Software that had a data moat because it was hard to integrate with or migrate off of will have that moat disappear. A web site is a client now. Building data migration too for all of you competitors is easier now.
They will add flooded streets to the training simulation and this problem will go away. Eventually, the corner cases not in the training simulation will be so corner they basically never happen. Waymo can be incredibly successful without dealing with "surprise clown parade" or whatever.
Instagram is gone as well. Everything is fake in different ways. If the video isn't ai generated, then it's influencers acting out a scenario they think will get engagement. I realized that when it's a real video, there is a caption that says some scenario is happening, but there is nothing in the video that shows that is real. I think people are just reposting videos with different captions and testing out whatever invented scenario make the video have the most views.
If had to answer this question 2 years ago, I wouldn't have said software was a "don't see it's bad until later" category, with compilers and it needing to actually do something very specific. However, business slides are full of exacting facts and definitely never contains generic business speak masquerading as real insight /s.
This feels like telling a story after the fact to make it fit.
If llms are able to write better code with more declarative and local programming components and tailwind, then I could imagine a future where a new programming language is created to maximize llm success.
If a company can align it's business model with user goals, then it can work in the long run. Apple has somewhat aligned it's integrated hardware sales business model with user privacy. Google and Meta are advertising companies and capturing user data and attention will always drive the business.
The selling point software that monitors social media to do support is about brand reputation. If people are saying bad things about your company on social media, then that is hurting your public image, so you should respond to those issues and make sure they get resolved. In practice, this means that the people responding are less likely to be outsourced because the the funding is more than just a support cost. In practice, this means you can often get better support through Twitter than email.
Are there any successful models that weren't trained with RLHF, or using a system with RLHF. I'm curious if this could be done without a fine tune step that would't meaningfully bias this.
This article does a poor job of representing induced demand so it's can't be trusted to critique it. Traffic and congestion is defined by (number of trips) * (distance traveled). So, if 10 people take 1 mile trips every day, or 5 people take 2 mile trips every day, the traffic congestion will be the same.
Induced demand says that by building roads, you enabled people to buy a cheaper house further out of the city, and drive further, thus congestion stays the same. This is true for other trips as well. You might drive further to Costco to get cheaper groceries rather than to your neighborhood store if there is a fast road there.
I think there is a legitimate criticism of induced demand that it usually doesn't provide a tradeoff for when you have enough roads. 0 roads in all size cities isn't the answer. At some point a city has enough roads and should focus on mass transit or other transportation. I've never seen an induced demand argument attempt to define this threshold and why.
Doing REST microservices is incredibly slow because of the amount of work it is to agree on what a "clean" and "consistent" api looks like for each service. It's just such an endless well of trying to establish best practices without refactoring constantly.
It's worth it for your public API, but it's such a huge time sink for internal APIs.
When senior leadership is non-technical, they can't tell when non-customer visible changes are useful or they just have mediocre engineering. They also can't tell when their software stack is such a mess it's beyond fixing.
I think the author's point is that tracing is a better implementation of both logs and metrics, and I think it's a valid point.
* metrics are pre-aggregated into timeseries data, which makes cardinality expensive. You could also aggregate a value from a trace statement.
* Logs are hand crafted and unique, and are usually improved by adding structured attributes. Structured attributes are better as traces because you can have execution context and well defined attributes that provide better detail.
Traces can be aggregated or sampled to provide all of the information available from logs, but in a more flexible way.
* Certain traces can be retained at 100%. This is equivalent to logs.
* Certain trace attributes can be converted to timeseries data. This is equivalent to metrics.
* Certain traces can be sampled and/or queried with streaming infrastructure. This is a way to observe data with high cardinality without hitting the high cost.
Correct. I'm saying that private insurance is good at insuring uncorrelated risks, like car wrecks, but usually, only the federal government can enable insuring correlated risks. Suggesting that mandatory liability insurance can a good policy alternative to regulation is correct, but if you are regulating something large enough with correlated risks, only the federal government can every really provide insurance. Banks pay FDIC insurance, and bank failures are usually covered by this, but it's pretty clear that the pool of money to save banks is backstopped by the federal government in case of a cascading failure.
Even for uncorrelated risk, if the payout event is bad enough, it can't be insured. For example, insurance on a nuclear plant is backstopped by the federal government, because no private insurer could ever really insure the worst case scenario.
There is an issue with correlated vs uncorrelated risks. This is the reason that only the federal government can offer FDIC insurance for banks and disaster insurance in Florida. When banks fail, they tend to all fail at the same time and there is no insurer that could hold enough reserves to actually pay out if they all fail together. Private insurance can cover fire insurance for homes, but is unlikely to have enough in reserves to pay if a category 5 hurricane floods half the homes in Florida.