A new artificial intelligence system teaches itself to recognize a range of visual and audio concepts by watching short video clips. Researchers at Google's DeepMind unit have developed an artificial intelligence (AI) system that teaches itself to recognize a range of visual and audio concepts by watching short video clips. He notes the DeepMind project brings the field one step closer to the goal of creating AI that can teach itself by watching and listening to the world around it. Instead of relying on human-labeled datasets, the new algorithm learns to recognize images and sounds by matching what it sees with what it hears.
Now it has revealed that industrial robots from Universal Robots and consumer models from Softbank Group and UBTech Robotics also have some troubling security flaws that can allow hackers to "modify safety settings, violating applicable safety laws and, consequently, causing physical harm to the robot's surroundings by moving it arbitrarily," according to a report published by the company today. The devices produced by Universal Robots are uncaged industrial robots meant to work with humans. "We contacted all the vendors in January but sadly there's little to suggest that the 50-plus vulnerabilities we demonstrated have been fixed," Lucas Apa, IOActive's principal security consultant told Bloomberg. The company's North America general manager, John Rhee, said in a statement, "UBTech has been made aware of a sensationalistic video produced by IOActive featuring the Alpha 2.
Scientist Andrew Ng, right, works with others at his office in Palo Alto, Calif. Ng, one of the world's most renowned researchers in machine learning and artificial intelligence, is facing a dilemma: there aren't enough experts trained to train the machines. He has said he sees AI changing virtually every industry, and any task that takes less than a second of thought will eventually be done by machines. Andrew Ng poses at his office in Palo Alto, Calif. Ng, one of the world's most renowned researchers in machine learning and artificial intelligence, is facing a dilemma: there aren't enough experts trained to train the machines. More recently, he left his high-profile job at Baidu to launch deeplearning.ai Every time he's started something big, whether it's Coursera, the Google Brain deep learning unit, or Baidu's AI lab, he has left once he felt the teams he has built can carry on without him.
Due to our limitations as human beings, we are not able to visualize higher dimensions. For these reasons, we need to resort to Principal Component Analysis or PCA to reduce the dimensions in our data-set. Principal Component Analysis converts our original variables to a new set of variables, which are a linear combination of the original set of variables. In the next post, you will understand dimensionality reduction and the popular method of Principal Component Analysis to achieve that.
Security firm IOActive tested 12 devices made for the home, businesses and industrial purposes, and in a report said all were found to contain critical vulnerabilities which could allow attackers to control them remotely. The researchers demonstrated their ability to take control over an Alpha 2 robot, a home assistant device manufactured by UBTECH, simply by sending a command over Bluetooth. Of the companies tested by IOActive, only Rethink Robotics - which makes manufacturing robots and had been looked at in previous research by the firm - issued a statement to Sky News responding to the report. At the time of writing the other companies featured in the report did not respond to Sky's request for comment.
The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for you to continue learning from. A lot of data science is based on attempting to measure likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line. If you prefer video, check out Brandon Holtz's great series on statistics on Youtube!
At the Hack in the Box security conference later this week in Singapore, Argentinian security researchers Lucas Apa and Cesar Cerrudo plan to demonstrate hacker attacks they developed against three popular robots: the humanoid domestic robots known as the Alpha2 and NAO, as well as a larger, industrial-focused robotic arm sold by Universal Robots. In terms of actual, physical danger, the most serious of the three attacks Cerrudo and Apa developed affects Universal Robots' "collaborative" robots. That earlier study found more than 50 hackable security vulnerabilities in robots and robotics software sold by companies that also included Rethink Robots, Robotis, and Arsatec. Earlier this year, another team of researchers from Italy's Politecnico Milano showed that they could take over an even larger, potentially more dangerous industrial robot arm, the 220-pound ABB IRB140.
In the fourth example, the person pictured is labeled'woman' even though it is clearly a man because of sexist biases in the set that associate kitchens with women Researchers tested two of the largest collections of photos used to train image recognition AIs and discovered that sexism was rampant. However, they AIs associated men with stereotypically masculine activities like sports, hunting, and coaching, as well as objects sch as sporting equipment. 'For example, the activity cooking is over 33 percent more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68 percent at test time,' reads the paper, titled'Men Also Like Shopping,' which published as part of the 2017 Conference on Empirical Methods on Natural Language Processing. A user shared a photo depicting another scenario in which technology failed to detect darker skin, writing'reminds me of this failed beta test Princeton University conducted a word associate task with the algorithm GloVe, an unsupervised AI that uses online text to understand human language.
As the amount of data in the world multiplies, AI will only improve in helping us increase efficiency, save lives, reduce errors, solve complex problems and make better decisions in real time. Perhaps best known for defeating a chess master and winning the game show Jeopardy, IBM's Watson computer has also proven incredibly adept at connecting disparate pieces of information from medical journals, helping doctors save time and better treat their patients. Businesses are starting to use "voice prints" to quickly identify their customers over the phone, helping service reps save time and remove the customer frustration that comes with answering a myriad of security questions. Instead, by helping us better analyze data and make quicker, smarter decisions, it will help us realize our true potential and achieve previously unimaginable new heights.
Machine Learning algorithms have already begun helping teachers fill the gaps while indicating which subjects students are struggling with the most. Content Technologies, Inc. (CTI) is an AI company using Deep Learning to create customized textbooks that fit the needs of specific courses and students. CTI hopes to fill that gap and help publishers create effective textbooks right for each individual learner. With Netex, teachers can create customized student materials to be published on any digital platform while providing tools for video conferences, digital discussions, personalized assignments, and learning analytics that show visual representations of each student's personal growth.