ML

ProtonPizza

Uh, I’m about as anti-corporate as you can get but this is just flat out wrong. It has pretty good LLM integrations that are easy to use. Are you talking ML/ image classification?

templar7171

Make sense. I developed my own ML model but only use it as "guidance", I don't trade blindly on it

templar7171

Be careful of LLMs re: trading. Last night, I did a google search on "how to manage a 10DTE put debit spread with 0DTE put credit spreads". I got a result from Google's "AI" that was 100% opposite of what you want to do. Now if you get deep into AI/ML, ignore the pre-trained "cloud" stuff and make your own models, then you get a feel for how things really behave, and what ML is good for and what it is not good for...

No_Arm_6582

Parlay the Dodgers ML and under 9. Free money

Blu3Gr1m-Mx

AI’s been rebranded ML with better PR. It’s powerful, but not the revolution it’s sold as. We can’t deny it’s not a pipe dream with different AI companies but we also can’t deny it’s made people learn to levels that will make the world better. It has made technology fun for everyone and has made a lot of people learn things they never thought they could. Change robots and machine learning now know as AI will provide tools for every worker to become better and more successful. You’re spot on. AI’s been rebranded ML with better PR. It’s powerful, but not the revolution it’s sold as.

Academic-Image-6097

>Everyone has literally said the same thing about every other AI. That's because the lead in these benchmarks changes every few months. But indeed, right now I believe Gemini Flash 2.5 is on top on most of the well-known benchmarks llmarena, GPQA, Aider, etc. But the speed at which ML is currently innovatief makes any benchmarks quickly saturated, and no organization has really been able to keep a lead, any technical optimizations are quickly adopted in other models. The question is really whether these benchmarks are actually meaningful when it comes to real-world use cases, consumer preference, monetization. In my humble opinion, from a layman who is very interested in the ML field, the technical benchmarks that really matter are: 1. context length (for coding and RAG-like applications, long form conversation), 2. cost of inference, measured by dollar per million tokens 3.speed of inference, measured by tokens per second. Last time I checked, Gemini was ahead in all three, but please feel free to look it up yourself. Perhaps it has changed again by now. I suspect they do so well at these aspects because Google, unlike an OpenAI, DeepSeek, Grok, Anthropic, etc., have inhouse TPUs that may perform better on the dollar, or they have optimized their datacenters better somehow. And they kickstarted the whole AI boom in the first place back in 2017.

landed-gentry-

Predictions are probabilistic in many domains: statistics (see ML models), medicine (see predictive medicine), social sciences, epidemiology, even physics if you go beyond classical physics into quantum mechanics. If you take a prediction from a standard statistical model like a regression, it's given as a "point estimate" and a "confidence interval". The point estimate is the best guess at the outcome (the prediction), and the confidence interval is the range of possibilities.

landed-gentry-

Prediction is probabilistic in many domains such as statistics (see: ML models), medicine (see predictive medicine), social sciences, even physics if you go beyond classical physics into quantum mechanics.

Fauster

Yeah, I think technical analysis using your brain meat is a losing proposition for virtually everyone. But if you know how to program, you can find and back-test weak correlations that give you a small edge, especially when you set limits to enter and exit. But, the odds are more like 55% of a chance of being right and 45% being wrong over the ST. If you try to leverage those odds, you will quickly fall victim to the Gambler's Ruin paradox. Even if you have a small edge on a single stock, if you bet the farm on it, volatility will quickly kill you, because you should expect a weighted coin to land on tails ten times in a row if you flip it a lot. To be clear, techniques like using Fibonacci sequences or Elliot wave theory are almost certainly BS and so hand-wavy that I don't even know how to begin to code them and they are motivated by absolutely nothing, as far as I can tell. Other indicators like crossovers can be statistically confirmed and easily coded, but their predictive capability is so weak that they aren't worth it. However, you can use technical indicators as a basis to preprocess data for statistical (weaker) or modern ML (stronger) methods to create ensembles that allow you to quantify volatility and the risk of a trade. The good thing about price and volume data is that there is a lot of it, especially with intraday data, even though it is still extremely sparse in high-D space. The really bad thing about fundamental data is that there is so very little, which makes it that much harder to find any correlation that isn't near-zero over a market cycle. I tried and repeatedly failed to get fundamental screens to work for so many decades that I gave up, with the exception that it is a good thing when revenues and EBITDA reliably trend up and a really bad thing when they go down, especially if the PE and PS are high. But for me, growth is a screen to whittle down what companies to even consider. I will never bet on a turnaround of fundamentals until the data comes in and I don't care that this means that I will always exit after the top and always enter after the bottom. Companies like Renaissance Technologies beat indices in their closed private funds for many years (their pleb funds underperform the S&P) because there is useful information in the raw data. They started by sending peons to copy treasury data by hand so they could digitize it and use it and I think they started with hidden variable Markov models. Data is useful because the market is driven by big players following the Pareto distribution, and no big players can enter and exit a position without leaving a significant wake in the data. Of course, as a retail trader, your algorithms are going to be worse than those of quant shops, and whatever model you use, they presumably see the same correlations as part of their ensembles and potentially exploit traders with weaker models. I still have a buy-and-hold account though. I try not to think about it, especially in times like these. I still buy positions of index funds along with stock trades to easily track performance in real-time, so I always have VOO, FXAIX, or QQQ positions. Also, there is absolutely no reason to buy individual stocks if you don't have a reason to think that your trades will outperform the index, unless you just like collecting shares of a specific company because you like the company. This is because any time you buy a company vs. an index fund you are assuming extra risk with less diversification for the potential of extra reward. Less diversification is the only way you can outperform. The only way to begin quantify that risk is to use data-driven algorithms. Ignoring that risk is fine if your portfolio is small compared to your salary, but losing money really hurts when that isn't true. TL;DR: I'm 40% cash/inflation-linked bonds right now which is a lot for me, because I'm convinced there is a modestly greater chance of more carnage ahead. I hate dividend stocks and bonds except for times like these. If I'm wrong, I still make less money with less risk; if I'm right, I get to buy great companies at even better prices knowing that this insanity won't last forever.

