> warmth and empathy don't immediately strike me as traits that are counter to correctness
This was my reaction as well. Something I don't see mentioned is I think maybe it has more to do with training data than the goal-function. The vector space of data that aligns with kindness may contain less accuracy than the vector space for neutrality due to people often forgoing accuracy when being kind. I do not think it is a matter of conflicting goals, but rather a priming towards an answer based more heavily on the section of the model trained on less accurate data.
I wonder if the prompt was layered, asking it to coldy/bluntly derive the answer and then translate itself into a kinder tone (maybe with 2 prompts), if the accuracy would still be worse.
Alternatively, I've gotten exactly what I wanted from an LLM by giving it information that would not be enough for a human to work with, knowing that the llm is just going to fill in the gaps anyway.
It's easy to forget that the conversation itself is what the LLM is helping to create. Humans will ignore or depriotitize extra information. They also need the extra information to get an idea of what you're looking for in a loose sense.
The LLM is much more easily influenced by any extra wording you include, and loose guiding is likely to become strict guiding
Absolutely, I can see how that could be effective. The jobs may lean toward electrical experience. Power engineering is a subfield of electrical and may be relevant. I looked into it once for myself, seemed like a good fit.
Another field of tools that I'm looking at are the Geospatial ones. Being able to work with mapping software/data always felt like a good mix to me.
What tools are they teaching now?
I studied on like AMPL for linear/nonlin prog, ARENA for sims, Matlab for general but it's been a while.
Hey there! I have a B.S. in Information & Systems Engineering, which includes Operations Research (OR). Happy to chat about this!
When I graduated, the OR term was already fading, and from what I’ve seen, it’s pretty much gone as a standalone field. The tools are still strong, but OR isn’t often listed as a job specialization on its own.
I started as a business analyst, and while OR wasn’t in any job descriptions, it gave me an edge. I used OR methods to go above and beyond, working closely with branch and executive management to analyze cost-effectiveness, optimize decisions, and make strategic recommendations. This helped me stay at the top of my pay band. Of course, I still handled traditional BA tasks like dashboards, reports, automation, and SQL.
My advice? Cross-specialize. OR is incredibly valuable, but it works best when paired with another strong skill set. For me, a CS minor and SQL/database skills helped early in my career.
To put it simply: OR lets you optimize a warehouse layout—but most jobs also require you to move boxes. It aligns more with engineering management roles than entry-level work, and those management positions typically go to people with industry experience.
That said, I genuinely believe OR is one of the best specializations when combined with another field. You just need to polish it with the right complementary skills.
(Full disclosure I used AI to clean up this message, but it's still very close to my initial draft. Mostly just some grammar and phrasing changes, but it does kind of read like AI now so I wanted to call it out that the sentiment is still genuine)
As far as connecting to other practitioners, I mostly just stay active in forums and joined a few LinkedIn groups but I need to improve in this area too, which is my motivation for posting this.
Anecdotal but I told chatgpt to include it's level of confidence in its answers and to let me know if it didn't know something. This priming resulted in it starting almost every answer with some variation of "I'm not sure, but.." when I asked it vague / speculative questions and then when I asked it direct matter of fact questions with easy answers it would answer with confidence.
That's not to say I think it is rationalizing it's own level of understanding, but that somewhere in the vector space it seems to have a Gradient for speculative language. If primed to include language about it, it could help cut down on some of the hallucination. No idea if this will effect the rate of false positives on the statements it does still answer confidently however
No comment on if output analysis is all that is needed, though it makes sense to me. Just wanted to note that using file size differences as an argument may simply imply transformers could be a form of (either very lossy or very efficient) compression.
I cant help but chime in here because I used to feel this way and all the typical advice never felt right (ie that you shouldn't care how good you are at things)
Very quickly I will list the 3 main points that have helped me the most
1) the things you care to try to excel at is a statement about things worth excelling at and actual skill is often a minor detail. It's okay to identify with where the effort goes and how much you give rather than the result of it. In this way it is like voting, and there is no best person at voting. You identify with the tribe, not your ability
2) when being competitive does actually matter, the best in the world cannot be everywhere at once, so there is actually a lot of meaning behind being the best locally at something. Or even just not the worst locally. Identity is irrelevant on this one, but it does require you care and are self aware about how good you actually are at things.
