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Azure Machine Learning でオンラインエンドポイントの Mirror Traffic 機能を使ってみる - Qiita

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はじめに Azure Machine Learning のマネージドオンラインエンドポイントには、メインのデプロイに流れるライブトラフィックの一部を別のデプロイにコピー (ミラーリング)する機能があります。ただあまり情報がなかったた...


State of AI in Financial Services

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Recently, Nvidia released a new report called the State of AI in Financial Services. To learn more, I caught up with Pahal Patangia, Global Developer Relations Lead for Consumer Fintech at Nvidia. Below is the transcript of our conversation (slightly edited for clarity). Theodora: Now, I know oftentimes when we think about Nvidia, we think about graphics cards. Nvidia is also a full stack, accelerated computing platform company that has been in the financial services space for 15 years.


Precision, Accuracy, Scale – And Experience – All Matter With AI

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When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. But more than anything else, particularly at the beginning of any data processing revolution, it is experience that matters most. Whether to gain it or buy it. With AI being such a hot commodity, many companies that want to figure out how to weave machine learning into their applications are going to have to buy their experience first and cultivate expertise later. This realization is what caused Christopher Ré, an associate professor of computer science at Stanford University and a member of its Stanford AI Lab, Kunle Olukotun, a professor of electrical engineer at Stanford, and Rodrigo Liang, a chip designer who worked at Hewlett-Packard, Sun Microsystems, and Oracle, to co-found SambaNova Systems, one of a handful of AI startups trying to sell complete platforms to customers looking to add AI to their application mix. The company has raised an enormous $1.1 billion in four rounds of venture funding since its founding in 2017, and counts Google Ventures, Intel Capital, BlackRock, Walden International, SoftBank, and others as backers as it attempts to commercialize its DataScale platform and, more importantly, its Dataflow subscription service, which rolls it all up and puts a monthly fee on the stack and the expertise to help use it. SambaNova's customers have been pretty quiet, but Lawrence Livermore National Laboratory and Argonne National Laboratory have installed DataScale platforms and are figuring out how to integrate its AI capabilities into the simulation and modeling applications. Timothy Prickett Morgan: I know we have talked many times before during the rise of the "Niagara" T series of many-threaded Sparc processors, and I had to remind myself of that because I am a dataflow engine, not a storage device, after writing so many stories over more than three decades. I thought it was time to have a chat about what SambaNova is seeing out there in the market, but I didn't immediately make the connection that it was you.


25 AI Insurance Companies You Should Know

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The insurance industry has always dealt in data, but it hasn't always been able to put that data to optimal use. With the rise of artificial intelligence, which analyzes and learns from massive sets of digital information culled from public and private sources, insurers are embracing the technology's many facets -- from machine learning and natural language processing to robotic process automation and audio/video analysis -- to provide better products. Customers, too, are benefitting from practices like comparative shopping, quick claims processing, around-the-clock service and improved decision management. To get a better sense of how AI impacts the insurance industry, check out these 25 AI insurance applications. Liberty Mutual explores AI through its initiative Solaria Labs, which experiments in areas like computer vision and natural language processing. Auto Damage Estimator is one result of these efforts.


3 Different Approaches for Train/Test Splitting of a Pandas Dataframe

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Usually, the Train/Test Splitting process is one of the Machine Learning tasks taken for granted. In fact, data scientists focus more on Data Preprocessing or Feature Engineering, delegating the process of dividing the dataset into a line of code. In this tutorial, I assume that the whole dataset is available as a CSV file, which is loaded as a Pandas Dataframe. Scikit-learn provides a function, named train_test_split(), which automatically splits a dataset into a training and test set. As input parameters of the function either lists or Pandas Dataframes can be passed.


A neural network picks promising antibiotics from a library of chemicals

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Biochemists have had some success designing drugs to meet specific goals. But much of drug development remains a tedious grind, screening hundreds to thousands of chemicals for a "hit" that has the effect you're looking for. There have been several attempts to perform this grind in silico, using computers to analyze chemicals, but they had mixed results. Now, a US-Canadian team reports that it modified a neural network to deal with chemistry and used it to identify a potential new antibiotic. Two factors greatly influence the success of neural networks: the structure of the network itself and the training it undergoes.


Researchers develop AI to find previously undiscovered rock art

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Researchers have developed a process using Machine Learning (ML) methods to find rock art in remote, hard-to-access areas of Australia. The study, co-led by Dr. Andrea Jalandoni, a digital archaeologist from Griffith University's Center for Social and Cultural Research, was published in the Aug. 2022 issue of the Journal of Archaeological Science. In the study, university researchers trained a ML model to detect whether painted rock art was present in an image by feeding it hundreds of images of rock art found in Kakadu National Park. The model achieved an impressive 89% success rate. Dr. Jalandoni told the Australian Associated Press, "Our machine learning model picks up whether an area photographed potentially contains previously undiscovered rock art, scientists can then go in and verify if there is rock art present and do more research."


AI can generate visuals from text, and eventually it may produce movies.

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The company claims that Google's new text-to-image AI outperforms the competitors. The software, Imagen, delivers a result based on inputted words, such as "a picture of a Persian cat playing the guitar on a beach while wearing a cowboy hat." Imagen is capable of creating both photorealistic and creative renderings. Other text-to-image generators like DALL-E, VQ-GAN CLIP, and Latent Diffusion Models are followed by Imagen. People who were asked to examine images made by Imagen and other text-to-image generators discovered that Google's model fared better than rivals in terms of accuracy and visual fidelity.


Juro

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How do we separate fact from fiction in machine learning and AI? In this whitepaper, introduced by Artificial Lawyer's Richard Tromans, Juro's lead data scientist, Dr. Matthew Upson, explores the myths, realities and possibilities of applying machine learning to contracts.


Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks

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The study population were patients with dilated cardiomyopathy, in which an explainable pre-trained deep neural network (FactorECG) was trained for the outcome of life-threatening ventricular arrhythmias. This network encoded the median beat ECG into 21 factors to generate an ECG using only these factors, allowing to evaluate most characteristics that make up an ECG automatically, in a relatively small dataset. LVAD, left ventricular assist device.