My previous job was at a startup doing BMI, for research. For the first time I had the chance to work with expensive neural signal measurement tools (mainly EEG for us, but some teams used fMRI). and quickly did I learn how absolute horrible the signal to noise ratio (SNR) was in this field.
And how it was almost impossible to reproduce many published and well cited result. It was both exciting and jarring to talk with the neuroscientist, because they ofc knew about this and knew how to read the papers but the one doing more funding/business side ofc didn't really spend much time putting emphasis on that.
One of the team presented a accepted paper that basically used Deep Learning (Attention) to predict images that a person was thinking of, from the fMRI signals. When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?" and there wasn't really any answer to that (or rather the publication didn't take that in to account, although that can be experimentally determined). Still, a month later I saw tech explore or some tech news writing an article about it, something like "AI can now read your brain" and the 1984 implications yada yada.
So this is indeed something probably most practitioners, masters and PhD, realize relatively early.
So now that someone says "you know mindfulness is proven to change your brainwaves?" I always add my story "yes, but the study was done with EEG, so I don't trust the scientific backing of it" (but anecdotally, it helps me)
There are lots of reliable science done using EEG and fMRI; I believe you learned the wrong lesson here. The important thing is to treat motion and physiological sources of noise as a first-order problem that must be taken very seriously and requires strict data quality inclusion criterion. As far as deep learning in fMRI/EEG, your response about overfitting is too sweepingly broad to apply to the entire field.
To put it succinctly, I think you have overfit your conclusions on the amount of data you have seen
I would argue in fact almost all fMRI research is unreliable, and formally so (test-retest reliabilities are in fact quite miserable: see my post below).
EDIT: The reason being, with reliabilities as bad as these, it is obvious almost all fMRI studies are massively underpowered, and you really need to have hundreds or even up to a thousand participants to detect effects with any statistical reliability. Very few fMRI studies ever have even close to these numbers (https://www.nature.com/articles/s42003-018-0073-z).
That depends immensely on the type of effect you're looking for.
Within-subject effects (this happens when one does A, but not when doing B) can be fine with small sample sizes, especially if you can repeat variations on A and B many times. This is pretty common in task-based fMRI. Indeed, I'm not sure why you need >2 participants expect to show that the principle is relatively generalizable.
Between-subject comparisons (type A people have this feature, type B people don't) are the problem because people differ in lots of ways and each contributes one measurement, so you have no real way to control for all that extra variation.
Precisely, and agreed 100%. We need far more within-subject designs.
You would still in general need many subjects to show the same basic within-subject patterns if you want to claim the pattern is "generalizable", in the sense of "may generalize to most people", but, precisely depending on what you are looking at here, and the strength of the effect, of course you may not need nearly as much participants as in strictly between-subject designs.
With the low test-retest reliability of task fMRI, in general, even in adults, this also means that strictly one-off within-subject designs are also not enough, for certain claims. One sort of has to demonstrate that even the within-subject effect is stable too. This may or may not be plausible for certain things, but it really needs to be considered more regularly and explicitly.
Between-subject heterogeneity is a major challenge in neuroimaging. As a developmental researcher, I've found that in structural volumetrics, even after controlling for total brain size, individual variance remains so large that age-brain associations are often difficult to detect and frequently differ between moderately sized cohorts (n=150-300). However, with longitudinal data where each subject serves as their own control, the power to detect change increases substantially—all that between-subject variance disappears with random intercept/slope mixed models. It's striking.
Task-based fMRI has similar individual variability, but with an added complication: adaptive cognition. Once you've performed a task, your brain responds differently the second time. This happens when studies reuse test questions—which is why psychological research develops parallel forms. But adaptation occurs even with parallel forms (commonly used in fMRI for counterbalancing and repeated assessment) because people learn the task type itself. Adaptation even happens within a single scanning session, where BOLD signal amplitude for the same condition typically decreases over time.
These adaptation effects contaminate ICC test-retest reliability estimates when applied naively, as if the brain weren't an organ designed to dynamically respond to its environment. Therefore, some apparent "unreliability" may not reflect the measurement instrument (fMRI) at all, but rather highlights the failures in how we analyze and conceptualize task responses over time.
