Minutes ahead of President Trump’s televised address from the Rose Garden Monday evening, Attorney General William Barr ordered authorities to clear a crowd of protesters that had gathered nearby, according to a Justice Department official.
Barr and other top officials from agencies responsible for securing the White House had previously planned to secure a wider perimeter around Lafayette park, a federally-owned green space just north of the building, in response to fires and destruction caused by protesters on Sunday night. That plan would have cleared the area later used for the President’s walk to a nearby church for a photo-op by 4 p.m. ET, the official said.
But it never happened. When Barr arrived at Lafayette park just after 6 p.m. ET — in a scene that was captured on news cameras and elicited heckles from the large, peaceful crowd — Barr saw that the area had not been emptied, and told police to clear the area, the official said. If federal law enforcement was met with resistance by the protestors, crowd control measures should be implemented, Barr had said, according to the official.
Barr had been told that police believed protestors were gathering rocks to throw at law enforcement, and while he was in the park, water bottles were thrown in his direction, the official said. CNN did not witness any water bottles being thrown at the attorney general. Camera footage shows him standing and watching the crowd for several minutes, flanked by a security detail and two senior department officials.
Just before 6:24 p.m., police broadcast their first warning for the crowd to distance. A CNN correspondent reporting from the rooftop of a nearby hotel heard three warnings broadcast over the next ten minutes as authorities moved closer to the crowd.
At 6:35 p.m., authorities began charging the crowd in lockstep with their shields raised, some using their batons to strike the protestors as gas canisters were deployed.
What The Eff Is This Keystone XL Segment, CNN??
One of the weirdest segments I’ve ever seen published on CNN aired this past week. It was focused on a small town (population of 444) and a few people who were going to miss out economically from the Keystone XL Pipeline being canceled. There were several odd things about the video that made me think or even say out loud, “WTF, CNN?”
First of all, everyone acted shocked that the pipeline was being cancelled, as if they didn’t know it was one of the biggest political footballs in the past decade. It should have been surprising to absolutely no one that the pipeline would eventually be cancelled (again). Going even further, some people had built businesses or were counting on millions of dollars of income centered around the project going through, and then were highlighted talking about the loss as if there was no way at all of predicting this would happen and that it was risky to build one’s hopes on one of the most controversial infrastructure projects in modern US history.
The CEO of the West Central Electric Cooperative was asked how he felt when the announcement was made, and said, “Like I got kicked in the stomach.” He was anticipating generation about half a million dollars a month from a project that relied on the Keystone XL going through. That’s right — half a million dollars a month. Based on the idea the Keystone XL wouldn’t be canceled. West Central Electric Cooperative has had 99 new customers … in 30 years. (Side note: the segment narrator indicates that all of the profits would have gone back to the coop members, ~3700 people, and the CEO indicated that on average they’d get about $325/year. That pencils out to $1,202,500 a year, or $100,208 a month, approximately $400,000 a month less than the CEO indicated the project would generate for West Central Electric Cooperative. Do we want to know where that $400,000 a month goes? Probably not.)
Then there was the fact that CNN spent almost no time truly explaining why the project was canceled, why it was obvious the project would be canceled, or how letting the pipeline go through would costs innumerable risks to the climate as well as communities in the case of potential (or likely) oil leaks. It was all basically something like, “for some reason, Biden just killed this project and took away our income.” It is framed as if Biden just killed a bunch of jobs — because environmentalists. Biden said he was going to create jobs, but look what he’s doing! If you can’t sense the snark in those statements, I’ll tell you, it’s there. The false battle between “environmentalists” (not the stability of our climate, society, and human civilization) and money (there’s also money and jobs in clean energy that doesn’t ruin our precious and rare blue marble) is grating. The loss of income for a small coop in North Dakota is not due to some wildflowers being protected in the middle of nowhere, and the money isn’t vaporized by Biden’s evil ballpoint pen.
There was no talk of a greater shift to clean energy and electric vehicles and the fact that many more jobs are being created in this transition than lost, including in rural areas and small towns. Rather than explain that yes, some areas are declining economically but others are getting an economic boost, it was a completely one-sided story making it seem like there were either jobs or the environment, but not both — a false dichotomy that is probably older than Betty White. This is practically journalistic malpractice — instead of providing good, broad context and insight, it is cherry picking a few personal stories to perpetuate a mythical dichotomy that is harmful to both our economy and our environment. It is doing the opposite of what a good news company should do.
