Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Due to the prevalence of chronic pain worldwide, there is an urgent need to improve pain management strategies. While opioid drugs have long been used to treat chronic pain, their use is severely limited by adverse effects and abuse liability. Neurostimulation techniques have emerged as a promising option for chronic pain that is refractory to other treatments. While different neurostimulation strategies have been applied to many neural structures implicated in pain processing, there is variability in efficacy between patients, underscoring the need to optimize neurostimulation techniques for use in pain management. This optimization requires a deeper understanding of the mechanisms underlying neurostimulation-induced pain relief. Here, we discuss the most commonly used neurostimulation techniques for treating chronic pain. We present evidence that neurostimulation-induced analgesia is in part driven by the release of endogenous opioids and that this endogenous opioid release is a common endpoint between different methods of neurostimulation. Finally, we introduce technological and clinical innovations that are being explored to optimize neurostimulation techniques for the treatment of pain, including multidisciplinary efforts between neuroscience research and clinical treatment that may refine the efficacy of neurostimulation based on its underlying mechanisms.

Over 20% of people worldwide suffer from chronic pain disorders (Goldberg and McGee, 2011). In response to an unmet need for effective pain management, opioid drugs have been widely adopted. Opioid drugs harness the body’s endogenous opioid receptors, which are dispersed throughout the central and peripheral nervous system to modulate pain perception. While prescription opioids often provide effective pain relief, they have undesirable and potentially dangerous side effects including abuse liability and respiratory depression. Their contribution to the ongoing opioid epidemic and the enormous negative impact of chronic pain underscore the need for safe and effective pain therapies (Manchikanti et al., 2012). Neurostimulation therapies are potential alternatives for managing medically refractory pain. However, these therapies are hampered by inconsistent pain relief across patients and diminishing analgesic effects over time (Kumar K. et al., 1998).

Read more | >

How do you distinguish a galaxy from a mere cluster of stars? That’s easy, right? A galaxy is a large collection of millions or billion of stars, while a star cluster only has a thousand or so. Well, that kind of thinking won’t get you a Ph.D. in astronomy! Seriously, though, the line between galaxy and star cluster isn’t always clear. Case in point, UMa3/U1.

It’s easy to distinguish galaxies such as Andromeda and the Milky Way. They are large, gravitationally bound, and dominated by dark matter. It’s also easy to distinguish star clusters such as the Pleiades. They are loosely bound star groupings without dark matter. But for a type of small dwarf galaxy known as Ultra-Faint Dwarfs (UFDs) the dividing line gets fuzzy.

UFDs are dominated by dark matter. The mass of the Milky Way, for example, is about 85% dark matter. An ultrafaint dwarf galaxy, however, can have a thousand times more dark matter than luminous matter. This is why they are so faint. Since UFDs often contain some of the oldest stars in the Universe, astronomers love to study them for clues on the origins of galaxies. Which brings us to UMa3/U1.

Read more | >

Who knew that magnetic fields could be so useful? Astronomers are able to use magnetic fields to map our environment within the Milky Way using a technique called Faraday rotation.

It works like this. There’s a bunch of dust—literal dust grains—floating within the galaxy.

Well, I say there’s a lot of dust, but it’s at very, very low densities. Thankfully, the volumes within interstellar space are so vast that the total amount of dust can really add up. And all these little dust grains have little magnetic fields associated with them, because all the grains are made of electric charges and they’re spinning around themselves.

Read more | >

Investors hoping to back former OpenAI chief technology officer Mira Murati’s buzzy new AI startup are being asked to commit a minimum of $50 million, according to two sources with knowledge of the deal. Murati is raising around $2 billion of capital at a $10 billion valuation for Thinking Machines Lab, BI previously reported.

Multiple sources say the mega-round, led by Andreessen Horowitz, is nearing the final stages of fundraising.

A spokesperson for Thinking Machines Lab declined to comment. A spokesperson for A16z did not respond to a request for comment. The round is not finalized, and the details could change. The financing would almost certainly rank as one of the largest seed rounds in history, which typically range in the low to mid-single digits.

A $50 million check size is beyond the scope of most traditional seed investors because it would represent a substantial percentage, if not their entire fund.

The minimum requirement and rich valuation reflect feverish investor enthusiasm for generative AI and the reality that there are a very limited number of technical founders with Murati’s expertise and the team she has assembled. It’s also enormously expensive to train AI models and recruit and retain top talent.

Murati spent over six years at OpenAI, where she worked on the development of ChatGPT and other AI research initiatives. She was briefly appointed interim CEO in November 2023 after OpenAI’s board abruptly fired Sam Altman, a move that sparked turmoil within the company. After Altman’s reinstatement as CEO, Murati resumed her role as CTO.

It has been a widely discussed mystery what exactly Thinking Machines will do to distinguish itself in a crowded and well-funded field that includes not only OpenAI but also Anthropic, Elon Musk’s xAI, and Google’s Gemini.

Continue reading “Mira Murati Is Asking Investors To Commit To At Least A Million Dollar Investment” | >

If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving.

One approach to counter this is known as eco-driving, which can be installed as a control system in to improve their efficiency.

How much of a difference could that make? Would the impact of such systems in reducing emissions be worth the investment in the technology? Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing.

Read more | >