Category Archives: Machine Learning

(credit: Pablo Lagarto / Adobe Stock)

On 1 February 2020, YouTuber Simon Weekert posted a video on YouTube claiming to have redirected traffic by faking traffic jams on Google Maps. The video shows Weekert walking slowly along traffic-free streets in Berlin, pulling a pile of second-hand mobile phones in a cart behind him and Google Maps generating traffic jam alerts because the phones had their location services turned on.

Weekerts performance act demonstrates the fragility and vulnerability of our systems and their difficulty in interpreting outliers, and highlights a kind of decisional blindness when we think of data as objective, unambiguous and interpretation free, as he put it. There are many other examples of decisional blindness relating to drivers following Google Maps and falling off cliffs or driving into rivers.

Google has the resources, expertise and technology to rapidly learn from this experience and make changes to avoid similar situations. But the same vulnerability to hacking or outliers applies to the use of machine learning in childrens social care (CSC) and this raises the question of whether the sector has the means to identity and rectify issues in a timely manner and without adverse effects for service users.

Have you ever had the experience of asking the wrong question in Google search and getting the right answer? Thats because of contextual computing that makes use of AI and machine learning.

At its heart, machine learning is the application of statistical techniques to identify patterns and enable computers to use data to progressively learn and improve their performance.

From Google search and Alexa to online shopping, and from games and health apps to WhatsApp and online dating, most online interactions are mediated by AI and machine learning. Like electricity, AI and machine learning will power every software and digital device and will transform and mediate every aspect of human experience mostly without end users giving them a thought.

But there are particular concerns about their applications in CSC and, therefore, a corresponding need for national standards for machine learning in social care and for greater transparency and scrutiny around the purpose, design, development, use, operation and ethics of machine learning in CSC. This was set out in What Works for Childrens Social Cares ethics review into machine learning, published at the end of January.

The quality of machine learning systems predictive analysis is dependent on the quality, completeness and representativeness of the dataset they draw on. But peoples lives are complex, and often case notes do not capture this complexity and instead are complemented by practitioners intuition and practice wisdom. Such data lacks the quality and structure needed for machine learning applications, making high levels of accuracy harder to achieve.

Inaccuracy in identifying children and families can result in either false positives that infringe on peoples rights and privacy, cause stress and waste time and resources, or false negatives that miss children and families in need of support and protection.

Advocates of machine learning often point out that systems only provide assistance and recommendations, and that it remains the professionals who make actual decisions. Yet decisional blindness can undermine critical thinking, and false positives and negatives can result in poor practice and stigmatisation, and can further exclusion, harm and inequality.

Its true that AI and machine learning can be used in empowering ways to support services or to challenge discrimination and bias. The use of Amazons Alexa to support service users in adult social care is, while not completely free of concerns, one example of positive application of AI in practice.

Another is Essex councils use of machine learning to produce anonymised aggregate data at community level of children who may not be ready for school by their fifth birthday. This data is then shared with parents and services who are part of the project to inform their funding allocation or changes to practice as need be. This is a case of predictive analytics being used in a way that is supportive of children and empowering for parents and professionals.

The Principal Children and Families Social Worker (PCFSW) Network is conducting a survey of practitioners to understand their current use of technology and challenges and the skills, capabilities and support that they need.

It only takes 10 minutes to complete the survey on digital professionalism and online safeguarding. Your responses will inform best practice and better support for social workers and social care practitioners to help ensure practitioners lead the changes in technology rather than technology driving practice and shaping practitioners professional identity.

But its more difficult to make such an assessment in relation to applications that use hundreds of thousands of peoples data, without their consent, to predict child abuse. While there are obvious practical challenges around seeking the permission of huge numbers of people, failing to do so shifts the boundaries of individual rights and privacy vis--vis surveillance and the power of public authorities. Unfortunately though, ethical concerns do not always influence the direction or speed of change.

