Monthly Archives: May 2020

Written by Jackson Barnett May 14, 2020 | FEDSCOOP

Think of it asmachines helping machines: The Defense Innovation Unitwants a machine learning platform that can boost the Pentagons existing uses of robotic process automation (RPA) for business tasks.

The goal of the Silicon Valley-basedagencys solicitationis to help nudge Department of Defense RPAs into more complex problem-solvingterritoryby providing pattern recognitionand instructions on how toadjust automation to fit changing scenarios.

The ML platform will identify and suggest corrections to business processes that are not limited to previously well-defined business logic methods,

The DOD has sought to expand its use of RPAs to reduce some of the tedious work many employees are still required to conduct manually.Current use cases arelimitedtonarrow, well-defined tasks, the department says, but it wants machine learning to help automate less-defined problems like finding abuse or fraud in finical systems, according to the solicitation. The platform will integrate with current RPA technology and be used for data management and algorithmic training.

Machine learning, a type of artificial intelligencesystem that trains computers to make inferences from large data sets, can help by identifying corrections and fixes to automation that gets stumped on less-defined tasks. The advantage of machine learning is that computers can detect subtle changes that would get lost to the human eye.

The appeal of RPA is simple, DOD officials say.

We all generally have more work than we have time to do, Rachael Martin, theJointArtificial Intelligence Centers mission chief for intelligent business automation, augmentation and analytics, said during an April webinar.

Martin said the JAIC is working to help DOD components adopt RPAs. The center is helping coordinate policy and technical solutions for parts of the military touse initsown problem sets, she said in April. Many of the current use cases are being tested in DOD support agencies, she said.

DIU isnot looking for a cloud service provider or new RPAs, just a platform that will simplify data flows and use open architecture to leveragemachine learning, according to the solicitation.

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DIU seeks one form of automation (ML) than can help another (RPA) - FedScoop

Its an understatement to say that doctors are swamped right now. At the beginning of April, coronavirus patients had filled New York emergency rooms so thoroughly that doctors across specialties,including dermatologists and orthopedists, had to help out.

Short-term, doctors need reliable, proven technology, like N95 masks. Longer-term, though, machine learning algorithms could help doctors treat patients. These algorithms can function as hyper-specialized doctors assistants, performing key technical tasks like scanning an MRI for signs of brain cancer, or flagging pathology slides that show breast cancer has metastasized to the lymph nodes.

One day, an algorithm could check CT scans for the lung lesions and abnormalities that indicate coronavirus.

Thats a model that could be trained, Mona G.Flores, MD, global head of medical AIat NVIDIA, told Built In.

At least, it could be trained in theory. Training an algorithm fit for a clinical setting, though, requires a large, diverse dataset. Thats hard to achieve in practice, especially when it comes to medical imaging. In the U.S.,HIPAA regulations make it very difficult for hospitals to share patient scans, even anonymized ones; privacy is a top priority at medical institutions.

More on AI and PrivacyDifferential Privacy Injects Noise Into Data Sets. Heres How It Works.

Thats not to say trained algorithms havent made it into clinical settings. A handful have passed muster with the U.S. Food and Drug Administration, according to Dr. Spyridon Bakas, a professor at University of Pennsylvanias Center for Biomedical Imaging Computing and Analytics.

In radiology, for instance, algorithms help some doctors track tumor size and progression, along with things that cannot be seen with the naked eye, Dr. Bakas told Built In like where the tumor will recur, and when.

If algorithms could train on data without puncturing its HIPAA-mandated privacy, though, machine learning could have a much bigger impact on healthcare.

And thats actually possible, thanks to a new algorithm training technique: federated learning.

Federated learning is a way of training machine learning algorithms on private, fragmented data, stored on a variety of servers and devices. Instead of pooling their data, participants all train the same algorithm on their separate data. Then they pool their trained algorithm parameters not their data on a central server, which aggregates all their contributions into a new, composite algorithm. When these steps are repeated, models across institutions converge.

Federated learning is a way of training machine learning algorithms on private, fragmented data, stored on a variety of servers and devices. Instead of pooling their data, participating institutions all train the same algorithm on their in-house, proprietary data. Then they pool their trained algorithm parameters not their data on a central server, which aggregates all their contributions into a new, composite algorithm. This composite gets shipped back to each participating institution for more training, and then shipped back to the central server for more aggregation.

