ML

semitope

I've actually had an insurance where it was better. Google was giving me more crap. Usually Google is better but I can see them shifting towards sucking. Especially if Ms can get search results processed by ML models to some useful degree first.

Spara-Extreme

Yes but Tsla was stupidly valued to begin with - all of its insane market cap was based on the premise that it had a technical moat around batteries, charging, and ai. Turns out it can’t even compete on cars anymore and it’s ai got thrashed by Mercedes of all companies (which doesn’t even use ML). Tesla is the pimple that isn’t anywhere near done bursting.

natures3

Yeah Neural networks for unsupervised learning I guess. I think it was good move for FSD v12 and beyond to let the new ML framework learn real time on edge cases than to manually code edge cases with C++. Flywheel for ML.

thisoneismineallmine

ML was a thing years ago. DeepMind was acquired by Google in 2014... So nobody "copied", they simply accelerated their release dates. 

Maximum_Reputation96

Vegas Golden Knights ML since my old coach plays for them

Pirwzy

It looks like the data ML shows 401(k) account holders doesn't include the current month. It only shows details of the last previous month and the quarter ending in that month. Being three weeks into April right now there are three weeks of data not being shown to me, and most of the funds I've allocated into have had big drops just in April.

Pirwzy

[This](https://imgur.com/a/ntGALip) is everything that ML shows me.

thunder_jaxx

That seems very reductionistic. Have you ever tried using Full self driving before? Have you ever sat in a fully self driven car ? I understand people’s skepticism but the current tech is leaps ahead of the competition given what it can do. Additionally this is not an e-commerce site that any person on the road can copy it by just being clever. Machine learning is not child’s play. The work / setup Tesla has for its ML is decades ahead of the competition. Even if someone manages to copy it, they still don’t have the data advantage that Tesla lives with. On accidents: Yes there will be accidents, it is just not possible to launch something like this without accidents. other auto manufacturers don’t even have the same level of capabilities ( try making the car drive itself on the streets of SF) and I can gaurentee you when they reach the level Tesla is at, then they will have even more accidents. Additionally accidents are not growing at an alarming rate. If accidents are a reason for Tesla being a bad company then people should never board a Boeing plane since they have had way more casualties than Tesla cars.

ekdaemon

Is the info at ML with an "as of" date of a month ago? A recent drop could cause what you're looking at.

greytoc

Looks like about 7.59% ytd to me - [https://www.troweprice.com/personal-investing/tools/fund-research/TRBCX](https://www.troweprice.com/personal-investing/tools/fund-research/TRBCX) Perhaps you can call ML and have them explain what you are seeing. You may be looking at the effects of dollar cost average contributions - which is why dca is often recommended.

Pirwzy

My employer goes through Merrill Lynch for my 401(k). The information I get about funds from ML looks incorrect and I'm not sure how I'm supposed to make informed decisions on fund allocation when I'm not getting accurate information about the performance of the various funds, or if I'm even reading the information right. For example, the T ROWE PRICE BLUE CHIP GROWTH FUND (TRBCX) according to what ML is telling me, has a total return for the last month of 2.11% and year-to-date of 14.05%. However, when I look up the fund and check MarketWatch, the chart is clearly showing that the fund is negative for the month at -6.09% and a year-to-date of just 7.59%. Are they even the same metrics and which am I to believe?

Educational-Air-685

I have been using the ML web app for such. This is a 2 step process. First step is to get your buy order executed, typically market or limit price. Let’s assume you placed an order at $1 premium, so total expense is $100 + fees. Second immediate step is to put “SELL to Close” stop or stop limit order. Let’s say you want the initial SL to be at 25%, you put a stop price of 0.75, order cost would bd $75. I suggest to put this as GTC. Once the trade starts going in your favor, say the premium is gone up 25% ($125), you can edit the 2nd (Sell) order up, say to $1 ($100). You have to manually trail your profits, they don’t have automated way to setup percentage based orders. The challenge I have is with spreads, will create a new thread for suggestions or alternate / substitute approaches.

