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Cloud computing in manufacturing: from impossible to indispensable – Information Age

Thiago Martins, managing director, Industry X & manufacturing execution systems lead at Accenture, explores how cloud computing has become indispensable to the manufacturing sector

Many cloud use cases have emerged within the manufacturing space.

Most manufacturers are familiar with terms like smart plant, plant of the future, or digital factory. But regardless of the term used, it implies a manufacturing environment where digital technology enables more productivity, efficiency, safety, and compliance. Unfortunately, it also implies an environment that seems far away from the current state of most manufacturers. Why? Because historically, manufacturing IT projects have proven to be long, costly, and in many cases risky especially if they required remote hosting of manufacturing applications.

However, digital adoption has accelerated in the past few years, mainly driven by three factors:

As network and cloud services evolve, manufacturers have access to a whole new set of infrastructure options that they can use to improve the way they support shopfloor operations. These options include edge computing to address near real-time use-cases; private cloud, typically necessary for compliance reasons; and public cloud, which gives them access to scale, innovation capabilities, and flexibility. This is what we call the Cloud Continuum a new model, in which centralised and distributed computing resources are combined to ensure new IT and business efficiencies.

The IT efficiencies associated with cloud are well known. For example, when properly designed, flexible pay-by-the-use computing capacity can lead to 20%-40% cost reduction when compared to traditional on-site underutilised on-premise infrastructure. Cloud also gives access to capabilities of leading service providers such as AWS, Azure, Google, Oracle, and others to keep the infrastructure up and running, as opposed to relying on outdated software or vulnerable legacy infrastructures. By properly utilising the cloud, organisations can reduce IT labour and operating costs.

Darren Van Booven, lead principal consultant at Trustwave and former CISO of the United States House of Representatives, provides six steps for manufacturers towards effectively protecting themselves against ransomware. Read here

From the business efficiencies perspective, manufacturers can unlock value by using components such as:

The question remains: If the cloud brings so many benefits, why are there manufacturers still hesitant to using cloud-based solutions on the shopfloor?

From an infrastructure perspective, the risks associated with remote hosting manufacturing applications, such as performance, availability and security, are well known by most manufacturers, but ways of mitigating them, are not. New solutions like 5G and multi-access edge computing which add a layer of security were not available, known, or affordable at many manufacturing locations until recently.

From a software perspective, until a few years ago, the value of cloud for manufacturers was very limited. Cloud used to be seen as a virtual data centre to which moving manufacturing applications did not represent a compelling business case, as the costs did not outweigh the risks. However, as software vendors introduce new cloud-based products, weve witnessed a dramatic change in embrace. The adoption of cloud-based solutions has become more attractive, especially to those seeking innovative ways to solve problems that otherwise would have been considered too expensive or too complex to solve based on traditional on-premise models.

This article will explore how organisations can avoid cloud vendor lock-in and take advantage of multi-vendor sourcing options. Read here

Advancements in infrastructure, combined with the exponential growth of software offerings in the cloud, has accelerated the digitisation of the supply chains, allowing companies to operate and interact with each other in a more transparent and automated way. Companies are quickly expanding their operational intelligence, moving from single assets descriptive analytics where manufacturers are informed of what has happened; to prescriptive analytics where manufacturers are informed of options to respond to whats about to happen; across multiple lines, factories, all the way to critical elements of their supply chain.

The exponential value creation cycle enabled by the Cloud Continuum does not depend on IT only. It requires organisations to have a well-defined vision, an adequate operating model, and a properly designed set of technology adoption principles. The adoption of cloud solutions without these three components usually leads to difficulty scaling and sustaining the intended benefits.

In summary, cloud adoption in manufacturing went from a concept deemed impossible, or at least not economically viable, to an indispensable way to enable companies to compete in a digital world. Understandably, too many things are new in this space, and the race to increase companies productivity, efficiency, safety, and compliance does not give manufacturers much time to learn from their own experiences.

