Asia Pacific Artificial Intelligence in Fashion Market to 2027 – Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User…

The Asia Pacific artificial intelligence in fashion market accounted for US$ 55. 1 Mn in 2018 and is expected to grow at a CAGR of 39. 0% over the forecast period 2019-2027, to account for US$ 1015. 8 Mn in 2027.

New York, Jan. 15, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" - https://www.reportlinker.com/p05833586/?utm_source=GNW Real-time consumer behavior insights and increased operational efficiency are driving the adoption of artificial intelligence in fashion industry. Moreover, the availability of a large amount of data originating from different data sources is one of the key factors driving the growth of AI technology across the fashion industry. Artificial Intelligence has already disrupted several industries, including the retail and fashion industry. The fashion industry so far has been one of the primary adopters of the technology. The fashion retailers these days are leveraging several revolutionary technologies, including machine learning, like augmented reality (AR) and artificial intelligence (AI), to make seamless shopping experiences across the channels, from online models to brick and mortar stores. Fashion retailers are progressively moving towards the AI integration within their supply chain, where more focus is being on customer-facing AI initiatives. Further, an AI integrated search engine is expected to reshape the way fashion designers develop new product designs. Store operations and in-store services will also be greatly benefited from AI integration in the fashion industry.The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years.In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further.

The governments of various countries in this region are trying to attract FDIs in the technology sector with the increasing need for enhanced technology-related services.For instance, Chinas government relaxed the restrictions on new entries with an objective to encourage overseas and private capital to invest in its economy.

This factor is anticipated to drive the demand for artificial intelligence in fashion market in this region.The artificial intelligence in fashion market by deployment type is segmented into on-premise and cloud.During the forecast period of 2019 to 2027, the cloud-based segment is anticipated to be the largest contributor in artificial intelligence in fashion market.

The artificial intelligence in fashion market is experiencing a paradigm shift from traditional on-premise deployment to cloud-based deployments in the current scenario. This trend is predominantly driven by the presence of a new category of cloud-only solutions, which help in minimizing integration complexities and installation costs with quick setup.The overall artificial intelligence in fashion market size has been derived using both primary and secondary source.The research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the artificial intelligence in fashion market.

It also provides an overview and forecast for the artificial intelligence in fashion market based on all the segmentation provided with respect to the Asia Pacifica region.Also, primary interviews were conducted with industry participants and commentators to validate data and analysis.

The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers, and national sales managers, and external consultants such as valuation experts, research analysts, and key opinion leaders specializing in the artificial intelligence in fashion market. Some of the players present in artificial intelligence in fashion market are Adobe Inc., Amazon Web Services, Inc., Catchoom Technologies S.L., Facebook, Inc., Google LLC, Huawei Technologies Co., Ltd., IBM Corporation, Microsoft Corporation, Oracle Corporation, and SAP SE among others.Read the full report: https://www.reportlinker.com/p05833586/?utm_source=GNW

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Artificial intelligence and the future of rep visits – – pharmaphorum

As access to healthcare professionals (HCPs) declines, the challenges facing sales representatives continue to increase: less time with HCPs, Sunshine Act restrictions, and integration of practices into larger health systems. It can be daunting.

Once, influence was based on interactions between reps and HCPs more than just about anything else. But today, influence is spread across a variety of touch points, many digital, which can be accessed by an HCP at any time and place. To reinforce their value, sales reps are expected to have deep knowledge of the market and their customers, so that they can tailor their interactions to the unique needs of each.

How can todays rep succeed? Its all about data.

Data gathered judiciously, digested accurately, analysed rapidly, and used wisely makes the sales force more efficient and productive. This concept is nothing new: it dates back to the beginnings of CRM in the 20th century.

But todays digital world offers new possibilities, enabling connections and predictions that yesterdays rep never even dreamed of.

What if reps could anticipate relevance?

By combining the best in industry expertise, brand strategy, CRM technology, and artificial intelligence (AI) and machine learning, reps can have the tools to make anticipated relevance possible.

At a recent Digital Health Coalition Midwest Summit, Intouch demonstrated examples of what this could look like for a brand, using their AI assistant, EVA, which is short for embedded virtual assistant.

