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The B Side Of Artificial Intelligence, Around The Macho Algorithm – Nation World News

Bruno Fortea Miraso

Prague, 24 July. The social gaps of the analog world are also digitized at the hands of artificial intelligence (AI), as this technology often works with biased data on gender issues, and the solution to purging them is near, as consulted by EFE. Doing so also through the workings and legal imperative of AI to improve, according to experts.

Harvard University researcher Alexandra Przegalinska is convinced that, once they thoroughly examine artificial intelligence, public authorities will immediately see that perhaps there may be a certain abuse of power along with its potential.

In this sense, she celebrates the pioneering legislation that the European Commission has proposed, as it considers AI from a risk perspective, according to the professor attending the Womens Leadership School organized by the Chinese technology company Huawei. After, and which brought young talents from all over Europe together in Prague this week.

There is hope associated with AI, and we should uncover it. But there is also a risk associated with artificial intelligence, or the misuse of artificial intelligence. And I think its good that we have this regulation, he says .

The rules drawn up by Brussels consider the use of AI to be low risk to those who clearly carry high risk, a criterion which, according to Przegalinska, impacts on human life. varies depending on. This technology.

Lets think about a field like weather forecasting. Its not something thats going to hurt anyone if you tell them its going to rain. Its a trivial problem compared to a situation where a biased algorithm Says you have a disease that you dont have. You really dont have it, or where a very complicated algorithm tells you you cant get a loan and you dont know why, he says.

Women, the most hurt

In these situations, which Przegalinska uncovers, women are often most affected, given that their parameters are underrepresented in the databases with which algorithms operate and, therefore, they are a transmission belt of true discrimination. Which women have to suffer in the analog world.

Its not the technology thats the problem, its the people. Theres a saying in the AI world that garbage out, garbage in. So if the data is biased, there will certainly be bias in the system as well, says Tilburg University of Cognitive Science Thats the claim of the head of the region, Maria Postma, who also spoke with EFE.

The professor recalls that AI is an attempt to create an artificial version of human intelligence, for example in simultaneous language translation or autonomous driving of vehicles, so the original idea was, according to him, create a kind of human. Brain simulation.

AI feeds on the database and from this information, in turn, generates algorithms with mathematical models that guide its actions.

Suppose a database contains information about the profiles of job candidates based on decisions made in the past. And, in the past, many decisions were made with gender bias and female candidates were not selected. fixed position, he cites as an example.

And he continues: If the system works with this information, it will use gender as a variable in its decision-making process, because the AI will think that, if it excludes women, it will arrive at the same decision that humans did in the past, he warned.

Both Postma and Przegalinska claim the existence of solutions to stop this spiral of bias, which results either from systems excluding gender variables, according to Postma, or by deliberately screwing up mathematical models of AI. Przegalinska proposes scrambling and scrambling of the data to enhance the randomness of the algorithm.

AI researcher

For the director of the Spanish Womens Leadership School, Berta Herrero, the scope of work of these two experts shows that ethics experts are still needed in the field of AI.

The Spanish representative at the school, Maitane Gonzlez, a 22-year-old from Bilbao who has just graduated in law, is in favor of preventing pre-regulation of new technologies such as AI. EFE

bfm/cat/pi

(Photo/Video)

(This chronicle is part of a series supported by Huawei. Efes editorial content is independent of this companys posts)

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The B Side Of Artificial Intelligence, Around The Macho Algorithm - Nation World News

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Artificial intelligence (AI) market, Prevention of fraud and malicious attacks to boost market growth, Evolving Opportunities with Alphabet Inc. and…

Read the 120-page report with TOC on "Artificial Intelligence (AI) Market Analysis Report byEnd-user (retail, banking, manufacturing, healthcare, and others) and Geography (North America, Europe, APAC, South America, and MEA), and the Segment Forecasts". Buy Sample Report Now!

Major Five Artificial Intelligence (AI) Companies:

Technavio's sample reportscontain multiple sections of the report, such as the market size and forecast, drivers, challenges, trends, and more.Request a sample report

Parent Market Analysis

Technavio categorizes the globalartificial intelligence (AI) market as a part of the global application software market within the global information technology market. The end-to-end understanding of the value chainis essential in profit margin optimization and evaluation of business strategies. The data available in our value chain analysis segment can help vendors drive costs and enhance customer services during the forecast period.

