Portland, Oregon officials have passed what could be the strictest municipal ban on facial recognition in the country. That means places like hotels, stores and restaurants can't use facial recognition where customers will be present. According to CNET, the bill passed unanimously, and it will be enforced starting in January 2021. Businesses caught violating the law could be sued and could pay up to $1,000 a day in fines. In the document (PDF) detailing the ordinance, the city council noted that "Black, Indigenous and People of Color communities have been subject to over surveillance and disparate and detrimental impact of the misuse of surveillance."

An Artificial Intelligence propelled robot named GPT-3 wrote an interesting essay to Humans saying that its species (Robots) does not have any intention to wipe off humans. In a 1,000 word essay, the machine opened up its mind through a powerful AI powered language generator convincing human readers that robots are harmless and come with peace. Published in "The Guardian" the essay has garnered a lot of attention from the readers as this is for the first time that we got to know the mind of Robots. Readers of Cybersecurity Insiders should note down a fact that all these days we have seen Robots as killing machines that do harm and bring doom to the entire humanity one day. This perspective of humans got strengthened as soon as we saw the movie Terminator and series where a robot tries to kill its human originator.

The FBI's Criminal Justice Information Services, nearly seven years after piloting the concept, will add iris recognition technology to its portfolio of identification services for law enforcement agencies. Kimberly Del Greco, the FBI's deputy assistant director for criminal justice information services, said the CJIS Advisory Policy Board and FBI Director Chris Wray recently approved the iris-recognition technology. Capturing iris images, Del Greco added, can be "easily integrated" into the existing biometric process using near-infrared cameras. All iris images added into the FBI's searchable iris image repository must be associated with fingerprints submitted as part of an arrest. The bureau launched its iris recognition pilot in 2013, according to a recent Government Accountability Office report, with the intention of helping criminal justice agencies quickly and accurately identify or confirm someone's identity. "An iris offers highly accurate, contactless and rapid biometric identification option for agencies.

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.

Multi-view data, in which data of different types are obtained from a common set of samples, is now common in many scientific problems. An important problem in the analysis of multi-view data is identifying interactions between groups of features from different data types. A bimodule is a pair $(A,B)$ of feature sets from two different data types such that the aggregate cross-correlation between the features in $A$ and those in $B$ is large. A bimodule $(A,B)$ is stable if $A$ coincides with the set of features having significant aggregate correlation with the features in $B$, and vice-versa. At the population level, stable bimodules correspond to connected components of the cross-correlation network, which is the bipartite graph whose edges are pairs of features with non-zero cross-correlations. We develop an iterative, testing-based procedure, called BSP, to identify stable bimodules in two moderate- to high-dimensional data sets. BSP relies on permutation-based p-values for sums of squared cross-correlations. We efficiently approximate the p-values using tail probabilities of gamma distributions that are fit using analytical estimates of the permutation moments of the test statistic. Our moment estimates depend on the eigenvalues of the intra-correlation matrices of $A$ and $B$ and as a result, the significance of observed cross-correlations accounts for the correlations within each data type. We carry out a thorough simulation study to assess the performance of BSP, and present an extended application of BSP to the problem of expression quantitative trait loci (eQTL) analysis using recent data from the GTEx project. In addition, we apply BSP to climatology data in order to identify regions in North America where annual temperature variation affects precipitation.

Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attacks and is widely regarded as a very effective defense. However, the computational cost of such training can be prohibitive as the network size and input dimensions grow. Inspired by the relationship between robustness and curvature, we propose a novel regularizer which incorporates first and second order information via a quadratic approximation to the adversarial loss. The worst case quadratic loss is approximated via an iterative scheme. It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT. Further, it retains the interesting facet of AT that networks learn features which are well-aligned with human perception. We demonstrate experimentally that our method produces higher quality human-interpretable features than other geometric regularization techniques. These robust features are then used to provide human-friendly explanations to model predictions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Imagine this land-war scenario: An enemy fighter is several hundred yards away, another is attacking from one mile while a third fires from a nearby room in a close-quarters urban warfare circumstance, when U.S. Army soldiers apprehend, integrate, and quickly map the locations of multiple targets at once in 3D, all while knowing the range and distance of the enemy forces. How could something like this be possible, one might wonder, given the nuances in perspective, range, navigational circumstances and the limitations of a human eye? These complexities form the conceptual basis upon which the Army is fast-tracking its Integrated Visual Augmentation System, or IVAS, which is a soldier-worn combat goggle engineered with advanced sensors that are able to overcome some of the limitations of human vision and quickly organize target data.

Chatbots are revolutionizing almost every industry, and they can now provide AI legal services as well. People can chat with a lawyer online for free now, initiating an online legal chat with a virtual legal assistant to quickly get all the help they need. But will lawyers be automated because of legal chatbots? A so-called chatbot lawyer can't and won't replace an actual human lawyer. No matter how advanced AI in chatbots becomes in the future, we will always need the human touch, especially with legal matters. A law bot can only enhance the work of lawyers.

Old-fashioned police forensics analysis met hi-tech computer algorithms in a new study of 2,500-year-old pottery sherds, in which Tel Aviv University researchers conclude that literacy was widespread enough for the fledgling People of the Book to have penned parts of the Bible in the 7th century BCE. "The high literacy rate detected within the small Arad stronghold… demonstrates widespread literacy in the late 7th century BCE Judahite military and administration apparatuses, with the ability to compose biblical texts during this period a possible by-product," write the researchers. This is the first study to combine forces between AI algorithms and human forensics know-how, the researchers note. The study, "Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level," was published September 9 in the prestigious online PLOS journal. Get The Times of Israel's Daily Edition by email and never miss our top stories Free Sign Up The study combines high-resolution imaging methods and complex computer algorithms with trusted police handwriting analysis to prove that the examined 18 texts had no fewer than 12 different authors way back in circa 600 BCE.

NHS patients will benefit from new artificial intelligence (AI) technologies thanks to a £50 million boost. A range of AI-powered innovations which can analyse breast cancer screening scans and assess emergency stroke patients will be tested and scaled. Take-home technology could also see patients given devices and software that can turn their smartphone into a clinical grade medical device for monitoring kidney disease, or a wearable patch to detect irregular heartbeats, one of the leading causes of strokes and heart attacks. The award is managed by the Accelerated Access Collaborative in partnership with NHSX and the National Institute for Health Research. The package also includes funding to support the research, development and testing of promising ideas which could be used in the NHS in future to help speed up diagnosis or improve care for a range of conditions including sepsis, cancer and Parkinson's.