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Killexams : IBM Butterfly teaching - BingNews Search results Killexams : IBM Butterfly teaching - BingNews Killexams : Anti-butterfly effect enables new benchmarking of quantum computer performance

Research drawing on the quantum "anti-butterfly effect" solves a longstanding experimental problem in physics and establishes a method for benchmarking the performance of quantum computers.

"Using the simple, robust protocol we developed, we can determine the degree to which quantum computers can effectively process information, and it applies to information loss in other complex quantum systems, too," said Bin Yan, a quantum theorist at Los Alamos National Laboratory.

Yan is corresponding author of a paper on benchmarking information scrambling, published today in Physical Review Letters. "Our protocol quantifies information scrambling in a quantum system and unambiguously distinguishes it from fake positive signals in the caused by quantum decoherence," he said.

Noise in the form of decoherence erases all the quantum information in a complex system such as a quantum computer as it couples with the surrounding environment. Information scrambling through quantum chaos, on the other hand, spreads information across the system, protecting it and allowing it to be retrieved.

Coherence is a quantum state that enables , and decoherence refers to the loss of that state as information leaks to the surrounding environment.

"Our method, which draws on the quantum anti-butterfly effect we discovered two years ago, evolves a system forward and backward through time in a single loop, so we can apply it to any system with time-reversing the dynamics, including quantum computers and quantum simulators using cold atoms," Yan said.

The Los Alamos team demonstrated the protocol with simulations on IBM cloud-based quantum computers.

The inability to distinguish decoherence from information scrambling has stymied experimental research into the phenomenon. First studied in black-hole physics, information scrambling has proved relevant across a wide range of research areas, including quantum chaos in many-body systems, phase transition, quantum machine learning and quantum computing. Experimental platforms for studying information scrambling include superconductors, trapped ions and cloud-based quantum computers.

Practical application of the quantum anti-butterfly effect

Yan and co-author Nikolai Sinitsyn published a paper in 2020 proving that evolving quantum processes backwards on a quantum computer to damage information in the simulated past causes little change when returned to the present. In contrast, a classical-physics system smears the information irrecoverably during the back-and-forth time loop.

Building on this discovery, Yan, Sinitsyn and co-author Joseph Harris, a University of Edinburgh graduate student who worked on the current paper as a participant in the Los Alamos Quantum Computing Summer School, developed the protocol. It prepares a quantum system and subsystem, evolves the full system forward in time, causes a change in a different subsystem, then evolves the system backward for the same amount of time. Measuring the overlap of information between the two subsystems shows how much information has been preserved by scrambling and how much lost to .

More information: Joseph Harris et al, Benchmarking Information Scrambling, Physical Review Letters (2022). DOI: 10.1103/PhysRevLett.129.050602

Citation: Anti-butterfly effect enables new benchmarking of quantum computer performance (2022, July 26) retrieved 10 August 2022 from

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Tue, 26 Jul 2022 07:37:00 -0500 en text/html
Killexams : IBM partners with Japanese business, academia in quantum computing

U.S. tech firm International Business Machines Corp. on Thursday launched a research partnership with Japanese industry to accelerate advances in quantum computing, deepening ties between the two countries in an emerging and sensitive field.

Members of the new group, which includes Toshiba Corp. and Hitachi Ltd., will gain cloud-based access to IBM’s U.S. quantum computers. The group will also have access to a quantum computer, known as IBM Q System One, which IBM expects to set up in Japan in the first half of next year.

The “Quantum Innovation Initiative Consortium” will be based at the University of Tokyo and also includes Toyota Motor Corp., financial institutions and chemical manufacturers. It will aim to increase Japan’s quantum skill base and allow companies to develop uses for the technology.

It follows an agreement between IBM and the university, signed late last year to further co-operation in quantum computing, which holds the promise of superseding today’s supercomputers by harnessing the properties of sub-atomic particles.

“We’re trying to build a quantum industry,” Dario Gil, director of IBM Research, said. “It’s going to take these large-scale efforts.”

The partnership comes as the United States and its allies compete with China in the race to develop quantum technology, which could fuel advances in artificial intelligence, materials science and chemistry.

“We have to recognize quantum is an extremely important, competitive and sensitive technology and we treat it as such,” Gil said.

Last September, IBM said it would bring a quantum computer to Germany and partner with an applied research institute there.

IBM is targeting at least doubling the power of its quantum computers each year and hopes to see its system become a service powering corporations’ operations behind the scenes.

Quantum computers rely on superconductivity that can only be achieved in temperatures close to absolute zero, making developing viable systems a formidable technical challenge.

Sun, 24 Jul 2022 12:00:00 -0500 en text/html
Killexams : The Butterfly Encounter: Exhibit allows people to feed, learn about butterflies

Butterflies have invaded the Freeborn County Fair as part of a new exhibit. 

“You can come in and feed live butterflies,” said Annette Holt, a staff member of the Butterfly Encounter running the exhibit at the fair. “We show you how to feed them on the feeding stick as well as to how to gently get them off their feeding stick back onto the flowers.”

According to Holt, butterflies, along with bees, are one of nature’s main pollinators.

