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Facebook, YouTube, and Twitter warn that AI systems could make mistakes – Vox.com

To adjust to the social distancing required by the Covid-19 coronavirus pandemic, social media platforms will lean more heavily on artificial intelligence to review content that potentially violates their policies. That means your next YouTube video or snarky tweet might be more likely to get taken down in error.

As they transition their operations to a primarily work-from-home model, platforms are asking users to bear with them while acknowledging that their automated technology will probably make some mistakes. YouTube, Twitter, and Facebook recently said that their AI-powered content moderators may be overly aggressive in flagging questionable content and encouraged users to be vigilant about reporting potential mistakes.

In a blog post on Monday, YouTube told its creators that the platform will turn to machine learning to help with some of the work normally done by reviewers. The company warned that the transition will mean that some content will be taken down without human review, and that both users and contributors to the platform might see videos removed from the site that dont actually violate any of YouTubes policies.

The company also warned that unreviewed content may not be available via search, on the homepage, or in recommendations.

Similarly, Twitter has told users that the platform will increasingly rely on automation and machine learning to remove abusive and manipulated content. Still, the company acknowledged that artificial intelligence would be no replacement for human moderators.

We want to be clear: while we work to ensure our systems are consistent, they can sometimes lack the context that our teams bring, and this may result in us making mistakes, said the company in a blog post.

To compensate for potential errors, Twitter said it wont permanently suspend any accounts based solely on our automated enforcement systems. YouTube, too, is making adjustments. We wont issue strikes on this content except in cases where we have high confidence that its violative, the company said, adding that creators would have the chance to appeal these decisions.

Facebook, meanwhile, says its working with its partners to send its content moderators home and to ensure that theyre paid. The company is also exploring remote content review for some of its moderators on a temporary basis.

We dont expect this to impact people using our platform in any noticeable way, said the company in a statement on Monday. That said, there may be some limitations to this approach and we may see some longer response times and make more mistakes as a result.

The move toward AI moderators isnt a surprise. For years, tech companies have pushed automated tools as a way to supplement their efforts to fight the offensive and dangerous content that can fester on their platforms. Although AI can help content moderation move faster, the technology can also struggle to understand the social context for posts or videos and, as a result make inaccurate judgments about their meaning. In fact, research has shown that algorithms that detect racism can be biased against black people, and the technology has been widely criticized for being vulnerable to discriminatory decision-making.

Normally, the shortcomings of AI have led us to rely on human moderators who can better understand nuance. Human content reviewers, however, are by no means a perfect solution either, especially since they can be required to work long hours analyzing incredibly traumatic, violent, and offensive words and imagery. Their working conditions have recently come under scrutiny.

But in the age of the coronavirus pandemic, having reviewers working side by side in an office could not only be dangerous for them, it could also risk further spreading the virus to the general public. Keep in mind that these companies might be hesitant to allow content reviewers to work from home as they have access to lots of private user information, not to mention highly sensitive content.

Amid the novel coronavirus pandemic, content review is just another way were turning to AI for help. As people stay indoors and look to move their in-person interactions online, were bound to get a rare look at how well this technology fares when its given more control over what we see on the worlds most popular social platforms. Without the influence of human reviewers that weve come to expect, this could be a heyday for the robots.

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Global Machine Learning in Communication Market 2020 Industry Trend and Forecast 2024 – Daily Science

The study on Global Machine Learning in Communication Market, offers deep insights about the Machine Learning in Communication market covering all the crucial aspects of the market. Moreover, the report provides historical information with future forecast over the forecast period. Some of the important aspects analyzed in the report includes market share, production, key regions, revenue rate as well as key players. This Machine Learning in Communication report also provides the readers with detailed figures at which the Machine Learning in Communication market was valued in the historical year and its expected growth in upcoming years. Besides, analysis also forecasts the CAGR at which the Machine Learning in Communication is expected to mount and major factors driving markets growth.

