- the low-hanging fruits (where there is a Wikipedia page or similar for X, and both Google and Bing do a good job of mining these)
- the tougher nuts ("who was the UK prime minister when the wright airplane first flew" - Google and regular Bing fail at this, but Bing chat correctly brings up Arthur Balfour). This was just an example I made up to try - but the ability to connect more dots than plain old search, which is hard to explain but you get a sense of the capability as you use ChatGPT/Bingchat - helps a lot.
Oh I agree with that. LLM doesn't give you better search - better search {index, ranking, SEO-tolerance, ...} gives you better search.
To me, Bing chat - although imperfect - augments search positively to fill my specific needs.
0) I don't see half a page of ads before the first real result, disguised to look like real results.
1) The interface is clean and noise-free : it takes away a huge context switching load I incur when going into individual results (which for most searches these days, is the top 3-5). I just want the content summarized, with no ads, in a form my brain is used to.
2) I can ask follow-up questions with context, again without ever leaving the interface. Otherwise, the follow up question's answer is often on another website.
3) I can ask more creative questions, which is not really a 'search' feature. Something like 'write a snippet of code'. You can try "unique_ptr in rust - show examples too" and it gives me a passable and concise answer. It presents two options, but to get what I exactly want, I can ask "how to use Box?" as a follow on.
4) It's vastly better at 'connect the dots' queries - see my other comment please.
One underrated feature is the 'next query' suggestions - I can use a single click instead of typing out 'one more example' or see more subtle examples by clicking 'how can I use Box for recursive types?'.
They don't. They're fairly orthogonal concepts. You can have a concept of lifetimes without memory management (for example, the object changes state while you have a reference to it).
I don't treat it like a person, but rather as ChatGPT that has access to Bing's search index. For factual queries ("who invented X? at what company?") it's more reliable than ChatGPT and saves me quite a bit of time. Similarly for content aggregation tasks - I'm *scared* to click on a lot of "Top 10 X", "The best Y in Z" type pages because they're SEO and advert mined. Bing does the initial aggregation for me, and if interesting, I ask it to tell me more about that resource or visit the page myself.
There will always be a segment of people who troll on the internet - that doesn't detract from the immense productivity tool it can be when used correctly.
How would one go about finding such consulting gigs?
I’m semi-retired now, but with nearly 4 decades of experience, I still didn’t feel comfortable becoming a full-time consultant.
Could you share any of these “hosted” services? I’m thoroughly disillusioned after paying for 3 streaming services and still not finding quality content
I’ve posited repeatedly that when Reddit IPOs, I’ll be reallocating a significant chunk of my portfolio into their stock.
Their management has historically lacked focus, but if Reddit ever builds a half-competent search index, and positions itself as a search-first, discovery-second destination, they will be in the FANG tier of stocks.
They have the data. They have the dedicated, active user base. They have free moderation. The hard parts are solved. If only they get someone like Satya at the helm. (Also a big reason for me to believe that an acquisition may also be a good play for a AMZN/MSFT)
Sequencing in distributed systems is difficult to handle correctly. When faced with such a situation, it’s often a matter of philosophy rather than technical brilliance - are you willing to accept that failure is inevitable even with a neat “waiter” implementation? What happens if the waiter waits forever? Is shaving seconds tangibly benefitting customers (for Sentry it might) and worth the operational overhead?
Framing these problems slightly differently - is 10 second latency close enough to “real time”? - unlocks solutions like an online batch processor that reads out of your DB (if it doesn’t support NOTIFY) on an indexed incremental identifier.
I very much get a cargo-cult vibe from this statement.
In a 30-year career, I’ve pair-programmed under 100 times. Usually to get someone up to speed on a confusing part of the code base.
In fact, I’d go as far as to say that if pair programming is a recurrent need, you need one or more of better engineers, better documentation, or better code reviews.
Yes.. I thought that incident made him look very, uh, disgraceful.
He likes to predend Repl.it is some marvel of technology when it is unironically a weekend project (which the intern he attacked did better). Bruised egos are dangerous.
I’d really like to see the author write an explanation as to why. I completed by PhD in “AI” (as it was then called) in 2001. Very little DNN work then, but I haven’t a clue where the connection lies.
This author doesn’t have a clue about anything. After the first paragraph, this might as well have been written by GPT-3. Sounds like a consultant trying to cash in on the news of the week to me.
I think part of it has to do with black box models being easier to build incrementally. I’ve seen many teams start off with 3-5 features, which gets you to some threshold for launch (say 90% precision and recall) and get badly burnt on the other 10% post-launch
- the low-hanging fruits (where there is a Wikipedia page or similar for X, and both Google and Bing do a good job of mining these)
- the tougher nuts ("who was the UK prime minister when the wright airplane first flew" - Google and regular Bing fail at this, but Bing chat correctly brings up Arthur Balfour). This was just an example I made up to try - but the ability to connect more dots than plain old search, which is hard to explain but you get a sense of the capability as you use ChatGPT/Bingchat - helps a lot.