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  3. /Learning from failure to tackle hard problems
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  3. /Learning from failure to tackle hard problems
Oct 30, 2025 at 2:18 PM EDT

Learning from failure to tackle hard problems

djoldman
125 points
26 comments

Mood

thoughtful

Sentiment

mixed

Category

science

Key topics

Machine Learning

Problem-Solving

Research Methodology

Debate intensity60/100

The article discusses how machine learning research can learn from failure to tackle extremely hard problems, with commenters debating the novelty and practicality of the ideas presented.

Snapshot generated from the HN discussion

Discussion Activity

Active discussion

First comment

6d

Peak period

11

Day 6

Avg / period

7.3

Comment distribution22 data points
Loading chart...

Based on 22 loaded comments

Key moments

  1. 01Story posted

    Oct 30, 2025 at 2:18 PM EDT

    25 days ago

    Step 01
  2. 02First comment

    Nov 5, 2025 at 10:47 AM EST

    6d after posting

    Step 02
  3. 03Peak activity

    11 comments in Day 6

    Hottest window of the conversation

    Step 03
  4. 04Latest activity

    Nov 7, 2025 at 5:18 AM EST

    17 days ago

    Step 04

Generating AI Summary...

Analyzing up to 500 comments to identify key contributors and discussion patterns

Discussion (26 comments)
Showing 22 comments of 26
axus
19 days ago
4 replies
The most important clue to solving a difficult problem is knowing that somebody else has already solved it.
baxtr
19 days ago
2 replies
The problem is time and resources.

Take building a viable company. You know that many people have solved this. But you also know that 9/10 fail.

So you need the time and the money to try enough times to make it work.

djdjdhdh
19 days ago
1 reply
9/10 vc backed companies fail. Not "companies." Ignore the hype and you'll be more likely to succeed.
stonemetal12
19 days ago
1 reply
As far as I am aware it is 8/10 across the broader landscape. A little better, but not much.
fhuteedc
19 days ago
Twice as likely to succeed is not insignificant. It's a lot better chance to succeed. You're being led to by folks who want to make you their slave.

https://clarifycapital.com/blog/what-percentage-of-businesse...

That 80% number is after 20 years. That's far longer than almost anyone stays at the same employer. Maybe if those failures are the owners retiring.

You're being lied to. The myths of silicon Valley are not there for the benefit of founders.

shermantanktop
18 days ago
You're describing bruteforcing through repetition. The paper is essentially about increasing the chance of success by training model which learns on failure.

That may not apply to a building a viable company directly. It might suggest that new companies should avoid replicating elements of failed companies.

LPisGood
19 days ago
1 reply
I had a professor in an additive combinatorics class that would (when appropriate) say “hint: it’s easy” and as silly as it is, it usually helped a lot.
mcmoor
18 days ago
Hint as simple as that feels like spoiler sometimes.
truelson
19 days ago
1 reply
The 4 minute mile comes to mind
paulorlando
18 days ago
2 replies
While Bannister’s 4-minute mile record is used as an example of a psychological barrier, there’s also a reinterpretation of the meaning behind his record. Before his 1954 race, the record for the mile stood at just over 4 minutes (4:01.4) for 9 years. While speed records were set during WWII, they were all set by Swedish runners (Sweden being neutral in the war). The record today, which has stood since 1999, is 3:43.13. It's not a round number, so as a result gets less attention. Maybe that's why we don't think of it as a psychological barrier.
NooneAtAll3
18 days ago
1 reply
so it's all a question of marketing

343 is 7 cubed, so just call it "cube barrier!" and it becomes a worthy challenge

mpalmer
18 days ago
1 reply
343 is 5:43
NooneAtAll3
17 days ago
not for marketing
mcmoor
18 days ago
Reminds me of barriers in speedrunning. Technically all the times are arbitrary, but there's still prestige to be the first person to get under <nice number>. I don't think it really influences the speed of record breaking around it, except that time when there's literally a bounty raised.
Nevermark
19 days ago
I worked on a problem for a couple months once. As soon as my professor hit mid-sentence telling me he found someone with the solution, I rudely blurted it out.

My mind was so familiar with all the constraints, all I had to know was that there was a solution and I knew exactly where it had to be.

But before knowing there was a solution I hadn't realized that.

abtinf
19 days ago
4 replies
> The [goal] of machine learning research is to [do better than humans at] theorem proving, algorithmic problem solving, and drug discovery.

Naively, one of those things is not like the others.

When I run into things like this, I just stop reading. My assumption is that a keyword is being thrown in for grant purposes. Who knows what other aspects of reality have been subordinated to politics by the writer.

captainclam
19 days ago
You must not end up reading much scientific literature then.
dgacmu
19 days ago
These have all been stated as goals by various machine learning research efforts. And -- they're actually all examples in which a better search heuristic through an absolutely massive configuration space is helpful.
LinuxAmbulance
19 days ago
What's the issue with drug discovery? AI/ML assisted drug discovery is one of the better examples of successful AI utilization out there.
ants_everywhere
19 days ago
which one do you think is unlike the others?
chrisXOXO
17 days ago
That idea feels really relevant to me as a future research direction(not an expert). Could maybe someone explain what I am missing here? Why does this idea not get more attention?! Is it not new? And if so, could one state why it is not commonly employed?
richard___
19 days ago
How does this compare to just reducing the likelihood of negative samples?

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ID: 45763262Type: storyLast synced: 11/20/2025, 5:45:28 PM

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