elijahg

canukstorm said:

elijahg said:

canukstorm said:

avon b7 said:

I think it's fair to say that Apple is behind in this area. 

Objectively, this year has been about ChatGPT style usage and Apple hasn't brought anything to market while others have. 

It is also recruiting for specific roles in AI. So far, most of the talk has been only that, talk. 

Talking about ML as they made a point of doing, is stating the obvious here. Who isn't using ML? 

In this case of LLMs on resource strapped devices, again, some manufacturers are already using them. 

A Pangu LLM underpins Huawei's Celia voice assistant on its latest phones. 

I believe Xiaomi is also using LLMs on some of its phones too (although I don't know in which areas). 

The notion of trying to do more with less is an industry constant. Research never stops in that area and in particular routers have been a persistent research target, being ridiculously low on spare memory and CPU power. I remember, many years ago, doing some external work for the Early Bird project and the entire goal was how to do efficient, real time detection of worm signatures on data streams without impacting the performance of the router. 

Now, AI is key to real-time detection of threats in network traffic and storage (ransomware in the case of storage, which is another resource strapped area). 

LLMs have to be run according to needs. In some cases there will be zero issues with carrying out tasks in the Cloud or at the Edge. In other cases/scenarios you might want them running more locally. Maybe even in your Earbuds (for voice recognition or Bone Voice ID purposes etc). 

Or in your TV or even better across multiple devices at the same time. Resource pooling. 

"Objectively, this year has been about ChatGPT style usage and Apple hasn't brought anything to market while others have. "

This does not prove that Apple is behind.  Was Apple behind when it introduced the iPod years after other digital music players? or the iPhone? or the iPad? or the Apple Watch.  All this proves is that Apple is not first but being behind implies that other players are better than them in this technology which hasn't been proven yet since we're still in the early stages with respect to AI / ML.

The iPod was a massive advance on other MP3 players of the time. So was the iPhone, and so was the iPad. Siri was pretty good at the time of introduction, but everyone else has surpassed it since development seems to have essentially stopped in 2014. ChatGPT is as far ahead of the competition as the iPhone was in 2007. Siri is to ChatGPT as Blackberry was to iPhone in 2007.

"Siri is to ChatGPT as Blackberry was to iPhone in 2007."

Not the same thing even though they both utilize machine learning technology.  Wait till Apple introduces their take on an LLM and then we'll see if ChatGPT is far ahead.

canukstorm said:

avon b7 said:

I think it's fair to say that Apple is behind in this area. 

Objectively, this year has been about ChatGPT style usage and Apple hasn't brought anything to market while others have. 

It is also recruiting for specific roles in AI. So far, most of the talk has been only that, talk. 

Talking about ML as they made a point of doing, is stating the obvious here. Who isn't using ML? 

In this case of LLMs on resource strapped devices, again, some manufacturers are already using them. 

A Pangu LLM underpins Huawei's Celia voice assistant on its latest phones. 

I believe Xiaomi is also using LLMs on some of its phones too (although I don't know in which areas). 

The notion of trying to do more with less is an industry constant. Research never stops in that area and in particular routers have been a persistent research target, being ridiculously low on spare memory and CPU power. I remember, many years ago, doing some external work for the Early Bird project and the entire goal was how to do efficient, real time detection of worm signatures on data streams without impacting the performance of the router. 

Now, AI is key to real-time detection of threats in network traffic and storage (ransomware in the case of storage, which is another resource strapped area). 

LLMs have to be run according to needs. In some cases there will be zero issues with carrying out tasks in the Cloud or at the Edge. In other cases/scenarios you might want them running more locally. Maybe even in your Earbuds (for voice recognition or Bone Voice ID purposes etc). 

Or in your TV or even better across multiple devices at the same time. Resource pooling. 

"Objectively, this year has been about ChatGPT style usage and Apple hasn't brought anything to market while others have. "

This does not prove that Apple is behind.  Was Apple behind when it introduced the iPod years after other digital music players? or the iPhone? or the iPad? or the Apple Watch.  All this proves is that Apple is not first but being behind implies that other players are better than them in this technology which hasn't been proven yet since we're still in the early stages with respect to AI / ML.

• Siri doesn't use contemporary "AI" like ChatGPT, Siri's "intelligence" comes from predefined queries a in basic lookup table - which is why if you don't ask it something in the right way it breaks.
• Very few Siri queries are processed on-device. 
• LLMs need huge datasets unavailable on-device so efficiency of on-device processing is mostly irrelevant. 

jimcord said:

Chelgrian……. do you not see that removing features from a watch that a person already paid for Could and probably would lead to a large class action lawsuit against Apple. Do you think Apple really wants that backlash from their customers? People bought their product because it had those features, and now you’ve taken them away. I see nothing but problems with this

eriamjh said:

I'm going to go out on a limb to say that the 2024 MacOS release will be the last one that supports Intel machines unless Sonoma is already it.  There's is no way that Apple will support a 2020 iMac in 2025, five years later.