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We’ve discovered that evolution strategies (ES) , an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.

evolution strategies (ES) reinforcement learning (RL)

In particular, ES is simpler to implement (there is no need for backpropagation ), it is easier to scale in a distributed setting, it does not suffer in settings with sparse rewards, and has fewer hyperparameters . This outcome is surprising because ES resembles simple hill-climbing in a high-dimensional space based only on finite differences along a few random directions at each step.

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Our finding continues the modern trend of achieving strong results with decades-old ideas. For example, in 2012, the “AlexNet” paper showed how to design, scale and train convolutional neural networks (CNNs) to achieve extremely strong results on image recognition tasks, at a time when most researchers thought that CNNs were not a promising approach to computer vision. Similarly, in 2013, the Big Discount For Sale Clearance Henrik Vibskov Youngdo long dress Outlet The Cheapest JQiM2k
showed how to combine Q-Learning with CNNs to successfully solve Atari games, reinvigorating RL as a research field with exciting experimental (rather than theoretical) results. Likewise, our work demonstrates that ES achieves strong performance on RL benchmarks, dispelling the common belief that ES methods are impossible to apply to high dimensional problems.

ES is easy to implement and scale. Running on a computing cluster of 80 machines and 1,440 CPU cores, our implementation is able to train a 3D MuJoCo humanoid walker in only 10 minutes (A3C on 32 cores takes about 10 hours). Using 720 cores we can also obtain comparable performance to A3C on Atari while cutting down the training time from 1 day to 1 hour.

In what follows, we’ll first briefly describe the conventional RL approach, contrast that with our ES approach, discuss the tradeoffs between ES and RL, and finally highlight some of our experiments.

Reinforcement Learning

Let’s briefly look at how RL works. Suppose we are given some environment (e.g. a game) that we’d like to train an agent on. To describe the behavior of the agent, we define a policy function (the brain of the agent), which computes how the agent should act in any given situation. In practice, the policy is usually a neural network that takes the current state of the game as an input and calculates the probability of taking any of the allowed actions. A typical policy function might have about 1,000,000 parameters, so our task comes down to finding the precise setting of these parameters such that the policy plays well (i.e. wins a lot of games).

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Darshan Shankar
Founder, CEO of Bigscreen.

The iPhone X started shipping last week to the public and while much of the discussion about the phone focused on Face ID, Animoji and the notch, it’s far more interesting to read into what Apple is signaling for the future of iPhone with this device.

It is a glimpse at what will be possible in a few years, and what we’re seeing right now is clearly laying the foundation for Apple’s next big product: Augmented Reality glasses.

The TrueDepth sensor FaceID is the most advanced facial detection and recognition hardware and software in the world, but what it is capable of is not as exciting as what it will be capable of .

The play

It’s no coincidence that ARKit launched alongside the iPhone X. Apple’s next big platform is AR so by putting it on everything from the iPhone 6 and above it’s now in of peoples’ hands — making it the biggest augmented reality platform in the world.

By doing this Apple is building up the content library it needs for a successful platform launch years ahead of showing the world what the device that powers it might look like and gets developers comfortable with the building blocks needed to succeed in a 3D-first world.

Building out ARKit now gives developers time to create killer apps, and some have already launched many compelling real-world demonstrations of its power, like IKEA’s Place app .

This is an unusual departure for Apple. With the iPhone and Apple Watch, SDKs and toolkits were released to developers only the product was announced. This time, Apple is releasing ARKit a couple before the anticipated AR glasses.

Take a second to realize the magnitude of the accomplishment here: in a fraction of a second, a tiny device in the palm of your hand can recognize who we are and map our faces onto other objects, and track our emotions.

The real-time motion capture (“mocap”) technology used to map your face in the iPhone X to create Animoji was reserved for big-budget film studios until recently (side note: Apple also owns a company that creates those motion effects for the Star Wars franchise).

Apple is now able to accurately map out the details of your unique face in milliseconds, and drop that onto a 3D model, or understand who are uniquely — perfect for building out a database of people or letting you load on a face filter that makes you look like Darth Vader.

Fast forward a few years from this and put this sensor on a pair of augmented reality glasses: suddenly you can identify the people you’re talking to (or about to) at a meetup or conference.

