AI systems claiming to 'read' emotions pose discrimination risks

The Guardian

Artificial Intelligence (AI) systems that companies claim can "read" facial expressions is based on outdated science and risks being unreliable and discriminatory, one of the world's leading experts on the psychology of emotion has warned. Lisa Feldman Barrett, professor of psychology at Northeastern University, said that such technologies appear to disregard a growing body of evidence undermining the notion that the basic facial expressions are universal across cultures. As a result, such technologies – some of which are already being deployed in real-world settings – run the risk of being unreliable or discriminatory, she said. "I don't know how companies can continue to justify what they're doing when it's really clear what the evidence is," she said. "There are some companies that just continue to claim things that can't possibly be true."

K-Nearest Neighbors explained Codementor


Here on Codementor I usually see lots of students and developers trying to get into Machine Learning confused with complicated topics they are facing at the very beginning of their journey. I want to make a deep yet understandable introduction to the algorithm which is so simple and elegant that you would like it. If you are a Machine Learning engineer but have a limited understanding of this one, it could be also useful to read it. I was working as a software developer for years and everyone around me was talking about this brand new data science and machine learning thing (I understood that there is nothing new on this planet later), so I've decided to take masters studies in the University to get known to it. Our first module was a general introductory course to Data Science and I remember myself sitting and trying to understand what's going on.



A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on Android phones or the Raspberry Pi. The guide is broken into three major portions. Each portion will have its own dedicated README file in this repository. This repository also contains Python code for running the newly converted TensorFlow Lite model to perform detection on images, videos, or webcam feeds.

What math classes are relevant for machine learning?


Finally, you need to have practical experience. Nothing better than connecting with a community that has the same interests as you. You can also face competitions. Most of this answer comes from a previous answers 1 and 2 that I gave to this (Brazilian) site. I must have forgotten lots of great references... Sorry about that.

Matplotlib for Machine Learning


Python features a large number of visualization libraries. The most recent ones, like bokeh and holoviews, are very powerful. They allow us to easily create interactive plots that can be displayed in the browser. And the datashader extension makes it possible to show big data without killing the client browser. Matplotlib, on the other hand, has been around for quite a while and might seem a bit limited with respect to more recent libraries.

Artificial Intelligence in Food and Beverages Market Forecast Report Offers Actionable Insightss 2019 – 2027 – Instant Tech Market News


The study on the Artificial Intelligence in Food and Beverages Market Research offers a profound comprehension of the market dynamics like opportunities, drivers, trends, and the challenges. The analysis further elaborates on the micro and macro-economic aspects which can be predicted to shape the rise of the Artificial Intelligence in Food and Beverages Market throughout the forecast period (2019-2029). The introduced study elucidates the key indexes of Market growth which contains a comprehensive analysis of CAGR development the value chain, and Porter's Five Forces Analysis. This data will enable readers to know the qualitative growth parameters of their worldwide market. The development prospects of this Artificial Intelligence in Food and Beverages Marketplace in various Regions are analyzed in the report together with information such as political, the regulatory frame, and economic outlook of each region.

6 Billion People's Personal Biometrics Stolen by China for their Quantum Artificial Intelligence Military Program - THE AI ORGANIZATION


China's Communist Government has extracted over 6 billion peoples biometrics, including facial, voice and personal health data to empower their Quantum Artificial Intelligence program meant for military purposes. This includes almost every American, Canadian, and European persons living today, every person in China, and Less so from groups in Africa, the Middle East, and South America. I initially made the finding public by publishing the discovery in the book AI, Trump, China and the Weaponization of Robotics without providing company names. Later, I included the findings with company names in the updated book Artificial Intelligence Dangers to Humanity. More than 1,000 AI, Robotics and Bio-Metric companies were researched to obtain the results of over 6 billion human beings who have had their bio-metrics stolen or transferred to China.

Tinder Swipes Right on AI to Help Stop Harassment


On Tinder, an opening line can go south pretty quickly. And while there are plenty of Instagram accounts dedicated to exposing these "Tinder nightmares," when the company looked at its numbers, it found that users reported only a fraction of behavior that violated its community standards. Now, Tinder is turning to artificial intelligence to help people dealing with grossness in the DMs. The popular online dating app will use machine learning to automatically screen for potentially offensive messages. If a message gets flagged in the system, Tinder will ask its recipient: "Does this bother you?"

Deep learning robotic guidance for autonomous vascular access


Medical robots have demonstrated the ability to manipulate percutaneous instruments into soft tissue anatomy while working beyond the limits of human perception and dexterity. Robotic technologies further offer the promise of autonomy in carrying out critical tasks with minimal supervision when resources are limited. Here, we present a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. Robotic cannulation is driven by predictions from a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time servoing. We demonstrate, through imaging and robotic tracking studies in volunteers, the ability of the device to segment, classify, localize and track peripheral vessels in the presence of anatomical variability and motion.