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Amazon is collaborating with IIT Bombay to advance research within speech, language, and multimodal AI. The Amazon- IIT Bombay artificial intelligence / machine learning initiative is a multi-year tie up that will fund research projects, PhD fellowships, and community events.

Amazons growing research and development operations in India have powered engagement with Alexa users in Hindi and Indic languages, and their AI/ML innovations have delivered increasingly delightful shopping experiences, said Rohit Prasad, Senior Vice President and head scientist at Alexa.

Amazon presently serves about 600 million people in India. Its research centre in Bengaluru helps solve conversational AI challenges stemming from Indias diversity22 official languages with over 19,500 dialects.

"This investment at one of the world's premier academic institutions will bring together Amazon scientists and IIT Bombay students and faculty, leveraging India's multilinguality as a learning lab, to develop new AI systems that can learn and adapt to different languages, accents and dialects. These efforts will help advance the technology fundamental to the future of conversational AI, says Prasad.

IIT Bombay ranks among the top engineering institutes in India and is known for producing cutting-edge research in AI/ML. With 45 full-time faculty members, its computer science and engineering department is one of the largest in the subcontinent.

Milind Atrey, IIT Bombays Dean of Research and Development

This collaboration will foster innovation in three ways: through community projects, research projects, and fellowships, which will indeed spur development in AI and ML domains, as well as other areas, as the relationship progresses, said Milind Atrey, IIT Bombays dean of Research and Development.

Amazon and IIT Bombay have existing ties through the Amazon Research Awards program. The most recent award was granted in 2022 to Preethi Jyothi, associate professor of computer science and engineering at IIT Bombay, for her work on fairness in speech recognition.

Subhasis Chaudhuri, Director at IIT Bombay

Our top research minds have always attracted the attention of companies interested in scientific study. With industry collaborators like Amazon who have a deep sense of technology and global reach, we hope to be able to expedite the deployment of technologies/products in the field of AI/ML, says Subhasis Chaudhuri, director at IIT Bombay.

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If youre curious about how the rise of A.I. and machine learning might impact your job, its worth taking a few minutes to read Microsoft co-founder Bill Gatess paper breaking down the future of A.I. (at least as he sees it).

Gates believes that the current ways of interfacing with hardwarevia a graphical user interface, a keyboard, and a mousewill gradually give way to something right out of a science fiction movie. Your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes, he wrote. Instead, youll be able to write a request in plain English.

He compares this functionality to a white-collar worker or a co-pilot. Eventually, A.I. will impact a variety of job functions in different industries, from healthcare to education. On the software side, the algorithms that drive an AIs learning will get better. There will be certain domains, such as sales, where developers can make AIs extremely accurate by limiting the areas that they work in and giving them a lot of training data thats specific to those areas, he continued. But one big open question is whether well need many of these specialized AIs for different usesone for education, say, and another for office productivityor whether it will be possible to develop an artificial general intelligence that can learn any task.

Will artificial intelligence and machine learning become a job-destroyer? Thats a key worry among many tech professionals, although Gates believes A.I. will enhance rather than destroy jobs. At the moment, even the most sophisticated A.I.-powered chatbots cant fully reproduce what the average tech pro does on a daily basis; although such platforms can generate code and potentially debug software, they obviously cant replicate the soft skills so essential to many jobs, including teamwork, empathy, and communication.

Even from a low-level coding perspective, A.I. can deliver imperfect results, as our testing demonstrated. For the foreseeable future, mastering the best practices for coding and debugging will remain essentialcompanies arent going to turn that functionality over to a chatbot just yet.

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How Bill Gates Thinks A.I. Will Impact Your Workflow - Dice Insights

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NORTHAMPTON, MA / ACCESSWIRE / March 28, 2023 / Schneider Electric

Schneider Electric, Tuesday, March 28, 2023, Press release picture

Today's energy-intensive processes are looking to artificial intelligence (AI) technologies, including machine learning (ML), to help deliver smart automation capabilities needed to decrease machine downtime, expand asset utilization, and unlock immediate insights into real-time process optimization.

Organizations with a "digital-first" mindset understand the potential ML offers to vastly increase daily decision-making accuracy, speed, and flexibility. According to a recent study, 84% of C-suite executives believe AI is necessary to achieve their growth objectives, yet 74% concede that significant barriers to implementation exist.

