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) …
"I personally think that no matter which approach you use, you lose," said Emily Wenger, a Ph.D. student who helped create Fawkes. "You can have these technological solutions, but it's a cat-and-mouse game. And you can have a law, but there will always be illegal actors." Ms. Wenger thinks "a two-prong approach" is needed, where individuals have technological tools and a privacy law to protect themselves. Elizabeth Joh, a law professor at the University of California, Davis, has written about tools like Fawkes as "privacy protests," where individuals want to thwart surveillance but not for criminal reasons.
Imperial College London researchers claim they've developed a voice analysis method that supports applications like speech recognition and identification while removing sensitive attributes such as emotion, gender, and health status. Their framework receives voice data and privacy preferences as auxiliary information and uses the preferences to filter out sensitive attributes which could otherwise be extracted from recorded speech. Voice signals are a rich source of data, containing linguistic and paralinguistic information including age, likely gender, health status, personality, mood, and emotional state. This raises concerns in cases where raw data is transmitted to servers; attacks like attribute inference can reveal attributes not intended to be shared. In fact, the researchers assert attackers could use a speech recognition model to learn further attributes from users, leveraging the model's outputs to train attribute-inferring classifiers. They posit such attackers could achieve attribute inference accuracy ranging from 40% to 99.4% -- three or four times better than guessing at random -- depending on the acoustic conditions of the inputs.
A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape. In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used in a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue. Privacy is a foremost ethical concern. AI learns to make predictions from large data sets -- specifically patient data, in the case of surgical systems -- and it's often described as being at odds with privacy-preserving practices.
Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.
As the second installment in this series of posts, I will touch upon on the topic of privacy in data science and algorithms. In particular, I'm going to discuss a relatively novel concept of privacy called differential privacy that promises, similar to algorithmic fairness, a way of quantifying the privacy of AI algorithms. When we, as humans, talk about privacy, we mostly refer to a desire to not be observed by others. However, what does privacy mean in the context of algorithms that "observe" us by using data that has information on us? In a very general sense, we could say that privacy will be preserved if, after analysis, the algorithm that used our data (e.g. an application on our smartphones) doesn't know anything about us.
Leaders of the world's four most powerful companies will defend the Internet giants, painting them as US success stories in a fiercely competitive world during a major antitrust hearing Wednesday. The unprecedented hearing will feature chief executives Jeff Bezos of Amazon, Tim Cook of Apple, Mark Zuckerberg of Facebook and Sundar Pichai of Google and its parent firm Alphabet. The CEOs will testify remotely at the hearing, which comes less than 100 days before the US election. Zuckerberg is to say that the internet giant would not have succeeded without US laws fostering competition, but that the rules of the internet now need updating. "Facebook is a proudly American company," Zuckerberg said in prepared remarks ahead of what will be a closely watched House Judiciary Committee hearing.
San Francisco – Leaders of the world's four most powerful companies will defend the internet giants in Congress, painting them as U.S. success stories in a fiercely competitive world during a major antitrust hearing Wednesday. The unprecedented hearing will feature chief executives Jeff Bezos of Amazon, Tim Cook of Apple, Mark Zuckerberg of Facebook and Sundar Pichai of Google and its parent firm Alphabet. The CEOs will testify remotely at the hearing, which comes less than 100 days before the U.S. election. Zuckerberg is to say that the internet giant would not have succeeded without U.S. laws fostering competition, but that the rules of the internet now need updating. "Facebook is a proudly American company," Zuckerberg said in prepared remarks ahead of what will be a closely watched House Judiciary Committee hearing.
Can we perform Data Analysis and build Machine Learning Models without revealing any information about the data and the identity of the users involved?? Every day the world is producing a lot of data and share them to different communities without thinking much about how it can use their privacy for personal vendetta, the vast amount of data is being used by various companies for to build machines which can reduce human efforts drastically using cutting-edge technologies. These data from innumerable sources is being transferred to a central server for further analysis, and hence it becomes an integral part of these communities to protect privacy as a principle that is fundamental to safeguarding the dignity and welfare of their subjects. With the advent of new GDPR/Privacy Attorney Rules, Companies are feeling a sudden need for introducing privacy and security components in existing as well as new machine learning algorithms, data storage centers and data transfer techniques as well as building secure-VM. A great emphasis now a days is on preserving identity of individuals in the data, thus providing security against various kind of data theft. Companies are investing a lot of money and time techniques which promise users that their identity and private information will be kept highly confidential, which allow users to share data without any hesitation.
Alden Ehrenreich explores a Brave New World. When I first read Aldous Huxley's famous 1932 novel Brave New World, I expected something fusty and old-fashioned. I wasn't prepared for how scathingly direct or unsettlingly dark it was, and still is today. It certainly adds a dash of cursing, a touch of violence, some Radiohead and a load of people getting their kit off. But it lacks a certain directness. The Handmaid's Tale is about sexism.
In 2019, UnitedHealthcare's health-services arm, Optum, rolled out a machine learning algorithm to 50 healthcare organizations. With the aid of the software, doctors and nurses were able to monitor patients with diabetes, heart disease and other chronic ailments, as well as help them manage their prescriptions and arrange doctor visits. Optum is now under investigation after research revealed that the algorithm (allegedly) recommends paying more attention to white patients than to sicker Black patients. Today's data and analytics leaders are charged with creating value with data. Given their skill set and purview, they are also in the organizationally unique position to be responsible for spearheading ethical data practices.