> Would love to hear OpenAI's explanation behind this line of thinking.
My assumption is that the data centers would mostly be staffed by those who will have to manually audit data going in and out of the LLMs on a daily basis. There would also be a need to generate/curate the test data that the LLMs will have train on.
There is potential for half a million jobs, but is that what you would want to have human effort invested in, is the real question.
One of the restuarant chains mentioned in the author's post (Social), is an extremely crowded pub during the night and for the rest of the time, a place where freelancers or remote workers come in to work and socialize. At least that was the case in Bengaluru,India before Covid.
I would say that from the restuarant's point of view, having the order-from-app experience works out since the freelancers can order via their laptops whenever they want, without having to flag down a waiter. And during rush hours, tables could order what they want without having to spot and call a waiter among a very drunk dancing crowd.
Their decisions made the life of those around them miserable because they did not care about the consequences of it. Like for example, hiring an intern who had basic programming skills when there was no engineering bandwidth available to support the intern, and then bouncing them around multiple large projects every week, for the sake of showing upper management that they had people "working" on priority tickets. There is more to this story, but it's just more details painting that person in a bad light.
I, most certainly am not such a person.
> Caring about that work was stupid, and I should have just enjoyed my time more.
I agree. It's a work in progress for me to get to that state.
When I was junior/mid level engineer working in a 5-8 person engineering team, there was this particular senior engineer whom I disdained. It had nothing to do with them as a person, but I always had the feeling that they just did things at work without caring for the impact of it.
The team and company grew in size over a period of 2 years and I went through a lot of first hand experiences of having to deal with unrealistic deadlines, chaotic lines of communication and multiple nights of tailing production logs to fix bugs. It was around this time, that the senior engineer during a company even got drunk and talked to me about their first few jobs.I realized that what they described, was quite similar to what I was facing right now and I came to understand why they cared so little about work they do on a daily basis.It honestly scared me at to think that I might either end up like this person or worse or burning out and quitting the field.
Right now, I am close to age the senior engineer was when I met them and even though I may selectively decide not to involve myself with things that can overstretch me, I am in no way the same as that other person.
The reason I am sharing this tangential story is to highlight my opinion about old age. It does not matter much if you have gained a lot of experience as you grow older, unless you are able to use it effectively.
I do not believe that most of these `perfectionists` are trolls. Some have just very bad experiences either in their career or childhood that make them feel that making mistakes is not normal.
I was a `perfectionist` for a while due to certain bad experiences at work, and it was only through the help of really good teammates that I was able to slowly get rid of it. And that required pointing out things like what the author has done in their blog post and once someone sees that mistakes are things that anyone could make, they get more comfortable the concept.
The harder part is understanding why a perfectionist is so, and then not getting frustrated while you try to help them improve.
The screenshot of the tweet thread at the end seemed to indicate that the alignment team wanted to explore other avenues(or models) to get to AGI, while the company leadership wanted to keep improving on the transformer models.
From the business point of view, continuing to improve the goose that is laying the golden eggs makes sense, but as a researcher they might have already ended up seeing the limit of adding compute and data, and wanted to pivot. As someone who worked a mid level manager/engineer, I can understand the frustration involved in making the management level understand what's really happening on the ground level.
From my point of view the underlying problem here seems to be that someone in Quora product team feels that the best way to drive engagement is to predict what the user might want to know about and then spam them with it, rather than letting them discover on their own or use their feedback.
I stopped using Quora 8 or 9 years back, because despite how much time I spent curating the feed I was served, the questions always seemed to veer back into the questions on life experiences, `write a short story in 3 lines` or about relationships. This was most probably due to topics like these being a fad in India during the time and often received a lot of answers or upvotes.
Every time I marked an question as something I was not interested in, because of the topic, Quora assumed that I did not like the author's answer and then proceeded to show 5 other questions the author has responded to. I assumed it was because of some bug on their side ignoring my preferences of topics, until I spent a week in Dubai and saw most of the questions in my feed match the topics I marked as interested.