nanotothemoon

Have you heard of AI? “AI” is a predictive machine learning model that predicts the next word (in the subset of ML that is language processing). It requires a lot of hardware resources to run and train/build/test these, and in general ML and Deep Learning type computing is exploding in popularity. Right now, everyone is racing to get the hardware to power this stuff, but none of the hardware out there was specifically designed for this. Nvidia has a dominant lead on the hardware design and they can’t keep up with demand for production. But GPUs were designed to process graphics. In fact, ML/LLM computations are handled better on the CPU, but those are not specifically designed to leverage neural computing either. That’s what Neural Processing Units (NPUs) are for. And it just so happens that the new M chips have a 16 core NPU directly embedded with the RAM (which happens to be another major constraint for loading LLMs). Initially, it was unclear what these NPUs were going to be used for. Google also has its tensor neural chip (though it’s currently hampered by being on the backs of Samsung). And these companies put this stuff in place back in 2018-2019 to handle some niche ML closed projects like Google Lens, Translate, Face Unlock, Siri, Speech dictation, etc etc. But everything has changed now in the last year in regards to the demand for ML computing. Apple just announced the new M4 chip will have more cores in their NPU (the first ever increase of cores on the NPU) along with new “AI enhancements”. Nvidia is also rumored to be releasing an AI specific chip in 2025 (either some kind of NPU or possibly even an LPU like Grok’s commercial chip design which stands for Language Processing unit being designed for an even more specific ML computation). I’m not saying Nvidia and Apple are in the same boat. Nvidia is a leading company in this field for many other reasons. However, the way I see it, Apple has sort of accidentally lucked into a big heard start with their chip design. At least in the B2C arena. Because all they need to do is open up more access to those embedded NPUs for everyone to leverage their models on and many developers can get a lot of work done with simply their MacBooks instead of having to build a server with an expensive Nvdia GPU that wasn’t even designed to do neural processing. But currently MacBooks aren’t quite enough to do big big models. So here now we have an exploding demand and a clear path for Apple to meet that demand, and it sounds like that’s exactly what they plan on doing based on recent announcements. Nvidia has a similar setup. Google? Idk. They’ve been in the ML game a while now so software wise they are ahead (although their LLMs are not inspiring anyone lately). Hardware wise they are very behind, despite having experience with their Tensor chip design. Idk, maybe they will take advantage with all of the data they have? Their path is less clear to me.

nanotothemoon

They are ahead on hardware for ML. Can’t speak to 20 years, but the next 5? Yes

nanotothemoon

NPUs. I view Apple aligning with Nvidia for keeping ahead of the curve for hardware that can train LLMs and all ML tasks moving forward. If you think Nvidia is good long term, so is Apple imo

thunder_jaxx

Maybe I am baised here. But the only reason I like my tesla is because of FSD. I have the car drive me everywhere. in-city roads/ on free ways, you name it. And it is pretty impressive what it can do in city traffic. Maybe other cars can do the same but I haven't seen any other can in the same pricepoint that gave me such a feature. The technology is also not as trivial to build as most speculate. Huge leap between even current self driving and lane-centered-assist. For Ford to catch up to Tesla(in FSD), they will need to build out an entire Self driving ML/Software infra structure that only has value when most cars are integrated with it (which they wont be until a few years even after they launch it).

nandeep007

You can't used fsd in India, nobody follows rules. ML would get confused what to do and just ram into cars lol

chilpanduk

Traditional ML is closer to statistical learning, not GenAI. Sure, it’s nowhere close to AGI but it’s not some variation of statistics either.

btoned

This. It's no different than betting the ML on a perceived blowout. If the team in question turns around, those +1200 odds can turn -100 in a heartbeat. 10x gains if you time it right. In OPs case he did this overnight which is akin to flipping a coin.

2beatenup

These jokers came to me with the same 1% fees on total assets. I told them I’ll give you 10% on gross profits. Do you have the balls to take this offer if you think you are really that good? They could not say a word. I have ML, Fidelity, Sofi and multiple accounts. I do a good 20-27%. How? I educated myself and I very actively manage my own funds. I am in control and it’s fun. They still send me new guys to talk to… my response is the same 10% and beat the market more than I can then we will talk. And I am not even a financial or market person. Just a Joe blow in a 9-5 corporate job. Most IMPORTANTLY: Do I do mistakes and lose money. YES but that is within my scale of risk management and I still beat the market. Long story short. Educate yourself and manage your own money. 1% my arse…

Maximum_Reputation96

76ers ML

chimmychimchum

Nvidia have been working in the quantum space for a long time. They have sown seeds there with cuquantum the same way they sowed seeds with cuda for ML/AI. Nvidia will be at the forefront of the quantum wave as they are the forefront of the ai wave.

TQuake

Man I don’t doubt that NVIDIA has a huge lead on the competition, but no one, not even he, can accurately predict if/when others will catch up. He simply doesn’t have enough knowledge of the competition’s internal research, or how AI tasks may evolve. I agree it’s unlikely anyone beats or catches up to NVIDIA anytime soon, but that doesn’t mean no one could develop a compelling alternative. If a company with a huge need for GPUs could develop their own chips in house and get them at cost, even if they’re worse than NVIDIA’s offerings. It could be compelling enough from a cost perspective to disrupt NVIDIA’s business. Obviously not many companies are anywhere close to being able to do that, I think Google is the best positioned, maybe Apple too since they have a lot of chip design experience, but they seem to lack the motivation with their ML/AI services seemingly being much smaller.

script_sibi

This regard works in ML, doubt he’s competent enough on the hardware side for this claim to have any weight.

Shronx_

Sure, go believe that. Then, next year, IBM or some other company drops a quantum technology that accelerates ML training and inference by orders of magnitude and NVIDIA is going to fall like a stone, transferring it's market cap to the next star on the rising.

CriticallyThougt

Watching you regards talk about something you don’t know is fun. AMD will continue to eat INTELs lunch but NVIDIA’s moat mostly lies in its CUDA software, which is the industry’s gold standard for AI/ML training Theoretically AMD could make chips that are both faster and cheaper than NVIDIA and it wouldn’t matter much unless they also deliver on the program side. CUDA is a walled garden, a golden walled garden that is highly optimized to fuck.