The value chain of the application software marketincludes the following core components:

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Radio Frequency Identification Market by Product, End-user, and Geography - Forecast and Analysis 2022-2026:The radio frequency identification (RFID) market share is expected to increase to USD8.88 billion from 2021 to 2026,and the market's growth momentum will accelerate at a CAGR of 10.39%.

Artificial Intelligence (AI) Market Scope

Report Coverage

Details

Page number

120

Base year

2020

Forecast period

2021-2025

Growth momentum & CAGR

Accelerate at a CAGR of 21%

Market growth 2021-2025

$ 76.44 billion

Market structure

Fragmented

YoY growth (%)

19.84

Regional analysis

North America, Europe, APAC, South America, and MEA

Performing market contribution

North America at 56%

Key consumer countries

US, China, Germany, UK, and France

Competitive landscape

Leading companies, Competitive strategies, Consumer engagement scope

Key companies profiled

Alphabet Inc., CognitiveScale, Intel Corp., International Business Machines Corp., Microsoft Corp., Nuance Communications Inc., NVIDIA Corp., Oracle Corp., Tesla Inc., and Wipro Ltd.

Market dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID 19 impact and recovery analysis and future consumer dynamics, Market condition analysis for the forecast period

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table of Contents

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer landscape

Geographic Landscape

Vendor Landscape

Vendor Analysis

Appendix

About Technavio

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

With over 500 specialized analysts, Technavio's 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 Technavio's 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.

Contacts

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Outlook on the Artificial Intelligence in Aviation Global Market to 2027 – Increasing Adoption of AI to Improve Customer Services Presents…

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence in Aviation Market (2022-2027) by Offerings, Technology, Applications, Geography, Competitive Analysis and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Aviation Market is estimated to be USD 6.47 Bn in 2022 and is projected to reach USD 32.65 Bn by 2027, growing at a CAGR of 38.23%..

Market dynamics are forces that impact the prices and behaviors of the Global Artificial Intelligence in Aviation Market stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors. There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals.

As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Ansoff Analysis

The report presents a detailed Ansoff matrix analysis for the Global Artificial Intelligence in Aviation Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification. The matrix is also used for risk analysis to understand the risk involved with each approach.

The analyst analyses the Global Artificial Intelligence in Aviation Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position.

Based on the SWOT analysis conducted on the industry and industry players, The analyst has devised suitable strategies for market growth.

Why buy this report?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Market Segmentations

The Global Artificial Intelligence in Aviation Market is segmented based on Offerings, Technology, Applications, and Geography.

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/zbhai

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Outlook on the Artificial Intelligence in Aviation Global Market to 2027 - Increasing Adoption of AI to Improve Customer Services Presents...

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Artificial intelligence pioneers fund next generation of researchers – University of Sydney

Professor Gemma Figtree

"We have long known that smoking, high cholesterol, diabetes and hypertension are risk factors for heart disease, but many people develop a silent build-up of plaque in their arteries and suffer subsequent heart attack without any of these risk factors," Professor Figtree said.

As part of the project, Professor Figtree and her team will apply complex algorithms to clinical and state-of-the-art 'omic' data to unravel novel biomarkers of heart disease in its early phases.

"As part of our study, we will analyse blood samples of individuals who have advanced imaging of their coronary arteries and characterisation of their coronary plaque burden. We will use advanced technology platforms to measure hundreds of thousands of small molecules in the blood, including, RNA, protein and metabolites, as well as genomic variations. With the help of machine learning, we will then be able to train our systems to discover novel signatures of coronary plaque."

"This will allow us to develop new methods for early diagnosis. Our vision is for a simple blood test that your GP could order on a regular basis to detect the earliest phases of coronary heart disease, many years before a heart attack. If positive, your GP could prescribe life-saving drugs that stabilise the plaque, and prevent plaque progression and heart attack."

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Artificial Intelligence (AI) and the Risk of Bias in Recruitment Decisions – Lexology

As part of the UK data protection authoritys new three-year strategy (ICO25), launched on 14 July, UK Information Commissioner John Edwards announced an investigation into the use of AI systems in recruitment. The investigation will have a particular focus on the potential for bias and discrimination stemming from the algorithms and training data underpinning AI systems used to sift recruitment applications. A key concern is that training data could be negatively impacting the employment opportunities of those from diverse backgrounds.

