ML

SquirtleHerder

Doesn’t seem like much of an “AI” company besides having an LLM wrapper in one of their products and using a bit of ML/Data Science in a few of their other products. “AI” is getting thrown around too much these days. Unless you know a lot about health tech and testing, and have a good idea of whether or not hospitals would throw money at their products/services, it seems like a gamble. I’m not a hospital executive nor do I work in healthcare, so I really have no idea myself, but my uneducated guess would be ‘no’ lol Regardless, they seem significantly overvalued based on both their current value and growth potential. On a side note, checking out their job postings, it’s seems they’re throwing a lot of money at Google. Seeing all these startups using GCP makes me more confident about my GOOGL positions, haha.

YouMissedNVDA

AWS started in 2007 or so, and it was ridiculed for being an unprofitable money sink with no foreseeable ROI. Now, AWS is the largest profit center for them, as well as a fundamental backbone to the modern internet. AI/ML is the next evolution of this.

pocketaces27

Tell me you are not in the ML field without saying it

thisoneismineallmine

But not just search, pretty much all the major breakthroughs came out of Google: from ML for spellcheck, Translate, speech recognition, acquisition of DeepMind, TensorFlow, AlphaGo, Tensor Processing Units, WaveNet, Federated Learning, the paper on Transformers. Geoffrey Hinton, the "Godfather of Ai" quit his job at *Google* to warn the world of what was coming. 

UncleGrimm

Yeah, I don’t see a whole lot of a B2B case for these specialized LLMs. Even a year or so ago when people were hyping them up for stuff like supply chain optimization- I don’t understand why you’d use an LLM for that at all. ML analysis already exists in those fields, with the benefits that they don’t “hallucinate” and the outputs can be explained to leadership and stakeholders.

Exact-Tangerine-4121

it's irrational to think that the "competition" is for sure going to catch up. Look at google. huge company. shitload of engineers. look at every cell phone company in china. it's all garbage. the iPhone is only good one. that's not changing. search engine? hey there's a bunch of them. the only good one is google. cloud and AI/ML? Amazon and MSFT. googles cloud and AI is terrible. and all the big tech is buying the shit out of NVDA chips.

InternetSlave

I believe the future of AI monetization at GOOG is infact unknown. As stated they're spending a LOT on AI infrastructure. In the future they can use their own super computers for ML or rent the servers out for other customers to use for their own ML. I think this is why he was hesitant to comment, simply because it's unknown. Goog is spending a lot now because in the future they'll have more data and compute power than anyone. Ai is more than chatbots n

ADelacour

Haha yup. Lots of people have NO idea how fucked up most code bases are, especially really old ones. Show me the ML Algo or AI Method which is capable of solving those clusterfucks. I'd be so glad if I never had to work with an IBM or Oracle product in my life again. Absolute clown shoes.

Fungusjr

There is also the "time-gap-issue". Currently we have lots and lots of hardware that can provide ML/AI services. But we dont have any software services that actually revenues from this. Most manufacturers today only use AI-related services as sidekicks to their current product. Self-driving cars and robots like the Teslabot will be products where AI is close to the primary feature instead of being a sidekick like Siri is to the iPhone today. An iPhone with Siri "AI" features cost the same, because they dont sell non-AI smartphones anymore. But it is still a stupid lump in my pocket, just got some more voice commands. The companies that start to deliver products with like "AI Native" like someone else mentioned in this thread will be the new winners. But that takes time, and we might get a rather chilly "AI-winter" before those services/products arise.

Unusule

It’s just supervised training which has always been a thing with ML, just way larger scale. The idea is a human looks at the result and gives the model feedback, then the model tweaks its algorithm in response. A modern chatbot will definitely have a ton of custom logic outside of just calling the models tho

zennsunni

I hope you're right. I work in an ML division at a FANG and I wish we had instances with those cards (having nigh-on 200 GB of RAM would simplify some Xgboost models with enormous feature sets).

LunchPocket

I work in security field and have used both products. SentinelOne is an excellent product and a growing platform. Crowdstrike is a more mature platform, but I would say their technology on the EDR side is comparable. S1 is heavy ML and AI. Crowdstrike uses similar but they are heavier on the human interaction than S1, and it seems to have backfired due to the people.

StrangeRemark

Yes, I don't have a PHD but have a masters in data science and ML. So probably more than you? As said above, this study does not rule out all potential causative variables, but does control the big ones with a test and control group. And one of these meds is correlatively linked to this issue and the other ones? Like of there were hundreds if people per group and nearly 10% had an eye stroke vs. like 2% in control. The p-value is .001. What other variables would you control for?

NaNaNaNaNaNaNaNaNa65

What’s the ML on the 21 year plan of?

SleepMoneyMaker

Awesome—this is mainly a Monte Carlo quantile estimation-based system - nothing fancy, but it helps encapsulate a nice forecast. I have been using this (and an AI-based system I developed) very successfully in my weekly options trading. I am a data scientist with 20+ years of experience in building decision-support systems based on AI/ML (part of my PhD). Addicted to data and passive income streams :)

entropyweasel

Well you are an admin. Or your windows security posture is pretty relaxed to the point you may as well be. You could just uninstall it, or just null route the traffic in the hosts file. Or any number of things. If you don't have a posture check and on top of that are an admin then sure you can make things not run. But it's a trivial thing to detect that happening and automatically remediate or isolate you. I don't work with the tool myself but seems that your security team got an advanced tool but also needs to mature security basics. Or you are a VIP or noisy developer and they choose to ignore this for business reasons. Since it sounds like your security team is struggling maybe shoot them a link on how to add exceptions to noisy rules, especially ML/behavioral ones for power users like developers. Then they can detect the obvious hacks/malware and contain block things in true emergencies, and you can play with all the scripts, containers, and shellcode that you want. Security is hard. I promise they don't think they are too dumb to use your laptop. More likely they are seeing a ton of stuff all over and other companies breached and trying to secure things as they mature.

