systems, says Raji. People of color are not outliers. Were not edge cases authors can just forget.
The fact that some researchers seem keen to only address the data side of the bias problem is what sparked larger arguments about the Obama image. Facebooks chief AI scientist Yann LeCun became a flashpoint for these conversations after tweeting a response to the image saying that ML systems are biased when data is biased, and adding that this sort of bias is a far more serious problem in a deployed product than in an academic paper. The implication being: lets not worry too much about this particular example.
Many researchers, Raji among them, took issue with LeCuns framing, pointing out that bias in AI is affected by wider social injustices and prejudices, and that simply using correct data does not deal with the larger injustices.
Others noted that even from the point of view of a purely technical fix, fair datasets can often be anything but. For example, a dataset of faces that accurately reflected the demographics of the UK would be predominantly white because the UK is predominantly white. An algorithm trained on this data would perform better on white faces than non-white faces. In other words, fair datasets can still created biased systems. (In a later thread on Twitter, LeCun acknowledged there were multiple causes for AI bias.)
Raji tells The Verge she was also surprised by LeCuns suggestion that researchers should worry about bias less than engineers producing commercial systems, and that this reflected a lack of awareness at the very highest levels of the industry.
Yann LeCun leads an industry lab known for working on many applied research problems that they regularly seek to productize, says Raji. I literally cannot understand how someone in that position doesnt acknowledge the role that research has in setting up norms for engineering deployments.
When contacted by The Verge about these comments, LeCun noted that hed helped set up a number of groups, inside and outside of Facebook, that focus on AI fairness and safety, including the Partnership on AI. I absolutely never, ever said or even hinted at the fact that research does not play a role is setting up norms, he told The Verge.
Many commercial AI systems, though, are built directly from research data and algorithms without any adjustment for racial or gender disparities. Failing to address the problem of bias at the research stage just perpetuates existing problems.
In this sense, then, the value of the Obama image isnt that it exposes a single flaw in a single algorithm; its that it communicates, at an intuitive level, the pervasive nature of AI bias. What it hides, however, is that the problem of bias goes far deeper than any dataset or algorithm. Its a pervasive issue that requires much more than technical fixes.
As one researcher, Vidushi Marda, responded on Twitter to the white faces produced by the algorithm: In case it needed to be said explicitly - This isnt a call for diversity in datasets or improved accuracy in performance - its a call for a fundamental reconsideration of the institutions and individuals that design, develop, deploy this tech in the first place.
Update, Wednesday, June 24: This piece has been updated to include additional comment from Yann LeCun.
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What a machine learning tool that turns Obama white can (and cant) tell us about AI bias - The Verge
SLAM + Machine Learning Ushers in the "Age of Perception – Robotics Business Review
The recent crisis has increased focus on autonomous robots being used for practical benefit. Weve seen robots cleaning hospitals, delivering food and medicines and even assessing patients. These are all amazing use cases, and clearly illustrate the ways in which robots will play a greater role in our lives from now on.
However, for all their benefits, currently the ability for a robot to autonomously map its surroundings and successfully locate itself is still quite limited. Robots are getting better at doing specific things in planned, consistent environments; but dynamic, untrained situations remain a challenge.
Age of PerceptionWhat excites me is the next generation of SLAM (Simultaneous Localization and Mapping) that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.
We are calling it the Age of Perception, and it combines recent advances in machine and deep learning to enhance SLAM. Increasing the richness of maps with semantic scene understanding improves localization, mapping quality and robustness.
Simplifying MapsCurrently, most SLAM solutions take raw data from sensors and use probabilistic algorithms to calculate the location and a map of the surroundings of the robot. LIDAR is most commonly used but increasingly lower-cost cameras are providing rich data streams for enhanced maps. Whatever sensors are used the data creates maps made up of millions of 3-dimensional reference points. These allow the robot to calculate its location.
The problem is that these clouds of 3D points have no meaning they are just a spatial reference for the robot to calculate its position. Constantly processing all of these millions of points is also a heavy load on the robots processors and memory. By inserting machine learning into the processing pipeline we can both improve the utility of these maps and simplify them.
