与「TheAnswer」相关的搜索结果

お知らせ🍏 よくあるお金の悩みについてお金の専門家であるFPが悩みを解決する番組 #TheAnswer# にMCとして出演してます！ぜひご覧ください☺️ #住宅購入編# #保険編# #資産運用編# #スカパーの人生設計サポートサービスLIVNAL# 動画はここから👇

can a neurotypical person please explain where they draw the line between a “reason” and an “excuse” because apparently i still don’t understand the rules. you ask me, “why did you do it that way?” and naturally i’m going to explain exactly what was going through my head because… you literally asked. then three sentences into my explanation you hit me with “i don’t want excuses.” okay??? you wanted the explanation or you wanted me to guess the answer you already had in mind?

⚓️ Ethra Ship Notes｜Vol.050 Today, I came across a number that made me stop. Nearly 1,900 vessels are now considered part of the maritime "dark fleet" according to Windward, roughly tripling since the Russia-Ukraine invasion. The interesting part isn't just the number. It's what the number tells us about visibility. A ship can disappear from AIS. That doesn't mean the ship disappears from the ocean. It just disappears from one information layer. And that's a very different thing. This is where I think Sea Verity's approach gets interesting. Instead of asking a single system to tell us the truth, it combines different sources. Reporters on the ground. AI analysis. Controller nodes. Each piece adds another layer of evidence. And rather than forcing every observation into a simple "true" or "false", the system is designed around confidence scores. I like that approach. Because the physical world rarely gives us perfect information. A satellite image can be obscured. A reporter can only see part of a vessel. AIS can disagree with visual evidence. Weather can make everything harder. The honest answer isn't always certainty. Sometimes it's: We're 90% confident this is what happened, and here's why. That's much more useful than pretending the other 10% doesn't exist. For insurers, traders, logistics companies and regulators, knowing the confidence behind a piece of maritime intelligence could be just as important as the information itself. The ocean is enormous. Maybe the answer isn't one perfect signal. Maybe it's many imperfect signals learning how to verify each other. @EthraShip #EthraShip# #EthraShipProtocol#

Reddit is disappearing from ChatGPT citations right now, but I honestly think everyone is focusing on the wrong part of the story: Reddit isn't what matters most here. Because something much bigger seems to have changed in how ChatGPT searches. 8 August: ChatGPT started citing Reddit less. 14 August: Basically stopped citing Reddit at all. But at almost exactly the same time, ChatGPT’s query fan-outs started changing too. It began using way more specific searches like: > site:ibm .com > ibm official pricing > ibm docs [feature] Which suggests ChatGPT is doing less: search the web > see what ranks > cite it And more: choose where to search > search those sites or brands > build the answer But Reddit isn't the only thing changing, another dataset found that it’s the entire citation mix: > Reddit: 15% → 0% > review sites/forums: 7% → 0% > smaller company sites: 66% → 32% While: > help centres/docs: 2% → 32% > established companies: 4% → 18% > app marketplaces: 2% → 17% This is the part I find most interesting. Is ChatGPT starting to decide which sources it trusts before it searches? Does that make the authority of the source more important than where an individual page ranks? And how long does any of this last? Because if AI search keeps proving one thing, it’s how quickly things change. Reddit looked incredibly important 2 weeks ago. In another 2 weeks, this could all look different again.

Should enterprise AI follow a workflow — or think for itself? The answer is: both. Real business work is a mix of open-ended problems and predictable processes. That’s why Claw Mode in Tencent Cloud ADP 4.0 lets Agents and Workflows call each other. 🔄

When language models first started using tools well, I was sympathetic to the narrative that instead of scaling up language models, all we needed was a strong enough "cognitive core", say 1B parameters, and anything else could be done with tool use, like browsing the internet or executing code. I think a lot of people were sympathetic to this argument, and indeed it is pretty hard to come up with a meaningful task that cannot be in principle achieved by a 1B model with adequate access to tools. For example, any esoteric fact that a large language model would know can be, in principle, retrieved from the internet and reasoned over by a 1B language model. However I now think this is totally wrong for one simple reason: doing tasks quickly and naturally without tool use matters a lot. The way that I internalized this reason was actually in my personal journey learning badminton this year. In badminton I am very much like a "1B cognitive core". While I can physically do every movement in a badminton shot that my coach teaches me, it requires a lot of work to mentally remember every cue and put it together. In practice I can do a shot almost perfectly, but I struggle to do it across a point and I definitely can't do it consistently in a game. This is obviously different from someone who has practiced a shot ten-thousand times and effortlessly executes it as a natural instinct. In the same way, language models knowing a fact internally, without tool calls, is meaningful. The first reason is that we obviously care about speed; you'd much rather get an answer immediately than have the model think a long time to be sure of its answer or browse the web. A second reason is that there are some things that are simply best learned via backpropagation over lots of data. If you ask about how people generally think of the Shambhala music festival, you'd rather a large language model give you an aggregate opinion based on all the data on the internet, than get a regurgitation of the first three reviews that show up in a web search. A third reason is that having to do a lot of work to find an answer is not as reliable as already knowing the answer. While this does not have to be true in theory, it is probably true in practice, at least for now. If you have to re-look up facts or redo a mathematical derivation all the time there is a higher chance of mistakes, which can compound in a long-horizon task. Once you buy that it is valuable to do things parametrically without tool use, then you must buy the argument that a 1B cognitive core is not sufficient. There is an information limit to how much knowledge can be internalized by a 1B model, and we will surely want AI to know more than that. Even 1T probably won't be enough. We will want the AI to know as much about our world as possible, we will want it to be updated with new information, and our expectations of what AI can do for us will continue to grow. In summary, tool use enables small models to do a lot more, but those who demand the highest quality intelligence will always want larger models. Bitter lesson strikes again.

Booking Tony Robbins today costs $1 million for a single day. this is a 21-minute tape from inside his own house, filmed over 30 years ago, where he breaks down exactly how to get anyone to say yes. same $1 million material. completely free. this is a rare, unfiltered tape from decades before this man started charging billionaires just to be in the room with him. people give you two excuses when they say no, he says. not enough time. not enough money. neither is true. the real reason is they don't believe it's worth it yet, and that's not a money problem, that's a state problem. so he teaches something he calls attack and confess. instead of arguing with the objection, you confess your own. "I had a chance to go to this thing six months ago and I didn't go until two months ago," he tells the room. "I can't even imagine the time I lost." the room goes quiet. nobody argues back. he calls it getting someone on the yes train. every small yes you get compounds into the next one, until saying no to the final ask feels harder than saying yes. by the time he asks someone to sign, he says, they've already agreed to it five times over without realizing it. 21 minutes. that's all it takes to walk away knowing the exact two moves people pay $1 million a day to learn: how to read anyone's state, and how to move it. most people spend years in sales guessing at this. he wrote it down on a flip chart in his living room in under half an hour. a seat in that room cost $125 back then. today it's a $1 million-a-day to sit in front of him. The tape is free right now, and the answer is in this video.