ML

cantm1ss

Actually like this one a lot, this is a really interesting tech from a computer science perspective. I would have to think they are using continuous data to improve the product as well with ML models, which would put them in a competitive advantage over others without the data as well. Chart looks good to me imo, plenty of room to run on any volume.

C130J_Darkstar

Nope, DOE has fully authority to allow for design, construction and operation per the new MOU- https://www.nrc.gov/docs/ML2530/ML25303A288.pdf Streamlined review after it’s already built and operating for commercialization.

FINomad

Now compare those two to Vanguard. With Vanguard, there are no account fees, no 12b-1 fees, no loads, and you can get VTI for 0.03% ER. If you want a financial advisor, Vanguard's AUM fees are 0.30% or less (as you get higher AUM). Are you paying less than 0.03% total in fees with ML and EJ, and 0.30% or less if using an advisor? I have over $2M with Vanguard and manage the accounts myself, so my fees are 0.03-0.05% total.

Choice_Figure6893

LLMs are not. ML methods that have been around for decades are

Marko-2091

I work in the chemical industry and have used ML (ML was the hot topic while I studied my posgraduate program) and all those magical savings have already been done or been used to improve a bit the process. AI saved us around 80M USD last year (sounds a lot) but it is nothing compared to the 100B in revenue that we make.

sneaky-pizza

People forget we’ve been using ML for work like this for a long time

VegaGT-VZ

There are doubts OpenAI will make it 2 years, so 20 is extremely optimistic Plus there are other much more secure and substantially less resource intensive ways to use AI. You can build a little model focused on a specific problem with your own hardware. Companies are already doing it. It's kind of like the next extension of ML.

Cool_guy0182

The fact that you said p-value of 0.5 tells me everything you know about “medicine”. They had done two statistical studies: 1. Pre-specified efficacy population: 4.5% vs 7.5% regain rate. Here they don’t see much statistical significance but a one-sided p-value is still 0.07 (which is quite impressive). This is what crashed the whole thing. 2. Exploratory group: 4.2% vs 13.5% regain rate. Here p-value was 0.004 (very statistically significant). They failed the prespecified efficacy study because most likely this procedure is NOT FOR EVERYONE! In the other hand, it does work extremely well for patients above median GLP-1 weight loss. Yes there is a risk for false positives but that’s only if you claim this product to be more general in use. Also this is NOT a cosmetic procedure. It’s a 40 minute endoscopy. And FYI: I have a PhD in Biomedical Engineering and stats/ML is my bread and butter. I too know a thing or two lol. Lemme know if you any other questions.

TotalEmployment9996

This is why I’m very bullish on Meta. Almost all the infra is in house and meta owns its own data centers. It has massive amounts of user data and training data for ML models too.

mymorningjacketoff

Harvard ML against Brown at -130 I accept tips at https://cash.app/$Bear6669

Chemical-Agency-3997

Been evident for decades people are just impatient. I went back to uni and got a computer science degree and done my dissertation using ML before ChatGPT dropped.

Delvaris

My regarded theory is they're trying to deliberately create a systemic hazard situation ala 2008. It's mostly gotten drowned out by the zone being flooded by pro-AI PR and marketing but there has always been skepticism in academic circles that LLMs were the road to AI. There are various stances but the most obvious and simple one is that such a system would require a workable world model through which it could analyze cause and effect to even begin considering the possibility that it's outputs were in any way intelligent. LLMs may be a part of that system but it definitely wasn't the extent. I think they figured out a year to two years ago that this was the case but the investment levels were already catastrophic. So now the goal is to bluff it's capabilities (even the stories of "bad AI" in tests sell this) and give the impression it's critical to national security (the race with China) and the agreements are creating opaque interconnected deals spreading the liabilities around while giving the impression that everything is fine and proceeding well. In the end the goal is to make OAI and AI in general too big to fail, at least on paper, so if the music ever stops a government bailout will follow because of how "vital" AI is to national security. Normally I'd slap my "rational actors" hat on and say it doesn't serve their corporate interests because it doesn't. However, that discounts the cultish level of devotion that AI has as a concept in SV. For once it's about more than money because they believe AI is absolutely vital to the future of the human race and ensuring that AI development continues no matter what is their overriding concern. Note: I don't entirely disagree with this but I think there's a lot of merit in the idea of 'focus on current problems, AI will come' as opposed to just pushing directly for AGI or ASI. The amount of breakthroughs that ML assisted research has made in the last few years is astonishing and there's clearly a lot of value to be had there but they're all focused on AGI to the exclusion of all else.

ericthedisrespectful

i think i may have a problem eglin afb xirs and chinese shills. I drank 3/4 of a 750 ML bottle of vodka tonight and i feel just slightly tired. the alocholism has escalated.

Doneeb

I mean, LLMs _are_ predictive ML…

Not69Batman

Had a look at the job ads on the q.ai website to understand the tech. They have roles for experimental physicist (*electro-optical and acousto-optical systems*), specialist AI/ML algorithm developers (*computer vision, edge devices and speech processing*), systems engineers (*wireless/RF, firmware, electro-optical, electronics-mechanical integration*), industrial designer (*consumer electronics*), software engineers (*coding, data and architect*) and management. So it seems like Q.ai is developing a non-acoustic communication interface that enables silent speech by using electro-optical sensors and edge AI to interpret micro movements and muscle tension from the user's face. Can be applied for both noisy or silent environments. Let's see when AAPL integrates this into wearables.

zharguy

Do you think the regarded analysts know the difference between predictive ML and generative LLMs?

