"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Artificial intelligence and machine learning bring exponential changes to the way physical security processes input from video cameras and sensors. Data is the fuel that feeds AI, and cameras provide massive amounts of video for review. AI's deep learning algorithms automatically detect differences between human and vehicle movements as opposed to animals, blowing leaves or reflections of light. One result is a tremendous reduction in false alarms and potentially related fines. We view AI as an added layer of security, helping, not replacing, humans to do a better job of securing people and assets.
Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.
Atomwise, which is using artificial intelligence for small molecule drug discovery, received a cash infusion of $123 million in an oversubscribed Series B financing. San Francisco-based Atomwise touts being the creator of the first convolutional neural networks, or visual imagery, using AI technology for drug discovery, a market estimated to reach $40 billion in value by 2027, according to Fior Markets research. To date, Atomwise has provided AI technology to more than 750 academic research collaborations addressing over 600 disease targets, Abraham Heifets, co-founder and CEO told Crunchbase News. B Capital Group and Sanabil Investments led the investment that also included existing investors DCVC, BV, Tencent, Y Combinator, Dolby Ventures, AME Cloud Ventures, as well as two undisclosed insurance companies. This brings the total amount of capital raised, since Atomwise's inception in 2012, to almost $175 million.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.
The UK Court of Appeal has unanimously reached a decision against a face-recognition system used by South Wales Police. The judgment, which called the use of automated face recognition (AFR) "unlawful", could have ramifications for the widespread use of such technology across the UK. But there is disagreement about exactly what the consequences will be. Ed Bridges, who initially launched a case after police cameras digitally analysed his face in the street, had appealed, with the support of personal rights campaign group Liberty, against the use of face recognition by police. The police force claimed in court that the technology was similar to the use of closed-circuit television (CCTV) cameras in cities.
This tutorial shows you how to perform supervised classification (e.g., Classification and Regression Trees [CART]) in Earth Engine. The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.
Training an image segmentation model on new images can be daunting, especially when you need to label your own data. To make this task easier and faster, we built a user-friendly tool that lets you build this entire process in a single Jupyter notebook. The main benefits of this tool are that it is easy-to-use, all in one platform, and well-integrated with existing data science workflows. Through interactive widgets and command prompts, we built a user-friendly way to label images and train the model. On top of that, everything can run in a single Jupyter notebook, making it quick and easy to spin up a model, without much overhead.
I love the sci-fi movie genre. Futuristic scenarios, jaw-dropping visuals, a tight storyline knitting it all together – that's a recipe for a box office hit. Anyone who grew up in the 80s and 90s will be intimately familiar with the Terminator franchise. And once I moved into the machine learning space, my appreciation and interest in these movies grew multifold! What we once thought of as unrealistic scenarios are now playing out in the real world.
I'd like to let you in on a secret: when people say'machine learning' it sounds like there's only one discipline here. There are two, and if businesses don't understand the difference, they can experience a world of trouble. Imagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that's the kind of mistake I see businesses making over and over. If you're opening a bakery, it's a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry.
In a decade, facial-recognition technologies will be seen as inherently corrosive to civil liberties and democratic engagement. Attempts to detect or predict people's criminality or predict people's internal emotional state by looking at pictures of their faces will ultimately be seen as unscientific and discriminatory. We need much more than ethical guidelines to ensure that AI is going to be safe and sustainable in the future. What are the labor practices required to make an AI system work? Does it rely on gig workers and warehouse workers [and "click workers," who tag data to train machine-learning algorithms] to put themselves in unsafe workplaces to make AI systems function?