CEO Elon Musk teased it in April for the first time, and it was set to bring unprecedented momentum to the company’s years of development of Full Self-Driving and fully autonomous driving technologies.
First reported by Bloomberg, Tesla is said to need more time to build the first units of the Robotaxi. Because it is built upon the automaker’s next-generation platform, which is to blame for the company’s lack of growth in 2024, more development is needed.
The country’s current progress appears to be on par with Elon Musk’s Neuralink.
China has created a committee to steer the nation’s development of brain-computer interfaces (BCIs), with the hope of becoming the global leader in brain chip technology.
The committee will reportedly develop nationwide standards for development to compete with Western technology outfits, such as Elon Musk’s Neuralink.
Brain-computer interfaces
The term “brain-computer interface” was coined in the early 1970s. A BCI refers to any device that translates the brain’s signals into language that can be interpreted by a computer.
Advances in artificial intelligence are making it increasingly difficult to distinguish between uniquely human behaviors and those that can be replicated by machines. Should artificial general intelligence (AGI) arrive in full force—artificial intelligence that surpasses human intelligence—the boundary between human and computer capabilities will diminish entirely.
In recent months, a significant swath of journalistic bandwidth has been devoted to this potentially dystopian topic. If AGI machines develop the ability to consciously experience life, the moral and legal considerations we’ll need to give them will rapidly become unwieldy. They will have feelings to consider, thoughts to share, intrinsic desires, and perhaps fundamental rights as newly minted beings. On the other hand, if AI does not develop consciousness—and instead simply the capacity to out-think us in every conceivable situation—we might find ourselves subservient to a vastly superior yet sociopathic entity.
Neither potential future feels all that cozy, and both require an answer to exceptionally mind-bending questions: What exactly is consciousness? And will it remain a biological trait, or could it ultimately be shared by the AGI devices we’ve created?
1/ OpenAI and Los Alamos National Laboratory (LANL) are partnering to explore how multimodal AI models can be safely used by laboratory scientists to advance life science research.
2/ As part of an evaluation study, novice and advanced laboratory scientists will solve standard experimental tasks…
OpenAI and Los Alamos National Laboratory (LANL) are collaborating to study the safe use of AI models by scientists in laboratory settings.
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The partnership aims to explore how advanced multimodal AI models like GPT-4, with their image and speech capabilities, can be safely used in labs to advance “bioscientific research.”
Humanoids, robotic or virtual systems with body structures that resemble the human body, have a wide range of real-world applications. As their limbs and bodies mirror those of humans, they could be made to reproduce a wide range of human movements, such as walking, crouching, jumping, swimming and so on.
Computationally generating realistic motions for virtual humanoid characters could have interesting implications for the development of video games, animated films, virtual reality (VR) experiences, and other media content. Yet the environments portrayed in video games and animations are often highly dynamic and complex, which can make planning motions for humanoids introduced in these environments more challenging.
Researchers at NVIDIA Research in Israel recently introduced PlaMo (Plan and Move), a new computational approach to plan the movements of humanoids in complex, 3D, physically simulated worlds. Their approach, presented in a paper published on arXiv preprint server, consists of a scene-aware path planner and a robust control policy.
Researchers at the Artificial Intelligence and Machine Learning Lab (AIML) in the Department of Computer Science at TU Darmstadt and the Hessian Center for Artificial Intelligence (hessian. AI) have developed a method that uses vision language models to filter, evaluate, and suppress specific image content in large datasets or from image generators.
Artificial intelligence (AI) can be used to identify objects in images and videos. This computer vision can also be used to analyze large corpora of visual data.
Researchers led by Felix Friedrich from the AIML have developed a method called LlavaGuard, which can now be used to filter certain image content. This tool uses so-called vision language models (VLMs). In contrast to large language models (LLMs) such as ChatGPT, which can only process text, vision language models are able to process and understand image and text content simultaneously. The work is published on the arXiv preprint server.
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In this video, we explore 20 emerging technologies changing our future, including super-intelligent AI companions, radical life extension through biotechnology and gene editing, and programmable matter. We also cover advancements in flying cars, the quantum internet, autonomous AI agents, and other groundbreaking innovations transforming the future.