> Additionally, members of the program receive priority placement and “richer brand expression” in chat conversations, and their content benefits from more prominent link treatments. Finally, through PPP, OpenAI also offers licensed financial terms to publishers.
> A recent model from The Atlantic found that if a search engine like Google were to integrate AI into search, it would answer a user’s query 75% of the time without requiring a clickthrough to its website.
If the user searching for the information finds what they want in ChatGPT's response (now that they have direct access to the publisher data), why would they visit the publisher website ? I expect the quality of responses to degrade to the point where GPT behaves more like a search engine than a transformer, so that the publishers also get the clicks they want.
I believe what the parent comment is trying to imply is that the search results are fetched/retrieved from Bing's own internal ranking vector(?) database and then passed to the LLM, which then converts the received documents into a more human readable format and fills in any missing gaps in the information with it's own data.
So the gaps are the only areas where the LLM can hallucinate on and if your search query is easily available information on the internet, then hallucinations will be less or none.
Edit: I have used RAG with a project that I am working on and it's quite hard to ascertain if the LLM used the information provided as part of the RAG documents or just made up information on it's own, since even without RAG, we were getting similar responses 7 times out of 10.
Based on the way you have described their API, I can make 2 assumptions
1. The might have had well supported API until the point at which they realized they wanted to have more people to use the webapp, rather than having the data sent through an API. More time spent on the Hubspot webapp means, more chances the customer notices the other services Hubspot provides and pays for them.
2. The versioning APIs that you see are most likely due to an enterprise deal Hubspot made with a large company who asked for variations in the API, and Hubspot introduced a v2/v3/v4 instead of modifying v1 (to not cause disruptions for their customers using v1).
I agree with your comment, and feel that in a software engineering context, the quote
> To find the sweet spot between impostor syndrome and overconfidence, you first need to understand that you can make mistakes without it reflecting on your competence.
is incomplete without describing the environment and people you are around when you make these mistakes. The teams I have worked with until now have all been extremely kind in dealing with other engineers making mistakes and focused on how we could reduce such mistakes as part of a team. It helped me build a mental checklist on dealing with such a situation and when/how to take the risk and when to bring in additional help. I never really noticed the impact of this until I worked with a colleague who seemed to have trouble releasing their work to production and always kept delaying things out.
> I think the main factor that will be key to generate a whole movie is being able to pass some reference images of the characters/places/objects so they remain congruent between two generations.
I partly agree with this. The congruency however needs to extend to more than 2 generations. If a single scene is composed of multiple shots, then those multiple shots need to be part of the same world the scene is being shot in.
If you check the video with the title `A beautiful homemade video showing the people of Lagos, Nigeria in the year 2056. Shot with a mobile phone camera.` the surroundings do not seem to make sense as the view starts with a market, spirals around a point and then ends with a bridge which does not fit into the market.
If the the different shots generated the model did fit together seamlessly, trying to make the fit together is where the difficulty comes in. However I do not have any experience in video editing, so it's just speculation.
While Sora might be able to generate short 60-90 second videos, how well it would scale with a larger prompt or a longer video remains yet to be seen.
And the general logic of having the model do 90% of the work for you and then you edit what is required might be harder with videos.
Based on my experience the generalist path works out well when startups have enough capital to make a slightly risky hire. In the current job market, specialists in a specific language or framework is more sought after so that the hiring is risk free.
It really depends on who(or what) is screening your resume. If it's someone with several years of techinical experience, they might consider the resume based on your generalist work. If it's a junior engineer or someone on HR side, they would just reject purely based on keywords on the resume.
I was once rejected for a python role because the last time I pushed code in python to production was 2 years ago.
I live in Berlin and buy all of my vegetables from a REWE or a Netto (both are supermarkets and are quite close to me). I have tried the Bio/Organic stores here and would like to buy from them, but they are a bit too far from where I live to buy regularly.