Immediate-Product167

He's a great ML researcher but he has no advantage in hardware.

UsefulReplacement

Don't you think that if Apple was capable of fixing Siri, in terms of both AI know-how and in-house NLP and ML talent, they would've done it by now? Even before GenAI, their assistant was at least 10x worse than Google Assistant. They also moved the compute to on-device, which will prevent them from running any of the large / advanced GenAI models. Apple's core competency is human interfaces and the integration of software and hardware. AI has nothing to do with that. They have a lot of cash and a bunch of software engineers, but, you know, so do a lot of tech companies. So even if they go "all in on AI", what makes you think they'll be able to pull it off and won't be another Apple Car-like project? Also, who in upper management has any deep knowledge in AI? Seems nobody, as they learned about GenAI about the same time as the general public.

mortgagepants

glad to see another money stuff reader. (unless you are ML himself, in which case, everything is securities fraud!)

The-Kingsman

That's incorrect. They're an AI/ML company, they just don't have any good AI/ML platforms yet.

SwellsInMoisture

Amazing and thanks for the offer! I recently took some ML/AI courses and it's hard not to see EVERYTHING as an MLE problem now!

moosebearbeer

It will be nvidia. The market has yet to appropriately price in the cost of video-based generative AI. Video takes orders of magnitude more compute resources than text-based LMs, at the minimum let's say 10x more compute resources. The results from Sora look very promising, far better than was expected. And when it was released on Feb 15th, nvidia stock didn't react at all. I believe nvidia will reach 10 trillion by 2026, as more video-based ML models are experiment with.

DD_equals_doodoo

Are you intentionally missing the point? Someone claimed that CUDA is a moat for NVIDIA. You can run it on an AMD. Even then, there's other options that are essentially equivalent and/or growing. It's nonsensical to argue that CUDA is a moat for NVIDIA. It isn't. Don't get me wrong, I use NVIDIA for ML/AI, but there are other options to CUDA and will be more in the future also.

SwellsInMoisture

Ticker Symbol: JPM P/E: 11.27 P/E Rank: 83.53 P/S: 3.08 P/S Rank: 38.18 P/B: 1.77 P/B Rank: 59.49 P/FCF: 40.46 P/FCF Rank: 43.29 SHYield: 4.31% SHYield Rank: 74.24 EV/EBITDA: 10000.00 EV/EBITDA Rank: 16.44 Overall Score: 315.18 6 month price momentum: 25.07% Ticker Symbol: META P/E: 34.36 P/E Rank: 50.42 P/S: 9.67 P/S Rank: 15.39 P/B: 8.56 P/B Rank: 16.07 P/FCF: 29.76 P/FCF Rank: 48.84 SHYield: 1.83% SHYield Rank: 55.87 EV/EBITDA: 20.81 EV/EBITDA Rank: 46.82 Overall Score: 233.41 6 month price momentum: 56.15% Ticker Symbol: VRTX P/E: 28.55 P/E Rank: 55.11 P/S: 10.41 P/S Rank: 14.65 P/B: 5.81 P/B Rank: 22.43 P/FCF: 30.70 P/FCF Rank: 48.00 SHYield: 0.29% SHYield Rank: 41.75 EV/EBITDA: 20.55 EV/EBITDA Rank: 47.21 Overall Score: 229.15 6 month price momentum: 8.57% Ticker Symbol: GOOGL P/E: 27.17 P/E Rank: 56.83 P/S: 6.38 P/S Rank: 20.90 P/B: 6.94 P/B Rank: 19.32 P/FCF: 28.22 P/FCF Rank: 50.21 SHYield: 3.04% SHYield Rank: 65.25 EV/EBITDA: 18.77 EV/EBITDA Rank: 49.93 Overall Score: 262.45 6 month price momentum: 12.22% Ticker Symbol: PFE P/E: 71.95 P/E Rank: 39.96 P/S: 2.50 P/S Rank: 44.58 P/B: 1.64 P/B Rank: 62.43 P/FCF: 30.47 P/FCF Rank: 48.25 SHYield: 6.57% SHYield Rank: 86.33 EV/EBITDA: 18.14 EV/EBITDA Rank: 50.99 Overall Score: 332.55 6 month price momentum: -21.90% Ticker Symbol: PYPL P/E: 16.79 P/E Rank: 71.90 P/S: 2.29 P/S Rank: 47.42 P/B: 3.29 P/B Rank: 36.66 P/FCF: 16.11 P/FCF Rank: 65.00 SHYield: 6.69% SHYield Rank: 86.87 EV/EBITDA: 12.31 EV/EBITDA Rank: 66.68 Overall Score: 374.52 6 month price momentum: 11.23% Ticker Symbol: TSN P/E: 10000.00 P/E Rank: 17.15 P/S: 0.38 P/S Rank: 92.82 P/B: 1.11 P/B Rank: 79.22 P/FCF: 34.45 P/FCF Rank: 45.58 SHYield: 3.56% SHYield Rank: 69.14 EV/EBITDA: 13.90 EV/EBITDA Rank: 61.90 Overall Score: 365.81 6 month price momentum: 19.85% Ticker Symbol: QCOM P/E: 24.82 P/E Rank: 59.68 P/S: 5.27 P/S Rank: 24.17 P/B: 8.31 P/B Rank: 16.58 P/FCF: 19.33 P/FCF Rank: 60.00 SHYield: 3.01% SHYield Rank: 64.83 EV/EBITDA: 17.93 EV/EBITDA Rank: 51.30 Overall Score: 276.55 6 month price momentum: 54.15% I don't have a GitHub directory, but it's not a bad idea to create one! I've got a couple different ML-based financial modelling projects I've been tinkering on, but they're definitely not ready for sharing.

