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Searching for an application toolset that provides the required tools to scope, develop, roll-out and manage #AI applications? Take a look at #CodexAISuite, well-suited to AI applications with strong #machinelearning and #deeplearning components.


Digital transformation projects don't fail because of a shortage of 'tech'

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As analytics adoption increases, data scientists churn out more models of greater complexity, using a variety of tools and programming languages. Getting these models into frontline operations should not require time-consuming and error-prone recoding steps to integrate the models into an operational system. At the same time, models that are built on softer features, such as customer behavior, can decay more quickly than those built on more stable features, such as customer age. And organizations increasingly view algorithms and machine learning models as an additional source of risk, for example, reputational risk from a potentially biased model.


2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

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We're excited to announce that Gartner has recognized TIBCO Software as a Leader in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms for the 2nd year in a row! We believe Gartner's evaluation validates the innovative digital transformation success our customers have realized across many industries--including financial services, telecom, healthcare, retail, travel and logistics, manufacturing, energy and utilities and the pharmaceuticals. From reporting and modern BI to descriptive and predictive analytics to streaming analytics, TIBCO can help you compete and win. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact.


Huawei Atlas 900 AI Cluster Wins the GSMA GLOMO Tech of the Future Award

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Atlas 900 stood out with its world-leading AI computing power, ultimate heat dissipation system, and best-in-class cluster network. Atlas 900 accelerates global basic AI research and quickly brings AI applications to industries to advance the AI era with unparalleled AI computing power. Innovative technology has propelled the mobile industry far beyond the wildest expectations of early tech pioneers. GSMA awards the GLOMO Award – Tech of the Future Award to recognize technology that is ahead of its time and reshapes the world. Atlas 900 is the world's fastest AI training cluster.


Saving and Loading Keras model

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How can you share a deep learning model you trained that is giving awesome results with other team members working in a different part of the world or How to save a deep learning model and it's trained weights during or after training or How to resume training of a model from where you left off? Sharing the saved files will allow others to recreate the model to make inferences or to resume further training from you left off. The model can be recreated by loading the model, and it's trained weight from the saved files containing the model architecture and the pre-trained weights. In this article, you will learn how to save a deep learning model developed in Keras to JSON or YAML file format and then reload the model. To save the model, we first create a basic deep learning model.


Turning Machine Learning into Business Applications

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The exponential growth of data has had an inverse impact on the ability of businesses to gain value from their data through traditional rules-based programming. Machine Learning is viewed as an essential enabler that will allow applications to act on the collection of new data sets to improve their predictive capabilities. This white paper shows how Talend and AWS are bridging the gap between data scientists and data engineers to operationalize ML.


HeartVista Announces Formation of Medical and Scientific Advisory Board with Leaders from Stanford University and the University of Wisconsin

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HeartVista, a pioneer in AI-assisted MRI solutions, announced the formation of its Medical and Scientific Advisory Board, including notable thought leaders from Stanford University and the University of Wisconsin. "The past year was an inflection point for HeartVista, which was full of significant milestones as we received FDA 510(k) clearance for our AI-assisted One Click Cardiac Package," said Itamar Kandel, CEO of HeartVista. "This year, we will continue to progress our MRI software platform and expand its use across additional radiology centers within the US and globally. Our Medical and Scientific Advisory Board will provide strategic direction to our leadership team, enabling us to continue advancing the MRI field." "Automated, AI-driven prescription, as pioneered by HeartVista will change the way we perform advanced MRI exams, by dramatically reducing exam time, standardizing acquisitions, reducing error and rework, and ultimately improve the patient experience," said Dr. Scott Reeder, Vice Chair of Research and Chief of MRI, University of Wisconsin School of Medicine.


FarmBeats: AI, Edge & IoT for Agriculture - Microsoft Research

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Several studies have demonstrated the need to significantly increase the world's food production by 2050. However, there is limited amount of additional arable land, and water levels have also been receding. Although technology could help the farmer, its adoption is limited because the farms usually do not have power, or Internet connectivity, and the farmers are typically not technology savvy. We are working towards an end-to-end approach, from sensors to the cloud, to solve the problem. Our goal is to enable data-driven farming.


How long to make you start using a trading platform?

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I heard of Metatrader in 2006. Before knowing the name of that tool, I had heard of several trading platforms' names already. To be honest, compared with other tools, the first sight of MT didn't attract me. The design, UI is ordinary, seems nothing special. One of the difficulties is that I need to learn how to code a program and make it run on the tool.


Global Big Data Conference

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Efforts to benchmark computer systems, known as MLPerf, are essential to measure the expanding world of artificial intelligence silicon, according to Jeff Dean, head of AI efforts at Google, but the benchmarks will also have to evolve to better reflect real-world concerns. If computer systems are to evolve to handle ever larger machine learning models, a standard way to compare the effectiveness of those systems is essential, according to Google head of AI, Jeff Dean. But that system of measurement itself must evolve over time, he said. "I think the MLPerf benchmark suite is actually going to be very effective," said Dean, in an interview with ZDNet, last week, referring to the consortium of commercial and academic organizations known as MLPerf, founded within the last few years. The MLPerf group have formulated test suites that measure how different systems do on various AI tasks such as the number of image "convolutions" per second.