Another controversial recent application of technology is the use of live facial recognition cameras in London. An independent report by Essex Universitylast year suggested concerns with inaccuracies in use of live facial recognition, while the Met Polices senior technologist, Johanna Morley said millions of pounds would need to be invested in purging police suspect lists and aligning front- and back-office systems to ensure the legality of facial recognition cameras. Despite these concerns, the Met will begin using facial recognition cameras in London streets, with the aim of tackling serious crime, including child sexual exploitation.

Research published in November 2015, meanwhile, showed that a flock of trained pigeons can spot cancer in images of biopsied tissue with 99% accuracy; that is comparable to what would be expected of a pathologist. At the time, one of the co-authors of the report suggested that the birds might be able to assess the quality of new imaging techniques or methods of processing and displaying images without forcing humans to spend hours or days doing detailed comparisons.

Although there are obvious cost efficiencies in recruiting pigeons instead of humans, I am sure most of us will not be too comfortable having a flock of pigeons as our pathologist or radiologist.

Many people would also argue more broadly that fiscal policy should not undermine peoples health and wellbeing. Yet the past decade of austerity, with 16bn in cuts in core government funding for local authorities by this year and a continued emphasis on doing more with less, has led to resource-led practices that are far from the aspirations of Children Act 1989 and of every child having the opportunity to achieve their potential.

Technology is never neutral and there are winners and losers in every change. Given the profound implications of AI and machine learning for CSC, it is essential such systems are accompanied by appropriate safeguards and processes that prevent and mitigate false positives and negatives and their adverse impact and repercussions. But in an environment of severe cost constraints, positive aspirations might not be matched with adequate funding to ensure effective prevention and adequate support for those negatively impacted by such technologies.

In spite of the recent ethics reviews laudable aspirations, there is also the real risk that many of the applications of machine learning pursued to date in CSC may cement current practice challenges by hard-coding austerity and current thresholds into systems and the future of services.

The US constitution was written and ratified by middle-aged white men and it took over 130 years for women to gain the right of suffrage and 176 years to recognise and outlaw discrimination based on race, sex, religion and national origin. Learning from history would suggest we must be cautious about reflecting childrens social cares operating context into systems, all designed, developed and implemented by experts and programmers who may not represent the diversity of the people who will be most affected by such systems.

Dr Peter Buzzi (@MHChat) is the director of Research and Management Consultancy Centre and the Safeguarding Research Institute. He is also the national research lead for the Principal Children and Families Social Worker (PCFSW) Networks online safeguarding research and practice development project.

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'Technology is never neutral': why we should remain wary of machine learning in children's social care - Communitycare.co.uk

The worlds of artificial intelligence and cybersecurity have become deeply entwined in recent years, as organizations work to keep up with and ideally block increasingly sophisticated malicious hackers. Today, a startup thats built a deep learning solution that it claims can both identify and stop even viruses that have yet to be identified has raised a large round of funding from some big strategic partners.

Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised $43 million in a Series C.

The funding is being led by Millennium New Horizons, with Unbound (a London-based investment firm founded by Shravin Mittal), LG and Nvidia all participating. The investment brings the total raised by Deep Instinct to $100 million, with HP and Samsung among its previous backers. The tech companies are all strategics, in that (as in the case of HP) they bundle and resell Deep Instincts solutions, or use them directly in their own services.

The Israeli-based company is not disclosing valuation, but notably, it is already profitable.

Targeting as-yet unknown viruses is becoming a more important priority as cybercrime grows. CEO and founder Guy Caspi notes that currently there are more than350,000 new machine-generated malware created every day with increasingly sophisticated evasion techniques, such as zero-days and APTs (Advanced Persistent Threats). Nearly two-thirds of enterprises have been compromised in the past year by new and unknown malware attacks originating at endpoints, representing a 20% increase from the previous year, he added. And zero-day attacks are now four times more likely to compromise organizations. Most cyber solutions on the market cant protect against these new types of attacks and have therefore shifted to a detect-response approach, he said, which by design means that they assume a breach will happen.

