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Neuromorphic computing a neuron spike now leverages speedy AI

The neuronal structure of the human brain is emulated in neuromorphic computer research. The next generation of AI will extend AI into domains similar to human cognition, like interpretation and independent adaptability. This is important for overcoming the fragility of AI solutions based on neural network training and testing, which rely on literal, deterministic interpretations of events that lack perspective and common understanding. To automate everyday human operations, next-generation AI must be able to address unexpected circumstances and abstract. Let’s explore more about neuromorphic computing and neuron spikes in the sections below.  

What is Neuromorphic Computing?

Neuromorphic computing is an engineering method that models computer components after principles found in the human brain and nervous system. The phrase refers to the development of both hardware and software computer components. To build artificial neural systems found in biological architecture, neuromorphic engineers depend on a variety of fields, including computer science, biology, mathematics, electrical engineering, and physics.  

Why are Neuromorphic Systems Needed?

Most modern hardware is built on the von Neumann architecture, which separates memory and computation. Von Neumann chips waste time and energy since they must shuttle information back and forth between the memory and the CPU. Chipmakers have long been able to increase the amount of processing power on a chip by squeezing more transistors onto these von Neumann computers, thanks to Moore’s Law. However, the challenges with reducing transistors much more, their energy needs, and the heat they emit imply that without a change in chip principles, that won’t be possible for much longer. Von Neumann’s designs will make it increasingly difficult to achieve the needed improvements in computational power as time goes on. To stay up, a new non-von Neumann design will be required: neuromorphic structure. Both quantum computing and neuromorphic systems have been proposed as solutions, with neuromorphic computing, or brain-inspired computing, expected to be commercialized first. A neuromorphic computer might channel the brain’s functioning to solve various challenges in addition to perhaps bypassing the von Neumann barrier. Brains employ massively parallel computation, whereas von Neumann systems are mostly serial.  

Computer like a Human Brain

An action potential can be activated by a large number of stimuli at once (spatial) or by the input that accumulates over time (temporal). Because of these mechanisms, as well as the massive interconnectedness of synapses (one synapse may be linked to 10,000 others), the brain can transport information rapidly and effectively. Memristors might potentially be effective in simulating another valuable aspect of the brain: synapses’ capacity to retain as well as transfer information. Memristors, which can hold a range of values rather than simply one and zero, can imitate the way the intensity of a connection between the two synapses can change.  

Neuromorphic Systems Uses

For compute-intensive activities, edge devices like smartphones must presently delegate processing to a cloud-based platform, which executes the query and returns the response to the device. That question wouldn’t have to be sent back and forth with computational models; it could be answered within the device itself. However, arguably the most compelling reason for investing in neuromorphic computing is the potential it holds for AI. Current AI is primarily rules-based, educated on datasets until it learns to create a certain output. However, that is not how the brain operates: our grey matter is far more at ease with ambiguity and plasticity. It is believed that the next version of artificial intelligence will be able to cope with another few brain-like issues, such as constraint fulfillment, in which a system must discover the best solution to a problem with many constraints. Neuromorphic systems are also more inclined to aid in the development of better AIs since they are more at ease with other sorts of issues, such as probabilistic computing, which requires systems to deal with noisy and uncertain input. Others, such as determinism and non-linear thinking, are still in their infancy in neuromorphic computer systems, but once confirmed, they have the potential to dramatically extend the applications of AI.  

