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michelangelo


Artist Uses Artificial Intelligence To Reconstruct Realistic Portraits of Historical Figures

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Have you ever wondered what famous historical figures like Nefertiti and Cleopatra looked like in real life? Well, Bas Uterwijk might be able to show you a pretty good guess. The Dutch photographer and digital artist creates amazing AI portraits of famous historical figures using innovative neural network reconstructions. To create these portraits, Uterwijk uploads numerous references of the person's likeness to the AI applications. Then, he makes small adjustments to the program until he is satisfied with the result.


Art is Dead

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I am bad at painting but I do treasure and respect the art created by people hundreds of years ago. What is the difference between Leonardo Da Vinci and you? That may not be easy to answer, so, let me change the perspective. What is the difference between the paintings created by Da Vinci and you? The answer is probably a lot.


Michelangelo Lives Again Thanks to Artificial Intelligence - IssueWire

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The Opera di Santa Maria del Fiore commissioned Michelangelo two of his most celebrated masterpieces the DAVID and ST. More than 500 years after his birth (6 March 1475), Michelangelo "lives again", and is ready to answer your questions! This is not fake news, but a joint project of the Opera di Santa Maria del Fiore (Florence Cathedral Foundation), and Querlo, Customized Artificial Intelligence Solutions based in New York, who for the first time ever have realized a virtual Michelangelo, using Artificial Intelligence technology. MICHELANGELO AI is an educational tool available to all who want to know something about the art, life, and thought of the greatest Renaissance artist. Anyone can ask him questions (his language is now English) at the sites: https://duomo.firenze.it/it/home


5 Lessons Uber Learned From Running Machine Learning at Scale

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The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.


A Tour of End-to-End Machine Learning Platforms

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Michelangelo can deploy multiple models in the same serving container, which allows for safe transitions from old to new model versions and side-by-side A/B testing of models. The original incarnation of Michelangelo did not support deep learning's need to train on GPUs, but that the team addressed that omission in the meantime. The current platform uses Spark's ML pipeline serialization but with an additional interface for online serving that adds a single-example (online) scoring method that is both lightweight and capable of handling tight SLAs, for instance, for fraud detection and prevention. It does so by bypassing the overhead of Spark SQL's Catalyst optimizer. Noteworthy is that both Google and Uber built in-house protocol buffer parsers and representations for serving, avoiding bottlenecks present in the default implementation. Airbnb established their own ML infrastructure team in 2016/2017 for similar reasons. First, they only had a few models in production, but building each model could take up to three months. Second, there was no consistency among models. And third, there were large differences between online and offline predictions.


Learn AI and ML on the go!

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As the great Michelangelo said once -- "I'm still learning". Aren't we all still learning and always continue to do so? In today's world, everything is super connected and we can get anything on-demand. What an exciting time to be in this world! The field of Artificial Intelligence and Machine Learning has grown multiple-folds in just the last decade.


Productionizing ML Models with Proper Data - Gestalt IT

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In recent years, a paradigm shift has occurred. Following a Tecton briefing, Andy Thurai delves into what is involved in Machine Learning. From a code-based/compute-based economy, the enterprises have moved to data-based economies. Data happens to be the most important part of the data economy and Machine Learning (ML) models. It's also the very difficult part of the whole ML process to get it right.


Artificial intelligence can save the Food Industry.

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Rarely has a crisis accelerated the adoption of a technology in the manner that is occurring today with AI in the food industry. The business of selling food to consumers is being disrupted to a degree not since the last pandemic, over 100 years ago. It is increasingly apparent that our food system was ill prepared ('anti-fragile') for this Covid-19 induced crisis. With restaurants shuttered, a dramatic return to home cooking, a re-ignition in the meal-kit movement, shut-downs of meat factories and office canteens, and explosion of home delivery it may seem as though the world will never be the same again. This too, of course, will pass, but instead of being a 6 month blip, the continued deconstruction and automation of the food supply process makes it clear that we are entering a new norm, and that returning to the world as we knew it won't be possible.


How Big Tech use Machine Learning?

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Let's begin with what is machine learning? Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Popular uses of Machine learning are Play Store and App Store recommendations, google maps, email filtering, google translate, google search and so on and so forth. Let's look at 5 machine learning use case in details: They tried 3 different solution which involved using LSTM (Long short-term memory) which gained notable accuracy gains but led to serving delay since LSTM are computationally extortionate. The second solution was to replace LSTM with a Transformer model which is used for sequence-to-sequence prediction and has produced a significant result in NLP.


A Tour of End-to-End Machine Learning Platforms - KDnuggets

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Michelangelo can deploy multiple models in the same serving container, which allows for safe transitions from old to new model versions and side-by-side A/B testing of models. The original incarnation of Michelangelo did not support deep learning's need to train on GPUs, but that the team addressed that omission in the meantime. The current platform uses Spark's ML pipeline serialization but with an additional interface for online serving that adds a single-example (online) scoring method that is both lightweight and capable of handling tight SLAs, for instance, for fraud detection and prevention. It does so by bypassing the overhead of Spark SQL's Catalyst optimizer. Noteworthy is that both Google and Uber built in-house protocol buffer parsers and representations for serving, avoiding bottlenecks present in the default implementation. Airbnb established their own ML infrastructure team in 2016/2017 for similar reasons. First, they only had a few models in production, but building each model could take up to three months. Second, there was no consistency among models. And third, there were large differences between online and offline predictions.