Earlier this year (May 2018) Microsoft announced ML.NET, an open source and cross-platform machine learning framework built for .NET developers. It is exciting news to be able to integrate custom machine learning with .NET/C# applications. Although ML.NET is still in preview release version 0.5.0 at the time of writing, you can test drive it to explore the potential power of the framework. There are already a number of tutorials for ML.NET available from Microsoft and third parties. However, the example data sources are mostly flat files in the format of TSV (Tab Separated Values).

Facebook is being forced to apologise once again – this time for the biggest hack in its history. On Friday it announced that that it had discovered an attack that exposed the personal details of 50 million accounts, including those of co-founder Mark Zuckerberg and chief operating officer Sheryl Sandberg. The attack also gave the hackers access to other services that people logged into using their Facebook account, such as Tinder, Instagram, Spotify and Airbnb. To break in, the attackers exploited an interaction between several different bugs in Facebook's code, tricking the site into handing over the digital keys to individual accounts. When using the "View As" feature, which lets you see what your profile looks like to another user, a video-upload box was incorrectly left activated.

More bad news: Facebook has announced that a security exploit allowed attackers to gain control of at least 50 million user accounts. According to the company, the exploit impacted a feature that lets users see what their profile looks like to another user. In this case, the breach doesn't appear to involve extracting data from servers. Instead, the defect--introduced by a change to the way videos get uploaded--allowed users to gain control of a user's account directly, without a password. Facebook says they have fixed the vulnerability and taken steps to protect other users who could have been impacted.

"We have a responsibility to protect your data, and if we can't then we don't deserve to serve you." Mark Zuckerberg wrote that in a Facebook post on March 21, at the height of the furor over Cambridge Analytica's use of ill-gotten personal information from Facebook users to help in its work for political campaigns, possibly including Donald Trump's. On Friday, Facebook announced a massive security breach--probably the most significant in its history. Taking advantage of three separate bugs in tandem, hackers gained full control of at least 50 million users' Facebook accounts. That meant that, in theory, the users could do just about anything on those accounts: post status updates for you, download your photos, send messages to your friends, download your friends' nonpublic photos, and much more.

"It's always the company's responsibility to identify when UK citizens have been affected as part of a data breach and take steps to reduce any harm to consumers," ICO Deputy Commissioner of operations, James Dipple-Johnstone said http://bit.ly/2y1ahTQ.

Scroll down to know more. Our APIs make it easy for developers to build applications and leverage the power of ML/AI to develop features like gender classification, sentiment analysis and card detection. Detect the presence of any card in an image and classify the type of card, e.g. Classify the polarity of a given text, sentence or expressed opinion, as positive, negative,or neutral.

We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including inference and granularity. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you'll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school. Start with a problem tied directly to a specific business outcome. Make sure the subject of your pilot is small enough to tackle and clear enough to measure.

The AFINN lexicon is perhaps one of the simplest and most popular lexicons that can be used extensively for sentiment analysis. The current version of the lexicon is AFINN-en-165. You can find this lexicon at the author's official GitHub repository. The author has also created a nice wrapper library on top of this in Python called afinn, which we will be using for our analysis. Let's look at some visualisations now.