Privacy advocates blasted Amazon's recently announced purchase of robot vacuum maker iRobot for fueling'pervasive surveillance' as the Federal Trade Commission opened a probe into the $1.7 billion buyout. The tech giant's planned acquisition of the maker of Roomba vacuum cleaners will give it access to the appliance's operating system that uses a front-facing camera to create complete maps of the inside of people's homes - all of which can then be fed into Amazon's existing, massive trove of data about hundreds of millions of consumers. 'There is no more private space than the home. Yet with this acquisition, Amazon stands to gain access to extremely intimate acts in our most private spaces that are not available through other means, or to other competitors,' over twenty privacy and civil rights groups say in a Friday letter to the FTC. 'Information collected by iRobot's devices goes beyond home floor plans, and includes highly detailed information about the interiors of consumers' homes and the schedules and lifestyles of the inhabitants,' the letter, shared by digital rights nonprofit Fight for the Future, states.

Now is the time to talk, to put in place standards and regulations to mitigate the risk of a society ... [ ] based on surveillance and other nightmarish scenarios. Artificial Intelligence has become commonplace in the lives of billions of people globally. Research shows that 56% of companies have adopted AI in at least one function, especially in emerging nations. AI is used in everything from optimizing service operations through to recruiting talent. It can capture biometric data and it already helps in medical applications, judicial systems, and finance, thus making key decisions in people's lives. But one huge challenge remains to regulate its use.

Hundreds of Google workers and their supporters gathered near the company's downtown San Francisco offices Thursday, raising signs that read "No Tech for Apartheid" and filling the air with chants of "Tech from Amazon and Google! You can't claim that you are neutral!" Similar scenes unfolded outside Google and Amazon offices in New York and Seattle, and a Google office in Durham, North Carolina. Google and Amazon employees were joined at the rallies by tech workers from other companies and Palestinian rights organizations. They all convened to protest Project Nimbus, Google and Amazon's cloud computing contract with the Israeli government.

Damien Hirst cut a cow and calf each lengthwise into two halves and displayed them in four separate baths of formaldehyde in clear display tanks. The title of the creation, "Mother and Child Divided," is a pun. The cow and calf were cut in two and were displayed physically separated. The macabre bifurcated bovine creation won top place in the 1995 Turner Prize art competition. This simple example reveals that, like judging the palatability of raw oysters, ranking the quality of art is highly subjective.

A Florida man is facing up to five years in prison after he pleaded guilty to committing financial fraud using a crypto Ponzi scheme and making away with approximately $100 million in investment funds. In a statement released on Sept. 8, the U.S. Department of Justice (DOJ) identified Joshua David Nicholas as the "head trader" for EmpiresX, a firm founded in 2020 and was publicized to investors as a legitimate cryptocurrency trading and investment platform. Nicholas admitted that he "fraudulently promoted EmpiresX by making numerous misrepresentations regarding, among other things, a purported proprietary trading bot and fraudulent'guaranteed' returns to investors and prospective investors in the company," according to the statement. Nicholas reportedly disclosed that he and his co-conspirators told investors that they had a trading bot, an algorithm based on AI technology that places trades and whose goal was to maximize profitability for investors. "EmpiresX operated a Ponzi scheme by paying earlier investors with money obtained from later EmpiresX investors," the DOJ noted in the statement.

Because Allen used AI as a tool in the creation of the prize-winning image. Allen's final image applied AI software several times before coming up with the final picture he presented to the art competition. Allen said, "I made the prompt [to the AI program], I fine-tuned it for many weeks, curated all the images." Allen claims to have gone through 900 iterations before the final submission. This is a lot more than the 409 tries used to perfect Formula 409TM or the 40 iterations needed to find a successful formula for WD-40 TM.

If you apply for this opportunity we will get you resume and its contain personal data whose treatment has been authorized by its owner for Digital OnUs, S. de RL de CV (the "Company"). If you are not the owner of this information or have no relation whatsoever with the subjects treated in it, you are requested in the most attentive way not to make copies of it and / or its attached files and delete it immediately, under the risk of being considered as responsible for the unauthorized treatment of personal data in accordance with the Federal Law on Protection of Personal Data Held by Private Parties, its Regulations, and other applicable regulations. Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Sign up for dot.LA's daily newsletter for the latest news on Southern California's tech, startup and venture capital scene. Computers can now write poems, paint portraits and produce music better than many humans. The case will now head to federal court as the AI program's owner, Stephen Thaler, plans to file an appeal, according to Ryan Abbott, a Los Angeles-based attorney representing Thaler. The case arrives as artists are increasingly using AI to help generate artwork, including works produced by autonomous machines. Abbott, a partner at L.A.-based law firm Brown, Neri, Smith & Khan, noted that AI-produced artwork is creating significant commercial value, such as an AI-authored painting that sold for $432,000 at auction in 2018.

We introduce a method for explaining the results of various linear and hierarchical multi-criteria decision-making (MCDM) techniques such as WSM and AHP. The two key ideas are (A) to maintain a fine-grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high-level comparison of two alternatives in an MCDM problem, presumably an optimal and a non-optimal one, illuminating why one alternative was preferred over the other one. We show the usefulness of our techniques by generating explanations for two well-known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malevolent leakage, available defences, followed by the currently available assessment metrics and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.