Month: January 2018

Google’s Cloud Auto-ML Vision

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A new service by Google named Cloud AutoML uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The initial release of AutoML Cloud is limited to image recognition. Its simple interface lets you upload images with ease, train and manage them, and finally deploy models on Google Cloud.

The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.

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In addition, rather than forcing enterprises to train their algorithms using Google’s data, Cloud AutoML ingests enterprise data assets and tunes the model accordingly. The key here is that Google helps enterprises to customize a model without having to do so de novo: There’s already a great deal of training baked in. Though initially focused on image data, Google plans to roll out the service to tackle text, video, and more.

Cloud AutoML Vision is built on Google’s transfer learning and neural architecture search technologies (among others). Disney has already started using the technology to annotate their products to improve the customer’s experience on their shop-Disney site. The Zoological Society of London is also using AutoML Vision to recognize and track wildlife in order to understand their distribution and how humans are impacting the species.

The video below simplifies and formulates the working of Cloud AutoML Vision.

AI in Radiology

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AI now helps in diagnosing dangerous lung diseases and adds it to its growing list of things.

A few months back a new arXiv paper by researchers from Stanford explains how CheXNet, the convolutional neural network they developed, achieved the feat. CheXNet was trained on a publicly available data set of more than 100,000 chest x-rays that were annotated with information on 14 different diseases that turn up in the images. The researchers had four radiologists go through a test set of x-rays and make diagnoses, which were compared with diagnoses performed by CheXNet. Not only did CheXNet beat radiologists at spotting pneumonia, but once the algorithm was expanded, it proved better at identifying the other 13 diseases as well. Early detection of pneumonia could help prevent some of the 50,000 deaths the disease causes in the U.S. each year. Pneumonia is also the single largest infectious cause of death for children worldwide, killing almost a million children under the age of five in 2015.

Stanford researchers trained a convolutional neural network on a data set of 40,895 images from 14,982 studies. The paper documents how the algorithm detected abnormalities (like fractures, or bone degeneration) better than radiologists in finger and wrist radiographs. However, radiologists were still better at spotting issues in elbows, forearms, hands, upper arms, and shoulders.

We’ve come a far way in AI, but still, we’ve miles of journey left. The results here clearly depict that AI is excelling humans, but does it mean that we don’t need humans? In the coming era of super intelligence, where are we standing?

 

(via; MitTechReview, arXiv)

Chip Flaws: Spectre and Meltdown Vulnerabilities

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Processors are of crucial importance in this digital age as their vitality in this computational era is unparalleled. The device you are reading this blog on and your smartwatch you see your time on, every device has a processor. These processors run the processes that are essential to show you your notification, run an application, play games as well as check some emails. As they run all the essential processes on your computer, these silicon chips handle extremely sensitive data. That includes passwords and encryption keys, the fundamental tools for keeping your computer secure.

The Spectre and Meltdown vulnerabilities, revealed a few days before could let attackers capture the information they shouldn’t be able to access, like your passwords and keys. As a result, an attack on a computer chip can turn into a serious security concern.

 

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Meltdown and Spectre

 

So what’s Spectre?

Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim’s confidential information via a side channel to the adversary. To make computer processes run faster, a chip will essentially guess what information the computer needs to perform its next function. That’s called speculative execution. As the chip guesses, that sensitive information is momentarily easier to access. In brief, Spectre is a vulnerability with implementations of branch prediction that affects modern microprocessors with speculative execution. Spectre is a vulnerability that forces programs on a user’s operating system to access an arbitrary location in the program’s memory space.