3) how you relate to others is also a big part of identity. being in the middle of the pack on most things makes you much more relatable than being best. For some person who is better than you at everything, are you able to deeply connect with them or do you get distracted by comparison thoughts, insecurity, or ideas to use them for something self-serving? If not you, still how often in their life do you think that happens for them with others?
Yes actually, I've been binging a lot of General Relativity content lately by coincidence and can say the Dialect YouTube channel has been the best resource for describing this. I'm not an expert so I cannot speak to its accuracy but it seems sound.
In particular the video "Conceptualizing the Christoffel Symbols". Also look at content on the Metric Tensor
Additionally, there is content from other sources (albeit less produced) on describing projective geometry which is also related
Maybe not exactly what you are describing, but I recently did some layman research on "Strange Attractors" and chaos theory, which covers very similar topics. I cannot summarize here, but it's a neat rabbit hole to go down
Yes, it can be net positive. Ill clarify a bit, because text is easily misunderstood. I would not assume someone is a "bad" person just because they grew up around gang culture and drugs. I am talking "positive" in terms of understanding the world and determining the type of person you are proud of being. I'm not saying everything is enjoyable. I think our brains are capable of recognizing good things regardless of how much bad exposure we've had. That's more what I mean by net positive, not that everything we do is net pleasant
I think everything is ultimately net positive if you strengthen and condition the way you internalize all the sensory exposure.
To me, the risks are being overwhelmed by internalizing too much at once, and the opportunity cost of having a limited amount of time to take things in to your senses.
Influence is a very nuanced thing so I don't want to sound too black and white here, but I think it's like a brainstorm session. You take what you like best from your senses/inputs/etc
I think demanding upfront pricing could hurt the customer. The company would be forced to overestimate their costs to cover the risk associated with unknown use-case details, making the enterprise price inflated. There would be a big "**" next to the price as well with a long list of conditions that need to be met.
The true costs are not front facing because it may not be clear which part of your product is going to be bearing most of the weight of the enterprise customers use-case until you know exactly what they are buying. Your costs may change a lot based on the nature and scale of what they are buying.
For example, you may be rate limited on a background service they use and the customer usecase will likely pish you over your limit. the next product tier from the background service vendor that's required to fulfill the enterprise company's use-case may cost way more and offer way more capacity than that company is going to use by itself. So you have to make a bet on how much cost to assign to that customer, and how much to assign to other/future customers given you buy that new tier and use it to change what you offer to other customers to try to make use of it. It may lower your cost overall if it supports a feature you can upsell relatively easily. Or it may be a service hardly used at all andyou really can't justify going up to that next tier unless this one enterprise customer pays for nearly all of the added cost
I specialize in costing/pricing, not in saas however. There are a lot of reasons why what you are asking for is likely not realistic. It unfortunately does get abused, though, and I fully agree with how bullshit some of the arrangements are. It's just the people taking advantage of the situation are doing so knowing that the "cover" for it is legitimate.
Maybe there are ways to address the abuse without forcing upfront pricing?
Jumping in to say I think it's important to try to control the incentives around how many empty units are "too many" empty units.
Having a tenant comes with cost and risk. Why charge 1200 for 100 apartments when you can charge 3500 for 34 apartments? Maybe even 3000 is still just as profitable
Every extra 100 you charge an existing tenant is pure profit, while reducing price to take on more tenants means more maintenance costs, managing staff costs, etc.
In an economic vacuum this may work itself out, but IF there is a widening wage gap where the you have 30 tenants who can afford up to 4500 a month and you have to drop the price, and then the next 50 potential tenants can only afford up to 1800.. there is financial incentive to squeeze more and more out of the 30 year after year rather than try to accommodate all 80.