Lifeboat Foundation

The American public intellectual and creator of the television series Closer to Truth, Robert Lawrence Kuhn has written perhaps the most comprehensive article on the landscape of theories of consciousness in recent memory. In this review of the consciousness landscape, Àlex Gómez-MarÃn celebrates Robert Kuhn €™s rejection of the monopoly of materialism and uncovers the radical implications of these new accounts of consciousness for meaning, artificial intelligence, and human immortality.

The scientific study of consciousness was not sanctioned by the mainstream until the nineties. Let us not forget that science stands on the shoulders of giants but also on the three-legged stool of data, theory, and socio-political wants. Thirty years later, the field has grown into a vibrant milieu of approaches blessed and burdened by covert assumptions, contradictory results, and conflicting implications. If the study of behaviour and cognition has become the Urban East, consciousness studies are the current Wild West of science and philosophy.

Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware.

Artificial intelligence (AI) is reshaping the future of human life across various real-world fields such as industry, medicine, society, and education¹. The remarkable success of AI has been largely driven by the rise of artificial neural networks (ANNs), which process vast numbers of real-world datasets (big data) using silicon computing chips ^{2, 3}. However, current AI hardware keeps AI from reaching its full potential since training ANNs on current computing hardware produces massive heat and is heavily time-consuming and energy-consuming ^{4– 6}, significantly limiting the scale, speed, and efficiency of ANNs. Moreover, current AI hardware is approaching its theoretical limit and dramatically decreasing its development no longer following ‘Moore’s law’^{7, 8}, and facing challenges stemming from the physical separation of data from data-processing units known as the ‘von Neumann bottleneck’^{9, 10}. Thus, AI needs a hardware revolution^{8, 11}.

A breakthrough in AI hardware may be inspired by the structure and function of a human brain, which has a remarkably efficient ability, known as natural intelligence (NI), to process and learn from spatiotemporal information. For example, a human brain forms a 3D living complex biological network of about 200 billion cells linked to one another via hundreds of trillions of nanometer-sized synapses^{12, 13}. Their high efficiency renders a human brain to be ideal hardware for AI. Indeed, a typical human brain expands a power of about 20 watts, while current AI hardware consumes about 8 million watts to drive a comparative ANN⁶. Moreover, the human brain could effectively process and learn information from noisy data with minimal training cost by neuronal plasticity and neurogenesis,^{14, 15} avoiding the huge energy consumption in doing the same job by current high precision computing approaches^{12, 13}.

Starting with the direct detection of gravitational waves in 2015, scientists have relied on a bit of a kludge: they can only detect those waves that match theoretical predictions, which is rather the opposite way that science is usually done.

Over the past decades, computer scientists have developed various computing tools that could help to solve challenges in quantum physics. These include large-scale deep neural networks that can be trained to predict the ground states of quantum systems. This method is now referred to as neural quantum states (NQSs).

Discover how the SeleniumGreed campaign exploits exposed Selenium Grid services for crypto mining, posing risks to automated testing frameworks.

In the last 60 years technology has evolved at such an exponentially fast rate that we are now regularly conversing with AI based chatbots, and that same OpenAI technology has been put into a humanoid robot. It’s truly amazing to see this rapid development. Above: OpenAI technology in a humanoid robot Continued advancement […].

He pushed the British left to accept capitalism. Now he’s asking the world to make peace with artificial intelligence.

SearchGPT is just a “prototype” for now. The service is powered by the GPT-4 family of models and will only be accessible to 10,000 test users at launch, OpenAI spokesperson Kayla Wood tells The Verge. Wood says that OpenAI is working with third-party partners and using direct content feeds to build its search results. The goal is to eventually integrate the search features directly into ChatGPT.

It’s the start of what could become a meaningful threat to Google, which has rushed to bake in AI features across its search engine, fearing that users will flock to competing products that offer the tools first. It also puts OpenAI in more direct competition with the startup Perplexity, which bills itself as an AI “answer” engine. Perplexity has recently come under criticism for an AI summaries feature that publishers claimed was directly ripping off their work.