big-rob512

Yea, it's a combination of hardware and software, gaming cards are easier to explain but AMDs cards just in regards to brute force are technically faster they have better rasterization performance. But over all Nvidias cards are better because they include tensor cores for ML algorithms, and Raytracing cores, on top of Cuda cores so they can do things that aren't even possible with AMD like give you a path traced scene at a playable frame rate because it takes advantage of the entire set of hardware. Nvidia will probably always have the best total package whether its for gaming, or AI.

FruitnVeggie

**Matt Laslo**: “Have you heard anything about DEA and the administration moving?” **Cory Booker:** “I have heard a lot about it.” **ML**: “I've heard rumors that it might be April 15th — have you heard that?” **CB:** “I do not want to comment on that.” *Booker laughs as he hops onto elevator with his aide.* **ML**: “Ooooh — I'm warm?” **CB:** “Yes...” [https://www.askapol.com/p/cory-booker-on-dea-rescheduling-marijuana](https://www.askapol.com/p/cory-booker-on-dea-rescheduling-marijuana) I'm hopeful but the vast majority of rumors turn out to be duds so be prepared for nothing to happen.

roloplex

There aren't the GOP votes to pass SAFE. been asking for years. NAME THE 10+ GOP senators that are voting yes on SAFE. You should also know better that the Senate ML and a governership are completely different positions. And Desantis? who is actively working against the cannabis industry? really?

take_five

https://www.reuters.com/article/us-usa-trump-fed/trump-calls-loco-federal-reserve-too-aggressive-idUSKCN1ML1TA/

CucumberPopcorn

A lot of companies are using "AI" and "ML" to attract investors. A lot of these companies aren't meaningfully using AI or ML in any way that would add anything to their business. If you want to do better than sports betting, don't gamble on individual companies without a deeper knowledge of the company. Even then, investing more broadly in a fund (ETF) that holds pieces of many companies that may benefit significantly from AI would be a safer bet. But at the end of the day, especially given that you say you don't understand the stock market well, doing some self-learning on fundamentals would help you to get a better sense of general investment strategies.

YouMissedNVDA

Yup - that is the "gamble", but considering the ChatGPT moment was not driven by a fundamental action of them, but an exceptional result from their regular business, I see blue skies ahead. Blackwell will be the first hardware release that at least knew the scale of demand ahead of time. Hopper got smothered with demand part way through a regular cycle. We literally have not even seen the first hardware iteration amidst this next-gen ML revolution. Imagine where we might be in 5 iterations? And sure, competition is coming. Competition was always there, and coming. Fact is, NVDA stands head and shoulders above their peers, always has, and there is no real indication or justification for that to suddenly change. If anything, the available resources for them to throw at the problem has never been greater. And on saturation - the market for compute is not like most. "There is incredible opportunity in markets where solutions are never good enough", paraphrased from Jensen. Think about it: compute costs are incredibly deflationary, on the order of 100x cheaper over 10 years. In consumer markets, that kind of deflation would cause a reckoning - who would buy today if they can just hold on to their phone/computer for another year and get the same thing for so much less? This is why the Fed abhors deflation - it freezes economies. But in compute markets, no one cares if next gen will be less for more, because there is demand/opportunity/productivity on the table NOW, as well as tomorrow. And as we see - compute at scale gates unfathomable possibilities. The world will never have enough compute. Never has, never will. And the platform that nvidia offers facilitates the widest breadth of customers for compute in the world, from robotics to genomics, and so much more. This is a moment decades in the making, and it is just the start.

putreaper

AI is overdone. Super Bowl ads. LLM’s and ML. AWS and TSLA are developing their own GPUs. Current GPU architecture is advanced beyond the processing needs of LLM and ML applications. NaCl batteries to store solar energy is the next boom.

