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An interview with Perpetuity Capital Co-founder & CEO KaramVeer Dhillon – TechGraph

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Speaking to TechGraph, KaramVeer Dhillion, Co-Founder & CEO of Perpetuity Capital said, "By implementing Artificial Intelligence and Machine Learning, NBFCs can profitably serve a vast unbanked population and help them attain financial inclusion." Could you help give a sense of how far Perpetuity Capital has come since its existence? KaramVeer Dhillon: Perpetuity Capital was started with a clear vision to become a one-stop solution for asset financing to the underserved sector of the economy. Given our proximity to the logistics industry, we saw firsthand the liquidity needs of single-owner drivers and small fleet owners. Even after clearing all costs & EMIs, an owner needs a minimum of Rs. 2 to 2.5 lakhs of liquidity a year to maintain a commercial vehicle.


Well Ahead Philly: Artificial intelligence system at Main Line Health detects strokes, cutting time to treatment and saving lives

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Stroke is no longer just a danger for seniors as more young adults are suffering them. Lieutenant Governor John Fetterman is 52. Now, Main Line Health has a new technology to help stop strokes faster, if they're recognized quickly. Dominique Jones from Wynnefield just didn't feel right one morning in March. "When I was taking my shower, I had a headache," she recalled.


Artificial Intelligence in SEO (2022 Extreme Edition) - Coursemetry

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Note: 3.8/5 (279 notes) 36,854 students Welcome to experience the course "Artificial Intelligence in SEO (2022 Extreme Edition)". Looking for the word called "Popularity" to come to life? It's good to be heard, of course, and everything, but is that really the point? To be able to applaud and say, yeah… I had 1,000,000 visits last year to my website… that may be amazing, but why is website traffic important to your business or any business, for that matter? Website traffic (or the number of visitors to your website) is significant because the number of visitors is equal to the number of new customer opportunities.


How Artificial Intelligence Is Helping To Make Better Content

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Artificial intelligence has been around for decades, but only recently has the technology gotten to the point where you can begin to reap its benefits of it in your everyday life. This includes your personal and business life as well. Businesses have been taking advantage of this technology's ability to create new strategies and content on their own, without any human input whatsoever, while consumers are now able to see AI's influence on popular applications such as Siri and Amazon's Alexa. With most content creators knowing everything there is to know about their subject, at least on a basic level, it can be difficult for them to distinguish themselves from others in their field. That is where artificial intelligence comes in.


Self-Supervised Learning And Its Applications - AI Summary

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The focus was largely on supervised learning methods that require huge amounts of labeled data to train systems for specific use cases. Bidirectional Encoder Representations from Transformers (BERT) a paper published by researchers at the Google AI team has become a gold standard when it comes to several NLP tasks such as Natural Language Inference (MNLI), Question Answering (SQuAD), and more. To make BERT handle a variety of downstream tasks, input representation is able to unambiguously represent a pair of sentences that are packed together in a single sequence. While autoencoding models like BERT utilize self-supervised learning for tasks like sentence classification (next or not), another application of self-supervised approaches lies in the domain of text generation. The inputs are passed through our pre-trained model to obtain the final transformer block's activation hm l, which is then fed into an added linear output layer with parameters W y to predict y: Translation Language Modelling (TLM): a new addition and an extension of MLM, where instead of considering monolingual text streams, parallel sentences are concatenated as illustrated in the following image.


How to Test a Recommender System - neptune.ai

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Recommender systems fundamentally address the question – What do people want? Although it is an extensive question, in the context of a consumer application like e-commerce, the answer could be to serve the best products in terms of price and quality for a consumer. For a news aggregator website, it could be to show reliable and relevant content. In a case where a user would have to look through thousands or millions of items to find what they are looking for, a recommendation engine is indispensable. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. It is so accurate that personalised recommendations from the engine drive 80% of Netflix viewer activity. However, building and evaluating a recommender system is very different compared to a single ML model regarding design decisions, engineering, and metrics. In this article, we will focus on testing a recommendation system. The second and third require a lot of user-item interaction data. If that is not available, one might start with the first type of recommender system.


Will this fruit-picking robot transform agriculture?

The Guardian

Robots can do a lot. They build cars in factories. Robotic dogs can, allegedly and a little creepily, make us safer by patrolling our streets. But there are some things robots still cannot do – things that sound quite basic in comparison. "It's a simple thing" for humans, says robotics researcher Joe Davidson.


Alexa, why have you charged me £2 to say the Hail Mary?

The Guardian

When my 87-year-old mother, Patricia Collinson, was given an Alexa speaker by my sister, she was delighted to find she could ask it to say the Hail Mary. Every morning for a week the devout Catholic asked Alexa to recite the prayer. What she was less delighted to learn was that she had unwittingly ordered a premium subscription payable through Amazon to a private company called Catholic Prayers. Patricia, a retired district nurse in Hastings, does not own a computer, and does not know how to use one. She had signed up by voice command, without being presented with the kind of outline or terms and conditions that now comes as standard when you pay for things online.


Dyson reveals home robot prototypes that can carry out domestic chores – Dezeen

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"Dyson is supercharging its robotics ambitions, recruiting 250 robotics engineers across disciplines including computer vision, machine learning, …


Strengthening Research and Innovation in Newfoundland and Labrador – News Releases

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… and Research and the Accelerated Analytics and Machine Learning project. … genetic analysis, artificial intelligence, machine learning, …