"Computers have been getting better and better at seeing movement on video. How is it that they read lips, follow a dancing girl or copy an actor making faces?"
– from Andrew Blake. Introduction to Active Contours and Visual Dynamics. Visual Dynamics Group, Department of Engineering Science, University of Oxford
Take a look at how AI companies are implementing AI. By automating procedures and operations that formerly required human intervention, Artificial Intelligence (AI) is increasing company efficiency and production. AI is also capable of comprehending data at a level that no human has ever achieved. This skill has the potential to be extremely useful in the workplace. AI has the potential to enhance every function, business, and industry.
The latest AI-powered cameras are clicking high-end pictures and help in face recognition in pictures and videos. In addition, the same application has found benefits in ease of video editing. From scanning text scripts to recognizing faces on videos, it can match the elements and automate the video editing task. If you have to do this daily, it is better to choose software that is artificial intelligence or AI-powered. The list below would help you go for the right one and make the most of it for editing purposes.
An artificial intelligence (AI) tool was able to distinguish, with great accuracy, Parkinson's patients from healthy peers by analyzing short videos of facial expressions, particularly smiles, a small study shows. The predictive accuracy of the new tool was comparable to that of video analysis that uses motor tasks to detect Parkinson's, pinpointing facial expressions as a potential digital, diagnostic biomarker of the disease. This type of biomarker could allow remote diagnosis without the need for personal interaction and extensive testing. This would be particularly relevant in situations such as a pandemic, in cases of reduced mobility, or in underdeveloped countries where few neurologists exist but most people have access to a phone with a camera, researchers noted. The study, "Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online," was published as a brief communication in the journal npj Digital Medicine.
The U.N. human rights chief is calling for a moratorium on the use of artificial intelligence technology that poses a serious risk to human rights, including face-scanning systems that track people in public spaces. Michelle Bachelet, the U.N. High Commissioner for Human Rights, also said Wednesday that countries should expressly ban AI applications which don't comply with international human rights law. Applications that should be prohibited include government "social scoring" systems that judge people based on their behavior and certain AI-based tools that categorize people into clusters such as by ethnicity or gender. AI-based technologies can be a force for good but they can also "have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights," Bachelet said in a statement. Her comments came along with a new U.N. report that examines how countries and businesses have rushed into applying AI systems that affect people's lives and livelihoods without setting up proper safeguards to prevent discrimination and other harms.
SAN FRANCISCO (REUTERS) - In September last year, Google's cloud unit looked into using artificial intelligence (AI) to help a financial firm decide whom to lend money to. It turned down the client's idea after weeks of internal discussions, deeming the project too ethically dicey because the AI technology could perpetuate biases like those around race and gender. Since early last year, Google has also blocked new AI features analysing emotions, fearing cultural insensitivity, while Microsoft restricted software mimicking voices and IBM rejected a client request for an advanced facial-recognition system. All these technologies were curbed by panels of executives or other leaders, according to interviews with AI ethics chiefs at the three US technology giants. Reported here for the first time, their vetoes and the deliberations that led to them reflect a nascent industry-wide drive to balance the pursuit of lucrative AI systems with a greater consideration of social responsibility.
Many of the smart/IoT devices you'll purchase are powered by some form of Artificial Intelligence (AI)--be it voice assistants, facial recognition cameras, or even your PC. These don't work via magic, however, and need something to power all of the data-processing they do. For some devices that could be done in the cloud, by vast datacentres. Other devices will do all their processing on the devices themselves, through an AI chip. But what is an AI chip?
Most people see Machine Learning as robots that will dominate the world, computers winning against people in board games, robot butlers. However, Machine Learning can be things simpler than that and also be used in thousands of different tasks. Personally, my first experience with Machine Learning was in 2019 during my internship at a startup, where I make a system that could automatically count insects using only an RGB image. I don't know when was the first time that you have heard about Machine Learning, but probably this can take less than a decade, however, Machine Learning is not a young approach. First of all, Machine Learning is not a magic trick, there is Math behind that, of course, our computer wasn't be able to learn if we didn't set up a well-defined mathematical model.
Tuya employee Ella Yuan demonstrates the company's facial recognition system at the Consumer Electronics Show in Las Vegas on January 9, 2019. Three US senators are calling for the Chinese Internet of Things platform operator to be added to a list of sanctioned Chinese companies, citing national security concerns.
In the earlier post, we have seen how data augmentation can be done with various transformations to output two variations for the same image. For this blog, we can see how the backbone, projection and prediction layers are added for model training. Followed by how can we measure the accuracy of the model during the evaluation stage. Model definition & Training: The backbone used here is'resnet50'. Next to the projection head is the prediction layer that has Linear, BatchNorm, ReLU and then a Linear layer.