The editors at Solutions Review have compiled this list of the best deep learning courses and online training to consider.

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Based on artificial neural networks and representation learning, deep learning can be supervised, semi-supervised or unsupervised. Deep learning models are commonly based on convolutional neural networks but can also include propositional f formulas or latent variables organized by layer.

With this in mind, weve compiled this list of the best deep learning courses and online training to consider if youre looking to grow your neural network and machine learning skills for work or play. This is not an exhaustive list, but one that features the best deep learning courses and online training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.

Platform: Coursera

Description: In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural networks architecture, and apply deep learning to your own applications.

Related paths/tracks: Introduction to Deep Learning, Applied AI with DeepLearning, Introduction to Deep Learning & Neural Networks with Keras, An Introduction to Practical Deep Learning, Building Deep Learning Models with TensorFlow

Platform: Codecademy

Description: Deep learning is a cutting-edge form of machine learning inspired by the architecture of the human brain, but it doesnt have to be intimidating. With TensorFlow, coupled with the Keras API and Python, its easy to train, test, and tune deep learning models without knowing advanced math. To start thisPath, sign up for Codecademy Pro.

Platform: DataCamp

Description: Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, youll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Related paths/tracks: Introduction to Deep Learning with PyTorch, Introduction to Deep Learning with Keras, Advanced Deep Learning with Keras

Platform: DataCamp

Description: Deep Learning Training with TensorFlow Certification by Edureka is curated with the help of experienced industry professionals as per the latest requirements and demands. This deep learning certification course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, and RBM using the latest TensorFlow 2.0 package in Python. In this deep learning training, you will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more.

Related path/track: Reinforcement Learning

Platform: edX

Description: This 3-credit-hour, 16-week course covers the fundamentals of deep learning. Students will gain a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning and will complete hands-on projects using TensorFlow and Keras.

Related paths/tracks: Deep Learning Fundamentals with Keras, Deep Learning with Python and PyTorch, Deep Learning with Tensorflow, Using GPUs to Scale and Speed-up Deep Learning, Deep Learning and Neural Networks for Financial Engineering, Machine Learning with Python: from Linear Models to Deep Learning

Platform: Intellipaat

Description: Intellipaats Online Reinforcement Learning course is designed by industry experts to assist you in learning and gaining expertise in reinforcement learning which is one of the core areas of machine learning. In this training, you will be educated on the concepts of machine learning fundamentals, reinforcement learning fundamentals, dynamic programming, temporal difference learning methods, policy gradient methods, Markov Decision, and Deep Q Learning. This Reinforcement Learning certification course will enable you to learn how to make decisions in uncertain circumstances.

Platform: LinkedIn Learning

Description: In this course, learn how to build a deep neural network that can recognize objects in photographs. Find out how to adjust state-of-the-art deep neural networks to recognize new objects, without the need to retrain the network. Explore cloud-based image recognition APIs that you can use as an alternative to building your own systems. Learn the steps involved to start building and deploying your own image recognition system.

Related paths/tracks: Neural Networks and Convolutional Neural Networks Essential Training, Building and Deploying Deep Learning Applications with TensorFlow, PyTorch Essential Training: Deep Learning, Introduction to Deep Learning with OpenCV, Deep Learning: Face Recognition

Platform: Mindmajix

Description: Mindmajix Deep learning with Python Training helps you in mastering various features of debugging concepts, introduction to software programmers, language abilities and capacities, modification of module and pattern designing, and various OS and compatibility approaches. This course also provides training on how to optimize a simple model in Pure Theano, convolutional and pooling layers, and reducing overfitting with dropout regularization. Enroll and get certified now.

Related path/track: AI & Deep Learning with TensorFlow Training

Platform: Pluralsight

Description: In this course, Deep Learning: The Big Picture, you will first learn about the creation of deep neural networks with tools like TensorFlow and the Microsoft Cognitive Toolkit. Next, youll touch on how they are trained, by example, using data. Finally, you will be provided with a high-level understanding of the key concepts, vocabulary, and technology of deep learning. By the end of this course, youll understand what deep learning is, why its important, and how it will impact you, your business, and our world.

Related paths/tracks: Deep Learning with Keras, Building Deep Learning Models Using PyTorch, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker

Platform: Simplilearn

Description: In this deep learning course with Keras and TensorFlow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project.

Platform: Skillshare

Description: Its hard to imagine a hotter technology thandeep learning,artificial intelligence, andartificial neural networks. If youve got somePython experience under your belt, this course will de-mystify this exciting field with all the major topics you need to know. A few hours is all it takes to get up to speed and learn what all the hype is about. If youre afraid of AI, the best way to dispel that fear is by understanding how it really works and thats what this course delivers.

Related paths/tracks: Ultimate Neural Network and Deep Learning Masterclass, Deep Learning and AI with Python

Platform: Udacity

Description: Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.

Related path/track: Become a Deep Reinforcement Learning Expert

Platform: Udemy

Description: Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors, and Google Deepminds AlphaGo beat the World champion at Go a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only deep learning can solve such complex problems and thats why its at the heart of artificial intelligence.

Related paths/tracks: Machine Learning, Data Science and Deep Learning with Python, Deep Learning Prerequisites: The Numpy Stack in Python, Complete Guide to TensorFlow for Deep Learning with Python, Data Science: Deep Learning and Neural Networks in Python, Tensorflow 2.0: Deep Learning and Artificial Intelligence, Complete Tensorflow 2 and Keras Deep Learning Bootcamp, Deep Learning Prerequisites: Linear Regression in Python, Natural Language Processing with Deep Learning in Python, Deep Learning: Convolutional Neural Networks in Python, Deep Learning: Recurrent Neural Networks in Python, Deep Learning and Computer Vision A-Z, Deep Learning Prerequisites: Logic Regression in Python

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

See more here:

The 13 Best Deep Learning Courses and Online Training for 2021 - Solutions Review

Artificial intelligence is being used in a variety of ways by those trying to address variants and for data management.