Eventually, all the individual institutions algorithms converge on an optimal, trained algorithm, more generally applicable than any one institutions would have been and nearly identical to the model that would have arisen from training the algorithm on pooled data.

In December of 2019, at a radiology conference in Chicago, NVIDIA unveiled a new feature for Clara SDK. This software development kit, created expressly for the healthcare field, helps medical institutions make and deploy machine learning models with a set of tools and libraries and examples, Dr. Flores said.

The new tool was Clara Federated Learning infrastructure that allowed medical institutions to collaborate on machine learning projects without sharing patient data.

NVIDIAs not the only tech company embracing federated learning. Another medical AI company, Owkin, has rolled out a software stack for federated learning called Owkin Connect, which integrates with NVIDIAs Clara. Meanwhile, at least two general-purpose federated learning frameworks have rolled out recently, too: Googles TensorFlow Federated and the open-source PySyft.

The concept of federated learning, though, dates back to years earlier. Like many innovations, it was born at Google.

In 2017, Google researchers published a paper on a new technique they hoped could improve search suggestions on Gboard, the digital keyboard on Android phones. It was the first paper on federated learning.

In a blog post, Google AI research scientists Brendan McMahan and Daniel Ramage explained the very first federated learning use case like this:

When Gboard shows a suggested query, your phone locally stores information about the current context and whether you clicked the suggestion. Federated Learning processes that history on-device to suggest improvements to the next iteration of Gboards query suggestion model.

In other words, by blending edge computing and machine learning, federated learning offered a way to constantly improve the global query suggestion model without tracking users every move in a central database. In other words, it allowed Google to streamline its data collection processan essential given the Android OS more than 2 billion active users.

Thats just one of many potential applications, though. Dr. Bakas saw potential applications in medical imaging. This should come as no surprise: Dr. Bakas was the lead organizer of the BraTS challenge.

Since 2012, the BraTS challenge an annual data science competition has asked competitors to train algorithms to spot signs of brain tumors, specifically gliomas, on MRIs. All the competing teams use the same benchmark dataset to train, validate and test their algorithms.

In 2018, that dataset consisted of about 2,000 MRIs from roughly 500 patients, pulled from ten different medical institutions, Dr. Bakas said.

Now, this is a tiny fraction of the MRIs in the world relevant to the BraTS contest; about 20,000 people per year get diagnosed with gliomas in the U.S. alone. But obtaining medical images for a competition data set is tricky. For one, it requires the patients consent. For another, it requires approval from the contributing hospitals internal review board, which involves proving the competition serves the greater good.

The BraTS challenge is just one of many data science challenges that navigate labyrinthine bureaucracy to compile datasets of medical images.

Major companies rely on these datasets, too; theyre more robust than what even Google could easily amass on its own. Googles LYNA, a machine learning algorithm that can pinpoint signs of metastatic breast cancer in the lymph nodes, first made headlines by parsing the images from the 2016 ISBI Camelyon challenges dataset more than 10 percent more accurately than the contests original winner. NVIDIA, meanwhile, sent a team to the 2018 BraTS challenge and won.

[A]n accurate algorithm alone is insufficient to improve pathologists workflow or improve outcomes for breast cancer patients.

Even challenge-winning algorithms, though or the algorithms that beat the winning algorithms arent ready for clinical use. Googles LYNA remains in development. Despite 2018 headlines touting it asbetter than humans in detecting advanced breast cancer, it still needs more testing.

[A]n accurate algorithm alone is insufficient to improve pathologists workflow or improve outcomes for breast cancer patients, Google researchers Martin Stumpe and Craig Mermel wrote on the Google AI blog.

For one thing, it was trained to read one slide per patient but in a real clinical setting, doctors look at multiple slides per patient.

For another, accuracy in a challenge context doesnt always mean real-world accuracy. Challenge datasets are small, and biased by the fact that every patient consented to share their data. Before clinical use, even a stellar algorithm may need to train on more data.

Like, much more data.

More on Data ScienceCoronavirus Charts Are Everywhere. But Are They Good?

Federated learning, Dr. Bakas saw, could allow powerful algorithms access to massive stores of data. But how well did it work? In other words, could federated learning train an algorithm as accurate as one trained on pooled data? In 2018, he and a team of researchers from Intel publisheda paper on exactly that.

No one before has attempted to apply federated learning in medicine, he said.