“Unfortunately butterflies are starting to become extinct,” she said. “Monarchs themselves are one of our most resilient butterflies that we have in America, and they just hit the extinction list.”

And, Holt argued, if there aren’t butterflies, there isn’t food.

By having the exhibit, she wanted to bring nature to those who may not get to see them closely in the wild.

The exhibit, which she described as peaceful and relaxing, is also a learning experience where people can learn about the different species of butterflies as well as the value butterflies bring to “our economy and the ecosystem.”

While at the exhibit, people will see Monarch and Southern Painted Lady butterflies and have the opportunity to take photos and videos with the bugs.

“You are within this little 20-by-10-foot space,” she said. “You have over 100 butterflies.”  

Besides preventing them from escaping and chasing them down, Holt said ensuring visitors handle the butterflies gently was a challenge.

“They like to hitchhike on people and go off because when you use any type of floral shampoo or soap or laundry detergent, they like to land on you,” she said.

To ensure those things do not happen, she makes sure the exhibit is sealed to the ground and fully sealed. The exhibit has two doorways, and they also check each visitor as they leave.

For anyone interested in visiting, the Butterfly Encounter is open 10 a.m. to 8 p.m. in the Commercial Building. Look for flowers and decor.

“Butterflies are fragile, but they’re not just an insect,” she said. “They have a very meaningful job to our ecosystem.”

Holt said this was either the first or second time the Butterfly Encounter, which travels around the United States for events, fairs and expos, was at the Freeborn County fair.

“They called us and brought us in as entertainment,” she said. “We’re here to entertain the kids, give the kids something to do.

“They’re also learning at the same time, and they get to experience nature.”

Butterfly Encounter is based in Florida.

Fri, 05 Aug 2022 14:00:00 -0500 en text/html
Killexams : New Raspberry Pi 400 Is A Computer In A Keyboard For $70

The newest Raspberry Pi 400 almost-all-in-one computer is very, very slick. Fitting in the size of a small portable keyboard, it’s got a Pi 4 processor of the 20% speedier 1.8 GHz variety, 4 GB of RAM, wireless, Ethernet, dual HDMI outputs, and even a 40-pin Raspberry Standard IDE-cable style header on the back. For $70 retail, it’s basically a steal, if it’s the kind of thing you’re looking for because it has $55 dollars worth of Raspberry Pi 4 inside.

In some sense, it’s getting dangerously close to fulfilling the Raspberry Pi Dream. (And it’s got one more trick up it’s sleeve in the form of a huge chunk of aluminum heat-sinked to the CPU that makes us think “overclocking”.)

We remember the founding dream of the Raspberry Pi as if it were just about a decade ago: to build a computer cheap enough that it would be within everyone’s reach, so that every school kid could have one, bringing us into a world of global computer literacy. That’s a damn big goal, and while they succeeded on the first count early on, putting together a $35 single-board computer, the gigantic second part of that master plan is still a work in progress. As ubiquitous as the Raspberry Pi is in our circles, it’s still got a ways to go with the general population.

By Gareth Halfacree  CC BY-SA 2.0

The Raspberry Pi Model B wasn’t, and isn’t, exactly something that you’d show to my father-in-law without him asking incredulously “That’s a computer?!”. It was a green PCB, and you had to rig up your own beefy 5 V power supply, figure out some kind of enclosure, scrounge up a keyboard and mouse, add in a monitor, and only then did you have a computer. We’ve asked the question a couple of times, can the newest Raspberry Pi 4B be used as a daily-driver desktop, and answered that in the affirmative, certainly in terms of it having adequate performance.

But powerful doesn’t necessarily mean accessible. If you want to build your own cyberdeck, put together an arcade box, screw a computer into the underside of your workbench, or stack together Pi Hats and mount the whole thing on your autonomous vehicle testbed, the Raspberry Pi is just the ticket. But that’s the computer for the Hackaday crowd, not the computer for everybody. It’s just a little bit too involved.

The Raspberry Pi 400, in contrast, is a sleek piece of design. Sure, you still need a power supply, monitor, and mouse, but it’s a lot more of a stand-alone computer than the Pi Model B. It’s made of high-quality plastic, with a decent keyboard. It’s small, it’s light, and frankly, it’s sexy. It’s the kind of thing that would pass the father-in-law test, and we’d suggest that might go a long way toward actually realizing the dream of cheaply available universal (open source) computing. In some sense, it’s the least Hackaday Raspberry Pi. But that’s not saying that you might not want one to slip into your toolbag.


You can’t send Hackaday a piece of gear without us taking it apart. Foolishly, I started by pulling up the sticker, thinking I felt a hidden screw head. Nope, injection molding mark. Then, I pulled off the rubber feet. More molding marks. (Kudos for hiding them so nicely!) Save yourself the trouble; all you have to do to get the Pi 400 open is to pry gently around the edge, releasing each little plastic clip one after the next. It only takes five minutes, and as it says in the motorcycle repair manuals, installation is the reverse of removal.

Inside, there’s a flat-flex that connects the keyboard, and you see that big aluminum heat sink. It’s almost the full size of the keyboard, and it’s thick and heat-taped to the CPU. You know it means business. It’s also right up against the aluminum bottom of the keyboard, suggesting it could get radiative help that way, and maybe keep your fingers warm in the winter. (I didn’t feel any actual heat, but it’s gotta go somewhere, right? There are also vents in the underside of the case.)

