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Nintendo Super Mario Bros. video game sets record selling for $114,000. Do you have a sealed copy?

USATODAY - Tech Top Stories

Time to check your attic for sealed old video games. An unopened Super Mario Bros. video game released in 1985 for Nintendo's NES console broke the world record for the most ever paid for any video game, according to Dallas-based Heritage Auctions. A bidder, who wished to remain anonymous, snapped up the game for $114,000 during an auction conducted by Heritage Friday during its Comics & Comic Art event, which continues through Sunday. The previous record was set in early 2019 when an unopened copy of the same game sold for $100,150. "The demand for this game was extremely high, and if any lot in the sale could hit a number like that, it was going to be this one," Valarie McLeckie, Heritage video games director, said in a news release.

Top 15 Cheat Sheets for Machine Learning, Data Science & Big Data


Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That's why we have cheat sheets. But there are a plethora of cheat sheets available out there, choosing the right cheat sheet is a tough task. So, I decided to write this article. Enjoy and feel free to share!

[Project] blendtorch: seamless PyTorch - Blender integration


Training with artificial images is becoming increasingly important to address the lack of real data sets in various niche areas. Yet, many today's approaches write 2D/3D simulations from scratch. To improve this situation and make better use of existing pipelines, we've been working towards an integration between Blender, an open-source real-time physics enabled animation software, and PyTorch. Today we announce blendtorch, an open-source Python library that seamlessly integrates distributed Blender renderings into PyTorch data pipelines at 60FPS (640x480 RGBA). Batch visualization from 4 Blender instances running a physics enabled falling cubes scene.

The Case for Causal AI (SSIR)


Much of artificial intelligence (AI) in common use is dedicated to predicting people's behavior. It tries to anticipate your next purchase, your next mouse-click, your next job move. But such techniques can run into problems when they are used to analyze data for health and development programs. If we do not know the root causes of behavior, we could easily make poor decisions and support ineffective and prejudicial policies. AI, for example, has made it possible for health-care systems to predict which patients are likely to have the most complex medical needs. In the United States, risk-prediction software is being applied to roughly 200 million people to anticipate which patients would benefit from extra medical care now, based on how much they are likely to cost the health-care system in the future. It employs predictive machine learning, a class of self-adaptive algorithms that improve their accuracy as they are provided new data. But as health researcher Ziad Obermeyer and his colleagues showed in a recent article in Science magazine, this particular tool had an unintended consequence: black patients who had more chronic illnesses than white patients were not flagged as needing extra care. The algorithm used insurance claims data to predict patients' future health needs based on their recent health costs.

The F-16's Replacement Won't Have a Pilot at All


The U.S. Air Force plans to have an operational combat drone by 2023. The service plans to build out a family of unmanned aircraft, known as Skyborg, capable of carrying weapons and actively participating in combat. The Air Force's goal is to build up a large fleet of armed, sort-of disposable jets that don't need conventional runways to take off and land. The Air Force, according to Aviation Week & Space Technology, expects to have the first operational Skyborg aircraft ready by 2023. Skyborg will be available with both subsonic and supersonic engines, indicating both attack and fighter jet versions.

Chatbot Development : Make conversations flawless with a Dialog Manager


In your journey of chatbot development, you must have always wondered how chatbots converse so effectively. The conversations become so flawless that we almost forget that we are actually talking to an automated agent. In this article, we are going to dive into conversational design, and how to make the agent learn example conversations for training the Dialogue Manager. So, let's start our exciting journey of … Whenever we start building a conversational agent, we just have one thing in mind… "How to make our Bot most Human like?" Every conversational agent is built on two important components -- Language Understanding and Dialogue Management System.



Artificial Intelligence, also known as (AI) is a branch of computer science that focuses more on the development of intelligent machines that thinks and works like humans. AI is gaining popularity at a quicker pace; it influences the way we live, improves, and interact, customers, experience. Presently, Artificial Intelligence is a subject widely discussed in technology, medical, education, and law, manufacturing, and business circles. Many experts and industry analysts now argue that Artificial Intelligence or machine learning is the future. But, if we look around, we all are convinced without a doubt that AI is no longer the future, it is present!

Pytorch 101 -- An Introduction to Deep Learning


Whether you've noticed it or not, Deep Learning (DL) plays an important part in all our lives. From the voice assistants and auto-correct services on your smartphone to the automation of large industries, deep learning is the underlying concept behind these meteoric rises in human progress. A major concept that we implement in deep learning is that of neural networks. A neural network is a computing algorithm of an interconnected system of mathematical formulae used to make predictions by "training" the algorithm on data relevant to the prediction to be made. This is partly inspired by the way neurons are connected in biological brains.

Amsterdam opens lab to explore fair use of Artificial Intelligence


A new research facility will explore the fair uses of artificial intelligence (AI) in Amsterdam, the municipality announced on Tuesday. Five researchers will be studying the application of artificial intelligence in the fields of education, welfare, the environment, mobility and health in a new lab at Amsterdam Science Park. AI, a collective name for technological applications capable of recognising patterns, following algorithms, learning and problem-solving, has proven its immeasurable benefits for citizens, business and governments. From improving traffic and safety to preventing accidents, crimes, or the progress of diseases – the list of spheres where AI can potentially outperform human intelligence is long. However, like with every revolutionary technology, AI can also be exploited with bad intentions, it can be biased or can simply make errors in its "assumptions."