Media
Boulder-based E Source acquires artificial intelligence firm
July 21, 2020 at 5:28 p.m.. E Source Cos. LLC, a Boulder research and consulting firm for the utilities industry, has acquired artificial intelligence โฆ
Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code)
Kazhuparambil, Subramaniam, Kaushik, Abhishek
The scope of a lucrative career promoted by Google through its video distribution platform YouTube has attracted a large number of users to become content creators. An important aspect of this line of work is the feedback received in the form of comments which show how well the content is being received by the audience. However, volume of comments coupled with spam and limited tools for comment classification makes it virtually impossible for a creator to go through each and every comment and gather constructive feedback. Automatic classification of comments is a challenge even for established classification models, since comments are often of variable lengths riddled with slang, symbols and abbreviations. This is a greater challenge where comments are multilingual as the messages are often rife with the respective vernacular. In this work, we have evaluated top-performing classification models for classifying comments which are a mix of different combinations of English and Malayalam (only English, only Malayalam and Mix of English and Malayalam). The statistical analysis of results indicates that Multinomial Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest and Decision Trees offer similar level of accuracy in comment classification. Further, we have also evaluated 3 multilingual transformer based language models (BERT, DISTILBERT and XLM) and compared their performance to the traditional machine learning classification techniques. XLM was the top-performing BERT model with an accuracy of 67.31. Random Forest with Term Frequency Vectorizer was the best performing model out of all the traditional classification models with an accuracy of 63.59.
Using Artificial Intelligence/ Machine Learning to Detect Domain Generation Algorithms
We will also cover some unique and cutting-edge methods leveraging artificial intelligence/machine learning (AI/ML) to counter these threats andย โฆ
AI and Machine Learning Critical to Tackling Cyber Threats Says NTT
Advanced artificial intelligence (AI) and machine learning tools are becoming increasingly critical in detecting and combatting cyber threats.
Sisu Identified as a 2020 "Hot Vendor" in Artificial Intelligence by Aragon Research
Sisu is built on a novel machine-learning engine, purpose-built to handle the kind of high-volume, high-dimensional data enterprises are collecting inย โฆ
Artificial intelligence will have big role in ag's future, scientists say
The use of artificial intelligence in agriculture is expected to surge as interest increases and technology develops, researchers say. "It is already very โฆ
Artificial Intelligence Startup, CEO Accused of Securities Fraud
The CEO of a startup that purportedly used artificial intelligence to deliver customer insights from internet videos fraudulently raised at least $11 millionย โฆ
Artificial Intelligence Is the Hope 2020 Needs
The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed toย โฆ
Five ways to bring a UX lens to your AI project โ TechCrunch
As AI and machine-learning tools become more pervasive and accessible, product and engineering teams across all types of organizations are developing innovative, AI-powered products and features. AI is particularly well-suited for pattern recognition, prediction and forecasting, and the personalization of user experience, all of which are common in organizations that deal with data. A precursor to applying AI is data -- lots and lots of it! Large data sets are generally required to train an AI model, and any organization that has large data sets will no doubt face challenges that AI can help solve. Alternatively, data collection may be "phase one" of AI product development if data sets don't yet exist.
Creepy Apollo 11 Nixon deepfake video created by MIT to show dangers of high-tech misinformation
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Scientists at MIT have digitally manipulated video and audio to create a creepy deepfake of President Nixon "delivering" a speech that would have been used in the event of an Apollo 11 disaster. Written in 1969, the contingency speech was to be used if NASA astronauts Neil Armstrong and Buzz Aldrin were unable to return from the moon. The video is part of a project entitled "In Event of Moon Disaster" that aims to highlight the dangers of deepfakes, which use artificial intelligence (AI) and machine learning to create false, but realistic-looking clips.