Four PZ1 screws and a little bit of courage to unstick the pad get you underneath the heat spreader to find, surprise!, a Raspberry Pi 4. This was a little anticlimactic, as I’ve just spent a couple weeks looking over the schematics for my review of the new Compute Module 4, and it’s just exactly what you’d expect. It’s a Raspberry Pi 4, with all the ports broken out, inside a nice keyboard, with a beefy heat spreader. Ethernet magnetics sit on one side, and the wireless module sits on the other. That’s it!

How hackable is the Pi 400? Not very. There’s not much room for any kind of foolery in here, because the heat spreader takes up most of the interior volume. Folks who want to replace the USB 3.0 with a PCIe could probably do that, of course, but they’d be better served with a compute module and some DIY. You could try to cram other stuff in here, but with the convenient 40-pin port on the back, you’ll want to connect anything of any size with a cable anyway. It’s not so much that it’s not hackable as I don’t know why you would. (As always, we’re happy to be proven wrong!)

The Whole Enchilada

There are two packages for the Raspberry Pi 400: the basic and the full kit, for $70 and $100 respectively. The extra $30 gets you a nice USB C power supply, a Raspberry mouse, a micro-HDMI to regular HDMI cable, a name-brand SD card preloaded with Raspberry Pi OS, and the Official Raspberry Pi Beginner’s Guide. In short, everything you need to get started except a monitor. All of these things are already available, but you can get them bundled in for convenience.

The book is a nice intro that’s basically a guided tour of the great learning content already available on the Raspberry Pi website. The cable, power supply, and mouse are all good to have, and it’s certainly nice not to have to get and burn another SD card, but these are more comfort than necessity. Aside from the micro-HDMI cable, I had everything on hand anyway, though if this were a permanent installation, I would probably need to source another USB C wall wart.

I don’t know if it was just for the review model, but it was a nice touch that the SD card was already in the slot. That saved me maybe 10 seconds, but it might have confused someone who is not used to thinking of an SD card as a hard drive.

Convenience, simplicity, and ease of getting set up is exactly the name of the game here, and I think the full kit makes good on that promise. It was about as plug-and-play as possible.

Is It For You?

The Pi 400 is the least Hackaday Raspberry Pi. It’s a very slick piece of inexpensive, consumer computing for the masses. The full package is absolutely what I would give to my father-in-law. And that makes it also the first Raspberry Pi computer to really make good on the accessibility aspect of the founding dream, where they had already hammered the price. Congratulations to the Raspberry Pi folks are in order. This computer, combined with their decade-long investment in producing educational material to guide a newbie along the path, embody that dream.

This may not be the computer you want for a hacker project. That’s what the Model B is for. It’s probably not full of modification possibilities, though we’ll see what you all think. And it’s not, as far as we know, available with the full range of memory options either. If you don’t need the frills of the full package, the $70 price is a small upsell from the $55 of the equivalent Model B, but when you don’t need a keyboard or the nice case, you could put the $15 to use elsewhere.

Still, combine this with a small touch screen, and run it all off of a 5 V power pack, and you’ve got a ton of portable computing in a very small package. If you’re not mousing around all the time anyway, there’s a certain streamlined simplicity here that’s mighty tempting. The 40-pin port on the back makes it easy to add your own gear too, say if you want to use it as a portable logic analyzer, microcontroller programmer, or JTAG platform. I actually prefer the horizontal orientation of this Pi port over the vertical of the Model B — my projects always end up looking like hedgehogs, and gravity wants cables to lie flat. These are small details, but that’s usability.

Finally, I have a Compute Module, a Pi 4 Model B, and now the Pi 400 all sitting on the desk. The Pi 4 is known to throttle when it overheats, which conversely means that it runs faster with a heatsink, even without overclocking. There was mention in the Compute Module datasheet about more efficient processing using less power, and presumably producing less heat. And this big hunk of aluminum inside the Pi 400’s case calls out “overclocking” to me. There’s only one way to figure out what all this means, and that’s empirical testing. Stay tuned.

Fri, 05 Aug 2022 12:00:00 -0500 Elliot Williams en-US text/html
Killexams : Startups News No result found, try new keyword!Showcase your company news with guaranteed exposure both in print and online Ready to embrace the fast-paced future we’re all experiencing? Join us for tech… Outstanding Women in Business are ... Sun, 07 Aug 2022 12:41:00 -0500 text/html Killexams : AI And The Ghost In The Machine

The concept of artificial intelligence dates back far before the advent of modern computers — even as far back as Greek mythology. Hephaestus, the Greek god of craftsmen and blacksmiths, was believed to have created automatons to work for him. Another mythological figure, Pygmalion, carved a statue of a beautiful woman from ivory, who he proceeded to fall in love with. Aphrodite then imbued the statue with life as a gift to Pygmalion, who then married the now living woman.