Key vendors/manufacturers in the market:

Market Segment by Companies, this report coversAmazonIBMMicrosoftGoogleNextivaNexmoTwilioDialpadCiscoRingCentral

Request a sample of this report @ https://www.orbisresearch.com/contacts/request-sample/3063584

The Global Machine Learning in Communication Market is a highly competitive market. It has some players who have been in the business for quite some time. Subsequently there are many startups coming up to seize the huge opportunity this market offers. Some players have a presence only in a particular geography. In addition, the projections offered in this report have been derived with the help of proven research assumptions as well as methodologies. By doing so, the Machine Learning in Communication research study offers collection of information and analysis for each facet of the Machine Learning in Communication market such as technology, regional markets, applications, and types. Likewise, the Machine Learning in Communication market report offers some presentations and illustrations about the market that comprises pie charts, graphs, and charts which presents the percentage of the various strategies implemented by the service providers in the Global Machine Learning in Communication Market. In addition to this, the report has been designed through the complete surveys, primary research interviews, as well as observations, and secondary research.

Likewise, the Global Machine Learning in Communication Market report also features a comprehensive quantitative and qualitative evaluation by analyzing information collected from market experts and industry participants in the major points of the market value chain. This study offers a separate analysis of the major trends in the existing market, orders and regulations, micro & macroeconomic indicators is also comprised in this report. By doing so, the study estimated the attractiveness of every major segment during the prediction period.

Browse the complete report @ https://www.orbisresearch.com/reports/index/global-machine-learning-in-communication-market-2019-by-company-regions-type-and-application-forecast-to-2024

Global Machine Learning in Communication Market by Type:

Market Segment by Type, coversCloud-BasedOn-Premise

Global Machine Learning in Communication Market by Application:

Market Segment by Applications, can be divided intoNetwork OptimizationPredictive MaintenanceVirtual AssistantsRobotic Process Automation (RPA)

The Global Machine Learning in Communication Market has its impact all over the globe. On Global Machine Learning in Communication industry is segmented on the basis of product type, applications, and regions. It also focusses on market dynamics, Machine Learning in Communication growth drivers, developing market segments and the market growth curve is offered based on past, present and future market data. The industry plans, news, and policies are presented at a global and regional level.

Major Table of Contents1 Machine Learning in Communication Market Overview2 Company Profiles3 Market Competition, by Players4 Market Size by RegionsContinued

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AI, machine learning to deliver ‘wave of discoveries’ – The Northern Miner

The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company.

One of the problems were facing in exploration is the huge increase in the amounts of data we have to look at, said Barnett, in his presentation at theManaging and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. And although its high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it.

By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.

But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies.

To illustrate his point, Barnett demonstrated how he and his partner at BW Mining, Peter Williams, are using machine learning to analyze data from geological, geochemical and geophysical surveys of the Yukon in northwestern Canada to locate new deposits.

The Yukon became famous for the Klondike gold rush during the late 1890s, which petered out after a few years as prospectors moved onto Alaska. Today the area is experiencing a renewed interest in what has become known as the Tintina Gold Belt, with significant lode deposits being found over the past two decades and, according to Barnett, more waiting to be discovered.

We used the Yukon bedrock geology map published by the Yukon Geological Survey, which is very detailed and shows over 200 different geological formations, explained Barnett. However, you cant simply put 200 formations into a machine learning process. First, the data requires special treatment.

By representing each of the formations with a separate grid and by continuing the grids upward, they were able to see overlaps between formations, allowing them to consolidate the data by grouping the formations by rock type and age, and thereby reducing the data set down to around 50 discrete and different formations. They then used the same process to represent structural data provided by the map.

The structural data is important because it represents the pathways that the mineralization generally took to reach the surface, explained Barnett. We then used geophysical maps of the area provided by Natural Resources Canada, which contain enormous amounts of information that can be extracted and subjected to the same statistical treatment.

Applying the same approach to geochemical, gravity, topographical and satellite data, they were able to generate detailed data sets covering over 300,000 400,000 square kilometers of the study area.

The most critical layer of data for our machine learning process is the known deposits because this is used to train our artificial neural network against all the other layers to identify deposit formations, said Barnett.