How does it work?

EVA connects with Veeva to access a reps calendar of appointments to obtain information about where they need to go and who they need to see. Combined with marketing segmentation, EVA tells a sales rep the segmentation of todays calls. Data further informs the conversation with helpful facts like script-writing history, marketing plan, prior messages presented, and online activity, giving our rep a prediction of what their next best actions should be. These suggestions can be offered through the voice assistant, or sent by text or email for later reference, and can power the flow of the in-office detail. After the call, EVA can help a rep record a call quickly and easily in the CRM system.

An AI-powered ecosystem makes sure no pertinent data goes to waste. Whether its an email open, a website visit, a rep conversation, a script, or any other activity, the rep can quickly and easily understand what their HCP cares about and what information will be most helpful to their practice.

By anticipating relevance, the rep can provide an HCP with information thats useful to them, in the format, time, and place that helps them most. And EVA is able to use the most relevant assets efficiently and minimise the burden of administrative tasks. Time is used wisely on both sides, making it possible for the right information to help patients that much sooner.

Want to learn more about AI and modern pharma marketing? Download Intouchs comprehensive ebook.

Interested in learning how AI can work for your reps? Reach out to the Intouch team today.

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Artificial intelligence and the future of rep visits - - pharmaphorum

Frost & Sullivan: Value creation from artificial intelligence remains a priority – Tech Wire Asia

Artificial intelligene is making all business operations more intelligent. Source: Shutterstock

ENTERPRISE technology has reached a new high as business leaders are not only making an effort to understand things like cloud computing, artificial intelligence, and the internet of things but are also making related and relevant investments.

As we enter a new decade, Frost & Sullivan believes that more organizations will start to sharpen their focus on artificial intelligence and aim to really unleash its full potential.

The analyst firms ICT APAC VP Richard Wong believes that artificial intelligence will be the game-changing technology that organizations must leverage to ensure their long-term sustainability.

According to Wong, there is already a pervasive adoption of artificial intelligence across industries such as manufacturing, healthcare, retail, and the public sector.

Early success in those industries is likely to further influence the examination of new applications of the technology to enhance competitiveness in todays digital age.

Value creation from artificial intelligence will remain a priority. It is important to note that any artificial intelligence implementation should be outcome-focused, leveraging the right data sets to create value, Wong told delegates at its APAC ICT Outlook conference in Singapore recently.

Despite the optimistic forecast, Frost & Sullivan believes that numerous challenges remain in the journey to get more out of the technology.

Artificial intelligence is one of the few technologies that require a multi-disciplinary approach, addressing areas such as sociology and philosophy to ensure success. In its current state, the technology is heavily reliant on data, and hence, issues such as privacy, ethics, and governance are vital challenges that need to be addressed.

With regulators getting especially serious about how businesses capture, store, and manage data, and the transparency with which they run their operations, business leaders need to pay more attention to the basics before they can leapfrog into the future with the technology.

In the coming months, as more businesses think about deploying and scaling artificial intelligence solutions, thinking about value and maximizing value will be top of mind.

Failing to dial into the technology and unleash its full potential equals failing to get the most out of the investment. Thats the reality and business leaders understand that.

Soumik Roy | @soumikroy

Soumik Roy is a business and technology specialist. He helps small and medium enterprise owners understand what's most important to their company's growth and success.

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Frost & Sullivan: Value creation from artificial intelligence remains a priority - Tech Wire Asia

Artificial Intelligence Expert Neil Sahota Says AI Will Have Major Impact On 2020 Elections And In Medicine – Yahoo Finance

Sahota Offers AI Predictions for the New Year

LOS ANGELES, Jan. 15, 2020 /PRNewswire/ --Artificial intelligence, or AI, will play a significant role in the 2020 election campaign and may also lead to major breakthroughs in solving personal medical issues, according to futurist and AI expert Neil Sahota.