StripClubJedi

When you're the first mover on an easy-to-replicate tech, sure. There's nothing easy-to-replicate around the parallel processing power and throughput needed to perform ML, LLM and inference though.

newwobblywheeler

Given thet Blackberry Cylance also provides ML/AI cybersecurity and is a competitor of CRWD, how does it compare to CRWD? Would appreciate your comments and analysis.

TechTuna1200

No, ML is a broad field within AI. Things like deep learning is a technique within ML.

ModestJicama

NBD, I think the most ghastly thing about AI/ML is all these statisticians showing up in the software field and writing god awful python code that looks like it was written by an SE intern.... (and then they come to guys like me asking how to "put it in the cloud" 🙄)

merleau-ponty

nope, ML is broader than what is now referred to as "AI"--you've got it back asswards

merleau-ponty

ML was already being used in many industries in the '00s

AlpsSad1364

ML is just one of the many techniques that have been lumped into "AI", which range from very complex and impressive deep learning algos to very simple rules based decison makinv ("I see you just bought a new bike. Perhaps you would like to buy another new bike?") Most investors aren't smart enough understand or care about the underlying techniques though, what set the boom off was chatGPT in 2023, which is a very impressive demonstration that everyone can use. It is sadly though just a parlour trick which has already reached it's technological limit. The come down is inevitable and only the chip guys will ever make any money out of it.

Western_Objective209

> Here is an easy one. Integrate your LLM in your business with your internal wiki. Large organizations have massive amounts of internal knowledge. And it’s hard to organize and find the right howto page when I need it. Atlassian is going to make so much money if they integrate an LLM with their confluence product. Tie the above into your codebase and you have a much more capable support team that can tie an incident or outage back to the product/owner/jira/codebase instantly. You should actually spend time on r/machinelearning. People have been working on RAG assisted LLMss for search for a year now, and results are pretty poor when the data set becomes large, and you have to spend a shitload of money on ML engineers to work on it and then come back with mediocre results. Literally every company is trying this. You know what's great for searching documents? Search engines. That's solid and well understood computer science, and you can run an Elasticsearch server for a few hundred dollars a year, and the engineer working on it will cost $200k/year less then an ML engineer building a novel LLM with RAG. I use LLMs all the time, but outside of ChatGPT, they all kind of suck. Building a server rack for tens of thousands of dollars that will let you run an LLM that has 1/10th the capability of GPT-4o that costs a few cents per query is just a massive waste of money

cake97

So the tech companies that are driving the increase are in a large part doing that with cloud computing heavily related to AI/ML. Do you think young investors are the group buying MSFT? Are they statistically significant?

BookkeeperNo3239

I have both TSM and INTC for awhile now. Keeping my INTC for geopolitical reasons. With how technologies is evolving, semi will be a demanding sector. Look at all the massive computing platforms that are being built at national labs to perform large computational simulations... and it will continue as other countries are doing the same. New weapon systems can't be developed without massive computations/simulations anymore.... the demand for chips will be insane. Notice that i am not talking about AI/ML here.

geon2k2

Yes, there is a famous case, with ai and tumor detection, in which AI "learned" that in every picture which has a ruller, there is a malignant tumor. And btw that is not what these guys with the GPT call AI, but machine learning, which was available and used for restricted domains since the 60s. In fact GPT is also a more advanced form of ML. But neither of them are inteligences, as most people understand it, but vector and matrix multiplication engines. It has applications but quite limited for now, and considering the 200 billion per year costs with very limited returns i don't see it going very far.

TheDiligentDog

[https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) "It’s estimated to drive a $40 million USD in profit improvement to Klarna in 2024" I'm not saying this completely proves AI will be profitable for everyone but saying "We have no idea that AI will do anything for businesse's top and bottom lines" is a bit off imo. And I'm talking about GenAI ofc if we count ML as well (which we technically should) then it's a no-brainer.

zennsunni

I've never even heard of someone doing enterprise ML on an AMD chip.

No-Elk3522

Wow, that’s wild! Sounds like ML Edge is pulling some sneaky shenanigans.

Metacog_Drivel

ML casually dumping 20% today. Ok, I guess fuck me

MagicalTissue

Hold, these companies have good earnings and have products in the pipeline. Folks are upset about AI/ML being too slow to deliver. But it takes time to mature and the need for stronger datasets is critical for AI/ML growth.

gonemad16

> Machine learning (ML) is a field of study in **artificial intelligence** concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.

gonemad16

ML is still under the AI umbrella

Worf_Of_Wall_St

And it "might" as in some kind of ML was used for finding a slightly more optimal bristle configuration or material or some other part of the design but whatever it is it's not some game changing revenue multiplier.

Ok-Zucchini-1237

Why do people make fun of this? GPUs and ML cores will become extremely important, but the market doesnt even fill basic demand right now, and what's available is overpriced and way beyong possibilities. There are not many companies on this planet that can even build these things, and GPUs increase productivity in tech sectors by large factors, so there's enourmous potential to grow. It's saying that boat constructors are a tiny niche, when we actually just invented sails. \[ML = machine learning\]

herejusttolooksee

People are slightly sensationalizing what LLMs can do. Very powerful. It takes NLU to the next level. It doesn’t solve every problem better than traditional ML approaches.

kaze_san

Are the big players buying regular GPUs for their ML stuff or the specialized AI chips only?

thejumpingsheep2

$2b on around $50m rev? They work in a small segment that isnt particularly high profit and they are unlikely to land long term contracts with any big corporations because those guys will have and own their own system. They are not particularly hard to make any more, especially not if you piggy back on any of the major LLMs, all of which, offer customized solutions (aka custom ML training). Without knowing exactly what the Stellantis deal is, this is highly overbought. Fair valuation, to me would be maybe 10x rev, and only if you can convince me that they are worth the risk, because its still a huge risk given their competitors. That means I would only consider it at the low $1 range. Im a very risk averse investor so even at that price I would likely pass because I dont think these guys survive. I think they need to be bought out and will be bought out once their funds are gone.

new_name_who_dis_

All the "GenAI" tech that came out in the last few years has almost zero relevance or use to self-driving. I say this as someone who actually works in ML.