Pygmalion by Jean-Baptiste Regnault, 1786, Musée National du Château et des Trianons

Throughout history, myths and legends of artificial beings that were given intelligence were common. These varied from having simple supernatural origins (such as the Greek myths), to more scientifically-reasoned methods as the idea of alchemy increased in popularity. In fiction, particularly science fiction, artificial intelligence became more and more common beginning in the 19th century.

But, it wasn’t until mathematics, philosophy, and the scientific method advanced enough in the 19th and 20th centuries that artificial intelligence was taken seriously as an actual possibility. It was during this time that mathematicians such as George Boole, Bertrand Russel, and Alfred North Whitehead began presenting theories formalizing logical reasoning. With the development of digital computers in the second half of the 20th century, these concepts were put into practice, and AI research began in earnest.

Over the last 50 years, interest in AI development has waxed and waned with public interest and the successes and failures of the industry. Predictions made by researchers in the field, and by science fiction visionaries, have often fallen short of reality. Generally, this can be chalked up to computing limitations. But, a deeper problem of the understanding of what intelligence actually is has been a source a tremendous debate.

Despite these setbacks, AI research and development has continued. Currently, this research is being conducted by technology corporations who see the economic potential in such advancements, and by academics working at universities around the world. Where does that research currently stand, and what might we expect to see in the future? To answer that, we’ll first need to attempt to define what exactly constitutes artificial intelligence.

Weak AI, AGI, and Strong AI

quote-ai-vision-finds-sunsetYou may be surprised to learn that it is generally accepted that artificial intelligence already exists. As Albert (yes, that’s a pseudonym), a Silicon Valley AI researcher, puts it: “…AI is monitoring your credit card transactions for weird behavior, AI is reading the numbers you write on your bank checks. If you search for ‘sunset’ in the pictures on your phone, it’s AI vision that finds them.” This sort of artificial intelligence is what the industry calls “weak AI”.

Weak AI

Weak AI is dedicated to a narrow task, for example Apple’s Siri. While Siri is considered to be AI, it is only capable of operating in a pre-defined range that combines a handful a narrow AI tasks. Siri can perform language processing, interpretations of user requests, and other basic tasks. But, Siri doesn’t have any sentience or consciousness, and for that reason many people find it unsatisfying to even define such a system as AI.

Albert, however, believes that AI is something of a moving target, saying “There is a long running joke in the AI research community that once we solve something then people decide that it’s not real intelligence!” Just a few decades ago, the capabilities of an AI assistant like Siri would have been considered AI. Albert continues, “People used to think that chess was the pinnacle of intelligence, until we beat the world champion. Then they said that we could never beat Go since that search space was too large and required ‘intuition’. Until we beat the world champion last year…”

256px-hal9000-svgStrong AI

Still, Albert, along with other AI researchers, only defines these sorts of systems as weak AI. Strong AI, on the other hand, is what most laymen think of when someone brings up artificial intelligence. A Strong AI would be capable of actual thought and reasoning, and would possess sentience and/or consciousness. This is the sort of AI that defined science fiction entities like HAL 9000, KITT, and Cortana (in Halo, not Microsoft’s personal assistant).

Artificial General Intelligence

What actually constitutes a strong AI and how to test and define such an entity is a controversial subject full of heated debate. By all accounts, we’re not very close to having strong AI. But, another type of system, AGI (Artificial General Intelligence), is a sort of bridge between weak AI and strong AI. While AGI wouldn’t possess the sentience of a Strong AI, it would be far more capable than weak AI. A true AGI could learn from information presented to it, and could answer any question based on that information (and could perform tasks related to it).

While AGI is where most current research in the field of artificial intelligence is focused, the ultimate goal for many is still strong AI. After decades, even centuries, of strong AI being a central aspect of science fiction, most of us have taken for granted the idea that a sentient artificial intelligence will someday be created. However, many believe that this isn’t even possible, and a great deal of the debate on the Topic revolves around philosophical concepts regarding sentience, consciousness, and intelligence.

Consciousness, AI, and Philosophy

This discussion starts with a very simple question: what is consciousness? Though the question is simple, anyone who has taken an Introduction to Philosophy course can tell you that the answer is anything but. This is a question that has had us collectively scratching our heads for millennia, and few people who have seriously tried to answer it have come to a satisfactory answer.

What is Consciousness?

quote-thing-therefore-amSome philosophers have even posited that consciousness, as it’s generally thought of, doesn’t even exist. For example, in Consciousness Explained, Daniel Dennett argues the idea that consciousness is an elaborate illusion created by our minds. This is a logical extension of the philosophical concept of determinism, which posits that everything is a result of a cause only having a single possible effect. Taken to its logical extreme, deterministic theory would state that every thought (and therefore consciousness) is the physical reaction to preceding events (down to atomic interactions).

Most people react to this explanation as an absurdity — our experience of consciousness being so integral to our being that it is unacceptable. However, even if one were to accept the idea that consciousness is possible, and also that oneself possesses it, how could it ever be proven that another entity also possesses it? This is the intellectual realm of solipsism and the philosophical zombie.

Solipsism is the idea that a person can only truly prove their own consciousness. Consider Descartes’ famous quote “Cogito ergo sum” (I think therefore I am). While to many this is a valid proof of one’s own consciousness, it does nothing to address the existence of consciousness in others. A popular thought exercise to illustrate this conundrum is the possibility of a philosophical zombie.

