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Why digital transformation success depends on good governance

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The COVID-19 crisis forced businesses everywhere to fast track their digital transformation efforts. Faced with the stark choice of becoming a digital-first business, or having no business at all, companies that were previously behind the curve had to implement everything from remote working to entire digital storefronts in a matter of days. According to research by McKinsey, the digital initiatives unleashed in response to the pandemic leapfrogged seven years of progress in a matter of months as companies acted 20 to 25 times faster than they had believed was possible. In the process, this acceleration of digital during the crisis brought about a sea change in executive mindsets with regard to the role of technology in business. Fast forward to today, and corporate leaders are now investing in technology for competitive advantage, refocusing their entire business around cutting-edge technologies, and initiating a business culture where experimentation and innovation is actively encouraged.


Artificial intelligence in oncology: current applications and future perspectives – Docwire News

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Br J Cancer. 2021 Nov 26. doi: 10.1038/s41416-021-01633-1. Online ahead of print. ABSTRACT. Artificial intelligence (AI) is concretely reshaping …


If AI only had a brain: Is the human mind the best model to copy?

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Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. The Holy Grail of AI research is called "general artificial intelligence," or GAI. A machine imbued with general intelligence would be capable of performing just about any task a typical adult human could. The opposite of general AI is narrow AI – the kind we have today.


The 9 Principles of Ethical AI in Healthcare Industry

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It is noticed that AI systems are not neutral and not providing valid outcomes. AI in Healthcare can raise ethical issues and can harm patients by not giving intended outcomes. Therefore it is necessary to use Ethical AI in the health industry. Akira AI provides Ethical AI systems that are taking care of ethical issues and values. The 9 principles that are responsible for ethical AI in healthcare and provide a framework to help technologists while designing, developing, or maintaining systems.


AI must have its own goals to be truly intelligent

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Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Is it the capacity to solve complicated mathematical problems at very fast speeds? The power to beat world champions in chess and go? The ability to detect thousands of different objects in images? Those are all manifestations of intelligence. And thanks to advances in artificial intelligence, we have been able to replicate them in computers, to different degrees of success.


The 4 Top Artificial Intelligence Trends For 2021

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Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry. The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn't diminished the impact AI is having on our lives. In fact, it's become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future. AI undoubtedly remains a key trend when it comes to picking the technologies that will change how we live, work, and play in the near future. So, here's an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.


Building value-chain resilience with AI

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Across industries, value chains are facing increasing uncertainty from climatic anomalies, market volatility, and the COVID-19 pandemic, among other factors. Industries as diverse as agriculture, oil and gas, and mining face essentially the same problem: they need the ability to both run with increased efficiency and recover quickly from unforeseen or unexpected challenges. But these two goals often conflict. If companies simply increase production levels, they'll inevitably run into bottlenecks--and if failures occur that worsen those bottlenecks, the entire network can slow down and become less resilient. For more on how COVID-19 has affected supply chains, see Knut Alicke, Richa Gupta, and Vera Trautwein, "Resetting supply chains for the next normal," July 21, 2020. Resolving this conflict presents several challenges.


Retinal Vessel Segmentation Algorithm based on Residual Convolution Neural Network

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Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal blood vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90% and 96.88%, and the average specificity is 98.85% and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.


La veille de la cybersécurité

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Artificial intelligence is present in everyday life, from booking flights and applying for loans to steering driverless cars. It is also used in specialized fields such as cancer screening or to help create inclusive environments for the disabled. According to UNESCO, AI is also supporting the decision-making of governments and the private sector, as well as helping combat global problems such as climate change and world hunger. However, the agency warns that the technology'is bringing unprecedented challenges'. "We see increased gender and ethnic bias, significant threats to privacy, dignity and agency, dangers of mass surveillance, and increased use of unreliable AI technologies in law enforcement, to name a few. Until now, there were no universal standards to provide an answer to these issues", UNESCO explained in a statement.


Artificial intelligence in oncology: current applications and future perspectives - British Journal of Cancer

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Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.