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Looking for Alien Life? Seek Out Alien Tech

WIRED

Back in 1950, Enrico Fermi posed the question now known as the Fermi Paradox: Given the countless galaxies, stars, and planets out there, the odds are that life exists elsewhere--so why haven't we found it? The size of the universe is only one possible answer. Maybe humans have already encountered extraterrestrial (ET) life but didn't recognize it. Maybe it doesn't want to be found. Maybe it doesn't find us interesting.


what is AI,ML,DL,CV,NLP… and Why do we need them?

#artificialintelligence

Before understanding all these fancy and buzz words, we need to understand something else called'Automation'. Need for Automation: Because we are bored of doing the routine things everyday. Look at the things around you, how you used to operate them and now how are you operating. For example: 1)The mobile phone we use, typically we used to type the phone numbers using the dial pad and make a call. The motivation of these transformation is boredom and laziness. How Automation can be Achieved: Automation can be achieved through many ways.


How Tom Cruise got viral on TikTok

#artificialintelligence

Earlier this year, a viral video of Tom Cruise was posted on social media of him doing magic, playing golf and much more, which in reality wasn't even him. Let me explain, a VFX artist named Chris Ume deepfaked these videos using AI, resulting in an internet frenzy. The question now arises, what is deepfake technology? To explain it simply, it's when real photos & videos are manipulated with the help of AI, and are then turned into fake photos & videos for a specific purposes. For obvious reasons, deepfakes are pretty dangerous since this technology can easily be used for blackmailing, fake news and scamming. But surprisingly, there are a few pros too.


Components of Transformer Architecture

#artificialintelligence

Sequence modelling is popularly done using Recurrent Neural network(RNN) or its advancements as gated RNNs or Long-short term memory(LSTM). Handling events sequentially hinders parallel processing and when sequences are too long, then the model could potentially forget long-range dependencies in the input or could mix positional content.


At the intersection of two Eras

#artificialintelligence

The evening captured in the above click is of Kanyakumari, India's southernmost point. I remember it was a warm evening with cool breeze, the sunset was surreal, and I wondered why? Beautiful phenomenon materialize when different realms intersect. Extrapolating form the current strides in technology I believe it is safe to assume we are at an intersection of two eras. From a time when most of our decisions are taken based on human intuition and a repository of collectively learned knowledge, to a time were a swarm of AI agents collectively collaborate to take decisions for us.


AI discovers over 300 unknown exoplanets in Kepler telescope data

#artificialintelligence

A new artificial intelligence algorithm has discovered over 300 previously unknown exoplanets in data gathered by a now-defunct exoplanet-hunting telescope. The Kepler Space Telescope, NASA's first dedicated exoplanet hunter, has observed hundreds of thousands of stars in the search for potentially habitable worlds outside our solar system. The calatog of potential planets it had compiled continues generating new discoveries even after the telescope's demise. Human experts analyze the data for signs of exoplanets. But a new algorithm called ExoMiner can now mimic that procedure and scour the catalog faster and more efficiently.


Your Brain Is an Energy-Efficient 'Prediction Machine'

WIRED

How our brain, a three-pound mass of tissue encased within a bony skull, creates perceptions from sensations is a long-standing mystery. Abundant evidence and decades of sustained research suggest that the brain cannot simply be assembling sensory information, as though it were putting together a jigsaw puzzle, to perceive its surroundings. This is borne out by the fact that the brain can construct a scene based on the light entering our eyes, even when the incoming information is noisy and ambiguous. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Consequently, many neuroscientists are pivoting to a view of the brain as a "prediction machine."


Titanic Predictions with LDA

#artificialintelligence

The titanic is one of the most iconic and at the same time saddest stories in the history of human beings. There are barely any individuals who are not familiar with its story and how lucky some people were on that liner, because of certain characteristics that they took with them. Whether they were kids or had a higher purchasing power, there was a pattern to follow when predicting the probability of getting a safe boat, leaving unharmed the ship. The cleaning of the data is by far the most challenging part in most of the machine learning projects since you can extremely improve (or harm) your model according to the individual features and the types of features you train your model with. For feature selection, we will go through three main aspects.


INHUBBER - Contract management and digital signature platform is on AppRater

#artificialintelligence

INHUBBER simplifies contracts, making them comprehensible, interactive, and easy to manage. It eliminates inefficiencies associated with manual contract analysis, management, and approval processes. INHUBBER provides AI-powered management of contractual obligations and deliverables. Business flow is improved through automated AI reminders, contract fulfillment, and fast approvals. The platform's own digital signature approves ZIP, Excel, video, and any other file format. Due to its 100% security, large SMEs and corporations trust INHUBBER with their contracts.


A Brief Overview of Methods to Explain AI (XAI)

#artificialintelligence

I know this topic has been discussed many times. But I recently gave some talks on interpretability (for SCAI and France Innovation) and thought it would be good to include some of my work in this article. The importance of explainability for the decision-making process in machine learning doesn't need to be proved any longer. Users are demanding more explanations, and although there are no uniform and strict definitions of interpretability and explainability, the number of scientific papers explaining artificial intelligence (or XAI) is growing exponentially. As you may know, there are two ways to design an interpretable machine learning process.