NFL teams use machine learning.

Dont believe me? Take it from Andrew Berry, the new general manager of the Cleveland Browns, who said in a recent interview (while he was still working for the Eagles) that machine learning techniques and data are having more of an impact in all the different spaces of football operations. In a talk given while he was working for the team, former Patriots senior software engineer Sean Harrington said his job included machine learning, data warehousing and visualizations.

As more teams embrace the use of data and advanced statistical techniques to inform decision-making, they are looking to hire analysts to make sense of the information and avoid being left behind. But without connections to the technical world, finding the right people can be challenging.

Enter the Big Data Bowl, the NFLs annual competition in which data scientists and anyone else...

Go here to read the rest:
How Big Data Bowl winners used machine learning to better understand the game - The Athletic

(Dmitriy Rybin/Shutterstock)

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company thats attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code.

Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the companys co-founder and CEO, the original plan called for Qeexo to be a machine learning application company.

The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate. Huawei liked the ML-based finger-gesture application that Qeexo (pronounced Key-tzo) developed, and the company wanted Qeexo to ensure that it could run across all of its phone lines. That was a good news-bad news situation, Lee says.

Our first commercial implementation with Huawei kept the whole company in China for two months, to finish one model with one hardware variant, Lee tells Datanami. We came back and it was difficult to keep the morale high for our ML engineers because nobody wanted to constantly go abroad to do this type of repetitive implementation.

Qeexos AutoML solution handles many aspects of ML model development and deployment for customers

It quickly dawned on Lee that, with more ML models and more hardware types, the amount of manual work would quickly get out of hand. That led him to the idea of developing an automated machine learning, or AutoML, platform that could automatically generate ML models based on the data presented to it, automatically flash it to a group of pre-selected microcontrollers.

Lee and his team of developers, which is led by CTO and co-founder Chris Harrison (who is an assistant professor at Carnegie Mellon University), developed the offering nearly five years ago, and the company has been using it ever since for its own ML services engagements.

Huawei continues to utilize Qeexos AutoML solution to generate ML applications for its handsets. In 2018, we completed 57 projects for Hauwei, and most of the projects were completed by our field engineers just using the AutoML platform, without the help of ML engineers in the US, Lee says.

Sang Won Lee is the co-founder and CEO of Qeexo

In October 2019, Qeexo released its AutoML offering as a stand-alone software offering. The product automates many steps in the ML process, from building models from collected data, comparing performance of those models, and then deploying the finished model to a microcontroller all without requiring the user to write any code.

The offering has built-in support for the most popular ML algorithms, including random forest, gradient boosted machine, and linear regression, among others. Users can also select deep learning models, like convolutional neural networks, but many microcontrollers lack the memory to handle those libraries, Lee says.

Qeexos AutoML solution automatically handles many of the engineering tasks that would otherwise require the skills of a highly trained ML engineer, including feature selection and hyperparameter optimization. These feature are built into the Qeexo offering, which also sports a built-in C compiler and generates binary code that can be deployed to microcontrollers, such as those from Renasas Electronics.

Lee says ML engineers might be able to get a little more efficiency by developing their own ML libraries, but that it wont be worth the effort for many users. There are always more improvements that you can get with ML experts digging into it and doing the research, he says. But this is giving you the convenience of being able to build a commercially viable solution without having to write a single line of code.

Today Qeexo announced its new AWS solution. Instead of training a model on a laptop, customers can use now AWS resources to train their model. It also announced more ML algorithms, including deep learning algorithms and traditional algorithms. The visualization that Qeexo provides have also been enhanced to give the user the ability to better spot outliers and trends in data. Support for microphone data has been supported. And it also added support for the Renesas RA Family of Cortex-M MCUs, which are geared toward low-power IoT edge devices.

Having Huawei as a client certainly gives Qeexo some experience with scalability under its belt. But the Mountain View, California-based company is bullish on the potential for a new class of application developers to get started using its software to imbue everyday devices with the intelligence of data.

What we really want to tell the market is that even or those microcontrollers that are already out and that have very limited memory resource and processing power, you can still have a commercially viable ML solution running on it, if you use the right tool, Lee says. You dont want to neglect all the sensor data thats connected to the microcontroller. We can provide a tool that you can use to build intelligence that can be embedded into those tools.