Advanced-Speaker-729

cocoa futures are overheated more than the surface of Venus. There is no cataclysmic yield event. The drop is predicted to be 10% at maximum. So the 450% price increase is hugely overblown. Also an AI ML software has been developed to recognise hidden cocoa plantations that aren't recorded in efforts to disguise deforestation. They estimate that second biggest producer, Ghana are understating their cocoa plantation figures by 43%.... That is massive. There is not a supply issue at all. Simply a cornering aided by bottlenecking.

pascalsAger

H100 is 4 years old now? That must mean NVDA already has another AI Accelerator in the market. Oh wait, no. Blackwell is still being sampled. H100 is NVDA most advanced AI chip in production and also the one they use for their ML perf benchmark LAST MONTH. 500+ upvotes for a demonstrably false comment:

IHadTacosYesterday

Also, idiot CNBC analysts that have no idea that ChatGPT4 wouldn't even exist if for not a whitepaper on "Transformer Networks" that was published by Google ML engineers in 2017. All they could talk about was how Google had to play catchup to everybody else, when Google was actually leading the field

That-Intern-7452

Intel builds chips? Last I checked, they build electric heaters that sometimes compute a bit on the side. AI chip? More like a heater that tries to run an ML model

Fikkafikation

Yup, that's very true. I was so impressed by their digital currency revolution since covid and demonetization. So well integrated from the smallest of the sellers to the biggest corporate. I mean challenging VISA & Mastercard becoz they charge exorbitant fee due to which small businesses suffer and creating your own is a massive feat for the betterment. I don't recommend AD, ML/AI is riddled with false positives atm. Of course 25yrs down the line - who knows.

butts-kapinsky

I am a physicist. From time to time, I dabble with ML. Both for work and for personal projects. Amazon was lying about their product. It's not that hard to understand.

Fikkafikation

That's not how it works. My example was not related to Tesla, it was a generic issue with ML/AI about false positives. Irrespective of your training data they currently exist at high enough levels which cannot be deemed safe wrt to autonomously driving a car.

Fikkafikation

I'll just say, I work in the ML/AI field too and the amount of false positives it gives is ridiculously high. For image generation where you might end up with some anomalies is acceptable. When dealing with people's lives - it's not. A beta testing software is not for mainstream prime time. Period.

xRICHxPIANAx

Iowa ML?

LivingDracula

Forecast volatility with ML, generate strikes with ML for P, R1-4, S1-4, plug those into a modified Black-Scholes to generate probabilities of assignment/expiration. Downvote me for being having a wrinkled brain...

Moterboat76

AI/ML All Indian Manual Labor

forrestthewoods

AMD CPUs thoroughly defanged Intel. Their gaming GPUs are ok but not spectacular. Nvidia drivers have an outrageous amount of “per application” support – but that’s not relevant to ML server workloads. The world is *desperate* for an Nvidia competitor.  NVDA margins are what, like 70%? That’s bonkers. It’s not a matter of “if” AMD produces a decent AI competitor but “when”. However that when is likely 4 or 5 years away. NVDA has a safe lead now and with the upcoming Blackwell. So *maybe* after that. AMD is a buy and HODL stock. Not for regarded FDs.

New-Set-3059

I downloaded the activity into excel to show trades for easy to read, otherwise as they fill different transactions which makes very hard to read from a direct screenshot from ML app.

eht_amgine_enihcam

The problem is, you're regarded and you don't understand how AI works. The hype behind AI is the genius idea that if you can get a computer to do all of the work for you, it's free and you'll make a ton of money. Many CEO's have had this idea, but get tripped up with the pesky step of how to get the computer to do all the work for you. ML is good at maximising around an objective function by taking nonlinear relationships between a feature vector (pattern recognition and duplication). Much of the hype is generated by LLM's such as chatGPT. These learn by tokenising language, and then predicting what the most likely next token is based on the previous ones. This will by design only create generic stories. A suitable function to maximise around (audience reviews etc) may be found but I think LLM's are limited by design. AI is a ways off from creating "good" stories that you'd actually want to watch. I think AI will come to around 80% of a film you want to watch and then hit roadblocks, similar to what self driving cars are having. Things like Sora are very impressive, but they will likely be a supplementary tool. Images can be generated but there has to be advances to connect these into a cohesive story. Very plot light films and porn are likely exceptions.

BlurredSight

This is a big reason why later models of Teslas can't park for shit, the older 2015-16 models had no issues parallel parking because it was built upon sensor information. The later editions went for a more camera/ML based design leading to it needing human intervention. Yiannimize did this test and Tesla was the worst of all 6-7 cars he tested for parking, the others were BMW, Mercedes, and Audi

gamarrex21

Right now that problem is the only one I can think of. Interior facing cameras solve a lot of problems, especially for manned taxis. Alas, you’ve got a point. People tend to be malicious when nobody is around to immediately punish them. At the same time, I can also see Tesla offering all hours cleaning service at Tesla stores (not technically dealerships?) and other verified partners. Someone pukes, ML camera system detects abnormality, sends owner a push notification with snapshots of what it sees, owner can choose to send it home or to the nearest cleaning center for a $ fee. Owner submits complaint ticket about rider with video for review, Tesla judges the rider responsible for a $$ cleaning fee. I’m just getting carried away now