"I'm increasingly concerned about the impact of fake news, photo scams and other deceits designed to negatively influence voting this year," says Sahota, who works closely with the United Nations and other organizations to foster innovation and develop next generation products/solutions to be powered by AI. "We will see the effect of more AI tools generating fraudulent information and influencing voters. Thankfully, there will also be new tools to fight this kind of disinformation. What is certain is that machine vs machine battles will become more prevalent."

The author of the influential book Own the AI Revolution (McGraw Hill), Sahota is also an IBM Master Inventor, who led the IBM Watson Group and is a professor at the University of California/Irvine.

In addition to its potential impact on the election campaigns, Sahota predicts AI will be responsible for significant medical advances. "We will see more use of AI that will accelerate solutions for doctors, nurses, clinicians and researchers in providing personalized care," he said. "Each of us is genetically unique and there isn't a one-size fits all solution for us. But AI can solve this dilemma by providing personalized medicine based on a specific person's genomic sequence, lifestyle, medical history, environment and other differences. I think there will be great strides in these areas in the coming year."

"The election and medicine are only two areas where we will feel the impact of AI, which is coming into its own as an emerging technology," Sahota says. "We are likely to see it help combine tools such as block chain, virtual reality and artificial reality. For example, I envision a virtual reality courtroom where a law student interacts with an AI 'judge,' opposing counsel and jury. AI simulation is not only more 'real world' but has great variability, meaning each time the VR module is used, it's different. There's no memorization or 'cheat sheet' for the law student. It's a dynamic, highly interactive learning module and 2020 will start the wave of convergence: combining these technologies together.

About Neil Sahota: Neil Sahota is a futurist and leading expert on Artificial Intelligence (AI) and other next generation technologies. He is the author of Own the AI Revolution (McGraw Hill) and works with the United Nations on the AI for Good initiative. Sahota is also an IBM Master Inventor, former leader of the IBM Watson Group and professor at the University of California/Irvine. His work spans multiple industries, including legal services, healthcare, life sciences, retail, travel, transportation, energy, utilities, automotive, telecommunications, media, and government.

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Artificial Intelligence Expert Neil Sahota Says AI Will Have Major Impact On 2020 Elections And In Medicine - Yahoo Finance

Revolutionizing IoT Through AI: Why Theyre Perfect Together – IoT For All

The IoT global market revenue will reach approximately USD 1.1 trillion by 2025, predicts IDC.

IDC also says that the global IoT connections will rise with a 17 percent Compound Annual Growth Rate (CAGR) from 7 billion to 25 billion between the years 2017 to 2025.

Back in 2018, Sophia a humanoid robot performed a duet with Jimmy Fallon at his show. This performance left the audience awestruck. The entire world was spellbound of how Sophia (humanoid robot) could showcase human emotion while performing the song.

David Hanson, an American roboticist, who is the founder and Chief Executive Officer of Hanson Robotics, not only invented AI that could mirror human intelligence but also enabled it to show human emotion.

A major breakthrough in the technology world.

Well, this is just a teaser of whats coming for us.

While were here sitting in an era where science fiction is a popular genre of books has already engulfed how AI will be seen at the forefront were already living in the future thats beyond AI.

With leaps and bounds in the tech industry, AI holds a lot more in the technology world. Combining it with IoT has only further enhanced the usage of both the technologies. While IoT enables connecting two or more sensors, platforms, objects or networks to enable data transmission for several applications, AI offers the capability of analyzing the most critical information easily providing valuable insights and making highly informed decisions.

This simply means that smart AI experts will get the opportunity to bring in new IoT-enabled solutions to life.

IoT is described as the network of physical objects. For instance, these can be things that can be embedded with technologies, software or sensors that further helps in connecting or the exchange of data with other devices or systems via the internet or vice versa.

Now, these devices could be a simple ordinary household object or even sophisticated industrial tools.

There are over 8.3 billion IoT devices connected today. The growth further projects to grow to 10 billion by next year (2020) and 22 billion in 2025.

If youre wondering how AI is being used with IoT, here are a few perfect examples for you to understand.

Created by Alibaba Cloud, ET City Brain is an AI platform solution which helps in optimizing the usage of public urban resources. This has been successfully implemented in Hangzhou, China that led to a decrease in traffic by 15 percent.