XYHopGuy

> II think AMD represents a great opportunity to invest in computing in general (not just GPUs). And I think AMD is an okay investment opportunity if their GPU division is unsuccessful. Again, it's an interesting place to invest but the opportunity cost is way too high until they show they can deliver in the ML space. >I'll repeat, what you're paying for earnings growth is what matters. Not earnings or earnings growth in a vacuum. I understand, and again, their primary competitor, even with a giant market cap, is showing better growth y/y, q/q, for the price as the leader in the biggest growth market on earth. Over the next 5 years it's certainly possible this outlook could change, but again, until we see MI400X there is a big opportunity cost to allocate resources in AMD instead of the alternatives.

XYHopGuy

And down q/q during an AI boom. Semis are cyclical but the ML boom has not been. We are 2.5 years into this and AMD is still "cyclical."

TitsMcgeexMustafa

Just cashed nuggets ML +850 live bet

Low_Finance_3874

What the market doesn’t know is when he’ll do something stupid again, reverse/increase a tariff, attack Powell, etc. They can only price in what they know and they can’t price in ignorance and chaos. Coming from an a guy whose job is ML/AI, we have no models for what he brings.

XYHopGuy

>And you can't build AI model without high end AMD CPU. Most don't use AMD CPUs. CPU is not a very important piece of ML hardware. >30%+ GPU market share How TF you get this number. Revenue don't lie. Gaming rev alone is 25% of NV. And this number is dominated by consoles for AMD. Datacenter segment looks much worse (and is dominated by CPU for AMD)

King_Awesomeland

saying it works vs deploying at scale is not the same. unfamilair user experience vs windows would greatly burden those migrating away. we're years away from ML capabilities that will make my above point moot. it's biz as usual for at least 3plus years...and by that time we may have weathered the storm. fingers 🤞

Echo-Possible

I lived the dot com bubble as well. Is there any reason to believe this is the same? The dot com bubble implosion was due to speculation in a bunch of Internet companies that didn’t have products or revenue growth to back it. Today we have massive tech companies pulling trillions in revenue and growing that are leading the AI charge. Apples and oranges. If you truly believe we won’t see huge productivity gains and earnings growth from automation with AI then go ahead and sit on the sidelines the next decade. I work as an applied scientist in AI/ML and I’ve built tools for clients that generated outsized returns for the investment.

SuffiecientSapian

Warriors ML & Over on points. However now that I said that it’ll now be a donation 

rocknes88

big data -> cloud computing -> internet of things / ML -> Block chain -> AI -> Quantum computing?

lafindestase

I see the value in Reddit now, but I don’t know how they’re going to deal with the inevitable swarm of low-cost, convincing-enough ML bots. They going to make everyone link their account to government issued ID?

SectorBudget406

Team looks better on paper but are streaky even within games. Timberwolves are the opposite. For some reason they were +205 ML last night and looked like free money so I took it.

Radiant_Rip_4037

My ML says spy going to next level 596 check out my post history your welcome 

Radiant_Rip_4037

Look at my post history I’ve been right 4 days in a row not bragging just saying hmu my ML can analyze any stock 

Dogmanq

lol fr, just go take a -300 ML and call it a day

Livid-Ad141

I completely agree with you, I was just using them as an example. I couldn’t get my personal broker at ML to make the trade for me. The entire industry lined up against retail.

f8worksbothways

My puts gonna print harder than the -19 I threw up on Golden Tee last night while hammering about 17 ML smoothies with the boys

suzisatsuma

I'm a ML/AI engineer - have worked in big tech for decades. uh believe it. The agentic ai stuff is actually pretty good for a lot of the more tedious code writing. It's getting dramatically better. (there is all the incentive) There's vast amounts of boilerplate / refactoring / maintenance / and testing shit. It's not like it's off by its self. I used a lot of agentic ai integration in my IDE. They're pretty damn effective for writing code for the shit I find tedious. It's code generated at the prompt and validation of engineers tho - so making individual devs more productive.

pboswell

So the 49% of people didn’t downsize INCLUDES the 30% of people who upsized. Check the [report](https://agewave.com/wp-content/uploads/2016/07/2015-ML-AW-Home-in-Retirement_More-Freedom-New-Choices.pdf) page 9 for the pie chart. 51% of retirees downsized.

Gingeneration

You’d start with ML. They have investor relations

suzisatsuma

hi OP - I'm a machine learning engineer/data scientist that has worked in tech giants for decades doing ML/AI. What do you not believe about AI? What do you think AI is?

benjaminprinter

Timbys ML was an obvious lock today sorry bron and bronny

Echo-Possible

I'm an applied scientist working in AI/ML in a tangential field (computer vision). It sounds like you don't understand the basics of neural networks. Localized parameters means the models require additional training specifically for that location and the deployed models have different weights for every location. This is not a generalizable solution.

acceptablerose99

Google has been investing in ML research for longer than most AI companies existed. Their own transformer paper that they published for free is what set off race in AI.  They will not abandon the space. 