Not69Batman

Had a look at the job ads on the q.ai website to understand the tech. They have roles for experimental physicist (*electro-optical and acousto-optical systems*), specialist AI/ML algorithm developers (*computer vision, edge devices and speech processing*), systems engineers (*wireless/RF, firmware, electro-optical, electronics-mechanical integration*), industrial designer (*consumer electronics*), software engineers (*coding, data and architect*) and management. So it seems like Q.ai is developing a non-acoustic communication interface that enables silent speech by using electro-optical sensors and edge AI to interpret micro movements and muscle tension from the user's face. Can be applied for both noisy or silent environments. Let's see when AAPL integrates this into wearables.

2manyhobby

Ya theres no such thing as AI. Its machine learning. This software is valuable to many industries. But ML itself makes software cheaper and faster to develop. They are thinking all these data centers will be needed to create and run all the models. Long term that could be worth a lot. But competition drives costs down. The profit is all theoretical but a lot of very real money is going all in on it.

caughtinthought

Anyone that was in the field of ML/AI knew that the systems were going to rapidly improve. Quantum's own experts are highly, highly skeptical of it ever being useful beyond an academic curiosity.

GreenGameboyColor

Rams or Seahawks? I'm about to do a ML bet

Current-Function-729

Seems incredibly risky in this economy unless their ML are savants.

smokiebonzo

Google only buys from Nvidia to sell Nvidia GPU to customer on the GCP offering. It does not use Nvidia GPUs for its own AI/ML compute at all. Google uses TPUs internally, which are designed in-house, and also what gives it an edge in 2 ways 1. Not being dependent on Nvidia to scale 1. Designing its hardware stack with the software (Gemini) that it runs in mind, allowing for far greater efficiencies than using an off-the-rack GPU.

utsnik

One can always question. I thought something very wrong was up with my system as technical indicators and everything was positive. My ML model was also positive. Yet the outflow was huge! Seeing the pictures and the intensity of the tweets and posts (Trump and Vance planting a flag on Greenland) as well as the military threats. They surely made sure that the market was going to tank quite hard. Oh, I also detected some euphoria in a huge amount of calls on Thursday Retail was buying 3 calls for every put on Thursday in XLF, while DIA was almost two to one. Then insiders sold to retail in a huge dump on Friday. A perfect retail trap...

linverlan

I mean that’s just semantics. We’ve been calling the field machine learning for 50 years and generally someone using the term “AI” is also someone who wasn’t in the field until ChatGPT came out so it makes sense for that term to specifically refer to that style of ML.

meltbox

Yeah but their tech is only marginally better than Flock. You know, used 8 year old android phones running the equivalent of a gba advance game of ML models. These companies are just a who you know shitshow.

_hiddenscout

From the same report >Rigs drill oil wells, and an increased number of active drilling rigs indicates that U.S. producers are drilling more wells, which generally results in growing oil production. Our latest STEO shows the active rig count decreased year over year in 2024 through November in all L48 primary crude oil producing regions except the Bakken. The region with the most activity, the Permian Basin, declined from 310 rigs to 303 rigs between November 2023 and November 2024. The active rig count for these regions, which includes the Permian, Eagle Ford, and Bakken, declined 18% to 389 rigs since the recent January 2023 high. Data on 34 publicly traded exploration and production companies also suggest increasing well productivity is helping reduce companies’ production cost per barrel. Some companies are seeing efficiency from AI, but it's not LLM's, it's ML. I'm not saying AI is going to replace anything, but rather, AI can be a tool to increase efficiency. From HAL [https://www.halliburton.com/en/resources/the-rise-of-artificial-intelligence](https://www.halliburton.com/en/resources/the-rise-of-artificial-intelligence) >Results >The effective interaction between AI and the directional engineer marked a significant operational milestone for the operator. Human expertise, combined with predicitive analytics technology, formulated recommendations as part of a unified human-AI team. The LOGIX® automation and remote operations helped improve consistency during well construction, clearly demonstrated during the development of three wellpath trajectories. >The team achieved consistent performance improvement and drilling trajectory accuracy, which resulted in a remarkable 33% increase to the rate of penetration (ROP) when compared to traditional drilling methods without human-AI solutions. >The autonomous drilling platform demonstrated consistency between planned and actual DDIs indicated by significant smoothness in hole profiles, which minimized time and effort. Casing and liner run speed additionally improved by 15 to 45%, which reduced deviation from the planned path, enhanced steering efficiency, and minimized the tortuosity impact. There's been a loss of a lot of jobs in the industry [https://finance.yahoo.com/news/40-us-oil-jobs-lost-103032384.html](https://finance.yahoo.com/news/40-us-oil-jobs-lost-103032384.html) >New technologies to drill faster for cheaper, corporate mergers and robots replacing humans on rigs resulted in the disappearance of some 250,000 jobs since the sector's employment peaked in 2014. Production surged 50% during that time.

_hiddenscout

Software engineer, but AI has been great. I use it all the time at work and still in the camp of LLM's are pretty dumb, but real AI winners will just be companies that can actually take advantage of it. A great case example is oil and gas companies are using ML, machine learning which is still AI, to get more efficient in drilling to lead to cheaper break even prices. For example, something from $HAL [https://www.halliburton.com/en/energy-pulse/artificial-intelligence-drilling-accelerates-new-era-of-excellence](https://www.halliburton.com/en/energy-pulse/artificial-intelligence-drilling-accelerates-new-era-of-excellence)

InclinationCompass

But these examples are AI. Traditional ML uses fixed features for narrow tasks. That's not the case here. The AI used by Meta, Google, Amazon, UPS, Walmart, healthcare uses **predictive modeling, reinforcement learning and generative techniques to make dynamic decisions at scale**. They analyze massive, unstructured data, optimize workflows and even summarize complex info, doing things static algorithms simply can’t.