While there is already a large profusion of AI-based cybersecurity tools on the market today, Caspi notes that Deep Instinct takes a critically different approach because of its use of deep neural network algorithms, which essentially are set up to mimic how a human brain thinks.

Deep Instinct is the first and currently the only company to apply end-to-end deep learning to cybersecurity, he said in an interview. In his view, this provides a more advanced form of threat protection than the common traditional machine learning solutions available in the market, which rely on feature extractions determined by humans, which means they are limited by the knowledge and experience of the security expert, and can only analyze a very small part of the available data (less than 2%, he says). Therefore, traditional machine learning-based solutions and other forms of AI have low detection rates of new, unseen malware and generate high false-positive rates. Theres been a growing body of research that supports this idea, although weve not seen many deep learning cybersecurity solutions emerge as a result (not yet, anyway).

He adds that deep learning is the only AI-basedautonomous system that can learn from any raw data, as its not limited by an experts technological knowledge. In other words, its not based just on what a human inputs into the algorithm, but is based on huge swathes of big data, sourced from servers, mobile devices and other endpoints, that are input in and automatically read by the system.

This also means that the system can be used in turn across a number of different end points. Many machine learning-based cybersecurity solutions, he notes, are geared at Windows environments. That is somewhat logical, given that Windows and Android account for the vast majority of attacks these days, but cross-OS attacks are now on the rise.

While Deep Instinct specializes in preventing first-seen, unknown cyberattacks like APTs and zero-day attacks, Caspi notes that in the past year there has been a rise in both the amount and the impact of cyberattacks covering other areas. In 2019, Deep Instinct saw an increase in spyware and ransomware on top of an increase in the level of sophistication of the attacks that are being used, specifically with more file-less attacks using scripts and powershell, living off the land attacks and the use of weaponized documents like Microsoft Office files and PDFs. These sit alongside big malware attacks like Emotet, Trickbot, New ServeHelper and Legion Loader.

Today the company sells services both directly and via partners (like HP), and its mainly focused on enterprise users. But since there is very little in the way of technical implementation (Our solution is mostly autonomous and all processes are automated [and] deep learning brain is handling most of the security, Caspi said), the longer-term plan is to build a version of the product that consumers could adopt, too.

With a large part of antivirus software often proving futile in protecting users against attacks these days, that could come as a welcome addition to the market, despite how crowded it already is.

There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis, said Ray Cheng, partner at Millennium New Horizons, in a statement. What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems is a paradigm change.

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Deep Instinct nabs $43M for a deep-learning cybersecurity solution that can suss an attack before it happens - TechCrunch

Image: ShutterstockThe 2020 outlook for Asset Management re-affirms impact of globalization and outperformance of private equity. While the developed worlds economy has sent mixed signals, all eyes are now on Asia and especially India, to drive the next phase of growth. The goal is to provide Investment Solutions for its mix of young as well as senior population. Its diversity cultural, economic, regional & regulatory, will pose the next challenge.

The application of Data Science & Machine Learning has delivered value for portfolio managers through quick and uniform decision-making. Strategic Beta Funds which have consistently generated added value, rely heavily on the robustness of their portfolio creation models which are excruciatingly data driven. Deploying Machine Learning algorithms helps assess credit worthiness of firms and individuals for lending and borrowing. Data Science and Machine Learning solutions eliminate human bias and calculation errors while evaluating investments in an optimum period.

Investment management is justified as an industry only to the extent that it can demonstrate a capacity to add value through the design of dedicated investor-centric investment solutions, as opposed to one-size-fits-all manager-centric investment products. After several decades of relative inertia, the much needed move towards investment solutions has been greatly facilitated by a true industrial revolution taking place in investment management, triggered by profound paradigm changes with the emergence of novel approaches such as factor investing, liability-driven and goal-based investing, as well as sustainable investing. Data science is expected to play an increasing role in these transformations.

