aabajian
Those are the four use cases featured by the Microsoft 365 Copilot App (https://m365.cloud.microsoft/).
Conversely, I bet there are a lot of people who want AI to improve things they are already doing repeatedly. For example, I click the same button in Epic every day because Epic can't remove a tab. Maybe Copilot could learn that I do this and just...do it for me? Like, Copilot could watch my daily habits and offer automation for recurring things.
Think about how you learned anatomy. You probably looked at Netter drawings or Grey's long before you ever saw a CT or MRI. You probably knew the English word "laceration" before you saw a liver lac. You probably knew what a ground glass bathroom window looked like before the term was used to describe lung findings.
LLMs/LVMs ingest a huge amount of training data, more than humans can appreciate, and learn connections between that data. I can ask these models to render an elephant in outer space with a hematoma on its snout in the style of a CT scan. Surely, there is no such image in the training set, yet the model knows what I want from the enormous number of associations in its network.
Also, the word "finite" has a very specific definition in mathematics. It's a natural human fallacy to equate very large with infinite. And the variation in images is finite. Given a 16-bit, 512 x 512 x 100 slice CT scan, you're looking at 2^16 * 26214400 possible images. Very large, but still finite.
Of course, the reality is way, way smaller. As a human, you can't even look at the entire grayscale spectrum. We just say, < -500 Hounsfield units (HU), that's air, -200 < fat < 0, bone/metal > 100, etc. A gifted radiologist can maybe distinguish 100 different tissue types based on the HU. So, instead of 2^16 pixel values, you have...100. That's 100 * 26214400 = 262,440,000 possible CT scans. That's a realistic upper-limit on how many different CT scans there could possibly be. So, let's pre-draft 260 million reports and just pick the one that fits best at inference time. The amount you'd have to change would be miniscule.
On a basic level, software exists to expedite repetitive human tasks. Diagnostic radiology is an extremely repetitive human task. When I read diagnostics, there’s a voice in the back of my head saying, “I should be writing code to automate this rather than dictating it myself.”
4 years med school
2 years computer science
6 years of residency (intern year, 4 years of DR, 1 year of IR)
16 years...
Can AI read diagnostic images better than a radiologist? Almost certainly the answer is (or will be) yes.
Will radiologists be replaced? Almost certainly the answer is no.
Why not? Medical risk. Unless the law changes, a radiologist will have to sign off on each imaging report. So say you have an AI that reads images primarily and writes pristine reports. The bottleneck will still be the time it takes for the radiologist to look at the images and validate the automated report. Today, radiologist read very quickly, with a private practice rads averaging maybe 60-100 studies per day (XRs, ultrasounds, MRIs, CTs, nuclear medicine studies, mammograms, etc). This is near the limit of what a human being can reasonably do. Yes, there will be slight gains at not having to dictate anything, but still having to validate everything takes nearly as much time.
Now, I'm sure there's a cavalier radiologist out htere who would just click "sign, sign, sign..." but you know there's a malpractice attorney just waiting for that lawsuit.
Here is the original version of their book: https://www.amazon.com/Inbound-Marketing-Found-Google-Social...
IBM reached out to Microsoft sometime in 1980 about an operating system, so SCP would've had at least 8 months to look into why Microsoft wanted their DOS before selling it entirely to them.
Did Microsoft resell 86-DOS to anybody before changing the name to MS-DOS? Did SCP make any effort find out why Microsoft wanted their DOS?
Conversely, if I say, "Put the button in the upper right corner exactly like the existing button in the upper left," I'll get a button in the upper right, but not exactly like the upper left button.
I went to a Java school. I remember my operating systems class involved writing simulated OS code in Java (for example, round robin for context switching). The argument was that it would be easier to understand the algorithms if the hardware complexities were minimized. I understand that sentiment, but I don't think Java was the right choice. Python would have accomplished the same task even better (understanding algorithms). I think there was a huge influence from industry to teach college students Java from day one. I had taught myself BASIC and some C back in high school, so it was a bit of a step backwards to learn a high-level language just to do simulated low-level OS programming.
I learned years later that there are a huge number of changes between 3.1 and 3.0. The biggest being support for more memory and multimedia extensions. The latter was the first time I learned what a dynamic link library (DLL) was and that took me down the rabbit hole of C++.
If you do naive matrix multiplication, you get a sense that you're doing similar work multiple times, but it's hard to quantify just what that duplicated work entails. Compare it to, for example, calculating the size of the union of two sets:
Total size = size(A) + size(B) - size(intersection(A, B))
You have to take out that extra intersection amount because you've counted it twice. What if you could avoid counting it twice in the first place? That's easy, you just iterate over each set once, keeping track of the elements you've already seen.
