Tag: ML

  • AI Wrote Better Phishing Emails

    Phishing Email (from the article)

    WIRED schreibt, dass es Forschern gelungen ist, mit Hilfe von GPT3, dem Generative Pre-trained Transformer 3 ML Netzwerk, Phishing Mails zu erzeugen, die deutlich wirksamer sind als von Menschen geschriebene Mails.

    Endlich ein Einsatzbereich für AI, der sich auch ohne VC Geld lohnt.

    Source: AI Wrote Better Phishing Emails Than Humans in a Recent Test | WIRED

  • Protect your images from abuse by KI

    From the “Daily Dystopia Department”: Protect your images from abuse by KI. Headlines that’d be absolutely unthinkable only a decade ago don’t seem to be shocking in the year of the pandemic, 2021.

  • Introducing TensorFlow Recommenders

    TensorFlow, the open-source machine-learning library, introduced a library to make recommendations easier. Recommendations are a crucial component for e-Commerce but also other web-services. Good recommendations help build a better user experience and drive customer engagement. The more time consumers spend on a site, the quicker customers find what they are looking for, the better their satisfaction. Recommendations help achieve this and TensorFlow now makes it easier to improve such functionality.

    Introducing TensorFlow Recommenders, a library for building flexible and powerful recommender models.

    From the blog.

    Source: Introducing TensorFlow Recommenders — The TensorFlow Blog

  • Magic Email

    Magic Email

    It’s not like email has been a perfect solution ever, to start with. In fact, email has been broken for most of its existence. Imagine all the rules and filters you need to stay on top of your inbox. When Internet became popular, soon spam became popular.

    Email lists were usable only before eternal September began. Just the other day somebody at my employer responded to an email list that has thousands of subscribers. And so did everybody else.

    Not to mention those emails that come with a good intention and make it past all filters into your inbox. Those typically span many pages and make you feel guilty for not reading because you’re busy.

    Admit it, email is broken.

    With the advent of new technology, there are new solutions. Magic Email, as announced by Producthunt in it’s weekly newsletter, is something that I’m totally not sure whether it’s an improvement or total troll. Built on GPT-3, that created some bus on the Internet recently, Magic Email allows you to do two things:

    a) It will summarize long emails for you. That actually seems to be a good idea for those emails you just couldn’t get around to reading full detail. At least it will tell you whether it’s worth it to invest more time and go into the details buried in long prose.

    b) The much more interesting feature it is, that magic email can write text for you. You just give it a bunch of keywords a simple statement and it will extend to a long email. This is exactly that part that makes me wonder whether the product is meant as a troll. Imagine all those guys responding to an email list. Instead of replying “please unsubscribe me”, GPT-3 will write an exhaustive email basically saying the same. The same level of detail will pull much more of your time. Unless you have Magic Email installed yourself of course.

    Nevertheless, the product is amazing.

    Magic Email is your AI-powered email assistant that summarizes your emails and generates professional emails from brief one-line descriptions. Get through all of your emails 5x faster so you can free up more time for your important work.

    From Producthunt

    Source: Magic Email – Summarize and generate emails using GPT-3 in one click | Product Hunt

  • Internet Powertoy

    pointer pointer

    Internet of the day. Enjoy the rest of your day. You are welcome.

    https://pointerpointer.com

  • Machine Learning Confronts the Elephant in the Room 

    Machine Learning helps identifying the elephant in the room. Literally.

    A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.

    Source: Machine Learning Confronts the Elephant in the Room | Quanta Magazine