LazyFalcon7165

You’re absolutely correct. And, the people that work on manufacturing product  validation and sustaining production are completely different from the people that do software, ML and AI. Recalled 462k trucks in nov last year and a separate 35M settlement this year for older trucks.  Tesla has auto business problems, (brand damage, aging lineup, inability to produce a true mass market vehicle), and those are separate from the higher value, autonomy goals.  Autonomy failure is a very real risk, but it’s pretty unrelated to a minor recall on a new auto product

mtnbiketech

Its not a casino, let me explain. With NVDA, they make products for average people. Graphics cards and ML accelerators are pretty straight forward - you give NVDA money, they give you compute power in a box. When economy gets bad, people stop buying those things, which is as expected. TSLA on the other hand used meme magic. Generally, cars like Prius and other up and coming plug in hybrids should have been the tech bro cars. They are functionally better, and way easier to live with. But through sheer power of memes, Tesla took that spot. The problem is, Tesla cars are all shit. The only thing the cars have going for them is the torque, but once people buy it and figure out how quickly flooring it everywhere or driving fast drains the battery, and how much of a pain it is to recharge, they stop having fun with the car, and then it becomes basically a more expensive worse eco car, with trim that is falling apart, reliability issues. The only people that buy Teslas even before Musk destroyed his brain on Ketamine were people who were clueless about cars in general, or your run of the mill software engineer Indians trying to fit in socially. But the thing about meme magic is self perpetuating. So lets say TSLA starts lowering prices. You better believe that every Rajesh in any second rate software company is gonna be picking one up cause they know fuck all about politics, but their friend that works at Amazon drives one so they want one too. Investors sense this demand. Like even you probably can sense if Elon goes away, and TSLA gets ran by someone else, the product can be the same but the cars will gain back their popularity despite still being shit. So naturally when stock is cheap, people buy. Then TSLA going up in price means it can raise money and develop more meme cars, which are again going to be absorbed by the tech bros, and so on. Its the same reason Apple dug itself out of the grave with the iPhone. The original iPhone was a piece of shit compared to other devices. Blackberry was the defacto standard for productivity, web browsing, e.t.c. , and there were [other phones](https://maddox.xmission.com/c.cgi?u=iphone/) that were better. But the meme of having a giant touch screen and no buttons took hold and allowed it to propel itself into top spot for mobile phones for quite some time.

Historical-Apple8440

The pattern of growth in their Energy Generation and Storage segment is really impressive, and the loss for their Cars given \*gestures widely\* the past 3 months is not as bad as people thought (\~100,000 less "cars, 400 something thousand to 300 something thousand). The FSD 13.2.8 roll out is also a solid innovation, it really just works that well. There is a solid AI/ML play here that differentiates them enough. Reddit lives in this bizarre echo chamber and wants Tesla to fail because of Musk. But the company is really doing interesting, cool and potentially profitable, sustainable things no one else is doing stateside. They held up much better to the protests, and the numbers are proof. Investors *know* that *outrage* or being *against something*, when that something is *a car,* does not and will not scale.

tonydtonyd

You’re on the right path however you have several key details incorrect. Waymo does all the ML using Google services, which it pays for. Google has essentially no interaction with Waymo aside from funding and being on the board. Waymo does not *just* run on Uber app. Actually their largest markets are on their own Waymo One app. Hyundai does not build the cars, Waymo does. Waymo has always ordered modified versions of base vehicles from manufacturers and unfitted their sensing and hardware suite themselves. Not sure what they are planning on doing with the next gen Zeekr and Hyundai vehicles though, with more scale it makes sense to push more of the upfitting to vehicle manufacturers.

Far-Fennel-3032

Waymo's robotaxis are a bit complicated as its sort of a three way joint venture to get robo taxis working. With Google doing all the ML and self-driving software, Uber does the customer-facing side and Hyundai does the car and hardware. Atm to ride a Waymo car you summon it via the uber app, and an empty car turns up and you just get in and it drive itself and you don't sit in the driving seat.

Defol-420

ofc, using huggingface libraries for ML flow and stuff

ArmmaH

Okay now that tech discussion on ML is more interesting to me. I know that AI has had big impact on computer vision, security and defense (palantir type stuff) etc. However, I feel most of the products that came out are just slightly more efficient or faster but they are not revolutionary. Im sure governments will spend millions on facial recognition and sentiment analysis, using those tools to manipulate the masses more efficiently. They will also use the tech for drones and automated warfare. This is the only place I see revolutionary impact. Everything else like search engines and social media are still the same product. Adobe has made an investment in AI and they are just staying competitive, it does not increase their sales. Please let me know if I am missing something here, I am open to being proven wrong and will appreciate your insight as someone directly working on those products.

Echo-Possible

That’s one thesis. The reality is the vast majority of people trying to time short term movements have logical theses but they still underperform the market long term. You simply don’t know what’s going to happen. What you think is a storm may just end a squall that clears up overnight. You can certainly try but the odds are against you. As someone who works in AI/ML as an applied scientist I fundamentally disagree. AI has already transformed many industries and I’ve developed products that generate tangible returns.

dkrich

As someone who really really dislikes Elon and think he’s completely deranged, here’s why you’re wrong: Tesla is still an amazingly forward thinking company with ML experts working every day to bring AV to bear at a cost nobody else will be able to compete with.

Serious-Regular

I'll give you 100 shrute bucks if you can define any of the ML terms in this pasta

Apprehensive_Bit2685

ChatGPT is only the use case for OpenAI’s LLM/Advanced machine learning algorithm. ChatGPT is not the product, the LLM/ML itself is the product.

