jkldotio
Joined 13 years ago
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962
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221
Member since
Mar 9
Recent submissions
Comment · 5 months ago
It's 15 companies that adopted a technique and it's part of a broader constellation of experiments in this area which have been confounding the traditional logic that productivity would decrease. https://www.4dayweek.com/research
The people representing the businesses in the preprint hold titles like co-founder, CEO/founder, COO, General Manager, and CEO. The size of the business and sector are also noted. I think your framing of them as "unidentified people" is therefore off, it is certainly not the same as a journalist conveniently using "unnamed sources", this is standard academic practice.
Different companies measure different things, but they do measure and that is addressed in the paper. "revenue (DM10), profit (DM4), other financial targets (DM2, DM6), customer/client satisfaction ( DM8, DM6), story points (DM14), sprint goals (DM7), billable hours (DM12), capacity ( DM4), response rates (DM10), standard operating procedure metrics (DM9), sick leave (DM1, D M 4. DM9, DM15), lodgements (DM12), employee happiness (DM6, DM15), projects delivered on time (DM15), and net promoter score ( DM4)". There were also other benefits like hiring and retention.
So this is not what "unidentified people" "thought" about productivity, this was founders and the c-suite using their existing favoured metrics. On those metrics a large number of them reported an increase in productivity, and a larger group reported no deleterious effects on productivity. This is broadly consistent with the trends in the wider research into this area globally, which continually go against the predictions that productivity will drop. Is it universally applicable? I don't think anyone is claiming that.
I've followed this area for a while and, sorry to be impolite, it is your summary that is less accurate than the the one you accuse of being AI-generated.
Comment · 10 months ago
Soft launched an early alpha of a customisable news aggregator last month.
Comment · 5 years ago
So you've conflated regional with generic, a distinction I pointed out first, without any worked reasoning at all.
People generally lose their trademarks quickly if they don't enforce them and allow them to go into the language https://en.m.wikipedia.org/wiki/List_of_generic_and_generici...
I honestly don't see you as providing a real argument from first principles.
Comment · 5 years ago
Feta comes from φέτα/~"slice", so it's not named after a region. Melbourne has one of the largest Greek populations in the world. Why don't these Greek people have right to their heritage? If the British were to start trying to say that "sandwich" was protected and American sandwiches were "inauthentic and deceptive" would you take such a claim seriously?
Comment · 10 years ago
https://www.maxlaumeister.com/blog/google-is-deleting-your-f... has some, but anyone who's been following it knows it's happening beyond that. I am surprised that infinitesoup is apparently unaware of these issues given his account on HN is 591 days old and over it's history has posted about absolutely nothing but YouTube. Indeed an account on Reddit called infinitesoup, possibly unrelated I concede, has wall-to-wall comments on YouTube too. They all read like a superfan or possibly an employee of Google providing support. The usual etiquette on HN is to simply disclose an interest and then argue a point. While it's not against the rules I don't like having to do my own research and finding out the person posting links from Google support is likely a Google employee (the person connected with the Reddit account seems to be a Google employee, maybe this guy isn't).
Even if they are different accounts and this guy isn't a Google employee they haven't presented any other credible evidence for this wonderful support anyway. They have only presented a policy aspiration. Google is not Amazon; Google is notorious for bad customer support. Our prior belief for "will YouTube provide good support" shouldn't be very high given they are part of Google. Therefore it doesn't take many data points in the direction of poor support to confirm that.
There are clear and sometimes good reasons for what Google does, it's a great company with competent employees but let's not drink the Kool-Aid and pretend they have great customer support just because there is a policy document aspiring to have good response times.
Comment · 10 years ago
Censorship and demonetisation on YouTube seems to be happening to people fairly regularly, and without appeal, via poorly calibrated algorithms, corporate pressure and to satisfy mobs of complaining people for various reasons. The rules seem to be vague to the point of meaninglessness and it seems even the stars of the platform who often share managers and production companies with other stars have difficulty getting in touch with YouTube to resolve issues.
