Lifeboat Foundation

Researchers have combined research with real and robotic insects to better understand how they sense forces in their limbs while walking, providing new insights into the biomechanics and neural dynamics of insects and informing new applications for large legged robots. They presented their findings at the SEB Centenary Conference 2023.

Campaniform sensilla (CS) are force receptors found in the limbs of insects that respond to stress and strain, providing important information for controlling locomotion. Similar force receptors exist in mammals known as golgi tendon organs, suggesting that understanding the role of force sensors in insects may also provide new insights into their functions in vertebrates such as humans.

Continue reading “Big robot bugs reveal force-sensing secrets of insect locomotion” »

Neuromorphic computers perform computations by emulating the human brain¹. Akin to the human brain, they are extremely energy efficient in performing computations². For instance, while CPUs and GPUs consume around 70–250 W of power, a neuromorphic computer such as IBM’s TrueNorth consumes around 65 mW of power, (i.e., 4–5 orders of magnitude less power than CPUs and GPUs)³. The structural and functional units of neuromorphic computation are neurons and synapses, which can be implemented on digital or analog hardware and can have different architectures, devices, and materials in their implementations⁴. Although there are a wide variety of neuromorphic computing systems, we focus our attention on spiking neuromorphic systems composed of these neurons and synapses. Spiking neuromorphic hardware implementations include Intel’s Loihi⁵, SpiNNaker2⁶, BrainScales2⁷, TrueNorth³, and DYNAPS⁸. These characteristics are crucial for the energy efficiency of neuromorphic computers. For the purposes of this paper, we define neuromorphic computing as any computing paradigm (theoretical, simulated, or hardware) that performs computations by emulating the human brain by using neurons and synapses to communicate with binary-valued signals (also known as spikes).

Neuromorphic computing is primarily used in machine learning applications, almost exclusively by leveraging spiking neural networks (SNNs)⁹. In recent years, however, it has also been used in non-machine learning applications such as graph algorithms, Boolean linear algebra, and neuromorphic simulations^10,11,12. Researchers have also shown that neuromorphic computing is Turing-complete (i.e., capable of general-purpose computation)¹³. This ability to perform general-purpose computations and potentially use orders of magnitude less energy in doing so is why neuromorphic computing is poised to be an indispensable part of the energy-efficient computing landscape in the future.

Neuromorphic computers are seen as accelerators for machine learning tasks by using SNNs. To perform any other operation (e.g., arithmetic, logical, relational), we still resort to CPUs and GPUs because no good neuromorphic methods exist for these operations. These general-purpose operations are important for preprocessing data before it is transferred to a neuromorphic processor. In the current neuromorphic workflow— preprocessing on CPU/GPU and inferencing on neuromorphic processor—more than 99% of the time is spent in data transfer (see Table 7). This is highly inefficient and can be avoided if we do the preprocessing on the neuromorphic processor. Devising neuromorphic approaches for performing these preprocessing operations would drastically reduce the cost of transferring data between a neuromorphic computer and CPU/GPU. This would enable performing all types of computation (preprocessing as well as inferencing) efficiently on low-power neuromorphic computers deployed on the edge.

Sophia, the AI-embedded humanoid robot, is not just mirroring emotions; she’s leading a revolution in emotional intelligence.

So AI says they can run the world better than humans.

A panel of AI-enabled humanoid robots told a United Nations summit on Friday that they could eventually run the world better than humans.

But the social robots said they felt humans should proceed with caution when embracing the rapidly-developing potential of artificial intelligence.

Continue reading “AI robots at UN reckon they could run the world better” »

The nine humanoid robots gathered at the ‘AI for Good’ conference in Geneva, where organizers are seeking to make the case for Artificial Intelligence and the robots it is powering to help resolve some of the world’s biggest challenges such as disease and hunger.

AI For Good Summit.

https://dig.watch/event/ai-for-good

Continue reading “Robots say they won’t steal jobs, rebel against humans” »

A team of Brown brain and computer scientists developed a new approach to understanding computer vision, which can be used to help create better, safer and more robust artificial intelligence systems.

An older research article and I really hope I didn’t already post this, but isn’t this scary? Nevermind AI or nano, but the fact you don’t need that to mess with your mind? Oh and I’ve searched google and there’s nanoparticles in meds, including psych meds. EMF could potentially mess with that or the minerals in your body but I’m not an expert. But we do have iron in our blood. I read that EMF can affect the blood brain barrier as well. I know there’s issues with people saying they’re targeted individuals, but with instructions online on how to make a microwave gun, especially on youtube, and there’s a Wired Magazine article about a court case where a judge ordered a man to stop EMF targeting a former business partner over an argument over a business deal. Yup, the 21st centure is bringing more than guns and knives and fists into the foray.

From our archives. This important article first published by GR in August 2004 brings to the forefront the role of Psychotronic weapons as an instrument of modern warfare.

A new study from the University of Georgia aims to improve how we evaluate children’s creativity through human ratings and through artificial intelligence.

A team from the Mary Frances Early College of Education is developing an AI system that can more accurately rate open-ended responses on creativity assessments for elementary-aged students.

“In the same way that hospital systems need good data on their patients, educational systems need really good data on their students in order to make effective choices,” said study author and associate professor of educational psychology Denis Dumas. “Creativity assessments have policy and curricular relevance, and without assessment data, we can’t fully support creativity in schools.”

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. The following sections share our initial findings.

For the full version of this report, download the PDF.

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.