FuckElonMuskkk

TEAM UPDATE. TIME NOW (1144et): Weather is FAVOURABLE . Just CONFIRMED w/CENTCOM we are a GO for mission launch. 1215et: MILLER 6-PACK LAUNCH (1st strike package) 1345: CAPTAIN MORGANS SHOTS 5ML BASED FROM MY CUPBOARD (Wife is @ her boyfriend's so SHOULD BE OKAY FOR SHOTS – also, remote start car (Toyota Prius) 1410: MORE MILLER TALL BOYS SHOTGUNNED (2nd strike package) 1415: Drive to work (MILLER 16oz CANS DEPLOYED FROM MY CARS CENTER CONSOLE) 1536: LOCATION: WHITE HOUSE (Last 4 Miller Cans deployed in Coca-Cola Koozies for MAXIMUM OPSEC) MORE TO FOLLOW (per timeline) Godspeed to our Warriors.

Fun-Author3767

Even basic ML models using knn methodology would suggest a spike in unemployment from the cyclical patterns alone. Doing VAR using inflation rates and Treasury yields without any other info puts the unemployment at 7% by August. Given this factor of tariffs into the VAR would probably multiply that effect. I don't have time to pull tariffs from 1962 to present to add it to a VAR, but I imagine it would not react kindly to the addition.

IvorTheEngineDriver

Quite [older](https://m.media-amazon.com/images/I/71mgFrH86ML._AC_UF1000,1000_QL80_.jpg)than that

Khrinshaw

u/xevaviona Love the “GPT wrapper” callout — you’re right to question what’s under the hood. The AI isn’t meant to trade for you — no tool can reliably predict markets, and I’m not pretending otherwise. Instead, it’s a dead-simple coach for retail options traders (9-to-5ers dreaming of going full-time). It analyzes your trades to spot sloppy habits, like FOMO buys or bad risk-reward, and gives clear, data-backed tips to improve, like “Your late-day calls lose 20% more — try morning setups.” Why use it? Because small fixes add up, helping you trade smarter without complex apps. On the “GPT wrapper”: Fair point — the AI uses a language model (like GPT) to make advice conversational, but it’s not just a chatbot. It’s paired with trade-specific logic that crunches your trade data (win rate, risk-reward, etc.) to generate tailored tips. We’re also exploring lightweight ML to make it smarter over time — think clustering your trades to find patterns (e.g., “Your scalps on low-volume stocks tank 15% more”) or ranking your worst habits by impact. This ML doesn’t need massive datasets; it learns from your trade history to personalize advice, not predict markets. Connecting to trading accounts: The tool would link to your brokerage (e.g., Robinhood, TD Ameritrade) via a secure API — no manual uploads. You’d authenticate once, and it pulls your trade history (entry/exit prices, volume, etc.) to calculate metrics and feed the AI. This makes it effortless: you trade, it analyzes, you get tips like “Cut trades after 2 PM to boost your win rate.” Security’s key — we’d use OAuth and encryption to keep your data safe.

Khrinshaw

20 years in data analysis and ML gives you a heavy-hitting perspective, and I’m glad you’re calling out the potential pitfalls. I hear you loud and clear on the risks of oversimplifying a complex problem like trading, and I want to unpack your points to clarify what I’m aiming for and address your concerns. You’re spot-on that trading is an optimization problem, and chasing recent patterns can slam you into constraint boundaries. I’ve seen enough models overfit to noise to know that’s a real danger. The AI coach I’m proposing isn’t about “boosting returns” through some magic formula or overfitting to recent trades. Instead, it’s focused on helping retail traders spot basic, repeatable mistakes — like consistently poor risk management, FOMO-driven entries, or ignoring stop-losses. The AI would analyze their trade history and suggest practical, principle-based improvements (e.g., “Your risk-reward ratio is below 1:1 on 70% of trades — aim for 1:2”), not chase hot trends. It’s more about discipline than optimization. Does that framing reduce the “shoot yourself in the foot” risk, or do you see other traps in even this approach? And great point about the challenge of finding a large, appropriate training set. Complex ML models for trading need massive, clean datasets, and retail traders’ data is often messy or limited. To clarify, I’m not envisioning a full-blown ML model predicting market moves or requiring huge datasets. The “AI” here is more about lightweight, rule-based analysis combined with a language model (like those powering chatbots) to interpret trade data and generate human-readable advice. For example, it calculates metrics like win rate or average loss from a user’s trade log and uses predefined logic to suggest improvements, with the language model making it conversational. It’s less “deep learning” and more “smart analytics with a friendly face.” Would a simpler approach like this still seem naive, or is the data hurdle still a dealbreaker in your view? On Credibility and Trust: Ouch, the “random Redditor” jab hits hard, but it’s a fair challenge. You’re right — without credibility, a free tool sounds like a gamble, especially in a space full of sketchy promises. To be transparent, this is an early-stage idea to gauge interest, not a finished product. My background is in building user-focused tools, not finance, so I’d need to partner with trading experts or analysts like yourself to make it legit. The tool would likely be built on a platform like [Bolt.new](http://Bolt.new), integrating basic trade data APIs and a proven language model API (e.g., OpenAI). To earn trust, I’d open-source parts of the logic, share clear documentation, and start with a beta for feedback from traders. What would it take for a tool like this to earn your trust — a track record, expert endorsements, or something else? “AI can’t profitably trade but can tell you how to profitably trade” does sound like nonsense, and I appreciate you calling it out. Let me clarify: I don’t believe AI can autonomously trade profitably (like a quant fund’s algo) due to market complexity and unpredictability. But I do think AI can help traders improve by analyzing their past trades and pointing out behavioral or strategic flaws, like “You’re overtrading after losses — try capping daily trades at 3.” It’s about coaching the trader, not trading for them. Does that distinction make sense, or do you still see it as a contradiction? With your experience in predictive analytics and optimization, you’ve probably seen tools that promised big and flopped. I’d love your take on how a tool like this could avoid those traps. For example, what guardrails would you put in place to prevent recency bias or over-optimization? And what’s one feature you’d want in a trading analysis tool to actually make it useful for someone with your background? Thanks again for the reality check — your insights are gold for shaping this idea. No BS, I’m here to learn from feedback like yours.

