Let's start with some comments about a recent ACLU blog in which they run a facial recognition trial. Using Rekognition, the ACLU built a face database using 25,000 publicly available arrest photos and then performed facial similarity searches of that database using public photos of all current members of Congress. They found 28 incorrect matches out of 535, using an 80% confidence level; this is a 5% misidentification (sometimes called'false positive') rate and a 95% accuracy rate. The ACLU has not published its data set, methodology, or results in detail, so we can only go on what they've publicly said. To illustrate the impact of confidence threshold on false positives, we ran a test where we created a face collection using a dataset of over 850,000 faces commonly used in academia.

When it comes to work, workers, and jobs, much of the angst of the modern era boils down to the fear that we're witnessing the automation endgame, and that there will be nowhere for humans to retreat as machines take over the last few tasks. The most recent wave of commentary on this front stems from the use of artificial intelligence (AI) to capture and automate tacit knowledge and tasks, which were previously thought to be too subtle and complex to be automated. Is there no area of human experience that can't be quantified and mechanized? And if not, what is left for humans to do except the menial tasks involved in taking care of the machines? At the core of this concern is our desire for good jobs--jobs that, without undue intensity or stress, make the most of workers' natural attributes and abilities; where the work provides the worker with motivation, novelty, diversity, autonomy, and work/life balance; and where workers are duly compensated and consider the employment contract fair. Crucially, good jobs support workers in learning by doing--and, in so doing, deliver benefits on three levels: to the worker, who gains in personal development and job satisfaction; to the organization, which innovates as staff find new problems to solve and opportunities to pursue; and to the community as a whole, which reaps the economic benefits of hosting thriving organizations and workers. This is what makes good jobs productive and sustainable for the organization, as well as engaging and fulfilling for the worker. It is also what aligns good jobs with the larger community's values and norms, since a community can hardly argue with having happier citizens and a higher standard of living.1 Does the relentless advance of AI threaten to automate away all the learning, creativity, and meaning that make a job a good job?

"Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world." The first step to understanding machine learning is understanding what kinds of problems it intends to solve, based on the foregoing definition. It is principally concerned with mapping data to mathematical models -- allowing us to make inferences (predictions) about measurable phenomena in the world. From the machine learning model's predictions, we can then make rational, informed decisions with increased empirical certainty. Take, for example, the adaptive brightness on your phone screen. Modern phones have front- and rear-facing cameras that allow the phone to constantly detect the intensity of ambient light, and then adjust the brightness of the screen to make it more pleasant for viewing.

WIRE)–Progress (NASDAQ: PRGS), the leading provider of application development and digital experience technologies, today announced that Progress NativeChat, the artificial intelligence-driven platform for creating and deploying chatbots, has been selected as the winner of the "Best Chatbot Solution" award from AI Breakthrough, an independent organization that recognizes the top companies, technologies and products in the global Artificial Intelligence (AI) market today. "We are thrilled to be recognized with the AI Breakthrough award for'Best Chatbot Solution'. Progress continues to invest to deliver solutions that enable organizations to deliver the most modern capabilities in their applications," said Dmitri Tcherevik, Chief Technology Officer, Progress. "As the need for solutions around machine learning, AI, AR and VR grow, we will continue to be at the forefront, delivering innovative solutions to drive the future success of our customers and partners." Unlike other chatbot technologies, NativeChat platform is based on patent-pending CognitiveFlow technology that can be trained with goals, examples and data from existing systems, similar to the process for training new customer service agents.

Amazon's Rekognition facial surveillance technology has wrongly tagged 28 members of Congress as police suspects, the ACLU says. Amazon's Rekognition facial surveillance technology has wrongly tagged 28 members of Congress as police suspects, according to ACLU research, which notes that nearly 40 percent of the lawmakers identified by the system are people of color. In a blog post, Jacob Snow, technology and civil liberties attorney for the ACLU of Northern California, said that the false matches were made against a mugshot database. The matches were also disproportionately people of color, he said. These include six members of the Congressional Black Caucus, among them civil rights legend Rep. John Lewis, D-Ga.

It's hard to get lawmakers to understand privacy issues, let alone commit to taking action. Yet in one day, the American Civil Liberties Union (ACLU) pulled off an incredible feat. They showed the public how facial recognition technology can lead to false and biased accusations. By the afternoon, two prominent members of Congress sent a letter to Amazon CEO Jeff Bezos demanding an immediate meeting. The Congressmen were angry at the results of a report which showed that Amazon's facial recognition software, Rekognition, had identified 28 legislators as potential criminals.

A test of Amazon's facial recognition technology by the ACLU has found it erroneously labelled those with darker skin colours as criminals more often. Bias in AI technology, when used by law enforcement, has raised concerns of infringing on civil rights by automated racial profiling. A 2010 study by researchers at NIST and the University of Texas in Dallas found that algorithms designed and tested in East Asia are better at recognising East Asians, while those designed in Western countries are more accurate at detecting Caucasians. The ACLU (American Civil Liberties Union) ran a test of Amazon's facial recognition technology on members of Congress to see if they match with a database of criminal mugshots. Amazon's Rekognition tool was used to compare pictures of all members of the House and Senate against 25,000 arrest photos, the false matches disproportionately affected members of the Congressional Black Caucus.

In the early to mid-90's while my friends were getting into Indie Rock, I was hacking away at robots and getting them to learn to map a room. A computer science graduate student, I programmed LISP algorithms for parsing nursing records in order to predict intervention codes. I was no less a nerd (or to put it a better way, a technology enthusiast) in law school, when I wrote about how natural language processing can improve legal research tools. I didn't put much thought, either as a computer scientist or law student to whether artificial intelligence (AI) should be regulated. Frankly, we were in such the early days of the technology, that AI regulations seemed like science fiction a la Isaac Asimov's three laws of robotics.

APAC regional legal technology conference LexTech has launched an AI assistant to help facilitate a better conference experience, making its services available through Facebook Messenger -- a first in the local legal tech industry. Called, LEXi, the Artificial Intelligence chatbot was developed by Malaysia's CanChat and will focus on addressing enquiries of LexTech delegates in real-time via Facebook Messenger. Notably, between now and August 31, 2018, potential conference delegates can secure a 20% discount code by asking LEXi for one. Lee Su Wen, Director of the LexTech Conference 2018 said that: "By leveraging on AI capabilities, the chatbot is a smart and intuitive way to provide information and directions to our audience with great convenience and speed. With LEXi taking on these tasks, we are now able to more efficiently re-allocate our manpower to the more strategically-intensive departments, and by extension creating a better experience for all delegates."

SAN FRANCISCO -- Amazon's controversial facial recognition program, Rekognition, falsely identified 28 members of Congress during a test of the program by the American Civil Liberties Union, the civil rights group said Thursday. In its test, the ACLU scanned photos of all members of Congress and had the system compare them with a public database of 25,000 mugshots. The group used the default "confidence threshold" setting of 80 percent for Rekognition, meaning the test counted a face match at 80 percent certainty or more. At that setting, the system misidentified 28 members of Congress, a disproportionate number of whom were people of color, tagging them instead as entirely different people who have been arrested for a crime. The faces of members of Congress used in the test include Republicans and Democrats, men and women and legislators of all ages.