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Wired reports that consumer neurotechnology is rapidly moving from the lab into workplaces and homes, with companies including Apple, Meta, and Snap developing products that can monitor or interpret brain activity. Researchers warn that as these systems improve, neural data could become the next major privacy battleground. Some neuroscientists are already calling for a series of core rights, coined "neurorights," that protect "mental privacy," "identity," and personal "agency." Here's an excerpt from the article: Over the past two decades, researchers using functional magnetic resonance imaging (fMRI), which tracks the iron in the hemoglobin supplying oxygen to neurons, have been building up increasingly detailed maps and inventories of the mammalian cortex. Thanks to huge advances in machine-learning artificial intelligence -- computer algorithms that are able to sort through enormous amounts of information and use statistical methods to make classifications and predictions -- fMRI scans can now be used to identify everything from depressive thoughts to the nuanced feelings of envy and schadenfreude. Other algorithms have been able to accurately piece together reconstructions of movie clips watched by subjects, just by analyzing their brain scans; or have detected, in probing the brain activity of swing voters in the US presidential election, responding to photographs and videos of presidential candidates, which candidates provoked anxiety or even disgust, and which elicited positive responses or feelings of empathy. In just the last few years, neuroscience researchers have progressed from decoding images and emotions as they play across the cortex to sounds, words, phrases, and even language. In 2023, in a remarkable demonstration of this emerging technology, a woman called Ann Johnson, who had been paralyzed for 18 years by a brain-stem stroke, was able to speak again through the insertion of a grid of 253 electrodes onto the surface of her brain, which translated her neuronal signals into sentences, in real time, at a rate of 78 words per minute (just about half the speed of standard conversation). The research team at the University of California, led by neurosurgeon Edward Chang, had combined this brain-computer interface with an animated avatar of Johnson's head, which spoke in her own voice, as reconstructed from a recording of a 15-minute toast she had given at her wedding. Just as the avatar's mouth spoke Johnson's words as she thought them, so its expressions were similarly influenced by the nuances of her brain activity, which turned her thoughts about facial gestures into displays of emotion -- from smiles to pursed lips and frowns. [...] The more invasive the recording equipment, the richer and more detailed the data. Surgical interventions are at the vanguard of neuroscience and remain very rare -- fewer than 100 people on the planet have brain-computer interfaces like Johnson's embedded beneath their skulls. Yet almost inevitably, a concerted trickle-down effect is occurring. In the summer of 2023, a team at the University of Texas demonstrated that they could use fMRI to translate brain scans into words and sentences, after subjects listened to 16 hours of the storytelling podcasts The Moth Radio Hour and The New York Times' Modern Love to train an AI model. When the subjects then listened to new podcasts, the algorithm was able to convert the gist of what they heard, as it manifested in their brains, into words, phrases, and sentences that roughly captured the stories. As the team's lead computational neuroscientist, Alexander Huth, put it in an interview with Science, "Our thought when we actually had this working was, 'Oh my God, this is kind of terrifying.'" Now noninvasive, wearable brain scanners are beginning to proliferate beyond the lab, making their way into our workplaces and, through the vast global consumer market, into our homes too.

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Fei-Fei Li, aka The Godmother of AI, says the biggest AI risk in education may be students losing the motivation and agency to learn, rather than simply using the technology to cheat. She argues against banning AI outright, saying it can be valuable when used to support engaged students instead of replacing the thinking and struggle that learning requires. Techspot reports: Speaking on an episode of the science podcast Huberman Lab this week, Li talked about the fears around how students are using AI. "The absolute bad outcome is that our young generation, their agency and human-level motivation of learning and living is taken away by tools," the ImageNet inventor warned. "It should not be taken away by humans nor should it be taken away by machines." If we see a generation of graduates who have completely relied on AI without actively trying to learn anything for themselves, they risk leaving education without having "properly developed the brain," Li said. [...] Li certainly isn't advocating for a complete ban on students using AI. She believes it can genuinely help struggling students who are already engaged in their learning. Li herself noted how she struggled with organic chemistry as a premed student, when teaching assistants and professors didn't have enough time to answer every question. While there's no obvious solution, Li says we need to find a way to keep children and students' motivation and agency. "Let's find a way to give them the access and the right way of using these tools," she said. Li added that if AI is used properly in education, it could make the students of the future "way smarter than us because they are superpowered."