Spy300

>**BREAKING: BANK OF SPAIN CHIEF - MASSIVE RISE IN ILLNESS ACROSS ENTIRE POPULATION DUE TO COVID-19** >https://x.com/SamuelHurtadoBE/status/1809161477619622062?t=bg8x5Z3m9hPp6ML-MujWvg&s=19 LLY 3000

TrebleInTheChoir

Tech valuations have inflated at different rates than general inflation over that period of time. One example is that their data is also a lot more valuable given they can leverage it for AI/ML.

jdick4297

Nice I got a few too. All ML. Yankees, Mets, Philly, reds, cubs.

bigtimebonerboy

Couple parlays one is Taking Yankees over 9 Sox -1.5 Phillies ML dodgers ML marlins +1.5

ykoreaa

Only the ML whose in love with the girl dressed as a guy bc of her poverty and in the middle of questioning his sexuality, doesn't know the girl, who clearly looks like a girl with short hair, isn't a girl

p-wk

Oh dude. YOLO. Keep the msft on the off chance that it skyrockets with a surprise advancement in AI. I hold Microsoft, Google, Nvidia and Apple for that reason alone. ETFs I’m across VOO and SCHG. Counting on those single stocks long term as I think they’re best set up for dominating AI, ML, and Quantum Computing… and if it doesn’t pan out and I’m wrong, then we’ll still be solid in the ETFs.

Impossible-Roll-6622

Blockchain was a threat to an established industry: banking, and an established control system: central governance. Machine Learning and Artificial Intelligence has been in use for…at least 50 years? The mathematical models underpinning ChatGPT were described in like 1927 or something absurd for classifying and cataloging stars. Its essentially a linear autoregressor…same shit used for automated stock picking, that looks at “what word came next” on billions, trillions, indescribeable-illions of examples and guesses when it sees new things. And obviously that’s a gross simplification. But that’s the gist. And the difference between v1 and v2 and v3 and vCurrent is how many features can we cram into it, How many little on off switches, Yes no, True false, Can we slice this up into to fine tune the output… GPT4 has 1.7 trillion tuneable knobs. 1000x more than GPT3 which was 1000x more than GPT2. And to be honest it still kinda sucks. Thats why NVIDIA is going to keep crushing it. Their cards will be obsolete 2 generations out as these nerds make bigger and bigger neural nets which will spawn an entire secondary market of “budget” ML/AI on the 1 or 2 gen back stuff. The reason “AI” is now huge is not because its “the next big thing” its because general computing is now fast enough and networking is now fast enough and data collection on ***everything*** and ***everyone*** is now robust enough to leverage these insanely hungry algorithms and do it in a way that’s reasonably cost effective to churn out products at scale with insane margins. 40 years ago the computer vision sensors on maverick’s ruskie busting sidewinder missiles costs millions of dollars a piece to build and used lead sulfide and old vacuum tube diodes to adjust voltage to flight surface controllers based on how far away from 0 or 1 the signal was. Neural nets do the same shit today and ukraine can throw some code on a DJI drone with a brick of c4 for a couple thousand bucks to do the same exact thing. We’ve been leveraging and refining this technology for decades, it has just been very expensive, very niche, very private. Now its going mainstream because its comparatively cheap and we can solve problems at scale in a way thats commercially profitable. We weren’t concerned about the per missile profitability of shooting down MiGs during the cold war. Facebook is 100% concerned about the profitability of their algorithm vs tiktok’s algorithm vs not having an algorithm at all. In the eyes of the powers that be, Blockchain is a bug. AI is a feature.

moosebearbeer

Interesting. Among the chip startups, people were calling this one a complete failure. I personally don't know anyone who uses their chips for ML models.

notseelen

very insightful. I work at a security software company and have seen a lot of the softwares we buy and use (like ticketing systems) roll out AI companions that I rarely use Our devs use it more for calculating things quickly, but half the time I'm able to get the answers just as fast from stack exchange, with a higher probability of being right I'm looking forward to AI being used in threat assessments though, I'm thinking that eventually all red and blue teams will need to leverage AI, and that'll be a total game changer....but we're some years off that being generalized. have seen bots that defeat advanced captchas through ML though, that's been kinda cool! so, investing-wise, I think the big question is, which companies will still be around when they finally make this work in 5-15 years

Spy300

>BREAKING: BANK OF SPAIN CHIEF - MASSIVE RISE IN ILLNESS ACROSS ENTIRE POPULATION DUE TO COVID-19 https://x.com/SamuelHurtadoBE/status/1809161477619622062?t=bg8x5Z3m9hPp6ML-MujWvg&s=19

jckstrwfrmwcht

nah, OpenAI is still a partner and even then I'd worry more about the fate of azure, office, windows... they won't be losing any steam on ML R&D by giving up this board seat.

littlered1984

1. Won’t happen. People are already locked in to trust Apple, Microsoft, Google, etc with their precious data. Worst case is each company has their own data center, which is already happening and great for Nvidia. Second, AI training would still require data center sized computing and can’t be done on device. 2. Yes, but that’s also a bet that AI market won’t grow, or that Nvidia will lose their dominance. That’s a general concern that’s been known for a long time that the market has shaken off, except for bears. 3. There’s a lot of hype around those chips, mostly propaganda from the companies developing them. Most are vaporware, terrible, or have limited uses. Go look at ML Perf benchmarks and you’ll see that these companies don’t have either the guts or capability to compete on apples-apples benchmarks.

pascalsAger

Ofcourse. It runs pretty much all the important high level ML libraries. Developers use high level libraries at the end of the day. CUDA is not as big a moat as NVDA bulls say it is.

