In a darkened room, a fractured symphony of rattles, hums and warbles bounces off the walls – like an orchestra tuning up in some parallel universe. If you look closely there is a small fragment of a performer. In the centre of the room, visitors hover around a raised plinth, craning to glimpse the brains behind the operation. Under a magnifying lens sit two white blobs, like a tiny pair of jellyfish. Together, they form the lab-grown "mini-brain" of the late US musician Alvin Lucier – composing a posthumous score in real time.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence will likely not reach human-like cognition unless the programs are connected to robots and designed with evolutionary principles, researchers in the U.K. found. Revolutionary AI platforms that mimic human conversation, such as the wildly popular ChatGPT, will never reach human-like cognition despite their large their neural networks and the massive datasets they are trained on if they remain disembodied and only appear on computer screens, researchers at the University of Sheffield reported in a new study. ChatGPT, a chatbot that can simulate conversations with human users who provide prompts to the AI platform, learns in a similar way to human children through supervised and unsupervised learning. Unsupervised learning entails the system learning through trial and error, such as a human telling the chatbot an answer to a prompt was wrong and building off of that information.

Running ChatGPT costs millions of dollars a day, which is why OpenAI, the company behind the viral natural-language processing artificial intelligence has started ChatGPT Plus, a $20/month subscription plan. But our brains are a million times more efficient than the GPUs, CPUs, and memory that make up ChatGPT's cloud hardware. And neuromorphic computing researchers are working hard to make the miracles that big server farms in the clouds can do today much simpler and cheaper, bringing them down to the small devices in our hands, our homes, our hospitals, and our workplaces. "We have to give up immortality," the CEO of Rain AI, Gordon Wilson, told me in a recent TechFirst podcast. "We have to give up the idea that, you know, we can save software, we can save the memory of the system after the hardware dies."

Collective behavior is widespread across the animal kingdom. To date, however, the developmental and mechanistic foundations of collective behavior have not been formally established. What learning mechanisms drive the development of collective behavior in newborn animals? Here, we used deep reinforcement learning and curiosity-driven learning -- two learning mechanisms deeply rooted in psychological and neuroscientific research -- to build newborn artificial agents that develop collective behavior. Like newborn animals, our agents learn collective behavior from raw sensory inputs in naturalistic environments. Our agents also learn collective behavior without external rewards, using only intrinsic motivation (curiosity) to drive learning. Specifically, when we raise our artificial agents in natural visual environments with groupmates, the agents spontaneously develop ego-motion, object recognition, and a preference for groupmates, rapidly learning all of the core skills required for collective behavior. This work bridges the divide between high-dimensional sensory inputs and collective action, resulting in a pixels-to-actions model of collective animal behavior. More generally, we show that two generic learning mechanisms -- deep reinforcement learning and curiosity-driven learning -- are sufficient to learn collective behavior from unsupervised natural experience.

Despite their overlapping interests, it is rare for developmental neuro biologists to consult artificial intelligence (AI) experts in the course of their research and vice versa. But in his new book, The Self-Assembling Brain, neurobiologist Peter Robin Hiesinger argues that doing so would likely be of great benefit to both parties. In 10 chapters, he describes a series of imagined conversations between four hypothetical individuals--a developmental geneticist, a neuroscientist, a robotics engineer, and an AI researcher--that offer readers insight into the information that is needed both to understand the workings of the brain and to create an artificial system that mimics the brain. These fictional conversations are followed by "seminars" in which the author discusses specific topics in greater detail. Hiesinger elegantly moves through a variety of topics, ranging from biological development to AI and ending with a discussion of the advances that deep neural networks have brought to the field of brain-machine interfaces.

Artificial intelligence has simultaneously been one of the most promising and at the same time worrying topics of the past few years. It unavoidably draws attention with its latest achievements and many are worried that AI could overtake a large percentage of the jobs in the world in the near future. But besides that, artificial intelligence can also end up being a powerful military tool. The National Office of Military Space Intelligence has already developed a powerful artificial brain named Sentient, which was revealed in 2019 by the National Intelligence Office (NRO) under the freedom of information law. Unlike any other AIs in existence, it differs in the degree of independence.

Artificial Intelligence has impacted almost every aspect of human life, leaving large amounts of manual jobs to computers, and allowing humans to pursue intelligent jobs. However, in the future, there may be a time when intelligent jobs are also done artificially. Even more unsettling is the possibility that machines will become more intelligent than humans. Although there is little consensus on this topic, such an event is almost certain to happen. We can only hope that we have found a way to make AI "friendly", or share humans interests beforehand.

Artificial intelligence prophets including Elon Musk, Stephen Hawking and Raymond Kurzweil predict that by the year 2030 machines will develop consciousness through the application of human intelligence. This will lead to a variety of benign, neutral and terrifying outcomes. For example, Musk, Hawking and dozens of other researchers signed a petition in January 2015 that claimed AI-driven machines could lead to "the eradication of disease and poverty" in the near future. This is, clearly, a benign outcome. And then there's the neutral result: Kurzweil, who popularized the idea of the technological singularity, believes that by the 2030s people will be able to upload their minds, melding man with machine. On the terrifying side of things, Musk envisions a future where humans will essentially be house cats to our software-based overlords, while Kurzweil takes it a step further, suggesting that humans will essentially be eradicated in favor of intelligent machines.

There are myriad benefits of hunkering down for a good night's sleep. Adequate rest has a wide range of positive effects ranging from enhanced problem solving and reasoning skills to reduced stress and irritability. Humans are not alone in this regard by any means. From praying mantises to giant armadillos, sleep is common across the animal kingdom. New research from Los Alamos National Laboratory suggests that artificial computational brains may also benefit from catching a few Z's.

We take our sense of touch for granted. Simple tasks like opening a jar or tying our shoelaces would be a whole lot more complex if we couldn't feel the object with our hands. Robots typically struggle to replicate this sense, restricting their ability to manipulate objects. But researchers from the National University of Singapore (NUS) might have found a solution: pairing artificial skin with a neuromorphic "brain." The system was developed by a team led by Assistant Professors Benjamin Tee, an electronic skin expert, and Harold Soh, an AI specialist.