How are you dealing with all the negative side effects of AI on the web?
It seems to me that the AI hype killed the web for good, but not for the reasons we immediately associate with it.
Search engines now rank links full of slop at the top, which ironically make LLM summaries acceptable, not because they are better than what we had before (they are not), but because they at least mention the good links which are now buried at the bottom of the page. Also, they've been rate limiting ordinary users like crazy: more often than I'd like to, Google and DuckDuckGo now either show me either an error message or tell me to wait a bit. Sometimes I waste at least a minute refreshing the page and waiting for a result.
I also can't browse GitHub freely without being logged in. Anubis is now everywhere, occasionally blocking me. I feel like my productivity has actually gone down thanks to all of that.
Edit: I see that most of the replies here are suggesting relying less on the web and going after more fundamental resources, which are great suggestions, don’t get me wrong! But a book from 5 years ago will not help you understand why your custom firmware doesn’t boot in your 1-year old mobile chipset. A existing bug report on the web probably will. Not quite my predicament at the moment, but something very similar for which the web seemed to work just fine before the AI craze.
Can I become an ML/AI Engineer with a BS Mathematics and Data Science degree instead of Computer Science? [D]
I'm currently pursuing a BS Mathematics with Data Science at COMSATS University in Pakistan. My goal is to become an ML/AI Engineer, eventually specializing in Deep Learning and LLMs.
My degree includes Calculus, Linear Algebra, Statistics, Python, OOP, Machine Learning, Deep Learning, Data Science and other computer-related courses.
However, since my degree title isn't Computer Science, I'm concerned about how employers might view it.
I'd like to ask people working in the industry or studying in this field:
Is this degree a good academic foundation for becoming an ML/AI Engineer?
Can mathematics and data science graduates compete with CS graduates for ML engineering positions?
Are there any important CS fundamentals I should learn separately?
Would a Master's in AI or Computer Science be beneficial later?
I'd appreciate honest advice, especially from people who have transitioned into AI/ML from a Mathematics or Data Science background.
[link] [comments]
Combinatorial Optimization with Pointer Networks: Theory, Benchmarks, and Limitations [P]
Hello everyone,
This is the first article I’ve decided to share publicly. I'm really interested in solving combinatorial optimization problems using machine learning, and this is the opening piece in what will hopefully become a broader series exploring this domain.
This part focuses on the classical Pointer Network architecture trained via Reinforcement Learning (REINFORCE). It shows how neural models can construct high-quality, near-optimal solutions to NP-hard problems (specifically, the 2D Euclidean Travelling Salesman Problem) in fractions of a millisecond.
I benchmarked the model against Google OR-Tools (Guided Local Search with a 10s timeout) across problem scales from N=20 up to N=200.
TL;DR: While the network finds solutions almost instantly, tour quality decays significantly as the problem size scales up. Why? Long story short: using an LSTM encoder on coordinate sets introduces an artificial sequential order and recency bias, which fundamentally mismatches the permutation-invariant nature of geometric graphs.
Here is the link to the full article on Medium
[link] [comments]
Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]
![Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]](https://preview.redd.it/w1848clxk40i1.jpeg?width=640&crop=smart&auto=webp&s=83f3c4ff3352971e08d608ef0a32750c4ab588ea "Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]") | Ant Ling has released Ling-3.2-flash, a native multimodal MoE with 560B total parameters, 25B activated parameters and a maximum 1M-token context window. The official documentation specifies text, image and video inputs. The release evaluation reports: MathVision(w/CI): 96.80 Humanity's Last Exam-MM: 30.12 ZEROBench / ZEROBench(Sub): 18.00 / 43.71 BabyVision: 69.85 OmniDocBench 1.5: 92.21 CharXiv_RQ: 85.15 GDP.pdf: 22.25 MedXpertQA-MM: 79.93 Vision2Web: 85.86 AndroidWorld: 85.63 BlenderBench: 67.77 AntBench-Medical is reported separately as 89.52% comprehensiveness and 99.22% correctness. HLE-MM uses the multimodal subset; full ZEROBench and its Sub result are separate entries. The chart identifies API-collected comparison results under the official evaluation settings. The release materials also demonstrate PDF-to-code implementation, GUI operation, 3D reconstruction workflows and multi-agent collaboration. Both OpenAl-compatible and Anthropic-compatible API access are documented. [link] [comments] |
ICLR27: Received a review that seems to be for an entirely different paper. Has anyone experienced this? [D]
Got my reviews yesterday, and honestly, I'm starting to question the credibility of the entire review process.
