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In a new paper published in The International Journal of Astrobiology, Joseph Gale from The Hebrew University of Jerusalem and co-authors make the point that recent advances in artificial intelligence (AI)particularly in pattern recognition and self-learningwill likely result in a paradigm shift in the search for extraterrestrial intelligent life.

While futurist Ray Kurzweil predicted 15 years ago that the singularitythe time when the abilities of a computer overtake the abilities of the human brainwill occur in about 2045, Gale and his co-authors believe this event may be much more imminent, especially with the advent of quantum computing. Its already been four years since the program AlphaGO, fortified with neural networks and learning modes, defeated Lee Sedol, the Go world champion. The strategy game StarCraft II may be the next to have a machine as reigning champion.

If we look at the calculating capacity of computers and compare it to the number of neurons in the human brain, the singularity could be reached as soon as the early 2020s. However, a human brain is wired differently than a computer, and that may be the reason why certain tasks that are simple for us are still quite challenging for todays AI. Also, the size of the brain or the number of neurons dont equate to intelligence. For example, whales and elephants have more than double the number of neurons in their brain, but are not more intelligent than humans.

The authors dont know when the singularity will come, but come it will. When this occurs, the end of the human race might very well be upon us, they say, citing a 2014 prediction by the late Stephen Hawking. According to Kurzweil, humans may then be fully replaced by AI, or by some hybrid of humans and machines.

What will this mean for astrobiology? Not much, if were searching only for microbial extraterrestrial life. But it might have a drastic impact on the search for extraterrestrial intelligent life (SETI). If other civilizations are similar to ours but older, we would expect that they already moved beyond the singularity. So they wouldnt necessarily be located on a planet in the so-called habitable zone. As the authors point out, such civilizations might prefer locations with little electronic noise in a dry and cold environment, perhaps in space, where they could use superconductivity for computing and quantum entanglement as a means of communication.

We are just beginning to understand quantum entanglement, and it is not yet clear whether it can be used to transfer information. If it can, however, that might explain the apparent lack of evidence for extraterrestrial intelligent civilizations. Why would they use primitive radio waves to send messages?

I think it also is still unclear whether there is something special enough about the human brains ability to process information that casts doubt on whether AI can surpass our abilities in all relevant areas, especially in achieving consciousness. Might there be something unique to biological brains after millions and millions of years of evolution that computers cannot achieve? If not, the authors are correct that reaching the singularity could be humanitys greatest and last advance.

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Reaching the Singularity May be Humanity's Greatest and Last Accomplishment - Air & Space Magazine

New Jersey, United States:The Quantum Computing Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the Quantum Computing market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the Quantum Computing Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the Quantum Computing market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the Quantum Computing market.

Global Quantum Computing Market was valued at USD 89.35 million in 2016 and is projected to reach USD 948.82 million by 2025, growing at a CAGR of 30.02% from 2017 to 2025.

Leading players of the Quantum Computing market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the Quantum Computing market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the Quantum Computing market. It also provides useful recommendations for new as well as established players of the Quantum Computing market.

Quantum Computing Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the Quantum Computing market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for Quantum Computing.

Analysts who have authored the report have segmented the market for Quantum Computing by product, application and region. All segments are the subject of extensive research, with a focus on CAGR, market size, growth potential, market share and other important factors. The segment study provided in the report will help players focus on the lucrative areas of the Quantum Computing market. The regional analysis will help the actors to strengthen their position in the most important regional markets. It shows unused growth opportunities in regional markets and how they can be used in the forecast period.

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Highlights of TOC:

Overview: In addition to an overview of the Quantum Computing market, this section provides an overview of the report to give an idea of the type and content of the study.

Market dynamics: Here the authors of the report discussed in detail the main drivers, restrictions, challenges, trends and opportunities in the market for Quantum Computing.

Product Segments: This part of the report shows the growth of the market for various types of products sold by the largest companies.

Application segments: The analysts who have authored the report have thoroughly evaluated the market potential of the key applications and identified the future opportunities they should create in the Quantum Computing.

