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A comprehensive collection of tools, libraries and resources that allows data scientists and machine learning engineers to build and deploy ML-powered business applications. ML platforms automate the delivery life-cycle of predictive applications capable of processing big data using machine learning algorithms.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why these are important and what organizations should do, no tools to help implement these principles have existed – until now.
Recently I attended the inaugural Emotion AI conference, organized by Seth Grimes, a leading analyst and business consultant in the areas of natural language processing, text analytics, sentiment analysis, and their business applications. So, what is emotion AI, why is it relevant, and what do you need to know about it?
SortSpoke’s novel approach to machine learning answers a longstanding problem in financial services – how to efficiently extract critical data from inbound, unstructured documents at 100% data quality.
Cloud Machine Learning Engine is a managed service that lets developers and data scientists build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. The training and prediction services within ML Engine are now referred to as AI Platform Training and AI Platform Prediction.
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Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.
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OpenAI builds free software for training, benchmarking, and experimenting with AI. OpenAI is an AI research company, discovering and enacting the path to safe artificial general intelligence.
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Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks. Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.
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The DataRobot Enterprise AI platform includes two independent but fully integrable machine learning model building products, and each can be deployed in multiple ways to match your business needs and IT requirements. All configurations feature a constantly expanding set of diverse, best-in-class algorithms from R, Python, H2O, Spark, and other sources, giving you the best set of tools for your machine learning and AI projects.
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H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
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Gamalon gets smarter faster by bringing together machine learning with domain-specific human understanding.
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Element AI is an artificial intelligence solutions provider that gives organizations unparalleled access to cutting-edge technology. Bringing together the best in entrepreneurship, technology and academic ecosystems, Element AI is building an AI-First World to elevate our collective wisdom.
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ViSenze powers visual commerce with artificial intelligence at scale for retailers, brands and publishers. Built on deep learning and computer vision, the company delivers intelligent solutions that simplify the consumer journey on search, discovery and recommendation of products.
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deepsense.ai is a data science company, which helps organizations gain competitive advantage by providing them with AI-based end-to-end solutions, with the main focus on computer vision, predictive analytics and natural language processing. The company also delivers machine learning and deep learning training programs to support enterprises in building AI capabilities in-house.
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Microsoft acquired Bonsai, an AI platform that empowers developers to build, teach and use intelligent systems, in June 2018. Its platform greatly simplifies the programming of control and optimization to create more intelligent systems and business processes. Bonsai's fundamentally different approach results in far more accessible, efficient and explainable models compared to alternatives. Bonsai’s platform combined with rich simulation tools and reinforcement learning work in Microsoft Research becomes the simplest and richest AI toolchain for building any kind of autonomous system for control and calibration tasks.
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TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production. TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models.
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Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.
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Torch is an open-source machine learning library, a scientific computing framework, and a script language based on the Lua programming language.[3] It provides a wide range of algorithms for deep learning, and uses the scripting language LuaJIT, and an underlying C implementation. As of 2018, Torch is no longer in active development.[4] However, PyTorch is actively developed as of August 2019.[5]
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Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical, graph and machine learning methods for structured and unstructured data.
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PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
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Chainer is a Python-based deep learning framework developed and provided by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively.Chainer quickly incorporates the results of the latest deep learning research. With additional packages such as ChainerRL (reinforcement learning), ChainerCV (computer vision), and ChainerChemistry (a deep learning library for chemistry and biology) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field.
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Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl, and Wolfram Language.) The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.
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Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Caffe is released under the BSD 2-Clause license.
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Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs.
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Amazon DSSTNE, Deep Scalable Sparse Tensor Network Engine (pronounced "Destiny") is an open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale. It is designed for production deployment of real-world applications which need to emphasize speed and scale over experimental flexibility.
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Aerosolve provides sophisticated machine learning features, such as geo-based features, controllable quantitization and feature interaction. Provide human intuition to machine models by specifying prior beliefs.
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At KNIME, we build software to create and productionize data science using one easy and intuitive environment, enabling every stakeholder in the data science process to focus on what they do best. One enterprise-grade software platform, two complementary tools. Open source KNIME Analytics Platform for creating data science and commercial KNIME Server for productionizing data science.
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Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why these are important and what organizations should do, no tools to help implement these principles have existed – until now.
Recently I attended the inaugural Emotion AI conference, organized by Seth Grimes, a leading analyst and business consultant in the areas of natural language processing, text analytics, sentiment analysis, and their business applications. So, what is emotion AI, why is it relevant, and what do you need to know about it?
SortSpoke’s novel approach to machine learning answers a longstanding problem in financial services – how to efficiently extract critical data from inbound, unstructured documents at 100% data quality.
