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Why Google believes machine learning is its future

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One of the most interesting demos at this week's Google I/O keynote featured a new version of Google's voice assistant that's due out later this year. A Google employee asked the Google Assistant to bring up her photos and then show her photos with animals. She tapped one and said, "Send it to Justin." The photo was dropped into the messaging app. From there, things got more impressive.


Three Ways To Leverage AI And Keep Pace With The Future Of Digital Marketing

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From personal assistants to legal counsel on parking fines, artificial intelligence (AI) and machine learning (ML) have established their potential as disruptive technology that will alter industries. With each passing day, further discoveries enable AI to become more sophisticated and viable in our world. Naturally, like all things digital, AI has had a profound impact on digital marketing as well. From Google's RankBrain search engine algorithm to Amazon's personalized recommendations, it is powering the world's leading organizations and changing the face of the modern digital marketing landscape. Currently, I work as senior vice president of marketing at CUJO AI, an AI-driven network security and intelligence company.


Google's AI boosts accuracy of lung cancer diagnosis, study shows - STAT

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One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.



Reaching New Heights with Artificial Neural Networks

Communications of the ACM

Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio spent their careers developing are today an integral component of everything from search to content filtering. Here, the three researchers share what they find exciting, and which challenges remain. There's so much more noise now about artificial intelligence than there was when you began your careers--some of it well-informed, some not. What do you wish people would stop asking you? GEOFFREY HINTON: "Is this just a bubble?"


The US Air Force is enlisting MIT to help sharpen its AI skills

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It's not yet clear how this collaboration will go down, especially since the military's previous efforts to collaborate with industry have proved problematic. Most notably, a project involving Google's Cloud AI team, established through a program known as Maven, sparked a backlash among employees. This involved using the Cloud platform to identify objects in aerial images, and some worried that it could eventually lead to using AI to target weapons. As a result, Google chose not to renew its contract with the Air Force and issued a new AI code of ethics, which precludes working on technology that could be weaponized.


Hey Siri, turn on 'The Voice' for my Vizio TV

USATODAY - Tech Top Stories

Sure, Siri can open Netflix for you and search for a George Clooney movie, but only if you spring $179 to $199 for the Apple TV accessory streamer. Now, Apple's personal assistant can turn on the TV, change the channel and find a specific TV show, on certain newer TVs from Vizio, Samsung, Sony and LG. It's part of a radical rethink on Apple's part to bring Apple outside of the ecosystem, and onto mainstream television sets. Samsung pushed out Apple's AirPlay features on new smart TVs that began shipping May 13. AirPlay lets you mirror what's on your device.


Google's lung cancer detection AI outperforms 6 human radiologists

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Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.


Artificial intelligence system spots lung cancer before radiologists

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CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Artificial intelligence system spots lung cancer before radiologists

#artificialintelligence

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.