Philosophical Zombies

A philosophical zombie is a human who does not possess consciousness, but who can mimic consciousness perfectly. From the Wikipedia page on philosophical zombies: “For example, a philosophical zombie could be poked with a sharp object and not feel any pain sensation, but yet behave exactly as if it does feel pain (it may say “ouch” and recoil from the stimulus, and say that it is in pain).” Further, this hypothetical being might even think that it did feel the pain, though it really didn’t.

No, not that kind of zombie
No, not that kind of zombie [The Walking Dead, AMC]
As an extension of this thought experiment, let’s posit that a philosophical zombie was born early in humanity’s existence that possessed an evolutionary advantage. Over time, this advantage allowed for successful reproduction and eventually conscious human beings were entirely replaced by these philosophical zombies, such that every other human on Earth was one. Could you prove that all of the people around you actually possessed consciousness, or if they were just very good at mimicking it?

This problem is central to the debate surrounding strong AI. If we can’t even prove that another person is conscious, how could we prove that an artificial intelligence was? John Searle not only illustrates this in his famous Chinese room thought experiment, but further puts forward the opinion that conscious artificial intelligence is impossible in a digital computer.

The Chinese Room

The Chinese room argument as Searle originally published it goes something like this: suppose an AI were developed that takes Chinese characters as input, processes them, and produces Chinese characters as output. It does so well enough to pass the Turing test. Does it then follow that the AI actually “understood” the Chinese characters it was processing?

Searle says that it doesn’t, but that the AI was just acting as if it understood the Chinese. His rationale is that a man (who understands only English) placed in a sealed room could, given the proper instructions and enough time, do the same. This man could receive a request in Chinese, follow English instructions on what to do with those Chinese characters, and provide the output in Chinese. This man never actually understood the Chinese characters, but simply followed the instructions. So, Searle theorizes, would an AI not actually understand what it is processing, it’s just acting as if it does.

An illustration of the Chinese room, courtesy of
An illustration of the Chinese room, courtesy of

It’s no coincidence that the Chinese room thought exercise is similar to the idea of a philosophical zombie, as both seek to address the difference between true consciousness and the appearance of consciousness. The Turing Test is often criticized as being overly simplistic, but Alan Turing had carefully considered the problem of the Chinese room before introducing it. This was more than 30 years before Searle published his thoughts, but Turing had anticipated such a concept as an extension of the “problem of other minds” (the same problem that’s at the heart of solipsism).

Polite Convention

Turing addressed this problem by giving machines the same “polite convention” that we give to other humans. Though we can’t know that other humans truly possess the same consciousness that we do, we act as if they do out of a matter of practicality — we’d never get anything done otherwise. Turing believed that discounting an AI based on a problem like the Chinese room would be holding that AI to a higher standard than we hold other humans. Thus, the Turing Test equates perfect mimicry of consciousness with actual consciousness for practical reasons.

Alan Turing, creator of the Turing Test and the "polite convention" philosophy
Alan Turing, creator of the Turing Test and the “polite convention” philosophy

This dismissal of defining “true” consciousness is, for now, best to philosophers as far as most modern AI researchers are concerned. Trevor Sands (an AI researcher for Lockheed Martin, who stresses that his statements reflect his own opinions, and not necessarily those of his employer) says “Consciousness or sentience, in my opinion, are not prerequisites for AGI, but instead phenomena that emerge as a result of intelligence.”

Albert takes an approach which mirrors Turing’s, saying “if something acts convincingly enough like it is conscious we will be compelled to treat it as if it is, even though it might not be.” While debates go on among philosophers and academics, researchers in the field have been working all along. Questions of consciousness are set aside in favor of work on developing AGI.

History of AI Development

Modern AI research was kicked off in 1956 with a conference held at Dartmouth College. This conference was attended by many who later become experts in AI research, and who were primarily responsible for the early development of AI. Over the next decade, they would introduce software which would fuel excitement about the growing field. Computers were able to play (and win) at checkers, solve math proofs (in some cases, creating solutions more efficient than those done previously by mathematicians), and could provide rudimentary language processing.

Unsurprisingly, the potential military applications of AI garnered the attention of the US government, and by the ’60s the Department of Defense was pouring funds into research. Optimism was high, and this funded research was largely undirected. It was believed that major breakthroughs in artificial intelligence were right around the corner, and researchers were left to work as they saw fit. Marvin Minsky, a prolific AI researcher of the time, stated in 1967 that “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved.”

quote-ai-solved-within-a-generationUnfortunately, the promise of artificial intelligence wasn’t delivered upon, and by the ’70s optimism had faded and government funding was substantially reduced. Lack of funding meant that research was dramatically slowed, and few advancements were made in the following years. It wasn’t until the ’80s that progress in the private sector with “expert systems” provided financial incentives to invest heavily in AI once again.

Throughout the ’80s, AI development was again well-funded, primarily by the American, British, and Japanese governments. Optimism reminiscent of that of the ’60s was common, and again big promises about true AI being just around the corner were made. Japan’s Fifth Generation Computer Systems project was supposed to provide a platform for AI advancement. But, the lack of fruition of this system, and other failures, once again led to declining funding in AI research.