therealjerseytom

A variety of reasons. Coming at this from the perspective of an engineer who has been involved for ~20 years in data analysis, modeling, predictive analytics, optimization, and dabble in machine learning now and then. Overall I'd say it comes across as a great way to shoot yourself in the foot at best, and disembowel yourself at worst. Anything that's aimed at "boosting" trades and returns is an optimization problem, and it's really easy—even among professional analysts—to "chase" in one direction based on recency bias and just hit your constraint boundaries. I could see it being difficult to really find a large enough and appropriate training set for ML to munch on. To think that "simple" AI can be a solution to financial or trade coaching seems naive. And lastly but perhaps most significantly, put yourself in our shoes. Why would any of us put *any* faith in a freebie tool put together by some random Redditor? What credibility do you have in someone else's eyes? *None whatsoever.* Especially when you're already making contradictory statements, e.g.: > personally don't think AI is at the point of being able to profitably trade [...] it can analyzing your trades and tell you how you could be more profitable "AI can't profitably trade but AI can tell you how to profitably trade." That is a complete contradiction and nonsense.

Ok_Rain_3864

You can look elsewhere in industry. Compute power will be a currency in the future, because it will determine the upper limit of what you can do with AI and ML. It's also the natural progression after datacenters - they'll be datacenters that think.

Sick_by_me

Copy pasta from chat gpt. I don't know if it even makes sense. Summary of Key Points: Purpose & Background: This Mortgagee Letter replaces ML 2025-06 and formalizes updates to FHA loss mitigation policies. It aims to tighten and expedite implementation of FHA’s new permanent loss mitigation options, ensuring sustainability, reducing defaults, and protecting the Mutual Mortgage Insurance Fund (MMIF). Key Findings: HUD observed misuse and increased default rates in COVID-19 Recovery Options. Permanent loss mitigation options are now limited to once every 24 months (previously 18 months) to prevent repeated reliance and strain on MMIF. Some borrower compensation increases (e.g., Cash for Keys, relocation assistance) were deemed not cost-effective and rolled back to prior levels. Language accessibility requirements were reduced, deemed too burdensome. Key Changes: FHA-HAMP and COVID-19 Recovery Options are officially sunset by September 30, 2025. New permanent options take effect October 1, 2025, with updated eligibility, definitions, and requirements. FHA Handbook 4000.1 is being updated extensively to reflect these changes. Updated Procedures Include: Revised servicing responsibilities, particularly during transfers of servicing or sale. Expanded guidance on escrow management, insurance administration, and loss mitigation waterfall. New reporting codes for claim types related to loan modifications and partial claims. Streamlined documentation and communication requirements with HUD and housing counseling agencies. Conclusions: HUD is emphasizing a more disciplined, sustainable approach to loss mitigation. These updates balance borrower support with fiduciary responsibility to taxpayers and MMIF stability. Ongoing policy review will continue under the Trump administration, especially around tools like the Payment Supplement.

Echo-Possible

As an applied scientist in AI/ML I couldn't disagree more. No one is using Amazon's custom silicon in industry. They're garbage with poor support. I've never seen anyone even discuss it. And as for Google they've been working on TPU for over a decade and its highly customized for their workloads. It's very difficult to get anything up and running on TPU if you don't work for Google. They simply don't have the support to make it useable by the AI community. They're focused on TPU for supporting their own training and inferencing workloads not making it a generalizable solution.

theorizable

Not the correct solution according to who? Elon? That's convenient for his stock price, lmao. If you work in tech (or anywhere really) you know that sometimes there are multiple approaches to solving a problem. If it works, and meets MVP criteria, it's a potential solution. Humans are limited to vision. Why would we limit the solution criteria to only allow vision as input? That makes no sense. Robots can see completely different spectrums of light, heat, radar, sonar, etc. Imagine you had FSD, but then you also wanted to build, I dunno, a submarine car. What good is vision going to be? LOL. I worked in the ML space for a bit. Their goal is ambitious but idiotic (negligent) from a business perspective. On top of that, Waymo is going to start collecting data in these more complex environments. Tesla might have a lot of data, but the quality of data matters a lot. I see Waymo everywhere I go. Residential streets. Urban. Freeways. They can run it 24/7 because they actually have unsupervised delivered. Eventually they will have enough data to pivot to a vision only solution. I doubt they will, because it'd be the stupidest decision they could make, but they'd be better posisitioned for it than Tesla. All this is ignoring the problem of Elon himself, lmao.

Responsible-Meringue

Source: 15yrs in this specific type of model & drug dev.  Predictive polypharmacy is gonna be so fkin cool when we've got AI models trained on 25 years of unified data using human tissue specific physiological in vitro systems and validated tissue specific qsp and mechanistic models. Not to mention the data brokerage market what will mine historic clin trials once we build the back end of Human1 into a causal ML loop.  We've already seen a couple INDs accepted without formerly required animal testing (some still performed, but reduced from normal)... and those drugs moved to phase 2 (not at work computer cant remember which exactly, but rare disease single-tissue targets).  It's a green light to keep developing and do it fast. FDA modernization act was a few yrs ago tho. Market is just catching on cause FDA finally released the framework. Hoping this actually causes industry to pick a goddamn standard cause the past decade of this type of work has been a shit show trying to get anyone to agree on anything cohesive to study over any long time period. DARPA 10-organ was the only 5+yr effort i can think of, and that was the field in it's infancy and had a hard split in the first year that cut the original grantees into 2 teams cause they just kept fighting. 