Image: iStock.com/Udom Pinyo

Millions of people across the world have already started the process of receiving a COVID-19 vaccines. More than half of all adults in the U.S. have gotten at least one dose of a COVID-19 vaccine while state and local officials seek to get even more people vaccinated as quickly as possible. Some health experts have said artificial intelligence will be integral not just in managing the process of creating boosters for the variants to COVID-19 but also for the distribution of the vaccine.

David Smith, associate VP of virtual medicine at UMass Memorial Health Care, explained that the difference between predictive modeling and AI, or machine learning, is that predictive models depend on past data to foretell future events.

AI, on the other hand, not only uses historical data, it makes assumptions about the data without applying a defined set of rules, Smith said.

"This allows the software to learn and adapt to information patterns in more real time. The AI utility for vaccine distribution could be applied in a variety of ways from understanding which populations to target to curve the pandemic sooner, adjusting supply chain and distribution logistics to ensure the most people get vaccinated in the least amount of time, to tracking adverse reactions and side effects," he noted.

SEE:AI in healthcare: An insider's guide (free PDF)(TechRepublic Premium)

Matthew Putman, an expert in artificial intelligence and CEO of Nanotronics, has been working with one of the top vaccine developers and said AI was helping teams manage the deluge of data that comes with a project like this.

While the number of vaccinated people in the country continues to rise by the millions each day, there is already some concern about how the vaccines will hold up against the multitude of variants.

The biggest challenge right now and the biggest opportunity for changing the way that therapeutics are both developed and deployed, Putman explained, is being able to handle new types of variants.

"In the case of mRNA vaccines, being able to actually do reprogramming as fast as possible in a way that is as coordinated as possible. The things that we have realized in many parts of our lives now is that as good as humans are at exploring things and being creative, being able to deal with enough data and to be able to make intelligent choices about that data is something that actually artificial intelligence agents can do at a pace that is required to keep up with this," Putman said.

"So it means a lot of multivariate correlations to different parts of the process. It means being able to detect potential intrusion and it's a way that we can avoid these lengthy phase three trials. Everything that's going on right now is so incredibly urgent."

Putman added that an AI system would help with building actionable data sets that allow doctors to examine root causes or things that researchers don't have time to spend on.

When researchers are dealing with things like lipid nanoparticles and the tasks of imaging and classifying different features and trends that are on a scale, it can be difficult for humans to manage. AI is now being used for analyzing these images in real time and has helped researchers try to pick out genetic mutations and variations, according to Putman.

"People are more open to AI than ever, and this emergency has brought a focus on things that probably would have been on the backburner. But AI is starting to be used for classification and to understand what genomic features and what type of nano compounding has been going on," Putman added.

"AI has been used for the development of components and much more. It's been crucial to the process and will be crucial to an alteration to the vaccine, which is looking like it will have to be done at some point. The way I look at contemporary AI systems, it's taking into account what move is being made next. This is Alpha Go for drug discovery. A virus will mutate in different ways and now a response to that can be developed in new ways."

Putman went on to compare the situation to the yearly creation of a new flu vaccine, noting that once you've grown a lot of biological specimens, it's a slow tedious process to change for new mutations.

"Using mRNA, it's not, and using AI for being able to see what changes are going on from everywhere from the sequence to the quality inspection is a big deal," Putman said.

When asked about the production of boosters for variants, Putman said adjusting a process usually takes years just for a normal product, and if you're doing something as new as what is going on with the vaccine and you're dealing with the entirety of the supply chain, the process has to be adjusted as fast as the science does.

"We have the science now. We've shown that these types of vaccines can be developed. Now, making sure that your production process stays the same, even if you've adjusted something else, is something that if it's put in place, the process will adjust," Putman said.

"If an AI system worked for this or an intelligent factory system is put into place, then the process can adjust as quickly as the R&D can. Without AI, it would be very difficult."

Cheryl Rodenfels, a healthcare strategist at Nutanix, echoed those remarks, explaining that AI can be an incredibly useful tool when it comes to vaccine distribution.

Organizations that utilize workflow improvement processes can harness AI tools to ensure that the processes are being followed as designed and that any missing elements are identified, Rodenfels said, adding that this process plays into vaccine tracking measures specifically, as AI will track vaccine handling, storage and administration.

"Relying on the technology to manage distribution data eliminates human error, and ensures that healthcare organizations are accurately tracking the vast amounts of data associated with the vaccine rollout," Rodenfels said.

"However, the biggest problem with using AI to assist with vaccine rollout is that each manufacturer has its own process and procedure for the handling, storage, tracking, training and administration of the vaccine. This is then complicated by the amount of manufacturers in the market. Another issue is that hospital pharmacies and labs don't have a lot of extra space to stage and set up the doses. In order to insert effective AI, a hospital would need to ensure a process architect and a data scientist work collaboratively."

These issues are compounded by the fact that there is no baseline for how these things are supposed to work, she noted. The measurements, analytics and information will be developed on the fly, and because it is unknown how many vaccines each organization will be required or allowed to have, it is difficult to predict the capacity or amount of data that will be produced.

The advantage to using AI in vaccine rollout is that it will set us up for success during round two of vaccine dosing. It will also positively impact future vaccine dissemination by creating a blueprint for the next mass inoculation need, both Rodenfels and Putman said.

Walter McAdams, SQA Group senior vice president of solutions delivery, said that AI will be useful in analyzing how the virus is mutating over time, how variations could affect current vaccine make-ups, and how to use that information to accelerate the development of virus countermeasures.

Researchers, he said, can leverage data about how COVID-19 has mutated and vaccine effectiveness to continuously refine the vaccine sequence and, in some cases, get ahead of COVID-19 and prepare new vaccines before additional strains fully develop.

Our editors highlight the TechRepublic articles, downloads, and galleries that you cannot miss to stay current on the latest IT news, innovations, and tips. Fridays

Link:

How AI is being used for COVID-19 vaccine creation and distribution - TechRepublic

In researching for this blog, I reached out to Brendan Bennett, a Reinforcement Learning Researcher at the University of Alberta, for his thoughts on how emerging digital technologies may be deployed in the energy industry. Brendan and I discussed how some recent landmark accomplishments in artificial intelligence might soon make their way into the energy industry.