Geographic Segments: Each regional market is carefully examined to understand its current and future growth scenarios.

Company Profiles: The top players in the Quantum Computing market are detailed in the report based on their market share, served market, products, applications, regional growth and other factors.

The report also includes specific sections on production and consumption analysis, key results, key suggestions and recommendations, and other issues. Overall, it offers a complete analysis and research study of the Quantum Computing market to help players ensure strong growth in the coming years.

Complete Report is Available @ https://www.verifiedmarketresearch.com/product/Quantum-Computing-Market/?utm_source=SGN&utm_medium=003

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Analysts with a high level of expertise in data collection and governance use industrial techniques to collect and analyze data in all phases. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise and years of collective experience to produce informative and accurate research reports.

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Tags: Quantum Computing Market Size, Quantum Computing Market Trends, Quantum Computing Market Forecast, Quantum Computing Market Growth, Quantum Computing Market Analysis

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Quantum Computing Market 2020 | Growing Rapidly with Significant CAGR, Leading Players, Innovative Trends and Expected Revenue by 2026 - Skyline...

CBI / Accenture research reveals that technology investment is shifting up a gear. Next on businesses investment horizon are technologies at the cutting edge of innovation: distributed ledger technology (DLT) like blockchain, artificial intelligence (AI), and quantum computing.

All three of these technologies have the potential to transform how we do business. AI in particular already is: the technology has been embedded by 33% of businesses, and is changing the game from sectors like law, where its improving how millions of documents are analysed, to the financial services, where its helping to combat increasingly sophisticated money laundering and fraud threats.

But cutting-edge tech isnt a universal answer. It can be hard to separate the hype from the reality and work out whether you would really benefit from familiar or emerging technologies. Whats more, successful technology adoption doesnt rely on just technology: it also requires factors like involving employees in innovation or understanding technology ethics, which can be a challenge.

Thats why the CBI has created an exclusive member guide, in partnership with Accenture. Based on the latest evidence and engagement with senior business leaders, we reveal where businesses are struggling to get the most out of their tech investments and the practical steps you can take to avoid common pitfalls.

The CBI is helping to drive change so that the UK can lead in emerging tech

To create a thriving ecosystem for key emerging technologies, CBI is calling for a pro-innovation regulatory environment that attracts companies to come to the UK, test new innovations here, and scale for long term success. The government must take a leading role to stimulate research and investment into new technologies like AI, quantum computing, and DLT, with a greater focus on horizon scanning.

The CBIs new practical guide will help your business move beyond the hype and get more from your technology

In partnership with Accenture, the CBIs exclusive member guide,Tech Reality Check, will help you understand:

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Tech reality check: business must move beyond the hype on digital technology - CBI

Fujitsu Laboratories Ltd., and Quantum Benchmark Inc. of Canada will conduct joint research on quantum algorithms using Quantum Benchmarks error suppression technology as they aim to advance the capabilities of current generation quantum computing platforms.

Quantum Benchmark, a startup founded by leading researchers from the University of Waterloos Institute for Quantum Computing, provides software solutions for error characterization, error suppression, and performance validation for quantum computing hardware.

In this collaborative research project, the companies will develop practical quantum algorithms utilizing Fujitsus AI algorithm development technology as well as its knowledge gained through Digital Annealer applications in finance, medicine and material development. The Digital Annealer is Fujitsus new quantum-inspired architecture that can rapidly resolve combinatorial optimization problems.

Overview of the joint research.

Quantum Benchmarks patented True-Q software system, which enables optimal performance of current hardware, is a key to this development. Accordingly, Fujitsu Laboratories and Quantum Benchmark will endeavor to solve problems in the fields of materials science, drug development and finance that are intractable to solve with conventional computers.

Quantum computers are expected to be able to perform a new form of computation by harnessing fundamental properties of the quantum world, such as entanglement and superposition. This is often explained by invoking the idea that they can process both 0 and 1 at the same time, and the continuum of states in between 0 and 1. This advantage comes by performing calculations using quantum bits, called "qubits", which is unlike conventional computers which process conventional bits, that can be only 0 or 1. However, quantum bits are fragile and highly vulnerable to errors and noise, and as time goes on, the effects of noise add up, making the quantum calculation results inaccurate. Since calculations for pharmaceuticals and materials are time-consuming, there is a need to develop error-suppression methods enabling algorithms to overcome the effects of noise.