One of the reviewers seems to have submitted a review for a completely different paper. And I don't mean they misunderstood our method or missed the main contribution. Their entire paper summary describes a completely different research problem, method, and set of experiments. It has practically nothing to do with our submission, yet they went ahead and gave it a rejection.
Of the other two reviewers, one actually provided a good, thoughtful review, correctly identifying the strengths and weaknesses of our work. The other, however, didn't seem to have read the paper carefully. They criticized our negative findings as a weakness, despite the fact that investigating and demonstrating the limitations of existing approaches is explicitly part of the paper's objective.
I'm perfectly fine with criticism, rejection, or reviewers disagreeing with our conclusions. That's part of research. But how is an author supposed to respond when one reviewer is apparently reviewing an entirely different manuscript?
Has anyone experienced something like this before? Should I contact the AC directly, and is there any realistic chance of getting such a review disregarded or corrected?
I'm genuinely at a loss as to how something like this happens at a conference of this scale.
[link] [comments]
Are ICLR 2027 reviews unusually shallow this year, or is it just me? [D]
I’ve been going through ICLR 2027 reviews, and most of them look extremely shallow. They’re so f short, like they're written for a random workshop.
Almost every review I've seen is two generic points under each section (strengths, weaknesses, questions) and nothing more. Then a score of 2, even when the issues they raise would justify a straight reject. There’s almost no discussion of the method's motivation, impact, or soundness, and they take it for granted.
Am I exaggerating, or are you seeing the same thing? Because I'm really questioning the point of PhD or research generally speaking, more than ever
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Try my dang hardest to come up with novel research ideas, but everything feels clunky [R]
Im an incoming masters student in ML, a bit older than most (31). I’ve read 100s of papers over the last 7/8 years from the foundational papers in the field to niche papers that never got adopted (Hinton’s forward forward, capsule networks). Ive also replicated dozens of papers during this time.
It may seem small to most here, but It would be an amazing dream to do publish a conference quality paper but all my research ideas feel so clunky, inelegant and crude. My ADHD lets me to spawn hundreds of ideas and hyperfocus on a couple, so far all my preliminary work doesnt point to anything really novel.
I dont understand how first authors come up with beautiful results like engrams, diffusion models, VAEs, MoEs, token superposition in pretraining, that seems so obvious when you read the paper but clearly are anything but obvious.
Are these people just built different or did their research also start off clunky? Is it just luck? Most PIs dont expect to lead the research so it cant be an astute PI?
[link] [comments]
The most scathing reviewer 2 review I have ever read
This is not my paper. Not my review either.
Official Review of Submission21893 by Reviewer Xz8e
Paper Summary:
The paper tries to learn correlations among components of each latent state in a world model to propagate the reward sensitivities to adaptively weight the uncertainties that most matter for long-horizon planning via a pessimistic objective.
Presentation: 1: Difficult to follow despite careful reading.
Informativeness: 1: I learned little or nothing new from this paper.
Soundness: 1: I have significant concerns about the paper’s soundness that do not appear easy to address.
Critical Strengths:
None
Critical Weaknesses:
First and foremost, the paper is highly likely to be fully AI-generated. The authors demonstrate a lack of technical accuracy and depth. They fail to communicate their ideas clearly and accurately. The paper makes few original contributions. See evidence below.
Neologistic and full of jagons that add little value of presenting research gaps, methodologies, and their contributions. I don’t believe authors even understand those terms before stitching them together.
