This is why I think trail running is so valuable. You must be aware of your changing environment and how to adjust your body's movement to accommodate. You are exercising your mind to calculate your current momentum, intended placement of your next step, and it's potential impact to the rest of your body. You are running your own mental physics simulation as you work the trail, nothing like running on regular roads.
It gave me a "living in the future" feeling the day someone sent me a picture of a phone number through imessage. Barely thinking, I pressed on the phone number in the image and I was prompted to call it. It was like technology and primitive intuition teamed up to create that moment.
There is data but nowhere near the amount of written language that is fairly normalized and without the need to account for additional features such as language, dialect, intonation, facial expression, hand gestures. Speech to text is used as the translation layer as it throws many of those other features away and contextualizes it into a set of tokens that are much more efficient to map between languages.
The reason I like line scan images is because it breaks our mental model of images. We are not looking at the image of a train. We are looking at a time series graph of what occupied a very small defined area in space.
The first definition of this type of procedural generation algorithm was called Model Synthesis by Paul Merrell [1] which built upon texture synthesis. You can even read Merrell's later comparison of the two algorithms [2].
The civilian government agencies spent 248B on contract services in 2023 [1]. Not all of that was professional services, but I expect that we will see an increase in that number as more services are contracted out and a decrease in direct government workforce; a government contractor can still work remotely.
The mindset for acquisition is typically anything not core to an agency's mission should be bought on the open market at the lowest price technically acceptable. This tends to select against small businesses who can provide stellar services but can't just cut rates willy nilly for extended delivery time periods.
In effect, government contracting is a large jobs program.
Government often goes too far. You should outsource not things that are not your core values, but things you cannot trust someone else to do. Maintenance often needs to be something you do in house because you cannot trust someone else to take care of it. That someone in house will of course outsource the labor (toilet clogged once - the in house person uses a plunger - if that toilet clogs often they call a plumber to fix what is wrong), but you need someone in house to decide if you need to hire the labor in the first place, otherwise you end up paying a plumber to replace a toilet that works fine but got too much put into it one time.
I am not qualified to make any assumptions but I do wonder if a massive investment into computing infrastructure serves national security purposes beyond AI. Like building subway stations that also happen to serve as bomb shelters.
Are there computing and cryptography problems that the infrastructure could be (publicly or quietly) reallocated to address if the United States found itself in a conflict? Any cryptographers here have a thought on whether hundreds of thousands of GPUs turned on a single cryptographic key would yield any value?
I'm not a cryptographer, nor am I good with math (actually I suck badly; consider yourself warned...), but am I curious about how threatened password hashes should feel if the 'AI juggernauts' suddenly fancy themselves playing on the red team, so I quickly did some (likely poor) back-of-the-napkin calculations.
'Well known' password notwithstanding, let's use the following as a password:
correct-horse-battery-staple
This password is 28 characters long, and whilst it could be stronger with uppercase letters, numbers, and special characters, it still shirtfronts a respectable ~1,397,958,111 decillion (1.39 × 10^42) combinations for an unsuspecting AI-turned-hashcat cluster to crack. Let's say this password was protected by SHA2-256 (assuming no cryptographic weaknesses exist (I haven't checked, purely for academic purposes)), and that at least 50% of hashes would need to be tested before 'success' flourishes (lets try to make things a bit exciting...).
I looked up a random benchmark for hashcat, and found an average of 20 gigahashs/second (GH/s) for a single RTX 4090.
If we throw 100 RTX 4090s at this hashed password, assuming a uniform 20 GH/s (combined firepower of 2,000 GH/s) and absolutely perfect running conditions, it would take at least eleven-nonillion-fifty octillion (1.105 x 10^31) years to crack. Earth will be long gone by the time that rolls around.
Turning up the heat (perhaps literally) by throwing 1,000,000 RTX 4090s at this hashed password, assuming the same conditions, doesn't help much (in terms of Earth's lifespan): two-octillion-two-hundred-ten septillion (2.21 x 10^27) years.
Using some recommended password specifications from NIST - 15 characters comprised of upper and lower-case letters, numbers, and special characters - lets try:
dXIl5p*Vn6Gt#BH
Despite the higher complexity, this password only just eeks out a paltry ~ 41 sextillion (4.11 × 10^22) possible combinations. Throwing 100 RTX 4090s at this password would, rather worryingly, only take around three hundred twenty-six billion seven hundred thirteen million two hundred seventeen thousand (326,713,217,000) years to have a 50% chance of success. My calculator didn't even turn my answer into a scientific number!
More alarming still, is when 1,000,000 RTX 4090s get sic'ed on the shorter hashed password: around thirty-two million six hundred seventy-one thousand (32,671,000) years to knock down half of this hashed password's strength.
I read a report that suggested Microsoft aimed to have 1.8 million GPUs by the end of 2024. We'll probably be safe for at least the next six months or so. All bets are off after that.
All I dream about is the tital wave of cheap high-performance GPUs flooding the market when the AI bubble bursts, so I can finally run Farcry at 25 frames per second for less than a grand.
Agreed. This was raised within our corp the other week and we read through the privacy and security documentation as it relates to Connected Experiences.
Microsoft has outlined specifically what Connected Experiences covers.[1] [2]
You could argue that predictive text is a product of machine learning but there is no clause allowing for training any generalized large language models using this data. The confusion may have arisen, if they read an article about CoPilot. If the user had a Microsoft Copilot 365 license, then the data would be used as grounding for their personal interaction with CoPilot. But still not used to train any foundational LLMs.
However, even this data is still managed in compliance with Microsoft's data security and privacy agreements.
The article covers this and I think the title is a bit too general. It is a byproduct of how CRISPR works as it targets a specific sequence. In this case the sequence is also present in areas that were non-targeted. Essentially, the sequence was not unique so the process impacted other areas in unintended ways.
> Here we evaluated diverse corrective CRISPR-based editing approaches that target the ΔGT mutation in NCF1, with the goal of evaluating post-editing genomic outcomes. These approaches include Cas9 ribonucleoprotein (RNP) delivery with single-stranded oligodeoxynucleotide (ssODN) repair templates [11,12], dead Cas9 (dCas9) shielding of NCF113, multiplex single-strand nicks using Cas9 nickase (nCas9) [14,15], or staggered double-strand breaks (DSBs) using Cas12a16.