The ET City Brain not only helped detect road accidents and illegal parking but also helps ambulances reach their destinations by changing the traffic signals.

Youve probably heard of the classroom monitoring system. Although this has raised certain controversy, a high school in Hangzhou, China is already making use of this system.

This camera scans the room once per 30 seconds. The algorithm is then able to determine the emotions of the student (sad, happy, angry or bored, etc.) along with their behavior such as writing, reading or raising their hand.

According to the Vice-principal of the school, its said that the system is managed locally and the behavior is focused on the entire class and not a single individual.

The data gathered is through cameras and the next step which is the image recognition step is done at the local servers.

The Tesla autopilot system enables GPS, sonars, cameras and forward-looking radars, in combination with specialized hardware, through which data can be fully utilized and coupled into Neural Network Architectures. This works like a self-enclosed system that gathers information from the sensors and further uses the Neural Network model that determines the next change in the movement of the car.

Dearth for talent and lack of expertise in the IoT market reveals the upsurge for AI professionals AI specialists and AI engineers.

AI and IoT are inseparable. The entire idea of artificial intelligence is to capture more actionable data from IoT devices.

The Internet of Things is already disrupting various industries impacting human lives in several ways.

Precisely, AI is more about making machines put in intelligent behavior. Whilst, the function of IoT is to make these machines connect. The reciprocal behavior of both these technologies manifests itself in forms that cannot be comprehended.

Thus, AI experts will play a significant role in the unprecedented growth of the IoT era.

Although the disruption of these technologies will not happen overnight, its already indubitably arriving at a much faster pace than expected.

Artificial Intelligence and IoT cannot be ignored anymore!

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Revolutionizing IoT Through AI: Why Theyre Perfect Together - IoT For All

Ancient Artificial Intelligence: The Mechanical Messiah and Other Automatons – Ancient Origins

It may not be long before Artificial Intelligence creates access to God via modern technology, however during the 19th century, a spiritualist by the name of John Murray Spear was inspired to build a Mechanical Messiah. Born on September 16, 1804, into a deeply religious family, John Murray Spear eventually became a member and minister of the Universalist Church. He forsook his ministry for spiritualism and was aided in his endeavor by illustrious technical advisors, the members of the Association of Electrizers: Thomas Jefferson (1743 - 1826), the third president of the United States; John Quincy Adams (1767 1848) an American statesman, diplomat, and lawyer who served as the sixth president of the United States; Benjamin Rush (1746 - 1813) a signer of the United States Declaration of Independence and a civic leader in Philadelphia, and Benjamin Franklin (1706 - 1790) a scientist, journalist, politician, inventor and one of the major protagonists of the American Revolution. This ambitious project had one flaw most of the technical team were deceased.

Three of the Electricizers: John Quincy Adams ( Public Domain ); Benjamin Rush by Charles Peale (1818) ( Public Domain )and Benjamin Franklin by Joseph Duplessis (1785) ( Public Domain)

The innovative Spear, under the influence of his first wife Sophronia, was intent on bettering the fate of this wretched humanity by providing it with all sorts of 'technical' information. With the guidance of his highly qualified technical team, Spear received instructions for realizing the unattainable perpetual death; a thinking machine; an electric ship and a global telepathic network. But his greatest achievement was a strange automaton, a curious device composed of electrical and mechanical parts that was to embody the 'New Motive Force', a technological 'Holy Spirit', a new 'Messiah' destined to awaken the whole of humanity from a demonic stupor. Spear believed he was spearheading a technological revolution (certainly not yet the Fourth) at a time when electricity was just beginning to enlighten the man in the street as to what 'miracles' human ingenuity was capable of.

John Murray Spear inventor of the Mechanical Messiah

The Mechanical Messiah was not born in a stable, nor warmed by the loving breath of an ox and a donkey, as the established Christian tradition would have one believe. No, this very strange, technological transformer between the Earth and Heaven, between the Immanent and the Transcendent was born in a laboratory at High Rock Cottage, Massachusetts.