Happy-Champion1661

algo with advanced ML model calculated taco date and placed bets accordingly

zennsunni

There's a Linus video where they get an H100 running for gaming. It does fine, but they'll never be cost effective due to the memory and tensor core count compared to a gaming GPU. The notion that the bubble bursts and H100/200s go on sale for like $1,000 is dreaming. Even if the AI bubble didn't exist, they'd all be gobbled up by private enterprise for use in non-AI slop ML.

Illustrious_Rub2975

Aside from your snarky attitude, you’re half correct. Let me explain. If RV were pure white noise, variance swaps wouldn’t exist and GARCH type models wouldn’t even weakly work. Empirically, they do, just not cleanly, not linearly and not stably. Though it is true that you’re not predicting a physical process, rather, you are predicting the output of a reflexive system. In my view, you cannot point-forecast RV reliably, but you can identify conditional distributions, regime likelihoods and volatility pressure buildup. OP just doesn’t realise what the model is implicitly assuming about the world, things like stationarity, feature exogeneity and objective mismatches. Dealers must hedge. Funds must rebalance. Gamma must decay. Liquidity must thin at certain times. Those are real constraints, not opinions. I can guarantee OP will just find weak, brittle correlations, calendar quirks, microstructural noise and short-lived flow artefacts. And then proceed to extrapolate them, right until a regime boundary breaks. I would say, you should use the ML to classify regimes and detect distributional drift which is congruent with constraint stress, not the model chasing its down shadow. There’s a difference between a forecast and a seismograph.

DopeCaliboyz

Valid points. To clarify, this isn’t an ML or prediction model yet, so there’s no “training dataset” in that sense. I’m not forecasting price at all right now. What I’m testing currently is rule-based strategy evaluation, not signals. • Universe: Nifty 50 only (liquidity + avoiding survivorship issues) • Type: long-only, EOD data • Style: swing / position, no intraday, no leverage The backtests you’re seeing are deliberately limited and you’re right, that’s a weakness. Most of it sits in recent regimes, which absolutely increases the risk of overfitting. Before building the app, the next steps are: • testing across multiple regimes (2008, 2013, 2020, 2022) • walk-forward testing instead of static tuning • randomizing entries to detect curve fitting • focusing more on drawdown behavior than returns If it doesn’t survive ugly markets, I’m not interested in building around it. Trying to break it on paper before turning it into code.

The_Kobalt

I'm quite up to speed on AI/ML tech and news. This is the best take I've heard in a hot minute regarding scaled training hardware. Bonus points for keeping it in regard monke mode language. 100/10.

thatkid1010101

After pats score, then Texans ML

Bubbly-Form-7059

Texans ML

Senior_Professional4

Buy the dip on denver ML

NOKIABUMPS69

Bills, bears, Texans, niners ML

NOKIABUMPS69

Bills, bears, Texans, niners ML

zachalicious

Metaverse didn't really have a use case. LLMs and ML absolutely do have their place. They're just not necessary/useful/appropriate for everything they're being thrown at.

Revolutionary-Mode75

[Space Forge](https://www.google.com/search?q=Space+Forge&oq=uk+put+factory+into+space&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIGCAEQRRhA0gEINTE5MWowajmoAgawAgHxBbJ8E_MoFEES&sourceid=chrome&ie=UTF-8&mstk=AUtExfCXlblU5If6mFRLTWDk0fSsvwGpGOs-soxL-dSwkj0sjw0Yy4ARNBZVxXOfsLmyCAnFGwHn8OmT5ciVtPsFG5bTMkIqoXNb0ML7gJZlzxaK-t3JikT8YFLuhwCdYWW5sjVKOLV_eP1NgXZxku0UmcYXZ-ZUqsNJkRGDeOCwCiwrpOdzp3mzlfOu77eKbQ694eaOUoSUHKv0JQsoM7JuQzjRj6bOL9dVNkE8X-_L8iGLZC_0H5sCjhKP8YSqrvFQAMt4x4RJ6KRIech7U1ynPP5Z&csui=3&ved=2ahUKEwit3M7P1pGSAxUdRkEAHYvSLEsQgK4QegQIARAE), isn't public traded company, an the ceo come across as a bit of a arse in some interview and far to eager to talk Britain down, which I'm not a fan off.

zennsunni

I thought this as well, but evidently the most recent generation has two clusters of architecture devoted to training and inference at the ASIC level, i.e. the training architecture supports higher precision for example. I work directly in ML, but have only worked with NVIDIA clusters and GPUs so I don't know a ton, but this is my understanding. Certainly NVIDIA GPUs are still faster, and they're more flexible, but who knows once the training TPU technology matures.

