New Algorithm that outperforms Deep Learning

Deep Learning is blooming and with it, all kinds of unthought applications have been sprung to life. Every tech company is trying to implement a form of AI in their businesses in some way or the other. Neural networks, after all, have begun to outperform humans in tasks such as object and face recognition and in games such as chess, Go, and various arcade video games. And as we all know, Deep Learning is a manifestation of the human brain and has tremendous potential.





Evolutionary computing is completely unlike Deep Learning. The conventional way to create code is to write it from first principles with a specific goal in mind. Evolutionary computing uses a different approach. It starts with code generated entirely at random. And not just one version of it, but lots of versions, sometimes hundreds of thousands of randomly assembled pieces of code, and each of these codes have been tested to check whether the end goal is achieved. As a result, a lot of code is generated. But just by chance, some pieces of code are a little better than others. These pieces are then reproduced in a new generation of code, which includes more copies of the better codes.


Each of the new generations is then tested to see how well it works. The best pieces of code are preferentially reproduced in another generation, and so on. In this way, the code evolves. Over time, it becomes better, and after many generations, if conditions are right, it can become better than any human coder can design.


via: MitTechReview, Medium

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.




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.



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



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)




Nvidia Pegasus





(sources: MitTechReview, NvidiaBlog)

MIT’s new prototype ‘3D’ Chip

Daily a plethora of data is being generated and the computing power to process this data into useful information is stalling. One of the fundamental problems being faced is the processor-memory bottleneck or the performance gap. Various methods such as caches and different software techniques have been used to eliminate this problem. But there’s another way which is, building the CPU directly into a 3D memory structure, connect them directly without any kind of motherboard traces, and compute from within the RAM itself.



A prototype chip built by researchers at Stanford and MIT can solve the problem by sandwiching the memory, processor and even sensors all into one unit. While current chips are made of silicon



The researchers have developed a new 3D chip fabrication method that uses carbon nanotubes and resistive random-access memory (RRAM) cells together to create a combined nanoelectronic processor design that supports complex, 3D architecture – where traditional silicon-based chip fabrication works with 2D structures only. The team claims this makes for “the most complex nanoelectronic system ever made with emerging nanotechnologies,” creating a 3D computer architecture. Using carbon makes the whole thing possible since higher temperatures required to make a silicon CPU would damage the sensitive RRAM cells.

The 3D design is possible because these carbon nanotube circuits and RRAM memory components can be made using temperatures below 200 degrees Celsius, which is far, far less than the 1,000 degree temps needed to fabricate today’s 2D silicon transistors. Lower temperatures mean you can build an upper layer on top of another without damaging the one or ones below.

One expert cited by MIT said that this could be the answer to continuing the exponential scaling of computer power in keeping with Moore’s Law, as traditional chip methods start to run up against physical limits. It’s still in its initial phases and it would take many years till we see the actual implementation of these chips in real life.

Response of an artificial iris to light like the human eye

An artificial iris manufactured from intelligent, light-controlled polymer material can react to incoming light in the same ways as the human eye. The Iris was developed by the Smart Photonic Materials research group from the TUT, and it was recently published in the Advanced Materials journal.


The human iris does its job of adjusting your pupil size to meter the amount of light hitting the retina behind without you having to actively think about it. And while a camera’s aperture is designed to work the same way as a biological iris, it’s anything but automatic. Even point-and-shoots rely on complicated control mechanisms to keep your shots from becoming overexposed. But a new “artificial iris” developed at the Tampere University of Technology in Finland can autonomously adjust itself based on how bright the scene is.

Scientists from the Smart Photonic Materials research group developed the iris using a light-sensitive liquid crystal elastomer. The team also employed photoalignment techniques, which accurately position the liquid crystal molecules in a predetermined direction within a tolerance of a few picometers. This is similar to the techniques used originally in LCD TVs to improve viewing angle and contrast but has since been adopted to smartphone screens. “The artificial iris looks a little bit like a contact lens,” TUT Associate Professor Arri Priimägi said. “Its center opens and closes according to the amount of light that hits it.”

The team hopes to eventually develop this technology into an implantable biomedical device. However, before that can happen, the TUT researchers need to first improve the iris’ sensitivity so that it can adapt to smaller changes in brightness. They also need to get it to work in an aqueous environment. However, this new iris is therefore still long ways away from being ready.

What is Blockchain?

Blockchains in Bitcoin.




Blockchain formation.




In the above representation, the main chain (black) consists of the longest series of blocks from the genesis block (green) to the current block. Orphan blocks (purple) exist outside of the main chain.




Working of Bitcoin and Blockchain



Intel Core i9












(via Engadget, The Verge)


A peek into NVIDIA’s self driving AI.


Sorta follow-up to my previous blog post: Is AI Mysterious?. Some of the most powerful machine-learning techniques available result in software that is almost completely opaque, even to the engineers that build it.


We still don’t know exactly how AI works, the people at Nvidia still don’t know why the AI they built wasn’t following a single instruction given by its creators. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.

It simply matches input from several video cameras to the behavior of a human driver and figures out for itself how it should drive. The only catch is that the system is so complex that it’s difficult to untangle how it actually works.

Nvidia is now working to open the black box by developing a way to visually highlight what the system is paying attention to. Explained in a recently published paper, the neural network architecture developed by Nvidia’s researchers is designed so that it can highlight the areas of a video picture that contribute most strongly to the behavior of the car’s deep neural network. Remarkably, the results show that the network is focusing on the edges of roads, lane markings, and parked cars—just the sort of things that a good human driver would want to pay attention to.


Nvidia’s Convolutional Neural Network architecture for Self-Driving AI.


Fascinatingly enough, this compares a lot to human intelligence where we don’t actually know how to describe certain actions we do, but we just do them.

This certainly highlights the fact that in the near future we might start seeing AI those are just like us or even Superintelligent. I highly recommend reading the book Superintelligence: Paths, Dangers, Strategies by Nick Bostrom.

(Sources: Nvidia, MitTechReview, Nvidia Blog)

Artificial Intelligence ready ARM CPUs (DynamIQ)

ARM processors are ubiquitous and many of the tech gadgets we used are powered by them, furthermore, the company is showing off its plans for the future with DynamIQ. Aimed squarely at pushing the artificial intelligence and machine learning systems we’re expecting to see in cars, phones, gaming consoles and everything else, it’s what the company claims is an evolution on the existing “big.Little” technology.








(source: ARM community, Engadget)

Razer’s Project Valerie

For those who missed out on Razer’s CES 2017 showcase, Project Valerie is an impressive gaming laptop with three 4K displays, virtually allowing for a portable panoramic display mode.

Each display is 17.3 inches in size and features a 4K resolution, offering a huge viewing space unprecedented for a laptop. When not in use, the extra Project Valerie screens are housed within a chassis. When the laptop is opened, the screens automatically slide out from the lid and extend to the sides of the main display to create a 180-degree viewing experience, without requiring any human input.


The machine is fuelled by a top-of-the-line Nvidia GeForce GTX 1080 graphics card with 8GB RAM, accompanied by an Intel Core i7 Skylake CPU and 32GB RAM as a foundation. It seems to be all about graphics power, and since Razer has a reputation for building high-end gaming laptops, Project Valerie is certainly no exception.

Since these two extra 17″ 4K displays need some extra space, the unit is somewhat thick in comparison to the paper-thin laptops of today and is not exactly lightweight either. But for what it is, it’s pretty impressive.

For more information, watch the video linked below.