Digital innovation in commercial spheres has largely been a story of improving efficiency and reliability while reducing costs. In the energy sector, these innovations have been a result of oil and gas companies doing what they do best: relying on talented engineers to improve on existing solutions. Improvements have quickly spread across the industry, bringing down costs and making processes more efficient.

I recently co-authored an article on the future of Artificial Intelligence in the Canadian Oil Patch, which discusses a number of examples of current innovations, including AI-powered predictive maintenance, optimized worker safety, and digital twin technology for better visualization of construction projects and formations. Looking forward, network effects, improving sensors, and algorithmic advances will continue to increase the rate of innovation and prevalence of new tech in the energy industry.

The most common example of network effects can likely be found in your pocket or in your hand right now. Because of the network effects of the smartphone, every new smartphone purchase increases the value of everyone else's smartphones by a little bit. Coupled with economies of scale in production, this means that the cost of these devices falls, while the value they provide increases. Some may view this as a virtuous cycle.

This same effect can be seen with sensors deployed in the oil and gas sector. Advances in technology and widespread use are pushing down the cost of sensors. This allows for more sensors to be deployed in a given application, creating a more complete and reliable data set when all measurements are taken together. Algorithms trained on larger, more comprehensive data sets can produce leaps in efficiency that were previously impossible.

DeepMind, an artificial intelligence research laboratory with a research office in Edmonton, recently combined prolific sensors with its own machine learning capabilities to reduce the cooling bill at Google's data centres by up to 40%. Cooling is one of the primary uses of energy in a data centre; the servers running services like Gmail and YouTube generate a massive amount of heat. Given that Google already runs some of the most sophisticated energy management technology in the world at its data centres, an energy savings of almost half is astounding.

The same combination of plentiful sensors and advanced machine learning will soon be applied throughout the energy value chain, and promises to deliver those same astounding results. Accurate sensors providing clear insight into power use relative to a variety of factors will soon allow power grids run by machine learning algorithms to more accurately predict periods of peak demand, and provide the energy to satisfy demand with dramatic efficiency. These systems could also be designed to optimize for multiple variables, providing low cost power while also minimizing CO2 emissions.

More abstractly, AlphaFold, another project from DeepMind, employed deep neural networks to model protein folding, providing a solution to a 50-year-old grand challenge in biology. The protein-folding problem has baffled biologists for decades. Cyril Levinthal, an eminent biologist, estimated in 1969 that it would take longer than the age of the known universe to describe all of the possible configurations of a typical protein through brute force calculation, an estimated 10300 possible configurations. AlphaFold's deep neural network can predict the configuration of a protein with stunning accuracy, in less time than standard complex experimental methods.

A similar approach might be applied to the problems of resource extraction and mapping of geological formations. Feeding the neural net with massive amounts of information generated from sensors that are cheaper and more plentiful in the oil and gas industry may lead to improvements in production efficiency. Further, the ability to map and test within the digital playground of these advanced neural nets may help producers avoid undesired consequences to human health and to the environment.

These advanced AI technologies will fundamentally change the way we explore for and develop our natural resources. Organizations like Avatar Innovations, which work with some of the province's leading entrepreneurs to bring innovations into the energy space, will be pivotal in helping Alberta lead the way in the development of these technologies.

Read more:

Digitization in the energy industry - the machine learning revolution - Lexology

Artificial Intelligence and other disruptive technology are spreading their wings in the current scenario. Technology has become a mandatory element for all kinds of businesses across all industries around the globe. Let us travel back to 1958 when Frank Rosenblatt created the first artificial neural network that could recognize patterns and shapes. From such a primitive stage we have now reached a place where machine learning is an integral part of almost all softwares and applications.

Machine learning is resonating with everything now, be it automated cars, speech recognition, chatbots, smart cities, and whatnot. The abundance of big data and the significance of data analytics and predictive analytics has made machine learning an imperative technology.

Machine learning, as the name suggests is a process in which machines learn and analyze the data fed to it and predict the outcome. There are different types of machine learning like supervised, unsupervised, semi-supervised, etc. Machine learning is the stairway to reach artificial intelligence and it learns from algorithms based on the database and derives answers and correlations from them.

Machine learning is an integral part of automation and digital transformation. In 2016, Google introduced its graph-based machine learning tool. It used the semi-supervised learning method to connect clusters of data based on their similarities. Machine learning technology helps industries identify market trends, potential risks, customer needs, and business insights. Today, business intelligence and automation are the norms and ML is the foundation to achieve these and enhance the efficiency of your business.

A term identified by Gartner, Hyperautomation is the new tech trend in the world. It enables industries to automate all possible operations and gain intelligent and real-time insights from the data collected. ML, AI, and RPA are some of the important technologies behind the acceleration of hyperautomation. AIs ability to augment human behaviour is aided by machine learning. Machine learning algorithms can automate various tasks once the algorithm is trained. ML models along with AI will enhance the capacity of machines and software to automatically improve and respond to changes according to the business requirements.

According to Industry Research, the Global Machine Learning market is projected to grow by USD11.16 billion between 2020 and 2024, progressing at a CAGR of 39% during the forecast period.

This data is enough to indicate the growth and acceptance of ML across the world. Let us understand how different industries are using ML.

Other industries leveraging ML include banking and finance, cybersecurity, manufacturing, media, automobile, and many more.

Executives and C-Suite professionals should consider it a norm to have a strategy or goal before putting out ML into practice. The true capability of this technology can only be extracted by developing a strategy for its use. Otherwise, the disruptive tech might remain inside closed doors just automating routine and mundane tasks. MLs capability to innovate should not be chained just to automate repetitive tasks.

According to McKinsey, companies should consist two types of people, quants, and translators to unleash the power of ML. Translators should be the ones connecting the vague lines between the complex data analysis by algorithms and convert it into readable and understandable business insights for the executives.

Machine learning is not an unfamiliar technology these days, but it still takes time and patience to leave the legacy systems behind and embrace the power of disruptive technologies. Companies should focus on democratizing ML and data analytics for their employees and create a transparent ecosystem to leverage the capabilities of these techs by demystifying them.