This trend poses a critical challenge to global academic institutions: educating a new breed of young professionals and equipping them with the right skills to address the situation, and who could seize the fast-developing new job opportunities in this field. Continuous education gives the opportunity to meet with new challenges of this ever-changing world, especially in the investment industry.

As recently emphasized by our colleague Vijay Vaidyanathan, CEO, Optimal Asset Management, former EDHEC Business School PHD student, and online course instructor at EDHEC Business School, our financial well-being is second only to our physical well-being, and one of the key challenges we face is to enhance financial expertise. To achieve this, we cannot limit ourselves to the relatively small subset of the population who can afford to invest the significant time and expense of attending a formal, full-time degree programme on a university campus. Therefore, we must find ways to elevate the quality of financial professional financial education to ensure that all asset managers and asset owners are fully equipped to make intelligent and well-informed investment decisions.

Data science applied to asset management, and education in the field, is expected to affect not only investment professionals but also individuals. On this topic, we would like to share insights from Professor John Mulvey, Princeton University, who is also one of EDHEC on-line course instructors. John believes that machine learning applied to investment management is a real opportunity to assist individuals with their financial affairs in an integrated manner. Most people are faced with long-term critical decisions about saving, spending, and investing to achieve a wide variety of goals.

These decisions are often made without much professional guidance (except for wealthier clients), and without much technical training. Current personalized advisors are reasonable initial steps. Much more can be done in this area with modern data science and decision-making tools. Plus, younger people are more willing to trust fully automated computational systems. This domain is one of the most relevant and significant areas of development for future investment management.

By Nilesh Gaikwad, EDHEC Business School country manager in India, and Professor Lionel Martellini, EDHEC-Risk Institute Director.

Original post:
How Machine Learning Will Reshape The Future Of Investment Management - Forbes India

With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.

The new virus emerged in Wuhan, China, in December, triggering a global health emergency. It remains uncertain how deadly or contagious the virus is, and how widely it might have already spread. Infections and deaths continue to rise. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities there Friday.

John Brownstein, chief innovation officer at Harvard Medical School and an expert on mining social media information for health trends, is part of an international team using machine learning to comb through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs the virus is taking hold in countries outside of China.

The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel. A company called BlueDot used a similar approachminus the social media sourcesto spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.

We are moving to surveillance efforts in the US, Brownstein says. It is critical to determine where the virus may surface if the authorities are to allocate resources and block its spread effectively. Were trying to understand whats happening in the population at large, he says.

The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization. Yet it isnt clear if the spread is really slowing or if new infections are simply becoming more difficult to track.

So far, other countries have reported far fewer cases of coronavirus. But there is still widespread concern about the virus spreading. The US has imposed a travel ban on China even though experts question the effectiveness and ethics of such a move. Researchers at Johns Hopkins University have created a visualization of the viruss progress around the world based on official numbers and confirmed cases.

Health experts did not have access to such quantities of social, web, and mobile data when seeking to track previous outbreaks such as severe acute respiratory syndrome (SARS). But finding signs of the new virus in a vast soup of speculation, rumor, and posts about ordinary cold and flu symptoms is a formidable challenge. The models have to be retrained to think about the terms people will use and the slightly different symptom set, Brownstein says.

Even so, the approach has proven capable of spotting a coronavirus needle in a haystack of big data. Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30. This was shared with the WHO, but it took time to confirm the seriousness of the situation.

Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk more quickly than using official medical sources.

Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still arent entirely clear. Its something new. We dont have historical data, Vespignani says. There are very few cases in the US, and most of the activity is driven by the media, by peoples curiosity.

Excerpt from:
How AI Is Tracking the Coronavirus Outbreak - WIRED

Emulators speed up simulations, such as this NASA aerosol model that shows soot from fires in Australia.

By Matthew HutsonFeb. 12, 2020 , 2:35 PM

Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take many hours on even the fastest supercomputers. Emulators, algorithms that quickly approximate these detailed simulations, offer a shortcut. Now, work posted online shows how artificial intelligence (AI) can easily produce accurate emulators that can accelerate simulations across all of science by billions of times.