Related Items:

On the Radar: Cortex Labs, RadicalBit, Qeexo

AutoML Tools Emerge as Data Science Difference Makers

Cloud Tools Rev Up AI Dev Platforms

Read this article:
Machine Learning on the Edge, Hold the Code - Datanami

Machine learning is helping Penn Medicine researchers identify the size and shape of brain networks in individual children, which may be useful for understanding psychiatric disorders. In a new study publishedin the journalNeuron,a multidisciplinary team showed how brain networks unique to each child can predict cognition. The study, which used machine learning techniques to analyze the functional magnetic resonance imaging (fMRI) scans of nearly 700 children, adolescents, and young adults, is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development.

The human brain has a pattern of folds and ridges on its surface that provide physical landmarks for finding brain areas. The functional networks that govern cognition have long been studied in humans by liningup activation patternsthe software of the brainto the hardware of these physical landmarks. However, this process assumes that the functions of the brain are located on the same landmarks in each person. This works well for many simple brain systems. However, multiple recent studies in adults have shown this is not the case for more complex brain systems responsible for executive functiona set of mental processes which includes self-control and attention. In these systems, the functional networks do not always line up with the brains physical landmarks of folds and ridges. Instead, each adult has their own specific layout. Until now, it was unknown how such person-specific networks might change as kids grow up, or relate to executive function.

The exciting part of this work is that we are now able to identify the spatial layout of these functional networks in individual kids, rather than looking at everyone using the same one size fits all approach, says senior authorTheodore D. Satterthwaite, an assistant professor of psychiatry in the Perelman School of Medicine. Like adults, we found that functional neuroanatomy varies quite a lot among different kidseach child has a unique pattern. Also like adults, the networks that vary the most between kids are the same executive networks responsible for regulating the sorts of behaviors that can often land adolescents in hot water, like risk taking and impulsivity.

Read more at Penn Medicine News.

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Machine learning identifies personalized brain networks in children - Penn: Office of University Communications

Artificial intelligence (AI) understood as intelligence demonstrated by machines and machine learning (ML) a subfield of AI that focuses on machines ability to automatically learn from experience without being explicitly programmed are two of the computer science technologies that will arguably have the biggest impact on our lives in the coming decades. AI and ML are not exactly new both terms date back to the mid-20th century but it is mainly in recent years that their huge potential has become evident.

The technologies are now being used more and more extensively across a whole range of fields. Artificial intelligence can be found in autonomous vehicles, search engines, online assistants and spam filtering programs, to name just a few of its numerous common applications. Applications of machine learning include computer vision, DNA sequence classification, financial market analysis, Internet fraud detection, medical diagnosis and speech recognition. Both AI and ML are currently transforming whole sectors and will continue to do so in the future.

Pushing the boundaries

Of course, behind that transformation are concrete people, outstanding engineers and scientists who continue to push the boundaries of how theoretical concepts related to computer science and other similar fields can be turned into practical use. Curious folks, always on the lookout for new possibilities. Consider Pierre-Habt Nouvellon, the CTO and Head of Machine Learning at Snipfeed, a California-based startup that has come up with an eponymous AI-based news and information recommendation engine, mainly targeted at young (Generation Z) users.

Snipfeed, which he co-founded, provides daily, highly personalized pieces of news and information (snippets) that are filtered and recommended to its users depending on their needs. The technology used by the engine ensures that Snipfeed avoids recommending articles which do not look credible. An internal tool called Fakebuster detects fake news articles by verifying content, assessing the reliability of the facts in it and the quality of the writing style and the headline.

Falling for ML

Nouvellon discovered machine learning while he was doing a Masters degree in aerospace engineering in France. He was working on a research project on theoretical supply chain problems and his supervisor advised him to see if reinforcement learning, a subfield of machine learning, could help him complete his task. I took the well-known Andre Ng course in machine learning and fell in love with it. It was the perfect mix of statistics, algebra and computer science, he recalled.

After that initial experience, Nouvellon realized that he wanted to study machine learning more thoroughly. He applied for a Masters program in computer science at the University of California, Berkeley, and was admitted. There he met some of the leading professors in the field. Once in Berkeley, Nouvellon and two of his friends started working on an AI-based tutor called Jenyai, a conversational bot on messenger that was able to answer students questions in math, history and science.

One of the applications features was a selection of news stories meant to help students better understand the discussed topics. This feature proved to be very popular with Jenyais users, which inspired Nouvellon and his partners to create Snipfeed. Nouvellon also had a chance to apply his knowledge of machine learning in a completely different field healthcare. He was involved in a medical research project done by the University of California, Berkeley, and the University of California, San Francisco.