Abstract is poorly written. This is not intended to communicate efficiently with readers. This is to create barriers in understanding their work. From the second sentence, “a scalar uncertainty”, “sparse latent dynamics” come out of nowhere and don’t know what they are referring to. And so many such examples follow thereafter: “factor-level residual bounds”, “retains initialization error”, “expected-error control”, “exact certificate”, “removing error-propagating dependencies and introducing approximation bias”, “localized mechanism-shift”, “comparator-dependent regret bound under simultaneous validity”, “a decision-relevant uncertainty bound”. And showcase experimental results without stating tasks?
Introduction is also disconnected, poorly motivated, fragmented. From the first paragraph “which model errors matter for a long-horizon decision, especially after an environmental mechanism changes?”, what is “environmental mechanism changes” here? Is it handling non-stationary environments, or continual learning tasks? Why brings something out of vacuum? Then the second paragraph pumps out “a global Lipschitz constant”, and reiterate what so-called “sparsity” And then jumps to uncertainty with a set of new terms “A small predicted covariance”, “Marginal calibration”, “a high-probability interval” and close by “Exploration should reduce uncertainty about mechanisms that affect decisions, rather than maximize uncertainty indiscriminately.”: okay, what uncertainty are you focusing on? epistemic or aleatoric? Methods and contributions are also vague and lack clarity.
Method section is horrible.
What do you mean by “a sufficient latent state”? An approximate model can hardly find sufficient statistics.
In the Gaussian transition model, what is z_t^(pa_e(i))?
What is the difference between z_(t+1)^i and ẑ_(t+1)^i, is the latter deterministic and why so?
What is “nominal imagination”? Where does “sparsity” come from and what is “a within-time DAG”? What is “nominal systems”? What is feedback policy? What problem setting are you looking at? Where does the “recurrent state” come from the encoder? What is mechanism i?
What is the motivation of introducing the adjacency matrix? If you intend to learn the dependency between different subcomponents of the latent state, use one sentence is enough.
And several notations are conflating, like parameter posterior q(θ|D) with the state posterior. The training objective is not motivated.
Experiments are not scientific. No random seeds reported, no task descriptions, no metrics explanations.
This paper is an affront to scientific research and reviewers’ time.
Questions For Authors:
None
Flag For Ethics Review: No
Rating: 1: Clear rejection
Confidence: 4: I have substantial direct research experience on this topic.
[link] [comments]
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The battle for megawatts: why restricting data centers is a strategic mistake
In Ireland, data centers consume nearly a quarter of the country’s electricity. New grid connections around Dublin were effectively frozen for three years. Under revised rules, new projects must provide generation or storage capacity onsite or nearby. Amsterdam previously imposed a moratorium on new facilities, while Frankfurt regulates their location through zoning.
In July 2026, New York State introduced a moratorium of up to one year on certain environmental permits for new large data centers. State officials said the pause would allow them to assess the facilities’ impact on the power grid and the environment. As of May 2026, data center projects representing nearly 12 GW of potential demand were in the state’s grid interconnection queue.
Should governments restrict data centers when power grids cannot keep pace? I believe they should expand power supply and grid capacity instead.
As co-founder and general partner of Roosh Investment Group, I have been investing in technology companies for over five years. Our portfolio includes AI infrastructure, and energy is now a key focus. This experience has shown me how access to energy infrastructure influences investment decisions. Reliable power is increasingly becoming a decisive factor in a company’s ability to scale and attract capital.
As businesses rely more on AI, control over its models and infrastructure matters more. A foreign provider’s decision to change terms, raise prices, or restrict access could affect entire industries. Domestic computing capacity gives countries more control over the infrastructure their businesses depend on.
Control over AI is becoming a source of economic power
This helps explain the intensity of U.S.–China competition in AI. American companies hold leading positions across the AI stack, from advanced models and chips to major cloud platforms. Washington views the power supply for data center infrastructure as a national security issue.
