Herbert Ong Brighter with Herbert.
Category: robotics/AI – Page 408
This paper introduces a novel theoretical framework for understanding consciousness, proposing a paradigm shift from traditional biological-centric views to a broader, universal perspective grounded in thermodynamics and systems theory. We posit that consciousness is not an exclusive attribute of biological entities but a fundamental feature of all systems exhibiting a particular form of intelligence. This intelligence is defined as the capacity of a system to efficiently utilize energy to reduce internal entropy, thereby fostering increased order and complexity. Supported by a robust mathematical model, the theory suggests that subjective experience, or what is often referred to as qualia, emerges from the intricate interplay of energy, entropy, and information within a system. This redefinition of consciousness and intelligence challenges existing paradigms and extends the potential for understanding and developing Artificial General Intelligence (AGI). The implications of this theory are vast, bridging gaps between cognitive science, artificial intelligence, philosophy, and physics, and providing a new lens through which to view the nature of consciousness itself.
Consciousness, traditionally viewed through the lens of biology and neurology, has long been a subject shrouded in mystery and debate. Philosophers, scientists, and thinkers have pondered over what consciousness is, how it arises, and why it appears to be a unique trait of certain biological organisms. The “hard problem” of consciousness, a term coined by philosopher David Chalmers, encapsulates the difficulty in explaining why and how physical processes in the brain give rise to subjective experiences.
Current research in cognitive science, neuroscience, and artificial intelligence offers various theories of consciousness, ranging from neural correlates of consciousness (NCCs) to quantum theories. However, these theories often face limitations in fully explaining the emergence and universality of consciousness.
On Saturday, Chinese scholars unveiled a preliminary proposal draft in Beijing that could potentially shape the nation’s forthcoming artificial intelligence (AI) law.
The proposal draft pays attention to the development issues of industrial practice in the three areas of data, computing power and algorithms, Zhao Jingwu, an associate professor from BeiHang University Law School, told the Global Times.
Zhao said that the proposal also introduces the AI insurance system that encourages the intervention of the insurance market through policy incentives, exploring insurance products suitable for the AI industry. In addition, it proposes the enhancement of citizens’ digital literacy, aiming to prevent and control the security risks of the technology from the user end.
A team from the University of Geneva (UNIGE) has succeeded in modeling an artificial neural network capable of this cognitive prowess. After learning and performing a series of basic tasks, this AI was able to provide a linguistic description of them to a “sister” AI, which in turn performed them. These promising results, especially for robotics, are published in Nature Neuroscience.
Performing a new task without prior training, on the sole basis of verbal or written instructions, is a unique human ability. What’s more, once we have learned the task, we are able to describe it so that another person can reproduce it. This dual capacity distinguishes us from other species which, to learn a new task, need numerous trials accompanied by positive or negative reinforcement signals, without being able to communicate it to their congeners.
A sub-field of artificial intelligence (AI)—Natural language processing—seeks to recreate this human faculty, with machines that understand and respond to vocal or textual data. This technique is based on artificial neural networks, inspired by our biological neurons and by the way they transmit electrical signals to one another in the brain. However, the neural calculations that would make it possible to achieve the cognitive feat described above are still poorly understood.
1:05 — OpenAI board saga 18:31 — Ilya Sutskever 24:40 — Elon Musk lawsuit 34:32 — Sora 44:23 — GPT-4 55:32 — Memory & privacy 1:02:36 — Q* 1:06:12 — GPT-5 1:09:27 — $7 trillion of compute 1:17:35 — Google and Gemini…
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TRANSCRIPT:
From satellites to thermal cameras, technology has always been a powerful tool for conservation. Artificial Intelligence could be the most important yet.
“Our research in the past decade has made analog memristor a viable technology,” said Dr. Qiangfei Xia. “It is time to move such a great technology into the semiconductor industry to benefit the broad AI hardware community.”
Digital computing has become the norm in our everyday lives, but their limits are being reached in terms of computing power. Can analog computing step in and outperform them? This is what a recent study published in Science hopes to address as a team of researchers from the University of Southern California, TetraMem Inc., and the University of Massachusetts, Amherst (UMass Amherst) have spent the last decade developing memristors, which are capable of overcoming the computing limits of digital computing. This study holds the potential to help researchers develop more efficient methods in storing data without the drawbacks of holding too much of it, thus creating a clog.
“In this work, we propose and demonstrate a new circuit architecture and programming protocol that can efficiently represent high-precision numbers using a weighted sum of multiple, relatively low-precision analog devices, such as memristors, with a greatly reduced overhead in circuitry, energy and latency compared with existing quantization approaches,” said Dr. Qiangfei Xia, who is a professor of Electrical & Computer Engineering at UMass Amherst and a co-author on the study.
YouTube is now requiring creators to disclose to viewers when realistic content was made with AI, the company announced on Monday. The platform is introducing a new tool in Creator Studio that will require creators to disclose when content that viewers could mistake for a real person, place or event was created with altered or synthetic media, including generative AI.
The new disclosures are meant to prevent users from being duped into believing that a synthetically created video is real, as new generative AI tools are making it harder to differentiate between what’s real and what’s fake. The launch comes as experts have warned that AI and deepfakes will pose a notable risk during the upcoming U.S. presidential election.
Today’s announcement comes as YouTube announced back in November that it was going to roll out the update as part of a larger introduction of new AI policies.
Apple is looking to team up with Google for a mega-deal to leverage the Gemini AI model for features on iPhone, Bloomberg reported. This will put Google in a commanding position as the company already has a deal with Apple as the preferred search engine provider on iPhones for the Safari browser.
The publication cited people familiar with the matter saying that Apple is looking to license Google’s AI tech to introduce AI-powered features with iOS updates later this year. Additionally, the company also held discussions with OpenAI to potentially use GPT models, Bloomberg said.