Bias is a particular risk in AI or machine learning systems designed not to solve a problem by following a set of rules, but instead to learn from examples of what the solution looks like. If the data sets used to provide those examples have bias built in, then an AI system is likely to replicate and amplify that bias. For example, if successful candidates reflected in the training data share certain characteristics (such as gender, demographic profile or educational profile) then there is a risk of excluding candidates whose profiles do not match those criteria.

The ICO also plans to issue refreshed guidance for AI developers on ensuring that algorithms treat people and their information fairly. However, even where algorithms and training data reflect ethical guidance, it will remain best practice to retain meaningful human involvement in decision-making. In effect, AI systems should produce recommendations for human review, rather than decisions. Under EU and UK GDPR Article 22, decisions based solely on automated processing, including profiling, which produce legal effects concerning him or her or similarly significantly affects the data subject are restricted unless they are:

The making or withholding of employment offers would clearly constitute legal or similarly significant effects.

Where special category personal data is involved, decisions based solely on automated processing are permissible only:

In addition, because decisions based solely on automated processing are considered to be high risk, UK GDPR requires a Data Protection Impact Assessment (DPIA), showing that risks have been identified and assessed, and how they are addressed. From there, compliance obligations include:

The ICOs indication that investigating AI in the context of recruitment will be one of its priorities over the next three years is significant. AI and machine learning tools are an increasingly valuable resource, but they come with compliance obligations that are likely to come under intense scrutiny as an area of particular interest to the ICO as the UKs data protection authority. To learn more, or to discuss the practicalities of compliance, please contact the authors.

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Four artificial intelligence myths debunked experts reveal the TRUTH about robots taking over… – The Sun

There are many fears out there when it comes to artificial intelligence some rational, some irrational. Here's the truth.

By now, most people have integrated some form of artificial intelligence (AI) into their daily lives.

1

For example, if you use Alexa to check the weather or ask Siri to tell you jokes, that's AI.

However, despite the widespread use of such technology around the world, people are still concerned about AI.

While many fears are valid such as AI taking over certain jobs, or making humans lazier others are unfounded.

Here are four myths surrounding AI.

Many people believe that AI is the end-all-be-all of data computation, however, this is not the case.

Users cannot simply load an AI algorithm with any data to get desired results it can only work with the right data.

Experts from American technology company TTEC defined this as "data that is relevant to the problem being solved and specific to a set of use cases and a domain of knowledge."

"Many in the technology industry erroneously claim that an AI solution can just be pointed at data and that the right answer will be produced by powerful machine learning algorithms."

Because many AI technologies are considered 'cognitive', some people believe they can function in the same way human brains do.

The truth is, 'cognitive technologies' can notsolve problems they werent programmed to solve.

Furthermore, most AI is labeled as 'narrow' or 'weak', meaning that it can only apply its knowledge to one or a few tasks.

The fear of AI taking over has developed from the idea that machines will somehow gain consciousness and turn on their creators.

In order for AI to achieve this, it would not only need to possess human-like intelligence, but it would also need to be able to predict the future or plan ahead.

As it stands, AI is not capable of doing either.

When prompted with the question "Is AI an existential threat to humanity," Matthew O'Brien, a robotics engineer from the Georgia Institute of Technologywrote onMetafact: "The long-sought goal of a 'general AI' is not on the horizon. We simply do not know how to make a general adaptable intelligence, and it's unclear how much more progress is needed to get to that point".

A recent study from John Hopkins University and the Georgia Institute of Technology found that some AI algorithms can display signs of racism and sexism.

These biases could prove to be extremely harmful to the groups of people affected, experts have claimed.

However, these unfair biases are not a product of the AI themselves.

Instead, they come from "human decisions about how an AI application is designed, tested, and deployed," experts from Google said.

"There are many instances where human decision-making from employment decisions to credit allocation results in unfair outcomes for vulnerable groups, and if AI is trained to mimic the behavior of those human decision-makers it can also reflect those biases."

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Artificial Intelligence could be the future of mental illness detection – Times Now

Functional magnetic resonance imaging (fMRI) scans are used to produce brain imaging data to evaluate dynamic brain activity by spotting minute variations in blood flow

Instead of a snapshot like an x-ray or the more popular structural MRI, he likened this type of dynamic imaging to a movie, noting that "the accessible data is so much larger, so much richer than a blood test or a standard MRI." But that's the problem, it is difficult to understand so much data.