Core constraints to building automated analytics into automation and control applications are a lack of access to technical skills, diversity in domain expertise, and deployment tools.

Numerous organizations, including Schneider Electric, have found that a fundamental enabler of successful and ground-breaking ML deployment is to team up with expert outside partners. Dynamic collaborations can significantly enhance the skills and abilities of cross-functional and interdisciplinary teams. Such cooperation is the core philosophy behind our Partnerships of the Future program, an initiative designed to develop mutually beneficial professional relationships to speed innovation and generate superior business outcomes for customers and partners alike.

A collaborative approach to digital development strategy pays off

With valuable input from specialists at Alkhorayef Petroleum, Schneider Electric was able to develop edge analytics-enabled AI capabilities into the EcoStruxure Autonomous Production Advisor platform for oil and gas production facilities. It's one example of the several successful digital co-innovation efforts Schneider is currently executing across multiple industrial segments. The goal of both partners was to build an AI-based solution incorporating ML and pattern recognition models that could detect anomalies in the Oil and Gas extraction process and positively impact several key challenges, including:

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Harnessing and replicating the insights and expertise of the most proficient well operators so their abilities could be automated and deployed across a broader range of production conditions.

Actively managing equipment lifecycles to optimize well intervention schedules and generate maximum value from the physical asset base.

Monitoring and reacting to downhole conditions in real-time to optimize petroleum production, reduce unplanned downtime, maximize oil volumes, and improve safety.

Because traditional automation architectures and strategies wouldn't deliver the required capabilities, particularly for remote oil and gas wells, a new cooperative development approach was undertaken. Together, the team was able to leverage Schneider's expertise in IIoT-enabled control systems and AI-based process optimization with Alkhorayef Petroleum's knowledge and expertise of electrical submersible pumps to create novel techniques to capture and automate expert knowledge.

Cloud computing and edge analytics combined

EcoStruxure Autonomous Production Advisor merges the power and flexibility of cloud and edge computing with the value-generating capabilities of artificial intelligence and machine learning. In conjunction with remote terminal units (RTUs), the platform runs on industrial-grade edge controllers that combine supervised and unsupervised ML models running directly at the edge.

Replicating the actions of highly skilled human operators, the AI monitors the pump operation, assesses production variables, and analyzes the interactions and relationships between them, to identify anomalous operations. As a next step, the AI model classifies the detected anomalous events (such as sand intrusion, interfering gas, or mechanical problems) as specific issues. Continuous validation of the AI model's event classifications by operators and experts helps to retrain the model, developing increasingly accurate diagnostic and predictive abilities.

The implementation of machine learning models in industrial applications forms an exciting new area because they can be trained to optimize operations and asset performance in a variety of important areas, such as:

Identification of asset deterioration

Early detection of abnormal behavior

Prediction of equipment failure and smart alarming

Asset performance management (digital twin)

An additional benefit of AI models is that such a solution can be trained for image recognition, enabling it to be an automation aid for several applications, including:

Product quality

Man down and intrusion detection and alarming

Leakage detection and contactless flow measurement

Machine vision and object and shape detection

Vendor-agnostic hardware enables the platform to be deployed to existing architectures without requiring significant modifications.

Co-innovation delivers tangible results

In offshore and onshore wells in the Middle East, Africa, and Latin America, the EcoStruxure Autonomous Production Advisor model training process has proven to be very effective at capturing the skills and expertise of the most senior operators and having the system automate and reproduce them. In one use case run by Schneider Electric, the customer reported a 13% increase in production and a 33% reduction in energy consumption.

Innovation isn't just about technology; there's no "one size fits all" strategy for partnering to invent new solutions that deliver major dividends. Success depends on nurturing conditions for a dynamic, mutually beneficial partnership. With a depth of co-innovation experience unrivaled in the smart control systems and process automation space, Schneider Electric is ready to work with true partners looking to overcome our greatest challenges.

Click to learn more about EcoStruxure Autonomous Production Advisor and Alkhorayef Petroleum.

View additional multimedia and more ESG storytelling from Schneider Electric on 3blmedia.com.