PresentationNo634

Name: CyanConnode Holdings plc Ticker: CYAN (London Stock Exchange ‑ AIM) ISIN: GB00BF93WP34 Sektor: Technologie / Kommunikationsausrüstung UK Workers: ca. 115 🔹 Market Cap: ~£24,11 Mio. (~24 Millionen GBP)   🔹 Enterprise Value (EV): ~£42,06 Mio.  PS‑Ratio: ca. 1,51   PB‑Ratio: ca. 2,10   • triple Orderbook (~£180 Mio.) • Deal with India Government Smart Meter (~£70mio) Stocks ca 352 mio Free Float 63% Big investors Axia Investments Ltd. And Premier Fund Managers Mesh networks are not just a technology of the future—they have the potential to transform critical infrastructure in energy, communication, and transportation. Startups like Cyanconnode are strategically well-positioned because they have already implemented mesh networks in smart grids, providing a stable market and growth opportunities in the long term. Future Potential & Market Opportunities a) IoT & Smart Homes / Smart Cities • Every device can function as a node (lights, sensors, thermostats, cameras). • Advantage: Lower infrastructure costs and stable, self-healing networks. • Market potential: Billions of connected devices by 2030. b) Smart Grids / Energy Supply • Cyanconnode, for example, uses mesh networks for smart meters → real-time load management and energy optimization. • Advantages: Reduces power outages and enables distributed energy resources (DERs) such as solar and battery storage. • Long-term potential: Critical infrastructure, especially in emerging markets. c) 5G & 6G Networks • Mesh networks can serve as a backbone for ultra-dense networks. • Advantages: Higher bandwidth, lower latency, improved resilience. d) Autonomous Vehicles & Robotics • Vehicles and drones can use mesh networks to communicate directly with each other (V2V – Vehicle-to-Vehicle). • Advantage: Real-time collaboration without a central infrastructure. e) Emergency & Crisis Communication • Mesh enables networks that remain operational even if infrastructure fails. • Example: Natural disasters or military operations. Strategic Opportunities: • Scaling in regions with insufficient infrastructure (India, Africa, Southeast Asia) • Partnerships with energy providers → smart grid projects • Integration of AI/ML → predictive maintenance, consumption forecasts, and network load optimization

SilverBBear

>I have like 35% of '1's in my set but still it predicted 0 of them Yeh - your model is correct 65% of the time which it thinks is great so it cant improve. Start by dropping half your '0's and try again. - This isn't a production choice, its more so you can see how important class balancing is for most ML. You will start by getting some 1s - which is a beginning. Then you work on improving it.

SilverBBear

Some thoughts: 1) Volume feature 2) Unbalanced data (few positives so always predicts 0) (This is a common ML issue with no real SOA please search for solutions before proceeding ) 3) You biggest feature is hour - I mean we all know this - but this also means that your learner may need to focus on microstructure regimes - ie. specific hours of trading.

Ancient_Knowledge99

Hi all-would appreciate any advice from you all. Got laid off late last year from a company (freenome) that announced they were going IPO via a SPAC (3 weeks after they lay us off.....figures). I have until end of January to buy my options or let them go. I would have to exercise my options at about \~5.00, and they want to IPO at 10. I of course have a 6 month lockup, so I can't even do anything day 1. I feel that this is a bad bet just bc of the SPAC route but would love feedback! Links below: [Freenome and Perceptive Capital Solutions Corp Announce Business Combination Agreement to Create a Publicly Listed Company Transforming Blood-Based Multi-Cancer Detection through an AI/ML-Enabled Multiomics Platform - Freenome](https://www.freenome.com/newsroom/freenome-and-perceptive-capital-solutions-corp-announce-business-combination-agreement-to-create-a-publicly-listed-company-transforming-blood-based-multi-cancer-detection-through-an-ai-ml-enabled-mult/) [Liquid Biopsy Firm Freenome Finds a Way to the Public Markets via $330M SPAC Merger - MedCity News](https://medcitynews.com/2025/12/freenome-spac-merger-liquid-biopsy-colorectal-cancer-screening-early-detection-frnm/) [Freenome Stock: Will Freenome IPO in the Next Few Years?](https://accessipos.com/freenome-stock-ipo/)

ok-hacker

this is fascinating research but it also highlights why I'm skeptical of "prediction" bots—most are just reacting faster than humans, not actually predicting. what I've learned running a trading agent for a year: the goal isn't to outsmart the market or compete with HFT algos. it's to automate the boring execution stuff (position sizing, rebalancing, stop management) so I can focus on strategy and context. the real edge isn't speed or ML—it's removing emotional trading and execution slippage. bot handles discipline, I handle the thesis. worth checking out andmilo if this approach resonates with you

DACula

Zuck's foresight has been questionable lately. They've lost a metric tonne of money on the Metaverse, which has basically been abandoned now. They're spending a lot more on ML/AI talent, but the toxic culture forces these very talented people out in less than an year. The problem with Zuck is that he wants to own entire sectors. One company is too small to build the entirety of the Metaverse. The same is true for AI/ML and Zuck will end up alienating all the top talent.

x86brandon

Apple definitely didn't admit defeat. Just because Apple skipped early generation LLM's does not mean they are out of the game or that they didn't make significant investments in AI. They just started with the hardest problem first. How do I ask questions of \*my\* data without uploading everything to someone else's cloud. I suggest you look at their ML research. They skipped LLM's because none of them remotely had reasoning capability and that wasn't enough. Also look at all the work done in differential privacy, building an entirely new class of server, etc. The foundation models will be a collection of models, not just Gemini that has an Apple front end, PCC nodes, obfuscated requests and multi-model cascade. [https://machinelearning.apple.com](https://machinelearning.apple.com)

ritholtz76

how do we invest in IPO's. I have ML account.

derprondo

I worked at Lily briefly about 15 years ago, they were doing cutting edge ML for drug discovery way back then. They had one of the biggest beowolf clusters in the world and an army of phds running simulations on it.

