A big part of Microsoft's strategy after its brief risk of fading into obscurity in the late 2000s has involved major acquisitions. The acquisition of LinkedIn allowed Microsoft to move into social media, and the platform has thrived under Microsoft ownership. With all of that having been said and now out of the way, it is important to note that Microsoft is not stopping there. The latest acquisition made by the tech giant is second only to LinkedIn in terms of overall cost to the company, and it involves a completely different sector of the tech industry. The latest firm that has been acquired by Microsoft is called Nuance, and it specializes in AI based speech tech with an emphasis on voice recognition.

Twitter said Wednesday it was launching an initiative on "responsible machine learning" that will include reviews of algorithmic fairness on the social media platform. The California messaging service said the plan aims to offer more transparency in its artificial intelligence and tackle "the potential harmful effects of algorithmic decisions." The move comes amid heightened concerns over algorithms used by online services, which some say can promote violence or extremist content or reinforce racial or gender bias. "Responsible technological use includes studying the effects it can have over time," said a blog post by Jutta Williams and Rumman Chowdhury of Twitter's ethics and transparency team. "When Twitter uses (machine learning), it can impact hundreds of millions of tweets per day and sometimes, the way a system was designed to help could start to behave differently than was intended."

The digital bank founded by Prof. Amnon Shashua, among the founders of the self-driving auto-tech company Mobileye, officially began operations on Sunday, promising to shake up the Israeli banking sector and inject badly needed competition. First Digital Bank, Israel's first new banking institution in 43 years, aims to use artificial intelligence and other technology to create a personal ambiance without the actual human contact that comes with neighborhood branches. "Netflix killed off Blockbuster, Spotify disrupted the music industry and Tesla has left Ford and Mitsubishi in the dust. Banking is one of the few industries that hasn't undergone a revolution. Big, long-standing names control the market with too little competition and offer exactly the same products," said First Digital Bank's CEO, Gal Bar-Dea.

In an effort to address mounting concerns about algorithmic harms, Twitter has announced a new initiative that will subject some of the company's machine-learning systems to more scrutiny and pave the way for changes to any problematic AI models. Dubbed'Responsible ML', the initiative is designed not only to increase the transparency of the AI systems used by Twitter, but also to improve the fairness of the algorithms, and to provide users with "algorithmic choice" when it comes to the technologies that might affect them. Twitter has pledged to take responsibility for the platform's algorithmic decisions, and has appointed a Responsible ML working group to lead the initiative. This group, whose members are drawn from across the company, will be managed by Twitter's existing ML Ethics, Transparency and Accountability (META) team. With almost 200 million people using Twitter daily, the platform relies on machine-learning models for a many tasks, ranging from organizing content by relevance to identifying posts that violate terms of service.

Facebook is testing a new video speed dating app called Sparked which emphasises'kindness' and a'positive dating experience' between users. Developed by the social media giant's New Product Experimentation (NPE) team, the app requires users to have a Facebook profile, The Verge reported. The app requires users to type out what makes them a kind dater when signing up, and these responses will reportedly be "reviewed by a human at Sparked" before people can go on the video dates. The social media giant noted that Sparked would not contain any public profiles, or swiping, or DMs, and is likely to be free to use. Users also have to choose whether they want to date men, women, or nonbinary people, according to The Verge which reportedly accessed the app's website.

This work exploits a large source domain for pretraining and transfer the diversity information from source to target. Highlights: Anchor-based strategy for realism over regions in latent space A novel cross-domain distance consistency loss Existing models can be leveraged to model new distributions with less data Extensive results demonstrates qualitatively and quantitatively that this few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

AI technology used to be limited to advanced research teams. It is now a key capability for many businesses to improve sales and product quality, provide deep personalisation and new interfaces, and to improve safety and reduce risk. AI is materially changing how we interact with and benefit from technology. Having ready access to and consistent operational control over AI infrastructure is a game changer that democratizes AI for enterprises and opens access to many new use cases.

Ireland's Data Protection Commission (DPC) is investigating the recent leak of a Facebook user dataset that dates back to 2019. At the start of April, it came out that someone on a hacking forum had made the dataset public, exposing the personal information of about 533 million Facebook users in 106 countries. Depending on the account, there are details about phone numbers, birth dates, email addresses, locations and more. The source of the leak is an oversight Facebook fixed in August 2019. "The DPC, having considered the information provided by Facebook Ireland regarding this matter to date, is of the opinion that one or more provisions of the GDPR and/or the Data Protection Act 2018 may have been, and/or are being, infringed in relation to Facebook Users' personal data," the agency said in a statement spotted by TechCrunch.

It's highly unlikely that business owners are going to read this and begin to change their perspectives on how we define Data Science. Not because I doubt my influence or anything, but since I'm aware that the majority of my readers are at the beginning of their Data Science journey -- I really dislike the term "aspiring" -- but here is what I wish to tell you all… Stop trying to be good at everything in Data Science, and pick 1 (max 2) area's you want to specialize in and get really good at it! Let's face it... Breaking into Data Science is difficult for a number of reasons. However, I've come to a realization recently that much of the difficulty lies in the fact that the term "Data Scientist" encompasses so many different technical qualities that make it virtually impossible for one individual to meet all these criteria and stay up to date in each area -- and that's okay! I've been listening and speaking to Vin Vashishta, Chief Data Scientist and LinkedIn Top Voice 2019, and he believes that for roles to be defined better then more specialization amongst practitioners must occur.

Our entire financial system is built on trust. We can exchange otherwise worthless paper bills for fresh groceries, or swipe a piece of plastic for new clothes. But this trust--typically in a central government-backed bank--is changing. As our financial lives are rapidly digitized, the resulting data turns into fodder for AI. Companies like Apple, Facebook and Google see it as an opportunity to disrupt the entire experience of how people think about and engage with their money. But will we as consumers really get more control over our finances? In this first of a series on automation and our wallets, we explore a digital revolution in how we pay for things. This episode was produced by Anthony Green, with help from Jennifer Strong, Karen Hao, Will Douglas Heaven and Emma Cillekens.