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Category: robotics/AI – Page 745

In its quest to develop AI that can understand a range of different dialects, Meta has created an AI model, SeamlessM4T, that can translate and transcribe close to 100 languages across text and speech. Available in open source along with SeamlessAlign, a new translation dataset, Meta claims that SeamlessM4T represents a “significant breakthrough” in the field of AI-powered speech-to-speech and speech-to-text.

“Our single model provides on-demand translations that enable people who speak different languages to communicate more effectively,” Meta writes in a blog post shared with TechCrunch. “SeamlessM4T implicitly recognizes the source languages without the need for a separate language… More.

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In this video i will show everyone the Theoretical & Practical side of understanding and learning Reverse-Engineering, to modify Machine-Code/Code overall in the Memory inside Binary Software Files on Systems, and also the Fundamentals about the System Architechture x64/x32-x86 Bit, as how it works in the smallest of Bits/Bytes form on the Memory-Layout Architechture. I will be showing a variety of Techniques like Cracking Games, Manipulating basic “Hello World” compiled C++ code Binary, and overall i will show different kind of Debugging/Reverse-Engineering Techniques on the Tool x64DBG.
- Educational Purposes Only.

If the Video was helpful and useful for learning Reverse-Engineering in a sense to understand Problematic Bugs/Vulnerabilities or Code in a Binary, subscribe for more videos!. Thanks.

Reverse-Engineering Tools:

Ghidra: https://ghidra-sre.org/ // Mac OSX/Windows/Linux.

Continue reading “Reverse-Engineering — How Do Hackers Debug & Hack System Software/Services” | >

The latest advancements in AI for gaming are in the spotlight today at Gamescom, the world’s largest gaming conference, as NVIDIA introduced a host of technologies, starting with DLSS 3.5, the next step forward of its breakthrough AI neural rendering technology.

DLSS 3.5, NVIDIA’s latest innovation in AI-powered graphics is an image quality upgrade incorporated into the fall’s hottest ray-traced titles, from Cyberpunk 2077: Phantom Liberty to Alan Wake 2 to Portal with RTX.

But NVIDIA didn’t stop there. DLSS is coming to more AAA blockbusters; emotion is being added to AI-powered non-playable characters (NPCs); Xbox Game Pass titles are coming to the GeForce NOW cloud-gaming service; and upgrades to GeForce NOW servers are underway.

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Materials scientists aim to develop autonomous materials that function beyond stimulus responsive actuation. In a new report in Science Advances, Yang Yang and a research team in the Center for Bioinspired Energy Science at the Northwestern University, U.S., developed photo-and electro-activated hydrogels to capture and deliver cargo and avoid obstacles on return.

To accomplish this, they used two spiropyran monomers (photoswitchable materials) in the hydrogel for photoregulated charge reversal and autonomous behaviors under a constant electric field. The photo/electro-active materials could autonomously perform tasks based on constant external stimuli to develop intelligent materials at the molecular scale.

Soft materials with life-like functionality have promising applications as intelligent, robotic materials in complex dynamic environments with significance in human-machine interfaces and biomedical devices. Yang and colleagues designed a photo-and electro-activated hydrogel to capture and deliver cargo, avoid obstacles, and return to its point of departure, based on constant stimuli of visible light and applied electricity. These constant conditions provided energy to guide the hydrogel.

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Most adult humans are innately able to pick up objects in their environment and hold them in ways that facilitate their use. For instance, when picking up a cooking utensil, they would normally grab it from the side that will not be placed inside the cooking pot or pan.

Robots, on the other hand, need to be trained on how to best pick up and hold objects while completing different tasks. This is often a tricky process, given that the robot might also come across objects that it never encountered before.

The University of Bonn’s Autonomous Intelligent Systems (AIS) research group recently developed a new learning pipeline to improve a robotic arm’s ability to manipulate objects in ways that better support their practical use. Their approach, introduced in a paper published on the pre-print server arXiv, could contribute to the development of robotic assistants that can tackle manual tasks more effectively.

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“Because of the heterogeneity of this disease, scientists haven’t found good ways of tackling it,” said Olivier Gevaert, PhD, associate professor of biomedical informatics and of data science.

Doctors and scientists also struggle with prognosis, as it can be difficult to parse which cancerous cells are driving each patient’s glioblastoma.

But Stanford Medicine scientists and their colleagues recently developed an artificial intelligence model that assesses stained images of glioblastoma tissue to predict the aggressiveness of a patient’s tumor, determine the genetic makeup of the tumor cells and evaluate whether substantial cancerous cells remain after surgery.

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Enter AI. Multiple deep learning methods can already accurately predict protein structures— a breakthrough half a century in the making. Subsequent studies using increasingly powerful algorithms have hallucinated protein structures untethered by the forces of evolution.

Yet these AI-generated structures have a downfall: although highly intricate, most are completely static—essentially, a sort of digital protein sculpture frozen in time.

A new study in Science this month broke the mold by adding flexibility to designer proteins. The new structures aren’t contortionists without limits. However, the designer proteins can stabilize into two different forms—think a hinge in either an open or closed configuration—depending on an external biological “lock.” Each state is analogous to a computer’s “0” or “1,” which subsequently controls the cell’s output.

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A new robotic platform developed at the University of Chicago can adapt to its surroundings in real time for applications in unfamiliar environments.

The platform, dubbed the Granulobot, consists of many identical motorized units each a few centimeters in diameter. The units are embedded with a Wi-Fi microcontroller and sensors and use magnets to engage other units.

As its name suggests, the Granulobot is inspired by the physics of granular materials, which are large aggregates of particles that exhibit a range of complex behaviors. After water, these are the most ubiquitous material on the planet.

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https://www.deeplearning.ai/the-batch/issue-209/
https://arxiv.org/abs/2210.

Minecraft AI — SELF-IMPROVING 🤯 autonomous agent:

Continue reading “Proof that AI Understands? 👀 Andrew Ng on LLMs building mental models, Othello GPT, Geoffrey Hinton” | >