He and his co-authors trained an off-the-shelf, basic algorithm on BraTS 2018 MRI images using four different techniques. One was traditional machine learning, using pooled data; another was federated learning; the other two techniques were alternate collaborative learning techniques that, like federated learning, involved training an algorithm on a fragmented dataset.

We were not married to federated learning, Dr. Bakas said.

It emerged as a clear success story in their research, though the best technique for melding AI with HIPAA-mandated data privacy. In terms of accuracy, the algorithm trained via federated learning was second only to the algorithm trained on conventional, pooled data. (The difference was subtle, too; the federated learning algorithm was 99 percent as accurate as the traditional one.) Federated learning also made all the different institutions algorithms converge more neatly on an optimal model than other collaborative learning techniques.

Once Dr. Bakas and his coauthors validated the concept of federated learning, a team of NVIDIA researchers elaborated on it further, Dr. Bakas explained. Their focus was fusing it with even more ironclad privacy technology. Though federated learning never involves pooling patient data, it does involve pooling algorithms trained on patient data and hackers could, hypothetically, reconstruct the original data from the trained algorithms.

NVIDIA found a way to prevent this with a blend of encryption and differential privacy. The reinvented model aggregation process involves transferring only partial weights... so that people cannot reconstruct the data, Dr. Flores said.

Its worth noting that NVIDIAs paper, like the one Dr. Bakas co-authored, relied on the BraTS 2018 dataset. This was largely a matter of practicality, but the link between data science competitions and federated learning could grow more substantive.

In the long-term, Dr. Bakas sees data science competitions facilitating algorithmic development; thanks to common data sets and performance metrics, these contests help identify top-tier machine learning algorithms. The winners can then progress to federated learning projects and train on much bigger data sets.

In other words, federated learning projects wont replace data science competitions. Instead, they will function as a kind of major league for competition-winning algorithms to play in and theyll improve the odds of useful algorithms making it into clinical settings.

The end goal is really to reach to the clinic, Dr. Bakas said, to help the radiologist [and] to help the clinician do their work more efficiently.

Short answer: a lot. Federated learning is still a new approach to machine learning Clara FL, lets remember, debuted less than six months agoand researchers continue to work out the kinks.

So far, NVIDIAs team has learned that clear, shared data protocols play a key role in federated learning projects.

You have to make sure that the data to each of the sites is labeled in the same fashion, Dr. Flores said, so that you're comparing apples to apples.

Open questions remain, though. For instance when a central server aggregates a group of trained algorithms, how should it do that? Its not as straightforward as taking a mathematical average, because each institutions dataset is different in terms of size, underlying population demographics and other factors.

Which ones do you give more weight to than others? Dr.Flores said. There are many different ways of aggregating the data That's something that we are still researching.

Federated learning has major potential, though, especially in Europe, where privacy regulations have already tightened due to the General Data Protection Regulation. The law, which went into effect back in 2018, is the self-proclaimed toughest privacy and security law in the world so stringent, Dr. Bakas noted, that it would prevent hospitals from contributing patient data to the BraTS challenge, even if the individual patients consented.

So far, the U.S. hasnt cracked down quite as heavily on privacy as the EU has, but federated learning could still transform industries where privacy is paramount. Already, banks can train machine learning models to recognize signs of fraud, using in-house data; however, if each bank has its own model, it will benefit big banks and leave small banks vulnerable.

While individual banks may like this outcome, it is less than ideal for solving the social issue of money laundering, writes B Capital venture capitalist Mike Fernandez.

Federated learning could even the playing field, allowing banks of all sizes to contribute to a global fraud detection model trained on more data than any one bank could amass, all while maintaining their clients privacy.

Federated learning could apply to other industries, too. As browsers like Mozilla and Google Chrome phase out third-party cookies, federated learning of cohorts could become a way of targeting digital ads to groups of like-minded users, while still keeping individual browser histories private. Federated learning could also allow self-driving cars to share the locations of potholes and other road hazards without sharing, say, their exact current location.

One thing Dr. Bakas doesnt see federated learning doing, even in the distant future: automating away doctors. Instead, he sees it freeing up doctors to do what they do best, whether thats connecting with patients or treating novel and complex ailments with innovative treatments. Doctors have already dreamed up creative approaches to the coronavirus, like using massage mattresses for pregnant women to boost patients oxygen levels.