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Dr Roberto Volterri is the author of 40 books including Gli Stregoni Della Scienza

Top Image : Thetis receiving the arms of Achilles from Hephaestus by Peter Paul Rubens (1630)

Museum Boijmans Van Beuningen ( Public Domain )

By Dr Roberto Volterri

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Ancient Artificial Intelligence: The Mechanical Messiah and Other Automatons - Ancient Origins

It’s 2020 Stop Confusing Cognitive Automation With Artificial Intelligence – Analytics India Magazine

Artificial intelligence has revolutionised every piece of technology it has touched. However, this augmentation for better or worse has also brought up a lot of confusion. With more and more AI application coming up in different fields, specifically in automation like Cognitive Automation, the conditions associated with it give the impression that the technology is artificially intelligent and seems to dilute the real meaning behind it. This poses a more significant problem as what qualifies as a mere application of AI can be called artificial intelligence.

When we talk about automation and AI, there is a lot of buzz around cognitive automation as it uses technology to mimic human behaviour and precisely the reason why some people call it as cognitive automation artificial intelligence.

Artificial Intelligence Vs Cognitive Automation

If one had to define artificial intelligence regarding computing, then it can be defined as the area of computer science that focuses on the creating intelligent machines that work and interact like humans with each other or with living beings. Some activities include speech recognition, learning, among others. When it comes to AI creating intelligent machines that work like humans is what one has to keep in mind from the definition. The creation process depicts the intelligence part of the device.

For example, AI in healthcare has had many applications over the years. Now, if a doctor wants to take the help of an AI, then during a particular procedure, intelligence comes into play when AI suggests which course of action to choose based on its analysis.

Intelligence, especially artificial intelligence, requires a lot of information to carry out its analysis about a process.

On the other hand, cognitive automation mimics quantitative human judgement or augments human intelligence. In short, cognitive automation imitates human thinking. If you look at the technologies in cognitive automation like natural language processing, image processing and contextual analysis all are more profound concepts of perceptions and judgements and are heavily influenced by AI.

If one looks at the cognitive applications, it becomes evident that the automation happens via hardcoded human-generated rules or through dense inputs.

According to Franois Chollet, creator of the neural network library, Keras, Automation is, at best, robustly handling known unknowns over known tasks, which is already incredibly difficult and resource-intensive in the real world whether engineering or data.

Therefore, when it comes to automation, it can only work if it is made aware of the unknowns. Working with the unknown entirely on itself will only result in the failure when it comes to automation. For instance, in the healthcare sector, doctors do take the help of AI for deciding the course of action based on the suggestions made by the intelligent system. However, when it comes to automation, this technology is only here to enhance the doctors practice and not independently run any analysis.

Cognitive automation learns through different unstructured data and connects to creating tags, annotations and other metadata. Cognitive automation tries to find similarities between items to specific processes. It seeks to identify the mentioned items in the process and then searches for similar ones in order to connect them.

To carry out a process by an automation system requires data. And, once enough information has been provided during the automation process, there is no requirement for humans to build an additional model to carry out the analysis further. As the new data set is provided, the automation makes more connections with the old one, which allows the cognitive automation systems to keep learning without any supervision and can continuously adjust to the new information.

Whereas for AI it carries out its analysis after been given a different data set at the expense of a massive amount of information which has been fed to the system. This information/data is more than the required data for cognitive automation.

In the current scenario, when one reads about the cognitive applications, the process and its workings might be similar to artificial intelligence, and thus creating confusion between the two. This happens because ultimately, cognitive automation is an application of artificial intelligence itself, which is just a little less intelligent. Cognitive automation doesnt deal with the unknowns of a process or the real-world problems, and it can only work through them if there is data fed to it in.

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It's 2020 Stop Confusing Cognitive Automation With Artificial Intelligence - Analytics India Magazine

Global Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 | 31% CAGR Projection Through 2023 | Technavio – Business Wire

LONDON--(BUSINESS WIRE)--The global artificial intelligence (AI) market in manufacturing industry is expected to post a CAGR of around 31% during the period 2019-2023, according to the latest market research report by Technavio. Request a free sample report

Manufacturing companies are moving toward the implementation of Industry 4.0 standard to intensify automation to achieve higher operational efficiencies. This is increasing the adoption of a greater number of connected devices and technologies such as big data, ML, and IoT, which is resulting in the generation of high volumes of data. This, in turn, is compelling manufacturing firms to adopt AI-based solutions to extract insights from the data to improve the management of operations. Hence, the integration of industrial IoT and big data is crucial in driving the growth of the market.