BuckskinBound

Both! If you want to really boil things down to their pithiest: It’s hard work for a president to fix an economy, and absolute child’s play for a president to screw it up. Nobody has done tariffs like this for a hundred years because it turns out it’s incredibly difficult to make the free market do what you want it to with policy sticks, and only marginally easier to do it with policy carrots. And like 99.9% of people who studied and wrote about these types of tariffs prior to 2017 said they were the stupidest, worst, most god-forsaken fuckup a country could ever do. So yes, Trump is absolutely screwing the pooch because he is so goddamn stupid that he can’t even grasp the most basic concept of who literally pays the tariffs. But also, markets are incredibly complex because they’re the interactions of billions of people’s needs, wants, and work. And there are patterns and cycles because behavior is pretty consistent, and people will try to exploit systems in similar ways over and over, and people will be susceptible to fads and trends and gold-rushes. So there will always be bubbles expanding and then popping. There will always be hyped technologies that fail and take down supporting ecosystems with them, then something else will rise. The 80s was “computers”, the 90s was telecommunications, the 00’s was the web and then social media, the 10’s was devices and streaming content and global insta-commerce, now the 20’s is AI/ML and content creators and more. There will always be cycles. There will always be dreamers, and laborers, and parasites, and people just trying to get their cut anyway they can. And through periods of “deregulation for growth and greatness” followed by “holy shit, let’s regulate so THAT never happens again” we’ll see the boom-and-bust cycle, which will mostly never be stopped, but merely have its turning points delayed or detonated by wars, sound fiscal and monetary policy, and the occasional greediest motherfucking moron this planet has ever seen sitting in the hot seat.

poolsclsd

You do understand Ai and ML played a huge role in developing the actual vaccine right?

templar7171

Yes, it seems to me that until this settles, both TA and AI/ML are trash

TheMysteryCheese

I wrote it in the Web version of Word on my tablet, didn't use copilot, and just got the in-built grammar checker to make sure I was using my dashes right. I wrote it as a shitpost and an over exaggerated post. You guys really come off like the fun police. Please help me with a conundrum. If only AI can write with Em dashes, maintain rhetorical rigour, keep a consistent style, and affect a tone—where does the training come from? I read a lot. Like a lot a lot. When I post, I spend a decent amount of time writing them and editing them. In the comments, I'm more off the cuff. I try and present what inbound consider a publishable version of something, I mainly used reddit to post D&D memes but have started branching out a bit, and sadly, there is a saddening lack of standards. I expected people to make nonsense in a meme sub, but if it is a text post, why wouldn't I put some real effort in? I used ChatGPT to add markdown formatting to a post once, and it was called out, and I admitted it because I don't think it is that big of a deal. What is your yardstick for AI also? What about Grammarly? Or advanced spell check add-ons. Those aren't LLMs but rely on ML principles to function. I build AI tools, I advocate for the responsible use of AI, and I give a shit about making my posts high quality. As for a tag or flair, I support this in principle, but in practice, it is a mark of Cain used to target people for harassment. Considering people get death threats for using AI, can you really object to the hesitation?

TomatoSpecialist6879

That's because they aren't feeding ML with PhD and Doctorate thesis papers, those are locked behind healthcare related AIs and my god they're terrifyingly efficient. The recent new discovery in Alzheimer's and cancer treatments were from MLs running models based on all kinds of obscure thesis papers that would have been buried or left undiscovered for years because there's so many of them

thejumpingsheep2

No. Decoupling was going to happen no matter what, via normal tech advancement because that literally what advancement does. It makes things more efficient. This is obvious to everyone. No one needs to say it. But to get there you need robotics with good enough ML/AI to do the tasks. We are not there yet. If you bring manufacturing back right now, it will fail. There is no way to make anything cheap in the USA and trying to force it via tariffs or regulation will only result in making everyone poor except the government. See China. Now that said, the question you need to ask is why? If all we need to do is advance tech, why demonize education that has gotten us to this point? Why cut research funding? Why force something that is not sustainable? Why invite inflation? Why decouple from the rest of the world if its just China you are after? Because it was never about China. China is just an easy scapegoat.

Unassisted3P

Royals ML tonight.

andherBilla

I'm a data scientist, 12 YoE, while ML, deep learning, LLMs and GANs have made strides in the last decade. The current AI offerings in the market have been increasingly overpromising. There is no true AI, it's just math and numbers. LLMs aren't a panacea, the kind of problem-solving is being promised is unrealistic, so there is a lot of overinvestment in the whole thing. What used to happen was, the moment the average graduate used to understand these techniques, they were never referred to as AI ever again. 20 years ago we were calling ML as AI, 40 years ago we were calling search algorithms as AI, and 60 years ago even basic looping conditional flows were called AI. Now the education too has fallen prey to the hype cycle, and even new grads we interview do not really understand this tech, their mathematical understanding of models is extremely weak. There in lies the issue. I believe reality is going to eventually catch up to expectations on gains, and there will be a crash. However, algorithm and mathematics will keep improving, the current models will become more ubiquitous. It's just that it's going to fall short of heavy expectations it has, till the next breakthrough, then the hype cycle will start all over again.

buggysoftware

Huh. I am (embarassingly, but profitably) a holder of ML, but I was never asked for a vote.