Spiking neuron: Faster and Accurate AI

DEXAT is a novel spiking neuron model developed by researchers at IIT-Delhi, led by Prof Manan Suri of the Department of Electrical Engineering (Double EXponential Adaptive Threshold). The discovery is crucial because it will aid in the development of accurate, quick, and energy-efficient neuromorphic artificial intelligence (AI) systems for real-world applications such as voice recognition. The effort is multidisciplinary, straddling AI, neuromorphic hardware, and nanoelectronics. “We’ve demonstrated that memory technology can be used for more than just storage. We’ve successfully employed semiconductor memory for in-memory computing, neuromorphic computing, sensing, edge AI, and hardware security. “In a news release from IIT-Delhi, Suri adds, “This study especially exploits analog characteristics of nanoscale oxide-based memory devices for generating adaptive spiking neurons.” In comparison to previous state-of-the-art adaptive cut-off spiking neurons, the study revealed a neuron model with greater accuracy, faster convergence, and versatility in hardware implementation. With fewer neurons, the suggested method provides excellent performance. The researchers were able to effectively show a hybrid nanodevice-based hardware realization. Even with very significant device variability, the described nanodevice neuromorphic network was reported to attain 94% accuracy, showing resilience.  

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What Is Edge Computing For Cloud And Iot?

Edge computing is a term that’s getting thrown around more and more these days, though often unaccompanied by an easy-to-digest definition of what exactly Edge Computing means. Usually, explanations are either too aggressively full of technical jargon for a layman to decipher or too vague to provide a meaningful, clearcut understanding of what Edge Computing really is, why it’s useful, and why so many more organizations are turning to it as a way of handling emerging IT obstacles and improving the power of other technologies, namely Cloud Computing and IoT. 

What is Edge Computing?

Cloud Computing and IoT Explained

Before we can illustrate the mechanics of Edge Computing, it’s important to first understand how cloud computing — a completely different technology and term that is in no way interchangeable with Edge Computing — works and the current obstacles it faces.

Cloud computing delivers computing power over the Internet by connecting users to powerful servers maintained and secured by a third-party. This lets users leverage the computing power of those servers to process data for them.

Cloud computing services like the Microsoft Azure cloud, Amazon Web Services, the Google Cloud Platform and the IBM Cloud allow users to avoid the substantial upfront costs that come with creating a heavy-duty local server setup as well as the responsibility of maintaining and securing that server. This affords people and companies a “pay-as-you-go model” option for their information processing needs, with costs varying with use.

The Internet of Things, or IoT is a related concept that involves the networking of everyday devices over the Internet via cloud computing. This allows non-computer devices to speak to each other, gather data, and be controlled remotely without being directly connected to each other.

Take, for example, a home security camera. The camera can send its information to the cloud via the home Wi-Fi network, while the user can access the data via their phone while at work. Neither device needs to be directly connected to one another, only the internet.

This way the user can send and receive information through a server that both devices connect to via their internet connection.

This same model can be used in all sorts of ways; everything from smart home technology like smart lights, smart ACs, and other appliances, to industrial safety mechanisms like heat and pressure sensors can use IoT to increase automation and create actionable data.

By allowing devices to connect with one another wirelessly, IoT helps reduce human workload and improve overall efficiency for both consumers and producers.

Obstacles Facing Cloud Computing and IoT

While IOT continues to grow, with applications being used in nearly every industry, the burden on data centers used for cloud computing is increasing exponentially. The demand for computational resources is beginning to exceed the supply of said resources, reducing overall availability.

When cloud computing first emerged, the only devices connecting to it were client computers, but, as IoT has exploded, the amount of data that needs to be processed and analyzed has reduced the amount of computational power available at any one moment. This slows data processing speeds and increases latency, bringing down performance on the network. 

This Is Where Edge Computing Comes In

Now that you understand cloud computing, IoT, and the obstacles that face Both technologies, the concept of Edge Computing should be easy to understand.

In simple terms, edge computing places more of the workload locally where the data is first collected, rather than on the cloud itself. As its name suggests, Edge Computing aims to place more of the burden of data processing closer to the source of the data (i.e. at the “edge” of the network).

This means, for example, finding ways to do some of the work that would be done at the data center on the local device before sending it off, reducing both processing time (latency) as well as bandwidth. In the context of a security camera, this would mean developing software that discriminates against data based on certain priorities, picking and choosing which data to send to the cloud for further processing.