And let’s be honest — they found basically 3 individual stories in 2 tiny towns and gave them an enormous megaphone. (The second town, where a husband and wife had invested “their own money” in a wellness studio for pipeline construction workers … who wouldn’t live there long anyway, had a population of 779.) Have you ever seen CNN highlight 3 of the millions of solar, wind, and electric vehicle jobs that have been created from the same transition away from fossil fuels and toward clean energy? Have you seen them highlight entrepreneurs who saw where the world was headed and formed side businesses to complement the cleantech transition rather than betting that fossil fuels will be burnt haphazardly forever? Have you seen them show a balanced look at the number of jobs created in clean energy and electric vehicle technology versus those lost in oil, coal, and gas? Did the interviewer even ask, “But didn’t you notice that there’s been a full-scale movement for years to block this climate-destroying pipeline? Did you think it was a safe bet to create a business or expect your business’s income to blow up indefinitely with the assumption that this pipeline wouldn’t eventually be canceled (again)?” If there was one individual project in the whole country that was bound to close down if/when Biden won, it was the climate disaster that the Obama–Biden administration canceled a handful of years ago that Biden promised to shut down.
Getting back to the context CNN so magnanimously bungled, the narrator says, “Environmentalists had argued the pipeline and the oil would have added to climate change, and feared damage to water and wildlife where the pipeline went through.” First of all, it’s not environmentalists just claiming something. It’s basic, clear climate science. They could have said, “Science shows that the pipeline and the oil would have done great damage to the climate humans rely on for food, water, and basic livability.” Secondly, the concern regarding oil leaks isn’t just for water and wildlife! It’s also for humans and the economy! But that doesn’t fit the simplistic, ignorant, outdated narrative.
“But, stopping the pipeline has problems of its own.” Lions and tigers and bears, oh my! Seriously, CNN, you’re going to put these on the same level and play this ridiculous game of false balance in 2021? And where that statement leads gets even dumber: “Like, what happens to the land that was already bought? Another concern: what do you do with all this stuff? Pipeline assets were spread across hundreds of miles, much of it now … just stranded.” This is not an April Fool’s Day joke.
What happens to the land? It stays where it is. If I asked my 4-year-old that question, she’d probably be confused at how senseless it is.
What about the stranded assets? That’s why you shouldn’t invest in stupid, harmful projects. No one has to guarantee a return on a stupid investment that was clearly going to be canceled.
It’s gets even stupider, but I’m done. I saved the video to write about it last week when it irritated me with its stupidity and counterproductive narrative. I had to watch it again just now to write this piece, and the lack of logic, context, and point is driving me nuts again. Why, CNN, why?
When it comes down to it, why did CNN run a piece like this? Did someone sponsor it or did someone high up the food chain hint that it was a special request that needed to get done for some? Is it bringing anyone together or highlighting any issues that need to be resolved? Is it just stirring up a political food fight over the fact that the world changes? Is it bringing to light anything new and useful that the world wasn’t aware of? Is it putting the clear losses of some people and some businesses in the broader light of technology change and an effort to save humanity from itself? What is the actual point? Is it an effort to make CNN look like a joke?
When CNN does so much other useful stuff, why stoop down to such poor journalistic malpractice?
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Maximizing Edge AI Performance
Inference of convolutional neural network models is algorithmically straightforward, but to get the fastest performance for your application there are a few pitfalls to keep in mind when deploying. A number of factors make efficient inference difficult, which we will first step through before diving into specific solutions to address and resolve each. By the end of this article, you will be armed with four tools to use before building your system.
Why accelerate convolutional layers?
Broadly speaking, convolutions are all about sliding a function over something else. In the context of image data, we slide a window over pixels with three channels (RGB) and apply the same function on each window.
Fig. 1: Convolving a window over an image.
In a convolutional layer of a CNN, the function performed in every window is actually an element-wise multiplication with a matrix (necessarily of equal size) of fixed values called a filter. A set of multiple filters is also known as a convolutional kernel. The number of filters in this kernel will ultimately be the number of channels that the layer will output.
Fig. 2: In a convolutional layer, the actual function we are convolving is a series of element-wise matrix multiplications with different filters. Note: Each mathematical operation is actually a fused multiply and add (FMA) operation, also known as a ‘tensor op’.