The rest is here:

A Guide To Machine Learning: Everything You Need To Know - Analytics Insight

Is Facebook evil?

The answer to this simple question is not that simple. The tools that have enabled Facebook to enjoy its position are its access to massive amounts of data and its machine learning algorithms. And it is in these two areas that we need to explore if there is any wrongdoing on Facebooks part.

Facebook, no doubt, is a giant in online space. Despite their arguments that they are not a monopoly, many think otherwise. The role that Facebook plays in our lives, specifically in our democracy, has been heavily scrutinized and debated over the last few years, with the lawsuits brought on by the federal and dozens of state governments toward the end of 2020 being the latest examples. While many regulators and most regular folks will argue that Facebook exerts unparalleled power over who shares what and how ordinary people get influenced by information and misinformation, many still dont quite understand where the problem really lies. Is it in the fact that Facebook is a monopoly? Is it that Facebook willingly takes ideological sides? Or is it in Facebooks grip on small businesses and its massive user base through data sharing and user tracking? Its all of these and more. Specifically, its Facebooks access to large data through its connected services and the algorithms that process this data in a very profit-focused way to turn up user engagement and revenue.

Most people understand that there are algorithms that drive systems such as Facebook. But their view about such algorithms is quite simplisticthat is, an algorithm is a set of rules and step-by-step instructions that informs a system how to act or behave. In reality, hardly any critical aspect of todays computational systems, least of them Facebooks, are driven by such algorithms. Instead, they use machine learning, which by one definition means computers writing their own algorithms. Okay, but at least were controlling the computers, right? Not really.

The whole point about machine learning is that we, the humans, dont have enough time, power, or ability to churn through massive amounts of data to look for relevant patterns and make decisions in real time. Instead, these machine learning algorithms do that for us. But how can we tell if they are doing what we want them to do? This is where the biggest problem comes. Most of these algorithms optimize their learning based on metrics such as user engagement. More user engagement leads to more usage of the system, which in turn drives up ad revenue and other business metrics. On the user side, higher engagement leads to even more engagementlike an addiction. On the business side, it leads to more and richer data that Facebook can sell to vendors and partners.

Facebook can use their passivity in this process to argue that they are not evil. After all, they dont manually or purposefully discriminate against anyone, and they dont intentionally plant misinformation in users feeds. But they dont need to. Facebook holds a mirror on our society and amplifies our bad instincts because of how their machine learning-powered algorithms learn and optimize for user engagement outcomes. Unfortunately, since controversy and misinformation tends to attract high user engagement, the algorithms will automatically prioritize such posts because they are designed to maximize engagement.

A user is worth hundreds of dollars to Facebook, depending on how active they are on the platform. A user that is on multiple platforms that Facebook owns is worth a lot more. Facebook can claim that keeping these platforms connected is best for the users and the businesses and that may be the case to some extent, but the one entity that has most to gain by this is Facebook.

There are reasonable alternatives to WhatsApp and Instagram, but none for Facebook. And it is that flagship service and monopoly of Facebook that makes even those other apps a lot more compelling and much harder to leave for their users. Breaking up these three services will create good competition, and drive up innovation and value for the users. But it will also make it harder for Facebook to leverage its massive user base for the kind of data they currently collect (and sell) and the machine learning algorithms they could run. There is a reason Facebook has doubled its lobbying spending in the last five years. Facebook is also trying to fight Apples stand on informing its users about user tracking with an argument that giving the users a choice about tracking them or not will hurt small businesses. Even Facebooks own employees dont buy that argument.

I may be singling out Facebook here, but many of the same arguments can be made against Google and other monopolies. We see the same kind of pattern. It starts out by gaining users, giving them free services, then bringing in ads. Nothing wrong with ads; televisions and radio have done them for decades. But with the way the digital ad market works, and the way these services train their machine learning algorithms, its easy for them to go after data at any cost (such as user privacy). More data, more learning, more user engagement, more sales for ads and user data, and the cycle continues. At some point the algorithms take on a life of their own, disconnected from whats good or right for the users. Some of these algorithms goals may align with the users and businesses, but in the end, it is the job of these algorithms to increase the bottom line for their mastersin this case, Facebook.

To counteract this, we need more than just regulations. We also need education and awareness. Every time we post, click, or like something on these platforms, we are giving a vote. Can we exercise some discipline in this voting process? Can we inform ourselves before we vote? Can we think about a change? In the end, this isnt just about free markets; its about free will.

About the Author

Dr. Chirag Shah, associate professorin the Information Schoolat the University of Washington.

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Facebook and the Power of Big Data and Greedy Algorithms - insideBIGDATA

Quantum computers have been on my mind a lot lately. A friend who likes investing in tech, and who knows about my attempt to learn quantum mechanics, has been sending me articles on how quantum computers might help solve some of the biggest and most complex challenges we face as humans, as a Forbes commentator declared recently. My friend asks, What do you think, Mr. Science Writer? Are quantum computers really the next big thing?

Ive also had exchanges with two quantum-computing experts with distinct perspectives on the technologys prospects. One is computer scientist Scott Aaronson, who has, as I once put it, one of the highest intelligence/pretension ratios Ive ever encountered. Not to embarrass him further, but I see Aaronson as the conscience of quantum computing, someone who helps keep the field honest.

The other expert is physicist Terry Rudolph. He is a co-author, the R, of the PBR theorem, which, along with its better-known predecessor, Bells theorem, lays bare the peculiarities of quantum behavior. In 2011 Nature described the PBR Theorem as the most important general theorem relating to the foundations of quantum mechanics since Bells theorem was published in 1964. Rudolph is also the author of Q Is for Quantum and co-founder of the quantum-computing startup PsiQuantum. Aaronson and Rudolph are on friendly terms; they co-authored a paper in 2007, and Rudolph wrote about Q Is for Quantum on Aaronsons blog. In this column, Ill summarize their views and try to reach a coherent conclusion.

First, a little background. Quantum computers exploit superposition (a particle inhabits two or more mutually exclusive states at the same time) and entanglement (a special form of superposition, in which two or more particles influence each other in spooky ways) to do things that ordinary computers cant. A bit, the basic unit of information of a conventional computer, can be in one of two states, representing a one or zero. Quantum computers, in contrast, traffic in qubits, which are constructed out of superposed particles that embody numerous states simultaneously.