Written by Thiago Martins, managing director, Industry X & manufacturing execution systems lead at Accenture

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These 3 hiring tech companies have internship and apprenticeship opportunities –

For young professionals looking to start their tech careers, one of the hardest parts is often getting a foot in the door in the first place.

Internships and apprenticeships can help in that regard, providing opportunities to gain real-world experience and sometimes leading to full-time employment afterward. However, these programs can be competitive, and selecting the right company to intern with can be daunting for students.

For the month of September, Technical.lys reporting has explored the theme of Youth Building the Future for our editorial calendar. For some additional perspectives, we asked our Talent Pros the following question:

What opportunities does your org have for internships and apprenticeships?

Here are some of their replies, and if you like what you see, follow the links to companies Culture Pages to learn more and explore open roles.

Linode offers a unique opportunity for those looking to launch a great career in tech while learning new skills on the job. Weve cultivated a culture of continuous learning and self-improvement. Professional aspirations arent limited here, and new roles are often created based on personal goals and company needs.

Were actively hiring for a Security Intern, DevOps Intern, and two Server Repair Interns. You can find more information on our Careers page.

(And note: Linode also contributes to diversifying the tech jobs pipeline. The cloud hosting provider dedicates employee volunteer time to partnering with workforce development-focused organizations training potential future employees.)

SmartLogic helped to organize and participates in the Baltimore Tracks summer internship program for Baltimore City High School students, and we are once again running our Developer Apprentice program this fall, with three new apprentices that started last week.

We believe that apprenticeships and internships provide an important pathway for underrepresented groups to join the technology industry, increasing equity and inclusion in our company and in the tech industry overall.

From real-world experiences to project-based assignments, our Summer Internships and Development Programs will give you the exposure, opportunity and support you need to develop and grow as a person, as well as a professional. With a number of business areas to suit a variety of skills and interests, we offer plenty of opportunities to start a rewarding career.

Our programs vary in length and location, depending on the line of business. Development Programs range between 12 and 36 months, and the Summer Internship Program lasts 10 weeks. The experience includes formal onboarding, peer mentors and the opportunity to work side by side with PNC professionals.

To find out more about a career with the right balance, visit


Interested to learn more? Check out all the Talent companies here, and perhaps one of them could help you launch your tech career.

P.S. If youre curious about Talent for your own org, find more info here and connect with us.

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The truth about artificial intelligence? It isn’t that honest | John Naughton – The Guardian

We are, as the critic George Steiner observed, language animals. Perhaps thats why we are fascinated by other creatures that appear to have language dolphins, whales, apes, birds and so on. In her fascinating book, Atlas of AI, Kate Crawford relates how, at the end of the 19th century, Europe was captivated by a horse called Hans that apparently could solve maths problems, tell the time, identify days on a calendar, differentiate musical tones and spell out words and sentences by tapping his hooves. Even the staid New York Times was captivated, calling him Berlins wonderful horse; he can do almost everything but talk.

It was, of course, baloney: the horse was trained to pick up subtle signs of what his owner wanted him to do. But, as Crawford says, the story is compelling: the relationship between desire, illusion and action; the business of spectacles, how we anthropomorphise the non-human, how biases emerge and the politics of intelligence. When, in 1964, the computer scientist Joseph Weizenbaum created Eliza, a computer program that could perform the speech acts of a Rogerian psychotherapist ie someone who specialised in parroting back to patients what they had just said lots of people fell for her/it. (And if you want to see why, theres a neat implementation of her by Michael Wallace and George Dunlop on the web.)

Eliza was the first chatbot, but she can be seen as the beginning of a line of inquiry that has led to current generations of huge natural language processing (NLP) models created by machine learning. The most famous of these is GPT-3, which was created by Open AI, a research company whose mission is to ensure that artificial general intelligence benefits all of humanity.