I think most content producers have wised up to this and diversify their audience over multiple platforms. If you rely on the income putting all your eggs in the YouTube basket is a massive risk until they clean up their processes (there are many automated and social methods to do some of this but they seem completely uninterested in doing it).
Comment · 10 years ago
Google approaching this kind of challenge with neural networks[0]. Which is, perhaps, connected to why they are putting Boston Dynamics up for sale.
Comment · 10 years ago
What are we up to now? Three preloaded spyware scandals, possible remote execution via the Intel stack and now this vulnerability. That's just what we know about, who knows what else exists. I don't think I can buy another one, which is sad as I think it was a timeless and great design.
Comment · 10 years ago
I think in addition to histograms and features like SIFT, SURF, and others like DAISY[0] that would permit searching with images as a query it would be beneficial just to use a neural network to text index using the classes at very top of a neural network, although you are right some features in layers just below that could be used too as I understand it.
You could then use classical text indexing on the text, perhaps with a topic model like LDA. Then an image with a plane in it will be indexed by "plane" via the output of the neural network but would also come first, or in the top results, when using "flight" as the query via a topic model.
Ditto for word2vec or para2vec over those words, the benefit being you can bring the knowledge of relations contained in the textual training data, Wikipedia or something else, to bear on the problem. I.e. a golf club and a baseball glove might not be correlated in the neural network that annotated the images but might be correlated in the text based knowledge model trained on Wikipedia and so a query of "sport" might bring both images up.
The big players like Google already have caption generation that's capturing relationships between objects.[1]
[0]http://scikit-image.org/docs/dev/auto_examples/features_dete... [1]https://arxiv.org/pdf/1411.4555.pdf
Comment · 10 years ago
Here are two Facebook posts from Taleb related to the topic and a ~3 minute video that has the main point.
"The establishment composed of journos, BS-Vending talking heads with well-formulated verbs, bureaucrato-cronies, lobbyists-in training, New Yorker-reading semi-intellectuals, image-conscious empty suits, Washington rent-seekers and other "well thinking" members of the vocal elites are not getting the point about what is happening and the sterility of their arguments. People are not voting for Trump (or Sanders). People are just voting, finally, to destroy the establishment." - 7th March - https://www.facebook.com/nntaleb/posts/10153654273663375
"What we are seeing worldwide, from India to the UK to the US, is the rebellion against the inner circle of no-skin-in-the-game policymaking "clerks" and journalists-insiders, that class of paternalistic semi-intellectual experts with some Ivy league, Oxford-Cambridge, or similar label-driven education who are telling the rest of us 1) what to do, 2) what to eat, 3) how to speak, 4) how to think... and 5) who to vote for. With psychology papers replicating less than 40%, dietary advice reversing after 30y of fatphobia, macroeconomic analysis working worse than astrology, microeconomic papers wrong 40% of the time, the appointment of Bernanke who was less than clueless of the risks, and pharmaceutical trials replicating only 1/5th of the time, people are perfectly entitled to rely on their own ancestral instinct and listen to their grandmothers with a better track record than these policymaking goons. Indeed one can see that these academico-bureaucrats wanting to run our lives aren't even rigorous, whether in medical statistics or policymaking. I have shown that most of what Cass-Sunstein-Richard Thaler types call "rational" or "irrational" comes from misunderstanding of probability theory." - 9th March - https://www.facebook.com/nntaleb/posts/10153658794008375
"Nassim Taleb: Skin in the Game" - 2m48s - 22nd April - https://www.youtube.com/watch?v=0Uc4DI-BF28
Comment · 10 years ago
1. If that's the case perhaps another kind of blurring? "Intriguing properties of neural networks" (https://arxiv.org/pdf/1312.6199.pdf page 6) has examples where you get radically different classifications that I don't think would occur naturally or survive a blur with some random element, let alone two moving cameras and a sequence of images. As the title says it's an intriguing property, not necessarily a huge problem.