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In our previous post we looked at fixing a super 8 film projector. While watching filme with a real projector has its own charm, it is inconvenient to say the least. First of all you make the entire room properly dark or you can't see anything. This is regardless of the fact that the projector bulb is consuming 100 watts of power to show the image. Even if you manage not to burn the film merely running it through the projector causes wear, scratches and tearing. While film typically ages very well, eventually it will turn into magenta goop or gets eaten by vinegar syndrome. Thus you'd really want to convert all these films into high quality digital files.

There are several companies that offer this service. If you only have a few rolls, using those is the smart thing to do. I, on the other hand, have so much material that using a commercial service would cost thousands (possibly tens of thousands) of euros. Fortunately, this is a fairly common problem and there are dozens of existing projects on the Internet to be inspired by. 

The main technical problem with super 8 film is that it is very small. A sequence of 10 super 8 images is approximately as long as a matchstick. The fact that projectors can display 18 frames per second with sub millimeter registration is an astounding achievement of mechanical engineering. How do they do that? Very difficultly.

Many of the DIY solutions start by taking an existing projector and modifying it to run slower. Then you remove the projection lens and aim a digital camera with a macro lens at the gate. This yields incredible results quality-wise but requires a fairly expensive macro lens and typically the modification on the projector is destructive. So that's out. Some more searching eventually lead me to this Github project.

The basic idea is simple. Instead of using a projector or trying to replicate a film transport (which proper tension and all that) instead rely on the basic stiffness on film and drive it directly with stepper motor. Film is not aligned mechanically but instead by detecting the sprocket hole with some straightforward machine vision code. Time to fire up the ol' 3D printer and order components. This is what the end result looks like after assembly

The thing at the top left that looks like a space cannon prop from a scifi movie is actually a microscope lens. Not only can it do > 1x optical magnification, it can do so at a cost of about 25 euros. The downside is noticeable chromatic aberration. The small flat thing on the other end is the Raspberry Pi HQ camera module that can do 4k at 12 bits per channel. The whole thing is run via a single Raspberry Pi 3 with a stepper motor hat. The board at the bottom is used to distribute 12V DC power to the lamp and motors.

Before going further, let's just spend some time appreciating just how awesome colors look in this film. Props to the chemical engineers at Kodak. And remember, the original image is about one third of the size of your smallest fingernail.

The Github repo says that you probably need to adapt the code to your setup. I basically ended up rewriting all of it from scratch. In the process I learned that OpenCV has its own GUI toolkit which is both simple (one could even say simplistic) and perfect for this use case. The first attempt took nine hours to process one 3.5 minute reel of film. Then I realized that trying to do 4k on material that physically maxes out at approximately 2k with the processing power of a potato is not a recipe for success. Halving the capture resolution and a few other optimizations brought the runtime down to about one hour per reel.

With this, some more custom software for image processing and stabilization coupled with FFmpeg scripts one can start to go through the archive of films. Doing so raises a fair bit of questions. For example:

Is that a 3 year old child driving a jury-rigged go-kart on a frozen lake on his own without even wearing a helmet?

Yes it is. A bit later a grown up drives the car but he is too heavy so the ice cracks under him. No one seems particularly concerned. This may seem strange to us but you have to understand that this was the very early 70s. The concept of safety had not been invented yet.

The CFTC has ordered Kalshi to keep operating in New York, claiming the state's lawsuit against the prediction market created a "market emergency." They said it acted "to ensure market stability" and "ordered the exchange to continue to operate in accordance with the Commodity Exchange Act's Core Principles." Ars Technica reports: The market emergency alleged by the CFTC is that New York Attorney General Letitia James sued Kalshi on July 31. James' lawsuit seeks a court order to permanently enjoin Kalshi "from operating an unlawful gambling business" in the state. The lawsuit also demands that Kalshi "make full restitution to customers who have engaged in betting" and pay financial penalties. "New York intends to make event contract derivatives waste away under its iron curtain of state gaming laws before the courts get the chance to issue final rulings," CFTC Chairman Michael Selig said yesterday. "Congress did not intend for derivatives exchanges to be regulated under a patchwork of state gaming laws... New York has no business regulating these interstate financial markets. The commission is required by law to ensure order in these markets, and that is what we have done today." [...] The CFTC says it alone has the power to regulate platforms such as Kalshi and Polymarket under the Commodity Exchange Act, a US law that gives the CFTC exclusive jurisdiction over designated contract markets (DCMs). "These are financial exchanges that offer financial instruments and operate across state lines," Selig said yesterday. "They match the bid from a resident of one state with the offer of a resident from another state and submit the trade to a clearinghouse that backstops the transactions of customers throughout the country."

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