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Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

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Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#61

Post by tedivm »

tbalsam wrote: post #16
22SAS wrote: post #5

So, a LLM, trained extensively on StackOverflow and other data (possibly the plethora of LC solutions out there), is fed a bunch of LC questions and spits out the correct solutions? In other news, water is blue. It is one thing to train an AI on megatons of data, for questions which have solutions. The day ChatGPT can build a highly scalable system from scratch, or an ultra-low latency trading system that beats the c…

If it helps, this likely is coming. I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Years ago this would have been a similar level of impossibility. Since all a codebase like that is is a kind of directed graph, then augmentations to the processing of the network to allow for the simultaneous parsing of and generation of this kind of code may…

I've worked in ML for awhile (on the MLOps side of things) and have been in the industry for a bit, and one thing that I think is extremely common is for ML researchers to grossly underestimate the amount of work needed to make improvements. We've been a year away from full self driving cars for the last six years, and it seems like people are getting more cautious in their timing around that instead of getting more optimistic. Robotic manufacturing- driven by AI- was supposedly going to supplant human labor and speed up manufacturing in all segments from product creation to warehousing, but Amazon warehouses are still full of people and not robots.

What I've seen again and again from people in the field is a gross underestimation of the long tail on these problems. They see the rapid results on the easier end and think it will translate to continued process, but the reality is that every order of magnitude improvement takes the same amount of effort or more.

On top of that there is a massive amount of subsidies that go into training these models. Companies are throwing millions of dollars into training individual models. The cost here seems to be going up, not down, as these improvements are made.

I also think, to be honest, that machine learning researchers tend to simplify problems more than is reasonable. This conversation started with "highly scalable system from scratch, or an ultra-low latency trading system that beats the competition" and turned into "the parsing of and generation of this kind of code"- which is in many ways a much simpler problem than what op proposed. I've seen this in radiology, robotics, and self driving as well.

Kind of a tangent, but one of the things I do love about the ML industry is the companies who recognize what I mentioned above and work around it. The companies that are going to do the best, in my extremely bias opinion, are the ones that use AI to augment experts rather than try to replace them. A lot of the coding AI companies are doing this, there are AI driving companies that focus on safety features rather than driver replacement, and a company I used to work for (Rad AI) took that philosophy to Radiology. Keeping experts in the loop means that the long tail isn't as important and you can stop before perfection, while replacing experts altogether is going to have a much higher bar and cost.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#62

Post by WheelsAtLarge »

I think tests should be easy for ChatGPT to pass. It has been trained on data that has the answers and it's good at getting the data. I'm starting to doubt its long term usefulness since it does not seem to have good decision making abilities and even the slightest bit of cognitive ability.

I suspect the current crop of AIs will find very specific functions and hit a hard stop. They will change how we function but we won't be seeing a singularity type of revolution anytime soon. IBM's Watson is a good example of a system with a lot of possibilities but not finding a use. I think most of AI will fall in that realm. We have to get over the idea that it's smart. It's not.

An AI winter is coming so the improvements will come to a stop and we will find its limits. We are no where near general AI.

It's impressive that it can parse the question and write a relevant answer but it's not a robotic SWE.

For now, it's a good tool for cheating on tests.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#63

Post by ericmcer »

echelon wrote: post #35
22SAS wrote: post #5

So, a LLM, trained extensively on StackOverflow and other data (possibly the plethora of LC solutions out there), is fed a bunch of LC questions and spits out the correct solutions? In other news, water is blue. It is one thing to train an AI on megatons of data, for questions which have solutions. The day ChatGPT can build a highly scalable system from scratch, or an ultra-low latency trading system that beats the c…

> The rest of us aren't going to care that much. If you don't adapt, you'll be out of a job in ten years. Maybe sooner. Or maybe your salary will drop to $50k/yr because anyone will be able to glue together engineering modules. I say this as an engineer that solved "hard problems" like building distributed, high throughput, active/active systems; bespoke consensus protocols; real time optics and photogrammetry; etc.…

You really think in <10 years AI will be able to take a loose problem like: "our file uploader is slow" and write code that fixes the issue in a way that doesn't compromise maintainability? And be trustworthy enough to do it 100% of the time?

