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The cartoon gorilla that taught a generation to not click 'download'

Mashable

In the early 2000s, a purple, talking gorilla named BonziBuddy was billed as a free virtual assistant, ready for all your internet needs. It could talk, search for you, sing, send emails -- and anyone with a computer could download it for free. Turns out, that was the big problem. Bonzi wasn't your friend; it was malware, and it was released at the perfect time. Following the burst of the dot com bubble, investors pulled their money from the web and online companies needed a new way to profit.


How a purple gorilla made us regulate the internet — Kernel Panic

Mashable

In the early 2000s, a purple, talking gorilla named BonziBuddy was billed as a free virtual assistant, ready for all your internet needs. It could talk, search for you, sing, send emails -- and anyone with a computer could download it for free. Turns out, that was the big problem. In the third episode of Kernel Panic, we explore the rise and fall of one the friendliest-looking pieces of malware of all time. It's the story of how one seemingly harmless ape preyed on early internet users and then paid the price, teaching all of us how much we had to lose from so-called "free" downloads.


Deep Reinforcement Learning for AGV Routing

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This mapping will be included in an AGV Picking Simulation Model that will be used for testing our routing strategies. Dijkstra's algorithm is an optimization algorithm that solves the single-source shortest path problem for a directed graph with weighted edges (non-negative weights). This length can be the absolute length of the path, it can also be computed considering other constraints situated on the edges or the nodes. These parameters will vary in time, therefore let's use a reinforcement learning approach to select the optimal route from these candidates in accordance with this state.


Growing role for AI in fresh produce

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There is a growing role for artificial intelligence within horticulture, experts have claimed – but it is not the silver bullet many people think. Speaking at World of Fresh Ideas, Anthony Atlas, head of product and growth at agronomic machine-learning specialist ClimateAI, outlined the benefits and pitfalls of AI use on farms. Describing AI as "systems that generate predictions from past correlations – a giant pattern-identification machine", Atlas said AI is only as good as the training it receives. He stressed that it is not easy to build, and that there isn't one single system that does everything, but instead each task is done by a separate model trained to perform a particular task. In horticulture, AI is being used as a decision-support system in climate and weather forecasting, imagery interpretation and precision automation of greenhouses. Benefits of AI include more complexity, nuance and power, the ability to cheaply automate repetitive tasks, and the fact it is more lightweight than a supercomputer.


Artificial Intelligence for Rapid Exclusion of COVID-19 Infection

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An international retrospective study finds that infection with SARS-CoV-2, the virus that causes COVID-19, creates subtle electrical changes in the heart. An AI-enhanced EKG can detect these changes and potentially be used as a rapid, reliable COVID-19 screening test to rule out COVID-19 infection. The AI-enhanced EKG was able to detect COVID-19 infection in the test with a positive predictive value -- people infected -- of 37% and a negative predictive value -- people not infected -- of 91%. When additional normal control subjects were added to reflect a 5% prevalence of COVID-19 -- similar to a real-world population -- the negative predictive value jumped to 99.2%. The findings are published in Mayo Clinic Proceedings.


Random Forest Algorithm in Python from Scratch

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The intuition behind the random forest algorithm can be split into two big parts: the random part and the forest part. Let us start with the latter. A forest in real life is made up of a bunch of trees. A random forest classifier is made up of a bunch of decision tree classifiers (here and throughout the text -- DT). Each DT in an RF algorithm is completely independent of one another.


How to run (Model-Agnostic Meta-Learning) MAML algorithm

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MAML is a class of meta-learning algorithms created by Stanford Research and UC Berkeley Alum Dr. Chelsea Finn. MAML was inspired by the idea behind the question that how much data is really needed to learn about something. Can we teach algorithms to learn how to learn? Taken from Chelsea Finn's original research: MAML is designed such that it trains a model on a variety of tasks such that it can learn a new learning task with only a small number of training samples. MAML introduces an outer loop called meta-training.


A Startup Is Launching a New Brand Studio for Virtual Humans

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A startup that made a name for itself designing AI avatar clones of celebrities is turning its attention towards brands. Patrick Kulp is an emerging tech reporter at Adweek.


Everything You Need To Know About Google's Vertex AI

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Alphabet CEO Sundar Pichai has introduced Vertex AI, a managed machine learning platform for deploying and maintaining AI models, during his keynote speech at the recently concluded Google I/O conference. The new platform brings AutoML and AI Platform together into a unified API, client library and user interface. "When we were training algorithms before, we would have to run millions of test images," said Jeff Houghton, chief operating officer of L'Oréal's ModiFace, which develops augmented reality and AI digital services for the beauty industry. "Now, we can rely on the Vertex technology stack to do the heavy lifting. Vertex has the computing power to figure out complex problems. It can do billions of iterations, and Vertex comes up with the best algorithms," Houghton added.


How to Build An Image Classifier in Few Lines of Code with Flash

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Image classification is a task where we want to predict which class belongs to an image. This task is difficult because of the image representation. If we flatten the image, it will create a long one-dimensional vector. Also, that representation will lose the neighbor information. Therefore, we need deep learning for extracting features and predict the result.