Under the partnership, which is slated to run to March 2021, and planned for extension after April 2021, Fujitsu will develop quantum algorithms for applications such as quantum chemistry and machine learning, and develop performance analysis technology for quantum algorithms in simulations.

Quantum Benchmark will support the implementation of True-Q error diagnosis technology on current quantum computing platforms; support implementation of quantum algorithms on current quantum computing platforms; and support custom specific error suppression strategies and performance evaluation for quantum algorithms on current quantum computing platforms.

Fujitsu Laboratories and Quantum Benchmark will expand the scope of their joint research beyond finance, drug discovery, and materials, as they plan to develop quantum algorithms to be implemented in quantum computers for various applications which could not be solved with conventional computers. The companies aim to demonstrate new applications on a 100+ qubit quantum computer by 2023.

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Fujitsu Laboratories and Quantum Benchmark begin joint research on algorithms with error suppression for quantum computing - Green Car Congress

Preamble: Intermittently, I will be introducing some columns which introduce some seemingly outlandish concepts. The purpose is a bit of humor, but also to provoke some thought. Enjoy.

atom orbit abstract

God does not play dice with the universe, Albert Einstein is reported to have said about the field of Quantum Physics. He was referring to the great divide at the time in the physics community between general relativity and quantum physics. General relativity was a theory which beautifully explained a great deal of physical phenomena in a deterministic fashion. Meanwhile, quantum physics grew out of a model which fundamentally had a probabilistic view of the world. Since Einstein made that statement in the mid 1950s, quantum physics has proven to be quite a durable theory, and in fact, it is used in a variety of applications such as semiconductors.

One might imagine a past leader in computer science such as Donald Knuth exclaiming, Algorithms should be deterministic. That is, given any input, the output should be exact and known. Indeed, since its formation, the field of computer science has focused on building elegant deterministic algorithms which have a clear view of the transformation between inputs and outputs. Even in the regime of non-determinism such as parallel processing, the objective of the overall algorithm is to be deterministic. That is, despite the fact that operations can run out-of-order, the outputs are still exact and known. Computer scientists work very hard to make that a reality.

As computer scientists have engaged with the real world, they frequently face very noisy inputs such as sensors or even worse, human beings. Computer algorithms continue to focus on faithfully and precisely translating input noise to output noise. This has given rise to the Junk In Junk Out (JIJO) paradigm. One of the key motivations for pursuing such a structure has been the notion of causality and diagnosability. After all, if the algorithms are noisy, how is one to know the issue is not a bug in the algorithm? Good point.

With machine learning, computer science has transitioned to a model where one trains a machine to build an algorithm, and this machine can then be used to transform inputs to outputs. Since the process of training is dynamic and often ongoing, the data and the algorithm are intertwined in a manner which is not easily unwound. Similar to quantum physics, there is a class of applications where this model seems to work. Recognizing patterns seems to be a good application. This is a key building block for autonomous vehicles, but the results are probabilistic in nature.

In quantum physics, there is an implicit understanding that the answers are often probabilistic Perhaps this is the key insight which can allow us to leverage the power of machine learning techniques and avoid the pitfalls. That is, if the requirements of the algorithm must be exact, perhaps machine learning methods are not appropriate. As an example, if your bank statement was correct with somewhat high probability, this may not be comforting. However, if machine learning algorithms can provide with high probability the instances of potential fraud, the job of a forensic CPA is made quite a bit more productive. Similar analogies exist in the area of autonomous vehicles.

Overall, machine learning seems to define the notion of probabilistic algorithms in computer science in a similar manner as quantum physics. The critical challenge for computing is to find the correct mechanisms to design and validate probabilistic results.

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Is Machine Learning The Quantum Physics Of Computer Science ? - Forbes