Lifeboat Foundation

Dr. Nick Melosh at the BrainMind Summit hosted at Stanford, interviewed by BrainMind member Christian Bailey.

Nick Melosh is a Professor of Materials Science and Engineering, Stanford University. Nick’s research at Stanford focuses on how to design new inorganic structures to seamlessly integrate with biological systems to address problems that are not feasible by other means. This involves both fundamental work such as to deeply understand how lipid membranes interact with inorganic surfaces, electrokinetic phenomena in biologically relevant solutions, and applying this knowledge into new device designs. Examples of this include “nanostraw” drug delivery platforms for direct delivery or extraction of material through the cell wall using a biomimetic gap-junction made using nanoscale semiconductor processing techniques. We also engineer materials and structures for neural interfaces and electronics pertinent to highly parallel data acquisition and recording. For instance, we have created inorganic electrodes that mimic the hydrophobic banding of natural transmembrane proteins, allowing them to ‘fuse’ into the cell wall, providing a tight electrical junction for solid-state patch clamping. In addition to significant efforts at engineering surfaces at the molecular level, we also work on ‘bridge’ projects that span between engineering and biological/clinical needs. My long history with nano-and microfabrication techniques and their interactions with biological constructs provide the skills necessary to fabricate and analyze new bio-electronic systems.”

Continue reading “Nanotechnology meets Neuroscience — Nicholas Melosh at BrainMind” »

Gabriel Kreiman is a Professor at Harvard Medical School. He is on faculty at Children’s Hospital and the Center for Brain Science at Harvard University. He is Associate Director and Thrust Leader in the Harvard/MIT Center for Brains, Minds, and Machines. He received his MSc and PhD from the California Institute of Technology and pursued postdoctoral work with Professor Poggio at MIT.

The Kreiman laboratory combines behavioral metrics, neurophysiological recordings and computational models to understand cognitive function and to build biologically inspired Artificial Intelligence systems. Kreiman’s work has focused on two main themes: understanding the transformation of pixel-like inputs into rich and complex visual percepts; and elucidating the subjectively filters incoming inputs to create lasting narratives that constitute the fabric of our personal experiences and knowledge.

In 1959, Richard Feynman made the famous assertion that one day we will be able to swallow the surgeon. Advancements in nanomedicine are making that dream come true. Nanoroboticist Metin Sitti shows the tiny robot that can take pictures, biopsy, and deliver medicine inside of you.

Watch the full program here: https://youtu.be/FzFY5ms3AUc.
Original program date: May 30, 2013

Continue reading “The Nanorobot Surgeon You Can Swallow” »

Science fiction has become a reality with recent developments toward biohacking through nanotechnology. Soon, science and industries may soon realize the potential of human hacking… but at what risk versus reward? Medical nanotechnology is one of these such topics. Many experts believe nanotechnology will pave the way for a bright, new future in improving our wellbeing. Yet, at the core of this biohacking are machines and as we’ve seen with other technologies — there are very real risks of malicious intent. In this video, we share some of the applications being developed combining nanotechnology and medicine. We also look at the potential risks found in the practice and how we may mitigate issues before they’re problematic. We also share how companies can reduce security flaws and curb public perception so the nanotechnology industry can flourish without major setbacks. Want to learn more about this budding area of science and medicine?

See our accompanying blog post for the details and be sure to dig around the site, here:

Continue reading “Nanotechnology: Hacking Humans, Its Potential, and Real Risks” »

Veronica Roth (Tor)

THERE isn’t much world-building in Veronica Roth’s sci-fi retelling of Sophocles’s classic Greek tragedy Antigone. Then again, in Arch-Conspirator, there isn’t much world. A dusty dystopian city (Thebes in the original, but it isn’t clear where we are in the reboot) is all that remains after a thinly sketched environmental polycrisis has turned humanity into an endangered species.

Science Daily reports that the astronomers found out that the mass of this lone white dwarf is equivalent to 56% of the sun’s weight. It aligns with previous theoretical predictions regarding the white dwarf’s mass, and it also sheds light on persisting theories regarding the evolution of these white dwarfs as a result of usual star evolution. The interesting observation grants further understanding of theories regarding white dwarf composition and structure.

According to the Space Academy, the astronomers made use of the renowned Hubble Space Telescope to gauge this lone white dwarf’s mass. The dwarf is known as LAWD 37.

Many of the proteins that play a crucial role in living cells adhere to a core principle of biology: their form, or shape, fits their function. But there is also a vast number of proteins and their parts that defy that dogma.

Why it matters: New findings are revealing how these flexible, disordered proteins work — and deciphering their role in human diseases and potential treatments.

How it works: Whether many medicines, immune cells, or the moment-to-moment inner workings of cells function depends on the shape of proteins they interact with or use.

Researchers are starting to unravel one of the biggest mysteries behind the AI language models that power text and image generation tools like DALL-E and ChatGPT.

For a while now, machine learning experts and scientists have noticed something strange about large language models (LLMs) like OpenAI’s GPT-3 and Google’s LaMDA : they are inexplicably good at carrying out tasks that they haven’t been specifically trained to perform. It’s a perplexing question, and just one example of how it can be difficult, if not impossible in most cases, to explain how an AI model arrives at its outputs in fine-grained detail.

In a forthcoming study posted to the arXiv preprint server, researchers at the Massachusetts Institute of Technology, Stanford University, and Google explore this “apparently mysterious” phenomenon, which is called “in-context learning.” Normally, to accomplish a new task, most machine learning models need to be retrained on new data, a process that can normally require researchers to input thousands of data points to get the output they desire—a tedious and time-consuming endeavor.

Blue straggler stars are the weird grandparents of the galaxy: They should be old, but they act young. Finding and studying these strange stars helps us understand the complicated life cycles of normal, more well-behaved stars.

All stars follow a particular path in life, known as the main sequence. The moment they begin fusing hydrogen in their cores, they maintain a strict relationship between their brightness and temperature. Different stars will have different combinations of brightness and temperature, but they all obey the same relationship. For example, smaller stars, like red dwarfs, will be relatively dim but also cool, with their surfaces turning a characteristic shade of red. Medium stars, like the sun, will be both hotter and brighter, turning white. The largest stars will be both incredibly bright and extremely hot, making them appear blue.