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The G7 wants to regulate artificial intelligence. Should the US get on board? – News@Northeastern

With the introduction of new export controls on artificial intelligence software last week, the White House appealed to lawmakers, businesses, and European allies to avoid overregulation of artificial intelligence. It also maintained its refusal to participate in a project proposed by the Group of Seven leading economies, which seeks to establish shared principles and regulations on artificial intelligence, as the U.S. prepares to take over the presidency of the organization this year.

The U.S. has rejected working with other G-7 nations on the project, known as the Global Partnership on Artificial Intelligence, maintaining that the plan would be overly restrictive.

Kay Mathiesen is an associate professor of philosophy and religion in the College of Social Sciences and Humanities. Photo by Matthew Modoono/Northeastern University

Kay Mathiesen, an associate professor at Northeastern who focuses on information and computer ethics and justice, contends that the U.S.s refusal to cooperate with other nations on a united plan could come back to hurt its residents.

Advocates of the plan say it would help government leaders remain apprised of the development of the technology. The project, they say, could also help build consensus among the international community on limiting certain uses of artificial intelligence, especially in cases where its found to be controlling citizens or violating their privacy and autonomy.

U.S. leaders, including deputy chief technology officer Lynne Parker, counter that the proposal appears overly bureaucratic and could hinder the development of artificial intelligence at U.S. tech companies.

But Mathiesen says that many companies are already ahead of the curve in considering or implementing oversight mechanisms to guide the ethical development of their products. She says that its important to rein in the potentially harmful effects of artificial intelligence to ensure that the benefits of the technology are not overridden by the cost.

The idea that we should just not regulate at all or not even think about this, because maybe then we might limit ourselves, I think thats a pretty simplistic view, says Mathiesen, a professor of philosophy who studies political philosophy and ethics. Its not like the G-7 is going to have the power to all of a sudden impose regulations on U.S. industry. So that argument that merely by joining this [group] and beginning to think these things through, and do research on this, and develop [policy] recommendationsthat that by itself is going to put us behind on artificial intelligence doesnt hold a lot of water.

Mathiesen suggests that failing to work with other countries in addressing privacy issues stemming from the unchecked spread of artificial intelligence productssuch as facial recognitioncould result in consumer backlash, and thereby slow down the development of artificial intelligence in the U.S.

The technology is advancing incredibly rapidly and we want to make sure that were thinking ahead, and were building at the beginning protections for consumers before these things come out and its too late and we have to try to fix problems that we couldve prevented, she says.

The plan for the Global Partnership on Artificial Intelligence, which was introduced in December 2018, is to ensure that artificial intelligence projects are designed responsibly and transparently, in a way that prioritizes human values, such as privacy. The initiative received a major boost from Canada, which held the G-7s rotating presidency at the time, and was kept alive by France the following year. The U.S. will take over the presidency of the organization this year.

In addition to Canada and France, the other G-7 countries, including Germany, Italy, Japan, and the U.K., are on board with the project. The European Union, India, and New Zealand have also expressed interest. Mathiesen says that while she understands the concerns of some U.S. government officials about being out-competed, its important for the U.S. to be a participating member in this effort, especially while the technology is still in its nascent stages.

In a way, its better that the U.S. has buy-in at the beginning and is at the table to make these arguments about how do we balance concerns about things like privacy, security, and possible harm that could be produced by artificial intelligence? How do we balance that with also wanting to enable companies and inventors to create new things with artificial intelligence that can be economically and socially beneficial? she says.

Mathiesen suggested that failing to engage in these conversations with the wider international community could leave the U.S. trailing behind.

I think that the American citizens are going to suffer for that, just like they do now with the lack of data privacy, she says.

In conjunction with global professional services company Accenture, researchers at Northeasterns Ethics Institute last year produced a report that provided organizations a framework for creating ethics committees to help guide the development of smart machines.

For media inquiries, please contact Marirose Sartoretto at m.sartoretto@northeastern.edu or 617-373-5718.

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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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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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The hits and misses of using Artificial intelligence for recruitment – Livemint

Artificial intelligence is making huge strides and has been occupying some of the best minds of this century, but the hype around it is just as massive. Artificial intelligence is entering everyday lives and products, and many of us find ourselves in positions, both in our professional and personal lives, where we need to evaluate the genuineness of claims to using AI. And if we cant separate the hype from the truth, wed end up spending money on fake products and services.

Over the years that Ive spent with startups, Ive come across both genuine AI products and fakes. Ill start with the ones that truly solved problems using AI.

