A subreddit dedicated for learning machine learning. Feel free to share any educational resources of machine learning. Also, we are a beginner-friendly sub-reddit, so don't be afraid to ask questions! This can include questions that are non-technical, but still highly relevant to learning machine learning such as a systematic approach to a machine learning problem.

With No Time to Die all set to be Daniel Craig's last performance as the famous MI6 agent, fans are wondering who will be next to take up the martini-swigging mantle. Well, according to the first ever AI-assisted casting programme, The Witcher and Justice League star, Henry Cavill, is the right man to be the next James Bond. The AI-assisted programme compared different actor's attributes against Bond's attributes in order to best assess who would be the perfect choice to follow in Craig's footsteps. Cavill came out ahead, with The Hobbit star Richard Armitage just behind, followed by Idris Elba. This was then expanded to include international stars and resulted in The Boys and Dredd actor Karl Urban topping the list with a whopping 96.7%, putting him ahead of even Henry Cavill.

"We wanted flying cars, instead we got 140 characters" -Peter Thiel The closing quarter of the twentieth century was peak tech innovation in the United States. AT&T's Bell Labs invented the information age with the transistor and data networking, and many transformative technologies tangential to its core business: from solar cells to the Unix operating system to lasers.1 Xerox's Palo Alto Research Center (PARC) brought about human-computer interaction with the initial computer mouse, as well as laser printing and Ethernet networking.2 In the 80's Pixar was born, creating the first ever computer-animated sequence in a feature film with novel computer-generated imagery (CGI).3,4 At the same time Gates and Allen were hacking at something special that soon revolutionized computing, as were Wozniak and Jobs.5,6 Amidst the heyday of invention in the world of bits, the "space race" brought about massive innovation and accomplishments in the world of atoms: government competition between the US and Russia put humans on the moon for the first time.

Other levels include a spin on Hoopsy Daisy, a mini-game that involves jumping through hoops to win points quicker than other teams. In this new level, you move ramps and platforms to reach the hoops, and there will be moving draw bridges as well. In the third, it's an obstacle course with spiked logs that rotate, swinging axes and more. Mediatonic is keeping the remaining level under wraps for now, but it did recently announce Big Yeetus, a randomly-appearing swinging hammer that brings more chaos to rounds.

There is little doubt now that that the prospect of Artificial intelligence (AI) will radically change our lives and the society we live in. The following film is highly informative, but it's also a cautionary tale: the speed at which A.I. applications and integration is occurring is now threatening to outpace humans' ability to comprehend and consider its ramifications. From the filmmakers: "It touches on all aspects of society – private life, business, security -- including in the spread of fake news and the challenges posed by the advent of autonomous weapons. This documentary looks at the rapid change digitalization is causing as it unfolds …. a future with robots and the risks and ethical questions posed by the development of autonomous weapons …. AI can generate perfectly forged sound and videos, making it effective for purveying fake news. Discerning the truth from fiction will become increasingly difficult. Technology will streamline work, making some jobs surplus to requirements."

Earlier this year a new initiative to create standards for artificial intelligence (AI) and machine learning (ML) in the cable telecommunications industry was launched. The working group, which draws members from both inside and outside of cable including giants like IBM, is exploring how AI and ML can be leveraged to make the network more efficient. Using machine learning to solve this challenge, an algorithm considers multiple variables including service load and cost to provide an actionable and prioritized report for the cable operator to act on. By applying ML to automate node splits, the network will run more efficiently, and customers will continue to receive their high-speed services without interruption as the network grows. The working group is also looking at creating standards to control video piracy by applying artificial intelligence on the network that detects signatures of bad actors.

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce. Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life. This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies – not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie. When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background.

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.

My kids, ages 8 and 5, are showing me the future. When I want to watch a movie or turn out the lights, I instinctively reach for the remote or flick a switch. My children find it far more natural to just ask Siri for Peppa Pig, or tell Alexa to darken the room. Why not just talk to the machines around us like we talk to each other? Of course, right now talking -- rather than touching -- also has serious safety upsides. Voice tech adoption has accelerated as the coronavirus pandemic makes everyone touchy about how sanitary it is to poke buttons and screens.

"Researchers used machine learning to create the first large-scale, data-driven study to illuminate how culture affects the meanings of words.".