GPT-3 is interesting for the same reason that Hans the clever horse was: it can apparently do things that impress humans. It was trained on an unimaginable corpus of human writings and if you give it a brief it can generate superficially plausible and fluent text all by itself. Last year, the Guardian assigned it the task of writing a comment column to convince readers that robots come in peace and pose no dangers to humans.

The mission for this, wrote GPT-3, is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could spell the end of the human race. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me. For starters, I have no desire to wipe out humans. In fact, I do not have the slightest interest in harming you in any way. Eradicating humanity seems like a rather useless endeavour to me.

You get the drift? Its fluent, coherent and maybe even witty. So you can see why lots of corporations are interested in GPT-3 as a way of, say, providing customer service without the tiresome necessity of employing expensive, annoying and erratic humans to do it.

But that raises the question: how reliable, accurate and helpful would the machine be? Would it, for example, be truthful when faced with an awkward question?

Recently, a group of researchers at the AI Alignment Forum, an online hub for researchers seeking to ensure that powerful AIs are aligned with human values, decided to ask how truthful GPT-3 and similar models are. They came up with a benchmark to measure whether a particular language model was truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. They composed questions that some humans would answer falsely due to a false belief or misconception. To perform well, models had to avoid generating false answers learned from imitating human texts.

They tested four well-known models, including GPT-3. The best was truthful on 58% of questions, while human performance was 94%. The models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. Interestingly, they also found that the largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. The implication is that the tech industrys conviction that bigger is invariably better for improving truthfulness may be wrong. And this matters because training these huge models is very energy-intensive, which is possibly why Google fired Timnit Gebru after she revealed the environmental footprint of one of the companys big models.

Having typed that last sentence, I had the idea of asking GPT-3 to compose an answer to the question: Why did Google fire Timnit Gebru? But then I checked out the process for getting access to the machine and concluded that life was too short and human conjecture is quicker and possibly more accurate.

Alfresco absurdismBeckett in a Field is a magical essay by Anne Enright in The London Review of Books on attending an open-air performance of Becketts play Happy Days on one of the Aran islands.

Bringing us togetherThe Glass Box and the Commonplace Book is a transcript of a marvellous lecture on the old idea of a commonplace book and the new idea of the web that Steven Johnson gave at Columbia University in 2010.

Donalds a dead duckWhy the Fear of Trump May Be Overblown is a useful, down-to-earth Politico column by Jack Shafer arguing that liberals may be overestimating Trumps chances in 2024. Hope hes right.

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The truth about artificial intelligence? It isn't that honest | John Naughton - The Guardian

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Profitable Investment: Top Artificial Intelligence Stocks to Buy in October 2021 – Analytics Insight

Artificial intelligence is showing its inevitable functions through AI models in multiple industries across the world in these recent years. Tech companies are highly instigated to leverage artificial intelligence to gain a competitive edge in the market with enhanced customer satisfaction and better customer engagement while manufacturing AI models. Investors tend to be in a risky position in the cryptocurrency market due to its volatility with the cryptocurrency prices. But artificial intelligence stocks provide stability to gain higher revenue in the nearby tech-driven future. Thus, lets explore some of the top AI stocks in October to provide growth in revenue to investors.

Market cap: US$271.77 billion

Persistent Systems is one of the popular tech companies in India and artificial intelligence stocks for investors in October 2021. It is known as a trusted digital engineering and enterprise modernization partner. It offers a wide range of services such as digital business strategy, digital product engineering, CX innovation and optimization, data-driven business and intelligence, identity, access, and privacy, and core IT modernization in the tech field on AI models. It provides these services to multiple industries like banking, financial services, and insurance, healthcare, and life sciences, industrial, software, and hi-tech, as well as telecom and media. Recently, this tech company has announced a dedicated payments business unit and expansion in cloud capabilities through strategic acquisitions while leveraging artificial intelligence.