Driven by curiosity

The projects goal was to apply machine learning algorithms to predict the occurrence of a certain disease among a major pool of patients by analyzing those patients electronic medical records and genotype data, provided by a large medical insurance group. The application of ML technologies in medicine and healthcare is set to become more and more commonplace in the near future, but such technologies will also be transforming many other sectors. The revolution will be driven by people like Nouvellon, who are always inspired by the new and unknown.

Becoming an inventor is something that he thought of from his early years. Nouvellon revealed that he had grown up in a family were science and creativity played a central role. Already when he was a child, he liked innovating, playing with such ideas as making up a new language and building a complex electric circuit. He would dream of becoming an astronaut, a chemist or a mathematician. Inventing is doing something that has not been done before and thus there is an adventurous and exciting aspect to it that I have always loved, Nouvellon admitted.

This article does not necessarily reflect the opinions of the editors or management of EconoTimes.

See original here:
The perfect mix. The story of an ML engineer - EconoTimes

2020 has started as 2019 ended, with new cyberattacks, hacking incidents and data breaches coming to light almost every day.

Cyber criminals pose a threat to all manner of organisations and businesses, and the customers and consumers who use them. Some of the numbers involved in the largest data breaches are staggering, with personal data concerning hundreds of thousands of individuals being leaked each one potentially a new victim of fraud and other cybercrime.

SEE: Cybersecurity in an IoT and mobile world (ZDNet special report) | Download the report as a PDF (TechRepublic)

Businesses are doing their best to fight off cyberattacks, but it's hard to predict what new campaigns will emerge and how they'll operate. It's even harder to discern what the next big malware threat will be: the Zeus trojan and Locky ransomware were once major threats, but now it's things like Emotet botnet, the Trickbot trojan and Ryuk ransomware.

It's difficult to defend your perimeter against unknown threats -- and that's something thatcyber criminals take advantage of.

Artificial intelligence (AI) and machine learning (ML) are playing an increasing role in cybersecurity, with security tools analysing data from millions of cyber incidents, and using it to identify potential threats -- an employee account acting strangely by clicking on phishing links, for example, or a new variant of malware.

But there is a constant battle between attackers and defenders. Cyber criminals have long tried to tweak their malware code so that security software no longer recognises it as malicious.

Spotting every variation of malware, especially when it is deliberately disguised, is hard: increasingly it's by applying AI and ML that defenders are attempting to stop even the unknown, new types of malware attack.

"Machine learning is a good fit for anti-malware solutions because machine learning is well suited to solve 'fuzzy' problems," says Josh Lemos, vice-president of research and intelligence at Cylance, aBlackBerry-owned, AI-based cybersecurity provider working out of California.

The machine-learning database can draw upon information about any form of malware that's been detected before. So when a new form of malware appears -- either a tweaked variant of existing malware, or a new kind entirely -- the system can check it against the database, examining the code and blocking the attack on the basis that similar events have previously been deemed as malicious.

That's even the case when the malicious code is bundled up with large amounts of benign or useless code in an effort to hide the nefarious intent of the payload, as often happens.

It was these machine-learning techniques that enabled Cylance to uncover -- and protect users against -- a new campaign by OceanLotus, a.k.a. APT32, a hacking group linked to Vietnam.

"As soon as they came out with a new variant in the wild, we knew exactly what it was because we had some machine-learning signatures and models designed to orient to these variants when they appear. We knew they're close enough in their genetic make-up to be from this family of threat," Lemos explains.

SEE: This latest phishing scam is spreading fake invoices loaded with malware

But uncovering new kinds of malware isn't the only way machine learning can be deployed to boost cybersecurity: an AI-based network-monitoring tool can also track what users do on a daily basis, building up a picture of their typical behaviour. By analysing this information, the AI can detect anomalies and react accordingly.

"Think about what AI is really good at -- the ability to adapt and respond to a constantly changing world", says Poppy Gustafsson, co-CEO of Darktrace, a British cybersecurity company that uses machine learning to detect threats.

"What AI enables us to do is to respond in an intelligent way, understanding the relevance and consequences of a breach or a change of behaviour, and in real time develop a proportionate response," she adds.

For example, if an employee clicks on a phishing link, the system can work out that this was not normal behaviour and could therefore be potentially malicious activity.

Using machine learning, this can be spotted almost immediately, blocking the potential damage of a malicious intrusion and preventing login credentials being stolen, malware being deployed or otherwise enabling attackers to gain access to the network.