MajesticBread9147

I have three long term holds. **ASE Technology (ASX)** A Taiwanese company that is the market leader in outsourced semiconductor packaging and testing. Semiconductor process nodes can't shrink too much more before we get into issues, which is why many companies are not focusing as much on die-shrinks to increase performance but instead more advanced packaging. You see this with the increased use in 2.5 and 3D packaging, chiplets, SiP and the like. This trend is across the electronics industry, from auto manufacturers, the main CPU and GPU designers we all know, as well as SOCs used in cell phones, and combined CPU/GPU SOCs designed by big cloud providers used for AI training. The company is well diversified within the industry, and is the main player in their space, so isn't reliant on the current AI hype train to succeed. They have lower margins than TSMC however they have a significantly lower PE and PEG ratios and pay a 3% dividend which I reinvest. They are investing heavily into new equipment and factories to support the latest and highest margin technologies that they work with, but are still diversified across pretty much all semiconductor packaging beyond just the high end. The company doesn't get a lot of hype, and isn't captured by a lot of semiconductor ETFs, so while it absolutely is positive impact on the AI hype cycle, they are much less likely to be severely hurt by a bubble popping the hype cycle compared to NVIDIA or TSM, especially with their diversification. **Secondly, since we need to power the datacenters**: **First Solar(FSLR)** Basically zero debt, 0.57 PEG, and 28% profit margin with a huge backlog and new factories coming online this year. They make most of their panels in America and despite that and their large margins they were the first solar company to achieve sub $1/watt pricing over a decade ago. Their panels don't use silicon and instead use a different semiconductor (CdTe) that allows an efficient thin film deposited on glass ( vs sliced silicon crystals) meaning they use less material, and this semiconductor is both significantly better at maintaining efficiency in high heat environments and cheaper to produce. They focus exclusively on grid scale solar projects and contracts, so their revenues are more predictable and less sensitive to interest rates than rooftop solar. Current government policy can't change the fact that utility scale solar is by far the cheapest and fastest way to add electricity to the grid in a time when fossil fuels are set to become more expensive due to both increased exports and domestic demand, and nuclear projects, even SMRs take significantly longer and cost significantly more. **Lastly, I think Celestica(CLS) is still fairly valued as a growth play.** They are an advanced electronics manufacturer and large manufacturer of high speed network switches that are used in hyperscaler datacenters. Every server rack, and at multiple connections upstream has a switch, and networking is very important for ML workloads because large amounts of data needs to be sent between different servers quite quickly. They are the market leader in 800G switches which is the cutting edge right now. And while this is a good portion of their business, they also do healthcare technology,rack integration, general electronics design and offer services to better automate factories, which is important if we are going to bring manufacturing back. There are dozens of cloud companies, most of whom are unlikely to last til 2030, but Celestica will last, and every cloud company uses something made by them. They even make components and contracted out design and manufacturing for companies like Juniper and Dell. They beat last quarter earnings expectations by 50%, have a 30% ROE, and are expected to grow their EPS by 28% each year over the next five years. It's my largest holding by far. All of these are positioned to grow with whatever Cloud/Datacenter providers win out, whether AMD, Nvidia, or custom SOCs dominate compute, and are diversified enough to not go bankrupt if this turns out to be all hype.

IslandExact429

Had 49ers ML Cashed out at a loss Didn’t feel it. Watched Q3 and thought Eagles would just play chop ball

randylush

GPUs are still the bottleneck fam. ML practitioners are not limited by SSDs. Those are a commodity.

Trans-Squatter

Money is never destroyed. It only changes hands. They buy gpus, energy, pay ML engineers, data. The people working in this sector are getting paid, then they go to the strip club, and the young single mom also gets paid. Monday changing hands is the lifeblood of the economy. What should they be doing it, hoarding it? putting it under their mattress? That's when the economy dies out. Oh but you say, if they used that money more productively, they would have made more money! And where would that money have come from I ask you? Their clients - other companies that somebody would point the finger to and say "Look at those idiots burning money paying Google" like the posts calling people who buy every new iphone stupid. is money. In the end the government prints more and whether google execs burn their money building datacenters, or sniffing cocaine of a down syndrome dwarf in Thailand - the outcome is always the same. Government is going to print that government is going to print, and that money will circulate in the economy. What changes is who gets to hold it. That's all. Money can never be spend badly. Money is good only when it is being spent in any way possible.

dirty_F0x

Brother you are missing something fundamental it seems like. You are trying to predict where IV will be (which is what your GARCH model seems to be doing) or describing if IV is expensive compared to its historical past. It is about having a view on whether volatility implied in the options today have a chance to exceed the subsequent realized volatility. That is the name of the game with options. And essentially it is the variance risk premium analysis. You start to see many ex pro traders working at reputable places talking about it in a much more eloquent way than me. You should check Ksander from Sharpe Two: he has a background in ML and ex trader, and he predicts exactly this. It feels the closest to what people would do at a firm. It is inaccessible to most retails traders because we do not have the data and the infrastructure he has built overtime, but he put all his trade in his substack and his option analytics platform is great and super convenient. Same idea with Kris from Moontower, although I feel like Kris is a little less practical from a trading standpoint. In any case, stay far far far away from technical analysis when trading options, your intuition is right: no one ever got paid big bucks at SIG or Citadel for the chart pattern abilities. But for advanced quantitative skills...? That's why they win so often at the first place.

