This gets at the concept of accountability for those at the top of government. This is an issue in all governments, not just in Canada. A good parallel would be the United States. The list of actions the current administration has taken which have been determined illegal is astounding, yet no one is held accountable in a way that would deter future breaches of the law.
This is the kind of advice I give incoming graduate students. The sooner you start to treat grad school like a full-time job, the better. I was in a similar boat: my wife and I were both in grad school at the same time. We worked 9-5 every day, even if we weren't going in to the office. We both finished on time, and generally didn't have a difficult time with our degrees.
Or lucky! I had a great time during mine because my advisory was amazing. However, my cohort mates, many of whom I'd say are much smarter/intelligent than I, got stuck with terrible mentors.
His guide to doing well in undergraduate courses is decent enough that I've sent it around to my students as well. I sometimes have to teach first or second year students and the amount of questions I get about how to study or how to do well is significant. We kind of forget that this is a learned behavior, and everyone learns it at different times in their lives (or not at all).
Yeah the sound stage definitely gets impacted. I tend to use leather cups in the winter and fabric ones in the summer. Nothing worse than sweating from your temples while you work.
My thoughts exactly. I reads a lot like they are trying to minimize the state's power to regulate AI. I'm not sure that's such a good thing. Regulation is one of the only ways that we can manage the ``bads'' that come with any new technology. In the US, we've never been very good at regulating new technologies before industry stakeholders entrench themselves in the lobbying circuit.
Pareto optimal is definitely a core concept in game theory. It says that no other vector beats it in every dimension (or at least as good in all but one, and better in at least one).
I wish more business / product people understood this concept. When a product has been refined enough to approach Pareto optimality (at least on the dimensions the product is easily measured), it's all too common for people to chase improvements to one metric at a time, and when that runs out, switch to another metric. This results in going in circles (make metric A go up-up-up, forcing metric B down-down-down, then make B go up-up-up while forcing A to go down-down-down - it's worse than this because multiple dimensions go up/down together, making it harder to spot). Sometimes these cycles are over a period of quarters or years, making it even harder to spot because cycles are slower than employee attrition.
This is not independent of Goodhart's Law[1]. I've seen entire product orgs, on a very mature product (i.e., nearing the Pareto frontier for the metrics that are tracked), assign one metric per PM and tie PM comp to their individual metric improving. Then PMs wheel and deal away good features because "don't ship your thing that hurts my metric and I won't ship my thing that hurts yours" - and that's completely rational given the incentives. Of course the best wheelers-and-dealers get the money/promotions. So the games escalate ("you didn't deal last time, so it's going to cost you more this time"). Eventually negative politics explode and it's all just a reality TV show. Meanwhile engineers who don't have an inside view of what's going on are left wondering why PMs appear to be acting insane with ship/no-ship decisions.
If more people understood Pareto optimality and Goodhart's Law, even at a surface level, I think being "data driven" would be a much better thing.
[1] Goodhart's Law: when a measure becomes a target, it ceases to be a good measure
Cybernetics has devolved into KPI metrics with accelerationism as a treat.
Apparently documents from Google's antitrust case revealed the search algorithm was adjusted to give worse results in order to force the KPI for AdSense to drive quarterly earnings reports.
> “I care more about revenue that the average person but think we can all agree that for all of our teams trying to live in high cost areas another $[redacted] in stock price loss will not be great for morale, not to mention the huge impact on our sales team.
> “I don’t want the message to be ‘we’re doing this thing because the Ads team needs revenue.’ That’s a very negative message.
> But my question to you is – based on above – what do we think is the best decision for Google overall?
> …Are there other ranking tweaks we can push out quickly?” - Dischler
Anil Sabharwal, the Chrome executive:
> “1…we were able to get launch approval to rollout two changes (entity suggest and tail suggest) that increase queries by [redacted]% and [redacted]% respectively.
> 2. We are going to immediately start experiments to improve search ranking in the omnibox (more search results and nudging search to the top).”
I think the most consumer friendly options would be a pay as you go model or a pay for tiers of use (e.g., $X for 500 queries, $XX for 1000 queries, etc.).
However they really are banking on the idea that people pay a bunch up front and use it fairly minimally. This allows them to make profit on the subscribers to pay for queries by free users. I have no idea where the pricing model will go in the future but it wouldn’t surprise me if pricing models become the primary method for fighting for market share as opposed to the AI’s actual ability.
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