Echo-Possible

Also Motional (Hyundai). Argo (Ford, VW). Nvidia is entering the field too and has an ungodly amount of money now and controls the critical hardware required. You've got a bunch of Chinese companies testing robotaxis (Baidu, Pony, etc). The reality is even if you believe Tesla's cheap camera only hardware setup will work for L5 operation, then this will be the easiest implementation to copy. Any major auto can retrofit their lineup to add cheap cameras to bumpers and B pillars and be collecting billions of miles of data within a couple years. If a company like Hyundai/Toyota/VW retrofitted their lineup and sold 10-20M of those cars then within 2 years they would be collecting hundreds of billions of miles of data annually. The models themselves aren't the secret sauce as ML models are quickly commoditized. This is a much simpler approach then all the other competitors are taking so it will be easier for them to dumb down their hardware and retrain end to end models based on camera data. If autonomous vehicles are truly as lucrative as Tesla investors think there will be tons of competitors copying their approach if it works. Within a number of years their advantage would be eroded because the other auto manufacturers still control 99% of the hardware for data collection (vehicles on the road).

k-selectride

There's more to CUDA than just ML/AI workloads, which is part of the appeal. I don't know anything about ROCm, but I needed to do gpu accelerated similarity search with a custom metric and since we were on GCP I looked into the TPUs, but unfortunately the only way from what I could tell to make use of the TPUs is through jax, pytorch, or whatever, which is too high level for pure similarity search.

_hiddenscout

It feels like there is some parallels between the two, but they aren’t the same.  Like during the dotcom era, anything that appeared to be an appeared to be an online company got a crazy valuation. If anything, the spac craze a few years ago is probably closer.  So many of those spac companies are down like 90% or so with a good chance they’ll never recover due to the insane valuation.  A lot of the companies in the AI craze are companies that actually make money. The thing about AI, there’s like two main types right now, with machine learning and large language models. ML has been out for a long time and a ton of companies already use it. I think it offers the ROI.  A lot of the hype around AI now and a lot of the upgrade cycles is based around LLMs. This is still pretty early and not sure if the ROI will be as good. I do think at some point, investors will want to see some revenue increases due to the capex spent for the LLMs. 

imposter22

Tools are becoming avail and libraries are being updated so expect to see an increase in AMD and Intel share, but that takes time. Meanwhile Intel and AMD are also building their own AL and ML purpose built fpgas and accelerators. Intels Gaudi 3 seems pretty legit, and available much easier than a 2+ year waitlist for Nvidias Blackwell

SushiAssassin-

Certainly! Here's a detailed write-up on why Apple stock is likely to grow over the next five years: --- **The Case for Apple Stock Growth Over the Next Five Years** Apple Inc. (AAPL) has been a dominant force in the technology sector for decades. Its stock has seen substantial growth, driven by its innovative products, strong brand loyalty, and robust financial performance. As we look to the future, several key factors suggest that Apple stock will continue to grow over the next five years. ### 1. **Innovation and Product Ecosystem** Apple's commitment to innovation remains a cornerstone of its strategy. The company continually enhances its product lineup, which includes the iPhone, iPad, Mac, Apple Watch, and AirPods. With the anticipated releases of new technologies, such as augmented reality (AR) and virtual reality (VR) devices, Apple is well-positioned to lead in emerging tech markets. The rumored Apple Car project also promises to disrupt the automotive industry, potentially opening a new revenue stream. ### 2. **Services Expansion** Apple's services segment, which includes the App Store, Apple Music, Apple TV+, iCloud, and Apple Pay, has become a significant revenue driver. This segment has shown strong growth, with high margins contributing positively to Apple's overall profitability. As more consumers enter Apple's ecosystem, the demand for these services is expected to rise, providing a stable and growing income source. ### 3. **Brand Loyalty and Customer Retention** Apple enjoys unparalleled brand loyalty, with customers frequently upgrading to the latest models and expanding their use of Apple products and services. This loyalty is fostered by Apple's seamless integration across its product lines, creating a cohesive and user-friendly ecosystem. High customer satisfaction and retention rates are likely to sustain strong sales and recurring revenue. ### 4. **Financial Strength and Shareholder Returns** Apple's financial health is robust, with a substantial cash reserve that provides flexibility for strategic investments, acquisitions, and shareholder returns. The company's consistent revenue growth, strong margins, and disciplined cost management underpin its financial stability. Apple also has a track record of returning value to shareholders through dividends and stock buybacks, which can boost stock price and investor confidence. ### 5. **Global Market Penetration** While Apple is already a global brand, there remains significant growth potential in emerging markets. Regions such as India, Southeast Asia, and parts of Africa present opportunities for expanding Apple's customer base. The growing middle class in these areas, coupled with increasing internet penetration, can drive demand for Apple's premium products. ### 6. **Sustainability and Corporate Responsibility** Apple's commitment to sustainability and corporate responsibility resonates with consumers and investors alike. The company has made significant strides in reducing its environmental impact, with goals to become carbon neutral across its entire supply chain by 2030. This focus on sustainability enhances Apple's brand image and aligns with the values of socially conscious investors. ### 7. **Advancements in Artificial Intelligence and Machine Learning** Apple is heavily investing in artificial intelligence (AI) and machine learning (ML) to enhance its products and services. These technologies enable improved user experiences, such as more efficient Siri interactions, personalized content recommendations, and advanced health monitoring features. As AI and ML continue to evolve, Apple's integration of these technologies will likely create additional value for customers and investors. ### Conclusion Apple's combination of innovative product development, expanding services, strong brand loyalty, financial strength, global market potential, commitment to sustainability, and advancements in AI and ML positions the company for continued growth. While the stock market is inherently uncertain, these factors suggest that Apple is well-equipped to navigate challenges and capitalize on opportunities, making it a compelling investment over the next five years. --- This write-up highlights the key reasons why Apple stock is likely to see sustained growth, providing a comprehensive view of the company's strengths and future potential.

