ARM’s Machine Learning Processors

ARM’s latest mobile processors are tuned to crunch machine-learning algorithms as efficiently as possible. ARM announced a few days ago that it has created its first dedicated machine-learning chips, which are meant for use in mobile and smart-home devices. The company says it’s sharing the plans with its hardware partners, including smartphone chipmaker Qualcomm, and expects to see devices packing the hardware by early 2019.

Currently, small and portable devices lack the power to run AI algorithms, so they enlist the help of big servers in the cloud. But enabling mobile devices to run their own AI software is attractive. It can speed things up, cutting the lag inherent in sending information back and forth. It will allow hardware to run offline. And it pleases privacy advocates, who are comforted by the idea of data remaining on the device.


Jem Davies, the lead of Machine Learning group at ARM, said, “We analyze compute workloads, work out which bits are taking the time and the power, and look to see if we can improve on our existing processors.”  The new chips use less power than the company’s other designs to perform the kinds of linear-algebra calculations that underpin modern artificial intelligence. They’re also better at moving data in and out of memory.

Artificial Synapse could make Brain-On-A-Chip Hardware a Reality

Let’s start by understanding what does the title mean! This is a part of Neuromorphic Engineering aka Neuromorphic Computing, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. Microprocessors configured more like brains than traditional chips could soon make computers far more astute about what’s going on around them.


Neuromorphic computer chips are designed to work like the human brain. Instead of being controlled by binary, on-or-off signals like most current chips, neuromorphic chips weight their outputs, mimicking the way different neurons fire at different strengths through their synapses. In this way, small neuromorphic chips could, like the brain, efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers. But one significant hangup on the way to such portable artificial intelligence has been the neural synapse, which has been particularly tricky to reproduce in hardware.

Now engineers at MIT have designed an artificial synapse in such a way that they can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team has built a small chip with artificial synapses, made from silicon germanium. In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting, with 95 percent accuracy. The design, published last month in the journal Nature Materials, is a major step towards building portable, low-power neuromorphic chips for use in pattern recognition and other learning tasks.

Most neuromorphic chip designs attempt to emulate the synaptic connection between neurons using two conductive layers separated by a “switching medium,” or synapse-like space. When a voltage is applied, ions should move in the switching medium to create conductive filaments, similarly to how the “weight” of a synapse changes.

The research was led by Jeehwan Kim, the Class of 1947 Career Development Assistant Professor in the departments of Mechanical Engineering and Materials Science and Engineering, and a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories.

In conclusion, Artificial neural networks are already loosely modeled on the brain. The combination of neural nets and neuromorphic chips could let AI systems be packed into smaller devices and run a lot more efficiently.


(via ScienceDaily, MitTechReview, Wiki)

Google’s Cloud Auto-ML Vision

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.


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

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

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.



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

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.



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)

AI Helps in Making New Materials

The machine-learning system finds patterns in materials “recipes,” even when training data is lacking.  In the previous month, 3 of the MIT material scientists and their colleagues published a paper describing a new artificial intelligence system that can pore through scientific papers and extract “recipes” for producing particular types of materials.

The researchers envision a database that contains materials recipes extracted from millions of papers. Scientists and engineers could enter the name of a target material and any other criteria — precursor materials, reaction conditions, fabrication processes — and pull up suggested recipes. For instance, the new system was able to identify correlations between “precursor” chemicals used in materials recipes and the crystal structures of the resulting products. The same correlations, it turned out, had been documented in the literature.

The system also relies on statistical methods that provide a natural mechanism for generating original recipes. In the paper, the researchers use this mechanism to suggest alternative recipes for known materials, and the suggestions accord well with real recipes.

Like many of the best-performing artificial-intelligence systems of the past 10 years, the MIT researchers’ new system is a so-called neural network, which learns to perform computational tasks by analyzing huge sets of training data. Traditionally, attempts to use neural networks to generate materials recipes have run up against two problems, which the researchers describe as sparsity and scarcity.

