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) …
If you want to understand what's happening with artificial intelligence (AI) and cybersecurity, look no further than this week's news. On Monday, Palo Alto Networks introduced Magnifier, a behavioral analytics solution that uses structured and unstructured machine learning to model network behavior and improve threat detection. Additionally, Google's parent company, Alphabet, announced Chronicle, a cybersecurity intelligence platform that throws massive amounts of storage, processing power, and advanced analytics at cybersecurity data to accelerate the search and discovery of needles in a rapidly growing haystack. So, cybersecurity suppliers are innovating to bring AI-based cybersecurity products to market in a big way. OK, but is there demand for these types of advanced analytics products and services?
Machine learning (ML) has become an important part of the modern cybersecurity landscape, where massive amounts of threat data need to be gathered and processed to provide security solutions the ability to swiftly and accurately detect and analyze new and unique malware variants without requiring extensive resources. Some machine learning algorithms are typically trained on a large dataset. Malware outbreaks pose a challenge for machine learning in security since samples are scarce during the critical first hours. In our research paper entitled "Generative Malware Outbreak Detection," we demonstrated how machine learning technology for security solutions can identify a malware variant not only from large quantities of malware samples but also from only a small handful of observable variants. But how effective is machine learning if the only information available is from a single sample?
Last week at the Black Hat cybersecurity conference in Las Vegas, the Democratic National Committee tried to raise awareness of the dangers of AI-doctored videos by displaying a deepfaked video of DNC Chair Tom Perez. Deepfakes are videos that have been manipulated, using deep learning tools, to superimpose a person's face onto a video of someone else. As the 2020 presidential election draws near, there's increasing concern over the potential threats deepfakes pose to the democratic process. In June, the U.S. Congress House Permanent Select Committee on Intelligence held a hearing to discuss the threats of deefakes and other AI-manipulated media. But there's doubt over whether tech companies are ready to deal with deepfakes.
One astronomer had jumped the gun, tweeting ahead of an official announcement by LIGO (the Laser Interferometer Gravitational-Wave Observatory). The observatory had detected an outburst of gravitational waves, or ripples in spacetime, and an orbiting gamma-ray telescope had simultaneously seen electromagnetic radiation emanating from the same region of space. The observations--which were traced back to a colliding pair of neutron stars 130 million light-years away--marked a pivotal moment for multimessenger astronomy, in which celestial events are studied using a wide range of wildly different telescopes and detectors. The promise of multimessenger astronomy is immense: by observing not only in light but also in gravitational waves and elusive particles called neutrinos, all at once, researchers can gain unprecedented views of the inner workings of exploding stars, galactic nuclei and other exotic phenomena. But the challenges are great, too: as observatories get bigger and more sensitive and monitor ever larger volumes of space, multimessenger astronomy could drown in a deluge of data, making it harder for telescopes to respond in real time to unfolding astrophysical events.
Pass the frames from the camera to the VNCoreMLRequest so it can make predictions using a VNImageRequestHandler object. The VNImageRequestHandler object handles image resizing and preprocessing as well as post-processing of your model's outputs for every prediction. To pass camera frames to your model, you first need to find the image orientation that corresponds to your device's physical orientation. If the device's orientation changes, the aspect ratio of the images can also change. Because you need to scale the bounding boxes for the detected objects back to your original image, you need to keep track of its size.
Thirteen years ago, in Silicon Valley, a company was born. One simple goal of this company was to prove that electric cars could be better in every way over the traditional fuel-powered cars. Since then, Tesla led by Elon Musk has become a household name in the automotive industry, especially when it comes to manufacturing electric cars. They are said to be the pioneer in manufacturing electric cars, but they were not the first one to make electric cars. What they did first was to create a large consumer base who would want to try out new technology, and Tesla did not let their car owners down.
The Matrix reached US cinemas just over 20 years ago and articulated society's fear of the power of artificial intelligence (AI) and its potential to overpower the human. The film taps into ongoing human anxiety around technology and our ability to control it, best epitomised by Mary Shelley's 19th century trope of the Frankenstein's Monster-- the notion that we may well lose control of our own creations as we strive to push the boundaries of science. The human relationship with technology remains a fraught one, but there is little question that AI has the potential to be revolutionary. The McKinsey Global Institute Study reported that in 2016 alone, between $8bn and $12bn was invested in the development of AI worldwide, and Goldstein Research predicts that by 2023, AI will be a $14bn industry. While few of us yet use driverless cars and interact regularly with the animated robots of another science fiction story, I Robot, AI is nonetheless beginning to affect our daily life.
The majority of experts and opinion leaders believe that artificial intelligence (AI) is going to revolutionise many industries, including healthcare . In the short term, the power and potential of AI appear most suitable for complementing human expertise. In other words, machines will help humans do a better job. Consequently, it is anticipated that AI will help with repetitive tasks, in-depth quantification and classification of findings, improved patient and disease phenotyping and, ultimately, with better outcomes for patients, physicians, hospital administrators, insurance companies and governments . This focus issue of the Netherlands Heart Journal aims to help general cardiologists explore the state of the art of AI in cardiology.
The digital age has brought with it an unparalleled opportunity for progress, greater connectivity and efficiency. However, where there is opportunity there is also criminality. Fraud has become truly globalised, with the internet serving as its most lucrative vector. While a great deal of fraud is still committed by opportunistic lone operators, there is a growing contingent of organised, well-resourced outfits able to use the latest technologies to scam their victims. Indeed, between 31 per cent and 45 per cent of UK frauds are linked to organised crime groups (OCGs).