YoSmokinMan

Phillies ML O8.5 sgp

Brief-Frosting405

I’ll grant you that no one knew exactly when ChatGPT would be released and start this generative AI craze. But people knew that AI and ML applications used Nvidia chips. That was common knowledge.

pootersperman

NVDIA has a crazy moat and failing for a black swan event or anti trust shenanigans they will remain a behemoth and benefit directly from an rate of development taking place in AI/ML. They're R&D is top of the game, they can also rely on cash to acquire any potentially tech disrupting incumbents. Not to mention that they have a long standing relationship with the US Gov, Darpa, etc.

SnooWoofers345

It was hyperbole. But meant to get the point across that ML has been around and used in tech for lots of things — recommender systems, consumer segmentation, and really a slew of other things.

YUNG_SNOOD

No i’ve actually trained ML models unlike these dummies

elmorepalmer

Snood is right, read up on double descent and overparametrization. There are no free lunches, but there are some surprising things in ML that we don't rlly have a great explanation for.

Histericalswifty

People talk like ML aren’t just multivariate-fitting problems. I Love it. The more degrees of freedom a space has, the emptier the space… meaning, the harder it is to find meaningful solutions. But go ahead and burn your money, don’t mind me.

YUNG_SNOOD

This is not what the math says for ML models tbh

Draiko

BMW, Siemens, Mercedes, and GE are using nvidia omniverse digital twins to preplan and simulate everything from factories to product engineering to wind turbine placement. These have already reduced costs of new product development and new production infrastructure projects by over 8 figure amounts so far. Drug companies are already using ML tools to reduce cost of pharmaceutical research and development. Cost reduction typically automatically increases profit. Rare to see one without the other.

RadicalAlchemist

I always assume ML is MoneyLion, but I'm always wrong

MattEberjuice

All of tech was NOT built on ML lol. What did you even just say

Upstairs_Shelter_427

Machine learning? As in a quite old statistical algorithm first utilized massively in the 1980’s? Companies have been using ML for a long time for data science, since the 1980’s to optimize operations. I’m an industrial engineer, I’ve utilized machine learning for years to reduce costs for logistics and manufacturing operations. Why did I say what I said? ML is very different than AI. Shouldn’t confuse the two, I think you’re actually talking about AI, not just ML - which AI definately utilizes, as one of dozens of toolkits.

SnooWoofers345

ML = machine learning? Is so, all of tech was built on machine learning. GenAI, that’s a different story

IstariParty

Still waiting for a company to announce they either: 1.) reduced cost due to ML 2.) increased profit due to ML

Dietmar_der_Dr

I've never claimed I could have predicted that openai would release chatgpt. Though I must admit that me, and every other guy in ML, trains on Nvidia so I definitely could have done better (not 8 years ago, but maybe 3 years ago). I cannot explain to you where price will go, I am explaining to you why the price is where it is. While this can help you form a better thesis, it's not going to make you or me rich. I don't know which company will figure out semi-autonomous agents.

Brojess

Lol LLMs are a ML model. Neural networks are a ML model that LLMs are built off of. And ML is just fancy stats that take a lot of data and training iterations that use things like back propagation and other correct algorithms to move the estimates closer to the bottom of the error space. I don’t want models that “hallucinate” making decisions about which parts go where lol I want teams Aerospace engineers and other experts doing that. I think maybe you’re the one that’s confused my dude. But I’m sure you’ve worked in the field for years and are a SME /s

Ding-Dongon

So what does floating precision quantization have to do with Nvidia's success? I get there will be less demand for its GPUs (due to lower memory requirements), but maybe the model sizes will grow proportionally. Also if quantization results in lower quality of output then maybe for the big players (OpenAI, Microsoft, Google) it's not worth it. And the quantization regards inference only. > literally replacing the activations with 1s and 0s You mean replacing the output of an activation function with 1s or 0s? I mean a 16-bit float is also just 1s and 0s. There are already used 8- or 4-bit floats and even integers. I guess you mean replacing output of individual neurons with *either 1 or 0* (so using a single bit)? I don't see how that'd work without greatly reducing the accuracy (if you don't modify the model architecture at the same time). > And for the brain, well it seems to be quite binary in it's activations, either a neuron fires or it doesn't Yes, but it doesn't mean the same idea should be used for ML in general. Human brain is very efficient, while our models and compute capabilities are suboptimal.

Brojess

Humans have empathy and emotion. Take that out of war and war will never end. I personally love ML for things like flagging tumors in 1000s of MRI images. Or for taxi 🚕s. Or for creating haikus out of Reddit post. But keep them tf out of war. You’ve got experience in research in ML then? You’ve seen the kind of error these models produce and how bias is passed from the data to the models? You’ve studied numerical analysis and understand error propagation?

Brojess

Yes I am because it’s impossible to get to 100% accuracy with systems built on perceptions which is ALL neural networks. And guess what LLMs are built with! Transformer Neural Networks. https://www.cloudflare.com/learning/ai/what-is-large-language-model/# You are holding an extremely ideological viewpoint and it’s simply not true and has been shown to be MATHEMATICALLY IMPOSSIBLE. Have you have head of the perception convergence theorem? It shows that these types systems can be 100% accurate IF AND ONLY IF they’re trained on a INFINITE ♾️ amount of data and time and I’m sorry to break it to you but that’s impossible to do given we don’t have either either. Until someone disproves it then it stands to reason we should listen to it - by the way that theorem was written in 1962 and people have been trying ever since. For more apt readers. https://www.cs.princeton.edu/~tcm3/docs/mapl_2017.pdf TLDR https://medium.com/@adnanemajdoub/perceptron-convergence-theorem-c5b44cc06a08 These models are black boxes to us plain and simple. Yes we can train them and yes we can see how it “flows” through the system but we do not understand them and might not ever to be able too. It also very well may be IMPOSSIBLE to protect these prompt systems from jailbreaks (lots of research going on surrounding them too) and if we let them talk to each other it gets worse. Computers work at the speed of light minus the resistance on the wire and we cannot keep up. What happens when one in charge of our power grid gets jailbroken? Or ones in charge of our middle defense systems? Or fuck ones it charge of getting the radiation levels right for an X-ray? https://medium.com/@SamiRamly/prompt-attacks-are-llm-jailbreaks-inevitable-f7848cc11122 I for one don’t want to deify these systems and put them into systems scanning resumes much less in killer robots that could TALK TO EACH OTHER. They could literally jailbreak themselves. Please share your credentials. Are you a PhD data scientist that works in the field? Are you a machine learning engineer? Do you work in the data field at all? I’m a senior data engineer with degrees in both computer science and applied mathematics who has both studied and worked in the machine learning field for almost 15 years. I don’t care if you hold NVIDA bags or not. It doesn’t matter because math is math and physics is physics and I can say with certainty neither care about humans. There is no infinite time and infinite data thus you cannot have 100% accurate ML models. The media pushes these things as gods because they want to make money. Nothing more

