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.

While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at https://github.com/QData/TextAttack.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Senate on Tuesday rejected a bipartisan proposal to curtail the transfer of military-grade weapons and gear to local police departments. Senators voted 51-49 on the proposal, falling short of the 60 votes needed to pass. Spearheaded by Sen. Brian Schatz, D-Hawaii, the amendment to the National Defense Authorization Act (NDAA) proposed limiting tracked combat vehicles, armed drones, grenade launchers and tear gas to local police departments across the U.S. U.S. Sens. Brian Schatz, D-Hawaii, left, and Dick Durbin, D-Ill., attend a news conference on defunding military projects to pay for the border wall on Capitol Hill.

Researchers have achieved a breakthrough in the development of artificial intelligence by using light instead of electricity to perform computations. The new approach significantly improves both the speed and efficiency of machine learning neural networks – a form of AI that aims to replicate the functions performed by a human brain in order to teach itself a task without supervision. Current processors used for machine learning are limited in performing complex operations by the power required to process the data. Such networks are also limited by the slow transmission of electronic data between the processor and the memory. Researchers from George Washington University in the US discovered that using photons within neural network (tensor) processing units (TPUs) could overcome these limitations and create more powerful and power-efficient AI. A paper describing the research, published today in the scientific journal Applied Physics Reviews, reveals that their photon-based TPU was able to perform between 2-3 orders of magnitude higher than an electric TPU.

The University of Florida today announced a public-private partnership with NVIDIA that will catapult UF's research strength to address some of the world's most formidable challenges, create unprecedented access to AI training and tools for underrepresented communities, and build momentum for transforming the future of the workforce. The initiative is anchored by a $50 million gift -- $25 million from UF alumnus Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA, the Silicon Valley-based technology company he cofounded and a world leader in AI and accelerated computing. Along with an additional $20 million investment from UF, the initiative will create an AI-centric data center that houses the world's fastest AI supercomputer in higher education. Working closely with NVIDIA, UF will boost the capabilities of its existing supercomputer, HiPerGator, with the recently announced NVIDIA DGX SuperPOD architecture. This will give faculty and students within and beyond UF the tools to apply AI across a multitude of areas to improve lives, bolster industry, and create economic growth across the state.

Stocks surged this morning on some positive earnings report in what will be a busy week in that area. Dow components IBM IBM and Coca-Cola gained over 2.5% and 2.8% after reporting better-than-expected quarterly numbers, alleviating some concern over how bad corporate earnings might have been during the lockdown quarter in the U.S. Sentiment also got a boost on Tuesday after the European Commission, which is the EU's executive branch, agreed to a 750-billion-euro stimulus package, much quicker than expected given the political maneuvering that was needed there. Designed to help the most affected countries in the region, the stimulus was well received by financial markets. In the US, lawmakers are going to face pressure to pass legislation by the end of July to pass their own stimulus, set to expire. Gold seems to be benefitting from all the fiat currency printing, gaining over 1% and nearing all-time highs.

I started doing some home baking recently. It started, like with a lot of other people, during the pandemic lockdown period when I got tired of buying the same bread from the supermarket every day. In all honesty, my bakes are passable, not very pretty but they please the family, which is good enough for me. Yesterday I stumbled on a YouTube video on how a factory makes bread in synchronised perfection and it broke a bit of my heart. All the hard work kneading dough amounts to nothing compared to spinning motors tumbling through a mechanised giant bucket. As I watch rows and rows of dough rising in unison spirals up the proofing carousel then slowly rolling into a constantly humming monstrous oven to become marching loaves of bread, something died in me. When the loaves zipped themselves into sealed bags and dumped themselves into packing boxes, I tell myself that they don't have the same craftsmanship (in my mind) as someone who is making bread with love, for his family. But deep inside me, I understand that if bread depended on human bakers only, it would be a whole lot more expensive, a lot more people would go hungry.

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.

At the Diversity, Equity, and Inclusion breakfast at VentureBeat's AI-focused Transform 2020 event, a panel of AI practitioners, leaders, and academics discussed the changes that need to happen in the industry to make AI safer, more equitable, and more representative of the people to whom AI is applied. The wide-ranging conversation was hosted by Krystal Maughan, a Ph.D. candidate at the University of Vermont, who focuses on machine learning, differential privacy, and provable fairness. The group discussed the need for higher accountability from tech companies, inclusion of multiple stakeholders and domain experts in AI decision making, practical ways to adjust AI project workflows, and representation at all stages of AI development and at all levels -- especially where the power brokers meet. In other words, although there are systemic problems, there are systemic solutions as well. The old Silicon Valley mantra "move fast and break things" has not aged well in the era of AI.

Almost everyone knows this saying: "history repeats itself." It's amazing how true that statement is, especially when it comes to technology. We like to think that every new decade in the field of tech makes the previous decade completely obsolete. When we're dealing with the minutiae of version updates and patches we can sometimes see the past with tinted glasses. There's a great quote by Eleanor Roosevelt that expands on the idea of a continuous history, "Great minds discuss ideas; average minds discuss events; small minds discuss people."