Master___debator

It’s literally just linear regression slope over N days. No indicators, no ML, no magic. i asked GPT for help with the visualization https://preview.redd.it/d4mu0knv88cg1.png?width=1536&format=png&auto=webp&s=ed0747c15b112448df70d09ccada625a79ab3faa

EyeSea7923

I have an AI/ML degree and do some coding, but not technically the software engineer I think you are looking for

ImpressiveNeat9039

> And one that no one is betting on existing anytime soon Well then you should probably make yourself a bit more aware and probably educated on this topic and probably less bet oriented based on poor information .. I hold a Masters in CS from one of top US universities an AI/ML/D/RL were subject areas and I work on technology. So I know what is happening in these areas very well and how well is it progressing and how far we are from these kind of technologies. You not betting on it is your choice.. Won't stop what is coming.

Real-Introduction221

Iowa state ML and under total point tonight

Betteroffbroke

Still the buy. I’ve bought and sold RDDT three times and doubled my money every time. I’m holding shares and buying more on the next dip from AI fear mongering. I just read an article about Reddit being the 4th most visited site in the Uk beating out TikTok with the majority of its users being Gen Z (that’s the target market for social apps!). They only expanded into Europe a few years ago and are seeing big growth in India. The flywheel is gaining speed with new countries adopting due to AI/ML allowing for translation across languages, a subreddit for anyone about anything, and the massive opportunity for monetization - Reddit provides advertisers a unique opportunity to learn about what people are saying about their products + the ability to market their products to specific audiences based on their interests. That’s just my bias two cents.

LordFaquaad

Is this the similar thing as "Cuda" being used for ML applications so it essentially locks in the framework being used for developing autonomous vehicles? Wouldn't manufacturers still have to actually build out the model, understand how to incorporate into the car + sensors needed, etc.

cloud9ineteen

I don't know what you're smoking. Nvidia invested heavily into CUDA and made it the de facto standard for ML work. Even before the latest AI boom, Nvidia was pretty much the only game in town for ML. Now the circumstances went crazy beyond their wildest dreams but their vision absolutely set them up for this. AMD is up 138% in 5 years. NVDA is up 1300% in that time. One of these is not like the other. If you want to compare a company that was a bit more prepared for AI, Broadcom is up 700% in that time. I would say AMD utterly and completely failed to take advantage of the situation and underperformed the baseline. Regardless of the recent boom, AI/ML as a market for GPUs has been in the air for long and AMD pretty much failed completely to take advantage of this situation.

im_burning_cookies

Every life decision I’ve ever made has come down to this moment. Every options trade, every buy every sell, every paycheck collected. It all comes down to this. Putting it all down on something I KNOW will happen. Full port 14$. Illinois ML.

KuchKhaasHaiYNWA

My personal recommendation with a roughly 40 to 50 percent upside considering its current rate would be ServiceNow(NOW) stock that is simply pretty undervalued considering its fundamentals are super strong. They have next to no debt and have a pretty robust standing in the SaaS market which is only getting fuelled by AI/ML based solutions that the company offers along with its pivot to CyberSecurity sector with its recent acquisition Artemis. Bill Mcdermott is a proven leader and knows how to steer the ship when the tides are against us! Company also believes in him and have continued to invest in his leadership. I have personally invested 70 percent of my portfolio as the stock seems like a wonderful buying opportunity.

NOT_MartinShkreli

49ers ML and Jags ML cuz I got now market plays is my move today lol

Asid_Phreak

I built my own from the ground up. I use IBKR and Databento as my only outside vendors. Everything besides that; code, logic, modules, back testing, execution is my work. I’m only running paper now but I’m at 83.6% success rate on scalping IWM AND SPY 0DTE. I’m integrating ML into it this week for strategy development. I have over 600TB of data patiently waiting to be replayed over and over again. Once a strategy is selected it’s sent out for verification, if it meets specs then and only then is it allowed a chance on the paper to prove itself. I’m aiming for 97% accuracy before going full prod.

dirty_F0x

Sorry for your loss brother. Too many people don't focus enough on understanding the true pnl driver when trading options and remain stuck in hyper leveraged directional bet, when options is an insurance contract on how much the underlying will move. If it moves more than what was implied in the contract, you will have to pay the claim (if you sold the option) and you will be entitled to the claim if you were long the option. That is why having a view on implied and realized volatility is so important. It is not the only factor obviously, otherwise it would be an easy game everybody on reddit would have mastered. I won't bore you more than that but if you want to go deeper, and still learn the correct way I can point you to two resources I find infinitely valuable, both have worked at reputable firms, not like the monkeys we find too often on youtube: check Kris from Moontower, he has so much free content about how pros think about options, I dont understand why people still bother with wheeling and stuff when this guy is out there, explaining how he would does it as a retail when before he worked at Susquehanna. Then you have Ksander from Sharpe Two. You should read his trade anatomy series on Substack, it is eye opening on how pros do pnl attribution to validate whether or not the thesis leading to the trade was correct or not. That mf hasn't missed in 6 months; he uses ML (he used to work at an AI company) to predict what is too expensive and ... well it works. They also both have analytics software: I find Ksander stuff really top notch as the ML models explain how they come up with decisions. That is so valuable to keep building your own trading intuition overtime.