This is a big deal, says Donald Lucas, who runs climate simulations at Lawrence Livermore National Laboratory and was not involved in the work. He says the new system automatically creates emulators that work better and faster than those his team designs and trains, usually by hand. The new emulators could be used to improve the models they mimic and help scientists make the best of their time at experimental facilities. If the work stands up to peer review, Lucas says, It would change things in a big way.

A typical computer simulation might calculate, at each time step, how physical forces affect atoms, clouds, galaxieswhatever is being modeled. Emulators, based on a form of AI called machine learning, skip the laborious reproduction of nature. Fed with the inputs and outputs of the full simulation, emulators look for patterns and learn to guess what the simulation would do with new inputs. But creating training data for them requires running the full simulation many timesthe very thing the emulator is meant to avoid.

The new emulators are based on neural networksmachine learning systems inspired by the brains wiringand need far less training. Neural networks consist of simple computing elements that link into circuitries particular for different tasks. Normally the connection strengths evolve through training. But with a technique called neural architecture search, the most data-efficient wiring pattern for a given task can be identified.

The technique, called Deep Emulator Network Search (DENSE), relies on a general neural architecture search co-developed by Melody Guan, a computer scientist at Stanford University. It randomly inserts layers of computation between the networks input and output, and tests and trains the resulting wiring with the limited data. If an added layer enhances performance, its more likely to be included in future variations. Repeating the process improves the emulator. Guan says its exciting to see her work used toward scientific discovery. Muhammad Kasim, a physicist at the University of Oxford who led the study, which was posted on the preprint server arXiv in January, says his team built on Guans work because it balanced accuracy and efficiency.

The researchers used DENSE to develop emulators for 10 simulationsin physics, astronomy, geology, and climate science. One simulation, for example, models the way soot and other atmospheric aerosols reflect and absorb sunlight, affecting the global climate. It can take a thousand of computer-hours to run, so Duncan Watson-Parris, an atmospheric physicist at Oxford and study co-author, sometimes uses a machine learning emulator. But, he says, its tricky to set up, and it cant produce high-resolution outputs, no matter how many data you give it.

The emulators that DENSE created, in contrast, excelled despite the lack of data. When they were turbocharged with specialized graphical processing chips, they were between about 100,000 and 2 billion times faster than their simulations. That speedup isnt unusual for an emulator, but these were highly accurate: In one comparison, an astronomy emulators results were more than 99.9% identical to the results of the full simulation, and across the 10 simulations the neural network emulators were far better than conventional ones. Kasim says he thought DENSE would need tens of thousands of training examples per simulation to achieve these levels of accuracy. In most cases, it used a few thousand, and in the aerosol case only a few dozen.

Its a really cool result, said Laurence Perreault-Levasseur, an astrophysicist at the University of Montreal who simulates galaxies whose light has been lensed by the gravity of other galaxies. Its very impressive that this same methodology can be applied for these different problems, and that they can manage to train it with so few examples.

Lucas says the DENSE emulators, on top of being fast and accurate, have another powerful application. They can solve inverse problemsusing the emulator to identify the best model parameters for correctly predicting outputs. These parameters could then be used to improve full simulations.

Kasim says DENSE could even enable researchers to interpret data on the fly. His team studies the behavior of plasma pushed to extreme conditions by a giant x-ray laser at Stanford, where time is precious. Analyzing their data in real timemodeling, for instance, a plasmas temperature and densityis impossible, because the needed simulations can take days to run, longer than the time the researchers have on the laser. But a DENSE emulator could interpret the data fast enough to modify the experiment, he says. Hopefully in the future we can do on-the-spot analysis.