Cheap-Cold-5255

Look at that second sell 😂😂😂💀 and that massive pump afterwards man if you use ML algorithms then at least use a noise filter 😂 and think about fundamentals as well don’t trust it blindly. I hope you GFRITA with your shorts.

caseylolz

Snowflake - **Primary Focus**: Cloud-based data warehousing and analytics. - **Key Features**: - Data storage, processing, and analytics - Scalability and performance for large datasets - Seamless data sharing and collaboration - Integration with various BI tools - Data governance and security - **Target Users**: Data engineers, data analysts, businesses looking for scalable data storage and analytics solutions. ### Palantir - **Primary Focus**: Data integration, analytics, operational intelligence, and AI-driven insights. - **Key Features**: - **Palantir Foundry**: Data integration, management, and analytics platform. - **Palantir Gotham**: Designed for government agencies and large enterprises for intelligence and operations. - **Palantir AIP (Artificial Intelligence Platform)**: Provides advanced AI capabilities to help organizations build, deploy, and manage AI models. - Integration of AI/ML models into workflows - Real-time data processing and decision-making - Enhanced data analytics and insights - Support for custom AI applications - **Target Users**: Government agencies, large enterprises, organizations with complex data integration, analytics, and AI needs. ### Overlap - **Analytics and Insights**: Both Snowflake and Palantir (with AIP) offer robust analytics capabilities. Snowflake provides a strong foundation for data storage and basic analytics, while Palantir’s AIP enhances these capabilities with advanced AI/ML functionalities. - **Data Integration**: Palantir’s platforms, particularly Foundry, excel in integrating disparate data sources, which can complement Snowflake's data warehousing. - **Customer Base**: Both target large enterprises and organizations needing sophisticated data solutions, but Snowflake is more focused on modernizing data infrastructure, while Palantir, with AIP, emphasizes advanced AI-driven decision-making and operational intelligence. ### Competition - **Analytics and Insights**: Both platforms compete in providing data analytics, but Palantir’s AIP adds a layer of advanced AI/ML capabilities that Snowflake does not inherently possess. - **AI and Machine Learning**: Palantir AIP provides tools to build, deploy, and manage AI models, giving it an edge in organizations looking for integrated AI solutions. Snowflake, while powerful in data analytics, often relies on integrations with other AI/ML tools rather than providing built-in AI capabilities. - **Data Integration and Management**: Palantir's strength in integrating various data sources and enabling complex data analytics through its platforms (Foundry, Gotham, AIP) can be seen as complementary to Snowflake's data warehousing. ### Complementary Aspects - Organizations might use Snowflake for its robust data warehousing and basic analytics while leveraging Palantir AIP for more advanced AI/ML analytics and operational intelligence. - Snowflake’s scalable and performant data storage could serve as the foundation upon which Palantir’s AI and operational intelligence capabilities are built. In summary, while there is a competitive aspect between Snowflake and Palantir, especially with the introduction of Palantir's AIP, they also have complementary strengths. Snowflake provides powerful data warehousing and analytics, whereas Palantir, particularly with AIP, offers advanced AI capabilities and sophisticated data integration and operational intelligence solutions.

Valvador

> LLM based "garbage"? It is perhaps the best way to demonstrate the capabilities of generative AI, and the easiest to understand. Plenty of use case of LLM + RAGs, from customer service, to game development, to retail, to advertisement, etc. The subtle mistakes LLMs make as part of their nature makes them potentially dangerous for productivity tools because it empowers less skilled workers/employees to feel more confident about things they don't understand to begin with. I've been working with GitHub's Copilot in C++ for the last almost year and while in some cases it reduces how often I have to copy and paste code/patterns that are obvious, it still makes extremely subtle mistakes that could lead to a LONG debugging process if done by less experienced/attentive engineers. LLMs are interesting to experiment with on an artistic level because the weird pseudo-coherent BS they create is interesting the same way an acid-tripping musician's work is interesting and unique. The extend of usefulness is going to plateau (already is) pretty quickly. > So you admit that it's useful, and can be applied to multiple industries in their specific use case, but you don't believe companies who are working on those would see their valuation rise? There is a lot of technically cool stuff during "breakthrough tech" phases that seems better than it is. The specific cases I described that are interesting to me from an AI/ML perspective are not massive "profit multipliers" like people are acting. Most of the companies being rewarded for AI bullshit are not doing any of the interesting problems that have actual impact and instead are regurgitating their own LLM variants/integrations.

viperabyss

>Again, similar example to the physics simulation/ML work I linked in my reply. Again my point is that these useful applications are not what is currently generating the hype and inflated stock markets whenever someone mentions "AI". Once again, just because you don't see the potential, doesn't mean others don't see the potential. >My issue is that 80% of the stock value generated is nothing more than LLM-based garbage, and the actually good use cases of ML/AI are fewer between. LLM based "garbage"? It is perhaps the best way to demonstrate the capabilities of generative AI, and the easiest to understand. Plenty of use case of LLM + RAGs, from customer service, to game development, to retail, to advertisement, etc. Very easy to argue that LLM has way more use cases than physics informed machine learning. >I am well aware of it's capabilities and usefulness across multiple industries, but that is not what is driving the current AI-based evaluations. So you admit that it's useful, and can be applied to multiple industries in their specific use case, but you don't believe companies who are working on those would see their valuation rise?

