Why Litecoin Will Skyrocket 140% in Less Than 3 Months & Hit $220

By CCN: The Litecoin price has enjoyed a breakneck bull run in 2019, launching the cryptocurrency nearly 200 percent higher in less than six months. But while cautious investors might be tempted to take their profits and run, a crucial upcoming event could send Litecoin another 140% higher over the next three months, enabling LTC to eclipse the $220 mark for the first time in more than a year. The trigger from this mammoth ascent? Litecoin’s long-awaited “halvening,” which will slash block rewards by 50%, from 25 LTC to 12.5 LTC. Historically, such halvenings (or “halvings”) have proven to be

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DRIVE Labs: Predicting the Future with RNNs

Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series. With this series, we’re taking an engineering-focused look at individual autonomous vehicle challenges and how the NVIDIA DRIVE AV Software team is mastering them. Catch up on our earlier posts, here.

MISSION: Predicting the Future Motion of Objects

APPROACH: Recurrent Neural Networks (RNNs)

From distracted drivers crossing over lanes to pedestrians darting out from between parked cars, driving can be unpredictable. Such unexpected maneuvers mean drivers have to plan for different futures while behind the wheel.

If we could accurately predict whether a car will move in front of ours or if a pedestrian will cross the street, we could make optimal planning decisions for our own actions.



Autonomous vehicles face the same challenge, and use computational methods and sensor data, such as a sequence of images, to figure out how an object is moving in time. Such temporal information can be used by the self-driving car to correctly anticipate future actions of surrounding traffic and adjust its trajectory as needed.

The key is to analyze temporal information in an image sequence in a way that generates accurate future motion predictions despite the presence of uncertainty and unpredictability.

To perform this analysis, we use a member of the sequential deep neural network family known as recurrent neural networks (RNNs).

What Is an RNN?

Typical convolutional neural networks (CNNs) process information in a given image frame independently of what they have learned from previous frames. However, RNN structure supports memory, such that it can leverage past insights when computing future predictions.

RNNs, thus, feature a natural way to take in a temporal sequence of images (that is, video) and produce state-of-the-art temporal prediction results.

With their capacity to learn from large amounts of temporal data, RNNs have important advantages. Since they don’t have to only rely on local, frame-by-frame, pixel-based changes in an image, they increase prediction robustness for motion of non-rigid objects, like pedestrians and animals.

RNNs also enable the use of contextual information, such as how a given object appears to be moving relative to its static surroundings, when predicting its future motion (that is, its future position and velocity).

Using Cross-Sensor Data to Train RNNs

Radar and lidar sensors are very good at measuring object velocity. Consequently, in our approach, we use data from both to generate ground truth information to train the RNN to predict object velocity rather than seeking to extract this information from human-labeled camera images.

White boxes indicate current object locations predicted by the RNN, while the yellow boxes are the RNN’s predictions about where these objects will move in the future.

Specifically, we propagate the lidar and radar information into the camera domain to label camera images with velocity data. This lets us exploit cross-sensor fusion to create an automated data pipeline that generates ground truth information for RNN training.

The RNN output consists of time-to-collision (TTC), future position and future velocity predictions for each dynamic object detected in the scene (for example, cars and pedestrians). These results can provide essential input information to longitudinal control functions in an autonomous vehicle, such as automatic cruise control and automatic emergency braking.

With RNNs’ ability to learn from the past, we’re able to create a safer future for autonomous vehicles.

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AI Nails It: Startup’s Drones Eye Construction Sites

Krishna Sudarshan was a Goldman Sachs managing director until his younger son’s obsession with drones became his own, attracting him to the flying machines’ data-heavy business potential. Sudarshan quit Goldman after a decade in 2016 to found Aspec Scire, which pairs drones to a cloud service for construction and engineering firms to monitor their businesses. Read article >

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AI Nails It: Startup’s Drones Eye Construction Sites

Krishna Sudarshan was a Goldman Sachs managing director until his younger son’s obsession with drones became his own, attracting him to the flying machines’ data-heavy business potential.

