Thanks to advances in artificial intelligence (AI), society is now facing a unique challenge: how do we regulate the usage of human faces and voices? Facial recognition is the ability of computer systems to identify and us by our faces. Voice recognition is the ability of computer systems to do the same for our words. Both are powered by AI, and both create benefits for consumers and citizens. These technologies also raise difficult questions about privacy and personal rights.

AI systems excel in pattern recognition, so much so that they can stalk individual zebrafish and fruit flies even when the animals are in groups of up to a hundred. To demonstrate this, a group of researchers from the Champalimaud Foundation, a private biomedical research lab in Portugal, trained two convolutional neural networks to identify and track individual animals within a group. The aim is not so much to match or exceed humans' ability to spot and follow stuff, but rather to automate the process of studying the behavior of animals in their communities. "The ultimate goal of our team is understanding group behavior," said Gonzalo de Polavieja. "We want to understand how animals in a group decide together and learn together."

MIT researchers believe they've figured out a way to keep facial recognition software from being biased. To do this, they developed an algorithm that knows to scan for faces, but also evaluates the training data supplied to it. The algorithm scans for biases in the training data and eliminates any that it perceives, resulting in a more balanced dataset. MIT researchers believe they've figured out a way to keep facial recognition software from being biased. They developed an algorithm that's capable of balancing training data'We've learned in recent years that AI systems can be unfair, which is dangerous when they're increasingly being used to do everything from predict crime to determine what news we consume,' MIT's Computer Science & Artificial Intelligence Laboratory said in a statement.

Embattled Chinese telecom giant Huawei has some new problems. The US Department of Justice on Monday unsealed a 13-count indictment against Huawei and its CFO, Meng Wanzhou, alleging the company misled banking partners about violations of US sanctions against Iran. The charges include bank fraud, wire fraud, money laundering, and obstruction of justice. Meng, who is also the daughter of Huawei founder Ren Zhengfei, was arrested in Canada last month and is awaiting extradition to the US. In a separate case, the DOJ indicted Huawei for stealing intellectual property related to a cell-phone-testing robot from T-Mobile in 2012.

Originally published at medium.com by @l.mokrzycki on April 22, 2018. General Data Protection Regulation comes to life in the European Union from 25th May and will strongly influence how Machine Learning products are developed. Without a doubt, it will increase the amount of work required for shipping ML project to productions, but on the other hand, it will be solid protection for the rights of users, which also includes us, creators of these projects. The rights that help us keep control over data that is collected about us and protect us from unfair results of fully autonomous systems. Now, a user will have a legal basis for access, rectify, transfer or deletion of his private data. With this post, I would like to propose best practices for ML models developers allowing them to reduce the amount of work and issues implied by the new law.

Workplace sexual harassment continues to be one of the most under-reported crimes in India and the world over. A primary reason for this is the lack of awareness, both by'victims' as well as'perpetrators', on what constitutes sexual harassment, and the action to be taken when faced with it. Project Disha is here to help. This project consists of an Artificial Intelligence (AI) enabled chatbot at its core, which sits on top of an eLearning course on'Prevention of Sexual Harassment', and answers learner questions and alleviates concerns on the topic. This chatbot, called Disha the Learning Guide, also helps learners freely navigate the content without any restrictions and is the world's first chatbot powering a course developed in Articulate Storyline.

A hitchhiking robot was beheaded in Philadelphia. A security robot was punched to the ground in Silicon Valley. Another security bot, in San Francisco, was covered in a tarp and smeared with barbecue sauce. Why do people lash out at robots, particularly those built to resemble humans? It is a global phenomenon. In a mall in Osaka, Japan, three boys beat a humanoid robot with all their strength. In Moscow, a man attacked a teaching robot named Alantim with a baseball bat, kicking it to the ground, while the robot pleaded for help.

In May 1997, in a high-profile chess match held under tournament conditions, the reigning world champion, Garry Kasparov, took on Deep Blue, an IBM-developed computer, and lost. It was the first time that an artificial intelligence (AI) had defeated a world champion. The result received much coverage at the time and represented a triumph of late 20th century technology. The question of whether the practice of law exhibits an equivalent level of tactical dexterity to that of a chess match is not one to be answered here, and certainly not by a practising lawyer. But advances in AI, across many facets of life since the turn of the century, are undeniable.

There have been efforts to fight racist biases in face detection systems through better training data, but that usually involves a human manually supplying the new material. MIT's CSAIL might have a better approach. The code can scan a data set, understand the set's biases, and promptly resample it to ensure better representation for people regardless of skin color. The technology won't necessarily iron out all biases, but the results can be significant. In testing, MIT's system reduced "categorical bias" by 60 percent without affecting the precision. It also promises to save time, especially for larger data collections that are time-consuming.

Online social platforms have been the battlefield of users with different emotions and attitudes toward each other in recent years. While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techniques. Categorizing sexism as either benevolent or hostile sexism is so broad that it easily ignores the other categories of sexism on social media. Sharifirad S and Matwin S 2018 proposed a well-defined category of sexism including indirect harassment, information threat, sexual harassment and physical harassment, inspired from social science for the purpose of natural language processing techniques. In this article, we take advantage of a newly released dataset in SemEval-2018 task1: Affect in tweets, to show the type of emotion and intensity of emotion in each category. We train, test and evaluate different classification methods on the SemEval- 2018 dataset and choose the classifier with highest accuracy for testing on each category of sexist tweets to know the mental state and the affectual state of the user who tweets in each category. It is a nice avenue to explore because not all the tweets are directly sexist and they carry different emotions from the users. This is the first work experimenting on affect detection this in depth on sexist tweets. Based on our best knowledge they are all new contributions to the field; we are the first to demonstrate the power of such in-depth sentiment analysis on the sexist tweets.