Valvador

> Mercedes uses NVDA Drive Orin. Tesla uses its own special inference hardware. Toyota and VW uses QCOM. Honda partnered with Mobileye, and will partner with IBM in the future. > Medical fields are relying on AI to expand their understanding of genomics. Again, similar example to the physics simulation/ML work I linked in my reply. Again my point is that these useful applications are not what is currently generating the hype and inflated stock markets whenever someone mentions "AI". > Just because you're not interested in them, doesn't mean others are just as disinterested as you are. I am interested. I've literally been learning different AI/ML models for physics simulation relevant to different industries. My issue is that 80% of the stock value generated is nothing more than LLM-based garbage, and the actually good use cases of ML/AI are fewer between. I am well aware of it's capabilities and usefulness across multiple industries, but that is not what is driving the current AI-based evaluations.

viperabyss

>I only subscribe to Streaming services for a specific show I am watching and then unsubscribe. No amount of ML-based show promotions is going to generate extra revenue from me or most other people. This is nothing new, and companies are wasting money on this right now. Translated: I'm like this, so everybody else must be like that too. By the way, the "recommended" you see on Amazon or Walmart? The same tech as Netflix and Hulu. >Sure, but these things have been around pre-bubble and happen to fall into that 20% of actually useful things. Additionally, these things don't benefit from NVDA/specialized chips. Mercedes uses [NVDA Drive Orin](https://www.nvidia.com/en-us/self-driving-cars/partners/mercedes/). Tesla uses its own special [inference hardware](https://www.blogordie.com/2023/09/hw4-tesla-new-self-driving-hardware/). Toyota and VW uses [QCOM](https://kr-asia.com/qualcomm-lands-autonomous-driving-projects-with-toyota-and-faws-hongqi). Honda partnered with Mobileye, and will [partner with IBM](https://asia.nikkei.com/Business/Automobiles/Honda-and-IBM-team-up-on-next-gen-chips-for-software-defined-vehicles) in the future. Perhaps you're just misinformed? >The current Bubble isn't being driven by actually interesting AI/ML applications that are game-changing like self-driving and navigation. Oh my sweet child. Game developers would love to have a LLM platform augmented with RAG to comb through their engine documentation to find relevant code. Large corporations are testing this tech to augment their customer service team. Retailers are using this tech for IVA. Medical fields are relying on AI to expand their understanding of genomics. Just because you're not interested in them, doesn't mean others are just as disinterested as you are. >Who gives a fuck? What does this actually contribute to society/the economy? It's cool, sure. But so is a good cover singer at a Karaoke bar. Some find it very interesting, and (with permission from the estate) it might bring people's voice back to life in an authentic manner.

Valvador

> You shop with Amazon, that's AI. You watch Netflix / Hulu / Disney+ / HBO, that's AI I only subscribe to Streaming services for a specific show I am watching and then unsubscribe. No amount of ML-based show promotions is going to generate extra revenue from me or most other people. This is nothing new, and companies are wasting money on this right now. > Your car may have lane keep assist feature, or straight up self-navigation, that's AI. Sure, but these things have been around pre-bubble and happen to fall into that 20% of actually useful things. Additionally, these things don't benefit from NVDA/specialized chips. > When people hear AI, they think generative AI, without realizing they're already relying on AI for a huge part of their life. I'm well aware of ML being useful in a lot of things. I would even recommend a this [Youtube channel](https://www.youtube.com/@Eigensteve) for people who love Physics/Engineering and the application of ML models in simplifying simulations and helping discover new physics... But that is not what the current bubble is generating. The current bubble is generating bullshit AI bots that respond to Google search queries, shitty corporate bots that attempt to speed up communication internally by regurgitating poorly written wikis. The current Bubble isn't being driven by actually interesting AI/ML applications that are game-changing like self-driving and navigation. > Bonus: I'm going to get a lot of flak for this, but here's AI Chester Bennington singing Bring Me to Life. That's where we're at with generative AI. Imagine what we can achieve in a few years. Who gives a fuck? What does this actually contribute to society/the economy? It's cool, sure. But so is a good cover singer at a Karaoke bar.

Valvador

Based on how shitty every AI integration I've seen into things that don't actually need AI to be useful, I expect about 80% of AI hopefuls to drop out and the remaining 20% to remain strong. I did pull those numbers out of my ass based on feel, but regardless it seems like the demand for AI chips will only remain with the solutions where AI/ML is actually useful and not the bloat we have today.

cantadmittoposting

strictly, replication of human thought patterns by a machine. currently, a marketing term for algorithms such as those previously known as "deep learning" or "neural network" ML models. These approaches are typified by their ability to ingest and handle very large, unstructured, and non normalized feature sets and internally calculate a predictive model. They're further typified by the opaqueness of the actual decision chain, contrasting with models like CART which have very human-readable nodes.   "Current Generation" models being called AI are exemplified by "generative" AI, which ingests large amounts of text, image, etc, and successfully replicates (to varying degrees) human-like output. This is especially true of "large language models" which greatly improve on previous, simpler approaches like Markov Chains by having a far greater degree of calculation around "context" and methods of next token selection.   profitability of the current "flagship AI" (LLMs and more generally "generative AI") is somewhat uncertain. Certainly, cost reduction by replacing Customer Service with chat bots has been explored, as has rapid generation of marketing and advertising material. Efficiencies in developing documentation, writing proposal responses, recalling policy and regulatory details on command, and summarizing compliance requirements is also a low hanging use case. That said, accurate, powerful, context-sensitive predictive models, regardless of the "marketing term" for them, are wildly profitable in virtually any business context, so the public perception of LLMs as "AI" is somewhat irrelevant compared to general advances in distilling massive data sets into optimal decisions. Surprisingly, this comment not brought to you by AI, but ADHD and experience in the field.