Artificial neural networks operate much like the human brain. They can recognize patterns in the different layers of data and cluster or classify them into groups according to similarities in the input data. They are then capable of discriminating between zones of high and low mineral potential.

After scouring through geologic publications, company websites and NI 43-101 technical reports, Barnett and Williams were able to develop accurate mineral footprints for more than 30 deposits using their model, which, according to Barnett, reportedly contain over 46 million oz. of gold.

They then used an artificial neural network to establish the statistical favourability of a location containing an economically viable deposit across the entire region of interest. This approach is essentially an inversion process that uses exploration data relating to a given location as inputs to the network, which then produces the corresponding favourability as the output.

Image of a typical target. Red areas are highly favourable, while purple areas are shown as unfavourable for gold. Credit: BW Mining.

This requires very sophisticated software to analyze and interpret the data, so you cant just use off-the-shelf software, explained Barnett. We first started analyzing the data on a parallel-processor in the basement of the University of Sussex [in England] back in 1992, where my partner was a professor. But it would take five days to get an answer by which time wed forgotten what the question was.

However, with improvements to computer software and hardware, they are now able to generate an answer in a matter of hours using a common laptop.

Barnetts and Williams use of artificial intelligence and machine learning has led to a highly-focused target map that assigns numerical probabilities of making an economic discovery anywhere in the region of interest, and can be used to systematically rank and rate targets and plan cost-effective follow-up programs that take into account the expected return on investment for any given target.

Although Barnett believes there is currently a lack of understanding of artificial intelligence and machine learning in the industry, he is convinced that as these techniques become more widely used and available, machine learning and artificial intelligence will lead to a wave of discoveries. And within ten years, they will be commonly used tools in the mineral exploration industry.

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Brain Computer Interface: Definitions, Tools and Applications – AiThority

Weve finally reached a stage in our technical expertise where we can think about connecting our minds with machines. This is possible through brain-computer interface (BCI) technologies that would soon transcend our human capabilities.

The human race is looking at the past to create future tomorrows would be controlled by your mind, and machines will be your agents. If we look into the recent advancements in Computing, Data Science, Machine Learning and Neural Networking, the future looks very predictable, yet disarmingly tough. Imagine the future like this Were moving into a latent telepathy mode very soon. Its truly going to be a brain-power that will operate machines and get work done, AI or no AI.

In this article, we will quickly summarize the Brain-Computer Interface (BCI) definitions, key technologies, and their applications in the modern Artificial Intelligence age.

A Brain-Computer Interface can be defined as a seamless network mechanism that relays brain activity into a desired mechanical action. A modern BCI action would involve the use of a brain-activity analyzer and neural networking algorithm that acquires complex brain signals, analyzes them, and translates them for a machine. These machines could be a robotic arm, a voice box, or any automated assistive device such as prosthetics, wheelchair, and iris-controlled screen cursors.

This is a simple infographic about BCIs.

Advancements in functional neuroimaging techniques and inter-cranial Spatial imagery have opened up new avenues in the fields of Cognitive Learning and Connected Neural Networking. Today, Brain-Computer Interfaces rely on a mix of signals acquired from the brain and nervous systems. These are classified under

According to the US National Library of Medicine National Institutes of Health, there are three types of BCI technologies. These are

Brain-Computer Interface is used to complete a mental task using neuro-motor output pathways or imagery. For example, lifting your leg to climb steps.

This is a stimulus-based conditional Brain-Computer Interface that acts on selective attention. For example, crouching on your feet to cross a barbed fence. The principle behind Reactive BCIs can be better understood from the P300 settings. The P300 setting involves a mix of neuroscience-based decision making and cognitive learning based on visual stimulus.

It involves no visual stimulus. The BCI mechanism merely acts like a switch (On/Off) based on the cognitive state of the brain and body at work. It is the least researched category in BCI development.

Unlike general Cloud Computing and Machine Learning DevOps, the BCI developers come with a specialized background.

Hot Start-Ups:TIBCO Recognized as a Leader in 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

Brain-Computer Interface DevOps engineers have to constantly work with a team of Neuroscientists, Computer Programmers, Neurologists, Psychologists, Rehabilitation Specialists, and sometimes, Camera OEMs.

