On key metrics, a VR experience elicited a response indistinguishable from subjects who took medium doses of LSD or magic mushrooms.
Fifteen years ago, David Glowacki was walking in the mountains when he took a sharp fall. When he hit the ground, blood began leaking into his lungs. As he lay there suffocating, Glowacki’s field of perception swelled. He peered down at his own body—and, instead of his typical form, saw that he was made up of balled-up light.
“I knew that the intensity of the light was related to the extent to which I inhabited my body,” he recalls. Yet watching it dim didn’t frighten him. From his new vantage point, Glowacki could see that the light wasn’t disappearing. It was transforming—leaking out of his body into the environment around him.
This realization—which he took to signify that his awareness could outlast and transcend his physical form—brought Glowacki a sublime sense of peace. So he approached what he thought was death with curiosity: What might come next? Read More
Monthly Archives: August 2022
Digital Twins Are the Future, Here Are 5 Ways to Keep Them Secure While Manufacturing Innovation
As technology continues to revamp existing business models, novel methods of manufacturing and projection are increasingly being used. Digital twins are perhaps the best example of how companies marry technology with the natural world to create innovative solutions. A digital twin is an electronic version of a real-work entity. It allows companies to model business conditions and predict the impact of their choices.
Research by Capgemini reveals that digital twin usage is bound to increase by 36 percent over the next five years. Increased adoption will certainly help enterprises create better products. However, increased use often brings significant security risks.
Data freely flows between the real-world entity and the digital twin. For instance, manufacturers create data flows between a real-world assembly line and its digital twin. This situation makes digital twins prime targets for malicious hackers who can wreak havoc on enterprise systems.
Here are five ways your company can secure its digital twins while ensuring peak productivity. Read More
Top Trending Artificial Intelligence AI Technologies in 2022
A brand-new area of computer science was initially referred to as “artificial intelligence” in 1955. Many daily jobs are being replaced by artificial intelligence, requiring less human involvement. But what precisely is this new AI technology? AI refers to the process of teaching a computer system to function and think like a human brain. This is often accomplished through reinforcement learning, in which the computer learns from past errors and observed patterns. For instance, a trained model defeated the world champion in the difficult-to-learn and won a game of AlphaGO by self-training itself many times and learning from its mistakes each time it lost a game.
Applications of AI technology are rapidly gaining ground in every aspect of our daily life, just as AI is quickly transforming several sectors. People may easily carry out tasks like phoning a buddy, determining the best route to their destinations, and turning on or off electrical equipment by giving voice instructions to their virtual assistant. AI technology is also finding applications in the automotive, e-commerce, AI farming, healthcare, and several other industries.
It won’t be long before artificial intelligence (AI) technology has advanced to the point where people rely on their virtual assistants to wake us up, driverless cars to get us to work, and perhaps AI-powered robots to assist us in making decisions at work by forecasting, analyzing, and providing valuable insights. It all seems somewhat fictional when we read this, don’t you think? Thoughts of video calling were commonplace, but today everyone has access to it through their cellphones. Read More
MIT Claims New Artificial Neuron 1 Million Times Faster Than the Real Thing
Think and you’ll miss it: researchers at MIT claim to have successfully created analog synapses that are one million times faster than those in our human brains.
Just as digital processors need transistors, analog ones need programmable resistors. Once put into the right configuration, these resistors can be used to create a network of analog synapses and neurons, according to a press release.
These analog synapses aren’t just ultra-fast, they’re remarkably efficient, too. And that’s pretty important, because as digital neural networks grow more advanced and powerful, they require more and more energy, increasing their carbon footprint considerably.
As detailed in a new paper, the researchers hope their findings will advance the field of analog deep learning, a burgeoning field of artificial intelligence. Read More
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