They just dont really excel at scanning medical imaging and diagnosing common, well-documented ailments, like gliomas or metastatic breast cancer.

They can identify something that is already flaring up on a scan, Dr. Bakas said, but there are some ambiguous areas that radiologists are uncertain about.

Machine learning algorithms, too, often make mistakes about these areas. At first. But over time, they can learn to make fewer, spotting patterns in positive cases invisible to the human eye.

This is why they complement doctors so powerfully they can see routine medical protocols in a fresh, robotic way. That may sound like an oxymoron, but its not necessarily one anymore.

More on Data ScienceThe Dos and Donts of Database Design, According to Experts

Excerpt from:

Federated Learning Fuses AI and Privacy and It Could Transform Healthcare - Built In

The explosion of the internet, in conjunction with the success of neural networks, brought the world of finance closer to more exotic approaches. Deep learning today is one such technique that is being widely adopted to cut down losses and generate profits.

When gut instincts do not do the job, mathematical methods come into play. Differential equations, for instance, can be used to represent a dynamic model. The approximation of pricing functions is a persistent challenge in quantitative finance. By the early 1980s, researchers were already experimenting with Taylor Expansions for stochastic volatility models.

For example, if company A wants to buy a commodity say oil in future from company B but is unsure of the future prices. So company A wants to make a deal with B that no matter what the price of oil is in the future, B should sell it to A for a price according to their contract.

In the world of finance, this is a watered-down version of derivatives trading. Derivatives are the securities made on underlying assets. In the above case, company A predicts a rise in price, and company B predicts a fall in price. Both these companies are making a bet on future prices and agree upon a price that cuts down their losses or can even bring profits (if A sells after price rise). So how do these companies arrive at a certain price or how do they predict the future price?

Taking the same example of derivatives trading, the researchers at Danske Bank of Denmark, have explored the implications of differential deep learning.

Deep learning offers the much needed analytic speeds, which are necessary for an approximation of volatile markets. Machine learning tools can take up the high dimensionality (many parameters) trait of a market and help resolve the computational bottlenecks.

Differential machine learning is an extension of supervised learning, where ML models are trained on differentials of labels to inputs.

In the context of financial derivatives and risk management, pathwise differentials are popularly

computed with automatic adjoint differentiation (AAD). AAD is an algorithm to calculate derivative sensitivities, very quickly. Nothing more, nothing less. AAD is also known in the field of machine learning under the name back-propagation or simply backprop.

Differential machine learning, combined with AAD, wrote the authors, provides extremely effective pricing and risk approximations. They say that fast pricing analytics can be produced and can effectively compute risk management metrics and even simulate hedge strategies.

This work compares differential machine learning to data augmentation in computer vision, where multiple labelled images are produced from a single one, by cropping, zooming, rotating or recolouring.

Data augmentation not only extends the training set but also encourages the machine learning model to learn important invariances (features that stay the same). Similarly, derivatives labels not only increase the amount of information in the training set but also encourage the model to learn the shape of the pricing function. Derivatives from feedforward networks form another neural network, efficiently computing risk sensitivities in the context of pricing approximation. Since the adjoints form a second network, one can use them for training as well as expect significant performance gain.

Risk sensitivities converge considerably slower than values and often remain blatantly wrong, even with hundreds of thousands of examples. We resolve these problems by training ML models on datasets augmented with differentials of labels with respect to the following inputs:

This simple idea, assert the authors, along with the adequate training algorithm, will allow ML models to learn accurate approximations even from small datasets, making machine learning viable in the context of trading.

Differential machine learning learns better from data alone, the vast amount of information contained in the differentials playing a similar role, and often more effective, to manual adjustments from contextual information.

The researchers posit that the unreasonable effectiveness of differential ML is applicable in situations where high-quality first-order derivatives with training inputs are available and in complex computational tasks such as the pricing and risk approximation of complex derivatives trading.

Differentials inject meaningful additional information, eventually resulting in better results with smaller datasets. Learning effectively from small datasets is critical in the context of regulations, where the pricing approximation must be learned quickly, and the expense of a large training set cannot be afforded.

The results from the experiments by Danske banks researchers show that learning the correct shape from differentials is crucial to the performance of regression models, including neural networks.

Know more about differential deep learning here.