According to a paper on Brain-computer interfaces for communication and control, BCIs in 2002 could deliver maximum information transfer rates up to 10-25bits/min.

Since then, BCI development has gained major traction from large-scale innovation companies and futurist technocrats such as Teslas Elon Musk. We are already seeing logic-defying amalgamation of AI research and interdisciplinary collaboration between Neurobiology, Psychology, Engineering, Mathematics, and Computer Science.

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Huge Panic Grips Crypto Market: Ethereum, XRP and Other Altcoins Fall by… – Coinspeaker

The crypto market capitalization has fallen below $200 billion due to huge panic sell-off. Ethereum, XRP and other altcoins are losing their value.

Today will remain engraved in most crypto enthusiasts and investors minds, not for the good reasons, but rather since most altcoins, including Ethereum and XRP, have shed a huge portion of their prices by about 20-30% in the past 24 hours. The huge free fall has been attributed to a panic sell-off, as investors run for safety to avoid risky assets.

This proves the theory that crazy speculation is the key driving factor to most altcoins bull rally. Ethereum, the second by market capitalization, at the time of writing had shed close to 32% in the past 24 hours, leaving it at $135.5.

Ripples XRP which is the third by market capitalization seems to have not been affected as badly off than the rest. At the time of writing, it was down by 23.53% in the past 24 hours, leaving it trading at $0.1592.

As coronavirus continues proving to be a huge mountain than anticipated, investors will continue disposing of their huge amounts of coins to the market. As a result, most altcoins are left with the bare minimum value, whereby, only the diehard investors are holding. The whole crypto market cap has crippled down to hit below $200 billion levels.

XRP has been one altcoin with promising future, with Ripple pushing for its adoption worldwide through incentives given to its partners. Ripple as a company has been holding the largest share of coins in the escrow account. Therefore, its action to dump them into the market has been a significant stabilizer of XRP prices.

However, XRP has had a fair share of enthusiast who sees it as the future Bitcoin. The number has been steadily growing, considering Ripple was looking forward to dumping more coins into the recently opened Indian market. As a result of huge retail accumulation, XRP was not cushioned on any sell-off.

The free-fall might not be as huge also because it is backed by financial institutions that are on long term investment with RippleNet. With over 300 financial institutions worldwide using it for cross-border transactions, XRP might see the light of the day.

Ethereum being the second from bitcoin was the worst hit among the three (BTC, ETH, XRP) by the panic sell-off, as it fell with over 30% in the past 24 hours. It opened the day with hopes of pushing above the previous days lows, $190, however, it has drastically fallen to trade below $130.

As a result of the free fall, all the gains made since the calendar flipped have been wiped out. Having found a support at $130, if things dont change as per the time of writing, the next stopover will be at $84. A price that was last seen in Ethereum back in December 2018

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Litecoin, Tezos and Chainlink nosedive as the altcoin season rocket ship loses lift – FXStreet

The cryptocurrency market appears to have been painted with one brush dipped inred color. The majority of cryptocurrencies in the top 100 are buried deep in losses. Bitcoin, the largest digital asset by market capitalization is down 15% in the last seven days. Ethereum has lost over 22% of its value while Ripple is down 18.20% in the same period.

Apart from the fears regarding the Coronavirus, it is not clear what is behind the selloff. Cryptoassets have in less than two weeks erased most of the gains accrued since the beginning of the year. The Coronavirus was declared a pandemic by the World Health Organization (WHO) on Wednesday. The decision comes due to the rising number of infections around the world.

It appears that investors are afraid to buy the dip while others are preferring to keep their money in cash in the event the pandemic reaches uncontrollable limits. In this case, crypto enthusiasts should probably brace for more downside action likely to break under thelows in December 2019.

Altcoins have been performing relatively well in the last two months. Ethereum approached $300 butformed a high at $284, Bitcoin Cash shot up to $499 before succumbing to the ongoing downtrend while Ripple closed in on $0.35.