This way, the data center need only process perhaps 45 minutes or so of important data, rather than a full 24 hours of video. This lessens the burden on data centers, reduces the amount of information that needs to travel between the devices, increases the overall efficiency of the network. 

Speed and processing power have become especially important with the rise of more demanding technologies. Earlier uses of IoT in cloud computing required smaller amounts of data to be processed and were generally less time-sensitive.

A self-driving car requires cloud computing to be able to receive updates, send information, and communicate with other servers over the internet. It does not, however, have the luxury of limiting its processing power according to the availability of that connection.

This combination of increased local workload and sustained cloud connectivity is a prime example of edge computing and how similar system architecture can improve the efficiency of all the technologies involved.

Samsung Galaxy Nexus Review: Sleek And Speedy

The best Android phone to date, the Galaxy Nexus dazzles with its curved display, sleek design, fast performance, and, of course, the Ice Cream Sandwich update.

We’ve been clamoring to get our hands on the Galaxy Nexus ever since its unveiling in Hong Kong back in October. Finally, at long last, the U.S. version of the Galaxy Nexus has landed in our office. So is the Galaxy Nexus, the first phone to run Android Ice Cream Sandwich, everything we hoped it would be? Mostly, yes. The Galaxy Nexus ($300 with a two-year contract, as of December 16, 2011) impresses with lightning-fast performance, strong data speeds, a thin design, and, of course, all of that Ice Cream Sandwich goodness. It isn’t perfect, however. The camera isn’t outstanding, and the handset has no expandable memory slot. But as it stands, the Galaxy Nexus is the best Android phone currently available.

Design

The Galaxy Nexus is one fine-lookin’ piece of hardware. The glossy display, piano-black bezel, and textured back are all standard Samsung design elements. But unlike other Samsung Galaxy phones I’ve reviewed, the Galaxy Nexus feels high quality. At 5.1 ounces, it has a nice substantial weight to it without being too heavy. As you can see from the photos, the Galaxy Nexus has a subtle curve, which nicely contours to the hand. If you have small hands like me, however, you might find the Galaxy Nexus a bit large (it measures 5.33 by 2.67 by 0.37 inches).

The Galaxy Nexus has no physical hardware keys on its face. Instead, the touch-sensitive Back, Home, and Search keys are built into the display as soft keys.

Super AMOLED Display (No Plus)

The Galaxy Nexus has a high-def Super AMOLED display–not to be confused with the Super AMOLED Plus technology found in the Samsung Galaxy S II line of phones. This 1280-by-720-pixel display is actually based on a PenTile pixel structure in which pixels share subpixels. Engadget points out that the Galaxy S II phones have full RGB displays in which the pixels have their own subpixels. This means that the Galaxy Nexus has lower overall subpixel density, reduced sharpness, and degraded color accuracy than the Galaxy S II. But according to site FlatpanelsHD, the Galaxy Nexus has 315 pixels per inch, which is slightly lower than the iPhone 4/4S at 326 ppi.

To be quite honest, the only quality difference I saw between the Galaxy S II, the Galaxy Nexus, and the iPhone 4S was in color accuracy. Colors on the Galaxy Nexus had a slight yellowish tint, mainly in pictures or websites with a white background. Otherwise, blacks looked deep, while fonts and details appeared sharp. Unless you’re crazy about pixel density or have insanely sharp eyes, you probably won’t notice the slight display downgrade.

The display is a roomy 4.65 inches, but really only 4 inches of that real estate is usable. The remaining 0.65-inch space is occupied by a customizable shortcut bar that appears at the bottom of the home screens as well as some other internal screens. Even so, the screen feels plenty spacious for all of your gaming, video, and other multimedia desires.

Ice Cream Sandwich: Simply Sweet

We’ve written extensively on Ice Cream Sandwich, and will be doing much more in-depth coverage in the next few days. For this review, I’ll focus on how Ice Cream Sandwich performs on the Galaxy Nexus.

