The dating app Bumble has disabled its politics filter after it was supposedly used to reveal the identities of Capitol rioters, Mashable has reported. Bumble support posted on Twitter that it "temporarily removed our politics filter to prevent misuse," adding that it "prohibits any content that promotes terrorism or racial hatred." Bumble has promised in another tweet that it will "be reinstated in the future." It also stated that it has removed users confirmed as participants in the US Capitol attack. We've temporarily removed our politics filter to prevent misuse.

NASA has been forced to end its mission to drill down into the Martian soil after its unique geology proved too much for the InSight lander. The InSight probe was equipped with a probe -- dubbed the Mole -- which was going to drill up to 10 feet into the ground. However, the agency said that the soil's "unexpected tendency to clump" meant that the drill could never get enough purchase to function properly. It's the end of a long saga that began at the start of 2019 when the properties of Mars' soil proved tough to crack. After plenty of trial-and-error, and some help from InSight's robotic arm, the hardware only managed to reach a few centimeters into the ground.

In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via Medicaid.gov. But half came not from concerned citizens or even internet trolls. They were generated by artificial intelligence. And a study found that people could not distinguish the real comments from the fake ones.

NASA's "mole" on Mars has failed. After nearly two years of attempting to dig the InSight lander's heat probe – nicknamed the mole – into the Red Planet's surface, engineers have finally given up. The InSight lander arrived on Mars in November 2018. Its main purpose is to study the planet's deep interior in order to help us understand the history of the solar system's rocky worlds. The lander has three main instruments to help it do that: a seismometer to catch vibrations travelling through the ground, a radio to precisely measure Mars's rotation and learn more about its metal core and a setup called the Heat Flow and Physical Properties Package (HP3) to measure the heat flowing out of the planet's centre.

According to the National Oceanic and Atmospheric Administration (NOAA), more than 80% of the ocean "remains unmapped, unobserved, and unexplored" – despite constituting more than 70% of the planet's surface. Now, a pair of Navy veterans are looking to change that with a line of autonomous robot vehicles that will plunge the ocean's depths in search of big data for the company's clients. "The company really started when Joe [Wolfel] and I first got together, which was back in 2004," said Judson Kauffman, who shares the CEO role with Wolfel, in an interview with Datanami. "We met in [Navy] SEAL training together, and ended up being assigned the same unit, and then went into combat together and became very close friends. There, they developed the idea for Terradepth, which "stemmed from some knowledge that we gained in the Navy" – really, Kauffman said, "just of how ignorant humanity is of what's underwater, what's in the sea." "It was shocking to learn how little we know, how little the U.S. Navy knew," he continued – and the more they dug into the issue after their time in the Navy, the more surprised they were.

For the past 4 years, the Trump Administration has been committed to strengthening American leadership in artificial intelligence (AI). After recognizing the strategic importance of AI to the Nation's future economy and security, the Trump Administration issued the first ever national AI strategy, committed to doubling AI research investment, established the first-ever national AI research institutes, released the world's first AI regulatory guidance, forged new international AI alliances, and established guidance for Federal use of AI. Building upon this critical foundation, today the White House Office of Science and Technology Policy (OSTP) established the National Artificial Intelligence Initiative Office, further accelerating our efforts to ensure America's leadership in this critical field for years to come. The Office is charged with overseeing and implementing the United States national AI strategy and will serve as the central hub for Federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia, and other stakeholders. The National AI Initiative Office is established in accordance with the recently passed National Artificial Intelligence Initiative Act of 2020.

A Los Angeles man admitted in federal court Thursday that he flew a drone that struck a Los Angeles Police Department helicopter that was responding to a crime scene in Hollywood. Andrew Rene Hernandez, 22, made the admission in pleading guilty to one count of unsafe operation of an unmanned aircraft, a misdemeanor. A spokesman for the U.S. attorney's office in Los Angeles said Hernandez is believed to be the first person in the country to be convicted of that offense, which carries a punishment of up to one year in prison. In his plea agreement, Hernandez admitted that he "recklessly interfered with and disrupted" the operation of the LAPD helicopter, which was responding to a burglary of a pharmacy, and that his actions "posed an imminent safety hazard" to the chopper's occupants. Reached by phone Thursday, Hernandez declined to comment.

Deep learning models are yielding increasingly better performances thanks to multiple factors. To be successful, model may have large number of parameters or complex architectures and be trained on large dataset. This leads to large requirements on computing resource and turn around time, even more so when hyper-parameter optimization is done (e.g search over model architectures). While this is a challenge that goes beyond particle physics, we review the various ways to do the necessary computations in parallel, and put it in the context of high energy physics.

Despite the intricacies involved in designing a computer as a teampartner, we can observe patterns in team behavior which allow us to describe at a general level how AI systems are to collaborate with humans. Whereas most work on human-machine teaming has focused on physical agents (e.g. robotic systems), our aim is to study how humans can collaborate with information agents. We propose some appropriate team design patterns, and test them using our Collaborative Intelligence Analysis (CIA) tool.

Despite the high quality performance of the deep neural network in real-world applications, they are susceptible to minor perturbations of adversarial attacks. This is mostly undetectable to human vision. The impact of such attacks has become extremely detrimental in autonomous vehicles with real-time "safety" concerns. The black-box adversarial attacks cause drastic misclassification in critical scene elements such as road signs and traffic lights leading the autonomous vehicle to crash into other vehicles or pedestrians. In this paper, we propose a novel query-based attack method called Modified Simple black-box attack (M-SimBA) to overcome the use of a white-box source in transfer based attack method. Also, the issue of late convergence in a Simple black-box attack (SimBA) is addressed by minimizing the loss of the most confused class which is the incorrect class predicted by the model with the highest probability, instead of trying to maximize the loss of the correct class. We evaluate the performance of the proposed approach to the German Traffic Sign Recognition Benchmark (GTSRB) dataset. We show that the proposed model outperforms the existing models like Transfer-based projected gradient descent (T-PGD), SimBA in terms of convergence time, flattening the distribution of confused class probability, and producing adversarial samples with least confidence on the true class.