A few years ago, one of the co-founders of Liv.ai, a Bengaluru-based AI start-up, met me and demonstrated their product that used natural language processing (NLP) to convert speech to text in multiple Indian languages. Converting speech to text in multiple languages was a hard problem to solve. I was a bit skeptical at first, but when I saw the product, I was quite blown away. Flipkart acquired it and built a shopping assistant, Saathi, with text and voice interface to support shoppers in the smaller towns.

Facial recognition is another problem that has been solved and has wide applications that touch everyday lives, including unlocking ones smartphone. Work is in progress for image recognition applications in other fields, including in horticulture.

And now, I come to what I call fake products riding the AI wave. A vendor once approached us claiming their product could predict criminal tendency in an individual with an accuracy of 60%, and suggested we use this tool to evaluate our delivery boys. This means there is a 40% probability that it would classify someone with no criminal tendency as one with a criminal tendency. Do you need anything else to decide whether you should pay this vendor and run all your new hires through a test like this?

Another AI vendor once confidently bragged to us that their tool could look at a job description, evaluate 100 CVs and pick the best five suited for the job. When we asked, How?, they resorted to deep jargon: We use a deep learning algorithm. When we tested the tool and got it to look at 100-odd CVs and shortlist the best five, there was a zero match with what a good recruiter and hiring manager with years of experience had shortlisted.

Claims like these give AI a bad name. Arvind Narayanan, a computer science professor at Princeton, puts it more succinctly, Much of whats being sold as AI today is snake oil it does not and cannot work. Why is this happening? How can we recognize flawed AI claims and push back?"

He has classified AI into three broad buckets:

1. Areas where AI is genuine and making rapid progress like facial recognition, medical diagnosis from scans, etc.

2. Areas that are imperfect but improving like detection of spam, hate speech, etc.

3. Fundamentally dubious areas like predicting job success, recidivism, at-risk kids etc.

The last category, which is really about predicting social outcomes, is essentially the snake oil being sold to gullible users and used as a pretext for collecting a large amount of data. Users are made to believe that magical insights can somehow be extracted from large amounts of data and more the data better the insights.

Professor Narayanan writes that there has been no real improvement in the third category, despite how much data you throw at it; he further goes on to show that for predicting social outcomes, AI is worse off than manual scoring using just a few features.

In another questionable claim, Ginni Rometty of IBM said last year that IBM artificial intelligence can predict with 95% accuracy which workers are about to quit their jobs. In my opinion, using AI to predict human and social behaviour will always be flawed because human beings arent all that predictable. Theyre individualistic, and their behaviours depend on a number of factors that cant always be reduced to data points.

The protagonists of predicting social outcomes will no doubt claim that it is only a matter of time before AI gets better. I believe this is untrue.

Those who have heard of chaos theory (more commonly known as the butterfly effect) understand that small differences in initial conditionssuch as those due to rounding errorscan yield widely diverging outcomes even for deterministic systems where an approximate present cannot determine an approximate future. So, one can imagine how much more indeterminate or irrelevant the predictions would be for inherently non-deterministic systems like social behaviours and outcomes. Just as the Heisenbergs uncertainty principle places fundamental limitations at an atomic level, chaos theory places a similar limitation in areas like social outcomes.

Vested interests will always have a motive for creating the myth of being able to predict social outcomes using vast data. This myth needs to be dispelled.

T.N. Hari is head of human resources at Bigbasket.com and adviser to several venture capital firms and startups. He is the co-author of Saying No To Jugaad: The Making Of BigBasket.

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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.

Request a free sample report

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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Artificial intelligence is changing the world here’s how to invest – Telegraph.co.uk

This is the second part in aseries looking at the investments for the future sectors that will grow to become major industries and provide returns along the way. Part onelooks at clean energy. We will also focus on, water security, ageing populations and nutrition

"Alexa, can I make money investing in companiesthat buildartificial intelligence (AI) programmes?"

There is a lot of hype around the technology andit has thepotential to transform our lives. This naturally has led to investors approaching the sector with interest, looking to see whether they can invest in the next big technological change.

Investing in something as specific as AI is known as thematic investing or trend investing. Thisis a way of getting exposure to one niche area that is expected to expand significantly over time and therefore grow an investment.

Investing in AI is the second part in aseries looking at trend investing. Telegraph Money studies the outlook AI companies, how they would withstand a recession and what is the best way to invest for those enamoured with the sector.

AI is beginning to touch all areas of our lives, from suggesting films on Netflix to helping doctors diagnose diseases. It even interacts with us in our homes via smart speakers, which can play music and answer questionsamong an increasingly sophisticated array of "skills".