A dataset with over 10,000 individuals served as the initial training ground for the AI models as they were taught the fundamentals of fMRI imaging and brain activity. Following that, the researchers used multi-site data sets of more than 1200 people, including those with Alzheimer's disease, schizophrenia, and an autism spectrum disorder.

How does it function? It's similar to how Facebook, YouTube, or Amazon start to learn about you from your online behaviour and start to forecast your future behaviour, likes, and dislikes. The computer programme was even able to pinpoint the precise "moment" when the brain imaging data most strongly suggested a connection to the relevant mental condition.

These discoveries must be put to use before a disorder shows up for them to be clinically helpful.

We might be able to take action if we can identify risk factors for Alzheimer's disease in a 40-year-old and predict that risk using markers, according to Calhoun.

Similar to this, there might be ways to provide better or more efficient therapies if schizophrenia risks can be recognised before there are actual changes in brain structure.

"We are still unable to anticipate when exactly it will develop, even if we know through previous testing or family history that someone is at risk of an illness like Alzheimer's," Calhoun said. Brain imaging could shorten that window by spotting the pertinent patterns as soon as they emerge before the clinical illness becomes evident.

Disclaimer: This story has been published from a newswire service and nothing except the headline has been changed by Times Now.

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New Online MS in Artificial Intelligence in Business Now Offered at University of the Cumberlands – Williamson Source

University of the Cumberlands is offering a new onlineMaster of Science in Artificial Intelligence in Businessdegree program, with classes beginning in fall 2022.

The new online program will be offered in hybrid, synchronous, and asynchronous formats and can be completed within one year (12 months). Students in the program will study the application of AI in manufacturing, sales and marketing, healthcare, financial services and risk management as well as key theory and best practices in data visualization, AI and operations management, and AI in human resources. Students may begin courses during any term of the academic year. More information about the new program is available atwww.ucumberlands.edu/masters-artificial-intelligence.

According to Lois McWhorter, chair of the Department of Business at Cumberlands, the new online MS in artificial intelligence in business degree program is especially advantageous for individuals who already have an undergraduate degree in business, computer science, computer engineering, information technology, or information systems.

Said McWhorter, Typically, these undergraduate programs offer a broad curriculum but lack the depth of specialization provided in a masters degree. This new masters program in AI will build on students technical background and develop their knowledge of artificial intelligence and its applications in many facets of business.The program will provide appropriate knowledge and training to utilize these technologies in response to workplace demands.

Per the new online programs proposal submitted by the business faculty at Cumberlands, Predictions are that companies will devote over 11% of their marketing budgets to advanced analytics by 2022 (Diorio, 2020) and that organizations will invest $125B by 2025 to integrate AI into their business processes. The faculty added that the United States Bureau of Labor Statistics expects computer occupations to increase by about 13% between 2016 and 2026. This increase is almost twice the average growth rate for all trades (ZipRecruiter, 2021). The workforce need seemed high enough to necessitate creating a new online masters degree in AI in business to give future students a step up in the business world.

Dr. Daniel Kanyam, director of graduate business programs at the university, said, Whilethere are numerous programs available in artificial intelligence nationwide, the majority focus on preparing those students to develop AI applications. Our new masters in AI in business is unique as the focus is on preparing students to understand how AI can transform businesses to not only be more efficient, but develop entirely new services.

University of the Cumberlands offers high-quality hybrid and online graduate programs at competitive costs. To learn more, visitwww.ucumberlands.edu.

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Physics and Poetry in Radical Collaboration – Physics

July 22, 2022• Physics 15, 111

When physicists and poets work together, they can challenge existing paradigms. But these collaborations need larger platforms to realize their full artistic, scientific, and societal potential.

The root of the English noun poetry is the Ancient Greek word poi, which means to make, a verb that led to poiesis, the activity of bringing something into being. As a poet, I know that poetry is both a noun and the verb at its root. The word root comes from the Latin radicalis, which led to radical, the condition of fundamental change and reform. Poets, like scientists, radically challenge paradigms toward change and reform. When physicists construct the conditions for discoveries, such as the Higgs boson at CERN in Switzerland, and when poets construct interventions into culture and consciousness, both are practicing poiesis, the artistic activity of making.