Purple_Tangelo_399

this is the dumbest shit ever. electric cars are going to revolutionize transport? the highway system is fucked in the ass and is never going to be truly rehabilitated. it doesnt matter how smart the car is if it cant mother fucking fly and flying cars are actually only a wave or two of ML models after self-driving. its a hardware and regulation problem and those get solved real fast once there is money to be made

nero626

Nvidias advantage is definitely partially in hardware, the reason however is not as simple as "no one else is offering competitive hardware", but rather the network effect created by the immense popularity of PyTorch in the ML community and its reliance on over ~2200 unique operators that tensor and CUDA cores natively support, and that number is constantly growing, everyone else is just playing catch up, if they find the incentive to focus on it that is. the talent level required to train a massive model with high FLOPS utilization on a GPU grows increasingly higher because of all the tricks needed to extract maximum performance. the only way to break the vicious cycle is for the software that runs models on Nvidia GPUs to transfer seamlessly to other hardware with as little effort as possible. as model architectures stabilize and abstractions from PyTorch 2.0, OpenAI Triton, and MLOps firms such as MosaicML become the default, the architecture and economics of the chip solution starts to become the biggest driver of the purchase rather than the ease of use afforded to it by Nvidia’s superior software

butts-kapinsky

I'm a physicist who dabbles around with ML from time to time. Maybe hold off on disparaging the way I think until you get to know me a bit better. Amazon did not take a risk. They ran a scam. They were touting advanced AI when all along the product was just outsourced labour. 

butts-kapinsky

>  Reviewing and correcting data is still ML training regardless if it's 1% or 90% Technically correct. The required human overview would still be utilized for training purposes. But the primary reason behind reviewing 70% of purchases wouldn't be for annotation. It's quality control.

MarxKnewBest

This is stupid. I’m not saying Amazon can’t get up to shady stuff but they’re clearly talking about some sort of ML model training or audit work and trying to make it seem like it’s a man-behind-the-ATM thing. Ignorant and lazy journalist.

ogaat

It was a Hail Mary by Jensen Huang around 2015, I think. Nvidia was lagging in the market and he decided to put all the company's eggs in the software for GPU computing market. Luckily for him, the scientific community, the crypto community and then the ML communities developed Python libraries for number crunching using GPUs because those acted as coprocessors and did not bog the CPU down. Nvidia paid attention to their needs and went out of their way to serve those markets while others were focused on the much bigger gaming markets. Then OpenAI happened and rest is history.

ogaat

Nvidia's advantage is not in the hardware. It is in their proprietary CUDA software which is the gold standard for running AI/ML code only on Nvidia GPUs and has a near monopoly for that field. No one else has a similar moat.

leowashisname-2

-1. Betting the ML on an arbitrary baseball game. Jk.

Appropriate_Fold8814

That's not how this works. Reviewing and correcting data is still ML training regardless if it's 1% or 90% The goals is to reduce the input required until the model is self sufficient or at least requires very little correction. It is possible that that optimization wasn't progressing far enough which could lead to stopping the program. But it could also just be budgets or policy changes.

Appropriate_Fold8814

This is just blatantly not true. It's been grabbed up by social media as some giant gotcha when it's not. Training ML models requires not just training but also requires adjustment through retraining. That adjustment happens through feeding it new data and correcting mistakes. That data has to come from real world. So ya to optimize the model you need a high level of human input. What do you all think those inline captcha things are doing? Same thing. This is a non-story that doesn't understand ML development.

juancuneo

Anybody who has worked at Amazon over the past 10-15 years knows that every ML model was trained by thousands of Indians. It’s just how you train ML models. This is not news.

Brushermans

If it wasn't edge cases (70% is NOT an edge case) then it doesn't mean it wasn't training. A supervised ML model needs *someone* to label the data; if it could do it itself then we wouldn't need it in the first place.

NiceAsset

Believe it or not, AI and ML algos still utilize “actually Indians” to defeat captchas and continue their regularly scheduled automation

joshocar

This is one of those cases where when you know what about a topic it becomes clear that journalist are mostly terrible at getting basic science/engineering facts correct. I work in tech and this article gets basic facts about how the systems like this work plainly wrong. It reads like the author knows nothing about ML and doesn't care to learn about it. Those 1000 people in India were very likely reviewing low confidence events output by the model and likely random high confidence events to evaluate the model. That is how these things work. There are always edge cases that need a human in the loop to evaluate. When you do this long enough you can get the model to be very accurate, but you will always need a human in the loop path for edge cases, evaluation and training.

saucesultan

FYI, this is written super incorrectly. The Indian outsourced QA folks were not acting as checkout people at all. They were labeling data. Which, any good ML team will tell you is just standard practice. Google maps has tons of offshore folks labeling data as a truth-seeking strategy to make sure that as automated actions take place, that they are using human intervention at random samples to make sure the AI processes are taking places accurately. This wasn't just a bunch of random Indian dudes watching people check out and generating invoices for it lol.

soviet-sobriquet

The ML model struggled with [700 out of 1000 camera feed snippets](https://gizmodo.com/amazon-reportedly-ditches-just-walk-out-grocery-stores-1851381116). That's not a useful model.

joshocar

The 1000 people in India are not scanning every single camera feed like security guards, they are being sent snippets for things that the ML model struggled with. It's human in the loop verification of the ML model performance. This 1000 people will likely just move to another application that uses mmML models.