SPAC_Time

[MoneyLion Inc. Stockholders Approve Proposed Acquisition by Gen Digital Inc](https://www.businesswire.com/news/home/20250410138586/en/MoneyLion-Inc.-Stockholders-Approve-Proposed-Acquisition-by-Gen-Digital-Inc) \- ML ML.WS "All regulatory approvals have been obtained and MoneyLion and Gen Digital expect to complete the acquisition on April 17, 2025, subject to the satisfaction of customary closing conditions. Upon completion of the transaction, MoneyLion will become a subsidiary of Gen Digital, and its common stock will no longer be listed on any public market." "converted into the right to receive $82.00 in cash, without interest thereon, and one contingent value right that entitles the holder to a contingent payment of 0.7546 shares of Gen common stock if Gen Digital’s average volume-weighted average share price reaches at least $37.50 per share over 30 consecutive trading days from December 10, 2024 until 24 months after close." GEN was $29.82 on 12/10/2024, and was around $24 today, ML closed around $85 The warrants are trading around 25 cents. ML completed a 1 for 30 reverse split on 04/25/2023, so warrant terms are now 30 ML.WS warrants plus $345 exercise for one share of ML. The warrants do have the Black Scholes clause in the agreement. Will be interesting to see how close to 25 cents the final payout is to warrant holders.

Bobby_Marks3

Can't sell digital services to people who can't afford hardware. And social media companies that represent the foundation of the tech sector survive and thrive on the fact that _everyone you know_ is on a platform like Facebook so you have to be there too - it collapses if that coverage becomes spotty. Not to mention what happens when AWS has to start increasing their prices to offset hardware costs. That's the internet and ML-driven research up in smoke.

Klutzy_Parsnip_1933

Even hedge funds that have ML models with billions of parameters can't predict what the orange man will tweet next.

DIRTBOY12

It is all the Robinhood and basement traders crying. I spoke with my ML adviser and friend is January about stuff. He is a Biden guy and said, he was not sure how Trump's policies would play out, so we talked strategy. I took basically no hit and today I am up 4.82% I am whereI was 4 yrs ago when I switched for a 401K to IRA with my original amount after withdrawing close to 125K the last few years. Having a good professional manage your stocks is wise. I have a Robinhood account and made a $1K over the years and all house money now. Though Rivian and peloton losses with just about erase everything if I sold today. Its been a tough week, its not over yet but I feel secure in my account.

Vallyria

If my ML model is correct, trump will raise tariffs to 204% starting tomorrow. 

Big-Industry4237

For that, it probably is python for most people , especially if using a framework. For something enterprise (eg chatgpt) it would be built on lower level languages for optimizing, so c++ I don’t do any ML stuff just hitting things against APIs nowadays, some web dev stuff, and my work stuff which is heavy in powershell and a few other languages but I do a shit ton of powershell stuff

Echo-Possible

I work in AI/ML as an applied scientist lol.

thumpasaurus

missed out on generational wealth not livebetting florida ML as soon as ted cruz was spotted in the building

Chadsback42069

Then you’d love [this book](https://www.amazon.com/Vizmod-President-WallStreetBets-Nitka-Marga-ebook/dp/B0D12M9LWC/ref=mp_s_a_1_1?crid=2EGYX0VC6MZFM&dib=eyJ2IjoiMSJ9.mWgxNq-apLcKsWoF-m8zVQ.S08TF0KKij1m9ML5h--6AjnvzdMY8-JAfKutkCgMZNY&dib_tag=se&keywords=vizmod+for+president&qid=1744071113&sprefix=vizmod+for+president%2Caps%2C78&sr=8-1) , approved by the mechanic himself…

jpsreddit85

Maybe, maybe not, they're set up to make traditional circuitry, the quantum stuff will be quite different I'd imagine. OpenAI came out as the breakthrough moment, although backed by Microsoft, it wasn't a big existing company that set off this hype cycle. Nvidia benefitted hugely since their graphics cards were used and they almost fell into it (obviously the last few years of machine learning WAS something they focussed on but I don't think they designed the cards for ML initially).

MSFT400EOY

imagine staring at this chart where it’s just teasing around $500 instead of dumping your 401k into Houston ML

Malefic-Angel

So if Florida wins, my bracket wins $360, what do i hedge Houston ML? $100? Or wait to see if Florida gets an early lead then Houston ML?

shred-i-knight

You don't really understand how these social media algorithms work then and I'm not going to argue with you. Recommendation engines are like ML 101 class projects. Maybe try to do some research before you spread misinformation so confidently.

shred-i-knight

saying AI is not real when it's used in just about everything you do and half the reason we are even in this mess is hilarious. You understand that every algorithm you use--reddit, youtube, tiktok, google, netflix, spotify, etc., is entirely built off off machine learning? This is not even mentioning the ways businesses construct shipping routes, packaging optimization, fraud detection, cancer detection, tons and tons of things are driven by "AI". ML has been changing the world for a decade already.

Far-Fennel-3032

I suppose, but someone likely still needs to set them up and oversee what they are doing, as this is likely an extremely high-risk, high-reward moment where companies will make or lose more money today than they will over several years. I expect bot are likely not going to perform well in black swan events as ML works by making a fancy maths equation (its a stack of matrix interacting in strange ways, but yeah) that works based on past data, there might just not be enough data of black swan events for them to be reliable and there might be to risky for companies to use them so they need people doing everything.

dogcomplex

Deepseek R1 is a reasoning model, which relies on inference-time compute to derive a better result than pure LLMs, and with it they released a repeatable technique for retraining base models which improved several others significantly. That was from applying ML - a different technique - to the LLM base model. Similar other methods are being done which rely on more reasoning steps or multiple branches of reasoning and choosing the best - inference-time compute. As for open source - this has nothing to do with that. The claim is that LLMs are hitting a dead end. And it would take a year or two of no breakthroughs whatsoever before any real scientist would start to conclude that. We rarely hit more than a week without another significant leap in benchmarks or abilities. Compute costs have dropped 100x in 2 years, producing far smarter models that are far more useful in addition to being cheaper. The environmental/cost arguments are indeed regarded. Make whatever financial speculations you want (AI might be entirely deflationary and never profitable for AI companies), but technologically you're simply lying. There is nothing close to consensus on a scaling wall and the data does not back you up.