Use fast matrix multiplication algorithms
The first and biggest challenge with CNN inference is that each layer requires a massive amount of matrix multiplies, as mentioned above. The number of operations scale with the size of the image, as well as the number of filters in each layer. While there’s no way to avoid these computations, specialized inference solutions have hardware for fast matrix multiplication algorithms such as the Winograd transformation. On common 3×3 convolutional kernels, such transformations can have the effect of reducing the number of operations needed by 2.25x! Therefore, the first and most general optimization you can make is to ensure that your deployment solution is able to leverage the advantages that fast matrix multiplication algorithms like Winograd can provide. For example, dedicated SoCs like Flex Logix’s InferX X1 have circuitry built in that can dynamically perform the transformations necessary for Winograd multiplication.
Quantize to lower precision data types
Just as the number of multiplications can vary dramatically between layers, so too does the amount of data that needs to be passed between layers. This data is known as activation energies, or activations. Inherently neural networks are approximations, and once a function has been trained in FP32 or FP16, the extra precision that these data types provide is unnecessary for inference. The process of changing the data type of a CNN is known as quantization. In common frameworks like PyTorch and TensorflowLite, quantization to INT8 can be accomplished after training with a tiny fraction of the data required for training, and only a few extra lines of code. The benefit of quantizing for inference can result in an immediate 2x improvement in latency over inference even in FP16!
Choose hardware with flexibility
Next up, as inference proceeds through a CNN, each layer does a different convolution from the previous layer. Whether it’s changing the window size of the kernel or using a different number of filters, the operations that mold and shape the activations end up having different ratios of memory access to computation. An early layer may have many more computations relative to the amount of memory it requires, whereas a middle layer will be operating on a very large activation data but only perform a fraction of the computations. Inherently, then, an architecture that can adapt to these changing memory and computation access patterns will have an advantage over one that does not. For example, the InferX X1 leverages Flex Logix’s eFPGA technology to dynamically reconfigure between layers to maintain an optimal datapath throughout inference. So, when looking to deploy, choose an architecture that can adapt.
Lastly, when training models, in a process known as backwards propagation, much information is generated to update the weights of the model based on each piece of training data. One way to cut down the amount of memory bandwidth required is to ‘batch’ the data and sum up the different changes to these weights over that set of data. In the context of inference, the approach of batching and calculating multiple inferences in parallel, going layer by layer can also improve throughput, but at the cost of latency. For example, in realtime applications, you will have to wait for enough data to come in before starting, and with some hardware, instead of using all the processing elements on a single job, you end up splitting the resources to process multiple inferences in parallel. If the fastest possible inferences is a concern for your application, remember to infer on a batch size of 1.
Faster inference for real-time applications opens up new design possibilities and can ultimately save you and your customers not just time, but also money. As this article highlights, now you have a template you can apply to improve inference performance in your end application, whether that be for medical imaging, factory automation, ADAS, or something else entirely! Just remember these four key tools: 1) make sure you’re taking advantage of fast matrix multiplication algorithms, 2) quantize to INT8, 3) deploy on flexible hardware, and 4) use batch=1 for real-time applications. Leveraging these tools will ensure you get the fastest inference possible for your applications.
Vinay Mehta is the inference technical marketing manager at Flex Logix.
How To Spot Fake News
Who were the world leaders when the Berlin Wall fell? How many women have been heads of state in prominent governments? And who are the newest additions to the list of world leaders?
This graphic reveals the leaders of the most influential global powers since 1970. Countries were selected based on the 2020 Most Powerful Countries ranking from the U.S. News & World Report.
Note: Switzerland has been omitted due to the swiftly changing nature of their national leadership.
The 1970s: Economic Revolutions
Our graphic starts in 1970, a year in which Leonid Brezhnev ruled the Soviet Union, while on the other side of the Iron Curtain, Willy Brandt was presiding over West Germany.
In the U.S., Richard Nixon implemented a series of economic shocks to stimulate the economy, but resigned in scandal due to the Watergate tapes in 1974. In the same time period, China was undergoing rapid industrialization and economic hardship under the final years of rule of communist revolutionary Mao Zedong, until his death in 1976.
In 1975, the King of Saudi Arabia, Faisal bin Abdulaziz Al Saud was assassinated by his nephew. The decade also marked the end of Park Chung-Hee’s dictatorship in South Korea when he was assassinated in 1979.
To cap off the decade, Margaret Thatcher became the first female prime minister of the United Kingdom in 1979, transforming the British economy using a laissez-faire economic policy that would come to be known as Thatcherism.