And all of this is done without the day-to-day activity of the business being impacted, as the response is proportionate: if the potential malicious behaviour is on one machine, that doesn't require the whole network being locked down.

A key benefit of machine learning in cybersecurity is that it identifies and reacts to suspected problems almost immediately, preventing potential issues from disrupting business.

By deploying AI-based cybersecurity from Darktrace to automate some of the defence functions, the McLaren Formula One team aims to ensure that the network is going to be safe, without relying on humans having to perform the impossible the task of monitoring everything at once.

"If we can't see data coming off the car, if we're compromised, we stop racing -- so high-speed decision-making from machines makes it safer," Karen McElhatton, Group CIO at McLaren explains. "Data isn't just bits and bytes: we have video, we have chats, emails -- it's the variety of that input that's coming and the growing volume of it. It's too much for humans to be able to manage and automated tools are opening our eyes up to what we need to be watching."

That's especially the case when it comes to monitoring how employees operate on the network. Like other large organisations, McLaren employs training to help staff improve cybersecurity, but it's possible that staff will attempt to take shortcuts in an effort to do their job more efficiently -- which could potentially lead to security issues. Machine learning helps to manage this.

"We've got really clever people at McLaren, but with smart people come creative ways of getting around security, so having that intelligence response is really important to us. We can't slow decision-making or innovation down, but we need to enable them to do it safely and securely -- and that's where Darktrace helps us," McElhatton explains.

But while AI and ML do provide benefits for cybersecurity, it's important for organisations to realise that these tools aren't a replacement for human security staff.

It's possible for a machine learning-based security tool to be programmed incorrectly, for example, resulting in unexpected -- or even obvious -- things being missed by the algorithms. If the tool misses a particular kind of cyberattack because it hasn't been coded to take certain parameters into account, that's going to lead to problems.

SEE: IoT security is bad. It's time to take a different approach.

"Where AI and machine learning can get you into trouble is if you are reliant on it as an oracle of everything," says Merritt Maxim, researcher director for security at analyst firmForrester .

"If the inputs are bad and it's passing things through it says are okay, but it's actually passing real vulnerabilities through because the model hasn't been properly tuned or adjusted -- that's the worst case because you think you're fully protected because you have AI".

Maxim notes that AI-based cybersecurity has "a lot of benefits", but isn't a complete replacement for human security staff; and like any other software on the network, you can't just install it and forget about it -- it needs to be regularly evaluated.

"You can't assume that AI and machine learning are going to solve all the problems," he says.

Indeed, there's the potential that AI and machine learning could create additional problems, because while the tools help to defend against hackers, it's highly likely that cyber criminals themselves are going to use the same techniques in an effort to make attacks more effective.

A report by Europol has warned that artificial intelligence is one of the emerging technologies that could make cyberattacks more dangerous and more difficult to spot than ever before. It's even possible that cyber criminals have already started using these techniques to help conduct hacking campaigns and malware attacks.

"A lot of it is, at the moment, theoretical, but that's not to say that it hasn't happened. It's quite likely that it's been used, but we just haven't seen it or haven't recognised it," says Philipp Amann, head of strategy for Europol's European Cybercrime Centre (EC3).

It's possible that by using machine learning, cyber criminals could develop self-learning automated malware, ransomware, social engineering or phishing attacks. Currently, they might not access to the deep wells of technology that cybersecurity companies have, but there's code around that can provide cyber criminals with access to these resources.

"The tools are out there -- some of them are open source. They're freely available to everyone and the quality is increasing, so it's likely to assume that this will be part of a criminal's repertoire if it isn't already," Amann says.

While it may be unclear if hackers have used machine learning to help develop or distribute malware, there is evidence of AI-based tools being used to conduct cybercrime; last year it was reported that criminals used AI generated audio to impersonate a CEO's voice and trick employees into transferring over 220,000 ($243,000) to them.

This 'deepfake voice attack' added a new layer to business email compromise scams, in which attackers claim to be the boss and request an urgent transfer of funds. This time, however, the attackers used AI to mimic the voice of the CEO and request the transfer of funds.

The nature of a CEO's job means their voice is often in the public domain, so criminals can find and exploit voice recordings and -- and this case isn't the only example.

"Other industry partners have confirmed other cases that haven't been reported; so that's where we know an AI-based system has been used as part of a social-engineering attack," says Amann.

AI-based deepfake technology has already caused concern when it comes to spreading disinformation or even abuse of individuals via fake videos, leading tocalls for deepfake regulation.

