标签：#AR

Updates include a new souped-up Siri, lots of iOS enhancements, and some inkling on how an AI partnership with Google has come to power Apple’s products.

"It is actually challenging how you measure [Mach] from space."

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Polystyrene can be upcycled into carbon sponge material.

I said "maybe a couple days" on a call last Tuesday. By Wednesday morning it was in a Jira ticket as "2 days." By Thursday afternoon somebody was checking in to see if we were tracking against the two day commitment. Nobody did anything wrong. The person who wrote it down was capturing what I said. The person checking in was doing their job. I was the one who said the words. The system worked exactly as designed. The system is the problem. Something Ive learned is that theres no such thing as a rough number in meetings today with all of the AI note takers... The moment you say a number out loud, it stops being a feeling and starts being a quote. The hedge in front of it doesnt survive the transcription. "Maybe" disappears. "Couple" gets rounded to a specific integer. "Give or take" is the first thing that hits the cutting room floor. What lands in the document is the number, naked, with no caveats and no error bars. Everyone in the meeting heard what you heard. They heard the hedge. They watched you wave your hands. They understood, in the moment, that you werent committing. But the document doesnt remember any of that. The document just remembers the number. And the document outlives the conversation, which is where all the nuance lived. Ive watched myself do this for years and I still get caught by it. Someone asks how long something will take. I want to be helpful. I want to seem confident. I want to keep the meeting moving. So I say a number. The number is approximately right, or at least I think it is, but I havent actually thought about it the way you would think about it if you were going to commit to it. By saying it out loud, Ive committed to it. The fix, if theres one, is to refuse the number. Not rudely. Just clearly. "I need to look at it before I give you a real number. I can have one for you by Friday." This works about half the time. The other half, somebody in the room is going to ask you for a ballpark anyway, and youre going to give them one, and t

Siri is now directly embedded into the camera app, and there are more artificial intelligence tools in the Photos app to alter your images.

Apple Intelligence is coming to cameras connected to Apple Home. At WWDC, Apple announced that with iOS27, the Home app will use Apple Intelligence to analyze footage and generate descriptions summarizing what the camera saw. You can also search footage with natural language to find clips from across connected cameras, such as when a package […]

I am a last-year data science major at university who initially joined because of AI's exciting potential across numerous industries. However, after learning about multiple companies backtracking on their AI use on their platforms and cutting back on their data center expansions, I can't help but think that something is very wrong behind closed doors. I came to understand that the demand for AI is slowly decreasing in some areas and increasing exponentially in others. To me, it seems every major industry "needs" AI to make life easier, yet is backtracking when it doesn't perform the way they want it to. My concerns revolve around how unpredictable AI's usage is. If I get involved in an industry that actively destroys land, water, and other resources, I would hope that the environmental costs will be outweighed by the benefits everyone sees from AI. However, with the economic trend of AI's value decreasing for companies that initially went all in on it, I can't help but feel like I'm actively destroying the planet. Does anyone have any suggestions or moral redemption for me? I want to jump ship before the big explosion, but I'll stay if there's great potential for growth with AI. submitted by /u/Emergency_Ad6929 [link] [留言]

From a stand-alone app to a Google Gemini partnership, here’s everything you need to know from WWDC 2026 about Apple’s upcoming overhaul of Siri.

Yes, I'm calling it out. It IS racism. As an active member of r/MachineLearning and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber. I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups. This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field. submitted by /u/AffectionateLife5693 [link] [留言]

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The conversation around AI coding is still stuck on velocity and its completely missing the real operational bottleneck -> DEBUGGING I use a combination of tools like GitHub Copilot, Cursor, and generic agentic code gen tools(whichever give me the most credits that week) , dropping a 300-line functional block from a natural language prompt takes about a minute. On paper, developer velocity should have been increased by 69 times. but i feel like the bottleneck hasn't disappeared; it just shifted down the pipeline. Like i traded manual work for incredibly frustrating debugging. LLM code looks fine on surface but like when u go through line to line, you feel like its built on sand i mean sure if it works it works but like one thing i struggle with is ghost features, like if i accidentally suggest a feature then the LLM is gonna shove it in my code, even if i say no later on. (if someone knows how to fix do dm) idk about ya'll but i'd much rather have a ai llm that takes like 1 hour to write 500 lines of code if that means i have to debug less. another thing how are you handling validation boundaries? are u using runtime timeout scripts or smth open source like gitagent? also this is gonna sound weird but i kinda have trust issues when a llm spits like 300-400 lines in under a minute (idk why) sorry for my bad english, im not a native speaker submitted by /u/SpicyTofu_29 [link] [留言]

The British government thinks a state-backed infrastructure initiative will help supercharge homegrown chip startups.

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Amazon Web Services has recently announced the general availability of the next generation of Amazon OpenSearch Serverless, with a redesigned architecture that enables 20 times faster resource provisioning than the previous serverless architecture, true scale-to-zero capability, and up to 60% lower cost than a provisioned cluster for peak loads. By Gianmarco Nalin

The cars are too big to race well, but the competition for pole position is thrilling.

TL;DR: Future archaeologists will discover this post and conclude I traded a referral link for free AI credits. They will be correct. 500 free credits: https://manus.im/invitation/L722LISUH3EMDS?utm\_source=invitation&utm\_medium=social&utm\_campaign=system\_share Anyway... You know how in every sci-fi movie they promise us AI assistants? Yeah. Somehow we ended up with AI that needs constant supervision. Me: "Research this topic." AI: "Certainly. Before I begin, please provide your goals, audience, format, timeline, preferred writing style, risk tolerance, blood type, and your mother's maiden name." Thirty minutes later I'm managing the AI instead of the AI helping me. I've been messing around with Manus and the thing I like is that it behaves more like an actual assistant. I tell it what I need, and it goes off and fills in a lot of the blanks itself. I don't use it as my main model for everything. I use it like a second opinion. Research. Project planning. Finding blind spots. Comparing options. Figuring out what I'm forgetting. Basically all the stuff that happens before the actual work starts. For pure coding, there are better tools. For "here's the thing I'm trying to do, help me think through it from start to finish," it's been surprisingly useful. Full disclosure: if you use the link, I get some credits too. You get free credits. I get free credits. The robots get stronger. Honestly that's the healthiest relationship I've had with technology in years. submitted by /u/Mstep85 [link] [留言]

I've been admitted into both UPS and TUD for Applied Mathematics, and I wanted to hear some advice on which one would be better. For context, I'd like to work in some form of AI research, most likely within industry. At the moment, I'm most interested in privacy preserving machine learning or mechanistic interpretability. Which one do you think would leave me with better career opportunities after completion, alongside the best chances of getting admitted into competitive PhD positions? Thanks! submitted by /u/Far_Investigator6900 [link] [留言]