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR32119

As per Technavio, the increasing human-robot collaboration will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other important trends and market drivers that will affect market growth over 2019-2023.

Global Artificial Intelligence (AI) Market in Manufacturing Industry: Increasing Human-Robot Collaboration

Collaborative robots are designed to work in direct cooperation with humans in a well-defined workspace. They offer better productivity, reduced downtimes, and higher load capacity. Collaborative robots also improve safety in the manufacturing facility and prevent accidents and injury to humans. Moreover, they are affordable, highly adaptable, and are easy to install. Owing to such benefits, many organizations, including SMEs are increasingly adopting collaborative robot technologies. Over the next few years, the demand for collaborative robot technologies is expected to further increase with the development of better sensors and the integration of AI and ML algorithms.

Advances in AI related to intelligent business process and the increasing demand for generative designs will further boost market growth during the forecast period, says a senior analyst at Technavio.

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Global Artificial Intelligence (AI) Market in Manufacturing Industry: Segmentation Analysis

This market report segments the global artificial intelligence market in manufacturing industry by application (predictive maintenance and machine inspection, production planning, quality control, and others) and geography (APAC, Europe, MEA, North America, and South America).

The APAC region led the market in 2018, followed by North America, Europe, South America, and MEA respectively. During the forecast period, the APAC region is expected to maintain its dominance over the market. This is due to the growing adoption of smart technologies by manufacturing facilities in the region.

Technavios sample reports are free of charge and contain multiple sections of the report, such as the market size and forecast, drivers, challenges, trends, and more.

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Some of the key topics covered in the report include:

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation

Customer Landscape

Geographical Segmentation

Market Drivers

Market Challenges

Market Trends

Vendor Landscape

Vendor Analysis

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Global Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 | 31% CAGR Projection Through 2023 | Technavio - Business Wire

How to verify that quantum chips are computing correctly – MIT News

In a step toward practical quantum computing, researchers from MIT, Google, and elsewhere have designed a system that can verify when quantum chips have accurately performed complex computations that classical computers cant.

Quantum chips perform computations using quantum bits, called qubits, that can represent the two states corresponding to classic binary bits a 0 or 1 or a quantum superposition of both states simultaneously. The unique superposition state can enable quantum computers to solve problems that are practically impossible for classical computers, potentially spurring breakthroughs in material design, drug discovery, and machine learning, among other applications.

Full-scale quantum computers will require millions of qubits, which isnt yet feasible. In the past few years, researchers have started developing Noisy Intermediate Scale Quantum (NISQ) chips, which contain around 50 to 100 qubits. Thats just enough to demonstrate quantum advantage, meaning the NISQ chip can solve certain algorithms that are intractable for classical computers. Verifying that the chips performed operations as expected, however, can be very inefficient. The chips outputs can look entirely random, so it takes a long time to simulate steps to determine if everything went according to plan.

In a paper published today in Nature Physics, the researchers describe a novel protocol to efficiently verify that an NISQ chip has performed all the right quantum operations. They validated their protocol on a notoriously difficult quantum problem running on custom quantum photonic chip.

As rapid advances in industry and academia bring us to the cusp of quantum machines that can outperform classical machines, the task of quantum verification becomes time critical, says first author Jacques Carolan, a postdoc in the Department of Electrical Engineering and Computer Science (EECS) and the Research Laboratory of Electronics (RLE). Our technique provides an important tool for verifying a broad class of quantum systems. Because if I invest billions of dollars to build a quantum chip, it sure better do something interesting.

Joining Carolan on the paper are researchers from EECS and RLE at MIT, as well from the Google Quantum AI Laboratory, Elenion Technologies, Lightmatter, and Zapata Computing.

