Code used to encrypt sensitive radio communications around the world for years had major flaws that could be exploited by attackers, according to new research. A group of researchers from the Netherlands discovered multiple vulnerabilities in encryption algorithms used in the European radio standard TETRA, which is used in radio communications by police, critical infrastructure workers, mass transit and freight trains, and major government bodies. While the TETRA standard is public, the ciphers used to encrypt the communications were kept secret. One of the algorithms, known as TEA1, had a feature that reduces its 80-bit encryption down to just 32 bits--a backdoor, the researchers say, that made it vulnerable to eavesdropping and potentially other attacks. The body that develops and maintains TETRA--the European Telecommunications Standards Institute--rejects the "backdoor" label, saying that the weakened encryption was implemented to abide by encryption export controls in place when it was released in the 1990s. Regardless of what you call it, ETSI has released a replacement for the TEA1 algorithm and fixed another major flaw that made communications vulnerable to interception.

A coalition of AI researchers and health care professionals in fields like infectious disease, radiology, and ontology have found several common but serious shortcomings with machine learning made for COVID-19 diagnosis or prognosis. After the start of the global pandemic, startups like DarwinAI, major companies like Nvidia, and groups like the American College of Radiology launched initiatives to detect COVID-19 from CT scans, X-rays, or other forms of medical imaging. The promise of such technology is that it could help health care professionals distinguish between pneumonia and COVID-19 or provide more options for patient diagnosis. Some models have even been developed to predict if a person will die or need a ventilator based on a CT scan. However, researchers say major changes are needed before this form of machine learning can be used in a clinical setting.

People who think an almighty deity created the world are more likely to believe in conspiracy theories, scientists have discovered. Experts found the religion or God a person believes in is irrelevant, believers are still more likely to think the moon landing was a hoax or JFK was killed by the CIA. Scientists claim the'previously unidentified' link between the two is down to a brain bias that connects unrelated events. Teleological thinkers are willing to accept statements such as'the sun rises in order to give us light' and'the purpose of bees is to ensure pollination' as true. They hope that drawing the link between the two errors in rational thinking can highlight the'major flaws of conspiracy theories'.

DNNs, which are merely trained on raw data, do not recognize the semantics of the objects, but rather memorize the inputs. The short 3 page paper, titled "Deep Neural Networks Do Not Recognize Negative Images," provides support to its title via experimentation using a "state-of-the-art" deep (convolutional) neural network, which is separately trained on both MNIST and the German Traffic Signs Recognition Benchmark (GTSRB) datasets. The "regular" results of the MNIST and GTSRB recognition and classification reach greater than 99% and 98% accuracy on test data, respectively, while testing with the same negative images result in between 4% and 17% and between 5% and 14% accuracy, respectively. To assess the behavior of the image classification models, we evaluate the performance of DNNs on negative images of the training data. A negative is referred to an image with reversed brightness, i.e., the lightest parts appear the darkest and the darkest parts appear lightest.

In the 1950s when computer science was in its infancy and artificial intelligence was nothing but science fiction, Alan Turing, a pioneer in theoretical computer science felt that there needed to be a way to determine whether a machine could be said to be intelligent. So he created a test called the Turing Test, or Imitation Game. His test was very simple. A human and a machine would be separated so they could not see each other and an evaluator would listen as the human and the machine converse. If the evaluator, a third party that isn't part of the conversation, can't determine whether both parties are human or if one is a machine, then the machine is said to have passed the Turing Test.

Despite being a good few decades old, the basis of the Turing test -- developed by the computer science genius Alan Turning -- remains the exact same to this day. It asks whether a computer can trick a human into believing they are speaking with another fellow human. In one part of the test, a human judge is asked to interact with two hidden objects -- one is a human and the other a machine -- to determine if they can distinguish between the two. And if not, that AI has passed the Turing test. There has been a lot of challengers who have claimed that AI has actually passed the test.

The Turing test, developed by legendary computer scientist Alan Turing and used to test the artificial intelligence of computers, has a major flaw. A new study, published in the Journal of Experimental and Theoretical Artificial Intelligence, points out that the test, which was devised in the 1950s, could be successfully passed if the computer pleaded the Fifth Amendment and remained silent. Authors Kevin Warwick and Huma Shah from Coventry University argue that a machine could plausibly pass the test by saying very little, or nothing at all. Previous attempts at defeating the Turing test have seen computers pretend to be children, but no matter how great their general knowledge, they are often unable to convince a human interacting with them (over typed messages) that they are also a human. A critical point raised by the study is how the Turing test is based around a machine being discovered as a human based on what it does wrong, rather than what it does right.