SEE: California takes on deepfakes in porn and politics

But the problem here is that while most people would see this as a warning not to meddle in this field, cyber criminals will simply look to take advantage of any technology in order to make money from malicious hacking. For example, machine learning could be employed to send out phishing emails automatically and learn what sort of language works in the campaigns, what generates clicks and how attacks against different targets should be crafted.

Like any machine-learning algorithm, success would come from learning over time, meaning that it's possible that phishing attacks could be driven in the same way security vendors attempt to defend against them.

"There's a whole cyber criminal element here for financial gain, which can leverage AI and machine learning effectively," warns Lemos.

However, if AI-based cybersecurity tools continue to develop and improve, and are applied correctly alongside human security teams, rather than instead of them, this could help businesses stay secure against increasingly smart and potent cyberattacks.

"One thing we can be certain of is the offices of tomorrow aren't going to like those of past. AI is how technology responds to our ever-changing world. It updates automatically and learns how humans react. We can't second-guess technology, but we can watch it and learn from it and adapt," says Gustafsson.

"You could move into a world where your whole cybersecurity posture is enhanced, with the ultimate vision being you could end up with a self-learning and self-healing network that can learn negative behaviours and stop them happening," she says.

Read this article:
AI is changing everything about cybersecurity, for better and for worse. Here's what you need to know - ZDNet

Global Artificial Intelligence and Machine Learning Market 2020, presents a professional and in-depth study on the current state of the industry globally, providing basic overview of Artificial Intelligence and Machine Learning market including definitions, classifications, applications and industry chain structure. Historical data available in the report elaborates on the development of the Artificial Intelligence and Machine Learning market on a global and regional level. The report compares this data with the current state of the Artificial Intelligence and Machine Learning market and thus discuss upon the upcoming trends that have brought the Artificial Intelligence and Machine Learning market transformation.

Industry predictions along with the statistical implication presented in the report delivers an accurate scenario of the Artificial Intelligence and Machine Learning market. The market forces determining the shaping of the worldwide Artificial Intelligence and Machine Learning market have been evaluated in detail. In addition to this, the supervisory outlook of the Artificial Intelligence and Machine Learning market has been covered in the report from both the Global and local perspective. The demand and supply side of the Artificial Intelligence and Machine Learning market has been broadly covered in the report. Also the challenges faced by the players in the Artificial Intelligence and Machine Learning market in terms of demand and supply have been listed in the report.

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In Global Artificial Intelligence and Machine Learning Industry report, development policies and plans as well as market size, share, end users are analyzed. Growth prospects of the overall Artificial Intelligence and Machine Learning industry have been presented in the report. This industry study segments Artificial Intelligence and Machine Learning global market by types, applications and companies. However, to give an in-depth view to the readers, detailed geographical segmentation of Artificial Intelligence and Machine Learning market within the globe has been covered in this study. The key geographical regions along with Artificial Intelligence and Machine Learning revenue forecasts are included in the report.

The Artificial Intelligence and Machine Learning market is segmented on the basis of key players, types and applications.

The leading players of worldwide Artificial Intelligence and Machine Learning industry includes

AIBrainAmazonAnkiCloudMindsDeepmindGoogleFacebookIBMIris AIAppleLuminosoQualcomm

Type analysis classifies the Artificial Intelligence and Machine Learning market into

Deep LearningNatural Language ProcessingMachine VisionOthers

Various applications of Artificial Intelligence and Machine Learning market are

HealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturing

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Global Artificial Intelligence and Machine Learning Market regional analysis covers:

The industry research presents Artificial Intelligence and Machine Learning market in North America mainly covers USA, Canada and Mexico. Artificial Intelligence and Machine Learning market in Asia-Pacific region cover-up China, Japan, Korea, India and Southeast Asia. Artificial Intelligence and Machine Learning market in Europe combines Germany, France, UK, Russia and Italy. Artificial Intelligence and Machine Learning market in South America includes Brazil, Argentina, Columbia etc. Artificial Intelligence and Machine Learning market in Middle East and Africa incorporates Saudi Arabia, UAE, Egypt, Nigeria and South Africa.

The competitive framework of the market in terms of the Global Artificial Intelligence and Machine Learning industry has been evaluated in the report. The Artificial Intelligence and Machine Learning market top companies with their overall share and share with respect to the global market have been included in the Artificial Intelligence and Machine Learning report. Furthermore, the factors on which the companies compete in the worldwide Artificial Intelligence and Machine Learning industry have been evaluated in the report. So the overall report helps the new aspirants to inspect the forthcoming opportunities in the Artificial Intelligence and Machine Learning market.