You’ve probably heard a lot of buzz about the ability to unlock your phone with your face. The front-facing camera snaps a picture of you and then uses facial recognition software the next time you unlock your phone. It’s cool, most definitely, but it’s not the most secure way of protecting your phone. As Google warns, somebody who looks similar to you can unlock your phone with their face. Nevertheless, face unlock works well, and it is a pretty neat–although somewhat gimmicky–feature.

The Android software keyboard in Ice Cream Sandwich has larger, more square keys so it is easier to type on (though I still made a few errors here and there). You now have an option to verbally dictate your text, as well, though I didn’t always find it accurate. For example, “This is a test of the auto-dictate feature” translated into “Types of the otter dictate feature.”

Developers will delight in the dedicated “Developer options,” which let you access tools such as a CPU usage meter and controls for touchscreen feedback and the background process limit. It is features like this that truly make Android a standout operating system. There’s something for everyone.

The Core Apps

Gmail gets a face-lift, with a new context-sensitive Action Bar at the bottom of the screen. The bar changes depending on where in the app you are. For example, when you’re looking at an email message, you see options to archive it, trash it, label it, or mark it as unread. When you’re viewing your inbox, the bar changes to display options for composing new messages. Adding attachments from your gallery or other folders is now much easier as well. If you’re a heavy Gmail user like me, you’ll really appreciate these updates.

Google Calendar pretty much runs my life, so I was pleased to see a cleaner, easier-to-read version of it in Ice Cream Sandwich. I also appreciate the fact that you can pinch-to-zoom in on a particular calendar event to see more information about it; previously you had to tap on the calendar event, and it would open a new window. Like all of the other core-apps updates, Google has made everything in the Calendar more efficient and easier to use.

Unfortunately, Google Wallet is not supported on the Galaxy Nexus–despite the fact that the phone’s hardware supports NFC.

Performance

The Galaxy Nexus is powered by a dual-core 1.2GHz Texas Instruments OMAP 4460 processor, with 1GB of RAM and 16GB or 32GB of storage. The Galaxy Nexus scored well on all of our benchmark tests (which includes the Sunspider JavaScript benchmark and the GLBenchmark). Interestingly, the Nexus’s overall score was about the same as the mark of the Motorola Droid Razr, which has a 1.2GHz TI OMAP 4430 processor. The Samsung Galaxy S II for T-Mobile scored slightly higher overall than the Galaxy Nexus.

We also ran the Qualcomm-developed Vellamo benchmarking app, on which the Galaxy Nexus earned a score of 803. (The Droid Razr got a score of 1040, which put it ahead of the Samsung Galaxy S II.) This score puts the Galaxy Nexus ahead of the Samsung Skyrocket and the HTC EVO 3D. Because Vellamo was made by a competitor to Texas Instruments, we tend to take these results with a grain of salt.

We’re lucky enough to get very strong 4G LTE coverage here in San Francisco. In my tests using the FCC-approved Ookla Speedtest app, the Galaxy Nexus achieved download speeds ranging from 6.69 to 12.11 megabits per second and upload speeds of 21.18 mbps. In other words, the Galaxy Nexus is blazingly fast.

Call quality over Verizon’s network in San Francisco was consistently good. I had great coverage everywhere I went in the city. My friends and family sounded natural, with an ample amount of volume. One of my friends remarked that my voice sounded “hollow,” but other people I spoke with were pleased with the quality.

We have not yet finished our formal battery life tests, but the Galaxy Nexus survived through a whole day of heavy use before I needed to charge it again.

Camera

At the Hong Kong unveiling, Google bragged that the camera on the Galaxy Nexus has zero shutter lag. In my hands-on tests, I found these claims to be accurate: It processes your photo almost instantly after you press the shutter key. Another nice feature is the ability to access the camera from the lock screen rather than having to unlock and then dig through menus.

Unfortunately, the camera just isn’t of the same caliber as the rest of the phone. The photos I shot with the Galaxy Nexus’s 5-megapixel camera looked a bit flat. Colors seemed a touch washed out, and details were a little fuzzy.

But even if your photos don’t come out perfect, Ice Cream Sandwich has your back with its suite of photo-editing tools. You get an array of filters (like your very own Hipstamatic app), the capability to adjust the image angle, red-eye removal, cropping functions, and more. Any edits you make to a photo will create a copy, in case you ever want to revert to the original.

In camcorder mode, you can record video in up to 1080p. Video in my tests looked quite good. The camera handles motion well, with no artifacting or pixelation. Check out the test clip below.

Bottom Line

The Samsung Galaxy Nexus is a superb phone, and a great vehicle for introducing Android Ice Cream Sandwich to the world. Android has clearly come a long way, and the tweaks and updates Google has implemented throughout the operating system make a huge difference in efficiency and ease of use. Right now, the Galaxy Nexus is the best Android phone you can buy.

Edge Computing: Definition, Characteristics, And Use Cases

Traditional cloud computing networks are significantly brought together, with data being collected on the fringe edges and sent back to the essential servers for taking care.

This plan grew out of the way that most of the devices arranged near the edge came up short on computational power and limited capacity to separate and then again process the data they accumulated.

How much data is ceaselessly being made at the edge is turning out to be decisively speedier than the limit of associations to manage it.

As opposed to sending data to a cloud or a distant server homestead to achieve the work, endpoints should send data to an Edge Enrolling contraption that cycles or separates that data.

What is Edge Computing?

Edge Computing is closer to data source and limit, and figuring tasks should be possible in the edge enrolling center point, which diminishes the center data transmission process.

Carrying this handling ability to the edge of the association helps address the data trial by creating, generally, shut IoT structures.

A conclusive goal is to restrict cost and lethargy while controlling association bandwidth.

A huge benefit Edge Figuring offers that would be helpful is the reduction of data ready to be sent and taken care of in the cloud.

It underlines closeness to clients and outfits clients with better shrewd organizations, thus further creating data transmission execution, ensuring consistent taking care, and decreasing conceded time.

Benefits of Edge Computing

Edge registering has arisen as one of the best answers for network issues related to moving gigantic volumes of information created today. Here are the absolute most significant benefits of edge processing −

Reduces Latency − Inactivity alludes to the time expected to move information between two organizational focuses. Huge distances between these two focuses and network clogs can create setbacks. As edge figuring carries the focuses nearer to one another, idleness issues are nonexistent for all intents and purposes.

Saves Bandwidth − Transmission capacity alludes to the rate at which information is moved in an organization. As all organizations have a restricted transmission capacity, the volume of information that can be moved and the number of gadgets that can cycle this is restricted too. By sending the information servers to the places where information is created, edge registering permits numerous gadgets to work over much more modest and effective data transmission.

Execution Expenses − The expenses of executing an edge foundation in an association can be complicated and costly. It requires a reasonable degree and reason before the organization and extra gear and assets to work.

Inadequate Information − Edge figuring can handle incomplete data arrangements that should be characterized during execution. Because of this, organizations might wind up losing important information and data.

Security − Since edge registering is a circulated framework, guaranteeing sufficient security can be challenging. There are takes a chance engaged with handling information outside the edge of the organization. The expansion of new IoT gadgets can likewise build the chance for the aggressors to invade the gadget.

Edge Computing Use-Cases

Edge figuring draws information handling closer to business activities. It has numerous varieties, with numerous IT experts seeing it as a development of the conveyed ‘lights out’ server farm idea. Regardless of how savvy the end-point is; all Edge approaches share similar engineering.

Center information center(s) with satellite areas store and cycle information and cooperate with end-focuses.

Edge comprises organization doors, server farms, and everything IoT.