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Xanadu Receives $4.4M Investment from SDTC to Advance its Photonic Quantum Computing Technology – Yahoo Finance

Xanadu's unique photonic approach to quantum computing will be much more energy efficient than traditional computing methods, thereby saving energy and emissions from power generation.

TORONTO, Jan. 16, 2020 /PRNewswire/ - Xanadu, a Canadian quantum hardware and technology company has received a $4.4M investment from Sustainable Development Technology Canada (SDTC). The investment will expedite the development of Xanadu's photonic quantum computers and make them available over the cloud. This project will also further the company's overall progress towards the construction of energy-efficient universal quantum computers.

Xanadu (CNW Group/Xanadu)

"Canadian cleantech entrepreneurs are tackling problems across Canada and in every sector. I have never been more positive about the future. The quantum hardware technology that Xanadu is building will develop quantum computers with the ability to solve extremely challenging computational problems, completing chemical calculations in minutes which would otherwise require a million CPUs in a data center," said Leah Lawrence, President and CEO, Sustainable Development Technology Canada.

Despite efforts to improve the power efficiency of traditional computing methods, the rapid growth of data centres and cloud computing presents a major source of new electricity consumption. In comparison to classical computing, quantum computing systems have the benefit of performing certain tasks and algorithms at an unprecedented rate. This will ultimately reduce the requirements for electrical power and the accompanying air and water emissions associated with electricity production.

Xanadu is developing a unique type of quantum computer, based on photonic technology, which is inherently more power-efficient than electronics. Xanadu's photonic approach uses laser light to carry information through optical chips, rather than the electrons or ions used by their competitors. By using photonic technology, Xanadu's quantum computers will one day have the ability to perform calculations at room temperature, and eliminate the bulky and power-hungry cooling systems required by most other types of quantum computers.

The project will be undertaken by Xanadu's team of in-house scientists, with collaboration from the University of Toronto and Swiftride. The project will be carried out over three years and will encompass the development of Xanadu's architecture, hardware, software and client interfaces with the overall goal of expediting the development of the company's technology, and demonstrating the practical benefits of quantum computing for users and customers by the end of 2022.

"We are thrilled by the recognition and support that we are receiving from SDTC for the development of our technology. We firmly believe that our unique, photonic-based approach to quantum computing will deliver both valuable insights and tangible environmental benefits for our customers and partners," said Christian Weedbrook, CEO of Xanadu.

About XanaduXanadu is a photonic quantum hardware company. We build integrated photonic chips that can be used in quantum computing, communication and sensing systems. The company's mission is to build quantum computers that are useful and available to people everywhere, visitwww.xanadu.ai or follow us on Twitter @XanaduAI.

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‘How can we compete with Google?’: the battle to train quantum coders – The Guardian

There is a laboratory deep within University College London (UCL) that looks like a cross between a rebel base in Star Wars and a scene imagined by Jules Verne. Hidden within the miles of cables, blinking electronic equipment and screens is a gold-coloured contraption known as a dilution refrigerator. Its job is to chill the highly sensitive equipment needed to build a quantum computer to close to absolute zero, the coldest temperature in the known universe.

Standing around the refrigerator are students from Germany, Spain and China, who are studying to become members of an elite profession that has never existed before: quantum engineering. These scientists take the developments in quantum mechanics over the past century and turn them into revolutionary real-world applications in, for example, artificial intelligence, self-driving vehicles, cryptography and medicine.

The problem is that there is now what analysts call a quantum bottleneck. Owing to the fast growth of the industry, not enough quantum engineers are being trained in the UK or globally to meet expected demand. This skills shortage has been identified as a crucial challenge and will, if unaddressed, threaten Britains position as one of the worlds top centres for quantum technologies.

The lack of access to a pipeline of talent will pose an existential threat to our company, and others like it, says James Palles-Dimmock, commercial director of London- and Oxford-based startup Quantum Motion. You are not going to make a quantum computer with 1,000 average people you need 10 to 100 incredibly good people, and thatll be the case for everybody worldwide, so access to the best talent is going to define which companies succeed and which fail.

This doesnt just matter to niche companies; it affects everyone. If the UK is to remain at the leading edge of the world economy then it has to compete with the leading technological and scientific developments, warns Professor Paul Warburton, director of the CDT in Delivering Quantum Technologies. This is the only way we can maintain our standard of living.

This quantum bottleneck is only going to grow more acute. Data is scarce, but according to research by the Quantum Computing Report and the University of Wisconsin-Madison, on one day in June 2016 there were just 35 vacancies worldwide for commercial quantum companies advertised. By December, that figure had leapt to 283.