The worst-hit digital assets in the last seven days include Tezos (XTZ) which dived 30.29% to trade at $2.23, Chainlink (LINK) is dancing at $3.20 after losing 32.96% and Litecoin suffered a 26.35% loss and is now exchanging hands at $45.23.

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Bitcoin vs Altcoins: Which One Stands as an Investment Option? – CryptoNewsZ

The cryptocurrency sector comes with a lot of different currencies. Apparently, we have a new altcoin coming into the scene almost every day that can change the cryptocurrency field. Knowing where to invest your money can save a lot of hassles and time. There is no right decision or wrong decision in the crypto market, as everything depends on objectives and application areas. Having the right knowledge can help the user to make well-informed decisions and can minimize their investment risks.

One of the main advantages of Bitcoin is its acceptance and extensive adoption. Further, it had been widely accepted as a form of payment. Several financial institutions are supporting Bitcoin and people have heard of Bitcoin because of its popularity. Bitcoin has a wide network of users who are committed to long-term development. Lastly, it has a large pool of miners who continue to support the network and makes sure it is secure.

Altcoins can be used as an alternative to Bitcoin which has great potential. Bitcoin is looked at as an original cryptocurrency; hence any new currency is looked at as an alternative. Currently, thousands of altcoins are available for users to invest, and several more are developed regularly. Few of them have high demand, such as Ethereum and XRP, while others fall through. Some of the Altcoins are given below

One of the major advantages of altcoins is they serve as an alternative to Bitcoin. If the Bitcoin collapses, then users can switch to altcoins. However, several altcoins have a unique function. Few altcoins offer various processes and systems when compared to Bitcoin and have a wider scope to develop in the future. Ethereum and XRP are two different altcoins that have been adopted widely and used by several industries.

One of the disadvantages of an altcoin is the lack of acceptance and exposure. Altcoins like XRP, Ethereum, and Bitcoin cash are well received and have great support from users; other altcoins are not much recognized. Besides, there are restricted numbers of outlets where few altcoins can be used as they have not been widely adopted as Bitcoin.

Even though Bitcoin is the biggest currency in supply and has wider support, it does not mean that altcoins are useless. People can consider diversifying their investment by purchasing a few Bitcoins and some major altcoins. The advantage is it helps to minimize the risk of their investment portfolio. Moreover, Altcoins can make more money compared to Bitcoin, yet it is a lot risky and people could lose their investment abruptly.

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Bitcoin vs Altcoins: Which One Stands as an Investment Option? - CryptoNewsZ

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IOTA (MIOTA) Price Analysis: IOTA Regains $0.10 Mark With Signs Of Market Recovery – The Coin Republic

Source: coinmarketcap

On the 7day-weekly chart, IOTA started by breaking the crucial level of $0.20. it indicated that there is a potential of major downfall. The altcoin slipped from the price level of $0.90 to $0.10 within two days. However, the support level of $0.10 kept the price mark intact.

However, todays major downfall in the overall market was enough to break the support level and the cryptoasset plunged below the level of $0.10. It indicated that the bears are providing heavy price damages to the crypto asset in the market.

But the overall recovery followed in the market provided the altcoin the much-needed boost to the level of $0.10. The coin is still facing an overall loss of -23.56% with a market cap of $295,996,316 and a volume traded of $15,968,761.

Source: tradingview

The technical graph reflects on the significant downtrend faced by IOTA during the overall bearish momentum in the market. Fib retracement level reached a low level of 0.236 and Iota bounced back from the level to again reach $0.10

The symmetric triangle was also broken indicating that a major downfall may be coming for the altcoin but the market recovery prevented it.

MACD levels also attempted to reach the bullish zone but were unsuccessful. This also indicates that buying volume increases may provide MACD some positive phase.

The 24hr-RSI is showing bearish nature and currently is in the oversold region.

The 24hr-CCI is showing volatile nature by having sharp negative divergence from the overbought region to the oversold region.

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IOTA (MIOTA) Price Analysis: IOTA Regains $0.10 Mark With Signs Of Market Recovery - The Coin Republic

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Cloud of uncertainty for restaurants, bars, and servers – WKBW-TV

EAST AURORA, N.Y. (WKBW) Changes are coming to restaurants and bars as all establishments will have to close their dining and bar areas effective Monday at 8 p.m. Businesses will have to work under a take-out service only.