ElectricalGene6146

Google has better price/performance for their TPU and it supports full end to end ML workflows.

Western_Objective209

> I both use and constantly shit on AI stuff. Me too, but I spend like $50/month on it. Hardly anyone around me uses it at all. > Like Gemini taught me how to code a game in Unity from scratch with absolutely no knowledge of coding over two days, and did it while role playing as the Koolaid Man. It's great for learning stuff, especially around coding. But it's pretty surface level. Once you past a certain point, it actually hurts your coding because it writes code that looks correct but has a bug, and it's really hard to figure out why. > Companies just need to find ways to use it on their backends and then they're buying Nvidia tech to use it too. My company spent $1B on acquiring an AI start up, and spends about $10M/month on AWS bills developing AI products, and god only knows how much for the ML engineers. It's all around medical coding. It's by far our worst selling product. It's just a bottomless black hole where we dump money into.

WorkerPrestigious958

$14 price per sale is a very expensive tech company. If subscription revenue does not maintain a very high growth rate, that multiple will quickly drop. Taking an all in approach over the next 6 months is you arguing that Snowflake has a fundamental advantage over Google, Amazon, and Microsoft that will keep the acceleration. While Snowflake is investing in On-premise to expand their potential market share, I think it still has headwinds compared to the big 3 who are going to invest heavily in AI/ML which I think is ultimately the future of data warehousing. Sometimes you have so much data, it's hard to draw connections. You also have a high interest rate environment. If rates fall, that would benefit SNOW as investors move more towards growth. I'd be interested in what catalyst you see in SNOW?

OverlordBluebook

Hard to tell. If you would have said in 2010 they would be this huge have a marketcap multiple times over than Intel and AMD they'd throw you in the padded room. I'm in a business that involved customers using NVIDIA directly or indirectly through cloud usage. All I can say is there are still lots of backorders and nobody asks for anything else but NVIDIA for AI/ML hardware. In many cases it's the most expensive piece to the hardware. I attribute it to like an weapons arms race in a sense for AI and Nvidia GPU's are the weapon of choice by far. Like your tech company won't survive unless your fully armed to the teeth with Nvidia as part of your war machine.

psiviz

I'm a ML scientist at a big tech company. The thirst for gpus remains unquenchable. Nvidia could charge 20x for h200 and you'd see companies paying it no problem. Look at the trajectory from a100 to h200 and tell me where it ends and maybe we can figure out a rational value but as long as they keep cranking out more powerful processors I don't know... I do agree We're at the point where valuation seems to be pointing to bigger moves, Nvidia cloud or something where they can increase profit. If they don't sell their best chips, just use them to host, and if that's the only thing that makes gpt5 run in near real time what's the value of that? What if you can host multiple gpt4/llama70b copies on the same chip and have them work together? See criticgpt. They kinda control the limits of AI at this point more than any tech company. Also look at how they're already a big part of ai minshare, very prominent at cvpr iclr etc.  It's a crazy valuation but no one else is anywhere close on gpu architecture. Afaik Amd is blocked by parents to fit their stuff into cuda which is how all this stuff works. It's just nvidia or nothing if you're talking about cloud ML workloads. Anyway that's my take, I still agree the numbers are nuts but I don't see why anyone would sell if you believe ai is going to be a multi trillion dollar industry and that seems pretty reasonable long term 

AdMaleficent2789

I’ve never heard so many lies… I’ve never heard so much malarkey, I’ve never heard so much foolishness. I’m betting the next one coming is “I’ve never heard so much nonsense” +190 ML

Allinmoney

If you are having a gambling addiction in the market……. It’s time to see a Merrill Lynch’s advisor.. That should be an add. ML send me my marketing fee!

janon330

It will likely pop in 18-24 at minimum. Nvidia has a strong moat with a proprietary technology stack with CUDA that is used by engineers in the AI/ML space. Even if AMD or competitors come out with a chip that performs at 75% the level, hell even 85% as good as NVDAs chips in terms of raw processing. They still will not have the driver support or the proprietary driver library with CUDA to be effective.

Vincent_Waters

Go balls deep in TSLA calls. They are going all-in on RL/ML unlike other regarded robotics companies that are like “let’s write a million if statements and use shitty MPC”

Soybeanrice

Forced diamond hand mode on a buttload of 0DTE SPY calls... F ML

cantadmittoposting

novel analysis is mainly feature engineering and feature selection, not slapping the button to run a packaged ML model.

Previous_Code9994

So, let me put my two cents as a machine learning academic. There is a huge disconnect between the ML/AI reality and what the management is promising. The large language models breakthrough unfortunately only enlarged this gap. Calm-headed researchers pointing this out are called out and face immediate backlash from fanboys. Anything you say about large models and robustness gets called out as "copium". Is AI going to change the global economy entirely and dramatically? Yes. Do I believe it will happen in the next 5 years? No. The market seems to be over-optimistic about the timeline. I don't like it, because when the market is hyper-optimistic, you can only disappoint the market, which would lead to another maybe not AI winter but an AI autumn this time.

serhanaydin

You should consider the Expected Value (EV) of the trade with the probability of each outcome. For example, if this was a trade with 2 outcomes only; simple EV would be: p . MP+(1-p) . ML p is the prob. of achieving Max. Profit(MP), (1-p) is the complimentary probability, or the probability of incurring Maximum Loss(ML)

the_sound_of_a_cork

AMZN is RIvian's biggest shareholder. Amazon in partnership with BB in developing Blackberry Ivy, which standardizes data from across all systems in the vehicle and enables ML processing and data driven services, reduces operational costs, and creates new business and driver value. VW noW creates the scale.