Chapter 1, to describe Artificial Intelligence and Machine Learning product scope, market overview, market opportunities, market driving force and market risks.

Chapter 2, to profile the top manufacturers of Artificial Intelligence and Machine Learning, with price, sales, revenue and global market share of Artificial Intelligence and Machine Learning in 2018 and 2019.

Chapter 3, the Artificial Intelligence and Machine Learning competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.

Chapter 4, the Artificial Intelligence and Machine Learning breakdown data are shown at the regional level, to show the sales, revenue and growth by regions, from 2015 to 2020.

Chapter 5, 6, 7, 8 and 9, to break the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2015 to 2020.

Chapter 10 and 11, to segment the sales by type and application, with sales market share and growth rate by type, application, from 2015 to 2020.

Chapter 12, Artificial Intelligence and Machine Learning market forecast, by regions, type and application, with sales and revenue, from 2020 to 2025.

Chapter 13, 14 and 15, to describe Artificial Intelligence and Machine Learning sales channel, distributors, customers, research findings and conclusion, appendix and data source.

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Orbis Reports is constantly motivated to offer superlative run-down on ongoing market developments. To fulfill this, our voluminous data archive is laden with genuine and legitimately sourced data, subject to intense validation by our in-house subject experts. A grueling validation process is implemented to double-check details of extensive publisher data pools, prior to including their diverse research reports catering to multiple industries on our coherent platform. With an astute inclination for impeccable data sourcing, rigorous quality control measures are a part and parcel in Orbis Reports.

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Artificial Intelligence and Machine Learning Market by Application, Global Industry Share, Growth Opportunities, Regions & Forecast by 2025 - News...

Grand View Research, Inc. Market Research And Consulting.

According to report published by Grand View Research, The global machine learning market size was valued at USD 6.9 billion in 2018 and is anticipated to register a CAGR of 43.8% from 2019 to 2025.

The globalmachine learning marketsize is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025.

Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data. Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.

The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.

Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.

Request a sample Copy of theMachine Learning Market Research Report @https://www.grandviewresearch.com/industry-analysis/machine-learning-market/request/rs1

Key Takeaways Of The Report :

The emergence of connected AI is expected to enable ML algorithms to learn continuously based on newly available information. Such developments are anticipated to drive the market in the coming years

The advertising and media sector accounted for the largest share in 2018 owing to capabilities such as buyers optimization, data processing, and analysis provided by the technology

H2O.ai announced a partnership with IBM Corporation, a multinational IT company, in June 2018. Through this partnership, H2O.ai will offer its GPU-powered machine learning, next-generation AI platform, and best-of-breed deep learning on IBMs Power Systems platform

Key players in the machine learning market include Amazon Web Services, Inc.; Baidu Inc.; Google Inc.; H2O.ai; Hewlett Packard Enterprise Development LP; Intel Corporation; International Business Machines Corporation; Microsoft Corporation; SAS Institute Inc.; and SAP SE.

Have Any Query? Ask Our Experts @https://www.grandviewresearch.com/inquiry/7023/ibb

Grand View Research has segmented the global machine learning market based on component, enterprise size, end use, and region:

Machine Learning Component Outlook (Revenue, USD Million, 2014 2025)

Hardware

Software

Service

Machine Learning Enterprise Size Outlook (Revenue, USD Million, 2014 2025)

Machine Learning End-use Outlook (Revenue, USD Million, 2014 2025)

Machine Learning Regional Outlook (Revenue, USD Million, 2014 2025)

North America

Europe

Asia Pacific

South America

Middle East and Africa

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Education And Learning Analytics Market:The global education and learning analytics market size was valued at USD 17.01 billion in 2018. It is anticipated to witness a CAGR of 17.4% over the forecast period.

Smart Education and Learning Market:The global smart education and learning market size was valued at USD 135.5 billion in 2017 and is expected to register a CAGR of 15.2% from 2018 to 2025.

About Grand View Research

Grand View Research provides syndicated as well as customized research reports and consulting services on 46 industries across 25 major countries worldwide. This U.S.-based market research and consulting company is registered in California and headquartered in San Francisco. Comprising over 425 analysts and consultants, the company adds 1200+ market research reports to its extensive database each year. Supported by an interactive market intelligence platform, the team at Grand View Research guides Fortune 500 companies and prominent academic institutes in comprehending the global and regional business environment and carefully identifying future opportunities.