"We all have children and we all have homes and mortgages and payments that we have to make," Lori Cubins, a server at Bar-Bill Tavern said. "It creates a lot of uncertainty."

Cubins has been a server at the restaurant in East Aurora for 17 years. For the single mother of three, serving is her main source of income.

"If we can get back to business within two weeks, we're probably going to be okay," Cubins said. "But if it continues to go on longer than that, a lot of us will struggle with that."

The location in East Aurora has always had a take-out service but every patron knows how packed the dining area normally is.

"Ultimately we're buckling down and preparing for an extended negative impact to the business," Bar-Bill owner Clark Crook said.

Not only is the hit to business expected to be painful, Crook says he's worried about his staff.

"The impact on them is enormous," he added. "It can't be understated so either way, our employee impact is something we're the most concerned about."

In Tonawanda, Mississippi Mudds has moved completely to curbside service and are offering free delivery within a two-mile radius. It's all to help provide customers with a quick meal.

"It's gonna impact everybody but it's the right move to do," part-owner Tony Berrafato said. "I still believe people want the food they enjoy and we're keeping people working as best you can."

And during this time of uncertainty, employers can only look for the light at the end of the tunnel and hope one day soon, it's business as usual.

"It will be a struggle if it continues on for more than the two weeks," Cubins said. "But we will get through this, absolutely."

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Google: This is what caused CPU throttling at our cloud data center – ZDNet

Google says a set of crushed wheels used for moving its server racks triggered a chain reaction that may have disrupted Search, Gmail, and other services for some users.

A rack of servers at one of its data centers started overheating to the point where CPUs were automatically throttled, ultimately because a set of rack wheels couldn't bear the weight of Google's cloud kit.

Steve McGhee, a solutions architect at Google Cloud, says Google users "most likely" wouldn't have noticed errors caused by the rack's crushed wheels. But the chain of events resulted in enough CPU throttling to cause "user harm".

Fortunately, the incident wasn't as serious as one from June last year,caused by a failure in Google's automation software, which took down Gmail, YouTube, and customers' applications. That incident prompted a big apology to customers and a commitment to do better in future.

SEE: Cloud v. data center decision (ZDNet special report) | Download the report as a PDF (TechRepublic)

This time the company has decided to tell the story to illustrate the lengths it goes to to find the root cause of disruptions even when they don't noticeably impact users.

The latest event came to light when Google recently kicked off an investigation after a site reliability engineer noticed a spike in errors from machines on its edge network that cache content users frequently access. The machines were immediately taken offline to stop them impacting customers, allowing other machines to take up the slack.

Google engineers noticed some border gateway protocol (BGP) network errors but their characteristics suggested issues with the machines rather than the router. Further investigation turned up kernel messages in machines on the edge network that revealed CPU clock throttling.

The engineers found that failing systems were isolated to machines on a single rack. All of this investigation was happening remotely. Unable to explain why the rack was overheating enough to cause kernel errors, the engineers then requested Google's on-site data-center workers to physically check out the problem rack.

Soon after the data-center team reported back with a brief message and a picture of the rack's crushed wheels.

"Hello, we have inspected the rack. The casters on the rear wheels have failed and the machines are overheating as a consequence of being tilted," the team explained.

"The wheels (casters) supporting the rack had been crushed under the weight of the fully loaded rack," said McGhee.

"The rack then had physically tilted forward, disrupting the flow of liquid coolant and resulting in some CPUs heating up to the point of being throttled."

SEE: There's more to Google than Google: Dataset Search comes out of beta

It's not clear why the wheels were crushed but Google engineers feared it could be a more widespread problem and so they replaced all the racks that could be vulnerable to the same broken-wheel tilting issue.

The problem has caused Google to reconsider how it moves new racks into its data centers when they're being built.

Google's engineers discovered that casters on the rear wheels had failed, ultimately causing the machines to overheat.

The alarming tilt of a refrigeration unit also pointed to the underlying problem.

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