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#64

Post by object-object »

ALittleLight wrote: post #27
ipnon wrote: post #20

Earlier quoted context omitted.

Until ChatGPT can slack my PM, attend my sprint plannings, read my Jira tickets, and synthesize all of this into actionable tasks on my codebase, I think we have job security. To be clear, we are starting to see this capability on the horizon.

One issue is that there are a much larger number of people who can attend meetings, read Jira tickets, and then describe what they need to a LLM. As the number of people who can do your job increases dramatically your job security will decline.

If one's ability to describe what they need to Google is at all a proxy to the skill of interacting with an LLM, then I think most devs will still have an edge.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#65

Post by alephnerd »

klyrs wrote: post #59
alephnerd wrote: post #46

Earlier quoted context omitted.

That's a bad PM then to be honest. I think ChatGPT will definetly commodify a lot of "bitch work" (pardon my french). The PMs who are only writing tickets and not participating in actively building ACs or communicating cross functionally are screwed. But so are SWEs who are doing the bare minimum of work. The kinds of SWEs and PMs who concentrate on stuff higher in the value chain (like system design, product market…

To be fair to the people that I hear that from, they're essentially complaining about the worst part of their job. They're active participants in those meetings, they are genuinely thinking about the complexities of the mismatch between what management asks for and what their ICs can do, etc. I see their value. But the awful truth is that a $10k/project/yr license for PMaaS software will be very appealing to executiv…

And as a Product Manager, I'd support that. Most PMs I see now in the industry are glorified Business Analysts who aren't providing value for the amount of money spent on them. But that's also true for a lot of SWEs and any role. Honestly, the tech industry just got very fat the past 5-7 years and we're just starting to see a correction.

edit with additional context:

Writing Jira tickets and making bullshit Powerpoints with graphs and metrics is to PMs as writing Unit Tests are to SWEs. It's work you need to get done, but it has very marginal value. When a PM is hired, they are hired to own the Product's Strategy and Ops - how do we bring it to market, who's the persona we are selling to, how do our competitors do stuff, what features do we need to prioritize based on industry or competitive pressures, etc.

That's the equivalent of a SWE thinking about how to architect a service to minimize downtime, or deciding which stack to use to minimize developer overhead, or actually building an MVP from scratch. To a SWE, while code is important, they are fundamentally being hired to translate business requests that a PM provides them into an actionable product. Haskell, Rust, Python, Cobol - who gives a shit what the code is written in, just make a functional product that is maintainable for your team.

There are a lot of SWEs and PMs who don't have vision or the ability to see the bigger picture. And honestly, they aren't that different either - almost all SWEs and PMs I meet when to the same universities and did the same degrees. Half of Cal EECS majors become SWEs and the other half PMs based on my friend group (I didn't attend cal, but half my high school did, but this ratio was similar at my alma mater too, but with an additional 15% each entering Management Consulting and IB)

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#66

Post by SpeedilyDamage »

hacym wrote: post #45
SpeedilyDamage wrote: post #38

Earlier quoted context omitted.

You don't understand the take that just because ChatGPT can pass a coding interview doesn't mean the coding interview is useless or that ChatGPT could actually do the job? What part of that take do you not understand? It's a really easy concept to grasp, and even if you don't agree with it, I would expect at least that a research scientist (according to your bio) would be able to grok the concepts almost immediately.…

I don’t think your militant attitude helps them understand any better.

I don't think I had a militant attitude, but I do think saying, "I don't understand..." rather than "I disagree with..." puts a sour note on the entire conversation.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#67

Post by ianbutler »

brhsagain wrote: post #58
SpeedilyDamage wrote: post #38

Earlier quoted context omitted.