Yeah, when you start getting into this stuff and see your first dataset with over a hundred MRIs, and actually start manually inspecting things like skull-stripping and stuff, it is shocking how dramatically and obviously different people's brains are from each other. The nice clean little textbook drawings and other things you see in a lot of education materials really hide just how crazy the variation is.
And yeah, part of why we need more within-subject and longitudinal designs is to get at precisely the things you mention. There is no way to know if the low ICCs we see now are in fact adaptation to the task or task generalities, if they reflect learning that isn't necessarily task-relevant adaptation (e.g. the subject is in a different mood on a later test, and this just leads to a different strategy), if the brain just changes far more than we might expect, or all sorts of other possibilities. I suspect if we ever want fMRI to yield practical or even just really useful theoretical insights, we definitely need to suss out within-subject effects that have high test-retest reliability, regardless of all these possible confounds. Likely finding such effects will involve more than just changes to analysis, but also far more rigorous experimental designs (both in terms of multi-modal data and tighter protocols, etc).
FWIW, we've also noticed a lot of magic can happen too when you suddenly have proper longitudinal data that lets you control things at the individual level.
Yes on many of those fronts, although not all those papers support your conclusion. The field did/does too often use tasks with to few trials, with to few participants. That always frustrated me as my advisor rightly insisted we collect hundreds of participants for each study, while others would collect 20 and publish 10x faster than us.
Yes, well "almost all" is vague and needs to be qualified. Sample sizes have improved over the past decade for sure. I'm not sure if they have grown on median meaningfully, because there are still way too many low-N studies, but you do see studies now that are at least plausibly "large enough" more frequently. More open data has also helped here.
EDIT: And kudos to you and your advisor here.
EDIT2: I will also say that a lot of the research on fMRI methods is very solid and often quite reproducible. I.e. papers that pioneer new analytic methods and/or investigate pipelines and such. There is definitely a lot of fMRI research telling us a lot of interesting and likely reliable things about fMRI, but there is very little fMRI research that is telling us anything reliably generalizable about people or cognition.
I remember when resting-state had its oh shit moment when Power et al (e.g. https://pubmed.ncbi.nlm.nih.gov/22019881/) showed that major findings in the literature, many of which JD Power himself helped build, was based off residual motion artifacts. Kudos to JD Power and others like him.
The small sample sizes is rational response from scientists in the face of a) funding levels and b) unreasonable expectations from hiring/promotion committees.
cog neuro labs need to start organizing their research programs more like giant physics projects. Lots of PIs pooling funding and resources together into one big experiment rather than lots of little underpowered independent labs. But it’s difficult to set up a more institutional structure like this unless there’s a big shift in how we measure career advancement/success.
+1 to pooling funding and resources. This is desperately needed in fMRI (although site and other demographic / cultural effects make this much harder than in physics, I suspect).
I'm not an expert, but my hunch would be that a similar Big(ger) Science approach is also needed in areas like nutrition and (non-neurological) experimental psychology where (apparently) often group sizes are just too small. There are obvious drawbacks to having the choice of experiments controlled by consensus and bureaucracy, but if the experiments are otherwise not worthwhile what else is there to do?
I think the problems in nutrition are far, far deeper (we cannot properly control diet in most cases, and certainly not over long timeframes; we cannot track enough people long enough to measure most effects; we cannot trust the measurement i.e. self-report of what is consumed; industry biases are extremely strong; most nutrition effects are likely small and weak and/or interact strongly with genetics, making the sample size requirements larger still).
I'm not sure what you mean by "experimental psychology" though. There are areas like psychophysics that are arguably experimental and have robust findings, and there are some decent-ish studies in clinical psychology too. Here the group sizes are probably actually mostly not too bad.
Areas like social psychology have serious sample size problems, so might benefit, but this field also has serious measurement and reproducibility problems, weak experimental designs, and particularly strong ideological bias among the researchers. I'm not sure larger sample sizes would fix much of the research here.
> Areas like social psychology have serious sample size problems, so might benefit, but this field also has serious measurement and reproducibility problems, weak experimental designs, and particularly strong ideological bias among the researchers. I'm not sure larger sample sizes would fix much of the research here.