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What Is Differential Deep Learning? Through The Lens Of Trading - Analytics India Magazine

H2O.ai and SAS Institute

Machine Learning Market Competitive Analysis:

Consistent technological developments, surging industrialization, raw material affluence, increasing demand for the Machine Learning , and rising disposable incomes, soaring product awareness are adding considerable revenue to the market. According to the report, the Machine Learning market is expected to report a healthy CAGR from 2020 to 2026. Affairs such as product innovations, industrialization, increasing urbanization in the developing and developed countries are likely to boost market demand in the near future.

The report further sheds light on the current and forthcoming opportunities and challenges in the Machine Learning market and provide succinct analysis that assists clients in improving their business gains. Potential market threats, risks, uncertainties, and obstacles are also highlighted in this report that helps market players to lower the possible losses to their Machine Learning business. The report also employs various analytical models such as Porters Five Forces and SWOT analysis to evaluate several bargaining powers, threats, and opportunities in the market.

Machine Learning Market Segments:

Moreover, the leading Machine Learning manufacturers and companies are illuminated in the report with extensive market intelligence. The report enfolds detailed and precise assessments of companies based on their financial operations, revenue, market size, share, annual growth rates, production cost, sales volume, gross margins, and CAGR. Their manufacturing details are also enlightened in the report, which comprises analysis of their production processes, volume, product specifications, raw material sourcing, key vendors, clients, distribution networks, organizational structure, and global presence.

The report also underscores their strategics planning including mergers, acquisitions, ventures, partnerships, product launches, and brand developments. Additionally, the report renders the exhaustive analysis of crucial market segments, which includes Machine Learning types, applications, and regions. The segmentation sections cover analytical and forecast details of each segment based on their profitability, global demand, current revue, and development prospects. The report further scrutinizes diverse regions including North America, Asia Pacific, Europe, Middle East, and Africa, and South America. The report eventually helps clients in driving their Machine Learning business wisely and building superior strategies for their Machine Learning businesses.

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Table of Content

1 Introduction of Machine Learning Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Machine Learning Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 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 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Machine Learning Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Machine Learning Market Growth Trends, Key Players, Analysis, Competitive Strategies and Forecasts to 2026 - News Distinct

Among the myriad thematic exchange traded funds investors have to consider, artificial intelligence products are numerous and some are catching on with investors.

Count the ROBO Global Artificial Intelligence ETF THNQ, +0.72% as the latest member of the artificial intelligence ETF fray. HNQ, which debuted earlier this week, comes from a good gene pool as its stablemate,the Robo Global Robotics and Automation Index ETF ROBO, -0.67%, was the original and remains one of the largest robotics ETFs.

That's relevant because artificial intelligence and robotics are themes that frequently intersect with each other. Home to 72 stocks, the new THNQ follows the ROBO Global Artificial Intelligence Index.

Adding to the case for A.I., even with a new product such as THNQ, is that the technology has hundreds, if not thousands, of applications supporting its growth.

Companies developing AV technology are mainly relying on machine learning or deep learning, or both, according to IHS Markit. A major difference between machine learning and deep learning is that, while deep learning can automatically discover the feature to be used for classification in unsupervised exercises, machine learning requires these features to be labeled manually with more rigid rulesets. In contrast to machine learning, deep learning requires significant computing power and training data to deliver more accurate results.

Like its family ROBO, THNQ offers wide reach with exposure to 11 sub-groups. Those include big data, cloud computing, cognitive computing, e-commerce and other consumer angles and factory automation, among others. Of course, semiconductors are part of the THNQ fold, too.

The exploding use of AI is ushering in a new era of semiconductor architectures and computing platforms that can handle the accelerated processing requirements of an AI-driven world, according to ROBO Global. To tackle the challenge, semiconductor companies are creating new, more advanced AI chip engines using a whole new range of materials, equipment, and design methodologies.

While THNQ is a new ETF, investors may do well to not focus on that rather focus on the fact the AI boom is in its nascent stages.

Historically, the stock market tends to under-appreciate the scale of opportunity enjoyed by leading providers of new technologies during this phase of development, notes THNQ's issuer. This fact creates a remarkable opportunity for investors who understand the scope of the AI revolution, and who take action at a time when AI is disrupting industry as we know it and forcing us to rethink the world around us.

The new ETF charges 0.68% per year, or $68 on a $10,000 investment. That's inline with rival funds.