Beijing is pursuing a state-led strategy. Its “Eastern Data, Western Computing” program directs the development of computing capacity toward western China, where energy and land resources are more readily available. At the same time, the country is expanding power generation at scale. According to the International Energy Agency, China added nearly 500 GW of renewable capacity in 2025—more than 60% of the global increase.
Europe, meanwhile, risks falling further behind. Brussels plans up to seven AI gigafactories and at least a tripling of EU data center capacity within five to seven years. Yet some countries are slowing construction, while European businesses still rely primarily on American cloud providers. Restrictions do not remove demand for AI; they risk pushing investment abroad and reinforcing that dependence.
The bottleneck is local
Data centers’ hunger for power should be understood in the context of wider electricity demand. According to the International Energy Agency, they accounted for only around 70 TWh of the 800 TWh increase in global electricity consumption in 2025. Industry, air conditioning, heat pumps, and electric vehicles contributed far more.
The challenge is rapidly growing demand concentrated in a handful of locations. Power must reach new computing capacity in time.
What is holding up connections?
In the U.S., lead times for large transformers now exceed two years, compared with roughly 50 weeks before the pandemic. Morgan Stanley estimates that U.S. data centers could face a power shortfall of around 49 GW by 2028. Microsoft CEO Satya Nadella has acknowledged that power has become the company’s binding constraint. Some of its GPUs are sitting in inventory with nowhere to plug them in.
Dedicated gas-fired plants offer no quick fix. GE Vernova, Siemens Energy, and Mitsubishi Power produce around two-thirds of the world’s gas turbines, and their factories are booked years ahead. GE Vernova reported 116 GW in gas power equipment orders and slot reservations, while targeting annual turbine output of 20 GW in the third quarter of 2026.
The hardware itself adds another challenge. A server rack once required 7–12 kW. Systems built around NVIDIA’s GB200 consume 120–132 kW per rack. Conventional air cooling is no longer sufficient at that power density, so new facilities must be designed for liquid cooling from the outset.
Tech giants are securing power themselves. Microsoft agreed to buy electricity from the 835 MW Three Mile Island reactor, which is being restarted to serve its needs. Google’s agreement with Kairos Power calls for small modular reactors, with the first expected online by 2030.
Governments cannot rely on corporate initiative alone. They must plan energy and digital infrastructure together: expand grids, accelerate transformer production, streamline connections, and encourage long-term power purchase agreements, generation near data centers, and storage.
How war accelerated distributed generation in Ukraine
Ukraine’s experience sho
This CEO lived on canned soup and took just two days off for his daughter’s birth. Now, he admits he lost sight of proper work-life balance
The grind to success can be long, punishing, and it can quietly take more than it gives.
Ron Schneidermann knows that better than most.
After scaling his first company, Liftopia, into a business with more than $60 million in annual revenue, he went on to become CEO of AllTrails, the popular hiking map app. Today, he leads test-prep startup Acely. But looking back across his career, he says he had to learn the hard way what real work-life balance actually costs—and what it’s worth.
While building Liftopia, a digital marketplace for ski resorts, that mindset came at a personal price. Schneidermann worked out of a cramped San Francisco apartment, turned spending less than $15 a day into a kind of endurance test, and went two years without taking a salary, surviving largely on canned soup.
The tradeoffs even extended into his family life. When his first daughter was born four years into Liftopia, he took just two days off work. Three years later, he allowed himself just a week for his son’s birth—and thought that was progress.
“I look back, I was just able to justify it as ‘that’s just part of the grind’… but you never get that time back,” Schneidermann told Fortune. “That was a mistake.”
It’s not an easy admission in startup culture, where sacrifice is often worn as a badge of honor and overwork is normalized as the price of ambition. But Schneidermann, now in his late 40s, has since reframed the experience entirely.
“For everything that was frustrating, that went wrong, that I regretted about Liftopia, I was able to take the inverse and turn it into a strength,” he said.