I can believe it; but a change doesn't have to be sufficient to be ncessary.
You got downvoted, but I think you are right in a way. Direct neurophysiological recording is not a panacea because either 1) you can't implant electrodes in your participants ethically, 2) Recordings usually are limited in number or brain areas. That said, I think the key is "convergent evidence" that spans multiple levels and tools of analysis. That is how most progress has been made in various areas, like autism research (my current work) or memory function (dissertation). We try to bridge evidence spanning human behavior, EEG, fMRI, structural MRI, post-mortem, electrode, eye-tracking, with primate and rodent models, along with neuron cultures in a dish type of research. We integrate it and cross-pollinate.
There is actually a field where subjects do have electrodes implanted (see https://www.humansingleneuron.org/). - this is done when they are doing pre-operative recordings in preparation for brain surgery for the treatment of epilepsy, and the electrodes are already there for clinical diagnostic purposes and they volunteer a few hours of their time while they are sitting around in the hospital, to participate in various cognitive tasks/paradigms. The areas you can go are limited for sure, but some areas are near regions of interest depicting in fMRI scans.
Then there are also papers w/ recordings done in primates...
But overall yes - you integrate many modalities of research for a more robust theory of cognition
But none of this (signal/noise ratio, etc) is related to the topic of the article, which claims that even with good signal, blood flow is not useful to determine brain activity.
I think you're throwing the baby out with the bathwater, while also pointing to the missing pieces in our understanding of the brain and consciousness.
I also work in the field, specifically with sleep slow-wave enhancement.
Blood flow as a proxy for brain activity I always felt was a weak measure, as the brain activity involved across all manner of operating our biological systems, so is the increased blood flow measured in fMRI a response to cognition, or autonomic activity? What does that oxydation mean.
EEG is similarly flawed when we try to equate "brainwaves" to emotions and consciousness. I think we're almost better off measuring HRV, a much simpler measure, and more reliable.
I'm fascinated that so many people who discuss brainwaves think of them as actual "waves", when it is just how we plot electrical activity which creates a visual wave like pattern.
However, and this is specifically related to our work in sleep, we can detect slow-waves (I dislike that term, it's the synchronous firing of neurons) and we are able to stimulate this restorative brain function through sensory perception during sleep, and even create slow-waves in a lab using TMS.
Research linked on our website [1]
I agree the industry needs to stop projecting what we hope we're seeing with what is actually being measured, and we don't understand enough about how the brain works, but I think completely throwing away any brain related measures we have is going too far.
The difference is that EEG can be used usefully in e.g. biofeedback training and the study of sleep phases, so there is in fact enough signal here for it to be broadly useful in some simple cases. It is not clear fMRI has enough signal for anything even as simple as these things though.
Yes, there are a few medical cases where fMRI makes good simple basic sense, and TBI/Concussion sounds immediately like one of those to me. I seem also to recall them being useful in some cases prior to brain surgeries and the like.
This all makes sense because fMRI tracks metabolic activity via oxygenation changes, which is much more clearly and plausibly related to tissue health and recovery. In these cases, it is also most likely being used within-subject (i.e. longitudinally) to make comparisons to baselines, rather than in an attempt to make speculative inferences about the mind using groups of people, and likely is a simple comparison to baseline rather than bespoke statistical analyses relying on questionable assumptions about the BOLD response being related to overly-specific kinds of neural activity.
fMRI can track oxygenation changes, and indirectly where the blood flow is, or isn't, and perhaps some ideas on where to get it.
All to say, this application might not fall in the 40%.
I just find articles like these can't help but feel like they have an agenda to undermine something instead of simply acknowledge the kinds of things it is and isn't working for.
There's no doubt these researchers have found something, but the need for sensationalistic headlines is well known in academia as well.
Sometimes it's noticeable where the research is specific in scope, but the findings are more general and broad.
> fMRI's are being used in TBI/Concussion recovery
Interesting. Do you happen to have any more information on this topic? I ask because I was under the impression that concussions are a functional/metabolic injury and not a structural injury, therefore, concussions are not visible on any type of fMRI, CT Scan, etc.. Though, I haven't looked into this topic for almost half a decade, so I imagine things have likely progressed.
Concussions seem to be pretty physiological - first they're a brain bleed, and blood doesn't seem to pump the same as it did before the concussion... resulting in different symptoms.
That might be what you're referring to as functional?
Metabolically, or otherwise, if the brain can't operate, other things in the body such as metabolism would be impacted for sure when it can't oversee and run as it normally can?
While I'm not sure if a concussion directly is visible or not (some have sizeable enough brain bleeds that can be visible), concussions to the extent that they are a change in blood circulation changes and issues, can be visualized on fMRI, etc, where it's not regular, those areas suffer in a brain.
Things luckily have progressed and quite exciting.
Out of convenience, I'll share one I know about (no affiliation) that lay out their therapies and the science behind it as well.
Effectively (I hope I'm getting this accurately) it seems the blood vessels in the brain also have signalling from the blood and oxygen that gets affected which affects things downstream from there.
These guys do an fMRI baseline, have you jump on a bike, fMRI again, see what's not getting blood, and then give you exercises and activites for those regions of the brain. It's pretty interesting.
I'm not sure I understand. Wouldn't any prediction result above statistical random (in the image mind reading study) be significant? If the study was performed correctly I don't really need to know much about fMRI to tell whether it's an interesting result or not.
The study misleading claimed to produce images from brainwaves. In reality, they effectively built a combination of classifier from brainwaves to one of a few predetermined classifications of images shown (still cool, but less impressive) and a neural net to reproduce images it was trained on given a classification (boring).
90% of papers I read in computer science / computer security speak of software written or AI models they trained that are nowhere to be found. Not on git nor via email to the authors.
Saw the same thing first hand with Pathology data. Image analysis is far more straightforward problem than fMRI, but sorry, I do not trust your AI model that matches our pathologist’s scoring with 98.5% accuracy. Our pathologists are literally guesstimating these numbers and can vary by like 10-20% just based on the phase of the moon, whether the pathologist ate lunch yet, what slides he looked at earlier that day…that’s not even accounting for inter-pathologist variation…
Also saw this irl with a particular NGS diagnostic. This model was initially 99% accurate, P.I. smelled BS, had the grad student crunch the numbers again, 96% accurate, published it, built a company around this product —-> boom, 2 years later it was retracted because the data was a lot of amplified noise, spurious hits, overfitting.
I don’t know jack compared to the average HN contributor, but even I can smell the BS from a mile away in some of these biomedical AI models. Peer review is broken for highly-interdisciplinary research like this.
There's fancier ML studies on EEG signal but probably not consistent enough for clinical work. For now, the one thing EEG can reliably tell is if you're having a seizure or not, if you're delirious (or in a coma) or not, or if you're asleep.
> When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?"
So here you say quite a mouthful. If you train it on a pattern it'll see that pattern everywhere - think about the early "Deep Dream" trippy-dogs-pictures nonsense that was pervasive about eight or nine years ago.
I repaired a couple of cameras for someone who was working with a large university hospital about 15 years ago, where they were using admittedly 2010s-era "Deep Learning" to analyse biopsy scans for signs of cancer. It worked brilliantly, at least with the training materials, incredible hit rate, not too terrible false positive rate (no biggie, you're just trying to decide if you want to investigate further), really low false negative rate (if there was cancer it would spot it, for sure, and you don't want to miss that).
But in real-world patient data it went completely mental. The sample data was real-world patient data, too, but on "uncontrolled" patients, it was detecting cancer all over the place. It also detected cancer in pictures of the Oncology department lino floor, it detected cancer in a picture of a guy's ID badge, it detected cancer in a closeup of my car tyre, and it detected cancer in a photo of a grey overcast sky.
Aw no. Now what?
Well, that's why I looked at the camera for them. They'd photographed the biopsies with one camera on site, from "real patients", but a lot of the "clear" biopsies were from other sites.
You're ahead of me now, aren't you?
The "Deep Learning" system had in fact trained itself on a speck of shit on the sensor of one of the cameras, the one used for most of the "has cancer" biopsies and most of the "real patient under test" biopsies. If that little blob of about a dozen slightly darker pixels was present, then it must be cancer because that's what the grown-ups told it. The actual picture content was largely irrelevant because the blob was consistent across all of them.
I'm not too keen on AI in healthcare, not as a definitive "go/no-go" test thing.
The last one made me chuckle. Worked in Japan, didn't see many madogiwa zoku (probably because I only worked at startups) although it was talked about a lot. But I guess community manager-esque position did exist, and now it makes sense why so many big company blokes that went to tech meetup came off as very incompetent
If you're an Otaku it's not too surprising you don't like Ghibli. Ghibli is quite atypical and the reason it got popular is because it's not like the standard anime, that people used to look down on, in the 80s and forward until maybe 2015 when it got normalized.
>The stories don’t really have a proper conclusion, it’s often a pattern of a thing happened, let’s undo the thing, life goes on.
One could say this about Ghost in the Shell, Akira, and even Evangelion too.
I personally think that Ghibli is popular because it gives a sense of nostalgia, a beautiful depiction of nature and it feels alive because the care that goes in to the background and background characters movement.
It feel less like a theater, a story crafted to entertain, and more just like a snapshot of life of someone/something that will go on after the movie ends.
Also as for music. If you watched the American version, they've actually changed many scenes and added additional music. Disney said that American populace couldn't watch a scene where no music is present for happens for more than 3 min so they had to add some extra music. [1]
> If you're an Otaku it's not too surprising you don't like Ghibli.
That’s definitely something I felt the whole time. They’re anime for non-anime fans.
Story-wise, Gits and Akira do have a kind of logical story progression. I don’t understand Eva too. Cool visuals though.
Let’s take Princess Mononoke as an example. The Main Character goes west in search for something, discovers a Japanese Industrial Revolution underway led by some lady Eboshi. Eboshi and tall shoes guy kill a god, causing massive death and destruction, but they returned the god’s head at the end and suddenly everything is forgiven. Mononoke’s adopted mom is dead, several tribes of boars are dead, thousands of people are dead, the industrious village is destroyed and large numbers of their inhabitants sent to die in an ambush by their own boss but it’s all OK, because the people that started it ended it by returning something they stole. What?
As for Princess mononoke it's a bit hard to write down everything. It's Miyazakis magnus opus. (not spirited away. That was explicitly to woo western audience with oriental mysticism)
It's a story of conflict and mistakes. Do you watch a history movie about WW1 and also get perplexed "and then they did WW2, What?". As said, it's not a story about linear start-end like theater. It's a snapshot of a historical event.
And the idea is not industrial revolution. The idea is that tatara village is a sanctuary for the outcast. The place is for prostitutes and slaves that fled. Eboshi, is presented as this opportunistic woman but also simultaneously presented as a savior that even accommodate lepers. During the story, the village is also attacked by Samurais; the outer world "lords" that shunned the outcasts, and now that they're successful, come to ask for tithe. Something the village shuns by shooting at the emissary. This leads to war. The Eboshi is desperate and contact the Buddhist monk and get a formar letter from the emperor to hunt for the god, to gain legitimacy and protection.
The nature, Shishigami, doesn't care. They explicitly say that in the end. It's the process of nature and doesn't choose sides. and really, for everyone to move on, in a harsh world, is to be "OK" with what happened. This is more realistic than some story of tragedy or revenge that have an "end of history" synonymous as "the end of the story". Life goes on.
Miyazaki have been anti industrial, pro nature all his life. But, he also admits that industry is helping humans. The tatara village is just that. They are "evil" from mononoke and forest spirits point of view, but they're the down trodden, desperate that finally built a place for them.
The point of the movie is that Ashitaka, the outsider, that comes from a dying tribe in Japan peninsula, realize that his deadly curse is not caused by monochrome "evil, selfish people" or "revengeful nature". But that everyone (except the samurai and monks) have their reasons for the actions, and that lead to conflict. That in the end, would lead to destruction and the only way to live is to coexist.
The story of coexistence START at the end of the movie. Which is ofc underwhelming to watcher if you want a definitive answer of "will it work? Is this happy ever after? Who won? Whose story is this". The anser is "we don't know, we don't know, and the story is the event that transpired".
Most ghibli stories (that are actually written by Miyazaki) follow similar patterns. History continues
>There is an arrogance I have seen that is typical of ML (having worked in the field) that makes its members too comfortable trodding into adjacent intellectual fields they should have more respect and reverence for.
I've not only noticed it but had to live with it a lot as a robotics guy interacting with ML folks both in research and tech startups. I've heard essentially same reviews of ML practitioners in any research field that is "ML applied to X" and X being anything from medical to social science.
But honestly I see the same arrogance in software world people too, and hence a lot here in HN. My theory is that, ML/CS is an entire field around made-for-human logic machine and what we can do with it. Which is very different from anything real (natural) science or engineering where the system you interact with is natural Laws, which are hard and not made to be easy to understand or made for us, unlike programming for example. When you sit in a field when feedback is instant (debuggers/bug msg), and you deep down know the issues at hand is man-made, it gives a sense of control rarely afforded in any other technical field. I think your worldview get bent by it.
CS folk being basically the 90s finance bro yuppies of our time (making a lot of money for doing relatively little) + lack of social skills making it hard to distinguish arrogance and competence probably affects this further. ML folks are just the newest iteration of CS folks.
While I'd like to believe this, I also know that CCP have as of late tapped in to a dangerous remedy for the dissatisfaction of their rule(economic slowdown): Nationalistic fervor.
From my Chinese friends (and Hong Kong friends) it seems to be clear that the "century of humiliation" rhetoric is getting more prominent. Which includes rationalization such as "Japan and West (and Russia) humiliated us so it's our right to revenge. Whatever they're complaining about right now is just historical rebalancing". My British friend in HK seems to be getting tired of this rhetoric thrown at her every time she meets a Chinese person.
And CCP might be drinking that nationalism koolaid and get hooked to it just as US/West and recently Japan is. It's a very useful tool for the elite to dissipate discontent and I'd belive it will only accelerate.
And it's a strong rationalization rhetoric. Whatever "historical" you claim will probably be moot. Give us a decade or two and you'd probably be here posting something along the line, with multiple citations that have accumulated during the time
Sure, nationalism definitely serves that purpose. But please consider: in the most recent conflicts/flare-ups, the initiator has actually been Japan, not China.
Their new female prime minister is an extreme-right-wing politician who is not only provoking China, but also picking fights with South Korea and Russia at the same time, while pushing aggressively anti-immigrant and exclusionary policies.
Her approval ratings are also unusually high.
It feels pretty strange that Japan gets zero criticism for this while all the focus stays on China.
Takaichi is a slightly right of centre nationalist. Pushing a mild tightening of some immigration rules to maintain the social contract around immigration, and fend off the right wing populists. Her policies amount to things like tightening foreign land ownership rules and refusing visa renewals for people not paying their health insurance or pension (which is mandatory by law for all residents).
She’s had friendly relations with SK so far and recently met with the SK President and bowed in respect to the Korean flag.
Her “provocation” of China was to state, when asked in parliament, that an armed invasion of Taiwan by China would be a case of a potential existential threat to Japan.
Which frankly is utterly obvious to anyone, including of course China. Japan hosts American military bases. If China attacked Taiwan, triggering an American repose then there would at the least be Chinese missiles aiming for Tokyo (Yokosuka) and Okinawa.
Anyone have any advice for Arduino replacement? I recently (unknowingly) bought a R4 for some LED projects but knowing now the background, I'm wondering if there's any other alternative for hobby (noob level) micro controller project
I've played with a lot of boards, including Teensy, Seeed XIAO family, some boards from Adafruit and SparkFun, and one Chinese copy of a Chinese ESP32 board.
For my work projects, I use Teensy because it's the screaming-est processor, and I use its computing power. The cost isn't exorbitant since it's typically re-usable unless you want to turn something into a permanent installation.
I suggest play with what you've got until you get sick of it, or run into a hardware need that would be better served by another processor or board. Or choose a new board when your R4 goes into something that you want to keep.