I have a standard routine on the weekdays for dinner,
1. salad with some protein and carbs(potatoes mostly). Salad always has a standard base of vegetables like lettuce, carrot, cucumber and sweet corn, with dressing (apple cider vinegar or yoghurt) and salt. Sometime I add the salad dressing mixes or raw mangoes or green apples just to keep it interesting. It's always 60% salad, 20% protein and 20% carbs for the meal.
2. A semi-gravy vegetable stir fry of mostly carrots, bell peppers and mushrooms with a rotating list of vegetables such as beetroot, beans, brussels sprout or broccoli. I cut them up in small chunks and roast them in the oven mixed with some olive oil and salt. Then I prepare a sauce (mostly experimental) and add the cooked vegetables into it and saute for a bit. I sometimes add meat or meat stock and have portion of it as a full meal. In case I am really hungry, I mix in some carbs in the form of brown rice or wheat tortilla.
I alternate between these 2 every night and I cook enough for it to be carried over as the next day's lunch.
Even if I go outside this routine(on weekends or cravings for fast food), I try to ensure that the meal I have at home is at least 50-60% vegetables, and salads tend to be the most easiest to make up for this balance.
Based on my personal experience, interviewing for senior roles in technology feels much more difficult than the interviews I did for a junior or mid level roles. Interviewing in general, is like a Venn diagram where your experience and the interviewer's experience are the circles and you need to find a way to ensure that the questions being asked end up in the intersection where you both know well enough to showcase your skills.
As the role one is applying for more gets more senior, the larger the circles on both sides become, and it sometimes ends up being too hard to get to that intersection in the short duration of an interview.
That being said, I would recommend to to start applying for jobs, since the interview experience itself would be helpful to understand where you might need to improve.
> What can I do that would make you look past my lack of educational qualifications?
1. If you are in India, create profiles for yourself on job seeking websites such as AngelList, LinkedIn or Hirist. When I was part of a hiring team, I(and peers) used to go through profiles on these websites with filters for the skills we were looking for, and then ask the recruitment team to reach out to candidates we found interesting. If an engineer or a lead takes a look at your profile and feels that your skills meet what they are looking for, they are highly likely to overlook the lack of a degree.
2. Network. If your resume does not get your foot through the door, try to go to language specific meetups and socialize. The whole `need a degree to work here` is a HR thing and just a way for them to reduce the applications they receive. A referral from someone within the company would usually skip that barrier.
3. Apply to any and all openings that you feel that you are a suitable fit for. Do not let the `CV will not even be considered for even an entry level role at their companies because of that` thought stop you, and let the person looking at your resume decide if you are a fit or not.
> How do I make my skills visible?
Points 1 and 2 should help with this. Apply to one of the free courses on Udemy or Coursera and complete the courses to get a certificate. Have a colleague whom you work with leave a recommendation or endorse you for skills on LinkedIn. I used to receive offers for a IOS mobile dev roles even years after I stopped working with Swift just because of one single skill endorsement.
I believe you have not asked for this, but I would definitely recommend getting a degree, even if it were a distance learning course that you can do while you work.
I do not think that answer would correctly solve the original riddle. The LLM blindly just gave an answer without raising the question of which pair of objects together would be a problem if left alone.
The article misses out on the fact that, as human beings with varying tenure, exposure and interests, all mental maps are not similar. Which makes on often misjudge another engineer' skills or abilities as purely being "packers".
Lets take the example provided by the author about `Solving a Problem`,
It is always possible, that the engineer fixing the bug might not have a lot of time to spend on it, or the fix does not need to cover all edge cases and a quick fix is good enough based on their own mental map. But to an outsider, it might seems as if the engineer has not taken ample time to `understand the root cause of the error, and which assumptions are wrong in their mental model`, which is a valid thought, but not necessarily true.