Any recipe for a material can be represented as a vector, which is essentially a long string of numbers. Each number represents a feature of the recipe, such as the concentration of a particular chemical, the solvent in which it’s dissolved, or the temperature at which a reaction takes place.  To test the system’s accuracy, however, they had to rely on the labeled data, since they had no criterion for evaluating its performance on the unlabeled data. In those tests, the system was able to identify with 99 percent accuracy the paragraphs that contained recipes and to label with 86 percent accuracy the words within those paragraphs.

The research was supported by the National Science Foundation, the Natural Sciences and Engineering Research Council of Canada, the U.S. Office of Naval Research, the MIT Energy Initiative, and the U.S. Department of Energy’s Basic Energy Science Program.


(via MitTechReview, MitNews, Wiki)

Neural Networks Are Learning What (Not) To Forget

Deep Learning, which makes use of various types of Artificial Neural Networks, is the paradigm that has the potential to grow AI at an exponential rate. There’s a lot of hype going on around Neural Nets and people are busy in researching and making them useful to their potential.

The title of this blog might seem obscure to some of the readers but consider an example, humans have evolved from apes, and while evolving, we’ve forgotten some information or knowledge or a particular experience which is no longer useful or needed. In particular, humans have the extraordinary ability to constantly update their memories with the most important knowledge while overwriting information that is no longer useful. The world is full of trivial experiences or provides knowledge which is irrelevant or does little help to us, hence we forget those things or rather ignore those incidences.

The same cannot be said of machines. Any skill they learn is quickly overwritten, regardless of how important it is. There is currently no reliable mechanism they can use to prioritize these skills, deciding what to remember and what to forget. Thanks to Rahaf Aljundi and pals at the University of Leuven in Belgium and at Facebook AI Research. These guys have shown that the approach biological systems use to learn and to forget, can work with artificial neural networks too.

The key is a process known as Hebbian learning, first proposed in the 1940s by the Canadian psychologist Donald Hebb to explain the way brains learn via synaptic plasticity. Hebb’s theory can be famously summarized as “Cells that fire together wire together.”

What is Hebbian Learning?

Hebbian learning is one of the oldest learning algorithms and is based in large part on the dynamics of biological systems. A synapse between two neurons is strengthened when the neurons on either side of the synapse (input and output) have highly correlated outputs. In essence, when an input neuron fires, if it frequently leads to the firing of the output neuron, the synapse is strengthened. Following the analogy to an artificial system, the tap weight is increased with high correlation between two sequential neurons.

hebbian learning

Artificial neural nets being taught what to forget

In other words, the connections between neurons grow stronger if they fire together, and these connections are therefore more difficult to break. This is how we learn—repeated synchronized firing of neurons makes the connections between them stronger and harder to overwrite. The team did this by measuring the outputs from a neural network and monitoring how sensitive they are to changes in the connections within the network. This gave them a sense of which network parameters are most important and should, therefore, be preserved. “When learning a new task, changes to important parameters are penalized,” said the team. They say the resulting network has “memory aware synapses.”

Neural networks with memory aware synapses turn out to perform better in these tests than other networks. In other words, they preserve more of the original skill than networks without this ability, although the results certainly allow room for improvement.

The Paper: : Memory Aware Synapses: Learning What (Not) To Forget.

(via: MitTechReview, Wikipedia, arxiv)

Evolution of Poker with AI

Artificial Intelligence is happening all around us. With many businesses integrating AI in their technologies, there is an outburst of AI everywhere. From self-driving cars to GO playing AI, and most surprising of all, Poker! A few months ago, I had shared a blog on The Poker Playing AI (recommended read!). Well yeah, to those folks who didn’t know, AI has also kept its feet in Poker and it’s a major achievement!

To summarize the victory of AI in poker, a beautiful infographic has been created and designed meticulously by the folks at pokersites.



Source of the Infographic ->

What is Ethereum?

With all the hype going around Blockchain and it’s infrastructure users like Bitcoin and Ethereum are all over the media and magazines I had to write a blog on Ethereum. Briefly, Blockchain is to Bitcoin, what the internet is to email. A big electronic system, on top of which you can build applications.