Panoptic SegmentationPanoptic Segmentation techniques use machine learning to categorize collections of pixels from camera feeds into recognizable objects. For example, the millions of pixels representing a wall can be categorized as a single object. In addition, we can use machine learning to predict the geometry and the shape of these pixels in the 3D world. So, millions of 3D points representing a wall can be all summarized into a single plane. Millions of 3D points representing a chair can be all summarized into a shape model with a small number of parameters. Breaking scenes down into distinct objects into 2D and 3D lowers the overhead on processors and memory.
What excites me is the next generation of SLAM that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.
Adding UnderstandingAs well as simplification of maps, this approach provides the foundation of greater understanding of the scenes the robots sensors capture. With machine learning we are able to categorize individual objects within the scene and then write code that determines how they should be handled.
The first goal of this emerging capability is to be able to remove moving objects, including people, from maps. In order to navigate effectively, robots need to reference static elements of a scene; things that will not move, and so can be used as a reliable locating point. Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid. Combining the panoptic segmentation of objects in a scene with underlying map and location data will soon deliver massive increases in accuracy and capability of robotic SLAM.
Perceiving ObjectsThe next exciting step will be to build on this categorization to add a level of understanding of individual objects. Machine learning, working as part of the SLAM system, will allow a robot to learn to distinguish the walls and floors of a room from the furniture and other objects within it. Storing these elements as individual objects means that adding or removing a chair will not necessitate the complete redrawing of the map.
This combination of benefits is the key to massive advances in the capability of autonomous robots. Robots do not generalize well in untrained situations; changes, particularly rapid movement, disrupt maps and add significant computational load. Machine learning creates a layer of abstraction that improves the stability of maps. The greater efficiency it allows in processing data creates the overhead to add more sensors and more data that can increase the granularity and information that can be included in maps.
Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid.
Natural InteractionLinking location, mapping and perception will allow robots to understand more about their surroundings and operate in more useful ways. For example, a robot that can perceive the difference between a hall and a kitchen can undertake more complex sets of instructions. Being able to identify and categorize objects such as chairs, desks, cabinets etc will improve this still further. Instructing a robot to go to a specific room to get a specific thing will become much simpler.
The real revolution in robotics will come when robots start interacting more with people in more natural ways. Robots that learn from multiple situations and combine that knowledge into a model that allows them to take on new, un-trained tasks based on maps and objects preserved in memory. Creating those models and abstraction demands complete integration of all three layers of SLAM. Thanks to the efforts of the those who are leading the industry in these areas, I believe that the Age of Perception is just around the corner.
Editors Note: Robotics Business Review would like to thank SLAMcore for permission to reprint the original article (found HERE).
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SLAM + Machine Learning Ushers in the "Age of Perception - Robotics Business Review
Googles new ML Kit SDK keeps all machine learning on the device – SlashGear
Smartphones today have become so powerful that sometimes even mid-range handsets can support some fancy machine learning and AI applications. Most of those, however, still rely on cloud-hosted neural networks, machine learning models, and processing, which has both privacy and efficiency drawbacks. Contrary to what most would expect, Google has been moving to offload much of that machine learning activity from the cloud to the device and its latest machine learning development tool is its latest step in that direction.
Googles machine learning or ML Kit SDK has been around for two years now but it has largely been tied to its Firebase mobile and web development platform. Like many Google products, this creates a dependency on a cloud-platform that entails not just some latency due to network bandwidth but also risks leaking potentially private data in transit.
While Google is still leaving that ML Kit + Firebase combo available, it is now also launching a standalone software development kit or SDK for both Android and iOS app developers that focuses on on-device machine learning. Since everything happens locally, the users privacy is protected and the app can function almost in real-time regardless of the speed of the Internet connection. In fact, an ML-using app can even work offline for that matter.
The implications of this new SDK can be quite significant but it still depends on developers switching from the Firebase version to the standalone SDK. To give them a hand, Google created a code lab that combines the new ML Kit with its CameraX app in order to translate text in real-time without connecting to the Internet.
This can definitely help boost confidence in AI-based apps if the user no longer has to worry about privacy or network problems. Of course, Google would probably prefer that developers keep using the Firebase connection which it even describes as getting the best of both products.
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Googles new ML Kit SDK keeps all machine learning on the device - SlashGear