Amazon is offering its cashierless store technology to other retailers. The technology known as “Just Walk Out” eliminates checkout lines, offering an “effortless” shopping experience and shifting store associates to “more valuable activities”.
As the COVID-19 pandemic is shutting down whole countries, a few of you may be wondering whether AI can help create a vaccine for the virus responsible. After all, AI is magic, right?
Alphabet is facing backlash from its shareholders over its approach to digital privacy, reports the Financial Times. And not for the first time. This time, however, things will need to change.
The EU plans to invest €6 billion to build a single European data space, reports EURACTIV. The envisioned space will house personal, business, and “high-quality industrial data” and create the infrastructure for data sharing and use across businesses and nations.
“Facebook quietly acquired another UK AI startup and almost no one noticed,” reported TechCrunch on February 10. We looked into why.
In a landmark ruling, a Dutch court has ordered an immediate halt to the government’s use of an automated system for detection of welfare fraud.
Databricks, a data processing and analytics platform with a strong focus on AI and ML, has partnered with Immuta to deliver automated end-to-end data governance for AI, data science, and ML projects.
CognitiveScale has been named one of the 50 Smartest Companies of the Year 2019 by The Silicon Review. The recognition is for “transforming customer engagement and lifetime value with Artificial Intelligence.”
Facebook agreed to pay $550 million to settle a class action lawsuit with a group of users in Illinois over its use of facial recognition technology (FRT) to tag individuals in photographs, reports the BBC.
AI has been making headlines in healthcare for some time, and the current outbreak of the coronavirus in Wuhan, China, (with cases now in other parts of the world) – or, more specifically, the early warning of the outbreak – is another example.
Google founders Larry Page and Sergey Brin are stepping down as CEO and President of Alphabet, respectively. Google CEO Sundar Pichai will take over as Alphabet’s CEO. Both Page and Brin will remain actively involved as board members, shareholders, and cofounders.
I recently had an opportunity to speak with a KPMG partner in the Canadian risk consulting practice and with the head of data science for Canada about several things, including KPMG Ignite. This is what I learned.
SAS is creating a new agricultural technology business unit and has partnered with the North Carolina Plant Sciences Initiative to help next-generation farmers and agribusiness leaders harness artificial intelligence to transform agriculture and feed the world.
Facial recognition technologies (FRTs) are in the news again. This time, it is Clearview AI, a small company that until recently was virtually unknown to everyone except the 600 law enforcement agencies using its technology to match people’s photos to their online presence.
We recently covered Google’s lackadaisical approach to data privacy in the context of its partnership with Ascension, a US healthcare giant. Last month, Google was under fire again, along with Facebook, from Amnesty International.
Last week, Google’s CEO, Sundar Pichai, called for new AI regulations. The next day, IBM called for rules to eliminate AI biases that can discriminate against consumers, citizens, and employees based on their gender, age, and ethnicity.
Last month Amazon released SageMaker Studio, an IDE for machine learning (ML). The objective for this new-ish offering was to address “immature tooling” in ML and make it easier for data scientists to create and deploy ML models.
So, you know about AI biases but want to see a demonstration of what’s involved in identifying and removing them from a machine learning/AI application? A recent webinar by DataRobot does just that: it walks you through a small ML project and explains step by step what to do and how.
Doctors at the Moorfields Eye Hospital in London have built a data set that links patient retinal scans with nationally held data about people with Alzheimer’s. They plan to use it to see if they can detect the early stages of Alzheimer’s disease from retinal changes using machine learning.
DataRobot, a vendor of enterprise AI, recently released a report revealing that nearly half (42%) of AI professionals in the US and the UK are “very” or “extremely” concerned about AI bias.
The AI system developed by Google Health more accurately identifies breast cancer than human experts, reports The Guardian (and others). The system has been tested in the UK and the US, and the results are published in Nature, one of the most prestigious scientific journals in the world.
Dessa, a Canadian AI start-up previously known as Deeplearni.ng, announced today the availability of Atlas 2.0, one half of its Foundations suite of tools for building, deploying and maintaining enterprise-grade machine learning (ML) models at scale.
CognitiveScale’s Cortex Certifai product has been named the winner of the Responsible AI and Ethics award in the Global AI Awards 2019-2020. Cortex Certifai is the “world’s first AI Risk Scanner application that automatically detects and scores vulnerabilities in any black-box AI model.”
Google sets its sights on another vertical to dominate: it plans to start offering checking accounts, reports The Wall Street Journal (WSJ). The service, code-named Cache, will be available next year in partnership with Citigroup and Stanford Federal Credit Union.