Sandman_Is_Back

so i buy calls or take Dodgers ML

DickFineman73

Hi - robotics and automation professional here. I've been working with robots since I was 10, competing in FIRST programs, and hold a bachelor's in CS with focus on AI/ML. In my spare time, I have a hobby machine shop and 3D print farm. The jobs we're talking about aren't artisanal in nature. Replicating the "human hand" isn't something anyone cares about, because that's not what anyone is trying to do. We're not trying to automate the manufacturing of fine, expensive wood furniture - we're automating the manufacturing of flat pack furniture. Same with textiles; you're not automating super expensive designer clothes - but you are automating the fabrication of Gildan t-shirts or the crap you buy at Kohl's. Or cars or heavy equipment. You don't need a craftsmen's hands to build the frame of a car - especially considering it's already automated. In the grand scheme of things, automation is cheaper than American labor... but foreign labor is cheaper than automation. Two forces have been at work for the last fifty years that is changing this reality: 1) Machines get better and more efficient, all while costing less 2) Foreign labor asks for increases in wages The OP is right - jobs aren't coming back to America. Manufacturing might, but the work will be done by machine.

Mamuthone125

But addressing your comment: I do not sell anything, no ads, no bs. I do not repost nor AI generate content. At EverHint I use ML and AI do collect the news, analyze and create sentiment out of it. I am just a messenger sharing info. I have started about a month ago. Peace.

WarOnIce

Why are you showing everyone your ML account number lmfao?

elkab0ng

I use the same app and the way my accounts are broken up the layout is pretty much identical. I did NOT crap my pants, thinking I had posted my ML account while conversing with the ambien walrus, but I did come quite close 😂 😅

Fuehnix

Yeah, and it wasn't until 2022 that the consequences of our rescue efforts were realized. We managed to slow inflation with high fed rates, but it still hurt everyday prices. 2023 came in with a revolution in practical application of ML transformers with GPT 3.5, and the growth of AI carried the stock market out of the slump. But the braindeads think "durr biden made eggs expensive, I miss Trump"

UniqueUsername1125

Any idea why ML won't let me sell some sobr? Says it's bc of the reverse.

Campfire_Steve

When I instructed my ML financial adviser to reallocate 50% of my 401k into fixed income in January, he told me I was being way overcautious and things shouldn't get that bad. He just left a message that "we should talk on Monday". Sure buddy, looking forward to that.

parsley_lover

Software/ML

CarbonTail

You can't push tech for tech's sake (and I'm talking about digital technologies that power digital content consumption here, not "hard" tech like space faring technology and other tangible tech). You can only squeeze the consumers until a certain point before they stop using your platform or your digital product. I personally think we'll hit a fundamental limitation of transformer tech in ML soon, and a good chunk of the industry will be left holding the bag in trillions worth of raw compute buildouts.

iWolfeeelol

no US products, means no GPUs, no GPUs=no AI/ML. I’d love to see the EU compete with the U.S Tech industry but your definition of completely leave them behind as quickly as possible is vastly different than mine.

Babou_Ocelot

Brewers ML

Far-Fennel-3032

ML has largely been used by the medical industry long before LLM popped up with medical imaging and predictive healthcare largely being seen as the primary use case for ML along side self driving cars. For example, you can download apps on your phone that use ML image classification to examine your skin to detect skin cancer. The work for that was largely completed to have borderline perfect models over a decade ago. Another big part of this is the ML model do early detection better then humans as they are just better at noticing physically smaller parts of medical images. This is now a fairly mature but evolving area of research. I'm not familiar with the companies in their area but I suspect it just going to be tech developed by universities and deployed into the assorted software that comes with medical testing machines.

Klutzy_Parsnip_1933

Learning ML for trading is quite high level. Like how do I beat quant funds. But I think I'm MC so I can do it.

new_name_who_dis_

Waymo is owned by Google, who has their own chips called TPUs that are even more specialized towards AI/ML than GPUs since GPUs are also for graphics. I doubt Waymo cars are using NVDA...

vanisher_1

With all that data variance in think AI will allucinate at some point, some retailers decision are for some extent feeling oriented other than data oriented. Are you a ML engineering to be able to train such models or just self taught?

br0ast

Some claim the prolific hedge fund RenTech have been AI and ML pioneers over the last 30 years, and they trade options.

vol_street

Yeah pretty much. I'm training vol ML models as we speak. Average retail definitely doesn't have much competitive advantage, but to be honest, most models with structured data are best at market making and inefficiencies rather than longer term predictions. My thought process behind it is, the easier the data is to parse, the more competition there will be. I still think retail has as much as an edge as much people with general company predictions, because there is so much variance that can't be accounted for.

korstocks

What’s the basis for the $68k in ML? In other words, what’s the unrealized gain? I think it’s smart to not get student loans but you may owe income taxes on the gains. Have you held your investments for more than one year?

kebabmybob

I build ML models (I can say AI if I want to ride the hype train) for the most cutting edge digital native companies and I have no idea what it means for SAP to have transitioned to “AI Integration”.

Tandittor

I get your point and agree with your nuanced perspective. I just wouldn't discourage people that aren't close to retirement from being heavily overweight on tech. Diversifying in just tech is all one needs, in my (perhaps shitty) opinion. We are in the equivalent of the early 1990s of the internet era with ML.