Then what’s Ethereum? It is a public database that keeps a permanent record of digital transactions. Importantly, this database doesn’t require any central authority to maintain and secure it.


Like Bitcoin, Ethereum is a distributed public blockchain network. Although there are some significant technical differences between the two, the most important distinction to note is that Bitcoin and Ethereum differ substantially in purpose and capability. Bitcoin offers one particular application of blockchain technology, a peer to peer electronic cash system that enables online Bitcoin payments. While the Bitcoin blockchain is used to track ownership of digital currency (bitcoins), the Ethereum blockchain focuses on running the programming code of any decentralized application.

In the Ethereum blockchain, instead of mining for bitcoin, miners work to earn Ether, a type of crypto token that fuels the network. Beyond a tradeable cryptocurrency, Ether is also used by application developers to pay for transaction fees and services on the Ethereum network.

The Ethereum blockchain is essentially a transaction-based state machine, which means that on a series of specific inputs, it will transition to a new state. The first state in Ethereum is the genesis state, which means a blank state, wherein no transactions have happened on the network. After a transaction occurs, the genesis state transitions to a new state, possibly a final state denoting the current state of Ethereum. Of course, there are millions of transactions occurring concurrently, whereby these transactions are grouped in Blocks. So to simplify, a Block contains a series of transactions.

To cause a transition from one state to the next, a transaction must be valid. For a transaction to be considered valid, it must go through a validation process known as miningMining is when a group of nodes (i.e. computers) expend their compute resources to create a block of valid transactions. In the most basic sense, a transaction is a cryptographically signed piece of instruction that is generated by an externally owned account, serialized, and then submitted to the blockchain.

Any node on the network that declares itself as a miner can attempt to create and validate a block. Lots of miners from around the world try to create and validate blocks at the same time. Each miner provides a mathematical “proof” when submitting a block to the blockchain, and this proof acts as a guarantee: if the proof exists, the block must be valid.

For a block to be added to the main blockchain, the miner must prove it faster than any other competitor miner. The process of validating each block by having a miner provide a mathematical proof is known as a “proof of work.”

A miner who validates a new block is rewarded with a certain amount of value for doing this work. What is that value? The Ethereum blockchain uses an intrinsic digital token called “Ether.” The value token of the Ethereum blockchain is called ether. It is listed under the code ETH and traded on cryptocurrency exchanges. It is also used to pay for transaction fees and computational services on the Ethereum network. Every time a miner proves a block, new Ether tokens are generated and awarded. Ether can be transferred between accounts and used to compensate participant nodes for computations performed.

EVM – Ehtereum Virtual Machine

Ethereum is a programmable blockchain. Rather than give users a set of pre-defined operations (e.g. bitcoin transactions), Ethereum allows users to create their own operations of any complexity they wish. In this way, it serves as a platform for many different types of decentralized blockchain applications, including but not limited to cryptocurrencies.

Ethereum in the narrow sense refers to a suite of protocols that define a platform for decentralized applications. At the heart of it is the Ethereum Virtual Machine (“EVM”), which can execute code of arbitrary algorithmic complexity. In computer science terms, Ethereum is “Turing complete”. Developers can create applications that run on the EVM using friendly programming languages modeled on existing languages like JavaScript and Python.

Smart Contracts

Smart contracts are deterministic exchange mechanisms controlled by digital means that can carry out the direct transaction of value between untrusted agents. They can be used to facilitate, verify, and enforce the negotiation or performance of economically-laden procedural instructions and potentially circumvent censorship, collusion, and counter-party risk. In Ethereum, smart contracts are treated as autonomous scripts or stateful decentralized applications that are stored in the Ethereum blockchain for later execution by the EVM. Instructions embedded in Ethereum contracts are paid for in ether (or more technically “gas”) and can be implemented in a variety of Turing complete scripting languages.

There’s still a lot more to Ethereum, but this blog will help you get some insights before delving in deeper in the utopian world of blockchain and cryptocurrency.


(sources: Wiki, Telegraph, Medium, ethdocs)