If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In today's blog post you are going to learn how to perform face recognition in both images and video streams using: As we'll see, the deep learning-based facial embeddings we'll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We'll start with a brief discussion of how deep learning-based facial recognition works, including the concept of "deep metric learning". From there, I will help you install the libraries you need to actually perform face recognition. Finally, we'll implement face recognition for both still images and video streams.
Amazon may not have much choice but to address mounting criticism over its sales of facial recognition tech to governments. The American Civil Liberties Union has delivered both a petition and a letter from 17 investors demanding that Amazon drop its Rekognition system and exit the surveillance business. While the two sides have somewhat different motivations, they share one thing in common: a concern for privacy. Both groups are worried that Amazon is handing governments surveillance power they could easily use to violate civil rights, particularly for minorities and immigrants. They could use it to track and intimidate protesters, for instance.
Amazon is drawing the ire of its shareholders after an investigation found that it has been marketing powerful facial recognition tools to police. Nearly 20 groups of Amazon shareholders delivered a signed letter to CEO Jeff Bezos on Friday, pressuring the company to stop selling the software to law enforcement. The tool, called'Rekognition', was first released in 2016, but has since been selling it on the cheap to several police departments around the country, with Washington County Sheriff's Office in Oregon and the city of Orlando, Florida among its customers. Shareholders, including the Social Equity Group and Northwest Coalition for Responsible Investment, join the American Civil Liberties Union (ACLU) and other privacy advocates in pointing out privacy violations and the dangers of mass surveillance. 'We are concerned the technology would be used to unfairly and disproportionately target and surveil people of color, immigrants, and civil society organizations,' the shareholders write.
Here's how the rest of us can do meaningful things with artificial intelligence or machine learning since all of the current AI experts are already working for Google and Facebook. First and foremost, I'm going to argue that you don't necessarily need a PhD to be an AI expert. In order to get a PhD, you need to have completed a doctoral thesis, which is a lengthy research project usually mixed with additional education, done with close supervision by another academic. This is, in general, a good thing. We want research, we want knowledge, we want people to get PhDs.
If you are already using a pre-curated dataset, such as Labeled Faces in the Wild (LFW), then the hard work is done for you. You'll be able to use next week's blog post to create your facial recognition application. But for most of us, we'll instead want to recognize faces that are not part of any current dataset and recognize faces of ourselves, friends, family members, coworkers and colleagues, etc. To accomplish this, we need to gather examples of faces we want to recognize and then quantify them in some manner. This process is typically referred to as facial recognition enrollment.
The U.S. Department of Homeland Security (DHS) is quietly building what will likely become the largest database of biometric and biographic data on citizens and foreigners in the United States. The agency's new Homeland Advanced Recognition Technology (HART) database will include multiple forms of biometrics--from face recognition to DNA, data from questionable sources, and highly personal data on innocent people. It will be shared with federal agencies outside of DHS as well as state and local law enforcement and foreign governments. And yet, we still know very little about it. The records DHS plans to include in HART will chill and deter people from exercising their First Amendment protected rights to speak, assemble, and associate.
Facial recognition has made giant strides forwards since its invention in the 1960s thanks to massive advancements in computer hardware and software. With the new iPhone X, Apple has shined the spotlight on facial recognition for user authentication and it won't be long now until facial recognition becomes a mainstream technology. Facial or face recognition uses biometric data to identify and authenticate a person and involves comparing live capture or digital image data with stored data. The iPhone X's Face ID biometric system uses a powerful infra-red camera to scan and map your face in 3D. As the market leading smartphone brand, Apple's inclusion of this technology is likely to make us more accepting of face recognition in other applications, despite accuracy and privacy issues and the risk of misidentification.
If Artificial Intelligence (AI) is increasingly able to recognise and classify faces, then perhaps the only way to counter this creeping surveillance is to use another AI to defeat it. We're in the early years of AI-powered image and face recognition but already researchers at the University of Toronto have come up with a way that this might be possible. The principal at the heart of this technique is adversarial training, in which a neural AI network's image recognition is disrupted by a second trained to understand how it works. This makes it possible to apply a filter to an image that alters only a few very specific pixels but makes it much harder for online AI to classify. The theory behind this sounds simple enough, explains the University of Toronto's professor Parham Aarabi: If the detection AI is looking for the corner of the eyes, for example, it adjusts the corner of the eyes so they're less noticeable.
A study appearing today in the Proceedings of the National Academy of Sciences has brought answers. In work that combines forensic science with psychology and computer vision research, a team of scientists from the National Institute of Standards and Technology (NIST) and three universities has tested the accuracy of professional face identifiers, providing at least one revelation that surprised even the researchers: Trained human beings perform best with a computer as a partner, not another person. "This is the first study to measure face identification accuracy for professional forensic facial examiners, working under circumstances that apply in real-world casework," said NIST electronic engineer P. Jonathon Phillips. "Our deeper goal was to find better ways to increase the accuracy of forensic facial comparisons." The team's effort began in response to a 2009 report by the National Research Council, "Strengthening Forensic Science in the United States: A Path Forward," which underscored the need to measure the accuracy of forensic examiner decisions.
President Donald Trump's top tech advisor said on Tuesday during an MIT robotics and AI conference panel that the US government will disclose any federal data that could further AI research. "Anything we can do to figure that out, we will work very hard on," said Michael Kratsios, Trump's chief technology advisor, who holds the highest-ranking tech position in the administration, because Trump has yet to appoint an Office of Science and Technology head. SEE ALSO: Robert Downey Jr. is hosting a YouTube show about artificial intelligence because he is truly Iron Man The announcement was surprising, mostly because AI and tech have been regarded with caution by the administration, especially with Trump's outspoken rhetoric about the dangers of automation due to the job elimination potential. And in fact, Kratsios was at the conference to speak on a panel about empowering the workforce. Although he skirted direct queries of whether 45 supported AI, Kratsios did offer that "the White House has prioritized AI, and he [Trump] obviously runs the White House."