The 1980s: Reaganomics and the Fall of the Wall
The 1980s saw Ronald Reagan elected in the U.S., beginning an era of deregulation and economic growth. Reagan would actually meet the Soviet Union’s president, Mikhail Gorbachev in 1985 to discuss human rights and nuclear arms control amid the tensions of the Cold War.
The 1984 assassination of the Indian prime minister, Indira Gandhi was also a defining event of the decade. She was succeeded by her son, Rajiv Gandhi for only seven years before his own assassination in 1991.
The ‘80s were clearly turbulent times for world leaders, especially towards the end of the decade. In 1989, the Berlin Wall fell and Germany was reunified under chancellor Helmut Kohl. 1989 was also the year when the devastating events occurred at the Tiananmen Square protests in China, under president Deng Xiaoping. The event left a lasting mark on China’s history and politics.
The 1990s: War 2.0 and the Promise of the EU
The beginning of a new decade marked the end of the Cold War and the fall of the Soviet Union, leading to Boris Yeltsin’s position as the first president of the Russian Federation. A sense of peace, or at least the knowledge that a finger wasn’t floating above a nuclear launch button at any given moment, brought a sense of global calm.
However, this does not mean the decade was without conflict. The Gulf War began in 1990, led by the U.S. military’s Commander-in-Chief George H.W. Bush. In the mid-90s, prime minister Yitzhak Rabin of Israel was assassinated by Jewish extremists.
In spite of this, the ‘90s were a time of optimism for many. In 1993, the European project began. The European Union was founded with the support European leaders like the UK’s prime minister John Major, France’s president Francois Mitterrand, and chancellor Helmut Kohl of Germany.
The 2000s: Historic Firsts and Power Shifts
The dawn of a new century had people feeling both hopeful and scared. While Y2K didn’t end the world, many transformative events did occur, such as the 9/11 attacks in New York and the subsequent war on terror led by U.S. president George W. Bush.
On the other hand, Angela Merkel made history becoming the first female chancellor of Germany in 2005. A few years later, Barack Obama also achieved a momentous ‘first’ as the first African-American president in the United States.
The 2000s to early 2010s also revealed rapidly changing power shifts in Japan. Shinzō Abe rose to power in 2006, and after five leadership changes in seven years, he eventually circled back, ending up as prime minister again by 2013—a position he held until late 2020.
|Country||Number of Leaders Since 1970|
|🇰🇷 South Korea||10|
|🇸🇦 Saudi Arabia||5|
The 2010s: World Leaders Face Uncertainty
The 2010s were more than eventful. The Hong Kong protests under Chinese president Xi Jinping, and the annexation of Crimea led by Vladimir Putin, uncovered the wavering dominance of democracy and international law.
UK Prime Minister David Cameron’s move to introduce a Brexit referendum, resulted in just over half of the British population voting to leave the EU in 2016. This vote led to a rising feeling of protectionism and a shift away from globalization and multilateral cooperation.
Donald Trump’s U.S. presidential election was a shocking political longshot in the same year. Trump’s stint as president will likely have a longstanding impact on the course of American politics.
Two countries elected their first female leaders in this decade: president Park Geun-Hye in South Korea, and prime minister Julia Gillard in Australia. Here’s a look at which global powers have been led by women in the last 50 years.
|🇦🇺 Australia||Julia Gillard|
|🇨🇦 Canada||Kim Campbell|
|🇩🇪 Germany||Angela Merkel|
|🇮🇳 India||Indira Gandhi|
|🇮🇱 Israel||Golda Meir|
|🇰🇷 South Korea||Park Geun-Hye|
|🇹🇷 Turkey||Tansu Ciller|
|🇬🇧 UK||Margaret Thatcher|
|🇬🇧 UK||Theresa May|
2020 to Today
No one can avoid talking about 2020 without talking about COVID-19. Many world leaders have been praised for their positive handling of the pandemic, such as Angela Merkel in Germany. Others on the other hand, like Boris Johnson, have received critiques for slow responses and mismanagement.
The year 2020 packed about as much punch on its own as an entire decade does, from geopolitical tensions to a nail-biting 2020 U.S. election. The world is on high alert as the now twice-impeached Trump prepares his transfer of power following the riot at the U.S. Capitol.
The newest addition to the ranks of world leaders, Joe Biden, has recently taken his place as the 46th president of the United States on January 20, 2021.
Editor’s note: We’ll continue to update this graphic on world leaders as time goes on. Unfortunately, we were unable to include world leaders from more countries, as we were limited by the graphic format and user experience.
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