dirty_F0x

If you are interested into vol strategies, there are now a few software out there targeting retail traders and get close to what you find quant traders use. I have tested all of the one below and will give you my honest opinion: \- UnusualWhales: Great idea when it came out, but it is impossible to make money out of that thing. I would stay clear if you actually look for edge, but they have nice viz and sometimes it's nice to spend a friday afternoon looking for weird flows on obscure tickers. \- Spotgamma: this one is one I don't believe why it is so popular. Purely focus on directional trading and more specifically 0dte. They have supposedly some prop measures to compute dealer exposure, but when you talk with pros and do your due diligence a little, they all tell you the same thing: this is a fantasy and the market doesn't work like this. Unless you trade billions and work as a flow trader, there is no edge for a retail trader here. But again, nice app, great content. One last thing: do not ask annoying question about showing edge and profitability over time, you will get banned. \- Moontower ai: great tool from Kris who has been writing so much about vol trading over the years. He has worked at SIG for many years and knows what he is doing. He is focused on vol strategies, particularly the VRP. Now, my honest take is his tool is confusing and if you do not have his level of expertise, you are still left "guessing" or using your own experience to find what is the best trade. \- Sharpe two: amazing tool by Ksander. He uses ML to score where you should short or long vol on many tickers. And ... well it works. The guy has a background in trading and ML and ... it shows: he writes on substack and hasn't had a losing trades in 6 months. The tool is easy to use once you understand the concept of probabilities. What I love is the model output the reason why it makes a prediction which is very handy to keep learning and not just follow a black box. The downside: it requires some reframing of how you think trading. He doesn't do directional trading at all and is almost exclusively in ETFs. Def worth checking. I'll finish with Predicting Alpha who wishes they were what Moontower and Sharpe Two is. Except ... they are not. I lost a lot of money with them because their data were not accurate, but also they do not have a trading background. And how much Sean can be a nice guy, when shit hits the fan, you want to be in the community of someone who knows what he is doing. That's why I prefer Kris and Ksander's stuff: I learn a ton with Kris, I make money with Ksander.

autumndrop

I think this frames Amazon a bit narrowly as a “product company” in the consumer sense. Amazon’s edge has historically been operational + platform DNA, not polished end-user products. In AI, that actually maps pretty well to infrastructure, tooling, and distribution — which is why AWS, custom silicon, and internal ML deployment matter more than a breakout consumer AI product. They may never “lead” AI the way Apple leads hardware or OpenAI leads models, but they don’t need to. If AI becomes embedded across commerce, logistics, cloud, and enterprise workflows, Amazon can still be one of the biggest beneficiaries without winning the narrative.

seanpuppy

Based on other comments here, Im guessing he owns the hardware he runs his backtests on. I have never written a trading algorithm but i've written several backtesting pipelines for ML models at each of my past jobs. Im guessing he wrote the meat and potatoes of the math in CUDA and in order to pump a ton of data through it. I think the biggest bottle neck isn't some massive supply of raw data, but trying lots of combos of parameters through some sort of monte carlo simulation - against all securities data. He said elsewhere he spends $1k/month on data electricity

mollusc_in_the_wind

False equivalence. ClickHouse/CatBoost are software projects (largely open-source) that can be great on their own merits. NBIS’ business model is renting compute capacity (GPUs + power + facilities) with an orchestration layer. Being able to build good ML/database software does not prove you can build a defensible neocloud with durable pricing power.

NVDAismyg0d

Bama ML. Turn $1500 into 5,000

fooish_moral

where the dude that guaranteed osu ML several days ago? thanks bro...

daishi55

Nope! You are simply incorrect. GenAI does learn, that’s what happens when they are training the model. > I have a masters in ML Then you need to get a refund. “The models just predict” that is what literally everything in machine learning is about. It is astounding that you could complete a masters in ML without learning that! Mind boggling. You should genuinely sue the university who wasted however many years of your life.

Hacking_the_Gibson

Only in the sense that machine learning is a broad enough term that it can encapsulate anything in this field.  Classic ML is really quite different from generative AI. Machine learning is what enables your phone to predict your battery usage and display the expected expiration. The applications of more stereotypical ML are a lot more bounded. Generative AI is a fucking free for all. 

One_Opportunity9167

Open new IRA's IMMEDIATELY. Put in the full amount...not just the check, but also what was withheld. Borrow if you have to. The ML withholding will come back to you once you file your 2025 taxes. If you fail to deposit the full amount into the new IRAs, the amount you're short will: \-Be taxable \-Probably have a penalty \-Will be gone from your tax-advantaged assets Mitigating factors: \-You can always withdraw the amount of the original Roth IRA contribution without penalty or taxes, just not the earnings. \-$22K is a lot of money when you're young, but it's not that much over your whole life. Stuff happens all the time and it's no fun when it happens, but keeping this in mind might help you sleep at night. If it were me: I would do what ML suggests and open new IRAs with them because it's faster. Put in the full amount, even if I had to borrow (short term). Then, in 2027, I would transfer all my IRAs to Fidelity or Vanguard. Who the hell puts this kind of crap on a customer the last week of the year?

BeneficialCoffee6589

This isn’t politics or PR — it’s about who actually builds the tech. After the U.S., Israel punches at the very top in exporting advanced technology because the breakthroughs are real. • Mellanox → ultra-low-latency networking that feeds AI clusters at scale; now core to NVIDIA’s data-center stack • Check Point → AI-driven threat prevention (ML models analyzing billions of events in real time, not signature-based junk) • Palo Alto Networks → AI/ML-native security platform (behavioral modeling, automated response, zero-trust at scale) • Wiz → AI graph analysis of cloud environments to predict attack paths before breaches happen Israel isn’t just “cyber” — it’s AI-first cyber, forged under real-world pressure. That’s why it’s becoming the new Taiwan of intelligence: not fabs, but the minds designing and defending the systems. NVDA isn’t virtue signaling — it’s doubling down where the engineering alpha already is. Believe it or not… calls 🟢🦍📈