The Spectre paper displays the attack in four essential steps:

  1. First, it shows that branch prediction logic in modern processors can be trained to reliably hit or miss based on the internal workings of a malicious program.
  2. It then goes on to show that the subsequent difference between cache hits and misses can be reliably timed so that what should have been a simple non-functional difference can, in fact, be subverted into a covert channel which extracts information from an unrelated process’s inner workings.
  3. Thirdly, the paper synthesizes the results with return-oriented programming exploits and other principles with a simple example program and a JavaScript snippet run under a sandboxing browser; in both cases, the entire address space of the victim process (i.e. the contents of a running program) is shown to be readable by simply exploiting speculative execution of conditional branches in code generated by a stock compiler or the JavaScript machinery present in an extant browser.
  4. Finally, the paper concludes by generalizing the attack to any non-functional state of the victim process. It briefly discusses even such highly non-obvious non-functional effects as bus arbitration latency.

And What’s Meltdown?

In this form of attack, the chip is fooled into loading secured data during a speculation window in such a way that it can later be viewed by an unauthorized attacker. The attack relies upon a commonly-used, industry-wide practice that separates loading in-memory data from the process of checking permissions. Again, the industry’s conventional wisdom operated under the assumption that the entire speculative execution process was invisible, so separating these pieces wasn’t seen as a risk.

In Meltdown, a carefully crafted branch of code first arranges to execute some attack code speculatively. This code loads some secure data to which the program doesn’t ordinarily have access. Because it’s happening speculatively, the permission check on that access will happen in parallel (and not fail until the end of the speculation window), and as a consequence special internal chip memory known as a cache becomes loaded with the privileged data. Then, a carefully constructed code sequence is used to perform other memory operations based upon the value of the privileged data. While the normally observable results of these operations aren’t visible following the speculation (which ultimately is discarded), a technique known as cache side-channel analysis can be used to determine the value of the secure data.

The basic difference between Spectre and Meltdown is that Spectre can be used to manipulate a process into revealing its own data. On the other hand, Meltdown can be used to read privileged memory in a process’s address space which even the process itself would normally be unable to access (on some unprotected OS’s this includes data belonging to the kernel or other processes).

(via Wiki, cnet, spectreattack, meltdownattack, redhat, wired)

 

 

Capsule Nets

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A few months ago, Geoffrey Hinton and his team published two papers that introduced a completely new type of a neural network based on Capsules, further to in support of those Capsule Networks, the team published an algorithm called dynamic routing between capsules for the training of such networks.

With Hinton’s capsule network, layers are comprised not of individual Artificial Neural Networks (ANNs), but rather of small groups of ANNs arranged in functional pods, or “capsules.” Each capsule is programmed to detect a particular attribute of the object being classified, thus getting around the need for massive input data sets. This makes capsule networks a departure from the “let them teach themselves” approach of traditional neural nets.

A layer is assigned the task of verifying the presence of some characteristic, and when enough capsules are in agreement on the meaning of their input data, the layer passes on its prediction to the next layer.

 

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Capsule Net Architecture

 

A capsule is a nested set of neural layers. So in a regular neural network, you keep on adding more layers. In CapsNet you would add more layers inside a single layer. Or in other words, nesting a neural layer inside another. The state of the neurons inside a capsule capture the above properties of one entity inside an image. A capsule outputs a vector to represent the existence of the entity. The orientation of the vector represents the properties of the entity. The vector is sent to all possible parents in the neural network. For each possible parent, a capsule can find a prediction vector. Prediction vector is calculated based on multiplying its own weight and a weight matrix. Whichever parent has the largest scalar prediction vector product, increases the capsule bond. Rest of the parents decrease their bond. This routing by agreement method is superior to the current mechanism like max-pooling. Max pooling routes based on the strongest feature detected in the lower layer. Apart from dynamic routing, CapsNet talks about adding squashing to a capsule. Squashing is a non-linearity. So instead of adding squashing to each layer like how you do in CNN, you add the squashing to a nested set of layers. So the squashing function gets applied to the vector output of each capsule.

So far, capsule nets have proven equally adept at as traditional neural nets at understanding handwriting, and cut the error rate in half for identifying toy cars and trucks. Impressive, but it’s just a start. The current implantation of capsule networks is, according to Hinton, slower than it will have to be in the end.

 

(via arxiv, medium blogs, i-programmer, bigthink)