In the UK, Quantum Motion estimates that the industry will need another 150200 quantum engineers over the next 18 months. In contrast, Bristol Universitys centre for doctoral training produces about 10 qualified engineers each year.

In the recent past, quantum engineers would have studied for their PhDs in small groups inside much larger physics departments. Now there are interdisciplinary centres for doctoral training at UCL and Bristol University, where graduates in such subjects as maths, engineering and computer science, as well as physics, work together. As many of the students come with limited experience of quantum technologies, the first year of their four-year course is a compulsory introduction to the subject.

Rather than work with three or four people inside a large physics department its really great to be working with lots of people all on quantum, whether they are computer scientists or engineers. They have a high level of knowledge of the same problems, but a different way of thinking about them because of their different backgrounds, says Bristol student Naomi Solomons.

While Solomons is fortunate to study on an interdisciplinary course, these are few and far between in the UK. We are still overwhelmingly recruiting physicists, says Paul Warburton. We really need to massively increase the number of PhD students from outside the physics domain to really transform this sector.

The second problem, according to Warburton, is competition with the US. Anyone who graduates with a PhD in quantum technologies in this country is well sought after in the USA. The risk of lucrative US companies poaching UK talent is considerable. How can we compete with Google or D-Wave if it does get into an arms race? says Palles-Dimmock. They can chuck $300,000-$400,000 at people to make sure they have the engineers they want.

There are parallels with the fast growth of AI. In 2015, Ubers move to gut Carnegie Mellon Universitys world-leading robotics lab of nearly all its staff (about 50 in total) to help it build autonomous cars showed what can happen when a shortage of engineers causes a bottleneck.

Worryingly, Doug Finke, managing editor at Quantum Computing Report, has spotted a similar pattern emerging in the quantum industry today. The large expansion of quantum computing in the commercial space has encouraged a number of academics to leave academia and join a company, and this may create some shortages of professors to teach the next generation of students, he says.

More needs to be done to significantly increase the flow of engineers. One way is through diversity: Bristol has just held its first women in quantum event with a view to increasing its number of female students above the current 20%.

Another option is to create different levels of quantum engineers. A masters degree or a four-year dedicated undergraduate degree could be the way to mass-produce engineers because industry players often dont need a PhD-trained individual, says Turner. But I think you would be training more a kind of foot soldier than an industry leader.

One potential roadblock could be growing threats to the free movement of ideas and people. Nations seem to be starting to get a bit protective about what theyre doing, says Prof John Morton, founding director of Quantum Motion. [They] are often using concocted reasons of national security to justify retaining a commercial advantage for their own companies.

Warburton says he has especially seen this in the US. This reinforces the need for the UK to train its own quantum engineers. We cant rely on getting our technology from other nations. We need to have our own quantum technology capability.

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'How can we compete with Google?': the battle to train quantum coders - The Guardian

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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

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IBM heads US patent list for 27th consecutive year – Technology Decisions

IBM is celebrating its 27th year atop the annual US patent recipient list, having earned 9262 US patents in 2019 alone.

The technology giant was awarded more than 2500 cloud technology patents and 1800 AI patents, along with a number of blockchain, security and quantum computing patents, according to IBM.

Notable inventions include a method for teaching AI systems how to understand and deduce the nuances and implications behind certain text or phrases of speech by analysing other related content as well as a signature-based approach to homomorphic message encoding functions, which helps ensure data authenticity. Homomorphic encryption allows users to operate on encrypted data without decrypting it first, IBM explained.

The company also highlighted a method for scaling a quantum computer to support additional qubits and a technique to help blockchain users resist replay attacks where an attacker copies and uses signature information from one blockchain transaction to perform other, unauthorised transactions.

In addition, IBM inventors developed a method for jointly managing cloud and non-cloud computing platforms. Using a unified portal, the technique is designed to receive, organise and streamline incoming cloud and non-cloud tasks and requests, which could help organisations easily migrate to hybrid cloud platforms, IBM said.

The pace of innovation continues to accelerate and reach unprecedented levels, especially in IBMs Labs. Technology advances whether AI, cloud or quantum computing will all contribute to solving the biggest challenges facing business and society, IBM Executive Vice President Dr John E Kelly III said.

IBM sees no reason for technology development to slow down in or out of its labs, with the company recently joining the LOT Network a non-profit group of companies that builds a protective barrier for its members against patent assertion entities (PAEs). By preventing PAEs from acquiring patents from third parties and trying to make money through litigation against potential infringements, the LOT Network and IBM supports industry-wide open innovation. Currently over 80,000 IBM patents and patent applications are protected through the LOT Network.

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