They are probably just using 'men' and 'women' as an approximation that sort of matches the distribution of human beings. Without diving into the exact individual's relationships with sex/gender, you could get a somewhat accurate approximation of how a society operates. Then, from this 1st order approximation, if one was inclined, they could refine and add to the results by further breaking down the input parameters. I don't see any issues with approximating a binary distribution of male and female, even though everyone knows this is not a perfect description of reality. The fact that there is deviations from a binary is good, and can always be tapped into to add alternative views and refinements to the results.
Also, I think we should consider the people's race, their views on covid restrictions, whether or not they are indigenous, their interpretation of the merits and demerits of working from home, and what their political stance is on global warming and environmental issues. It's probably not intentional, but to me it feels like you are almost denying certain people's humanity by solely focusing on gender and sexual orientation. Those are important, but not the only issues that we face in modern society.
At the moment, if someone wants to store information on a blockchain using proof of work, proof of stake etc., arguably the best implementation that humans have invented is Ethereum (Bitcoin is arguably the best for slow stable $ preservation, think what you want..). The gas price of Ethereum is not fixed, but at the moment far too expensive to store large images. The purpose of Crypto technology is not specifically to store images, that will probably come in the future. It does work to store financial transactions, and to perform smart contracts that don't require large amounts of data. NFTs don't have to be used for images. They could be used for tickets, proof of membership, to run small algorithms etc. The value is always going to be subjective. But saying there is something 'wrong' I don't think is correct. There is definitely something wrong with many people's intentions obviously, but that is sociology, not technology.
In other words, don't blame the technology that is working the way it was designed to work. Blame the people who are intentionally or unintentionally being deceptive.
What are you talking about? I've never purchased an NFT, and I have always tried to warn all the people around me to make sure you understand what you are getting into before spending money on anything. There is plenty of good uses for blockchain and NFT technology. Paying for a URL to a stupid monkey picture that is not guaranteed to persist is not a good idea in my opinion.
No. If you read the documentation and understand how the technology works it is very obvious that it is too expensive to store the actual images on the blockchain, and in fact only the URL or some meta data in a small number of bytes is bound to the NFT. The 'functionality advertised by the NFT' does not include the image data. If someone lies to you and convinces you to buy an NFT with the assumption that the NFT and the image is one and the same then this only means someone lied and you got scammed because you were naive. That is bad behavior, but there is still nothing wrong with NFT or blockchain technology. It is working as is intended. Many people were warning that this exact situation was going to happen.
The NFTs are not broken at all. People just paid for the URL and meta-data stored on the blockchain. I don't think anything has changed on the blockchain. The real issue is that people didn't understand what they were actually paying for in the first place. The images were never physically, digitally or legally bound to the NFT in any way. This is how it was designed to work.
OK. That is an example of a reasonable argument. The article is not making reasonable arguments, it's just a wall of feelings based shit-posting. I don't care who the richest person is. At least in this moment a rich person is helping to drive innovative technology rather than hoarding Oil profits.
According to this woman's world view, Somehow the most incompetent bumbling fool on the planet got lucky for a decade and a half and stumbled his way to becoming one of the richest humans alive. Tesla cars are outdated heaps of poorly manufactured corner cut parts. Tesla has endured "Years of starving research and development". And any second now Tesla will utterly collapse in order for Elon to promulgate toxic hate speech because he is just so super mean. Please find some better drugs lady, this fantasy 'journalism' career you've invented is depressing as shit.
There is a huge difference between generating a series of notes, and generating actual audio. There can be dynamics in both. Just focusing on the actual notation is a much simpler problem. Trying to generate audio, requires not just notes, but tone harmonics, stochastic noise, rhythym, all sorts of properties that ears are extremely sensitive to.
Images are only correlated on very small space/time scales. Audio/Music has correlation on multiple over-lapping time scales.
We know this is true because Video filters are usually very small, bilinear, bi-cubic, operating on only a very small region. A large
video filter doesn't make sense because there is no correlation. Audio requires attention to tone, transients, harmonics, voice envelopes,
relationships between notes/scales, rhythym, recurring musical passages etc. etc. All these require filtering, or memory based processing
over different time scales. So audio/music from a raw data perspective is much more difficult for an AI system compared to images. This is why
image related AI is so prevalant, it is the low-hanging fruit due to small amount of correlation that the Neural Net needs to deal with.
This is one side of the argument. The other side of the argument is that what matters more than the raw sensor data is constructing an accurate representation of the actual 3D environment. So an argument could be made (which is what this guy and Tesla are gambling on and have designed the company around), is that the the construction & training of the Neural out-weighs the importance of the actual sensor inputs. In the sense that even with only two eyes (for example) this is enough when combined with the ability of the brain to infer the actual position and significance of real objects for successful navigation. So as a company with limited R&D & processing bandwidth, you might want to devote more resources to machine learning rather than sensor processing. I personally don't know what the answer is, just saying there is this view.
