Last week, like the Jews of Exodus painting blood on their lintels, hundreds of thousands of Instagram users posted a block of text to their accounts hoping to avoid the plague of artificial intelligence online. "Goodbye Meta AI," the message began, referring to Facebook's parent company, and continued, "I do not give Meta or anyone else permission to use any of my personal data, profile information or photos." Friends of mine posted it; artists I follow posted it; Tom Brady posted it. In their eagerness to combat the encroachment of A.I., all of them seemed to overlook the fact that merely sharing a meme would do nothing to change their legal rights vis-à-vis Meta or any other tech platform. It is, in fact, possible to prevent Meta from training its A.I. models on your personal data.

It used to be that you bought a new phone and got a fresh start. Now we just copy over all the junk from the old model onto the new one. Sure, it's faster and shinier, but it's packed with files you don't need, contacts you haven't talked to in years and photos you don't remember taking -- or want to see again. It doesn't take much effort to clear things out if you know what to do. You'll thank yourself (and me) later.

A dataset of videos, used for computer vision tasks, could be the key to success or failure. A clean dataset could lead the way to a great algorithm, model and ultimately system, while no matter how good the model or algorithm is, junk in -- junk out. Typically you will have several long videos, with multiple scenes in it, that might be used for different projects, while you need a one or more parts from each of the videos. How will you find these segments? Confirm they are all of the desired quality?

A dataset of images, used for computer vision tasks, could be the key to success or failure. A clean dataset could lead the way to a great algorithm, model and ultimately system, while no matter how good the model or algorithm is, junk in - junk out. So, how do we know what we have? How do we make sure the data is clean, high quality, contains what we really need? If the set was very little, we could check it manually.

SVM stands for support vector machine, and although it can solve both classification and regression problems, it is mainly used for classification problems in machine learning (ML). SVM models help us classify new data points based on previously classified similar data, making it is a supervised machine learning technique. The companion resources to this article can be found either on Google Colab or Github. Classification is a supervised ML task that requires machine learning algorithms that learn how to assign a class label to examples from a problem domain. An easy-to-understand example is classifying emails as "junk" or "not junk."

An important part of the International Space Station (ISS) been damaged after getting hit by orbital debris. The damage doesn't appear to be substantial, but it shows the threat posed by such "junk." The damage was discovered during an inspection of the ISS' Canadarm2 on May 12, the Canadian Space Agency (CSA) said in a statement. Canadarm2 is the CSA's contribution to the ISS and is essentially a robotic arm that performs various important tasks, from moving supplies and even astronauts to grabbing visiting vehicles then berthing them to the ISS. Images of the hit show small damage, but it was quite a "lucky strike," considering that Canadarm2 only has a diameter of 14 inches.

Artificial intelligence is a tool used by people all over the world to empower humans to make informed decisions. With a responsible use of AI, humans are able to solve problems faster because artificial intelligence can process more information at a time than a human can. As climate change, sustainability, and corporate social responsibility become of increased importance for brands across the globe, business, and communications professionals should keep an eye on how emerging technologies, like AI, are being used to solve real-world problems. Some of the problems AI is solving today are right out of science fiction movies. Waste recognition, space junk, and sustainability are a few of the challenges that artificial intelligence is helping humanity tackle.

Artificial intelligence is a tool used by people all over the world to empower humans to make informed decisions. With a responsible use of AI, humans are able to solve problems faster because artificial intelligence can process more information at a time than a human can. As climate change, sustainability, and corporate social responsibility become of increased importance for brands across the globe, business, and communications professionals should keep an eye on how emerging technologies, like AI, are being used to solve real-world problems. Some of the problems AI is solving today are right out of science fiction movies. Waste recognition, space junk, and sustainability are a few of the challenges that artificial intelligence is helping humanity tackle.

Preamble: Intermittently, I will be introducing some columns which introduce some seemingly outlandish concepts. The purpose is a bit of humor, but also to provoke some thought. "God does not play dice with the universe," Albert Einstein is reported to have said about the field of Quantum Physics. He was referring to the great divide at the time in the physics community between general relativity and quantum physics. General relativity was a theory which beautifully explained a great deal of physical phenomena in a deterministic fashion.

An Australian team is using machine learning to tackle the threat of space junk wrecking new satellites. Research to tackle the growing need to find, capture and remove junk from space is advancing at the Australian Institute for Machine Learning in Adelaide, South Australia. Machine Learning for Space director Tat-Jun Chin and his Adelaide-based team have won a $600,000 grant from Australia's SmartSat CRC to continue their work in detecting, tracking and cataloging space junk. SmartSat CRC was established last year to work with the Australian Space Agency based in Adelaide, contributing to the Australian government's goal of tripling the size of the space sector to $12 billion and creating as many as 20,000 jobs by 2030. The space junk project is based on developing a space-based surveillance network and tackling the growing challenge of crowding in space.