{"id":261,"date":"2023-09-12T05:10:12","date_gmt":"2023-09-12T05:10:12","guid":{"rendered":"https:\/\/techwyns.com\/tech\/?p=261"},"modified":"2023-09-12T05:15:55","modified_gmt":"2023-09-12T05:15:55","slug":"artificial-intelligence-news","status":"publish","type":"post","link":"https:\/\/techwyns.com\/tech\/blog\/2023\/09\/12\/artificial-intelligence-news\/","title":{"rendered":"Artificial Intelligence news"},"content":{"rendered":"

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Navigating privacy in a data-driven world with Microsoft Privacy<\/h2>\n<\/div>\n<\/header>\n
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Data protection and privacy have become business imperatives. In a global survey conducted by Microsoft and leaders in the academic privacy space, 90 percent of respondents said they would not buy from an organization that does not properly protect its data.1<\/sup>\u00a0More than ever, people are highly aware of their privacy, digital footprint, and, most importantly, how the organizations they work with treat both. According to Gartner\u00ae, by the end of 2024, three-quarters of the world\u2019s population will have personal data covered by modern privacy regulation.2<\/sup><\/p>\n

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People exercise their privacy rights either explicitly, through actions like subject rights requests, or implicitly, through declining to do business with organizations they do not trust. For organizations committed to respecting the privacy rights of individuals, it can be challenging to implement requirements and controls needed to meet data privacy needs<\/p>\n<\/div>\n<\/div>\n<\/article>\n

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Now generally available: The new Microsoft Dynamics 365 Customer Insights<\/h2>\n<\/div>\n<\/header>\n
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At Microsoft Inspire 2023, we\u00a0announced<\/a>\u00a0that we are bringing together Microsoft Dynamics 365 Marketing and Microsoft Dynamics 365 Customer Insights into one offer, enabling organizations to unify and enrich their customer data to deliver personalized, connected, end-to-end customer journeys across sales, marketing, and service. We are retaining the existing \u201cDynamics 365 Customer Insights\u201d name to encompass this new offer of both applications. Today, we\u2019re excited to share that the new\u00a0Dynamics 365 Customer Insights<\/a>\u00a0is now generally available for purchase.<\/p>\n

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For our existing Dynamics 365 Marketing and Dynamics 365 Customer Insights customers, this change signals an acceleration into our \u201cbetter together\u201d story, where we\u2019ll continue to invest in new capabilities that will enable stronger, insights-based marketing\u00a0read more\u2026<\/a><\/p>\n<\/div>\n<\/div>\n<\/article>\n

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IDC shares how generative AI transforms business processes within marketing, sales, and service<\/h2>\n<\/div>\n<\/header>\n
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Delving into the realm of customer-centric strategies, IDC analyst Gerry Murray casts a visionary light on the transformative influence of generative AI (Gen AI) on sales and service. Murray\u2019s perspective resonates powerfully with the groundbreaking nature of Gen AI, which is reshaping customer interactions into a new era of efficiency and effectiveness.<\/p>\n

Gen AI isn\u2019t just another technology; it\u2019s a strategic leap that orchestrates seamless data and workflows across marketing, sales, and service touchpoints<\/p>\n

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Microsoft and Epic expand AI collaboration to accelerate generative AI\u2019s impact in healthcare, addressing the industry\u2019s most pressing needs<\/h2>\n<\/div>\n<\/header>\n
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Today, the promise of technology to help us solve some of the biggest challenges we face has never been more tangible, and nowhere is generative AI more needed, and possibly more impactful, than in healthcare.\u00a0Read More\u2026<\/p>\n<\/div>\n

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Whisper: Revolutionizing Automatic Speech Recognition with Highly Trained, High-Performance OpenAI Technology<\/h2>\n<\/div>\n<\/header>\n
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Hope you have liked my earlier blog \u2018Next Generation ChatBot using OpenAI\u2019s ChatGPT vs. Google\u2019s Bard vs. Writesonic\u2019s Chatsonic\u2019. Pleased to introduce my next blog on OpenAI-whisper.\u00a0 Open AI\u2019s Whisper is a general-purpose, multi-tasking, Automatic Speech Recognition model (ASR) that can perform multilingual speech recognition, speech translation, language identification and voice activity detection.\u00a0 It has been trained on 680,000 hours of multilingual, supervised, and diverse audio data collected from the web.\u00a0 Use of such a large and diverse dataset leads to improved human level robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English.<\/p>\n

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The basic building block of Whisper models is a\u00a0Transformer<\/strong>\u00a0(Paper \u2018Attention is all you need\u2019 published in 2017 by Mr. Ashish Vaswani and 7 team members).\u00a0 The Transformer is a model architecture suited for solving problems containing sequences such as text or time series data.\u00a0 The architecture facilitates parallel computing and captures long range dependencies far better and more effective than the conventional RNN models thereby making it a high performing. Can be trained on a larger data set leading to high accuracy in responses. It has also been a basic building block for Open AI\u2019s GPT series models and DALL-E models, Google\u2019s LaMBDA and PaLM Models.\u00a0 It works on \u2018Self Attention\u2019 mechanism explained in diagram herewith.<\/p>\n

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It consists of Encoder and Decoder (In image, marked with RED boundaries) both having multiple layers of \u2018Self Attention\u2019 and Feed Forward Neural Network.\u00a0 The Encoder takes the text input (Called as Prompt), tokenize it, assign weightage to each token, analyses, and performs downstream tasks like context Summarization, Language Generation, Sentiment Analysis, Translation and many more.\u00a0 The Decoder uses context summary to create a response from the model.<\/p>\n

The tasks in Whisper Models are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.<\/p>\n

As of today, OpenAI has introduced Whisper with the following models.<\/p>\n

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Size<\/strong><\/td>\nLayers<\/strong><\/td>\nParameters<\/strong><\/td>\nEnglish-only model<\/strong><\/td>\nMultilingual model<\/strong><\/td>\nRequired VRAM<\/strong><\/td>\nRelative speed<\/strong><\/td>\n<\/tr>\n
tiny<\/strong><\/td>\n4<\/td>\n39 M<\/td>\ntiny.en<\/td>\ntiny<\/td>\n~1 GB<\/td>\n~32x<\/td>\n<\/tr>\n
base<\/strong><\/td>\n6<\/td>\n74 M<\/td>\nbase.en<\/td>\nbase<\/td>\n~1 GB<\/td>\n~16x<\/td>\n<\/tr>\n
small<\/strong><\/td>\n12<\/td>\n244 M<\/td>\nsmall.en<\/td>\nsmall<\/td>\n~2 GB<\/td>\n~6x<\/td>\n<\/tr>\n
medium<\/strong><\/td>\n24<\/td>\n769 M<\/td>\nmedium.en<\/td>\nmedium<\/td>\n~5 GB<\/td>\n~2x<\/td>\n<\/tr>\n
large<\/strong><\/td>\n32<\/td>\n1550 M<\/td>\nN\/A<\/td>\nlarge<\/td>\n~10 GB<\/td>\n1x<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
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