Mizz141

Because intel is literally pumping out new fabs in the US to get away from TSMC, And guess what sucks a lot of money: building fabs Along with Nvidia having the de-facto monopoly on ML due to CUDA, and AMD just dunking on them with Zen

xRICHxPIANAx

My buddy claims he’s up, all time, sports betting. I swear he has 5-10 bets going 24/7. His last 3 picks.. LSU women’s ML Houston ML Creighton ML 

Spara-Extreme

I think it’s important to understand the motivation here. The compute clusters for ML/AI require 20-50MW per cluster (or more)- and the current rate of energy’s investment isn’t keeping up. Nuclear is a good way to create base load, but investing in energy generation broadly would position you to take advantage of this long term trend.

Physical_Love6822

The really interesting thing is almost nobody who doesn't work with ML actually knows what "AI" of today actually is or what it can do. And what most people are aware of is just LLM's when that's just one facet of ML.

xFblthpx

I think it’s overblown since the largest price movements in the market come from news, not trends. Historical data is a poor predictor of future value when it comes to the stock market, and all ML is fundamentally trained on historic data. Sentiments role in the market may be subtracted out by ai, but ai modeling can’t predict beyond the latent space in its training data.

cc_apt107

The reason this may take longer than you think is that there is not sufficient information embedded within the stock market or even that plus all companies’ fundamentals alone to create models which remain valid in all circumstances. Every so often there will be an unforeseen black swan event like the ‘08 housing crisis or COVID which the ML models are poorly suited to handling well. On a smaller scale, oftentimes the run ups we see in stock prices are driven by intangible, qualitative shifts in market participants’ expectations and psychology. To make an ML model which can really predict price moves accurately, you would have to also include almost all news media and try to correlate that to moves. While this will happen one day, it is still prohibitively expensive at this point. While we don’t know what Renaissance technologies is doing with certainty, my understanding is they use complex mathematical techniques but not machine learning per se. Again, deploying ML models with the necessary sophistication to accurately predict the market will be extremely expensive. It’ll happen, but we will need to see the price come down. Just my two cents — I’m just some guy.

OrwellWhatever

Talk to anyone who actually works with ML models, though, and they'll tell you the opposite of what fortune 500 decision makers are saying 🤷‍♂️

ShaidarHaran2

They've already been deploying the *inferencing* portion for local compute in your pocket in neural engines for years. What I'm wondering about is the *training* portion, which companies like Meta which also have no AWS or Azure component are still buying ungodly amounts of H100s/B100s etc for, because CUDA already has extensive ML libraries built out for it so as they're all racing to train their own AI models, not using Nvidia is just too much of a leg down, hence why they have almost all the profit share in this space right now.

ShaidarHaran2

No shit lol, but I wonder what the state of them even buying training chips is, if it's as large as the rest, are they using CUDA ML libraries etc etc. Any major AI player that isn't is handicapping themselves by not using the massive ML libraries already built out for CUDA, which is the actual thing that makes Nvidia so sticky for training hardware more so than the hardware itself. I thought it was interesting that after many years of not mentioning each other at all after the MacBook Pro GPU failure spat, Jen-Hsun all of a sudden mentioned Apple a few times in as many days starting with the Vision Pro at GDC. When John Ternus and Jony Srouji were asked about if they were buying Nvidia chips they just said we can't say. Cramer even asked him how people could "get right" by Jen-Hsun and Jen-Hsun simply answered "oh, everyone's already right by me", indicating he'd work with anyone. There might be something, but if they're letting this decade+ old spat hold them back, that would be the stupidest thing

nightstalker30

I was just having a conversation with a former work colleague last week about the fact that 10 years ago, we were selling ML as the next big thing with hardware/software topology & dependency mapping while AI was basically coming along for the ride with things like chat bots. Somewhere along the way, AI got top billing and became the hot buzz term.

throwawaygoawaynz

And ChatGPT wasn’t the first AI model, or even the first LLM. Like the iPhone though it was the first one to enter the Zeitgeist of the consumer, and push AI from being something executives and business decision makers didn’t really understand (too techy like the first touchscreen smartphones), to something usable. Also the Nvidia moat is extremely strong due to a combination of price/performance, CUDA, and InfiniBand. Have you actually tried to use AMD’s equivalent? It’s awful and miles behind. And no company betting billions on ML training is going elsewhere until their tech is proven to work *at scale*.

TheDumper44

An acquaintance has been running a multivariate ML fund for at least 3 years now and I know he is not the first. He came from Bridgewater. I am sure they do it too.

reddit_0024

Their chip is for embedded controller stuff, like a general purpose ARM CPU, and may have certain level of GPU processing power or hardware accelerated ML, but far from special purpose chip for AI, unless there is something I don't know about.

