Daily Archives: May 14, 2020

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