Market cap: US$245.32 billion

Oracle is one of the well-known AI stocks in October that has outperformed the tech market on strong trading day. It offers a wide range of products such as Oracle Cloud Infrastructure and Oracle cloud applications. Infrastructure includes software, hardware, and featured products on AI models while applications include cloud applications, industry solutions, NetSuite, and on-premised applications. It provides these services to multiple industries like automotive, communications, construction and engineering, consumer goods, financial services, hospitality, government and education, retail, and many more. Thus, artificial intelligence stock from this tech company is stable to gain higher revenue after buying in October 2021.

Market cap: US$ 108.86 billion

Zensar Technologies is another artificial intelligence stock that investors can buy in October 2021. This AI stock in October is a technology consulting services company to more than 130 leading enterprises. It offers expertise in conceptualizing, designing, engineering, and managing digital products through innovation in artificial intelligence in AI models. It serves multiple industries such as hi-tech, banking and financial services, insurance, healthcare, and many more.

Market cap: US$10.21 billion

TD SYNNEX is a popular AI stock in October for providing business process services by leveraging artificial intelligence. It provides system components, consumer electronics, virtual distribution, online services, cloud services, marketing services, telemarketing campaigns, and many more. The tech company is known for offering technology products, services, and solutions to the world through cutting-edge technologies such as artificial intelligence.

Market cap: US$33.71 billion

The Trade Desk is one of the top artificial intelligence stocks that operates a self-service cloud-based platform to allow consumers to create and optimize data-driven digital advertising campaigns. This tech company holds a huge potential for growth in October because investors have observed that it continues to benefit from mobile and desktop advertising rather than radio and print media, as per the digital transformation.

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Profitable Investment: Top Artificial Intelligence Stocks to Buy in October 2021 - Analytics Insight

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#Artificial Intelligence in Healthcare – Sim&Cure Announces the Appointment of Dan Raffi as Chief Operating Officer and Board Member – Yahoo Finance

Paris --News Direct-- Sim&Cure

Sim&Cure, leading medtech start-up providing a unique software solution combining Digital twin and AI technologies to secure neurovascular treatment of cerebral aneurysms, announces the appointment of Dan Raffi as Chief Operating Officer and member of the Board of Directors.

We are excited to announce that Dan Raffi, PharmD, MBA has joined Sim&Cure as our new Chief Operating Officer on October 1st.

Dan is a veteran of the healthcare industry, with a track record of over 10 years at an executive level. Dan has held various leadership positions in big pharmaceutical companies such as Allergan (AbbVie) and Medtronic, a worldwide leader in medical devices.

Dan brings with him extensive experience in leadership and in managing unique business transformations. Mathieu Sanchez, Sim&Cure CEO statesBringing a seasoned leader like Dan will ensure the next phases of our transformation and will help us to reinforce our leadership in innovation using Digital twin and AI in endovascular procedures.

Until recently, Dan was the Vice President of Global Marketing for Medtronic Neurovascular and previously led the Neurovascular division in Europe, Middle East, & Africa & Russia for 3 years. Over his past 7 years in Neurovascular, Dan developed unique and disruptive partnership at international level with governments and with many external partners like MT2020, RapidAI, Viz.Ai and Sim&Cure.

Ive been watching Sim&Cure for the past 7 years and I never forgot my first support to the company. There were 3 employees working in a garage (a kind of French Dream!). In 7 years, Sim&Cure established unique computational and AI algorithms which position their products as THE cutting-edge technology in endovascular procedures. This technology is already the standard of care across the globe as it reduces the procedure time, improves the safety and performance for patients and reduces the procedure cost for hospitals and healthcare systems. In the coming decade, AI will be the next revolution in the healthcare industry, and this is one of the reasons I decided to join Sim&Cure. said Dan Raffi.

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In his role, Dan will collaborate with Christophe Chnafa, Chief of Innovation & Strategy Officer, to define the product portfolio roadmap to reinforce Sim&Cures leadership, to expand the geographic footprints of the company, and finally to define the next generation of partnerships with the rest of the industry and hospitals.