For decades, quantum computing has been little more than a hypothesis, or laboratory curiosity, as researchers wrestled with the technical complexities of maintaining superposition and entanglement for long enough to perform useful calculations. (Remember that as soon as you look at an electron or cat, its superposition vanishes.) Now, tech giants like IBM, Amazon, Microsoft and Google have invested in quantum computing, as have many smaller companies, 193 by one count. In March, the startup IonQ announced a $2 billion deal that would make it the first publicly traded firm dedicated to quantum computers.

The Wall Street Journal reports that IonQ plans to produce a device roughly the size of an Xbox videogame console by 2023. Quantum computing, the Journal states, could speed up calculations related to finance, drug and materials discovery, artificial intelligence and others, andcrack many of the defensesused to secure the internet. According to Business Insider, quantum machines could help us cure cancer, and even take steps to reverse climate change.

This is the sort of hype that bugs Scott Aaronson. He became a computer scientist because he believes in the potential of quantum computing and wants to help develop it. Hed love to see someone build a machine that proves the naysayers wrong. But he worries that researchers are making promises they cant keep. Last month, Aaronson fretted on his blog Shtetl-Optimized that the hype, which he has been countering for years, has gotten especially egregious lately.

Whats new, Aaronson wrote, is that millions of dollars are now potentially available to quantum computing researchers, along with equity, stock options, and whatever else causes ka-ching sound effects and bulging eyes with dollar signs. And in many cases, to have a shot at such riches, all an expert needs to do is profess optimism that quantum computing will have revolutionary, world-changing applications and have themsoon. Or at least, not object too strongly when others say that. Aaronson elaborated on his concerns in a two-hour discussion on the media platform Clubhouse. Below I summarize a few of his points.

Quantum-computing enthusiasts have declared that the technology will supercharge machine learning. It will revolutionize the simulation of complex phenomena in chemistry, neuroscience, medicine, economics and other fields. It will solve the traveling-salesman problem and other conundrums that resist solution by conventional computers. Its still not clear whether quantum computing will achieve these goals, Aaronson says, adding that optimists might be in for a rude awakening.

Popular accounts often imply that quantum computers, because superposition and entanglement allow them to carry out multiple computations at the same time, are simply faster versions of conventional computers. Those accounts are misleading, Aaronson says. Compared to conventional computers, quantum computers are unnatural devices that might be best suited to a relatively narrow range of applications, notably simulating systems dominated by quantum effects.

The ability of a quantum computer to surpass the fastest conventional machine is known as quantum supremacy, a phrase coined by physicist John Preskill in 2012. Demonstrating quantum supremacy is extremely difficult. Even in conventional computing, proving that your algorithm beats mine isnt straightforward. You must pick a task that represents a fair test and choose valid methods of measuring speed and accuracy. The outcomes of tests are also prone to misinterpretation and confirmation bias. Testing creates an enormous space for mischief, Aaronson says.

Moreover, the hardware and software of conventional computers keeps improving. By the time quantum computers are ready for the marketplace, they might lose potential customersif, for example, classical computers become powerful enough to simulate the quantum systems that chemists and materials scientists actually care about in real life, Aaronson says. Although quantum computers would retain their theoretical advantage, their practical impact would be less.

As quantum computing attracts more attention and funding, Aaronson says, researchers may mislead investors, government agencies, journalists, the public and, worst of all, themselves about their works potential. If researchers cant keep their promises, excitement might give way to doubt, disappointment and anger, Aaronson warns. The field might lose funding and talent and lapse into a quantum-computer winter like those that have plagued artificial intelligence.

Lots of other technologiesgenetic engineering, high-temperature superconductors, nanotechnology and fusion energy come to mindhave gone through phases of irrational exuberance. But something about quantum computing makes it especially prone to hype, Aaronson suggests, perhaps because quantum stands for something cool you shouldnt be able to understand.

And that brings me back to Terry Rudolph. In January, after reading about my struggle to understand the Schrdinger equation, Rudolph emailed me to suggest that I read Q Is for Quantum. The 153-page book explains quantum mechanics with a little arithmetic and algebra and lots of diagrams of black-and-white balls going in and out of boxes. Q Is for Quantum has given me more insight into quantum mechanics, and quantum computing, than anything Ive ever read.

Rudolph begins by outlining simple rules underlying conventional computing, which allow for the manipulation of bits. He then shifts to the odd rules of quantum computing, which stem from superposition and entanglement. He details how quantum computing can solve a specific problemone involving thieves stealing code-protected gold bars from a vault--much more readily than conventional computing. But he emphasizes, like Aaronson, that the technology has limits; it cannot compute the uncomputable.

After I read Q Is for Quantum, Rudolph patiently answered my questions about it. You can find our exchange (which assumes familiarity with the book) here. He also answered my questions about PsiQuantum, the firm he co-founded in 2016, which until recently has avoided publicity. Although he is wittily modest about his talents as a physicist (which adds to the charm of Q Is for Quantum), Rudolph is boosterish about PsiQuantum. He shares Aaronsons concerns about hype, and the difficulties of establishing quantum supremacy, but he says those concerns do not apply to PsiQuantum.

The company, he says, is closer than any other firm by a very large margin to building a useful quantum computer, one that solves an impactful problem that we would not have been able to solve otherwise (e.g., something from quantum chemistry which has real-world uses). He adds, Obviously, I have biases, and people will naturally discount my opinions. But I have spent a lot oftime quantitatively comparing what we are doing to others.

Rudolph and other experts contend that a useful quantum computer with robust error-correction will require millions of qubits. PsiQuantum, which constructs qubits out of light, expects by the middle of the decade to be building fault-tolerant quantum computers with fully manufactured components capable of scaling to a million or morequbits, Rudolph says. PsiQuantum has partnered with the semiconductor manufacturer GlobalFoundries to achieve its goal. The machines will be room-sized, comparable to supercomputers or data centers. Most users will access the computers remotely.

