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The mainframe, the hardware stalwart that has existed for decades, is continuing to be a force in the modern era.
Among the vendors that still build mainframes is IBM, which today announced the latest iteration of its Linux-focused mainframe system, dubbed the LinuxOne Emperor 4. IBM has been building LinuxOne systems since 2015, when the first Emperor mainframe made its debut, and has been updating the platform on a roughly two-year cadence.
A grim future awaits the United States if it loses the competition with China on developing key technologies like artificial intelligence in the near future, the authors of a special government-backed study told reporters on Monday.
If China wins the technological competition, it can use its advancements in artificial intelligence and biological technology to enhance its own countryâs economy, military and society to the determent of others, said Bob Work, former deputy defense secretary and co-chair of the Special Competitive Studies Project, which examined international artificial intelligence and technological competition. Work is the chair of the U.S. Naval Institute Board of Directors.
Losing, in Workâs opinion, means that U.S. security will be threatened as China is able to establish global surveillance, companies will lose trillions of dollars and America will be reliant on China or other countries under Chinese influence for core technologies.
In recent years, engineers and computer scientists have created a wide range of technological tools that can enhance fitness training experiences, including smart watches, fitness trackers, sweat-resistant earphones or headphones, smart home gym equipment and smartphone applications. New state-of-the-art computational models, particularly deep learning algorithms, have the potential to improve these tools further, so that they can better meet the needs of individual users.
Researchers at University of Brescia in Italy have recently developed a computer vision system for a smart mirror that could improve the effectiveness of fitness training both in home and gym environments. This system, introduced in a paper published by the International Society of Biomechanics in Sports, is based on a deep learning algorithm trained to recognize human gestures in video recordings.
âOur commercial partner ABHorizon invented the concept of a product that can guide and teach you during your personal fitness training,â Bernardo Lanza, one of the researchers who carried out the study, told TechXplore. âThis device can show you the best way to train based on your specific needs. To develop this device further, they asked us to investigate the viability of an integrated vision system for exercise evaluation.â
How can mobile robots perceive and understand the environment correctly, even if parts of the environment are occluded by other objects? This is a key question that must be solved for self-driving vehicles to safely navigate in large crowded cities. While humans can imagine complete physical structures of objects even when they are partially occluded, existing artificial intelligence (AI) algorithms that enable robots and self-driving vehicles to perceive their environment do not have this capability.
Robots with AI can already find their way around and navigate on their own once they have learned what their environment looks like. However, perceiving the entire structure of objects when they are partially hidden, such as people in crowds or vehicles in traffic jams, has been a significant challenge. A major step towards solving this problem has now been taken by Freiburg robotics researchers Prof. Dr. Abhinav Valada and Ph.D. student Rohit Mohan from the Robot Learning Lab at the University of Freiburg, which they have presented in two joint publications.
The two Freiburg scientists have developed the amodal panoptic segmentation task and demonstrated its feasibility using novel AI approaches. Until now, self-driving vehicles have used panoptic segmentation to understand their surroundings.
AN ARTIFICIAL intelligence text-to-image model has forecasted a disturbing end to mankindâs existence.
The popular Craiyon AI, formerly DALL-E mini AI image generator, designed some barren landscapes and scorched plains when prompted to predict the end of humans.
The AI has been trained to create its masterpieces using unfiltered data from the internet.
Being able to decode brainwaves could help patients who have lost the ability to speak to communicate again, and could ultimately provide novel ways for humans to interact with computers. Now Meta researchers have shown they can tell what words someone is hearing using recordings from non-invasive brain scans.
Our ability to probe human brain activity has improved significantly in recent decades as scientists have developed a variety of brain-computer interface (BCI) technologies that can provide a window into our thoughts and intentions.
The most impressive results have come from invasive recording devices, which implant electrodes directly into the brainâs gray matter, combined with AI that can learn to interpret brain signals. In recent years, this has made it possible to decode complete sentences from someoneâs neural activity with 97 percent accuracy, and translate attempted handwriting movements directly into text at speeds comparable to texting.
Superintelligent AI is âlikelyâ to cause an existential catastrophe for humanity, according to a new paper, but we donât have to wait to rein in algorithms.
THE artificial intelligence revolution has only just begun, but there have already been numerous unsettling developments.
AI programs can be used to act on humansâ worst instincts or achieve humansâ more wicked goals, like creating weapons or terrifying its creators with a lack of morality.
Artificial intelligence is a catch-all phrase for a computer program designed to simulate, mimic or copy human thinking processes.
A team of researchers at University College London, working with a colleague from Nylers Ltd. and another from XPCI Technology Ltd., has developed a new way to X-ray luggage to detect small amounts of explosives. In their paper published in the journal Nature Communications, the group describes modifying a traditional X-ray device and applying a deep-learning application to better detect explosive materials in luggage.
Prior research has shown that when X-rays strike materials, they produce tiny bends that vary depending on the type of material. They sought to take advantage of these bends to create a precision X-ray machine.
The researchers first added a small change to an existing X-ray machineâa box containing masks, which are sheets of metal with tiny holes in them. The masks serve to split the X-ray beam into multiple smaller beams. The researchers then used the device to scan a variety of objects containing embedded explosive materials and fed the results to a deep-learning AI application. The idea was to teach the machine what the tiny bends in such materials looked like. Once the machine was trained, they used it to scan other objects with embedded explosives to see if it could identify them. The researchers found their machine to be 100% accurate under lab settings.