By the time he joined AllTrails in 2015, that mindset shift was already underway. He eventually became CEO in 2019 and instituted a company-wide ritual: the first Friday of every month, AllTrails shut down and employees were encouraged to go outside.
In August 2025, he took on the new challenge of becoming CEO of Acely. This time, he’s bringing those hard-won lessons with him from the start. Instead of monthly trail days, the company now runs a monthly “hackathon,” where its less than a dozen employees pause routine work for the whole day and experiment with AI tools and new ideas—no meetings, no KPIs, no deliverables.
Ron Schneidermann says he’ll never take a job for its salary ever again
Even before Schneidermann had his eyes on entrepreneurship, he was learning career lessons the hard way.
In the late 1990s, he was a student at UCLA studying mass communication and business while also working at Abercrombie & Fitch to help pay for his studies. However, he ended up getting fired three separate times because he “never liked it” enough to muster the proper decorum.
Similarly, after graduating in 2000, he landed a job at Accenture working as a business process consultant. Once again, though, he found it not aligning with the life he wanted to have. And despite the lucrative salary of working at a Fortune 500 consulting firm, he quit.
“It was a great experience. I’m grateful for it, but I hated it,” he said. “I hated it, and I told myself, I am never going to take a job for money again. Life is too short.”
That realization eventually pushed him toward startups.
Through a friend of a friend, he landed a role at Hotwire, then a travel startup that was later acquired by Expedia, and it became his entry point into the tech world. Looking back, Schneidermann said that early career path offers a lesson he wishes more young workers would take seriously: relationships matter as much as résumés, especially in a job market where traditional entry-level pipelines are narrowing.
“Really invest in your network,” he said. “There’s a small pond at the end of the day. If you stay in it long enough, you are going to know everyone, and everyone you know are going to be leaders somewhere.”
Digital-native Gen Zers can boost their careers by working to build real relationships
Schneidermann’s point may resonate with Gen Z at a time when employers say early-career workers are struggling with basic professional norms. A 2024 survey found that six in 10 bosses admitted they had already fired Gen Z employees hired straight out of college, citing poor communication, lack of professionalism, and disorganization.
For Schneidermann, the issue isn’t just skills—it’s mindset. Too many young workers treat networking as transactional, when in reality it’s built on consistency, reputation, and how you show up over time. That starts with small, often overlooked behaviors.
“There’s a human on the other end. Don’t ghost people and just show up,” he said.
The same principle applies more broadly to career growth. Rather than chasing the perfect role or obsessing over credentials, Schneidermann emphasized curiosity as the real differentiator.
“Just approach everything with a curious mindset,” he added. “Just constantly try to be learning and exploring.”
That philosophy has carried through multiple career pivots, including his most recent move to Acely, a startup with no obvious connection to his background. After being approached by an executive search firm, he took the job in part because his high school–aged daughter had recently used the product for SAT prep. She was among its over 50,000 active users.
For Schneidermann, the challenge was one he was eager to take on. From his perspective, he didn’t need to be an expert in the industry—he just needed to be willing to learn it.
A version of this story was published on Fortune.com on April 25, 2026.
More on careers
Starter homes cost $1 million in a record 242 U.S. cities—proof the affordability crisis has hit the homes meant for first-time buyers
They waited out the pandemic boom. They saved longer, rented longer, delayed longer, and watched the typical first-time homebuyer age climb to a record 40. Now, the buyers who did everything right are running into a new problem: The Northeast just became the fastest-growing region for million-dollar starter homes in the country.
It’s especially imperfectly timed for millennials entering their peak household spending years and beginning to form their own families (or at least try to).
A Zillow report published in June counts a record 242 U.S. cities where starter homes cost $1 million or more—triple the 80 cities that cleared that mark before the pandemic, and up from 226 a year ago.
The share of first-time buyers has fallen to half the historical norm. For millennial parents in the Northeast—now in their late thirties and early forties, often with kids, hunting for more space—the numbers have a specific shape.