Contact Info:Spokesperson: Schneider ElectricWebsite: https://www.3blmedia.com/profiles/schneider-electricEmail: info@3blmedia.com

SOURCE: Schneider Electric

View source version on accesswire.com: https://www.accesswire.com/746202/Machine-Learning-and-AI-Combined-Can-Boost-Energy-and-Chemical-Production

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Machine Learning and AI Combined Can Boost Energy and Chemical Production - Yahoo Finance

Over the past few years, machine learning (ML) has completely revolutionized the technology industry. Ranging from 3D protein structure prediction and prediction of tumors in cells to helping identify fraudulent credit card transactions and curating personalized experiences, there is hardly any industry that has not yet employed ML algorithms to enhance their use cases. Even though machine learning is a rapidly emerging discipline, there are still a number of challenges that need to be resolved before these ML models can be developed and put into use. Nowadays, ML development and deployment suffer for a number of reasons. Infrastructure and resource limitations are among the main causes, as the execution of ML models is frequently computationally intensive and necessitates a large amount of resources. Moreover, there is a lack of standardization when it comes to deploying ML models, as it depends greatly on the framework and hardware being used and the purpose for which the model is being designed. As a result, it takes developers a lot of time and effort to ensure that a model employing a specific framework functions properly on every piece of hardware, which requires a considerable amount of domain-specific knowledge. Such inconsistencies and inefficiencies greatly affect the speed at which developers work and places restriction on the model architecture, performance, and generalizability.

Several ML industry leaders, including Alibaba, Amazon Web Services, AMD, Apple, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, and NVIDIA, have teamed up to develop an open-source compiler and infrastructure ecosystem known as OpenXLA to close this gap by making ML frameworks compatible with a variety of hardware systems and increasing developers productivity. Depending on the use case, developers can choose the framework of their choice (PyTorch, TensorFlow, etc.) and build it with high performance across multiple hardware backend options like GPU, CPU, etc., using OpenXLAs state-of-the-art compilers. The ecosystem significantly focuses on providing its users with high performance, scalability, portability, and flexibility, while making it affordable at the same time. The OpenXLA Project, which consists of the XLA compiler (a domain-specific compiler that optimizes linear algebra operations to be run across hardware) and StableHLO (a compute operation that enables the deployment of various ML frameworks across hardware), is now available to the general public and is accepting contributions from the community.

The OpenXLA community has done a fantastic job of bringing together the expertise of several developers and industry leaders across different fields in the ML world. Since ML infrastructure is so immense and vast, no single organization is capable of resolving it alone at a large scale. Thus, experts well-versed in different ML domains such as frameworks, hardware, compilers, runtime, and performance accuracy have come together to accelerate the pace of development and deployment of ML models. The OpenXLA project achieves this vision in two ways by providing: a modular and uniform compiler interface that developers can use for any framework and pluggable hardware-specific backends for model optimizations. Developers can also leverage MLIR-based components from the extensible ML compiler platform to configure them according to their particular use cases and enable hardware-specific customization throughout the compilation workflow.

OpenXLA can be employed for a spectrum of use cases. They include developing and delivering cutting-edge performance for a variety of established and new models, including, to mention a few, DeepMinds AlphaFold and multi-modal LLMs for Amazon. These models can be scaled with OpenXLA over numerous hosts and accelerators without exceeding the deployment limits. One of the most significant uses of the ecosystem is that it provides support for a multitude of hardware devices such as AMD and NVIDIA GPUs, x86 CPU, etc., and ML accelerators like Google TPUs, AWS Trainium and Inferentia, and many more. As mentioned previously, earlier developers needed domain-specific knowledge to write device-specific code to increase the performance of models written in different frameworks to be executed across hardware. However, OpenXLA has several model enhancements that simplify a developers job, like streamlined linear algebra operations, enhanced scheduling, etc. Moreover, it comes with a number of modules that provide effective model parallelization across various hardware hosts and accelerators.

The developers behind the OpenXLA Project are extremely excited to see how developers use it to enhance ML development and deployment for their preferred use case.

Check out theProject and Blog.All Credit For This Research Goes To the Researchers on This Project. Also,dont forget to joinour 16k+ ML SubReddit,Discord Channel,andEmail Newsletter, where we share the latest AI research news, cool AI projects, and more.

Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.

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OpenXLA Project is Now Available to Accelerate and Simplify Machine Learning - MarkTechPost