The motivation behind the Edge is to convey dispersed application administrations, give knowledge to the end-point, speed up execution from the center data frameworks or gather and forward data from the Edge end-point sensors and regulators.

The shortfall of a concurred and acknowledged Edge processing definition requested we make our own subsequent in three distinct kinds of purpose cases −

Remote ‘Lights Out’ Edge Server, farms can be a little hardware rack in different far-off areas or numerous enormous server farms. It is the most different, non-standard Edge climate. It requires new hierarchical models, modern programming application designs, and a high degree of reflection to the picture, conveying low touch control and the capacity to scale and deal with a heterogenous blend of gear.

Holder IT Edges, is where combined frameworks reside. This climate comprises an answering stack including at least one of the accompanying; servers, operating system, stockpiling, organization, and improved power and cooling to help all the hardware in the contained climate. The compartments are exceptionally normalized notwithstanding, customization is accessible to suit explicit Edge prerequisites with choices for extra parts.

Internet of Things (IoT), where profoundly accessible processors empower constant investigation for applications that can hardly hold on to decide. IoT end-directs go on toward getting more brilliant with a more remarkable capacity to work freely and settle on choices without routine correspondence with center stage.

Conclusion

With edge computing, things have become fundamentally more successful. Accordingly, the idea of business assignments has become higher. Edge figuring is a sensible solution for data-driven undertakings that require lightning-fast results and a raised level of flexibility, dependent upon the current status of things.

Neuromorphic Chips: The Third Wave Of Artificial Intelligence

The age of traditional computers is reaching its limit. Without innovations taking place, it is difficult to move past the technology threshold. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The Moore’s law (named after Gordon Moore, in 1965) states that the number of transistors in a dense integrated circuit doubles about every two years while their price halves. But now the law is losing its validity. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. This may indicate the third wave of Artificial Intelligence. The first generation of AI was marked with defining rules and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second generation was populated by using deep learning networks to analyze the contents and data that were largely concerned with sensing and perception. The third generation is about drawing parallels to the human thought process, like interpretation and autonomous adaptation. In short, it mimics neurons spiking like the nervous system of humans. It relies on densely connected transistors that mimic the activity of ion channels. This allows them to integrate memory, computation, and communication, at higher speed, complexity, and better energy efficiency. Loihi is Intel’s fifth-generation neuromorphic chip. This 14-nanometer chip has a 60-millimeter die size and contains over 2 billion transistors, as well as three managing Lakemont cores for orchestration. It contains a programmable microcode engine for on-chip training of asynchronous spiking neural networks (SNNs). Total, it has 128 cores packs. Each core has a built-in learning module and a total of around 131,000 computational “neurons” that communicate with one another, allowing the chip to understand stimuli. On March 16, Intel and Cornell University showcased a new system, demonstrating the ability of this chip to learn and recognize 10 hazardous materials from the smell. And this can function even in the presence of data noise and occlusion. According to their joint profiled paper in Nature Machine Intelligence, this can be used to detect the presence of explosives, narcotics, polymers and other harmful substances like signs of smoke, carbon monoxide, etc. It can purportedly do this faster, more accurate than sniffer dogs thereby threatening to replace them. They achieved this by training it constructing a circuit diagram of biological olfaction. They drew this insight by creating a dataset by exposing ten hazardous chemicals (including acetone, ammonia, and methane) through a wind tunnel, and a set consisting of the activity of 72 chemical sensors collected the signals. This tech has multifold applications like identifying harmful substances in the airport, detecting the presence of diseases and toxic fumes in the air. The best part is, it constantly re-wires its internal network to allow different types of learning. The futuristic version can transform traditional computers into machines that can learn from experience and make cognitive decisions. Hence it is adaptive like human senses. And to put a cherry on top, it uses a fraction of energy than the current state of art systems in vogue. It is predicted to displace Graphics Processing Units (GPUs). Although Loihi may soon evolve into a household word, it is not the only one. The neuromorphic approach is being investigated by IBM, HPE, MIT, Purdue, Stanford, and others. IBM is in the race with its TrueNorth. It has 4096 cores, each having 256 neurons and each neuron having 256 synapses to communicate with others. Germany’s Jülich Research Centre’s Institute of Neuroscience and Medicine and UK’s Advanced Processor Technologies Group at the University of Manchester are working on a low-grade supercomputer called SpiNNaker. It stands for Spiking Neural Network Architecture. It is believed to stimulate so-called cortical microcircuits, hence the human brain cortex and help us understand complex diseases like Alzheimer’s.