2. I honestly can't think of a situation where this could occur. It's the equivalent of kids shining lasers into the eyes of airline pilots, but the kids need a PhD in deep learning and specialised equipment to be able to do it. A hacker doing some update to the software via a network sounds much more plausible than attacking the system through its vision while it's traveling.
3. This is the real point in the end I guess, this Google presentation (https://www.youtube.com/watch?v=tiwVMrTLUWg) shows that the first autonomous cars to be sold will be very sophisticated with multiple systems and a lot of traditional software engineering. Hopefully LIDAR costs will come down.
Comment · 10 years ago
The examples where this happens have always seemed fairly weak to me. How many of the grave errors, not just where it's the wrong type of animal or container but actually thinking it's radically different, survive an application of Gaussian blur? Furthermore self-driving cars are a combination of signals; you are going to need to simultaneously fool both LIDAR and cameras.
On top of that you are going need to fool them over multiple frames, while the sensors get a different angle on the subject as the car moves. For example in the first Deep Q-learning paper, "Playing Atari with Deep Reinforcement Learning"[0], they use four frames in sequence. That was at the end of 2013.
I don't think anyone will be able to come up with a serious example that fools multiple sensors over multiple frame as the sensors are moving. Even if they do then inducing an unnecessary emergency stopping situation is still not the same as getting the car to drive into a group of people. Even if fooled in some circumstances the cars will still be safer than most human drivers and still have a massive utilitarian moral case in relation to human deaths, on top of the economic case, to be used.
The fooling of networks is still an interesting thing, but it's been overplayed to my mind and is not particularly more interesting than someone being fooled for a split second into thinking a hat stand with a coat and hat on it is a person when they first see it out of the corner of their eye.
[0]http://arxiv.org/pdf/1312.5602.pdf page 5
Comment · 10 years ago
The full results in the report seem to me be somewhat more complex to interpret than the summary, or the original submission title, suggest. On the first question (Q18jt), a 4 point scale without a "don't know" neutral option, there are large numbers of people putting global citizenship before national citizenship (51%-49%). However, when you get to a later question (M2), that asks people what their most important identity is, national citizenship overtakes global citizenship overwhelmingly (52% national, 17% global, 11% local, 9% religious and 8% race/culture). National citizenship is the largest in all countries besides three, and it's the second largest in two of those. When given a variety of choices in M2 the level of people supporting global citizenship (17%) as a primary identity is even lower than even those who "strongly agree" to global citizenship being more important to national citizenship in first question (22%).
In M2 it seems all of the "somewhat agree" and a few points of the "strongly agree" have gone elsewhere including back to national citizenship which creates a tension between the results of the two questions. It’s quite stark in the case of India where in the first question 67% place global citizenship over national citizenship but in M2 51% have national citizenship as their primary identity and only 6% have global citizenship.
I think the first question (Q18jt) would be more interesting if it was a 5 point scale with a "don't know" option provided rather than trying to railroad people into an answer with a 4 point scale. Even then I think the M2 question is a better instrument as it provides a clear set of choices. For example Pakistan goes from 56% global in the first question to 2% global in the second. Most of those seem to move to religion as their primary identity (43% answer religion on M2). Many religions are globally oriented toward all humanity but the M2 question lets us see more clearly what they believe.
Even though the second question (M2) is better it probably could still do with some examination. For example the only country where global citizenship was higher than national citizenship in M2 was Spain, but was that because many people in Catalonia and the Basque Country are choosing global over "Spanish" because the "local" option was translated in a particular way? If they used a word closer to "neighborhood" you could see how that might happen. Similarly was "local" translated more broadly in the Indonesian translation and became the dominant answer there (56%) as people could identify with an island/state/ethnic/linguistic group that was "local", like Bali for example. It's hard to tell without all the data and the exact translations of the questions.
email: 'tom' at the above website