Media ContactCompany Name: Grand View Research, Inc.Contact Person: Sherry James, Corporate Sales Specialist U.S.A.Email: Send EmailPhone: 1-415-349-0058, Toll Free: 1-888-202-9519Address: 201, Spear Street, 1100 City: San FranciscoState: CaliforniaCountry: United StatesWebsite: https://www.grandviewresearch.com/industry-analysis/machine-learning-market

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Machine Learning Market Anticipated to Expand at a CAGR of 43.8% For The Forecast Period From 2019 To 2025 | Grand View Reserach Inc. -...

Machine vision is a mature technology with established incumbents. However, significant advancements in chipsets, software, and standards are bringing deep learning innovation into the machine vision sector.

According to a recent analysis by global tech market advisory firm ABI Research, total shipments for machine vision sensors and cameras will reach 16.9 million by 2025, creating an installed base of 94 million machine vision systems in industrial manufacturing. Of that installed base, 11% will be deep learning-based.

Machine vision systems are a staple in production lines for barcode reading, quality control, and inventory management. These solutions often have long replacement cycles and are less prone to disruption. Due to the increasing demands for automation, machine vision is finding its way into new applications, said Lian Jye Su, Principal Analyst at ABI Research. Robotics, for example, is a new growth area for machine vision: Collaborative robots rely on machine vision for guidance and object classification, while mobile robots rely on machine vision for SLAM and safety.

A different breed from conventional machine vision technology, deep learning-based machine vision is data-driven and utilizes a statistical approach, which allows the machine vision model to improve as more data is gathered for training and testing. Major machine vision vendors have realized the potential of deep learning-based machine learning. Cognex, for example, acquired SUALAB, a leading Korean-based developer of vision software using deep learning for industrial applications, and Zebra Technologies acquired Cortexica Vision Systems Ltd., a London-headquartered leader in business-to-business (B2B) AI-based computer vision solutions developer.

At the same time, chipset vendors are launching new chipsets and software stacks to facilitate the implementation of deep learning-based machine vision. Xilinx, a Field Programmable Gated Array (FPGA) vendor, partnered closely with camera sensor manufacturer Sony and camera vendors such as Framos and IDS Imaging to incorporate its Versal ACAP System on Chip (SoC). Intel, on the other hand, offers OpenVINO for developers to deploy pre-trained deep learning-based machine vision models through a common API to deliver inference solutions on various computing architectures. Another FPGA vendor, Lattice Semiconductor, focuses on low-powered Artificial Intelligence (AI) for embedded vision through its senseAI stack, which offers hardware accelerators, software tools, and reference designs. These technology stacks aim to ease development and deployment challenges and create platform stickiness.

On the standards front, vendors are bringing 10GigE (Gigabit Ethernet) and 25GigE cameras into industrial applications. Continual upgrades on video capturing and compression technologies also generate a better image and video quality for deep learning-based machine vision models. This ensures the futureproofing of machine vision systems. Therefore, when choosing machine vision systems, end implementers need to understand their machine vision requirements, consider integration with their backend system, and identify the right ecosystem partners. Deployment flexibility and future upgradability and scalability will be crucial as machine vision technology continues to evolve and improve, concludes Su.

These findings are from ABI Researchs Machine Vision in Industrial Applications application analysis report. This report is part of the companys Artificial Intelligence and Machine Learning research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

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ABI Research: Installed base of machine vision systems in manufacturing to reach 100 million by 2025 - Modern Materials Handling

One of the seminal texts for anyone interested in technology and society is Melvin Kranzbergs Six Laws of Technology, the first of which says that technology is neither good nor bad; nor is it neutral. By this, Kranzberg meant that technologys interaction with society is such that technical developments frequently have environmental, social and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances.

The saloon-bar version of this is that technology is both good and bad; it all depends on how its used a tactic that tech evangelists regularly deploy as a way of stopping the conversation. So a better way of using Kranzbergs law is to ask a simple Latin question: Cui bono? who benefits from any proposed or hyped technology? And, by implication, who loses?

With any general-purpose technology which is what the internet has become the answer is going to be complicated: various groups, societies, sectors, maybe even continents win and lose, so in the end the question comes down to: who benefits most? For the internet as a whole, its too early to say. But when we focus on a particular digital technology, then things become a bit clearer.

