On any given day, Dallas motorists traveling along I-20 or I-45 are likely to be sharing the road with a self-driving truck that has the equivalent of a learner’s permit.
Why it matters: Dallas is the hub of autonomous truck testing and development, thanks to its vital freight corridors, business-friendly policies and generally favorable weather.
Through a scattering medium such as ground glass? Traditionally, this would be considered impossible. When light passes through an opaque substance, the information carried within the light becomes “jumbled up”, almost as if undergoes complex encryption.
Recently, a remarkable scientific breakthrough by Professor Choi Wonshik’s team from the IBS Center for Molecular Spectroscopy and Dynamics (IBS CMSD) has unveiled a method to leverage this phenomenon in the fields of optical computing and machine learning.
Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.
Today I’m checking out Replica’s AI-Powered Smart NPCs in their impressive new demo for unreal engine 5. In my opinion will quickly change the landscape of gaming and bring a whole new layer of depth to the already impressive worlds we all enjoy. I hope you enjoyed this look into the roots of AI in gaming, thanks for watching and liking.
Download the Demo &
https://www.replicastudios.com/blog/smart-npc-plugin-release.
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A postdoctoral researcher at the University of Southern Denmark in Odense, Bilal studies the evolution of the novel in nineteenth-century literature. Yet he’s perhaps best known for his online tutorials, in which he serves as an informal ambassador between academics and the rapidly expanding universe of search tools that make use of artificial intelligence (AI).
Developers want to free scientists to focus on discovery and innovation by helping them to draw connections from a massive body of literature.
NASA is cautiously testing OpenAI software with a range of applications in mind, including code-writing assistance and research summarization. Dozens of employees are participating in the effort, which also involves using Microsoft’s Azure cloud system to study the technology in a secure environment, FedScoop has learned.
The space agency says it’s taking precautions as it looks to examine possible uses for generative artificial intelligence. Employees looking to evaluate the technology are only invited to join NASA’s generative AI trial if their tests involve “public, non-sensitive data,” Edward McLarney, digital transformation lead for Artificial Intelligence and Machine Learning at the agency, told FedScoop.
In June, Microsoft announced a new Azure OpenAI tool designed for the government, which according to the company is more secure than the commercial version of the software. Last week, FedScoop reported that the Microsoft Azure OpenAI was approved for use on sensitive government systems. A representative for Microsoft Azure referred to NASA in response to a request for comment. OpenAI did not respond to a request for comment by the time of publication.
There’s no shortage of AI doomsday scenarios to go around, so here’s another AI expert who pretty bluntly forecasts that the technology will spell the death of us all, as reported by Bloomberg.
This time, it’s not a so-called godfather of AI sounding the alarm bell — or that other AI godfather (is there a committee that decides these things?) — but a controversial AI theorist and provocateur known as Eliezer Yudkowsky, who has previously called for bombing machine learning data centers. So, pretty in character.
“I think we’re not ready, I think we don’t know what we’re doing, and I think we’re all going to die,” Yudkowsky said on an episode of the Bloomberg series “AI IRL.”
The fear of artificial intelligence is largely a Western phenomenon. It is virtually absent in Asia. In contrast, East Asia sees AI as an invaluable tool to relieve humans of tedious, repetitive tasks and to deal with the problems of aging societies. AI brings productivity gains comparable to the ICT (information and communications technology) revolution of the late 20th century.
China is using AI as an integral part of the Fourth Industrial Revolution, which brings together different “Industry 4.0” technologies – high-speed (fifth-generation) communications, the Internet of Things (IoT), robotics, etc. Chinese ports unload container ships in 45 minutes, a task that can take up to a week in other countries.
Today’s fear of AI has many parallels to the fear of machines at the end of the 19th century. French textile workers, fearing mechanical weaving would endanger their jobs and devalue their craft, threw their “sabots” (clogs) into weaving machines to render them inoperable. They gave us the word sabotage.
The next generation of 2D semiconductor materials doesn’t like what it sees when it looks in the mirror. Current synthesizing approaches to make single-layer nanosheets of semiconducting material for atomically thin electronics develop a peculiar “mirror twin” defect when the material is deposited on single-crystal substrates like sapphire. The synthesized nanosheet contains grain boundaries that act as a mirror, with the arrangement of atoms on each side organized in reflected opposition to one another.
This is a problem, according to researchers from the Penn State’s Two-Dimensional Crystal Consortium-Materials Innovation Platform (2DCC-MIP) and their collaborators. Electrons scatter when they hit the boundary, reducing the performance of devices like transistors. This is a bottleneck, the researchers said, for the advancement of next-generation electronics for applications such as Internet of Things and artificial intelligence. But now, the research team may have come up with a solution to correct this defect. They have published their work in Nature Nanotechnology.
This study could have a significant impact on semiconductor research by enabling other researchers to reduce mirror twin defects, according to lead author Joan Redwing, director of 2DCC-MIP, especially as the field has increased attention and funding from the CHIPS and Science Act approved last year. The legislation’s authorization increased funding and other resources to boost America’s efforts to onshore the production and development of semiconductor technology.
Generative AI, in particular, is placing new demands on IT teams across nearly every industry. The GPUs required for generative AI are expensive and power-hungry, and you may need many. Aligning storage to keep those data-hungry GPUs fed requires adopting new technologies, such as NVIDIA’s GPUDirect, that enable applications to transfer data from primary storage directly into the GPU’s memory. The software stack looks unlike nearly anything else in enterprise IT. The list goes on and on.
Dell Technologies and NVIDIA are working together to reduce the complexity of building and deploying infrastructure for Generative AI. The two companies announced Project Helix earlier this year at Dell Technologies World, which Dell described as delivering full-stack solutions with technical expertise and pre-built tools based on Dell and NVIDIA infrastructure and software.