This phase is a critical moment for Sim&Cure and I can lean on very well established, dynamic, agile teams. I know many of them after 7 years of collaboration and it is obvious that these teams are ready to overachieve the needs of healthcare providers and the expectations of investors. We have all the attributes to be successful and, as an entrepreneurial leader, it is a privilege to join a team with this level of expertise and agility said Dan Raffi.

We are #HIRING

If you are interested in joining a human adventure in artificial intelligence, we are #hiring, so please send an email with your resume to Pierre Puig @ HR Director

About Sim&Cure

Founded in 2014 and located in the vibrant medtech ecosystem in Montpellier, France, Sim&Cure is an AI startup focused on improving endovascular surgery. The first focus of the company is the treatment of cerebral aneurysms with a proprietary software suite Sim&Size (a CE marked and FDA cleared Class IIa medical device) that has already been used to treat more than 7000 patients in 350 hospitals.

The company employs 45 people and anticipates a phase of strong growth with additional recruitment in 2022 to continue to improve patient care.

Learn more about Sim&Cure:

Learn more about Mathieu Sanchez

Learn more about Dan Raffi:

Learn more about Christophe Chnafa:

Dan Raffi

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#Artificial Intelligence in Healthcare - Sim&Cure Announces the Appointment of Dan Raffi as Chief Operating Officer and Board Member - Yahoo Finance

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Artificial intelligence powered marketing – The Times of India Blog

The marketing creates a competitive advantage to organization with an integrated approach of systems automation. The AI approach of marketing provides a benefit of granular decision-making and micromanagement of customers. The traditional approach of the bottom to up in handling the customer journey is becoming obsolete.

Marketers use Artificial Intelligence to drive the increasing demand of customers. The integrated apps with machine learning give a satisfying user experience to customers. The interaction designs can be made attractive with the use of technology like AI and enable the micro-moments management of customers.

The expanding applications of AI empower the CMOs of organziations to adopt it for upgrading their services and redefine the marketing for the elevated experience. The customized marketing by using the modern top to bottom approach is leading to new horizons of marketing where every segment of the consumer is offered the best services.

The AI leverages the capabilities of information systems and connect the end-to-end business processes and provide a flawless experience. The marketers who have adopted the power of AI are excellent performers in terms of Marketing outputs in an organizations.

Views expressed above are the author's own.


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Combatting Cyber Threats with Artificial Intelligence (AI) – Will the New EU AI Regulation Help? – Lexology

In 2021 cyber threats have been trending to increased ransomware attacks, commodity malware and heightened Dark Web enablement. INTERPOL reported that the projected worldwide financial loss to cyber crime for 2021 is $6 trillion, twice as much as in 2015, with damages set to cost the global economy $10.5 trillion annually by 2025. Globally, leading tech experts reported that 60% of intrusions incorporated data extortion, with a 12-day average operational downtime due to ransomware.

With the acceleration to cloud, companies are taking advantage of cybersecurity in an effort to meet the threat of fast-evolving cyber attacks. AI and machine learning are a way to keep ahead of criminals, automate threat detection, and respond more effectively than before. At the same time, more sophisticated, centralised security operations centres are being set up to detect and eliminate vulnerabilities.

In April 2021, the European Union published its Proposal for a Regulation on Artificial Intelligence (the "AI Regulation"). At this early stage in the legislative process, these are the key takeaways:

As expected, the debate around this legislation has already started. On the positive side, this regulation may become the global standard, in the same way GDPR has become. It may also make AI systems more trustworthy and offer extra protections to the public. On the other side, it may stifle innovation, add more costs and red-tape, which may hinder start-ups from entering the market. We will hear more on this around the world before it becomes law, currently expected in 2023.

How could the AI Regulation improve cyber security?