Maybe you're right, I don't care about Tesla drama.
Here is one possible perspective from an engineering standpoint:
Same amount of $$, same amount of software complexity, same size of engineering teams, same amount of engineering hours, same amount of moving parts. One company focuses on multiple different sensors and complex fusion with some reliance on AI. Another company focuses on limited sensors and more reliance on AI. Which is better? I don't think the answer is clear.
The other point is that I am arguing that many people are over-stating the importance of the sensors. They are important, but far more important is the post-processing. Any raw sensor data is a poor actual representation of the real environment. It is not about the sensors, but about everything else. The brain or the post-sensor processing is responsible for reconstructing an approximation of the environment. We have to infer from previous learned experiences of the 3D world to successfully navigate. There is no 3D information coming in from sensors, no objects, no motion, no corners, no shadows, no faces, etc. That is all constructed later. So whoever does a better job at the post-processing will probably out perform regardless of the choice of sensors.
I am an engineer working on autonomous vehicles. Nothing personal just responding to the thread as a whole. I don't believe this guy is conspiring to trick anyone. Business decisions, or course. I think they are in good faith gambling on this one approach. So I am interested to see if their idea will win, or if someone else figures out a better way.
Both methods have trade-offs as in there are positive and negative merits for both approaches. Using both systems requires the sensor data to be fused together to make real-time decisions. This is the whole point, why people are trivializing this problem, and why it is easy to believe that they are just trying to scam people by going cheap on using multiple sensors. If you want to argue that it is better to use Lidar then explain why apart from 'others do it'. The podcast, and previous explanations by this guy and others that agree with him (which occurred way before some shortage issues) is about what is the best way to solve autonomous driving. You don't solve it by simply adding more sensors. There are multiple hours of technical information about why this guy Andrej thinks this way is best. Others make arguments for why multiple sensors and fusion makes more sense. No one knows the correct answer, it will be played out in the future. Maybe what some people care about is cheaper cars. That is not what the podcast was about, that is not how the Lidar + stereo camera vs. stereo-camera only decision was made. And in terms of the advancement of human civilization it is not interesting to me whether Tesla has good or bad quarterly results compared to what is the best way to solve the engineering problems & the advancement of AI, etc. I don't really care very much but it is slightly offensive when many people just dismiss engineers who are putting in tons of effort to legitimately solve complicated problems as if they are just scam artists trying to lie to make quick money. That is also a stupid argument. No company is going to invest billions(?) of dollars and tons of engineering hours into an idea they secretly know is inferior and will eventually lose out because they can have a good quarter. That is not a serious argument.
Lidar vs. Stereo camera vs. multiple cameras vs. ultrasound is a separate problem that engineers are trying to solve, not how can we sell cheaper mops. The decision to not use Lidar, as he says, and is the common debate being explored by people working on autonomous driving is whether it makes more sense to focus on stereo image sensors with highly integrated machine learning, or maybe use Lidar or other sensors and include data Fusion processing. Both methods have trade-offs.
You have to eventually decide to trust one or the other, in real-time. So having multiple failure modes doesn't solve the problem entirely. This is called 'Fusion', meaning you have to fuse information coming from multiple sensors together. There are trade offs because while you gain different views of the environment from different sensors, the fusion becomes more complicated and has to be sorted out in software reliably in real-time.
Everyone has limited R&D and processing bandwidth. It's not just Andrej saying this, but anyone working on engineering autonomous vehicles. It has nothing to do with the cost of additional eyes. This is over-simplifying the problem. Our eyes don't work that way. The data coming from eyes and image sensors is very crude and relies on either your brain or very sophisticated post-sensor processing to construct a 3D approximation of the actual environment. The sensors themselves don't provide this information. They don't distinguish distinct objects, corners, shadows vs. changes in color, or all sorts of phenomena that doesn't actually exist in the sensor data. This has to be inferred later by a brain or processing that relies on prior assumptions and 'training' by previous experiences with 3D environments. I don't really care what Tesla is doing vs. what other companies are doing, but these 'cost cutting' arguments don't matter. I would suspect that the R&D invested into the Machine Learning infrastructure, and the custom IC, and the software engineering out-weighs whatever amount they could save by removing a small sensor. And I don't believe that this guy Andrej is conspiring to squeeze a few bucks out of his customers at the expense of degrading his life's work. He is not trying to sell mops.
You are greatly overestimating the functionality of the sensors, and underestimating the importance of the rest of the system. Sensors are important, but the majority of the work, effort and expense is involved with post-sensor processing. You can't just bolt a 'Lidar' on to the car and improve quality of results. Andrej and other engineers working on these problems are telling everyone the same story. The perfect solution is not obvious to anyone, and they have chosen one path. Engineers aren't trying to scam people out of a few dollars so they can weasel out of making high quality technology. This has Nothing to do with cost-cutting.