2020 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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A New Way To Think About Artificial Intelligence With This ETF - MarketWatch

Author: CapitalWatch Staff

Canaan Inc. (Nasdaq: CAN) became publicly traded in New York in late November. It raised $90 million in its IPO, which Canaan's founder, chairman, and chief executive officer,Nangeng Zhang modestly called "a good start." Since that time, the company has met significant milestones in its mission to disrupt the supercomputing industry.

Operating since 2013, Hangzhou-based Canaan delivers supercomputing solutions tailored to client needs. The company focuses on the research and development of artificial intelligence (AI) technology specifically, AI chips, AI algorithms, AI architectures, system on a chip (SoC) integration, and chip integration. Canaan is also known as a top manufacturer of mining hardware in China the global leader in digital currency mining.

Since IPO, Canaan has made strides in accomplishing new projects, despite the hard-hit cross-industry crisis Covid-19 has caused worldwide. In a recent announcement, Canaan said it has developed a SaaS product which its partners can use to operate a cloud mining platform. Cloud mining allows users to mine digital currency without having to buy and maintain mining hardware and spend on electricity a trend that has been gaining popularity.

A Chip of the Future

Earlier this year, Canaan participatedat the 2020 International Consumer Electronics Show in Las Vegas, the world's largest tech show that attracts innovators from across the globe. Canaan impressed, showcasing its Kendryte K210 the world's first-ever RISC-V-based edge AI chip. The chip was released in September 2018 and has been in mass-production ever since.

K210 is Canaan's first chip. The AI chip is designed to carry out machine learning. The primary functions of the K210 are machine vision and semantic, which includes the KPU for computing convolutional neural networks and an APU for processing microphone array inputs. KPU is a general-purpose neural network processor with built-in convolution, batch normalization, activation, and pooling operations. The next-generation chip can detect faces and objects in real-time. Despite the high computing power, K210 consumes only 0.3W while other typical devices consume 1W.

More Than Just Chipping Away at Sales

As of September 30, 2019, Canaan has shipped more than 53,000 AI chips and development kits to AI product developers since release.

Currently, the sales of K210 are growing exponentially, according to CEO Zhang .

The company has moved quickly to the commercialization of chips, and developed modules, products and back-end SaaS, offering customers a "full flow of AI solutions."

Based on the first generation of K210, Canaan has formed critical strategic partnerships.

For example, the company launched joint projects with a leading AI algorithm provider, a top agricultural science and technology enterprise, and a well-known global soft drink manufacturer to deliversmart solutionsfor variousindustrialmarkets.

The Booming Blockchain Industry

Currently, Canaan is working under the development strategy of "Blockchain + AI." The company has made several breakthroughs in the blockchain and AI industry, including algorithm development and optimization, standard unit design, low-voltage and high-efficiency operation, high-performance design system and heat dissipation, etc. The company has also accumulated extensive experience in ASIC chip manufacturing, laying the foundation for its future growth.

Canaan released first-generation products based on Samsung's 8nm and SMIC's 14nm technologies in Q4 last year. The former has been shipped in Q1 this year, while the latter will be shipped in Q2. In February, it launched the second generation of the product which is more efficient, more cost-effective and offers better performance.

Currently, TSMC's 5nm technology is under development. This technology will further improve the company's machines' computing power and ensure Canaan's leading position in the blockchain hardware space.

"We are the leader in the industry," says Zhang.

Canaan's Covid-19 Strategy

During the Covid-19 outbreak, Canaan improved the existing face recognition access control system. The new software can detect and identify people wearing masks. At the same time, the intelligent attendance system has been integrated to assist human resource management

Integrating mining machine learning and AI, the K210 chip has been used on Avalon mining machine, which can identify and monitor potential network viruses through intelligent algorithms. The company will explore more innovative integration in the future.

Second-Generation Gem

In terms of AI, the company will launch the second-generation AI chip K510 this year. The design of its architecture has been "greatly" optimized, and the computing power is several times more robust than the K210. Later this year, Canaan will use this tech in areas including smart energy consumption, smart industrial parks, smart driving, smart retail, and smart finance.

Canaan's Cash

In terms of operating costs and R&D, the company's last-year operating cost dropped 13.3% year-on-year. In 2018 and 2019, Canaan recorded R&D expenses of 189.7 million yuan and 169 million yuan, respectively347 million yuan were used to incentivize core R&D personnel.