Around the turn of the century, practical approaches to AI development and use were showing strong promise. With access to massive amounts of information (via the internet) and powerful computers, weak AI was proving very beneficial in business. These systems were used to great success in the stock market, for data mining and logistics, and in the field of medical diagnostics.

Over the last decade, advancements in neural networks and deep learning have led to a renaissance of sorts in the field of artificial intelligence. Currently, most research is focused on the practical applications of weak AI, and the potential of AGI. Weak AI is already in use all around us, major breakthroughs are being made in AGI, and optimism about artificial intelligence is once again high.

Current Approaches to AI Development

Researchers today are investing heavily into neural networks, which loosely mirror the way a biological brain works. While true virtual emulation of a biological brain (with modeling of individual neurons) is being studied, the more practical approach right now is with deep learning being performed by neural networks. The idea is that the way a brain processes information is important, but that it isn’t necessary for it to be done biologically.

Neural networks use simple nodes connected to form complex systems
Neural networks use simple nodes connected to form complex systems [Photo credit: Wikipedia]
As an AI researcher specializing in deep learning, it’s Albert’s job to try to teach neural networks to answer questions. “The dream of question answering is to have an oracle that is able to ingest all of human knowledge and be able to answer any questions about this knowledge” is Albert’s reply when asked what his goal is. While this isn’t yet possible, he says “We are up to the point where we can get an AI to read a short document and a question and extract simple information from the document. The exciting state of the art is that we are starting to see the beginnings of these systems reasoning.”

Trevor Sands does similar work with neural networks for Lockheed Martin. His focus is on creating “programs that utilize artificial intelligence techniques to enable humans and autonomous systems to work as a collaborative team.” Like Albert, Sands uses neural networks and deep learning to process huge amounts of data intelligently. The hope is to come up with the right approach, and to create a system which can be given direction to learn on its own.

Albert describes the difference between weak AI, and the more exact neural network approaches “You’d have vision people with one algorithm, and speech recognition with another, and yet others for doing NLP (Natural Language Processing). But, now they are all moving over to use neural networks, which is basically the same technique for all these different problems. I find this unification very exciting. Especially given that there are people who think that the brain and thus intelligence is actually the result of a single algorithm.”

Basically, as an AGI, the ideal neural network would work for any kind of data. Like the human mind, this would be true intelligence that could process any kind of data it was given. Unlike current weak AI systems, it wouldn’t have to be developed for a specific task. The same system that might be used to answer questions about history could also advise an investor on which stocks to purchase, or even provide military intelligence.

Next Week: The Future of AI

As it stands, however, neural networks aren’t sophisticated enough to do all of this. These systems must be “trained” on the kind of data they’re taking in, and how to process it. Success is often a matter of trial and error for Albert “Once we have some data, then the task is to design a neural network architecture that we think will perform well on the task. We usually start with implementing a known architecture/model from the academic literature which is known to work well. After that I try to think of ways to Excellerate it. Then I can run experiments to see if my changes Excellerate the performance of the model.”

The ultimate goal, of course, is to find that perfect model that works well in all situations. One that doesn’t require handholding and specific training, but which can learn on its own from the data it’s given. Once that happens, and the system can respond appropriately, we’ll have developed Artificial General Intelligence.

Researchers like Albert and Trevor have a good idea of what the Future of AI will look like. I discussed this at length with both of them, but have run out of time today. Make sure to join me next week here on Hackaday for the Future of AI where we’ll dive into some of the more interesting syllabus like ethics and rights. See you soon!

Tue, 26 Jul 2022 12:00:00 -0500 Cameron Coward en-US text/html
Killexams : Woxsen University Collaborates With Monmouth University, USA For Social Impact Project

Initiated by the Student Wellness Cell in collaboration with Centre for International Relations, Woxsen University, this will be a six-month program focused on a host of deliverables towards the upliftment of the underprivileged school students of Telangana. 

Elevate Program has come into existence with a strong vision to become a support system to the weaker sections of the society, in alignment to the United Nations Sustainable Development Goals. The program is also aligned to the ERS (Ethics, Responsibility & Sustainability) approach of leading international accreditation bodies such as AACSB, EFMD, and AMBA, of which Woxsen University holds prestigious memberships.

Another step towards contributing to the society and help build a better future for children, the Woxsen-Monmouth Elevate Program initiated its activities with 200+ children from 10 regions of the Telangana State - Kamkole, Buddhera, Lingampally, Digwal, Kohir, Melasangam, Ibrahimpur, Sadasivpet, and Zaheerabad. 

The significance of the project can be understood through these projected outcomes:

•    Providing children with the resources they need to exercise their right to quality education
•    Reducing inequality in educational access
•    Providing children with an access to a global platform and opportunity
•    Raised awareness of children's well-being

Various steps are being taken in order to make a significant influence on society by improving the lives of children, ensuring that they have access to their basic Right to Education.

Fundraising Campaign

The Elevate Program begun with a Fundraising Campaign in which people donate towards providing children in need, with the opportunity to receive a basic education.

