Our own party members will have a structured meeting and finilize the decision: Nasir Sogami
Our own party members will have a structured meeting and finilize the decision: Nasir Sogami.
Our own party members will have a structured meeting and finilize the decision: Nasir Sogami
Our own party members will have a structured meeting and finilize the decision: Nasir Sogami
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Education revolution in Punjab ❤️The transformation we all deserve! #punjab #punjabgovernmentschool
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Education revolution in Punjab ❤️The transformation we all deserve! #punjab #punjabgovernmentschool
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The conduct of Kerala’s LDF govt on Sabarimala issue will go down in history as one of the most shameful behaviours by any party and govt. The UDF is no better. Congress has multiple stands.
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Tukaram Parab exposed by his own members! Members doubts that Tukaram has ties with the BJP
Tukaram Parab exposed by his own members! Members doubts that Tukaram has ties with the BJP
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IDP uses AI to automate data processing
Despite major advances in digital security and cloud storage, paper remains an essential ingredient in the contracting process. Intelligent document processing (IDP) uses artificial intelligence to automate data processing, disrupting fintech by augmenting human intelligence. Unlike robotic process automation (RPA) and simple data extraction, AI for document insights enables knowledge management digital transformation with streamlined search, synthesis and analysis across large volumes of complex documents, helping employees quickly make better business decisions. But in 2021, where most business advances were built on some form of automation, weren’t we supposed to be living in a more efficient future? Even better: Why spend time assessing and sifting through thousands of documents when AI could do the job in a few minutes? As per the Adobe study, attitudes toward going all-digital with document management are rising, but old paper habits die hard. Did you know manual document-related challenges account for a 21.3% productivity loss and Indian companies waste a collective 82% annually managing paperwork!
India Inc. could potentially save up to Rs. 5000 to Rs. 6000 crore per annum by integrating smart offerings like NSE-backed Finstinct that allow financial organisations to cull out hyper-targeted, structured information from millions of unstructured paperwork and documents floating in the BFSI ecosystems. A report says, workers spend approximately 11 hours in a week to deal with documents related issues, which leads to low productivity and poor quality of the customer service. As per, Goldman Sachs it is estimated to spend $33mn every year on hiring analysts who can read reports, papers to make sense of millions of documents running through their system. It seems, perhaps the cost spent on the current manual system can be avoided and channelled towards better insi
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Deep learning, when applied to data science, can offer better and more effective processing models. Its ability to learn unsupervised drives continuous improvement in accuracy and outcomes. Deep learning, a computer program trains to conduct classification algorithms from text, images or sound. It also offers data scientists with more reliable and concise analysis results. 80% of the world’s data is unstructured. Much of your company’s data may be unstructured. In order to get the most mileage out of AI and machine learning, it helps to have your data formatted. There may be methods of incorporating AI and machine learning into your ongoing operations that you’re not yet aware of. That’s why it’s important to format as much of your data as you can within a database. This helps you get ahead of the curve. Deep Learning is called Deep because of the number of additional “Layers” we add to learn from the data. One of the key reasons deep learning is more powerful than classical machine learning is that it creates transferable solutions. Deep learning algorithms are able to create transferable solutions through neural networks: that is, layers of neurons/units.
It is important to acknowledge here that the definition of deep learning itself has been evolving as it embraces developments within the larger fold of AI. Gary Marcus, a pioneer in deep learning, has stated four possibilities for the future of deep learning, namely, unsupervised learning that would enable systems to determine their own objectives and do problem solving, integration of deep learning with symbolic systems that would make inferences more accurate, developing learning models through better insights from cognitive and developmental psychology and AI becoming multidimensional to deal with the complexities of the problem. According to Yann LeCun, Chief AI Scientist at Facebook, the next phase of AI will not be supervised but reinforced. He has elaborated on self- supervised learning, i.e. the
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