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A chaotic intro to all this machine learning hoo-ha everyone's on about

#artificialintelligence

Maybe it's that you spent all your best years with your nose to the grindstone, burning that midnight oil, ha ha. Complete Nutrition Backed By Science, they said. No way to prove them wrong! Or maybe it was those fumes. You are going to give yourself immortal life. No–you are going to create a new, better version of yourself that's immortal–a living replica of you made of metal that will act and say the things you would, if you were still alive. If that Soylent hadn't done you in. You saw a Black Mirror episode on it, and Microsoft filed a patent on the same concept this year.


AI analysis unveils the most effective email subject lines for the holidays

#artificialintelligence

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Retailers are already preparing for a 2021 holiday ecommerce season that mirrors last year. A recent survey by Radial found that 65% of consumers plan to spend the same as or even more than last year. Radial itself plans to hire 27,000 seasonal workers ahead of the shopping season to fulfill ecommerce orders. But to snag a share of that market, retailers will need to use the right email marketing language and tactics, according to a new report from AI-powered copywriting platform Phrasee.


I analyzed hundreds of user's Tinder data -- including messages -- so you didn't have to.

#artificialintelligence

I read Modern Romance by Aziz Ansari in 2016 and beyond a shadow of a doubt, it is one of the most influential books I've ever read. At the time, I was a snot-nosed college student who was still dating someone from high school. The numbers and figures given by the book about online dating success struck me as being callous. Millennials and their predecessors were blessed and cursed with the advent of the internet. The proliferation of partner-choice desensitizes us and gives us unrealistic expectations when it came to searching for our "soulmate." Instead of feeling dissuaded, I felt inspired.


Twitter's photo-cropping algorithm prefers young, beautiful, and light-skinned faces

#artificialintelligence

Twitter has announced the results of an open competition to find algorithmic bias in its photo-cropping system. The company disabled automatic photo-cropping in March after experiments by Twitter users last year suggested it favored white faces over Black faces. It then launched an algorithmic bug bounty to try and analyze the problem more closely. The competition, which was organized with the support of DEF CON's AI Village, confirmed these earlier findings. The top-placed entry showed that Twitter's cropping algorithm favors faces that are "slim, young, of light or warm skin color and smooth skin texture, and with stereotypically feminine facial traits."


Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning

arXiv.org Artificial Intelligence

Masked language models (MLMs) are pretrained with a denoising objective that, while useful, is in a mismatch with the objective of downstream fine-tuning. We propose pragmatic masking and surrogate fine-tuning as two strategies that exploit social cues to drive pre-trained representations toward a broad set of concepts useful for a wide class of social meaning tasks. To test our methods, we introduce a new benchmark of 15 different Twitter datasets for social meaning detection. Our methods achieve 2.34% F1 over a competitive baseline, while outperforming other transfer learning methods such as multi-task learning and domain-specific language models pretrained on large datasets. With only 5% of training data (severely few-shot), our methods enable an impressive 68.74% average F1, and we observe promising results in a zero-shot setting involving six datasets from three different languages.


What is Sentiment Analysis and how does it impacts Machine Learning

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Sentiment analysis (or opinion mining) may be a natural processing technique want to determine whether data is positive, negative, or neutral. Sentiment analysis is usually performed on textual data to assist businesses to monitor brand and merchandise sentiment in customer feedback and understand customer needs. Sentiment analysis is that the process of detecting positive or negative sentiment in text. It's often employed by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an important tool to watch and understand that sentiment.


Clippy is coming back to Windows and Microsoft Teams, but only as emoji

ZDNet

In it, Clippy stands tall on a long stream of the classic Office ribbon. While admitting Clippy wasn't loved by all Office users, Microsoft still likes to think of Clippy as the "true OG virtual assistant" -- before Apple Siri and Amazon Alexa.


Emoji Prediction using Deep Learning - DataFlair

#artificialintelligence

Emojis are a wonderful method to express oneself. This deep learning project automatically predicts emojis based on a given phrase. Stay updated with latest technology trends Join DataFlair on Telegram!! In this machine learning project, we predict the emoji from the given text. This means we build a text classifier that returns an emoji that suits the given text.


Walgreens used AI to optimize vaccine outreach emails

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As of May 18, nearly 40% of the U.S. population had been fully vaccinated against COVID-19, with close to 50% having received at least one shot. But outreach remains a major challenge. McKinsey estimated in December that vaccine adoption would require "unprecedented" public and private action and incremental investment of about $10 billion. Highlighting the unevenness in the rollout, a lower percentage of African Americans had been vaccinated by March than of the general population in every state reporting statistics by race. The government at the local, state, and federal levels is involved in distributing and administering vaccines, as well as private-sector partners like pharmacy chains, grocers, and retailers.


14 tasks for text preprocessing in NLP

#artificialintelligence

Natural Language Processing a subfield of Machine Learning mainly deals with text data. It analyses reviews of objects like books, movies, play store apps, etc, to find whether they are positive or negative, sentiment analysis, text generation for chatbots, query analysis and resolution for search engines, and many other text-related tasks. Preprocessing of datasets is one of the most arduous tasks of the machine learning pipeline. Text preprocessing also requires many steps. Some of the tasks while dealing with text datasets is given below.