Those who wish to make a contribution please visit: 

Educational and Economic Aid

The Elevate Program's aim to provide educational and economic aid to the children comprises infrastructural support to the recipient school, recorded class lectures to aid the teaching facility, transportation facilities, sports materials and stationery to boost their learning, and assist them with Covid-Relief Measures.

Academics and Co-Curricular Activities

The elevate program aims to teach educational subjects apart from their regular school curriculum like English Language, Mathematics, and Financial Literacy. 

Emphasis is also placed on encouraging students to learn life skills, building social dynamics, and computer foundations. The objective is to assist children learn concepts to be able to solve problems in an imaginative manner.

Developing co-curricular activities as well as the importance of wellness will provide children with creative opportunities to foster their out-of-the-box thinking.

Woxsen University looks forward to be the driving force for a positive impact.
About Woxsen

Woxsen University, located in Hyderabad, is one of the first private universities of the state of Telangana. Renowned for its 200-acre state-of-the-art campus and infrastructure, Woxsen University provides new-age, disruptive programs in the fields of Business, Technology, Arts & Design, Architecture, Liberal Arts & Law. With 60+ Global Partner Universities and Strong Industry Connect, Woxsen is reckoned as one of the top universities for Academic Excellence and Global Edge. 

-    Rank #13, All India Top 150 B-Schools by Times B-School Ranking 2022
-    Rank #16, All India Top Pvt. B-School, Business World 2021

Sun, 20 Mar 2022 22:22:00 -0500 en text/html
Killexams : Can YOU spot the tennis racquet in this Wimbledon-themed puzzle? No result found, try new keyword!Hopes Grove Nurseries, based in Tenterden, have challenged puzzlers to spot the butterfly among the peonies in a garden-themed brainteaser designed to put observation skills to the test. Fri, 24 Jun 2022 19:51:00 -0500 en-gb text/html Killexams : The global AI in medical imaging market is expected to grow at a CAGR of 45.68% during the forecast period 2022-2027


The constant increase in the number of diagnostic procedures and the decline in the number of radiologists, increasing work pressure on the radiologists, have increased the need for artificial intelligence adoption in the medical imaging space.

New York, June 22, 2022 (GLOBE NEWSWIRE) -- announces the release of the report "AI In Medical Imaging Market - Global Outlook & Forecast 2022-2027" -
The researchers are looking for multiple ways to implement artificial intelligence into medical imaging. The demand for artificial intelligence is constantly increasing in the medical imaging software market. From cardiac events, neurological conditions, fractures, or thoracic complications, artificial intelligence helps physicians to diagnose and provide treatment quickly. Implementing AI in medical imaging has enhanced medical screening, improved precision medicine software, reduced physicians’ load, etc.

Technological Advancements Revolutionizing AI in Medical Imaging

• There have been many technological advancements in AI-based medical imaging technologies, which have shown their increasing acceptance in high-income countries. Some of the improvements include the development of integrated rtiI software, which can directly be integrated into imaging equipment (MRI or CT scanner) which facilitates the automation of medical image analysis. Other advances include smartphone technology integration in AI in medical imaging in which front-line health workers could non-invasively screen for various conditions by leveraging a smartphone.
• AI in medical imaging has drawn the attention of several radiologists worldwide. It gives faster and more accurate results and reduces diagnostic errors at reduced costs compared to traditional medical imaging methods. Thus, radiologists believe that AI in medical imaging may bring an enormous opportunity for its increasing implementation in the upcoming years.
• In exact years, many large established companies, such as GE Healthcare and Siemens Healthineers, have enabled themselves to grow the AI in the medical imaging market by making huge investments for increasing partnerships and acquisitions. Other large healthcare-related or software companies not previously invested in health care, such as Thermo Fisher Scientific and Paraxel, have started making huge investments in the market.
Vendor’s Activity in AI-Based Medical Imaging Market
Siemens Healthineers, General Electric (GE) Company, Koninklijke Philips, and IBM Watson Health are the major players in the global AI in the medical imaging market. International players focus on developing innovative products with advanced technologies and expanding their product portfolio to remain competitive. They are continuously investing extensively in R&D to expand their product portfolio. Manufacturers such as GE Healthcare constantly focus on introducing new products with innovative technology platforms opening the platform (Edison Developer Program) for other companies offering artificial intelligence technologies to scale and deploy their developed applications across GE Healthcare’s customer base.
Many major players are engaged in strategic acquisitions and partnerships that continue to be a competitive strategy for the key players, thus helping them grow inorganically. Innovative product approvals coupled with R&D activities are also helping the vendors to expand their presence, enhance growth, and sustain their position in the global market. In 2021, more than 30 countries approved AI in medical imaging technologies that are FDA and CE approved.

There is increasing funding and investments by public and private entities, including large companies, which is also one of the major driving factors of AI in the medical imaging market. For instance, more than 20 start-ups from various regions have received funds to develop AI-based medical imaging technologies.