That data, when used as a ambient data point with machine learning, could remind you: “” and “”

The parts

The secret is that the iPhone X already contains many of the moving parts needed to build a great AR headset. If we zoom out and look at what a ‘mass adopted’ AR headset would need to succeed, some pieces are already in place:

This is a potent mix: your phone is learning to uniquely recognize millions of faces, understand the world around you, and identify what cups, tables, chairs, and other objects look like.

Apple will have a huge head start here because it’s already in your pocket and the machine learning models are baked in from day one.

The software

Before Apple’s September iPhone X announcement, ARKit looked to be the only mobile platform for AR. But Google took the wraps off ARCore just days before the event — a true competitor for Android.

ARCore is incredibly advanced — it’s built off the back of Google’s work on Tango phones, which required dedicated hardware with multiple cameras to function and recognize the world.

Today, ARCore is further ahead in some areas: it’s able to detect ramps, ceilings and vertical surfaces such as boxes. Both platforms are able to do horizontal surface detection, but that’s where it stops for ARKit.

That doesn’t actually matter, however: by having millions of devices in the market Apple is learning quickly and will be able to leverage millions of data points over the next year while Google tries to get ARCore adoption across its many vendors.

Apple will likely win out from sheer scale and their ability to catch up, slow and steady, over time.

Google has been loudly investing in AR since at least 2014 publicly, but Apple has been as well — in secret — for perhaps longer. Apple acquired the company behind the TrueDepth camera array in 2013, Primesense, and then FaceShift , a motion capture and animation company, in 2015. Yesterday, Apple boldly acquired Vrvana , an AR headset company.

PrimseSense’s technology was pretty advanced in 2013, but it’s clear how far it’s come since. The TrueDepth camera array is essentially an entire Kinect crammed into an inch of space, running using your phone’s processor.

Puzzle pieces

There’s one big piece missing for AR goggles to work: the back-facing camera array needs to be upgraded for AR as well to adopt more advanced sensors for measuring the environment in front of you.

Unsurprisingly, a rumor from just days ago confirms exactly that:

Earlier, I mentioned the need for sophisticated tracking, like SLAM, and the report mentions that as the focus for 2019:

Time-of-flight cameras are a bleeding edge technology and can be found in Microsoft’s Hololens (AR headset) and high-end drones . That’s what’s left in the puzzle of Apple’s future AR headset and it’s already rumored to be in the product pipeline.

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Yoni Heisler @edibleapple
July 23rd, 2018 at 8:00 PM
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One of Steve Jobs’ greatest talents was his ability to influence behavior and persuade people to buy into his own version of reality, an ability famously referred to as the reality distortion field. Even high-powered CEOs were sometimes rendered powerless when presented with a high-octane sales pitch from the charismatic Apple co-founder. One prime example which highlights Jobs’ power of persuasion involves the build-up to the original iPhone release. Famously, Gorilla Glass as a consumer-facing product came to be as a result of Jobs convincing Corning CEO Wendell Weeks that his company could, in fact, mass produce the protective glass within a six-month time frame.

As recounted by Walter Isaacson — who penned Jobs’ biography — Weeks initially told Jobs that mass production on the scale Apple wanted simply couldn’t be done. Jobs thought otherwise and, as the story goes, emphatically told Weeks: “Don’t be afraid. You can do this.”

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Even as a young executive in the late 70s, stories of Jobs willing things into existence are voluminous. That said, an early Apple employee named Joe Shelton recently sat down for an interview with Business Insider where he touched on a number of topics, including Jobs’ somewhat mythical reality distortion field. Interestingly enough, though not at all surprising, is that Jobs would sometimes back initiatives that were somewhat ill-conceived.

Back in the early 80s, Shelton was a product manager for Apple’s first Mac. Interestingly, Shelton recounts how Jobs — persuasive though he might have been — wasn’t always the champion of well-thought ideas. Citing one example, Shelton recalls how Jobs back in the 80s wanted the Mac to have a memory limit of 128k.

Standing in front of his employees, Jobs told them, “we want developers to write small, efficient code, not Microsoft code.” His logic was that would metastasize all over the place. The limit would become Apple’s advantage by forcing developers to do more with less headroom.

The staff seemed to like the logic — Jobs’ reality distortion field at work — but Shelton wasn’t convinced. “I was the only person who wasn’t accepting it because I knew how operating systems grow, how software grows.” Jobs thought developers would make their apps smaller, “but it wasn’t going to go that way,” Shelton says. Software code only ever balloons in size.

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