Sudarshan quit Goldman after a decade in 2016 to found Aspec Scire, which pairs drones to a cloud service for construction and engineering firms to monitor their businesses.

A colleague from the banking giant joined as head of engineering and former colleagues invested in the startup.

Business has taken flight.

Since its launch, Aspec Scire has landed work with construction management firms, engineering firms and owners and developers, including a large IT services company, Sudarshan said.

“The construction industry has very low levels of automation. This an industry that’s desperately in need of increased efficiency,” he said.

On-Demand Drones

Aspec Scire licenses its service to construction management firms, general contractors, surveyors and drone operators who offer it to their customers.

It can replace a lot of old-fashioned grunt work and record-keeping.

Its drones-as-a-service cloud business allows managers to remotely monitor the progress of construction sites. Videos and photos taken by drones can build up files on the status of properties, providing a digital trail of documentation for fulfilment of so-called SLAs, or service-level agreements, according to the company.

It can also show whether substructural building elements — such as pilings and columns — are keeping to the blueprints. The service holds promise for heading off safety issues and saving construction firms a lot of time and money if they can quickly catch mistakes before they become problems requiring major revisions.

Image recognition algorithms are also trained to spot hundreds of problems that could be dangerous or cause contractors major headaches — hot water lines next to gas lines, for example, or forgotten 50-amp outdoor plug-in receptacles for Tesla owners to charge from.

“We will be the ones that analyze data from construction sites to provide actionable insights to improve the efficiency of their operations,” he said.

AI Nails Construction

Sudarshan, who led technology for a division of Goldman, is like many who use NVIDIA GPUs to tap into fast processing for massive datasets. After studying the idea of drone data collection for construction, he put the two together.

He fits a banking industry adage: You can take the person out of Goldman, but you can’t take Goldman out of the person. “Goldman is so data driven at everything. And I can see this industry is not data driven, so I’m trying to see how we can make it more so,” he said.

Construction data is plentiful. Aspec Scire uses millions of images for training its image classification algorithms that apply to about 20,000 aspects of construction sites. Training data continues to grow as customers upload images from sites, he said.

Aspec Scire also provides trained models for the compact supercomputing power of Jetson TX2 onboard DJI drones to quickly process images. It trains its algorithms on NVIDIA GPUs on Google Cloud Platform, including the NVIDIA V100 Tensor Core GPU.

“Without GPUs we wouldn’t be able to do some of the things that we’re doing,” Sudarshan said.

Aspec Scire is a member of NVIDIA Inception, a virtual accelerator program that helps startups get to market faster.

 

Image credit: Magnus Bäck, licensed under Creative Commons

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This Painfully Boring CNBC Crypto Debate Is Bullish as Hell for Bitcoin

By CCN: If you’re looking for reasons behind bitcoin’s recent price surge, this video tells the whole story. Because it’s so completely dull. Has bitcoin gone too far, too fast? Here's how two traders are thinking about bitcoin futures. via @CNBCFuturesNow https://t.co/StSHvYGYD9 pic.twitter.com/aHKgt62GNe — CNBC (@CNBC) May 22, 2019 The two established analysts with decades of trading experience talk about bitcoin with maturity and logic. They make a strong case for allocating portfolio funds to crypto, and no-one mentioned bubbles or scams. The way people talk about bitcoin is changing, especially on mainstream finance outlets. It’s evolving. Bitcoin is a

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Dow Cringes as Xi Jinping Ominously Sparks Fears of Economic Cold War

By CCN: The Dow lurched back into decline on Wednesday after Chinese President Xi Jinping amped up the trade war rhetoric in an ominous speech that suggested that Beijing could not only permanently freeze negotiations with the Trump administration but also mire the two nations in an economic cold war. Dow Stumbles Toward Another Setback All three of Wall Street’s major indices braced themselves for significant pullbacks at the opening bell. As of 9:27 am ET, Dow Jones Industrial Average futures had lost 90 points or 0.35%, implying an 86.33 point decline. S&P 500 futures slid 0.44%, and Nasdaq futures

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