Colleges, businesses need to up their game to cope with AI, IoT: Report

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Estimates suggest that only 20 per cent of today's engineers are employable in this age of new technologies like artificial intelligence (AI), internet of things (IoT), blockchain and cyber security. And it's high time that educational institutions, businesses and the government upped their game. These are the findings of a report unveiled by the BML Munjal University, a higher education institution promoted by the Hero Group. The report, titled ÁI & Future of Work: Redefining Future of Enterprise, analyses the opportunities and challenges brought about by new-age tech changes and presents a roadmap for academic institutions, enterprises as well as the government on how to work together to fulfill the demand for qualified professionals in this new age where exponential technologies like AI and blockchain are going to rule the roost. "Today, legacy skills, tools and technologies have become obsolete. New-age digital professionals proficient in AI, IoT are being called upon to enter the talent workforce, with a new set of skills," said Sameer Dhanrajani, CEO of AIQRATE Advisory, who authored the report.


Processing Geospatial Data at Scale With Databricks

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The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is enabling organizations across industry to build new products and capabilities. Maps leveraging geospatial data are used widely across industry, spanning multiple use cases, including disaster recovery, defense and intel, infrastructure and health services. For example, numerous companies provide localized drone-based services such as mapping and site inspection (reference Developing for the Intelligent Cloud and Intelligent Edge).


KNIME on Amazon Web Services Now Available to Productionize AI/ML

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KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials. Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers.


C3.ai: accelerating digital transformation

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As one of the leading enterprise AI software providers, C3.ai is renowned for building enterprise-scale AI applications and harnessing digital transformation. The C3 AI Suite is software that uses a model-driven architecture to speed up delivery and reduce the complexities of developing enterprise-scale AI applications. Supply Chain Digital takes a closer look at the AI firm. The Suite propels organisations to deliver AI-enabled applications quicker than alternative methods while reducing the technical debt from maintaining and upgrading these applications. Its solutions cater to a range of different industries such as manufacturing, oil and gas, utilities, banking, aerospace and defence, healthcare, retail, telecoms, smart cities and transportation.


Robotic Arm in Note Sorting (RAINS) by ICICI Bank

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Sign in to report inappropriate content. ICICI Bank deployed the industrial'Robotic Arms' to digitise operations at its currency chests. The Bank is the first commercial bank in the country and among the few globally to customise and deploy industrial robots to automate and perform repetitive high volume steps in handling cash processing on high-end note sorting machines.


Saurabh Jha on Twitter

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"the biggest challenge to deployment of algorithms wasn't the AUC, or clever statisticians arguing endlessly on Twitter about which outcome measures the value of AI, or overfitting. It was a culture of doubt... which didn't so much fear change as couldn't be bothered..."


Saurabh Jha on Twitter

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Loved this line "In the Bollywood movie, Anand, an oncologist played by @SrBachchan diagnosed terminal cancer by glancing at the patient's radiograph for couple of seconds.


MIT's new tool predicts how fast a chip can run your code

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Folks from the Massachusetts Institute of Technology (MIT) have developed a new machine learning-based tool that will tell you how fast a code can run on various chips. This will help developers tune their applications for specific processor architectures. Traditionally, developers used the performance model of compilers through a simulation to run basic blocks -- fundamental computer instruction at the machine level -- of code in order to gauge the performance of a chip. However, these performance models are not often validated through real-life processor performance. MIT researchers developed an AI model called Ithmel by training it to predict how fast a chip can run unknown basic blocks.


Deep Learning with Taxonomic Loss for Plant Identification

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Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively.


Deep Learning with Taxonomic Loss for Plant Identification

#artificialintelligence

Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively.