The outage page has been updated to include context:
>> MathWorks experienced a ransomware attack. We have notified federal law enforcement of this matter. The attack affected our IT systems. Some of our online applications used by customers became unavailable, and certain internal systems used by staff became unavailable, beginning on Sunday, May 18. We have brought many of these systems back online and are continuing to bring other systems back online with the assistance of cybersecurity experts.
Tinnitus is sometimes neurological, seemingly caused by the brain compensating for a loss of sensation. I can imagine a horror story in which this just makes it a thousand times worse, on top of permanently losing all hearing.
Now, being able to use a hot-swappable audio sensor instead of an ear made of tissue would be pretty dope.
I hear that theory but I don't believe it - I have tinnitus. Nothing else in the nervous system behaves that way - lack of light doesn't suddenly make you see blinding light etc. It's much more likely the sound sensor in the ear is jammed in the on position.
There are various explanations about the genesis of the sound for T sufferers, and it obviously depends on the kind of T that one has (this chart [1] helps navigate the variants).
But if you are one of the "common kind", which is typically an insult to your hearing apparatus that damaged your cochlea, then the work from Susan Shore [2] is a reasonable explanation of what could actually be going on (genesis by the fusiform cells of the dorsal cochlear nucleus). You may be interested in checking out her publications listed in the wikipedia article quoted.
Amputees have phantom limb sensations including pain. I believe this is more than theory. Certainly medical science has collected at least some case studies over the past century about people who have had their auditory nerve severed for one reason or another. And, as I recall, the auditory system actually does behave unlike other parts of the nervous system like vision which is more mechanical and less dependent on the brain for basic functionality.
Well, perhaps some but I don't think it's the usual cause. Phantom limb isn't just loss of sensation, it's also having part of the body chopped off. Just having part of your body go numb doesn't usually cause that.
It does and it is called neuropathic pain. Phantom limb is just an extreme case of it, but malfunction or damage to nerves can cause all kinds of phantom “pain”. Experiencing phantom sensations due to nerve damage is well known and widely documented, so phantom sound in the ear due to nerve damage is well in line with that.
As a user, I'm under the impression YouTube uses click follow-through for algorithm feedback. For the past two years, I've consistently gotten more random content with ~100 views suggested to me in the side bar. I often click and check it out. Maybe prime the pump by diving for some random vids?
>> Unsurprisingly, the combined solver performed the best, solving the puzzle in an average of 4.77 moves. The quantum solver was next, with an average of 5.32 moves, while the classical solver came in last place with 5.88 moves on average.
This effect is pretty neat. From the paper [1], the quantum solver can only do what they call "square root SWAPs", which is like a tile swap that relies on certain superposition rules. The classical solver can only use standard tile SWAPs. The combined solver can do both. A little over half the puzzle states are solved faster by the classical solver, but certain tricky states benefit from this new "move type". So the game had this quantum-like computation option tacked on, but certain initial positions just don't benefit from it.
I don't see any sort of "applications" section in the paper. They talk about how I guess you could build the puzzle thing with "arrays of ultracold atoms in
optical lattices", but that still doesn't answer the question. My takeaway is that even problems which benefit dramatically from quantum algorithms in some cases (in a future where that's cheap and widely available) should have careful algorithm design built on other heuristics.
They also allow the solvers a move that measures the superposition, and if the state collapses to the solved state then that's a finish (otherwise the puzzle resets to the initial scrambled state). So a viable quantum strategy is to just repeatedly get decent overlap with the solved state until you get lucky; you don't need to be perfect.
Something I initially did't understand is why their classical solver ever takes more than 4 moves to solve the puzzle. At most one move to ensure a green square is in the top row, and then at most two moves to move the other green square into the other top row slot, and then a move to certify the solution. The issue is that the puzzle can start in superposed states, where the classical solver can only permute which states have which amplitudes and so always only has a chance of verification succeeding and relatively few variations on this. Whereas the quantum solver can use interference effects to make a big amplitude that it can then move to the solved state.
I was sort of hoping that they would show, for example, that superposed moves could transition from some classical unsolved states to the solved state in fewer steps deterministically. Some sort of known-source-known-destination variation on Grover's algorithm. But nothing like that unfortunately. An obvious obstacle to this is that the square-root-of-swaps don't commute with each other in a simple way, so almost all sequences of them don't correspond to a classical permutation; you basically have to undo what you did to get back to the classical manifold.
As all eight Ivy Leagues collectively received this amount in funding in 2024, it would appear that Yale is planning for a longer-term drought.
Aside, I tried to copy the title to search for context about this elsewhere:
>> To be able to copy & paste content to share with others please contact us at subscriptions@pei.group to upgrade your subscription to the appropriate licence
Well, that's... something. No ma'am, I don't think I will.
> Small M-dwarf stars ... operate through convection ... likened to what we see in a boiling cauldron of water.
> Larger stars like the Sun show a mix of radiative transfer – photons being absorbed and reabsorbed as they make their way to the surface – and convection.
> That enhances M-dwarf flare activity as their plasma is twisted and rotated, producing magnetic fields that snap open only to reconnect.
This is the first post I've seen targeted toward generic geeks that explained it that way. It makes total sense, is really cool, and I'm glad they wrote this article.
M dwarf stars, being fully convective (at least, the ones lighter than 0.35 Msun), cycle their entire material content through their cores. This is unlike the Sun, where the core is effectively isolated from overlying layers, and will run out of hydrogen while those outer layers still contain a great deal of it.
As a result, and due to their low luminosity, M dwarf stars can go on burning hydrogen for a very long time, perhaps as long as 12 trillion years for a 0.1 Msun star, much longer than the universe has existed so far.
It sure seems like the use of GenAI in these scenarios is a detriment rather than a useful tool if, in the end, the operator must interrogate it to a fine enough level of detail that she is satisfied. In the author's Scenario 1:
> You upload a protest photo into a tool like Gemini and ask, “Where was this taken?”
> It spits out a convincing response: “Paris, near Place de la République.” ...
> But a trained eye would notice the signage is Belgian. The license plates are off.
> The architecture doesn’t match. You trusted the AI and missed the location by a country.
Okay. So let's say we proceed with the recommendation in the article and interrogate the GenAI tool. "You said the photo was taken in Paris near Place de la République. What clues did you use to decide this?" Say the AI replies, "The signage in the photo appears to be in French. The license plates are of European origin, and the surrounding architecture matches images captured around Place de la République."
How do I know any better? Well, I should probably crosscheck the signage with translation tools. Ah, it's French but some words are Dutch. Okay, so it could be somewhere else in Paris. Let's look into the license plate patterns...
At what point is it just better to do the whole thing yourself? Happy to be proven wrong here, but this same issue comes up time and time again with GenAI involved in discovery/research tasks.
EDIT: Maybe walk through the manual crosschecks hand-in-hand? "I see some of the signage is in Dutch, such as the road marking in the center left of the image. Are you sure this image is near Place de la République?" I have yet to see this play out in an interactive session. Maybe there's a recorded one out there...
The advantage of the AI in this scenario is the starting point. You now can start cross referencing signage, language, license plates, landmarks. To verify or disprove the conclusion.
A further extension to the AI "conversation" might be: "What other locations are similar to this?" And "Why isn't it those locations?" Which you can then cross reference again.
Using AI as an entry point into massive datasets (like millions of photos from around the world) is actually useful. Correlation is what AI is good, but not infallible, at.
Of course false correlations exist and correlation is not causation but if you can narrow your search space from the entire world to the Eiffel tower in Paris or in Vegas you're ahead of the game.
The point about entry into massive, maybe untractable datasets makes sense to me. I get the usefulness of GenAI there for sure. Another commenter suggested:
>> Its more like, here are some possible answers where there were none before.
That's a good point. AI doesn't need to be an authority, but another way of generating leads, maybe when it would be time-intensive to do so yourself.
If it's not prohibitive to do the digging with a human, do that. Because if adequately trained and rested, the human will perform more reliably.
This. Skill sharing among a small tribe is the most effective way to survive a catastrophe, and I am concerned modern societies are poorly conditioned to do it well.
Speaking from a North American perspective, kids are educated in how to succeed in a national/global economy, not how to build small communities and develop/share useful skills. TBH, the latter feels "obsolete" nowadays. Maybe that's a problem.
>> MathWorks experienced a ransomware attack. We have notified federal law enforcement of this matter. The attack affected our IT systems. Some of our online applications used by customers became unavailable, and certain internal systems used by staff became unavailable, beginning on Sunday, May 18. We have brought many of these systems back online and are continuing to bring other systems back online with the assistance of cybersecurity experts.