Could PsiQuantum really be leading all the competition by a wide margin, as Rudolph claims? Can it really produce a commercially viable machine by 2025? I dont know. Quantum mechanics and quantum computing still baffle me. Im certainly not going to advise my friend or anyone else to invest in quantum computers. But I trust Rudolph, just as I trust Aaronson.

Way back in 1994, I wrote a brief report for Scientific American on quantum computers, noting that they could, in principle, perform tasks beyond the range of any classical device. Ive been intrigued by quantum computing ever since. If this technology gives scientists more powerful tools for simulating complex phenomena, and especially the quantum weirdness at the heart of things, maybe it will give science the jump start it badly needs. Who knows? I hope PsiQuantum helps quantum computing live up to the hype.

This is an opinion and analysis article.

Further Reading:

Will Artificial Intelligence Ever Live Up to Its Hype?

Is the Schrdinger Equation True?

Quantum Mechanics, the Chinese Room Experiment and the Limits of Understanding

Quantum Mechanics, the Mind-Body Problem and Negative Theology

For more ruminations on quantum mechanics, see my new bookPay Attention: Sex, Death, and Science and Tragedy and Telepathy, a chapter in my free online bookMind-Body Problems.

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Will Quantum Computing Ever Live Up to Its Hype? - Scientific American

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Cambridge Quantum Computing (CQC) hiring Stephen Clark as head of AI last week could be a sign the company is boosting research into ways quantum computing could be used for natural language processing.

Quantum computing is still in its infancy but promises such significant results that dozens of companies are pursuing new quantum architectures. Researchers at technology giants such as IBM, Google, and Honeywell are making measured progress on demonstrating quantum supremacy for narrowly defined problems. Quantum computers with 50-100 qubits may be able to perform tasks that surpass the capabilities of todays classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably, California Institute of Technology theoretical physics professor John Preskill wrote in a recent paper. We may feel confident that quantum technology will have a substantial impact on society in the decades ahead, but we cannot be nearly so confident about the commercial potential of quantum technology in the near term, say the next 5 to 10 years.

CQC has been selling software focused on specific use cases, such as in cybersecurity and pharmaceutical and drug delivery, as the hardware becomes available. We are very different from the other quantum software companies that we are aware of, which are primarily focused on consulting-based revenues, CQC CEO Ilyas Khan told VentureBeat.

For example, amid concerns that improvements in quantum hardware will make it easier to break existing algorithms used in modern cryptography, CQC devised a method to generate quantum-resistant cryptographic keys that cannot be cracked by todays methods. CQC partners with pharmaceutical and drug discovery companies to develop quantum algorithms for improving material discovery, such as working with Roche on drug development, Total on new materials for carbon capture and storage solutions, and CrownBio for novel cancer treatment biomarker discovery.

The addition of Clark to CQCs team signals the company will be shifting some of its research and development efforts toward quantum natural language processing (QNLP). Humans are good at composing meanings, but this process is not well understood. Recent research established that quantum computers, even with their current limitations, could learn to reason with the uncertainty that is part of real-world scenarios.

We do not know how we compose meaning, and therefore we have not been sure how this process can be carried over to machines/computers, Khan said.

QNLP could enable grammar-aware representation of language that makes sense of text at a deeper level than is currently available with state-of-the-art NLP algorithms like Bert and GPT 3.0. The company has already demonstrated some early success in representing and processing text using quantum computers, suggesting that QNLP is within reach.

Clark was previously senior staff research scientist at DeepMind and led a team working on grounded language learning in virtual environments. He has a long history with CQC chief scientist Bob Coecke, with whom he collaborated 15 years ago to devise a novel approach for processing language. That research stalled due to the limitations of classical computers. Quantum computing could help address these bottlenecks, and there are plans to continue that research program, Clark said in a statement.

The methods we developed to demonstrate this could improve a broad range of applications where reasoning in complex systems and quantifying uncertainty are crucial, including medical diagnoses, fault-detection in mission-critical machines, and financial forecasting for investment management, Khan said.

Continued here:

Cambridge Quantum pushes into NLP and quantum computing with new head of AI - VentureBeat

What youll learn:

A couple examples from history highlight our failure to secure the technology thats playing an increasingly larger role in both our personal lives and business. When computers were first connected to the internet, we had no idea of the Pandoras Box that was being opened, and cybersecurity wasnt even considered a thing. We failed to learn our lesson when mobile phones exploded onto the world and again with IoT still making fast to market more important than security. This has constantly left cybersecurity behind the 8 ball in the ongoing effort to secure data.

As we race to quantum computing, well see another, and perhaps the greatest, fundamental shift in the way computing is done. Quantum computers promise to deliver an increase in computing power that could spur enormous breakthroughs in disease research, understanding global climate, and delving into the origins of the universe.

As a result, the goal to further advance quantum-computing research has rightfully attracted a lot of attention and funding including $625 million from the U.S. government.1 However, it also will make many of our trusted security techniques inadequate, enabling encryption to be broken in minutes or hours instead of the thousands of years it currently takes.

Two important algorithms that serve as a basis for security of most commonly utilized public-key algorithms today will be broken by quantum computers:

As we prepare for a post-quantum world, we have another opportunity to get security right. The challenge of replacing the existing public-key cryptography in these applications with quantum-computer-resistant cryptography is going to be formidable.

Todays state-of-the-art quantum computers are so limited that while they can break toy examples, they dont endanger commercially used key sizes (such as specified in NIST SP800-57). However, most experts agree its only a matter of time until quantum computers evolve to the point of being able to break todays cryptography.

Cryptographers around the world have been studying the issue of post-quantum cryptography (PQC), and NIST has started a standardization process. However, even though were likely five to 10 years away from quantum computers becoming widely available, were approaching what can be described as the event horizon.

Data that has been cryptographically protected by quantum-broken algorithms up to Day 0 of the PQC deployment will likely need to remain secure for years decades in some cases after quantum computers are in use. This is known as Moscas Theorem (see figure).