Strassen's algorithm keeps track of calculations that are needed later on. It's all reminiscent of dynamic programming.
What I find interesting is that it seems the extra savings requires complex values. There must be something going on in the complex plane that is again over-counting with the naive approach.
For all of the examples, I knew what image to expect before seeing it. I think it's the user who is at fault for requesting a copyrighted image, not the LLM for generating it. The LLM is giving (nearly) exactly what the user expects.
Microsoft’s success was as much to do with them being a programming languages company first. DOS, Windows 3.1, and even Windows 95 shipped with an interpreter and compiler for their BASIC and C, respectively. This empowered developers to use and write code for the OS out-of-the-box.
With that said, I regard TCS as the study of discretized computation at scale. Or, more colloquially, what is the fastest way to do X, N times? This could be sorting, searching, indexing, drawing, tracking, balancing, storing, accessing, saving, reading, writing, whatever. The distinguishing characteristic is complexity analysis in time and space.
As a counterexample, I think cryptographic methods such as AES and RSA are much more pure math / number theory than TCS.
Neurosurgery is the hardest residency, and the hardest physician career. However, this difficulty is in-part due to the bravado of the neurosurgeons themselves. During neurosurgery residency, you have to learn to be a brain and spine surgeon, undertake two years of research, learn how to do endovascular interventions, and (of course) manage all of the pre-/post- operative care including clinic. It is not uncommon for a single PGY-2 neurosurgery resident to follow 100+ inpatients. Suffice to say neurosurgeons are overburdened, yet they themselves have fought against reducing training hours.
Patients can generally expect to spend less than 30 minutes speaking to their surgeon before/after their surgery.
For those unaware, you can become a spine surgeon via either the neurosurgery or orthopedic surgery routes. Just my simple opinion, but I think spine surgery should be its own residency. Brains should be left to the neurosurgeons and extremities to the orthopedists.
Second condo I found myself and my agent merely facilitated the transaction. She hired a home inspector who missed a couple things. Not saying I would've caught them with my own inspector, but when your agent is chummy with an inspector, you can't really call them out on their mistakes.
When it came to looking for houses, I saw a few with my agent, but the one I ended up buying I found myself on Redfin. I went to an open house myself and just told my agent I wanted to make an offer.
Of note, my agent got paid the least for the first condo (because it was the cheapest), but she was most valuable in that transaction. The other two transactions just required the agent to fill out the paperwork, yet they got paid much more. There has to be some relationship between the amount of work done and the cost of the buyer's agent, not just a percentage of the sale price.
-Not from a wealthy background, he got where he is due to hard work.
-Paid his share of the rent by trading stocks.
-He bought a used SUV off of Craigslist and negotiated the price down by almost 90%, afterwards the seller smiled and said something akin to "the balls on this guy!"
-Double majored in electrical engineering and economics in college.
-Worked at the US Patent Office reviewing video card patents, well before cryptocurrency, bitcoin mining, or AI.
-He built his own desk using woodworking skills.
-Studied contract law and has closed some multi-million dollar deals for other startups. He quit this high-paying, stable job to start Modern Realty.
-Has his own number of AirBnb properties.
Raffi has the very unusual combination of street smarts, book smarts and salesmanship.
-The company that makes Magnequench presumably knows their particles can be embedded in other materials. What's the most common use case for these types of particles? Are they typically embedded in fixed (non-flexible) solid medium or liquids?
-Is it necessary to use Dragon Skin? The idea of mixing magnetic particles in a semi-solid medium is intriguing to me. Like putting particles into Jello or Silly Putty. Could I then apply an external magnetic field and have the particles deform/shape the medium they are in?
In my opinion, the "monopolistic" aspect of it comes down to the fact that they tied it into an otherwise open messaging system - SMS. You cannot separate SMS messages from iMessages (to my knowledge). So, the only way to know a message was sent via SMS is the green background for incoming messages and the green background plus the "sent via SMS" for outgoing messages. This creates a disdain for SMS, and anyone who uses it over iMessage. It is such a strong feeling, that having green messages makes you "uncool", especially in the younger crowd.
On the other hand, I think the long-term sequalae of the blue-green message is to push people to use stand-alone apps like WhatsApp and FB Messenger. I think it'll be a hard sell at this point to convince a jury that iMessage is an overt monopoly.
The main question I want addressed is: If SMS messages can be directly shown in iMessage, and are not secure, then the argument of not allowing "insecure" 3rd-parties to integrate with iMessage goes out the window. All I want is Android messages to be shown in iMessage. Sure we can make them green, but at least they will be sent over the data network and not SMS.