A case in point is the technology known as machine learning, a manifestation of artificial intelligence that is the tech obsession de nos jours. Its really a combination of algorithms that are trained on big data, ie huge datasets. In principle, anyone with the computational skills to use freely available software tools such as TensorFlow could do machine learning. But in practice they cant because they dont have access to the massive data needed to train their algorithms.

This means the outfits where most of the leading machine-learning research is being done are a small number of tech giants especially Google, Facebook and Amazon which have accumulated colossal silos of behavioural data over the last two decades. Since they have come to dominate the technology, the Kranzberg question who benefits? is easy to answer: they do. Machine learning now drives everything in those businesses personalisation of services, recommendations, precisely targeted advertising, behavioural prediction For them, AI (by which they mostly mean machine learning) is everywhere. And it is making them the most profitable enterprises in the history of capitalism.

As a consequence, a powerful technology with great potential for good is at the moment deployed mainly for privatised gain. In the process, it has been characterised by unregulated premature deployment, algorithmic bias, reinforcing inequality, undermining democratic processes and boosting covert surveillance to toxic levels. That it doesnt have to be like this was vividly demonstrated last week with a report in the leading biological journal Cell of an extraordinary project, which harnessed machine learning in the public (as compared to the private) interest. The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.

The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brains architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources. They then asked the network to predict which would be effective against E coli but looked different from conventional antibiotics. This produced a hundred candidates for physical testing and led to one (which they named halicin after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.

There are a number of other examples of machine learning for public good rather than private gain. One thinks, for example, of the collaboration between Google DeepMind and Moorfields eye hospital. But this new example is the most spectacular to date because it goes beyond augmenting human screening capabilities to aiding the process of discovery. So while the main beneficiaries of machine learning for, say, a toxic technology like facial recognition are mostly authoritarian political regimes and a range of untrustworthy or unsavoury private companies, the beneficiaries of the technology as an aid to scientific discovery could be humanity as a species. The technology, in other words, is both good and bad. Kranzbergs first law rules OK.

Every cloud Zeynep Tufekci has written a perceptive essay for the Atlantic about how the coronavirus revealed authoritarianisms fatal flaw.

EU ideas explained Politico writers Laura Kayali, Melissa Heikkil and Janosch Delcker have delivered a shrewd analysis of the underlying strategy behind recent policy documents from the EU dealing with the digital future.

On the nature of loss Jill Lepore has written a knockout piece for the New Yorker under the heading The lingering of loss, on friendship, grief and remembrance. One of the best things Ive read in years.

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How artificial intelligence outsmarted the superbugs - The Guardian

30MHz is participating in the autonomous greenhouse challenge: growing tomatoes without entering the greenhouse. Theyre managing the greenhouse from behind their laptops, and have to guide their decisions based on the real-time data they receive from the indoor climate, outside conditions and weather forecasts. To be able to do this theyve been developing multiple machine learning applications. These applications guide the cultivation strategy and subsequently, the actions taken to reach the desired climate.

Machine learning challengesHowever, there are many challenges in developing and operationalising large scale machine learning applications. One reason is the inherent nature of machine learning. Data are ever-evolving and models are stochastic, which means you have no certainty about what will happen in advance.

In software engineering, code is version controlled to manage changes over time (i.e. the numbered software updates of your smartphone). In machine learning, there are no standardised solutions to manage changes in code, data and model characteristics at the same time. And this is largely due to the (im)maturity of the field. There are many initiatives trying to solve this problem, for example, MLflow and Data Version Control (DVC), but these have their own limitations which are out the scope of this blog.

AWS project & solutionsTo solve some of these problems 30MHz has been fortunate to receive the help of two machine learning engineers from Amazon Web Services (or AWS). AWS is a cloud provider, and the company is using their services to host among others servers, database and machine learning models. As a company, 30MHz has been closely working together with AWS for quite some years. For this reason, and because theyre excited about the work, 30MHz had the opportunity to learn from and work with AWS engineers at their own office in Amsterdam for more than two weeks.

The goals of the project were twofold:

Improve and automateWith AWS' knowledge and experience, 30MHz has been able to improve and automate a large part of their machine learning infrastructure. The result is a scalable and robust framework for machine learning applications on the 30MHz platform.

For more information:30MHzMoezelhavenweg 91043AM AmsterdamNetherlands+31 (0) 6 14551362contact@30mhz.comwww.30mhz.com

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Growing tomatoes with Amazon Web Services - hortidaily.com