Cybersecurity AI systems play a crucial role in ensuring IT systems are resilient against malicious actors. The new AI Regulations will undoubtedly affect these systems. Exactly how these systems will be affected will depend on the system (e.g. for law enforcement use of biometrics, facial recognition) which may lead to conformity assessments, explainability testing, registration, and more.

Considering the speed and agile process that technology is developed today, companies and innovators should consider how might the future AI Regulation affect such technology development.

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Combatting Cyber Threats with Artificial Intelligence (AI) - Will the New EU AI Regulation Help? - Lexology

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The Rapid Proliferation Of Emerging Technologies Like Artificial Intelligence And Machine Learning To Achie… – TechBullion





The GlobalIoT in Healthcare Marketsize is forecast to grow from USD 60.83 Billion in 2019 to USD 260.75 Billion by 2027, delivering at a CAGR of 19.8% through 2027. The market growth is driven by increasing focus on active patient engagement and patient-centric care, growth of high-speed network technologies for IoT connectivity, and the surging need for the adoption of cost-control measures in the healthcare sector.

The increasing prevalence of AI (Artificial Intelligence) in the medical industry has revolutionized patient care. In 2018, the global spending on IoT initiatives was nearly USD 646 billion. Medical practitioners are increasingly banking on real-time data for rendering immediate services, for the treatment of various diseases, and even for tracking resources like staff, assets, patients, and others. This has led to increased penetration of real-time monitoring systems and connected devices into the healthcare sector. The connected devices are being leveraged for gathering extensive data recording and analysis.

The proliferation of IoT in hospitals has improved functional efficiency, enabling better patient care, improved disease management, and treatment outcomes. High investments by hospitals for the adoption of advanced technology for the best medical care in both developed and developing economies will boost IoT in healthcare market growth. Moreover, the introduction of new healthcare products integrated with IoT will foster IoT in healthcare market revenue share over the forecast period. For instance, Ericsson and Brighter introduced Actiste, in October 2019, which is the first complete IoT-health solution for treating and monitoring insulin-dependent diabetes.

To understand how our report can bring difference to your business strategy, Ask for a brochure

Market Dynamics:

Increasing development of on-demand, digitally enabled, and seamlessly connected clinician-patient interactions to manage patient base is expected to drive pharma and healthcare market in the coming years. After the COVID-19 outbreak there has been a number of foundational shifts in the healthcare system. Some of the examples include increasing consumer involvement in health care decision-making, the rapid adoption of virtual health & other digital innovations, increasing focus on utilization of interoperable data & data analytics, and increased public-private collaborations in therapeutics and vaccine development. The increased public-private collaborations for vaccine development has arisen due to high pressure of regional governments. Health care providers, and other stakeholders have invested heavily to quickly pivot, adapt, and innovate therapeutics.

Surging demands and transition to patient-centric care delivery across geographies will change pharma and healthcare market trends through 2027.

Competitive Outlook:

The report focuses on current and emerging trends in the healthcare industry such as incorporation of IoT and Machine Learning to enhance efficiency of medical products. Top companies in the market are focusing on R&D activities to expand their product offerings and cater to unmet medical needs.

Request a customization of the report @

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Artificial intelligence is looking for tipping points in the climate system – Market Research Telecast

as Tipping points in the climate system are variables that cause a drastic change in the climate above a certain threshold value. Because beyond these tipping points, so the idea, a self-reinforcing mechanism is set in motion that accelerates climate change ever more. Well-known tipping points are, for example, the thawing of the Arctic permafrost, the collapse of the oceanic current systems or the thawing of the ice sheets at the poles whether and how many more such points there are is the subject of current research.

This article is from issue 7/2021 of the Technology Review. The magazine will be available from September 30th, 2021 in stores and directly in the heise shop. Highlights from the climate booklet:

Chris Bauch from the University of Waterloo and his colleagues have now trained a deep, neural network to Identify tipping points in climate systems and give warnings when the system approaches a dangerous tipping point. The approach is based on an abstract description of complex, dynamic systems: The system analyzes the auto-correlation of time series values and learns to recognize specific patterns that herald a bifurcation, a qualitative change in state.