WittyProfile

He's saying that AI is being slapped on ML to make it more sensational. AI used to refer to what is now called AGI. They made the term AGI because AI lost all meaning.

milnescapital

This is partially true Some of the ML algos make money but due to the nature of the models they don’t know exactly why

thicc_dads_club

What you think of as “AI” is really just a couple specific kinds of machine learning (ML). They’re all based on tokenized natural language and deep, non-recurrent models. They aren’t suitable for trading because trading has little to do with natural language. Most major developments in ML (not the products aimed at users but the math) are published, and there’s no secret about who the experts are in the field, so there’s no reason to think there’s a secret 10X capability that “they” have access to. ML is a very wide field. Yes, LLMs like ChatGPT are ML, but so are “boring” things like fitting nonlinear models to extract seasonality effects from weather records. The “boring” stuff is already used extensively in analysis and trading. The sexy LLM stuff is impressive because you can interact with it in natural language, but in trading you have hard numbers, so really the boring statistical stuff is more applicable. Unlike chat bots, which didn’t demonstrate much value or see much investment until recently, applications of ML in financial sectors are obvious and well-funded. So it isn’t likely that there’s a gap that can be filled by tech transfer from recent advances in chat bots or image generators. There is plenty of ongoing research and open questions about financial modeling that are investigated by universities and analytics companies and quantitative funds. It’s a competitive landscape so it’s hard for anybody to corner even a small market, much less the entire world economy! Fundamentals still matter. Yeah, there’s irrational exuberance at times, but there always is, that’s typical *human* behavior! tl;dr - you’re a high ass moron, chat gpt is neat but there’s no secret AI controlling the stock market into perpetual extreme growth, just a landscape of competitive firms using much-less-sexy ML to try and gain advantage over one another, which often means better prediction of fundamentals.

SomebodyElseKnows

As Elon once said our God is unlikely a csv file. There are plenty of things AI/ML can't do... just like humans. Quality of data is everything.

YeeterSkeeter9269

Snowflake is also working on a direct competitor to Pinecone (company that sells vector database) which are absolutely critical for ML/AI capabilities. I’m very bullish on Snowflake with the onset of the AI economic boom

MCX23

you should research python for finance. this guy is building his own ML model most likely. research GARCH on youtube, modeling vol, etc. there’s a whole community of quants that trade this way.

postachioo

Lakers and dodgers ML

hgreenblatt

ML ? Merrill Lynch ? Not exactly a bi-word in option trading. Tos Analytic Risk Profile on the other hand is. So what does this vaulted ML give you , 8 different models to use with the current option PRICES or an excel spread sheet with ONE vol for all strikes. https://app.screencast.com/ud9rmOQN1UCN1

LivingDracula

Surely you are joking lol That's not what we're talking about. We're talking about the math and logic behind the greeks, which is what these models do. Personally, I use a lot of ML with "technical features" and these models to get various strikes, then predict the probability of assignment and expiration based off forecasting the violatity and price of the underlying asset. It's very useful for marketing making.

nickerzb

dbt cloud is just a runner. No compute happening there. It submits the same SQL as dbt core. Snowflake for ML has some good stuff in private/public preview but I wouldn't use it for ML in production yet (except for very specific instances). Airflow is a great addition if you need to orchestrate things along with dbt. There's also cosmos which is an Airflow operator for dbt that takes your dbt DAG and integrates it into your Airflow DAG. You can also just trigger your dbt cloud job/runner from Airflow via API as well. However, if all you care about is orchestrating your transformations (separate of ingestion) then dbt is a great solution.

nickerzb

I don't think you have a correct understanding of what dbt is. It's just generating SQL and submitting that to Snowflake in an order driven by the DAG it generates. All the computer/transformation happens within Snowflake, thus no real cost savings directly (assuming equally performant and solid code being ran through streams/tasks/whatever. People like to say Databricks is cheaper than Snowflake, and while that's true from a strictly dollars per credit perspective, it's really not the full picture. In Databricks you also have to take into account cloud compute, networking, and all the personnel/training involved to upskill on cloud infrastructure and its best practices. You also generally need to know how to write pyspark (although for a lot of data engineering you can just use dbt or delta live table) Alternatively, Snowflake is all inclusive and everything is SQL. That means you can either leverage your existing DBAs or source someone much cheaper with a DBA skillset. Toss in dbt, and everything is still SQL. I work with billion dollar companies on both platforms from both a DE/ML/Analytics space as a chief architect in consulting for context.

po-handz2

The competition is like MS sql server, Amazon redshift, etc databases native to cloud service providers. Snowflake is alot better if you utilize it correctly however is really expensive if you're using it for your whole etl instead of databricks or dbt. From what I've seen and launched, I think snowflake has a role in the front end interaction with AI/ML/GENaI stuff. But I don't think they have a role in AI in terms of training, hosting, other heavy lift functions

arbitrageME

Write down your rules. The clearer, the better. You could be a chart reader or hardcore ML. But if you write down your rules, the numbers don't lie. Don't close a losing position because you can't stomach the 20k drop. Close the position because your written rules told you to.