The majority of comments surrounding stereo-camera/lidar questions have a ridiculously simplified idea of the problem. It's obviously the case that 'more sensor good, less sensor bad'. This is frankenstein's monster level technical analysis. Why don't the majority of large-brained animals have many eyes, and many different antannea appendages processing an array of diverse sensory input? You don't just automatically gain by adding lots of sensors. The signals have to be fused together and reliably cooperate and come to agreement in real time for any decision. Any sensor is only providing raw crude data. The majority of the work involved is done by processing this crude data and inferring a much more sophisticated approximation of the real environment from prior knowledge, hence using neural nets with pre-trained data. It is a good debate whether the
approximation can be done better by adding more sensor input and diverting R&D & processing resources towards fusion as opposed to improving the results that can be obtained from stereo image sensor. It's not obvious to anyone. And nature seems to inform us that most large brain animals evolve to rely heavily on two eyes instead of 16 eyes + lasers. This is an interesting discussion, but the issue isn't 'tesla could just bolt a Lidar box to the roof and magic, but they want to scam you out of a few extra bucks'. That is a moronic idea.
Agree, the SOM + Carrier board is the only thing that makes sense unless you are doing some large scale production making a million high end boards. The SOM's have suddenly made it possible for small scale or even independent bedroom projects to make use of high-end SoCs! KR260 / KV260 are both great boards.
After Intel took over Altera, the focus has switched primarily to Data Center processing. Just look at both AMD/Xilinx and Intel/Altera websites and you can see a big difference. If you are a Data Center Engineer than Altera is probably a good match. But AMD/Xilinx has a more diverse focus, specifically video, robotics and AI inference. Altera and Xilinx were about 50/50 competitors for many years and I think that was always healthy competition. Now that they've both been bought out, the future of FPGAs was very tenuous. In my opinion AMD has done a much better job at maintaining and even improving FPGA technology.
You met someone and you can confidently judge their life's work and how deserving they are of their position? And then you can make the correct judgement for when and to what degree someone has become too successful and how
to enact countermeasures to undo the effects of the 'power law' and unequal luck distribution? I don't think anyone has this amount of wisdom. You are correct that some people are disproportionally rewarded for their efforts, but the other side is true too. Some people are disproportionally punished for no reason at all. You can't do anything about either, it's part of nature. How do you know to what degree a very successful person is successful due to luck vs. hard work? You are going to track the minute details of every person on the planet's life path and make a fair judgement? It seems out of resentment society could arbitrarily punish the most fortunate people by redistributing their earnings and removing them from their position, but that doesn't stop the misfortunes on the other end. So the result would be a net negative direction. A person could certainly be born unlucky, or experience injustice, but we would guarantee that no one could be successful. This experiment has already been played out multiple times throughout history.
I would expect engineers to understand probability and statistics and know the difference between a bool and a floating point number. Maybe you mean emotionally under-developed A.I. ethics 'engineers'?
Direct involvement with drafting communications standards. Understanding Communication theory, advanced mathematics & algorithms. Computer Science involved with Hardware acceleration of the DSP algorithms. Quality of Results measurement and testing involving ridiculously expensive test equipment. (Like one piece of test equipment will cost as much as a house) Large scale chip manufacturing will require direct involvement with Semiconductor fabrication plants. the baseband comms chips are basically driving the latest small-scale wafer technology and requires the fab to evolve new techniques and new machinery. A 'winner takes all' environment, where the winning company will either win the mutli-million dollar contract, or take a huge hit. Because the baseband side is inherently centralized, it's not realistic to have many separate deployed solutions. I worked on comms for many years. It's interesting because so much human effort has been spent finding very sophisticated ways for processing large numbers of independent signals, and correcting errors. There are very intersting problems like digital pre-distortion, adaptive equalization etc. and crazy linear algebra problems. But the down side, is that you would most likely be working on a small piece of the large puzzle, and there is very little recognition for the amount of work required. There are very few 'famous' baseband engineers. The feeling is very much like a cog in the machine you you don't really see your direct contribution.
The socialist mind everyone just grows into their role in society like mushrooms, and some people accidentally fall into a role where they are able to hoard all the resources?!? No. Not everyone wants to wallow around in filthy tent communities begging for equal distribution. The billionaires are often the people who built significant portions of society by taking risks and out maneuvering everyone else to create massive economic value. They do that because there was incentive from the beginning. The laws society decides on should force everyone to play fairly. There might be political corruption that allows people to cheat or use loop holes that cause inequality. This is much different than stupidly chanting 'billionaires bad'. It requires a much different solution to actually fix problems in reality.
Not everyone is equally talented. Billionaires are the same as sports stars, rock stars, famous actors. You can't just decide to be a sports star, and it doesn't happen by random chance. If society can just decide on a whim to remove the top sports stars cause they are getting too much attention what kind of effect would that have on how sports are played?