• North America holds a dominant position in artificial intelligence in the medical imaging market. High adoption of advanced technologies is the primary factor for its larger industry share. In addition, the strong presence of the key players in the region is also a contributor to North America for holding a high market share.
• Europe is the second-largest market for AI-based medical imaging. The presence of prominent market players and high healthcare spending are the primary factors for the significant market share in this region. The industry in Europe is mainly driven by the increasing collaborative research with extensive funding from the government. Increasing funding and investments are also driving the AI-based medical imaging market in the region.
• The adoption rate of advanced artificial intelligence technologies in the medical imaging sector is still emerging in the Asia-pacific region. In exact years, there has been a noticeable investment and funding made by government and corporations and increasing collaborations and partnerships among the companies, research centers, and institutes. Due to these factors, the industry is growing exponentially in this region.
• Lack of awareness of the importance of artificial intelligence in medical imaging, radiologists’ job insecurity, a lack of sufficient IT infrastructure, a lack of proper training and knowledge among radiologists, concerns about data security, and a shortage of skilled professionals are all factors contributing to the industry’s slow growth in Latin America, the Middle East, and Africa.
• Among all the regions, APAC could witness the highest growth over the forecast period, with a CAGR of 51.06% in the global industry. The increasing prevalence of chronic diseases and diagnostic errors has opened enormous opportunities for APAC medical imaging AI market growth.

Segmentation by Geography

• North America
o US
o Canada
• Europe
o Germany
o UK
o France
o Italy
o Spain
o Japan
o China
o India
o Australia
o South Korea
• Latin America
o Brazil
o Mexico
o Argentina
• Middle East And Africa
o Turkey
o Saudi Arabia
o South Africa


Hospitals are purchasing the artificial intelligence medical software suits as a complete package for the usage or taking up one program at a time which is used the most in the industry. The diagnostic imaging center’s significant revenue generation is through imaging procedures, and they are primarily involved in implementing advanced products, which will attract customers. For instance, AI in medical imaging, along with clinical data, is helping physicians to predict heart attacks in patients accurately.

Neurology has accounted for the dominating share in the industry. The majority of the initial artificial intelligence product development focuses on downstream processing. This Downstream processing majorly includes artificial intelligence for segmentation, detecting anatomical structures, and quantifying a range of pathologies. Conditions like intracranial hemorrhage, ischemic stroke, primary brain tumors, cerebral metastases, and abnormal white matter signal intensities, which were unmet needs in the industry, has become commercially available solution within the radiology industry.

AI in medical imaging, especially cardiovascular magnetic resonance (CMR), is revolutionized by providing deep learning solutions, especially for image acquisitions, reconstructions, and analysis, which helps in supporting clinical decision-making. CMR is an established tool for routine clinical decision-making, including diagnosis, follow-up, real-time procedures, and pre-procedure planning.

Deep learning methods have enabled more tremendous success in medical image analysis. They have helped high accuracy, efficiency, stability, and scalability. Artificial intelligence tools have become assistive tools in medicine with benefits like error reduction, accuracy, fast computing, and better diagnostics. Natural language processing, Computer Vision, and Context-Aware Computing technologies are also used to create new analysis methods for medical imaging products.

In 2021, Philips showcased its new AI-enabled CT imaging portfolio. Their new CT 5100 with smart workflow application applies artificial intelligence at every step of CT image processing.

Siemens Healthineers’s AI-Rad companion chest CT detects and highlights lung nodules. The tumor burden is automatically calculated.
In addition, AI-Rad companion chest X-Ray played a significant role in patient management during the COVID-19 pandemic. The artificial intelligence-Rad companion file automatically processes upright chest X-ray images, pneumothorax, nodule detection, etc. This indicates the consolidations and atelectasis. This indicates the sign of pneumonia caused by the COVID-19 virus.

Artificial intelligence in CT scans and MRI dominated the industry, as much medical imaging using artificial intelligence tools falls into these modality categories. However, artificial intelligence in ultrasound and detecting mammography is largely adopted in the industry.

Segmentation by Technology
• Deep Learning
• Others

Segmentation by Application
• Neurology
• Respiratory & Pulmonary
• Cardiology
• Breast Screening
• Orthopedic
• Others

Segmentation by Modalities
• CT
• Ultrasound
• Nuclear Imaging

Segmentation by End-User
• Hospitals
• Diagnostic Imaging Centers
• Others

Key Vendors
• General Electric
• Siemens Healthineers
• Koninklijke Philips
• IBM Watson Health

Other Prominent Vendors
• Agfa-Gevaert Group/Agfa HealthCare
• Arterys
• Avicenna.AI
• AZmed
• Butterfly Network
• Caption Health
• CellmatiQ
• dentalXrai
• Digital Diagnostics
• EchoNous
• HeartVista
• iCAD
• Lunit
• Mediaire
• Nanox Imaging
• Paige AI
• Perimeter Medical Imaging AI
• Predible Health
• 1QB Information Technology
• Quantib
• Quibim
• Renalytix
• Therapixel
• Ultromics

1. How Big is the Artificial Intelligence in Medical Imaging Market?
2. What is the Growth Rate of the AI in Medical Imaging Market?
3. Who are is the Key Players in the AI in Medical Imaging Market?
4. What are is the Latest Trends in the AI-Based Medical Imaging Market?
5. Which Region Is Expected to hold the largest share in the AI-Based Medical Imaging Market?
Read the full report:

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Tue, 21 Jun 2022 20:58:00 -0500 en-CA text/html
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