Divide and conquer

The researchers work essentially traces an output quantum state generated by the quantum circuit back to a known input state. Doing so reveals which circuit operations were performed on the input to produce the output. Those operations should always match what researchers programmed. If not, the researchers can use the information to pinpoint where things went wrong on the chip.

At the core of the new protocol, called Variational Quantum Unsampling, lies a divide and conquer approach, Carolan says, that breaks the output quantum state into chunks. Instead of doing the whole thing in one shot, which takes a very long time, we do this unscrambling layer by layer. This allows us to break the problem up to tackle it in a more efficient way, Carolan says.

For this, the researchers took inspiration from neural networks which solve problems through many layers of computation to build a novel quantum neural network (QNN), where each layer represents a set of quantum operations.

To run the QNN, they used traditional silicon fabrication techniques to build a 2-by-5-millimeter NISQ chip with more than 170 control parameters tunable circuit components that make manipulating the photon path easier. Pairs of photons are generated at specific wavelengths from an external component and injected into the chip. The photons travel through the chips phase shifters which change the path of the photons interfering with each other. This produces a random quantum output state which represents what would happen during computation. The output is measured by an array of external photodetector sensors.

That output is sent to the QNN. The first layer uses complex optimization techniques to dig through the noisy output to pinpoint the signature of a single photon among all those scrambled together. Then, it unscrambles that single photon from the group to identify what circuit operations return it to its known input state. Those operations should match exactly the circuits specific design for the task. All subsequent layers do the same computation removing from the equation any previously unscrambled photons until all photons are unscrambled.

As an example, say the input state of qubits fed into the processor was all zeroes. The NISQ chip executes a bunch of operations on the qubits to generate a massive, seemingly randomly changing number as output. (An output number will constantly be changing as its in a quantum superposition.) The QNN selects chunks of that massive number. Then, layer by layer, it determines which operations revert each qubit back down to its input state of zero. If any operations are different from the original planned operations, then something has gone awry. Researchers can inspect any mismatches between the expected output to input states, and use that information to tweak the circuit design.

Boson unsampling

In experiments, the team successfully ran a popular computational task used to demonstrate quantum advantage, called boson sampling, which is usually performed on photonic chips. In this exercise, phase shifters and other optical components will manipulate and convert a set of input photons into a different quantum superposition of output photons. Ultimately, the task is to calculate the probability that a certain input state will match a certain output state. That will essentially be a sample from some probability distribution.

But its nearly impossible for classical computers to compute those samples, due to the unpredictable behavior of photons. Its been theorized that NISQ chips can compute them fairly quickly. Until now, however, theres been no way to verify that quickly and easily, because of the complexity involved with the NISQ operations and the task itself.

The very same properties which give these chips quantum computational power makes them nearly impossible to verify, Carolan says.

In experiments, the researchers were able to unsample two photons that had run through the boson sampling problem on their custom NISQ chip and in a fraction of time it would take traditional verification approaches.

This is an excellent paper that employs a nonlinear quantum neural network to learn the unknown unitary operation performed by a black box, says Stefano Pirandola, a professor of computer science who specializes in quantum technologies at the University of York. It is clear that this scheme could be very useful to verify the actual gates that are performed by a quantum circuit [for example] by a NISQ processor. From this point of view, the scheme serves as an important benchmarking tool for future quantum engineers. The idea was remarkably implemented on a photonic quantum chip.

While the method was designed for quantum verification purposes, it could also help capture useful physical properties, Carolan says. For instance, certain molecules when excited will vibrate, then emit photons based on these vibrations. By injecting these photons into a photonic chip, Carolan says, the unscrambling technique could be used to discover information about the quantum dynamics of those molecules to aid in bioengineering molecular design. It could also be used to unscramble photons carrying quantum information that have accumulated noise by passing through turbulent spaces or materials.

The dream is to apply this to interesting problems in the physical world, Carolan says.

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How to verify that quantum chips are computing correctly - MIT News

The dark side of IoT, AI and quantum computing: Hacking, data breaches and existential threat – ZDNet

Emerging technologies like the Internet of Things, artificial intelligence and quantum computing have the potential to transform human lives, but could also bring unintended consequences in the form of making society more vulnerable to cyberattacks, the World Economic Forum (WEF) has warned.