I've tried to maintain a "platform agnostic" approach, where I stick with the general Arduino API and processor-independent libraries as much as possible, and only drop down into the vendor-specific libraries when there's a real performance reason. This makes it easier to switch boards if needed -- a lifesaver during the chip shortage, and possibly important under present day supply chain uncertainty.
Doing it this way will give you the benefits of drawing from a broader range of tutorials and docs, while also providing a gentler learning curve on working with the low level chip-specific stuff.
Any ESP32, RP2040 or RP2350 board. The last two use external QSPI flash, so hobby projects only. There are no fuses to set to read protect the firmware.
Still can be reflashed if you have access to the right pins, but I believe there are more security features built into the later generations of these chips (plus some security fixed from the original 2350 design).
Just to be clear, some micros (STM32s come to mind) have what they call "on the fly decryption" for external flash. Basically, if the micros wanted to, they would. I think ESP32s are also using qspi flashes but they're integrated in package? Maybe that's changed but that's how I vaguely remember it
I believe only the ESP32 modules with 16M have QSPI flash, the 4M standard flash is on chip. For on the fly decryption you need on chip enclaves to store the keys. Anyway, it doesn't really matter for hobby projects and the ESP32 can also be used for commercial projects.
Indeed, the number of "non commercial" projects using these chips that are set up much less securely than even modest "hobbyist" projects boggles the mind.
I use a RP2040 in a commercial product, but it's for a niche portion of a niche industry and it's not intended to ever communicate with another device. Disabling USB mass storage and not adding a bootloader button is good enough for me.
Depending upon your project, perhaps the new MakerPort might be a good fit? https://makerport.fun
The basic kit comes with a built-in speaker, a bunch of LEDs, a servo, an LED matrix, and other goodies. The default firmware works with Scratch, Snap, and MicroBlocks, but I believe you can also use your own custom firmware.
Pine64 has products that are far more open than Raspberry Pi products, and still have good community support. They also have several RISC V products, which are quickly overtaking AVR, PIC, and ARM Cortex M0 microcontrollers.
A hacker should probably know that it's usually trade offs and blanket statements are very useless. Certain tools are good for certain tasks and situations, but bad for others. No free lunch and all that.
If you make that blanket statement, you're definitely not a hacker (or just a novice). But you'd make a heck of a politician or tech bro salesman
I learned about this 2 years ago when a racoon showed up in Tokyo close to where I lived. [1]
wild to think they spread even all the way to Japan because of anime. and probably south Korea now. They banned raccoons in Japan but it seems to not have caught up in SK and there are a lot of racoon pet videos from SK on YouTube)
The thing that most surprises me in these emails are that those two people care about housing price or student debt, or millennials in general.
And cynical me can't brush of the feeling that this is satire or that I need to read between the lines.
A dude that buys all houses around him just to get privacy and "fuck off" others (Zuck) cares about housing? A guy that think Greta is antichrist and think anyone that blocks AI advancement is antichrist, somehow thinks "we should look at why millennials choose socialism" as a thought process?
A more realistic, "what they mean" interpretation of this emailing is:
"FB product have influence over millennials (Instagram) and Boomers (Facebook). Boomers are dying out, so we should double down on our grip over millennials and tune the algorithms so we can manipulate them more than ever. We should lobby and push out the boomers and set millennials in the position of institutional power, and no other cohorts, so that we have a direct pipeline of controlling the public discord and communication between this generation "
And how it was almost impossible to reproduce many published and well cited result. It was both exciting and jarring to talk with the neuroscientist, because they ofc knew about this and knew how to read the papers but the one doing more funding/business side ofc didn't really spend much time putting emphasis on that.
One of the team presented a accepted paper that basically used Deep Learning (Attention) to predict images that a person was thinking of, from the fMRI signals. When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?" and there wasn't really any answer to that (or rather the publication didn't take that in to account, although that can be experimentally determined). Still, a month later I saw tech explore or some tech news writing an article about it, something like "AI can now read your brain" and the 1984 implications yada yada.
So this is indeed something probably most practitioners, masters and PhD, realize relatively early.
So now that someone says "you know mindfulness is proven to change your brainwaves?" I always add my story "yes, but the study was done with EEG, so I don't trust the scientific backing of it" (but anecdotally, it helps me)
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