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Explain Yourself - A Primer on ML Interpretability & Explainability

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The project to define what the late Marvin Minsky refers to as a suitcase word -- words that have so much packed inside them, making it difficult for us to unpack and understand this embedded intricacy in its entirety -- has not been without its fair share of challenges. The term does not have a single agreed-upon definition, with the dimensions of description shifting from optimization or efficient search space exploration to rationality and the ability to adapt to uncertain environments, depending on which expert you ask. The confusion becomes more salient when one hears news of machines achieving super-human performance in activities like Chess or Go -- traditional stand-ins for high intellectual aptitude -- but fail miserably in tasks like grabbing objects or moving across uneven terrain, which most of us do without thinking. But, several themes do emerge when we try to corner the concept. Our ability to explain why we do what we do makes a fair number of appearances in the list of definitions proposed by multiple disciplines.


Deep learning-based artificial intelligence applications in prostate MRI: brief summary

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Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement.


Top 12 AI and machine learning announcements at AWS re:Invent 2021

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This week during its re:Invent 2021 conference in Las Vegas, Amazon announced a slew of new AI and machine learning products and updates across its Amazon Web Services (AWS) portfolio. Touching on DevOps, big data, and analytics, among the highlights were a call summarization feature for Amazon Lex and a capability in CodeGuru that helps detect secrets in source code. Amazon's continued embrace of AI comes as enterprises express a willingness to pilot automation technologies in transitioning their businesses online. Fifty-two percent of companies accelerated their AI adoption plans because of the COVID pandemic, according to a PricewaterhouseCoopers study. Meanwhile, Harris Poll found that 55% of companies accelerated their AI strategy in 2020 and 67% expect to further accelerate their strategy in 2021.


This AI Reads Privacy Policies So You Don't Have To

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And of course, that's because they're not actually written for you, or any of the other billions of people who click to agree to their inscrutable legalese. Instead, like bad poetry and teenagers' diaries, those millions upon millions of words are produced for the benefit of their authors, not readers--the lawyers who wrote those get-out clauses to protect their Silicon Valley employers. But one group of academics has proposed a way to make those virtually illegible privacy policies into the actual tool of consumer protection they pretend to be: an artificial intelligence that's fluent in fine print. Today, researchers at Switzerland's Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis--short for "privacy policy analysis"--a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service's privacy policy, so you don't have to. In about 30 seconds, Polisis can read a privacy policy it's never seen before and extract a readable summary, displayed in a graphic flow chart, of what kind of data a service collects, where that data could be sent, and whether a user can opt out of that collection or sharing.


A quick survey of the AI/ML applications in Telecoms

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The telecom is an essential part of our lives in the modern world. With the advent of 5G era and the rapid advance in other technology, the telecom network equipment is growing dramatically, which brings new complexities and challenges to operations - the management of co-existence of new and legacy networks. This causes a huge interest in AI among telecoms in a hope to resolve this inherent complexity. According Tractica's prediction, the telecom industry is going to invest $36.7 billion annually in AI developments. The global AI in telecommunication market is expected to reach $14.99B.


Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking

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Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.


Digital twins and artificial intelligence - AIMed

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"Real excellence and humility are not incompatible with one another, on the contrary, they are twins." A digital twin was originally designed to be a virtualized representation of a physical object or system. Digital twin is more of a concept rather than a single technology, and entails the use of big data, Internet of things, blockchain, edge computing, cloud computing, and artificial intelligence. These digital twins are currently being applied, not only to objects and systems, but also as simulation tools for processes, operations, and even experiences and behaviors. With the escalating capabilities of cloud-based data storage and artificial intelligence, these simulations, enabled with digital twins, are reaching new heights.


How driverless cars will change our world

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Under the artificial glare of street lamps, a car can be seen slowly approaching. A green and blue'W' glows from the windscreen, giving off just enough light to see inside – to a completely empty driver seat. When they open the door to climb inside, a voice greets them over the vehicle's sound system. "Good evening, this car is all yours – with no one upfront," it says. This is a Waymo One robotaxi, hailed just 10 minutes ago using an app.


Council Post: 14 Ways AI Will Benefit Or Harm Society

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Artificial intelligence (AI) is on the rise both in business and in the world in general. How beneficial is it really to your business in the long run? Sure, it can take over those time-consuming and mundane tasks that are bogging your employees down, but at what cost? With AI spending expected to reach $46 billion by 2020, according to an IDC report, there's no sign of the technology slowing down. Adding AI to your business may be the next step as you look for ways to advance your operations and increase your performance. To understand how AI will impact your business going forward, 14 members of Forbes Technology Council weigh in on the concerns about artificial intelligence and provide reasons why AI is either a detriment or a benefit to society.


Creating a Tinder bot using artificial intelligence

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I first think about this idea when I was studying artificial intelligence in a bootcamp. We had to do a final project of our choice to apply what we learned and to face real case.