I experiment with poiesis by visiting scientific research centers, such as CERN, where I work alongside scientists. The scientists talk to me about their research and often give me behind-the-scenes access to their experiments. I talk to them about the poetry-science connection and sometimes give poetry readings and lectures. During my most recent CERN visit, I saw the Large Hadron Collider, where the Higgs boson was discovered, and CERN particle physicist James Beacham and I brainstormed an idea for a physics-poetry experiment. Working toward a book about the experiments physics and poetics, we wrote poems and are drafting a cowritten scientific paper. Ive also recently visited Cerro Tololo Inter-American Observatory in Chile, where I met astrophysicist Satya Gontcho A Gontcho, who researches dark energy at Lawrence Berkeley National Laboratory in California. Shes also a practitioner of Odissi, a classical Indian dance. We collaborated on a danced-poem, which involved me reciting a poem Id written about dark energy while she performed a dance that she choreographed to the words.

Physics-poetry collaborations like these ones help break down disciplinary silos that have existed for over a century, diversifying thinking and methodologies. But for such connections to reach their full potential, scientists need to be trained in the literary arts and poets in the sciences. For example, a physicist might receive lessons in reading and writing poetryone vessel through which we use language to reason and imaginein order to gain insights into literary theory and poetic craft. Similarly, a poet might take classes in quantum physics in order to learn about the imaginative questions that scientists explore. While this training occasionally happens, larger platforms for it are needed.

Todays physics-poetry collaborations draw from a long tradition of poets and artists responding to science. They also draw from scientific history where many early scientists were philosophers, artists, or poets. In the service of these traditions, I have helped establish a global network of science-engaged poets and arts-engaged scientists, organizing and participating in conferences, discussion projects, and class visits at universities, bookstores, and scientific research centers. These poetry-science connections represent a trailblazing, literary extension of the established art-science nexus where science interacts with the arts through education, outreach, and funding proposals. The art-science connection is part of an increasing number of collaborations across fields of knowledge and artistic practice.

Courtesy of A. Catanzano/Wake Forest University

Courtesy of A. Catanzano/Wake Forest University

Bringing science and the arts together also has the benefit of helping to combat ignorance, manipulation, bigotry, and violence. In addition to expanding knowledge and experience, which helps prevent and solve sociopolitical problems, the art-science connection helps protect science from destructive commercial and military aims through social critiques that are common in the arts. It also advances the influence of the arts, leading to more creativity and critical thinking.

When I use poetry and poetics to explore open questions in cutting-edge physics, questions mediated by translations between mathematics and language, I focus on quantum physics. I do so because quantum physics is central to the most pressing concerns in physics today, such as how to reconcile quantum physics with relativity, how to create a quantum computer, and the role of dark energy in cosmic acceleration. There is, however, another reason for my quantum-physics focus (see Arts & Culture: Poetry Takes on Quantum Physics). The quantum world is often described as strange when viewed through classical logic. But when I view the quantum world through poetic logic, I find it familiar and poetically sound.

Quantum physics, in my view, uses unacknowledged poetic principles to describe the properties of quantum phenomena such as uncertainty, observation, superposition, and entanglement. In the principle of indeterminacy or the uncertainty principle, a subatomic particles future position and momentum cannot be known with certainty, since its present state is measured in probabilities; in poetry, ambiguities that arise from uncertainty can be a form of artistic depth. In quantum physics, the observer affects the observed; in poetry, a reader can affect a poems meaning through interpretation. In quantum superposition, a subatomic particle can simultaneously exist in multiple states of spacetime; in poetry, alternative experiences with spacetime, such as simultaneity, can be evoked. In quantum entanglement, distanced quantum states of particles that were once near each other can instantaneously communicate; in poetry, which can exhibit quantum behavior, classical paradoxes need not be resolved. Poetry is an advanced technology like a particle collider or a telescope, extending the senses, intellect, and imagination. In poetry and quantum physics, the impossible is often probable and even inevitable.

Neither physics nor poetry are totalizing efforts leading to absolute truth. When theorized and conducted in union, both fields become far more wondrous: they carry new forms of information and experience, produce new ideas and technologies, and challenge dominant belief systems about the universe. It is the evolution of our questions, and not just our provisional answers, that advances scientific, artistic, and societal progress.

Amy Catanzano is a professor and the poet-in-residence at Wake Forest University, North Carolina. Author of three books and multimodal poetry projects involving physics, she is the recipient of the PEN USA Literary Award in Poetry and other honors. She recently wrote a poem for Physics Magazine on the Higgs boson (see Higgs Boson: The Cosmic Glyph).