The age of traditional computers is reaching its limit. Without innovations taking place, it is difficult to move past the technology threshold. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The Moore’s law (named after Gordon Moore, in 1965) states that the number of transistors in a dense integrated circuit doubles about every two years while their price halves. But now the law is losing its validity. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. This may indicate the third wave of Artificial Intelligence. The first generation of AI was marked with defining rules and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second generation was populated by using deep learning networks to analyze the contents and data that were largely concerned with sensing and perception. The third generation is about drawing parallels to the human thought process, like interpretation and autonomous adaptation. In short, it mimics neurons spiking like the nervous system of humans. It relies on densely connected transistors that mimic the activity of ion channels. This allows them to integrate memory, computation, and communication, at higher speed, complexity, and better energy efficiency. Loihi is Intel’s fifth-generation neuromorphic chip. This 14-nanometer chip has a 60-millimeter die size and contains over 2 billion transistors, as well as three managing Lakemont cores for orchestration. It contains a programmable microcode engine for on-chip training of asynchronous spiking neural networks (SNNs). Total, it has 128 cores packs. Each core has a built-in learning module and a total of around 131,000 computational “neurons” that communicate with one another, allowing the chip to understand stimuli. On March 16, Intel and Cornell University showcased a new system, demonstrating the ability of this chip to learn and recognize 10 hazardous materials from the smell. And this can function even in the presence of data noise and occlusion. According to their joint profiled paper in Nature Machine Intelligence, this can be used to detect the presence of explosives, narcotics, polymers and other harmful substances like signs of smoke, carbon monoxide, etc. It can purportedly do this faster, more accurate than sniffer dogs thereby threatening to replace them. They achieved this by training it constructing a circuit diagram of biological olfaction. They drew this insight by creating a dataset by exposing ten hazardous chemicals (including acetone, ammonia, and methane) through a wind tunnel, and a set consisting of the activity of 72 chemical sensors collected the signals. This tech has multifold applications like identifying harmful substances in the airport, detecting the presence of diseases and toxic fumes in the air. The best part is, it constantly re-wires its internal network to allow different types of learning. The futuristic version can transform traditional computers into machines that can learn from experience and make cognitive decisions. Hence it is adaptive like human senses. And to put a cherry on top, it uses a fraction of energy than the current state of art systems in vogue. It is predicted to displace Graphics Processing Units (GPUs). Although Loihi may soon evolve into a household word, it is not the only one. The neuromorphic approach is being investigated by IBM, HPE, MIT, Purdue, Stanford, and others. IBM is in the race with its TrueNorth. It has 4096 cores, each having 256 neurons and each neuron having 256 synapses to communicate with others. Germany’s Jülich Research Centre’s Institute of Neuroscience and Medicine and UK’s Advanced Processor Technologies Group at the University of Manchester are working on a low-grade supercomputer called SpiNNaker. It stands for Spiking Neural Network Architecture. It is believed to stimulate so-called cortical microcircuits, hence the human brain cortex and help us understand complex diseases like Alzheimer’s. Who knows what sort of computational trends we may foresee in the coming years. But one thing is sure, the team at Analytics Insight will keep a close watch on it.