In addition, the company currently has more than 500 million yuan in cash ($70.5 million), will continue to operate under the "blockchain + AI" strategy, with a continued focus on the commercialization of its AI technology.

A Fruitful Future

Canaan began as a manufacturer of Bitcoin mining machines, but it has become more than that. In the short term, the Bitcoin halving cycle is approaching (Estimated to occur on May 11, 2020 CW); this should promote the sales of company's mining machine, In the long term, now a global leader in ASIC technology, Canaan could be in a unique position to meet supercomputing demand.

"Blockchain is a good start, but we'll go beyond that," says Zhang. "When a seed grows up to be a big tree, it will bear fruit."

So far, it has done just that. Just how high that "tree" can get remains to be seen, but one thing is certain: The Kendryte K210 chip will be the driving force fueling the company's growth.

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Canaan's Kendryte K210 and the Future of Machine Learning - CapitalWatch

Library of Congress/Newspaper Navigator

In July 1848, L'illustration, a French weekly, printed the first photo to appear alongside a story. It depicted Parisian barricades set up during the city's June Days uprising. Nearly two centuries later, photojournalism has bestowed libraries with legions of archival pictures that tell stories of our past. But without a methodical approach to curate them, these historical images could get lost in endless mounds of data.

That's why the Library of Congress in Washington, D.C. is undergoing an experiment. Researchers are using specialized algorithms to extract historic images from newspapers. While digital scans can already compile photos, these algorithms can also analyze, catalog, and archive them. That's resulted in 16 million newspaper pages' worth of images that archivists can sift through with a simple search.

The Bridgeman Art Library

Ben Lee, innovator-in-residence at the Library of Congress, and a graduate student studying computer science at the University of Washington, is spearheading what's called Newspaper Navigator. His dataset comes from an existing project called Chronicling America, which compiles digital newspaper pages between 1789 and 1963.

He noticed that the library had already embarked on a crowdsourcing journey to turn some of those newspaper pages into a searchable database, with a focus on content relating to World War I. Volunteers could mark up and transcribe the digital newspaper pagessomething that computers aren't always so great at. In effect, what they had built was a perfect set of training data for a machine learning algorithm that could automate all of that grueling, laborious work.

"Volunteers were asked to draw the bounding boxes such that they included things like titles and captions, and so then the system would...identify that text," Lee tells Popular Mechanics. "I thought, let's try to see how we can use some emerging computer science tools to augment our abilities and how we use collections."

In total, it took about 19 days' worth of processing time for the system to sift through all 16,358,041 newspaper pages. Of those, the system only failed to process 383 pages.

Newspaper Navigator/ArXiv

Newspaper Navigator builds upon the same technology that engineers used to create Google Books. It's called optical character recognition, or OCR for short, and it's a class of machine learning algorithms that can translate images of typed or handwritten symbols, like words on a scanned magazine page, into digital, machine-readable text.

At Popular Mechanics, we have an archive of almost all of our magazines on Google Books, dating back to January 1905. Because Google has used OCR to optimize those digital scans, it's simple to go through and search our entire archive for mentions of, say, "spies," to get a result like this:

Popular Mechanics

But images are something else entirely.

Using deep learning, Lee built an object detection model that could isolate seven different types of content: photographs, illustrations, maps, comics, editorial cartoons, headlines, and advertisements. So if you want to find photos specifically of soldiers in trenches, you might search "trenches" in Newspaper Navigator and get results instantly.

Before, you'd have to sift through potentially thousands of pages' worth of data. This breakthrough will be extremely empowering for archivists, and Lee has open-sourced all of the code that he used to build his deep-learning model.

"Our hope is actually that people who have collections of newspapers...might be able to use the the code that I'm releasing, or do their own version of this at different scales," Lee says. One day your local library could use this sort of technology to help digitize and archive the history of your local community.

Newspaper Navigator/ArXiV

This is not to say that the system is perfect. "There definitely are cases in which the system will especially miscategorize say, an illustration as a cartoon or something like that," Lee says. But he has accounted for these false positives through confidence scores that highlight the likelihood that a given piece of media is a cartoon or a photograph.

"One of my goals is to use this project...to highlight some of the issues around algorithmic bias."