%{[ data-embed-type="image" data-embed-id="6081ce0f2f5c1329008b4613" data-embed-element="span" data-embed-size="640w" data-embed-alt="Illustration of a bad outcome under Mosca’s Theorem, where a quantum adversary can break the security requirements for recorded messages. The adversary could, for example, break the encryption on a recorded message or alter a legal document and generate a fake signature indistinguishable from a valid signature." data-embed-src="https://img.electronicdesign.com/files/base/ebm/electronicdesign/image/2021/04/PQC_Event_Horizon_Figure_1.6081ce0f24f07.png?auto=format&fit=max&w=1440" data-embed-caption="Illustration of a bad outcome under Moscas Theorem, where a quantum adversary can break the security requirements for recorded messages. The adversary could, for example, break the encryption on a recorded message or alter a legal document and generate a fake signature indistinguishable from a valid signature." ]}%

Deploying any secure solution takes time. Given the inherent longer development time of chips compared to software, chip-based security becomes even more pressing. Throw in the added challenge that PQC depends on entirely new algorithms, and our ability to protect against quantum computers will take many years to deploy. All this adds up to make PQC a moving target.

The good news is that, and I take heart in this, we seem to have learned from previous mistakes, and NISTs PQC standardization process is working. The effort has been underway for more than four years and has narrowed entrants from 69 to seven (four in the category of public-key encryption and three in the category of digital signatures) over three rounds.

However, in late January 2021, NIST started reevaluating a couple of the current finalists and is considering adding new entries as well as some of the candidates from the stand-by list. As mentioned previously, addressing PQC isnt an incremental step. Were learning as we go, which makes it difficult to know what you dont know.

The current finalists were heavily skewed toward a lattice-based scheme. What the potential new direction by NIST indicates is that as the community has continued studying the algorithms, lattice-based schemes may not be the holy grail we first had hoped.

Someone outside the industry may look at that as a failure, but I would argue thats an incorrect conclusion. Only by trial and error, facing failure and course correcting along the way, can we hope to develop effective PQC algorithms before quantum computers open another, potentially worse cybersecurity Pandoras box. If we fail to secure it, we risk more catastrophic security vulnerabilities than weve ever seen: Aggressors could cripple governments, economies, hospitals, and other critical infrastructure in a matter of hours.

While its old hat to say, Its time the world took notice of security and give it a seat at the table, the time to deliver on that sentiment is now.

Reference

1. Reuters, U.S. to spend $625 million in five quantum information research hubs

Continued here:

Are We Doomed to Repeat History? The Looming Quantum Computer Event Horizon - Electronic Design

When it comes to practical problems, including things such as the traveling salesman problem, a classic in optimization, the value of quantum is still to be decided, say Richard Moulds, left, head of Amazon's Braket quantum computing service, and Robert Liscouski, head of Quantum Computing Inc., which makes Qatalyst software to do optimization on both classical and quantum machines.

It's easy to imagine a problem for which, if one had a computer that magically leapt across steps of the computation, your life would be much better.

Say, for example, a computer that auto-magically searches through a vast space of possible solutions much faster than you can with a CPU or GPU.

That's the premise of quantum computing, and surprisingly, for all the hype, it's not clear if that premise is true.

"I don't think we've seen any evidence yet that a quantum machine can do anything that's commercially interesting faster or cheaper than a classical machine," Richard Moulds, head of Amazon Braket, the cloud giant's quantum computing service, said in an interview with ZDNet. "The industry is waiting for that to arrive."

It is the question of the "quantum advantage," the notion that the entangled quantum states in a quantum computer will perform better on a given workload than an electronic system.

"We haven't seen it yet," Robert Liscouski, CEO of Quantum Computing Inc, said of the quantum advantage, in the same Zoom interview with Moulds.

That aporia, the as-yet-unproven quantum advantage, is in fact the premise for a partnership announced this month, whereby QCI's Qatalyst software program will run as a cloud service on top of Braket.

QCI's corporate tag line is "ready-to-run quantum software," and the Qatalyst program is meant to dramatically simplify sending a computing task to the qubits of a quantum hardware machine, the quantum processing units, or QPUs, multiple instances of which are offered through Bracket, including D::Wave, IonQ, and Rigetti.

The idea is to get more people working with quantum machines precisely to find out what they might be good for.

"Our platform basically allows the democratization of quantum computing to extend to the user community," said Liscouski.

"If you look back on the quantum industry since it started, it's traditionally been very difficult to get access to quantum hardware," said Moulds, including some machines that are "totally unavailable unless you have a personal relationship with the the physicist that built it."

"We're trying to make it easy for everyone to have access to the same machinery; it shouldn't be those that have and those that have not, it should be everyone on the same flywheel," he said.

The spectrum of users who will be working with quantum comprise "two important communities" today, said Moulds, those that want to twiddle qubits at the hardware level, and those that want to spend time on particular problems in order to see if they actually gain any benefit when exposed to the quantum hardware.

"There's a lot of researchers focused on building better hardware, that is the defining force in this industry," said Moulds. "Those types of researchers need to be in the weeds, playing at the qubit level, tweaking the frequencies of the pulses sent to the chip inside the fridge."

On the other hand, "the other class of users is much more geared to Robert's view of the world: they don't really care how it gets done, they just want to understand how to program their problem so that it can be most easily solved."

That second class of users are "all about abstraction, all about getting away from the technology." As quantum evolves, "maybe it slides under so that customers don't even know it's there," mused Moulds.

When it comes to those practical problems, the value of quantum is still to be decided.

There has been academic work showing quantum can speed up tasks, but "that's not been applied to a problem that anybody cares about," said Moulds.

The entire quantum industry is "still finding its way to what applications are really useful," he said. "You tend to see this list of potential applications, a heralded era of quantum computing, but I don't think we really know," he said.

The Qatalyst software from QCI focuses on the kinds of problems that are of perennial interest, generally in the category of optimization, particularly constrained optimization, where a solution to a given loss function or objective function is made more complicated by having to narrow the solution to a bunch of variables that have a constraint of some sort enforced, such as bounded values.

"They are described at a high level as the traveling salesman problem, where you have multi-variate sort of outcomes," said Liscouski. "But it's supply-chain logistics, it's inventory management, it's scheduling, it's things that businesses do today that quantum can really accelerate the outcomes in the very near future."