Now in it's 15th year, the WEFGlobal Risks Report 2020 produced in collaboration with insurance broking and risk management firm Marsh details the biggest threats facing the world over the course of the next year and beyond.

Data breaches and cyberattacks featured in the top five most likely global risks in both 2018 and 2019, but while both still pose significant risks, they're now ranked at sixth and seventh respectively.

"I wouldn't underestimate the importance of technology risk, even though this year's report has a centre piece on climate," said John Drzik, chairman of Marsh & McLennan Insights.

SEE: A winning strategy for cybersecurity(ZDNet special report) |Download the report as a PDF(TechRepublic)

The 2020 edition of the Global Risks Report puts the technological risks behind five different environmental challenges: extreme weather, climate change action failure, natural disasters, biodiversity loss, and human-made environmental disasters.

But that isn't to say cybersecurity threats don't pose risks; cyberattacks and data breaches are still in the top ten and have the potential to cause big problems for individuals, businesses and society as a whole, with threats ranging from data breaches and ransomwareto hackers tampering with industrial and cyber-physical systems.

"The digital nature of 4IR [fourth industrial revolution] technologies makes them intrinsically vulnerable to cyberattacks that can take a multitude of formsfrom data theft and ransomware to the overtaking of systems with potentially large-scale harmful consequences," warns the report.

"Operational technologies are at increased risk because cyberattacks could cause more traditional, kinetic impacts as technology is being extended into the physical world, creating a cyber-physical system."

The report warns that, for many technology vendors, "security-by-design" is still a secondary concern compared with getting products out to the market.

Large numbers of Internet of Things product manufacturers have long had a reputation for putting selling the products ahead of ensuring they're secure and the WEF warns that the IoT is "amplifying the potential cyberattack surface", as demonstrated by the rise in IoT-based attacks.

In many cases, IoT devices collect and share private data that's highly sensitive, like medical records, information about the insides of homes and workplaces, or data on day-to-day journeys.

Not only could this data be dangerous if it falls into the hands of cyber criminals if it isn't collected and stored appropriately, the WEF also warns about the potential of IoT data being abused by data brokers. In both cases, the report warns the misuse of this data could be to create physical and psychological harm.

Artificial intelligence is also detailed as a technology that could have benefits as well as causing problems, with the report describing AI as "the most impactful invention" and our "biggest existential threat". The WEF even goes so far as to claim we're still not able to comprehend AI's full potential or full risk.

The report notes that risks around issues such as generating disinformation and deepfakes are well known, but suggests that more investigation is needed into the risks AI poses in areas including brain-computer interfaces.

A warning is also issued about the unintended consequences of quantum computing, should it arrive at some point over the course of the next decade, as some suggest. While, like other innovations, it will bring benefits to society, it also creates a problem for encryption in its current state.

SEE:Cybersecurity in an IoT and Mobile World (ZDNet sepcial report)

By dramatically reducing the time required to solve the mathematical problems that today's encryption relies on to potentially just seconds, it will render cybersecurity as we know it obsolete. That could have grave consequences for re-securing almost every aspect of 21st century life, the report warns especially if cyber criminals or other malicious hackers gain access to quantum technology that they could use to commit attacks against personal data, critical infrastructure and power grids,

"These technologies are really reshaping industry, technology and society at large, but we don't have the protocols around these to make sure of a positive impact on society," said Mirek Dusek, deputy head of the centre for geopolitical and regional affairs at member of the executive committee at the World Economic Forum.

However, it isn't all doom and gloom; because despite the challenges offered when it comes to cyberattacks, the World Economic Forum notes that efforts to address the security challenges posed by new technologies is "maturing" even if they're still sometimes fragmented.

"Numerous initiatives bring together businesses and governments to build trust, promote security in cyberspace, assess the impact of cyberattacks and assist victims," the report says.

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The dark side of IoT, AI and quantum computing: Hacking, data breaches and existential threat - ZDNet