Agentic and Physical AI Accelerate Automation and Real-World Integration

Key Quotes

"We have this you know first again is that conversational ai phase and i'll use a gaming analogy right this the conversational ai phase gives avatars gives agents i'll use them interchangeably today extremely little agency in doing anything other than speaking right it may be able to respond to my input if i ask it to do something but it's not going to physically change the state of something other than the dialogue it's going to tell me back is an adaptive partner phase where the ai is observing and responding to changes on its own it's not micromanaging every decision but it feels like you're collaborating with an agent or a unit that has just enough context to make smart decisions on its own like right a evolution of a recommendation engine being driven by you know a cognition engine here so it's not just learning but it feels like it's learning what we need even before we ask it again i think that's phase three i think there's still a phase four and i think that's a fully autonomous agent and that stage you know again continuing our analogy is a player two right where player three is its adapting to us stage four is hey this thing is an agent all on its own it feels like i'm playing against another human it is making decisions that feel optimal to its own objectives alike to mine or not"

Chris Colbert explains the evolution of AI agents, moving from simple conversational abilities to more autonomous partners. Colbert uses a gaming analogy to illustrate these phases, highlighting how agents gain agency and the ability to make independent, optimal decisions. This progression signifies a shift towards AI systems that can collaborate more effectively with humans.

"The immediate payoff of this capability is freeing human workers from repetitive error prone non creative tasks what we often refer to as toil but here's the key we don't need the agents to be perfect to be valuable in fact we don't even need them to do all of the work for us as nvidia's bartley richardson points out in episode 258 if it gets you 75 80 of the way there that's fantastic that's great because what's you know i'm i'm sure you know you do your fair share of writing right the hardest part for me about writing is that blank page the blank page totally right and if i can get something that's 80 of the way there it's great"

Bartley Richardson emphasizes that AI agents do not need to be perfect to provide significant value. Richardson argues that even achieving 75-80% of a task's completion is highly beneficial, particularly in creative endeavors like writing where overcoming the initial "blank page" is the most challenging aspect. This perspective suggests that AI can be a powerful assistant, even if it doesn't complete the entire task.

"You can think about it like data is exponential insight is linear every day percent data utilized to give insight is lower the analytical side of this and the ai solutions for this have been missing the field is still uh largely a manual field where you give people some data they sit in front of their computer you know they try to figure out they make some value and insight from this and i figured that's not a sustainable solution this field needs to move to ultimately build much larger integrated solutions that bring in many different angles of machine learning ai statistics and so forth to ultimately bridge this the data insight gap it's growing so you basically are constantly in a game in which you need to make it faster just it's actually what's called you know the in evolution and then remember alice in wonderland the red queen sure right where she said to alice you have to run just to stay in place but the red queen effect so this need for us to continuously run is a huge driver for automation acceleration and i would even say the cognitive meta analysis that we as humans need to do to somehow describe to a machine how we make decisions so that we can automate them"

Shai Shen Orr describes the challenge of exponential data growth outpacing linear insight extraction, likening it to the Red Queen effect from Alice in Wonderland. Orr argues that current manual approaches to data analysis are unsustainable, necessitating integrated AI solutions to bridge this gap. This continuous need for speed and automation is driven by the necessity to keep pace with the ever-increasing volume of data.

"So far in order to do ai you've had to send your data out to some kind of ai factory with a gpu do all your processing and copy it back right so the data has gravity and it turns out that instead of instead of sending all your data to the gpu you can actually send your gpu to the data and what that looks like is actually putting a gpu into your traditional storage system on that same storage network and letting it operate on the data in place where it lives without copying it out and the advantage of generating these ai representations with the source of truth data is that if the source of truth changes you can immediately propagate those changes to the representations the new version reverses this instead of shipping data out we bring the gpu compute to the data"

Jacob Lieberman explains a paradigm shift in AI infrastructure, moving from sending data to GPUs to bringing GPUs to the data. Lieberman highlights the concept of "data gravity," where large datasets are difficult to move, and proposes placing GPUs within storage systems to process data in place. This approach allows for immediate propagation of changes from the source of truth to AI representations, enhancing efficiency and reducing security risks associated with data transfer.

"The physical ai is really kind of the upcoming big industry um very likely larger than generative and agentic ai you know jensen typically says everything that moves all devices that move will be autonomous right so that's kind of the vision so a robot to operate in the real world obviously needs to understand the world what am i seeing what is everything i'm seeing doing how is it going to react to my my action right so understanding it needs to act but there is a catch you can't train a physical robot the same way you train a chatbot if a chatbot makes a mistake you might get a typo if a robot makes a mistake it'll probably break something"

Sonya Fidler posits that physical AI, which controls moving devices in the real world, is poised to become a larger industry than generative and agentic AI. Fidler emphasizes that robots operating in the physical world require a deep understanding of their environment and the potential consequences of their actions. Unlike chatbots, where mistakes might result in typos, errors made by physical AI systems can lead to tangible damage, underscoring the critical need for robust training and safety protocols.

"You know there is a group of people you know four thinking people and jensen very much included is near and dear to his heart that felt that um the time is ripe for this dream of humanoid robotics to finally be realized right you know let's let's actually go for it and you know this begs the question of why why humanoids at all you know why have people been so interested in humanoids why do people believe in humanoids and i think that the most common answer you'll get to this which i believe makes a lot of sense is that the world has been designed for humans you know right we have built everything for us for our form factors for our hands and if