GPT-4 Turbo (o1) is a BIG DEAL

But is it a step towards AGI?

Let’s get straight to it. The recent release of GPT-4 Turbo (aka o1) is making waves, and I’ve been down in the weeds figuring out whether this upgrade is more than just a flashy new model. My arms are all scratched up because these weeds are deep and prickly. TLDR: It’s not quite AGI, but damn, it’s closer than anything we’ve seen before.

Thought-by-Thought Optimization

In older versions, like GPT-3.5 and GPT-4, the models relied on autoregression. Essentially, they spat out responses based on the most probable next word, built from massive training sets. Think of it as a super-smart autocomplete that just happens to know more about the French Revolution than you ever cared to learn. The issue with the old approach — it often led to answers that were statistically likely but not necessarily…well… correct.

GPT-4 Turbo (o1) shifts away from a purely probabilistic approach. The new system uses a thought-by-thought pseudo-reinforcement learning method. It is a blend between A-star search (minimizing steps) and Q-star (maximizing reward). Each “thought” it generates is a step towards an optimized answer, not just the next statistically probable word.

Instead of being purely predictive, the AI now aims to optimize each step based on its reward model. It’s as if it’s thinking: “Am I on the fastest path to the most accurate answer?” And you can see this in real-time.

It’s no longer just filling in the blanks; it’s actually solving problems in a structured, deliberate way.

Autoregression Bias: Mostly Gone

By using this stepwise reinforcement approach, o1 has squashed a lot of the autoregression bias that haunted earlier models. ChatGPT is no longer lazily relying on past data to guess what’s next. Instead, it’s evaluating its own output and iterating towards a better result. This shift makes it substantially better at reasoning and handling complex queries without getting stuck in repetitive loops.

The recent calibration paper (see it here) shows us a new ability to gauge its own confidence. The AI actually quantifies its certainty in the answers it gives. Unlike humans (we just get a gut feeling and hope for the best), o1 assigns numerical confidence scores to its responses, correlating them with how likely it is to be correct. Spooky, no?

This Isn’t AGI (Yet)

To those ready to shout “AGI is here! All humans replaced!” — let’s not get ahead of ourselves. AGI (Artificial General Intelligence) means something entirely different — it means real, adaptive, self-improving intelligence. And while o1 is impressive, it’s not even all that close.

Here’s the deal: True AGI would require in-context training during inference. Basically, that means it needs to learn and adjust in real-time as it processes information, just like we do when our brain makes connections and adjusts neural pathways. Right now, even with o1, once you hit that context window limit, it’s like dropping off a cliff. Tokens and their associated knowledge just vanish into the ether, never to be seen again in that session.

In a fun twist of irony, I actually reached the context window on a thread that I was using to help me write this. You notice the thread starts to slow down and error out as you approach the context limit.

And then there’s interoperability, another key component of AGI, and something we haven’t cracked yet. A truly intelligent system won’t just talk to you in a vacuum. It needs to interact with other systems, pull in data, execute commands, and navigate the digital world fluidly. Today’s LLMs are isolated, stuck in their little sandbox environments, unable to integrate seamlessly with external APIs and systems.

The BotOracle Aproach

So, what are we doing about this? At BotOracle, we’re taking meaningful steps toward solving these challenges.

Memory That Matters: Unlike traditional thread-based models, BotOracle’s memory isn’t an afterthought. We’ve developed a dual-layer memory system:

• Generated Memory: Adaptive, self-pruning, and contextually aware. It learns what’s important and discards the rest.
• Logical Memory: Holds structured data like variables, schedules, and files. Users have complete control, with full transparency over what’s stored and why.

Real-Time Optimization Using Engines: We don’t solely rely on generative AI. Our Solving Engine and Logic Engine work in tandem to refine outputs in real time. The Solving Engine understands your intent while the Logic Engine makes sure that what gets delivered actually fits into a structured, predictable workflow.

Interoperability: We’re not just creating a chatbot that remembers your last question. Our robots are built to be fully interoperable. Want to automate a task, pull data from your CRM, or run scripts?

BotOracle’s Automations work like digital Legos. Each piece snaps together to build something truly scalable.

Final Thoughts

o1 is a big deal — it’s a step forward in creating more intelligent, context-aware AIs. But it is absolutely not AGI. Until we crack real-time learning and interoperability, we’re not even close.

At BotOracle, we’re building towards that future, one thoughtful step at a time. If you’re curious about how we’re pushing the boundaries of AI, check out our Developer Ambassador Program at www.botoracle.com/developers.

References:

• Calibration study on GPT-4 Turbo
• Stanford study on universal memory