This comment totally misunderstands why scientists get into science. Let me tell you, it isn't the money. It is for the joy of exploring the frontier. LLM's are taking this from us. Maybe they will be better than us but I'm not sure how many scientists will get into science for the joy of being an understudy to an LLM.
We live in a time where there isn't enough focus on giving life meaning. Without meaning, it is too easy to become cynical. Some of the happiest people I know believe that the greatest source of meaning is serving others. Many of those at the forefront of research believe there work, while individual, serves all of humanity.
Meaning in life comes from contributing to something larger than yourself. Work is one way but there are others. Just be careful when you dismiss work that you don't end up leading a meaningless life.
The entire cryptographic underpinning of the Internet is based on mathematics that, when I learned it as an undergraduate in the 1980's, was considered just an impractical curiosity. Non-Euclidean geometry became special relativity. Math underpins all science - "totally useless" phooey.
I'd like to believe Grant but I find this assault on mathematics to be dehumanizing and demoralizing. AI has destroyed a major source of "life's meaning" for many people who pursued mathematics. Imagine waking up one morning to find that an alien technology has solved the problem you had dedicated your life to (and many more to boot.) Not another mathematician but an alien technology. As a scientist, I don't want to end up as a laboratory assistant to an AI. I can take not being the smartest human in the room, but I'm pretty sure I don't want to spend my life serving a silicon master.
Professionally, writing my own code is critical. As a scientist, it is how I really understand what that journal article I just read really means. When I write the code, I discover the edge cases that the paper overlooked. If I left it up to AI, I would have the same superficial understanding of someone who just read the article. Code is where you assumptions are challenged. It is easy to make an theory that works 90% of the time. Writing the code forces you to go from 90% to 100% or at least understand why we are stuck at 90%. Not to mention the joys of wrestling with the code that the author describes.
I have no idea what you are babbling about. Sure, the evolution of the climate is recorded in the geological record. Climate scientists largely understand why these geological timescale events happened. What we are now undergoing is orders-of-magnitude faster than any geologically recorded event. Except for catastrophic events like meteorite impacts and mega-vulcanism there has never been such a rapid change. And climate scientists understand why. Look in the mirror. It is us.
Right, science, while not perfect (being a human endeavor), is our best mechanism for getting closer to the truth. Sure, fraud happens (occasionally). Not all "results" are a step forward. But the system is inherently self correcting. The problem is the politicization of science funding when scientific results don't fit into a dogma driven view of the world.
The product they sell is trustworthy news but they still have accountants. The high-quality news business is a rough business and few are profitable. I can understand why they might feel defensive and a more than a little spooked. How many profitable quality newspapers can you identify? NYT and WSJ - any others?
Not true for everyone. I learned Rust from The Rust Programming Language ("The Rust Book") and "Rust for Rustaceans." Sure, coming from C/C++, I could have learned the syntax online but learning best idioms and styles required the time and commitment to read a book cover-to-cover. In fact, I've probably read each page in "Rust for Rustaceans" at least twice to ensure that I understood some of the more subtle points. I could have developed a half-baked notion of how the borrow-checker worked by fooling around and reading blurbs on Stack Exchange. But Rust for Rustaceans made clear the more subtle points that might have taken years of tinkering to understand. Thank goodness people still write excellent books on computer programming.
There is necessary complexity and un-necessary complexity. Often the modern world seems to be layering on un-necessary complexity and frequently this is not to the individual's benefit. Consider for one, picking health insurance. It should be easy to line up 5 or 6 plans, compare them on coverage and price. However, it is against the insurance companies interest to compete directly. Much better to make it so complex that the average consumer can't compare realistically products. (Doubly so since we don't know what is covered until the doctor asks.) The government could make it easier on consumers by clearly defining coverage levels and allowing companies to compete on price.
This is why it is good lab procedure to always "run a blank." A blank is simply a sample that is constructed exactly like a real sample but without the thing you are studying. This way you quickly learn about contamination from tools/gloves/environment etc.
Both things can be true. A better O-ring with the same joint might have prevented the disaster. A better designed joint with the same O-ring might also. Feynman knew that a little theater would go a long way. The O-ring explanation, albeit a partial explanation, made for good theater.
Maybe not so much "oblivious to safety" as "oblivious to probable risk." We worry to much about low risk events (like airline flights) and don't worry enough about higher risk events (like trips-and-falls, driving a car, poor diet...)
I really enjoy how Julia handles images. The abstractions really streamline developing image processing algorithm that are independent of pixel representation. Like so much of Julia, the first time I saw it, my mind was warped. Julia is worth learning if just for all the clever design choices.
And yet all countries with socialized systems pay less per capita for healthcare than we do and pretty much all have better health outcomes. Further privatizing our system will only make it more dis-functional. Healthcare isn't a normal marketplace. * When you really need it, you can't shop around. * There is a knowledge asymmetry built in. * A civilized society can't just let poor children die of preventable causes.
This is just the lazy comment of someone who believes all the right-wing propaganda about government. In my experience, government employees take pride in doing a job worth doing and doing it well.
I don't care about dated looks. I do find MS Office's pressure to use OneDrive frustrating and annoying. Honestly, older UIs for office suite products just feel more direct and responsive than the clever ribbon bars. Excel used to be svelte (25 years ago or more...) Now it feels bloated and clumsy. LibreOffice Calc (same parentage as Collabora Office) feels more like Excel used to feel. Similar complaints about Word.
Hopefully, in coming years, we will see more practically designed EVs that are more affordable. A practical car doesn't need neck-snapping acceleration, every bell-and-whistle and room for a family of six with a dog. I'd like to believe that as batteries cost drop, the incentive to justify the extra cost will drop. Then we can get back to "just basic transportation" rather than a luxury product for the rich. While $31k isn't exactly cheap, the base new Leaf is heading the right direction.
This is a great example of the "I wonder if I could"-kind of research. It doesn't have to be practical. I doubt the authors intend it as a viable security product. It is the kind of "just playing around" thinking that can sometimes lead to brilliant insights. Keep up the good work.
A handful of the comments are skeptical of the utility of this method. I can tell you as a physical scientist, it is common to make the same measurement with a number of measuring devices of differing precision. (e.g. developing a consensus standard using a round-robin.) The technique Cook suggests can be a reasonable way to combine the results to produce the optimal measured value.
It is worth noting that the research that Martinis is being awarded the Nobel prize was largely performed while at NIST (National Institute of Standards and Technology), part of the Dept of Commerce.