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From models of galaxies to atoms, simple AI shortcuts speed up simulations by billions of times - Science Magazine

New Jersey, United States, The report titled, Machine Learning Market Size and Forecast 2026 in Verified Market Research offers its latest report on the global Machine Learning market that includes comprehensive analysis on a range of subjects like competition, segmentation, regional expansion, and market dynamics. The report sheds light on future trends, key opportunities, top regions, leading segments, the competitive landscape, and several other aspects of the Machine Learning market. Get access to crucial market information. Market players can use the report back to peep into the longer term of the worldwide Machine Learning market and convey important changes to their operating style and marketing tactics to realize sustained growth.

Global Machine Learning Market was valued at USD 2.03 Billion in 2018 and is projected to reach USD 37.43 Billion by 2026, growing at a CAGR of 43.9% from 2019 to 2026.

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Top 10 Companies in the Global Machine Learning Market Research Report:

Global Machine Learning Market: Competitive Landscape

Competitive landscape of a market explains strategies incorporated by key players of the market. Key developments and shift in management in the recent years by players has been explained through company profiling. This helps readers to understand the trends that will accelerate the growth of market. It also includes investment strategies, marketing strategies, and product development plans adopted by major players of the market. The market forecast will help readers make better investments.

Global Machine Learning Market: Drivers and Restrains

This section of the report discusses various drivers and restrains that have shaped the global market. The detailed study of numerous drivers of the market enable readers to get a clear perspective of the market, which includes market environment, government policies, product innovations, breakthroughs, and market risks.

The research report also points out the myriad opportunities, challenges, and market barriers present in the Global Machine Learning Market. The comprehensive nature of the information will help the reader determine and plan strategies to benefit from. Restrains, challenges, and market barriers also help the reader to understand how the company can prevent itself from facing downfall.

Global Machine Learning Market: Segment Analysis

This section of the report includes segmentation such as application, product type, and end user. These segmentations aid in determining parts of market that will progress more than others. The segmentation analysis provides information about the key elements that are thriving the specific segments better than others. It helps readers to understand strategies to make sound investments. The Global Machine Learning Market is segmented on the basis of product type, applications, and its end users.

Global Machine Learning Market: Regional Analysis

This part of the report includes detailed information of the market in different regions. Each region offers different scope to the market as each region has different government policy and other factors. The regions included in the report are North America, South America, Europe, Asia Pacific, and the Middle East. Information about different region helps the reader to understand global market better.

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Table of Content

1 Introduction of Machine Learning Market

1.1 Overview of the Market 1.2 Scope of Report 1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining 3.2 Validation 3.3 Primary Interviews 3.4 List of Data Sources

4 Machine Learning Market Outlook

4.1 Overview 4.2 Market Dynamics 4.2.1 Drivers 4.2.2 Restraints 4.2.3 Opportunities 4.3 Porters Five Force Model 4.4 Value Chain Analysis

5 Machine Learning Market, By Deployment Model

5.1 Overview

6 Machine Learning Market, By Solution

6.1 Overview

7 Machine Learning Market, By Vertical

7.1 Overview

8 Machine Learning Market, By Geography

8.1 Overview 8.2 North America 8.2.1 U.S. 8.2.2 Canada 8.2.3 Mexico 8.3 Europe 8.3.1 Germany 8.3.2 U.K. 8.3.3 France 8.3.4 Rest of Europe 8.4 Asia Pacific 8.4.1 China 8.4.2 Japan 8.4.3 India 8.4.4 Rest of Asia Pacific 8.5 Rest of the World 8.5.1 Latin America 8.5.2 Middle East

9 Machine Learning Market Competitive Landscape

9.1 Overview 9.2 Company Market Ranking 9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview 10.1.2 Financial Performance 10.1.3 Product Outlook 10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Highlights of Report

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Analysts with high expertise in data gathering and governance utilize industry techniques to collate and examine data at all stages. Our analysts are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research reports.

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TAGS: Machine Learning Market Size, Machine Learning Market Growth, Machine Learning Market Forecast, Machine Learning Market Analysis, Machine Learning Market Trends, Machine Learning Market

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Machine Learning Market 2020 Booming by Size, Revenue, Trend and Top Companies 2026 - Instant Tech News