You don't understand the take that just because ChatGPT can pass a coding interview doesn't mean the coding interview is useless or that ChatGPT could actually do the job? What part of that take do you not understand? It's a really easy concept to grasp, and even if you don't agree with it, I would expect at least that a research scientist (according to your bio) would be able to grok the concepts almost immediately.…

> doesn't mean the coding interview is useless or that ChatGPT could actually do the job Aren't these kind of mutually exclusive, at least directionally? If the interview is meaningful you'd expect it to predict job performance. If it can't predict job performance then it is kind of useless. I guess you could play some word games here to occupy a middle ground ("the coding interview is kind of useful, it measures som…

We've been saying for years these interviews are not predictive of job performance. Here's the proof.

Nothing you do in an interview like this resembles day to day work in this field.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#68

Post by phoehne »

tbalsam wrote: post #16
22SAS wrote: post #5

So, a LLM, trained extensively on StackOverflow and other data (possibly the plethora of LC solutions out there), is fed a bunch of LC questions and spits out the correct solutions? In other news, water is blue. It is one thing to train an AI on megatons of data, for questions which have solutions. The day ChatGPT can build a highly scalable system from scratch, or an ultra-low latency trading system that beats the c…

If it helps, this likely is coming. I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Years ago this would have been a similar level of impossibility. Since all a codebase like that is is a kind of directed graph, then augmentations to the processing of the network to allow for the simultaneous parsing of and generation of this kind of code may…

I think you’re right in one sense, and we both agree LLMs are not sufficient. I think they are definitely the death knell for the junior python developer that slaps together common APIs by googling the answers. The same way good, optimizing C, C++, … compilers destroyed the need for wide-spread knowledge of assembly programming. 100% agreed on that.

Those are the most precarious jobs in the industry. Many of those people might become LLM whisperers, taking their clients requests and curating prompts. Essentially becoming programmers over the prompting system. Maybe they’ll write a transpiler to generate prompts? This would be par of the course with other languages (like SQL) that were originally meant to empower end-users.

The problem with current AI generated code from neural networks is the lack of an explanation. Especially when we’re dealing with anything safety critical or with high impact (like a stock exchange), we’re going to need an explanation of how the AI got to its solution. (I think we’d need the same for medical diagnosis or any high-risk activity). That’s the part where I think we’re going to need breakthroughs in other areas.

Imagine getting 30,000-ish RISCV instructions out of an AI for a braking system. Then there’s a series of excess crashes when those cars fail to brake. (Not that human written software doesn’t have bugs, but we do a lot to prevent that.). We’ll need to look at the model the AI built to understand where there’s a bug. For safety related things we usually have a lot of design, requirement, and test artifacts to look at. If the answer is ‘dunno - neural networks, ya’ll’, we’re going to open up serious cans of worms. I don’t think an AI that self evaluates its own code is even on the visible horizon.

Re: Google testing ChatGPT-like chatbot 'Apprentice Bard' with employees

#69

Post by alchemist1e9 »

AussieWog93 wrote: post #11
6c737133 wrote: post #2

why does chatGPT passing tests or interviews continue to make headlines? all they're proving is that tests, and interviews, are bullshit constructs that merely attempt to evaluate someone's ability to retain and regurgitate information

You're not wrong, but until you actually play with ChatGPT yourself, you just don't understand how _dumb_ it is. All people see is the cheating, and possibly this scary new AI that's going to get smarter than humans in a short period of time. I suspect the best way to educate people on both the powers and limits of the technology is to get them to sit down for 15 minutes with it.

When I read this I feel people must be using it in the wrong way. I use it all the time to quickly solve tech problems I mostly know something about, however it’s so smart it regularly takes 1-2 hour problems for me and turns them into 10 mins ones. That is definitely not dumb from my perspective, but obviously it’s also not smart in it will give me profound understanding of something, but ok whatever, it’s still a massive productivity booster for many problem.

When you call it dumb, what do you mean? Can you give some examples?

Please don’t give computational examples we all already understand it does inference and doesn’t have floating point computational capabilities or reasoning, and so many give such examples for some silly reason.

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