New Jersey had just one million-dollar starter city before the pandemic. Now it has 26. New York used to have 12; now it has 41. The two states added 15 cities to the list in the past year alone—faster growth than anywhere else in the country. The New York City metro area now leads all with 63 cities where a starter home runs $1 million or more.
That growth didn’t happen in a vacuum. Six of the 10 most competitive housing markets in the country are in the Northeast, per Zillow’s 2026 analysis, a region where new construction has chronically lagged and inventory deficits run deepest. California still leads the raw count with 105 cities, but the Northeast is where the crisis is actively spreading.
“A housing shortage a decade in the making ran headlong into intense demand with mortgage rates at historic lows, driving up home values at a record pace,” Zillow senior economist Kara Ng wrote. For Northeast buyers, those forces have compounded.
The new typical first-time homebuyer is 40
First-time homebuyers are historically younger, although the typical age of first-time buyers climbed to an all-time high of 40, according to a National Association of Realtors report released in November 2025. In fact, the housing market has gotten so unaffordable that the number of first-time homebuyers fell to half the historical norm last July.
A rising price floor on starter homes only pushes that first home purchase further out of reach. And it’s a problem that’s worsened, especially during the past couple of years.
“For homebuyers, the prevalence of million-dollar cities significantly—greatly—strains what is already the worst housing affordability since at least the 1980s,” Christopher M. Naghibi, executive vice president and chief operating officer at First Foundation Bank, told Fortune in 2024 when Zillow released a report about the number of U.S. cities with $1 million median-priced homes. “Prospective buyers in these markets face higher barriers to entry, needing ridiculously large down payments and higher incomes to afford homes.” That still holds true today.
Zillow defines a starter home as one in the lowest third of home values in a given region. As of June, the typical starter home nationwide was worth $198,649, up 1.7% from a year earlier. But in well over 200 U.S. cities, starter home prices are a whopping five times as high as that. It’s a phenomenon that keeps spreading, and into places it never used to touch.
Zillow chalks it up to the effects of the pandemic housing boom.
It’s not just a California problem anymore
Before 2020, the list of cities with $1 million starter homes was almost all coastal. Colorado was the only non-coastal state with a million-dollar starter-home city. Now 26 states have at least one, up from nine before the pandemic. Texas, Wyoming, and Illinois each have multiple examples.
California still leads with 105 cities, but the fastest growth is in the Northeast. New York and New Jersey added 15 cities combined over the past year. New York is now up to 41, from just 12 before the pandemic. New Jersey is at 26, up from one.
The New York City metro area tops all metros with 63 cities where a starter home runs $1 million or more. San Francisco follows with 37, then Los Angeles with 33, San Jose with 13, and Miami and Seattle with eight each. The trend aligns with Zillow’s 2026 hottest markets analysis, which found six of the 10 most competitive housing markets are in the Northeast, where new construction has lagged and inventory deficits run deep.
The pandemic reset the cost of buying a home. A housing shortage a decade in the making collided with surging demand and historically low mortgage rates, pushing values up at a record pace. Those effects have stuck.
Baby boomers edge younger buyers out of the housing market
The squeeze on millennials and Gen Z has been building for years. Baby boomers, sitting on paid-off homes or cheap pandemic-era mortgages, have edged younger buyers out of the market in record numbers. The cost has pushed some Americans to delay milestones like marriage and kids, and others to co-buy homes with friends or partners just to split the bill.
Others have done the opposite—had kids first, rented longer, and are now hunting for space in a market that moved against them.
“We’re seeing a reshaping of the housing ladder,” Alexandra Gupta, a real-estate broker with the Corcoran Group, previously told Fortune. “Some first-time buyers are turning to long-term renting or even co-living models because the idea of owning a home has become so out of reach.”
There are modest signs of relief. Conditions are slowly turning friendlier for buyers. Zillow found the typical buyer now breaks even versus renting after about six years, down from more than eight in late 2023. More inventory and slower price growth mean buyers who are financially prepared are in better shape than they had