Florida could use drones to fight pythons and invasive species

The Japan Times

TALLAHASSEE, FLORIDA – Florida could turn to the sky to fight Burmese pythons on the ground under a bill a Senate committee unanimously approved Monday to allow two state agencies to use drones in the effort to eradicate invasive plants and animals. The bill would create an exception to a current law that prohibits law enforcement from using drones to gather information and bans state agencies from using drones to gather images on private land. It would allow the Florida Fish and Wildlife Conservation Commission and the Florida Forest Service to fly drones to manage and eradicate invasion species on public lands. Sen. Ben Albritton said he has been told that drones equipped with lidar, which stands for "light detection and ranging," might be able to identify pythons. "As you know, chasing those nasty critters down there in the Everglades is a difficult task," Albritton said.

Apple's self-driving car system could use voice, gesture guidance - Roadshow


Apple has its eye on self-driving car tech. Interacting with a future self-driving car could be a lot like working with some future interpretation of Apple iOS with voice, gesture and touch-enabled commands at your disposal. It's the overarching view gathered after reading through an Apple patent application filed last August and published last week for a self-driving car voice and gesture guidance system. CEO Tim Cook said in 2017 that Apple was working on an autonomous car system, rather than a car itself, as had been previously rumored. At its core, the system described in the patent application gives passengers three ways to give the autonomous car directions and input, and much of the described system is incredibly similar to commands we're used to today.

Python: Implementing a k-means algorithm with sklearn


Originally posted by Michael Grogan. The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm. The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective. The particular example used here is that of stock returns.

Small and Medium Enterprises (SMEs) rush for Machine Learning - BizAcuity Solutions Pvt. Ltd.


Back in in 1959, Arthur Samuel coined the term Machine Learning with a purpose. He wanted the computer systems to learn from data without being programmed. This latest approach not only helps the world perform computing processes in an efficient and cost-effective manner but also helps manage the gamut of data-driven affairs. Machine learning starts and sparks with the generic algorithms. It does mining, compiling, analyzing massive data and way beyond.

How to start machine learning as a software engineer in 5 steps!


These are 5 tips to keep in mind when switching from software engineering to machine learning. As a full time software engineer, it's difficult to spare time on the mathematical theory and algorithm internals of ML. The dropout rate in MOOQs is staggeringly high. I think a large part of this has to do with the motivation we are forced to synthesise. On top of this, very theoretical topics without visible results bore us.

r/MachineLearning - [P] Sightseer: SOTA Computer Vision and Object Detection models in 10 lines of code


This is the creator of gpt2-client here. Inspired by Hugging Face's Transformers, I built and launched Sightseer, a TensorFlow package that allows anyone to access SOTA Computer Vision and Object Detection models in 10 lines of code or less. In less than 10 lines of code, you can now access general-purpose SOTA models!!! For now, the Beta release supports YOLOv3 (Darknet by Joseph Redmon) and enables you to quickly load images and ingest them into the model. In the next release (coming very soon!), I'll be adding Facebook AI's Mask R-CNN model with support for video, webcam footage, and screen recordings and tools for data annotation and inter-format conversion (XML/CSV/JSON/TFRecords).

AI, machine learning and deep learning: What's the difference? - IBM IT Infrastructure Blog


It's not unusual today to see people talking about artificial intelligence (AI). When I was a kid in the 1980s, AI was depicted in Hollywood movies, but its real-world use was unimaginable given the state of technology at that time. While we don't have robots or androids that can think like a person or are likely to take over the world, AI is a reality now, and to understand what we mean when we talk about AI today we have to go through a -- quick, I promise -- introduction on some important terms. Simply put, AI is anything capable of mimicking human behavior. From the simplest application -- say, a talking doll or an automated telemarketing call -- to more robust algorithms like the deep neural networks in IBM Watson, they're all trying to mimic human behavior.

'Star Trek: Picard' breaks streaming records on CBS All Access – TechCrunch


CBS' streaming service, CBS All Access, credits a trio of high-profile events -- including the premiere of its new Star Trek series, "Star Trek: Picard," as well as the 62nd annual Grammy Awards, not to mention a busy month of football -- with helping it to achieve a new record for subscriber sign-ups in a given month. The company says January 2020 surpassed the service's previous record in February 2019 for subscriber sign-ups. In addition, last week was the second-best sign-up week ever, closely behind the week of the 2019 Super Bowl. Much of the record-setting had to do with the launch of the highly anticipated show, "Star Trek: Picard," which brings back fan-favorite Patrick Stewart as Jean-Luc Picard, now a retired Starfleet Admiral whose quiet life on his family's vineyard is about to be disrupted. The show, set 18 years after the events of the final "Star Trek: The Next Generation" movie, "Star Trek: Nemesis," not only capitalizes on Stewart's draw, it also brings back previous "Star Trek" actors including Brent Spiner (Data), Jeri Ryan (Seven of Nine), Marina Sirtis (Troi), and Jonathan Frakes (Riker).

Researchers And Army Join Hands to Protect the Military's AI Systems


As an initiative to provide protection to the military's artificial intelligence systems from cyber-attacks, researchers from Delhi University and the Army have joined hands, as per a recent Army news release. As the Army increasingly utilizes AI frameworks to identify dangers, the Army Research Office is investing in more security. This move was a very calculated one in fact as it drew reference from the NYU supported CSAW HackML competition in 2019 where one of the many major goals was to develop such a software that would prevent cyber attackers from hacking into the facial and object recognition software the military uses to further train its AI. MaryAnne Fields, program manager for the ARO's intelligent systems, said in a statement, "Object recognition is a key component of future intelligent systems, and the Army must safeguard these systems from cyber-attack. This work will lay the foundations for recognizing and mitigating backdoor attacks in which the data used to train the object recognition system is subtly altered to give incorrect answers."