Quantum Computing: The Chronicle Of Its Origin And Beyond

The history of quantum computing dates back to the 80s and the study still continues today

The spark about quantum computing is considered to have set out from a three-day discussion at the MIT Conference Center out of Boston, in 1981. The meeting, ‘ The Physics of Computation ’, was collaboratively sponsored by IBM and MIT’s Laboratory of computer science. The discussion aimed to formulate new processes for efficient ways of computing and bring the area of study into the mainstream. Quantum computing was not a popularly discussed field of science till then. The historic conference was presided over by many talented brains including Richard Feynman, Paul Benioff, Edward Fredkin, Leonid Levin, Freeman Dyson, and Arthur Burks, who were computer scientists and physicists.Richard Feynman was a renowned theoretical physicist who received a Nobel Prize in Physics, in 1965 with other two physicists, for his contributions towards the development of quantum electrodynamics. The conference was a seminal moment in the development of quantum computing and Richard Feynman announced that to simulate quantum computation, there is a need for quantum computers . Later, he went on to publish a paper in 1982, titled ‘Simulating Physics with Computers.’The area of study soon got attention from computer scientists and physicists. Hence, the work on quantum computing began. Before this, in 1980, Paul Benioff had described a first quantum mechanical model of a computer in one of his papers, which had already acted as a foundation for the study. After Feynman’s statement in the conference, Paul Benioff went on to develop his model of quantum mechanical Turing machine. However, almost a decade later, came Shor’s algorithm, developed by Peter Shor, which is considered a milestone in the history of quantum computing. This algorithm allowed quantum computers to factor large integers at a higher speed and could also break numerous cryptosystems. The discovery garnered a lot of interest in the study of quantum computing as it replaced the years taken by the classic, traditional computing algorithms to perform factoring by just some hours. Later, in 1996, Lov Grover invented the quantum database search algorithm, which exhibited a quadratic speedup that could solve any problem that had to be solved by random brute-force search and could also be applied to a wider base of problems. The year 1998 witnessed the first experimental demonstration of a quantum algorithm that worked on a 2-qubit NMR quantum computer. Later in the year, a working 3-qubit NMR computer was developed and Grover’s algorithm got executed for the first time in an NMR quantum computer. Several experimental progress took place between 1999 and 2009. In 2009, the first universal programmable quantum computer was unveiled by a team at the National Institute of Standards and Technology, Colorado. The computer was capable of processing 2 quantum bits. After almost a decade, IBM unveiled the first commercially usable integrated quantum computing system, and later in the year, IBM added 4 more quantum computing systems, along with a newly developed 53-qubit quantum computer. Google also gave a huge contribution to the field in late 2023, when a paper published by the Google research team claimed to have reached quantum supremacy . The 54-qubit Sycamore processor, made of tiny qubits and superconducting materials is claimed to have sampled a computation in just 200 seconds. Last year, IonQ launched its trapped ion quantum computers and made them commercially available through the cloud. There have been several experiments and research that are being carried on today. Each day becomes a new step for quantum computing technology since its proclamation back in the 80s. According to a report by Fast Company, IBM plans to complete the 127-qubit IBM Quantum Eagle this year and expects to develop a 1000-qubit computing machine called the IBM Quantum Condor by 2023. IBM has been keeping up in the path of developing the best quantum computing solutions since it hosted the conference in 1981. Charlie Bennet, a renowned physicist who was part of the conference as IBM’s research contingent, has a huge contribution to these innovations put forward by the company. The emerging era of quantum computing will invite many breakthroughs. The quantum computing revolution will increase processing efficiency and solve intrinsic quantum problems. Quantum computer works with quantum bits or qubits that can be in the ‘superposition of states that will cater to massive calculations at an extremely faster pace. Quantum computing will have a greater impact on almost all industries and business operations. It is capable of molecular modeling, cryptography , weather forecasting, drug discovery , and more. Quantum computing is also said to be a significant component of artificial intelligence, which is fuelling several businesses and real-life functions today. We might soon reach the state of quantum supremacy and businesses need to become quantum-ready by then.

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