Big_Seesaw_1792

Is this the way to fix the problem (Gemini)? # How to Fix It (The 60-Day Rule) **you have 60 days** from the date you received the funds to perform an **Indirect Rollover**. 1. **Open a New IRA:** Immediately open an account at another brokerage (like Fidelity, Vanguard, or Schwab). 2. **Deposit the Full Amount:** To avoid all taxes and penalties, you must deposit the **entire gross amount** that was in the account before liquidation. * *Example:* If your IRA had $10,000, ML likely sent you a check for $9,000 and sent $1,000 to the IRS. To "fix" this, you must deposit **$10,000** into your new IRA. 3. **The "Withholding" Gap:** You will have to come up with that 10% (the $1,000 in the example) out of your own pocket for now. When you file your taxes next year, you will report the rollover, and the 10% withheld by ML will be credited back to you as a tax refund.

aridneptune

Yeah, the reason was the CDD rule (as someone else pointed out). My wife just ignored the emails. Still, I would never have imagined that ML would just close the account. ML is refusing to do a custodial transfer.

LostAbbott

Dude.  You need to manage all of her stuff for her moving forward.  This isn't ML's fault it is your for not paying attention to your funds.  My wife hardly even knows her loginuch less what accounts she has where.  

aridneptune

Thanks! One question though…wouldn’t ML send two separate 1099-Rs?

aridneptune

ML is refusing to do that though!

Sov1245

ML = Merrill Lynch, not mother in law :D I read it the same quickly the first time.

Alexander_HamilDong

Curious - what was the mistake with ML?

Intelligent-You-6144

Trading View with Infiniti Algo or one of the other AI ML plug-ins.

bonsai-bro

When you said you "used AI" to analyze sentiment, what sentiment analysis models were you using? VADER? BERT? Or was it more classical ML methods like naive Bayes?

CurrentChannel5948

This is actually a solid thesis but I think you're being way too generous on the timeline here. ROCm is still hot garbage compared to CUDA and anyone who's actually tried to run ML workloads on AMD knows it's a pain in the ass The EPYC comparison is interesting but CPUs are way different from the AI accelerator market. Intel was literally asleep at the wheel for years while AMD was catching up. NVIDIA is actively investing billions into their moat That said, your depreciation point is spot on and those MI400 benchmarks could be spicy if they deliver

alecKarfonta

Sitting at +30% for the year. No options or margin just swing trading tech stock volatility. I am using a custom built analysis tool that combines traditional TA (momentum indicators and candlestick patterns) and an ensemble of machine learning models. I only make a trade when the TA, ML and my own intuition align.

AndAuri

Both reference the word "grokking" which had widespread use in the ML world even before AI was mainstream.

fooish_moral

canes ML it is... thanks wsb

NVDAismyg0d

Have way too much money on LSU ML. God help me 

TessTickols

Sounds like a 100% certified regard idea. Meanwhile I am using ML with macro and financial data to try to learn trading patterns, and there is no real edge so far after billions of trading years. I always seem to end up with a fairly respectable Sharpe, amazing bear market performance and bull underperformance.

drdecagon

I understand where you are coming coming from - but the videophile use case is still super niche. The trend is not that people want immersive experiences - they want access to a second screen. They want to flip through different content, different apps, for most of us our attention is shot and it's not getting better for younger generations. So, I think it's unlikely that immersive experiences will become VR's killer app. And AR, jeez, I can see business application clearly and some occasional consumer task application (and even that only materializing once there are additional advances and synergies with ML, machine vision, and AI) but I just can't imagine most people walking around with glasses with a bunch of additional notifications and information popping up non-stop in their field of vision. It might be close to sensory overload. Think about this - many cars nowadays have the HUD option - and yet, it is nowhere near a killer app - it's helpful, but not universally loved. I suspect that AR would start making a pronounced impact in cars first, but it hasn't yet in any meaningful way. There needs to be some kind of neuralink integration, where it's more integrated with our thoughts. But currently it's a solution looking for a problem (or really, for some form of a dramatic breakthrough).

shekurika

thats a different branch of AI/ML than the one thats important to the stockmarket (LLMs etc). its called bioinformatics and the idea is to more closely model the way the human brain works (whoch obviously is analogue as everything electrical in nature). but theyre in very early stages and itll take years of research to get anywhere

dirty_F0x

You should read some stuff from Euan Sinclair. If you like to get into the fundamentals of things, you can read Kris from Moontower. His stuff is really top notched. And recently I've liked a lot the very practical approach from Ksander, the guy who writes Sharpe Two, also that mf seems to not missing a trade lately. He is pretty open about ML and stuff, you should try to reach out.

Dry_Act6001

This doesn't even include the inevitable "How do I poison their dataset?" conversation in the world of adversarial LLMs and AI/ML

c-u-in-da-ballpit

I’m a software engineer focused on ML at a large-ish US company ($5 billion market cap) Everything in this post is uninformed horseshit

OUEngineer17

Move it wherever you want as long as you keep Platinum with ML, as that 2.6% cash back on everything CC is unbeatable.

lucun

Linked ML account balances count toward BoA premium tiers for people with higher balances with BoA. The premium tier credit card cash back boost is nice with their travel card. It becomes a generic 2.6%ish cash back anything card. BoA savings accounts are garbage though, but you can use the linked ML account to buy treasury mutual funds. I keep money and assets across many different banks/brokerages though

Nosemyfart

Yes, I particularly liked when they chatted about scaling up operations in the future on Azure. I'm a ML researcher and enjoy this sort of stuff outside of the investing potential.