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How Artificial Intelligence Will Transform Business - Planet Compliance

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Just like the invention of steam power in 1780, electricity in 1870, computers in 1960, AI changes our world today. Although it has been a while since AI reached our doorstep, the potential it has to offer is huge. So how artificial intelligence is changing business today? AI is good at processing large amounts of data. For businesses, it opens new horizons for quick and well-considered decision-making, risk management, forecasting, logistics optimization, marketing personalization, etc.


6 AI Myths Debunked

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"Artificial intelligence (AI)I will automate everything and put people out of work." "AI is a science-fiction technology." "Robots will take over the world." The hype around AI has produced many myths, in mainstream media, in board meetings and across organizations. Some worry about an "almighty" AI that will take over the world, and some think that AI is nothing more than a buzzword.


Through the looking glass…the future of AI (Artificial Intelligence) - Technology - Australia

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This is the sixth, and final episode in a series dedicated to all things A.I. In this episode, Tae Royle, Head of Digital Products APAC from Ashurst Advance Digital is joined by Tara Waters, Partner and Head of Ashurst Advance Digital. This is the sixth and final episode in a series dedicated to all things Artificial Intelligence. My name is Tae Royle head of digital products from Ashurst did that digital and today I'm joined by Tara Waters partner and head of Ashurst Advanced Digital based out of our London office. Naturally we come to the question of what's next? In Lewis Carroll's second novel, Alice enters Wonderland by climbing through a mirror.


Global Big Data Conference

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Artificial intelligence is already impacting virtually every industry and every human being. This incredible technology has brought many good and questionable things into our lives, and it will create an even bigger impact in the next two decades. According to Ray Kurzweil, one of the most-known futurists, computers will have the same level of intelligence as humans by 2029. Kurzweil stated to Futurism, "2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence. I have set the date 2045 for the'Singularity' which is when we will multiply our effective intelligence a billion fold by merging with the intelligence we have created."


Black and Queer AI Groups Say They'll Spurn Google Funding

WIRED

Three groups focused on increasing diversity in artificial intelligence say they will no longer take funding from Google. In a joint statement released Monday, Black in AI, Queer in AI, and Widening NLP said they acted to protest Google's treatment of its former ethical AI team leaders Timnit Gebru and Margaret Mitchell, as well as former recruiter April Christina Curley, a Black queer woman. "The potential for AI technologies to cause particular harm to members of our communities weighs heavily on our organizations," the statement reads. "Google's actions in the last few months have inflicted tremendous harms that have reverberated throughout our entire community. They not only have caused damage but set a dangerous precedent for what type of research, advocacy, and retaliation is permissible in our community."


Trust is a must: why business leaders should embrace explainable AI - Raconteur

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"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.


Thought Leadership Webcast -- AI Ethics

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Amita Kapoor is an Associate Professor in the Department of Electronics, SRCASW, University of Delhi and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her masters in Electronics in 1996 and Ph.D. in 2011, during Ph.D. she was awarded a prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded the Best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has co-authored four books including the best-selling book "Deep learning with TensorFlow2 and Keras" with Packt Publications.


Machine Learning vs. Artificial Intelligence: Which Is the Future of Data Science? - Dataconomy

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When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential.


Public must help guide future of artificial intelligence, expert say

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Artificial intelligence simulates human thought, using machines programmed to reason like humans and mimic their actions.


Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain

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In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine learning methods LIME and SHAP and the alternative explanation approach CIU, centered on the Contextual Value and Utility (CIU). The produced explanations were evaluated using human evaluation.