Such problems are "a very important use case," said Moulds. Quantum computers are "potentially good at narrowing the field in problem spaces, searching through large potential combinations in a wide variety of optimization problems," he said.

However, "classical will probably give you the better result" at this time, said Liscouski.

One of the reasons quantum advantage is not yet certain is because the deep phenomena at the heart of the discipline, things such as entanglement, make the field much more complex than early digital computing.

"A lot of people draw the analogy between where we are and the emergence of the transistor," said Moulds.

"I think that's not true: this is not just a case of making the computers we have today smaller and faster and cheaper, we're not anywhere near that regime, that Moore's Law notion of just scaling these things up."

"There's fundamental scientific discoveries that have to be made to build machines that can tackle these sorts of problems on the grand scale that we've been talking about."

Beyond the machines' evolution, there is an evolution implicit for programmers. Quantum brings a fundamentally different approach to programming. "These are physics-based machines, they're not just computational engines that add ones and zeros together, it's not just a faster slide rule," said Moulds.

That different way of programming may, in fact, point the way to some near-term payoff for the Qatalyst software, and Braket. Both Liscouski and Moulds expressed enthusiasm for taking lessons learned from quantum and back-loading them into classical computers.

"Typically, access to quantum computing is through toolkits and resources that require some pretty sophisticated capabilities to program to ultimately get to some result that involves a quantum computer," observed Liscouski.

"With Braket, the platform provides both access to QPUs and classical computing at the same time, and the quantum techniques that we use in the platform will get results for both," said Liscouski.

"It isn't necessarily a black and white decision between quantum and classical," said Moulds. "There's an emerging area, particularly in the area of optimization, people use the term quantum-inspired approaches are used."

"What that means is, looking at the ways that quantum computers actually work and applying that as a new class of algorithms that run on classical machines," he said.

"So, there's a sort of a morphing going on," he said.

An advantage to working with QCI, said Moulds, is that "they bring domain expertise that we don't have," things such as the optimization expertise.

"We've coined the phrase, 'Build on Braket'," said Moulds. "We're trying to build a quantum platform, and we look to companies like QCI to bring domain expertise to use that platform and apply it to problems that customers have really got."

Also important is operational stability and reliability, said Moulds. For a first-tier Web service with tons of users, the priority for Amazon is "running a professional service, a platform that is reliable and secure and durable" on which companies can "build businesses and solve problems."

Although there are "experimental" aspects, he said, "this is not intended to be a best-effort showcase."

Although the quantum advantage is not certain, Moulds holds out the possibility someone working with the technology will find it, perhaps even someone working on Braket.

"The only way we can move this industry forward is by pulling the curtains apart and giving folks the chance to actually see what's real," he said.

"And, boy, the day we see a quantum computer doing something that is materially advantageous from a commercial point of view, you will not miss that moment, I guarantee."

Originally posted here:

Quantum: It's still not clear what its good for, but Amazon and QCI will help developers find out - ZDNet

zapp2photo - stock.adobe.com

Published: 22 Apr 2021 7:07

Australia and India have joined hands to advance the development of critical and emerging technologies such as artificial intelligence (AI), 5G networks, the internet of things (IoT) and quantum computing through a research grant programme.

Through the programme, the two countries hope to help shape a global technology environment that meets Australia and Indias shared vision of an open, free, rules-based Indo-Pacific region.

The first three projects in the initial round of the programme, which prioritised proposals focused on strengthening understanding of ethical frameworks and developing technical standards for critical technologies, were recently announced by Australias department of foreign Affairs and trade.

This project, led by the Centre for International Security Studies at the University of Sydney and experts such as Rajeshwari Rajagopalan of the Delhi-based Observer Research Foundation and quantum physicist Shohini Ghose, aims to develop quantum accords to shape international governance of quantum technologies.

The team will build guiding principles on ethics, best practices and progressive applications of quantum technologies.

But rather than propose a formal set of universal rules, they will seek consensus among key stakeholders on what constitutes ethical or unethical behaviour, good or bad practices, productive or destructive applications for emerging quantum technologies.

The project, spearheaded by La Trobe University and Indian Institute of Technology Kampur, will provide Australian and Indian business with an ethics and policy framework when outsourcing their technology to Indian providers.

It will do by improving the understanding of how they translate being signatories of ethical codes to their actual practice. The project will also analyse the emotions and views of stakeholders expressed in social media on the ethical issues found to be important through business surveys.

In doing so, the project intends to advance knowledge in AI and cyber and critical technology, ethics and sustainability and risk by bringing together disciplines in business management and ethics, computer science and engineering, and AI and business analytics.

The outcomes expected include recommendations on revised ethical codes and practices and a framework for using AI and advanced analytics to review ethical practices of companies.

The explosive growth in wireless network usage and IoT systems is expected to accelerate. While 5G networks offer significant improvements in terms of capacity, data rates, and potential energy efficiency, there is a need to address critical privacy and security challenges.

The work will focus on the issues that arise from wireless tracking systems that rely on detecting variations in the channel state information (CSI) due to the users physical activities and wireless networking.

Based on a series of experiments in Australia and India, the project will develop a comprehensive understanding of the extent of private information and metadata exposed and related inferences. This will be used to engage with standards and regulatory agencies and government bodies to strengthen data protection regimes in Australia, India and globally.

The research will be the basis for a whitepaper detailing the emerging wireless network privacy and security threat landscape. This will be followed up with a workshop in Bangalore with key regulators, standards body officials, policy makers and researchers, with the goal of initiating action to effectively address the emerging threats.

The work will be led the University of Sydney, University of New South Wales, Orbit Australia, Reliance Jio Infocomm, Indian Institute of Technology Madras and Calligo Technologies.

The automation of the financial software that lies at the heart of any business & accountancy, budget management, general ledger, payroll, and so on & is a prize many organisations are eyeing up, with machine learning and robotic process automation close to mind. Find out everything you need to know by downloading this PDF E-Guide.

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Australia and India team up on critical technology - ComputerWeekly.com