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New phase of matter with 2D time created in quantum computer – Cosmos

Quantum computers hold the promise of revolutionising information technology by utilising the whacky physics of quantum mechanics. But playing with strange, new machinery often throws up even more interesting and novel physics. This is precisely what has happened to quantum computing researchers in the US.

Reported in Nature, physicists who were shining a pulsing laser at atoms inside a quantum computer observed a completely new phase of matter. The new state exhibits two time dimensions despite there still being only a singular time flow.

The researchers believe the new phase of matter could be used to develop quantum computers in which stored information is far more protected against errors than other architectures.

See, what makes quantum computers great is also what makes them exceedingly tricky.

Unlike in classical computers, a quantum computers transistor is on the quantum scale, like a single atom. This allows information to be encoded not just using zeroes and ones, but also a mixture, or superposition, of zero and one.

Hence, quantum bits (or qubits) can store multidimensional data and quantum computers would be thousands, even millions of times faster than classical computers, and perform far more efficiently.

But this same mixture of 0 and 1 states in qubits is also what makes them extremely prone to error. So a lot of quantum computing research revolves around making machines with reduced flaws in their calculations.

Read more: Australian researchers develop a coherent quantum simulator

The mind-bending property discovered by the authors of the Nature paper was produced by pulsing a laser shone on the atoms inside the quantum computer in a sequence inspired by the Fibonacci numbers.

Using an extra time dimension is a completely different way of thinking about phases of matter, says lead author Philipp Dumitrescu, a research fellow at the Flatiron Institutes Centre for Computational Quantum Physics in New York City, US. Ive been working on these theory ideas for over five years and seeing them realised in experiments is exciting.

The teams quantum computer is built on ten atomic ions of ytterbium which are manipulated by laser pulses.

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Quantum mechanics tells us that superpositions will break down when qubits are influenced (intentionally or not), leading the quantum transistor to pick to be either in the 0 or 1 state. This collapse is probabilistic and cannot be determined with certainty beforehand.

Even if you keep all the atoms under tight control, they can lose their quantumness by talking to their environment, heating up, or interacting with things in ways you didnt plan, Dumitrescu says. In practice, experimental devices have many sources of error that can degrade coherence after just a few laser pulses.

So, quantum computing engineers try to make qubits more resistant to outside effects.

One way of doing this is to exploit what physicists call symmetries which preserve properties despite certain changes. For example, a snowflake has rotational symmetry it looks the same when rotated a certain angle.

Time symmetry can be added using rhythmic laser pulses, but Dumitrescus team added two time symmetries by using ordered but non-repeating laser pulses.

Other ordered but non-repeating structures include quasicrystals. Unlike typical crystals which have repeating structure (like honeycombs), quasicrystals have order, but no repeating pattern (like Penrose tiling). Quasicrystals are actually the squished down versions, or projections, of higher-dimensional objects. For example, a two-dimensional Penrose tiling is a projection of a five-dimensional lattice.

Could quasicrystals be emulated in time, rather than space? Thats what Dumitrescus team was able to do.

Whereas a periodic laser pulse alternates (A, B, A, B, A, B, etc), the parts of the quasi-periodic laser-pulse based on the Fibonacci sequence are the sum of the two previous parts (A, AB, ABA, ABAAB, ABAABABA, etc.). Like a quasicrystal, this is a two-dimensional pattern jammed into a single dimension. Hence, theres an extra time symmetry as a boon from this time-based quasicrystal.

The team fired the Fibonacci-based laser pulse sequence at the qubits at either end of the ten-atom arrangement.

Using a strictly periodic laser pulse, these edge qubits remained in their superposition for 1.5 seconds an impressive feat in itself given the strong interactions between qubits. But, with the quasi-periodic pulses, the qubits stayed quantum for the entire length of the experiment around 5.5 seconds.

With this quasi-periodic sequence, theres a complicated evolution that cancels out all the errors that live on the edge, Dumitrescu explains. Because of that, the edge stays quantum-mechanically coherent much, much longer than youd expect. Though the findings bear much promise, the new phase of matter still needs to be integrated into a working quantum computer. We have this direct, tantalising application, but we need to find a way to hook it into the calculations, Dumitrescu says. Thats an open problem were working on.

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