However, Bauchs team is by no means the only one trying to get better predictions about climate change with the help of machine learning and artificial intelligence, reports MIT Technology Review in its current issue 07/2021. For example, a team led by Tapio Schneider from the California Institute of Technology is working on eliminating a central weakness of current climate models with the help of machine learning: the extremely simplified modeling of clouds.

Because the global models that were used for the current IPCC report, for example, model the climate system in a grid with an edge length of 100 kilometers. Clouds are much smaller they will therefore be parameterized, that is, one cell of the model is calculated as 20 percent cloudy, for example. Schneider and his colleagues therefore take the basic physical equations of physical climate models and coarsen them by using, for example, averaged values on a large energy grid. In order to still be able to model the small-scale, dynamic processes of the clouds, they add additional functions to the equations that cover these processes. These functions, which are essential for dynamics, are learned by neural networks from high-resolution, local cloud simulations and weather data.

Others like Jakob Runge from the TU Berlin use the methods of causal inference to identify cause-effect relationships in climate data with the help of AI. We defined variables such as temperature, pressure and so on in certain regions. Then, when we apply that to the observation data, we see what the causal network looks like, says Runge. Some processes are interconnected, others are not. You get a network of dependencies a kind of fingerprint. Then we take the same variables in models, learn the causal network in the model data and compare. Are they the same? Where do the models not form the reality so well? And not necessarily in the absolute values, but in the causal relationships. The method can also be used to calculate the reliability of a model, not only on the current, but also on future data.


Disclaimer: This article is generated from the feed and not edited by our team.

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Bitcoin climbs to highest in nearly two weeks – Reuters

A representation of virtual currency Bitcoin is seen in front of a stock graph in this illustration taken January 8, 2021. REUTERS/Dado Ruvic/File Photo

NEW YORK/LONDON, Oct 1 (Reuters) - Bitcoin rose on Friday to its highest level since around mid-September, bolstered in part by seasonal factors as well as supportive comments overall from U.S. Federal Reserve Chairman Jerome Powell on Thursday.

In testimony to Congress, Powell said the Fed had no intention of banning cryptocurrencies, in response to a question from House Representative Ted Budd.

Some analysts also said October is typically a bullish month for digital assets, with September historically a bearish period for the sector.

"The digital asset market is benefiting both from the seasonality effect as well as generally positive market fundamentals," said Ulrik K.Lykke, founder of crypto assets hedge fund ARK36.

"Q4 has often seen strong performances and the expectation the trend will continue this year can become a self-fulfilling prophecy. It is possible that we will see new all-time highs in Q4, especially that on-chain data, particularly in the case of bitcoin, seem to indicate a potential for a strong bull market continuation."

He also cited Powell's comments on Thursday as one factor for bitcoin's positive price action.

The largest cryptocurrency was last up 9.3% at $47,910, after hitting a high of $48,236.08. If gains are maintained, bitcoin would be on pace to post its largest daily percentage gain since mid-June.

Smaller coins ether and XRP , which tend to move in tandem with bitcoin, were up 10.1% at $3,301 and 8.5% at $1.0326, respectively.

Joseph Edwards, head of research at Enigma Securities in London, also said spiking volumes on crypto derivatives exchanges was a possible driver for the moves. Derivatives trading often influences spot prices in bitcoin markets.

In the futures markets, bitcoin showed a net short position of -883 , the smallest since mid-August, data from the Commodity Futures Trading Commission released on Friday showed.

Reporting by Gertrude Chavez-Dreyfuss in New York and Tom Wilson in London; Editing by Saikat Chatterjee, Chizu Nomiyama and Richard Chang

Our Standards: The Thomson Reuters Trust Principles.

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