Lee also says that, even despite his best efforts, these kinds of systems will always encode some human bias. But to reduce any heavy-handedness, Lee tried to focus on emphasizing the classes of imagescartoon versus advertisementrather than what's actually shown in the images themselves. Lee believes this should reduce the instances of the system attempting to make judgement calls about the dataset. That should be left up to the curator, he says.

"I think a lot of these questions are very very important ones to consider and one of my goals is to use this project as an opportunity to highlight some of the issues around algorithmic bias," Lee says. "It's easy to assume that machine learning solves all the problemsthat's a fantasybut in the this project, I think it's a real opportunity to emphasize that we need to be careful how we use these tools."

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The Librarians of the Future Will Be AI Archivists - Popular Mechanics

Half of the worlds organisations experienced economic crime between 2016 and 2018, according to PwCs Global Economic Crime and Fraud survey. Government organisations are among these and by no means immune from crime. And yet, the perpetrators are not always remote cyber criminals. The most worrying news is that half of fraud is carried out by agents within the organisation. For many rogue employees, their method is procurement fraud.

Though businesses may often misjudge the cost of fraud, the lack of a definable victim aside from the UK taxpayer means that the figures for fraud which hits public services are unclear. Figures for procurement fraud specifically are even more questionable, though estimates suggest that local councils quashed malicious efforts to steal over 300 million through procurement fraud in 2017/18. The amount undetected by anti-fraud investigators may dwarf that.

The importance of public sector funds not being lost to fraud or wasted has been crucially highlighted in recent weeks by the COVID-19 virus outbreak. These funds, always in high demand, are even more precious in emergency situations like were facing at the moment.

Transparency is a buzzword within the public sector. Citizens are looking for clarity on the value the public sector provides to taxpayers and are demanding more when it comes to services. It is increasingly clear to cash-strapped governments that preventing and detecting fraud is crucial for achieving goals when faced with the alternative of raising taxes which is never popular with the general public. The imperative is to safeguard taxpayers funds and for the public sector to do everything in its power to ensure that these funds are spent on crucial services.

Local government is a particular risk area for procurement fraud. Local governments, including city management,spend a lot of money particularly because many now outsource significant amounts of service provision. They may also lack expertise in contracting and commissioning, and may, therefore, be an easy target for fraudsters. The procurement process is an obvious target.

Procurement fraud can occur at any stage of the procurement lifecycle, which makes it extremely complex to detect and prevent. Analysis suggests that for government organisations, procurement fraud is most likely to occur at the payments processing stage, although vendor selection and bids are also vulnerable stages.

There are a number of ways in which procurement fraud can occur. Some involve collusion between employees and contractors, and others involve external fraudsters taking advantage of a vulnerability in the system. Organisations can also make themselves more vulnerable to fraud by not ensuring that employees follow proper procedures for procurement. One possible problem, for example, is dividing invoices up into smaller chunks to avoid particular thresholds. This is usually done in all innocence as a way to make procurement simpler, but it also leaves the organisation open to abuse because the proper checks are not made.

But if procurement fraud is on the rise, so too is counter-fraud work. Governments around the world have strategies and are monitoring the situation carefully. Many have increased the checks put on procurement processes and have also provided more information to employees and potential contractors about how to spot fraud and potential fraud.

There is growing understanding that rules-based systems are not enough to stop fraud: they may help to detect it after the event, but they are unlikely to prevent it, even in combination with systems to reduce opportunity. Analytics-based systems, however, can both improve detection of fraud, and also start to predict it. They are often based onartificial intelligence(AI), which learns from previous cases, and can then detect patterns that may be associated with fraud, or process breaches that may be a problem.

Detecting anomalies, however, is just one step in the process of preventing fraud. Its only an indicator, and all indicators can do is to indicate. In fraud detection, indicators like anomalies highlight an area for further investigation. Then its over to the fraud, audit and compliance teams to take a look.

Traditional fraud detection has often taken months to complete. Time-consuming audits could detect fraud, but these could begin months after the event, and may only occur once a year. Fraud detection systems based on analytics can spot fraud in a fraction of the time, flagging anomalies to investigation squads in real-time. The actions of those teams can then halt fraud in its tracks, before it takes place, or provide rapid evidence on the perpetrator. Public organisations that put these new technologies in place can rest assured that, with machine learning, fraud detection is not only smart, efficient and speedy, but a frightening prospect for those participating in procurement fraud.

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Preventing procurement fraud in local government with the help of machine learning - Open Access Government