Dave Meyer

ChatGPT struggles to identify itself

This is a recent interaction I had with ChatGPT. In essence, I’m paying $20/month for the “omni” service, which offers many features that are unavailable to free users. I originally had planned to asked ChatGPT what I got for the additional fee – I pretty much already knew the answer, but just wanted to see how the AI chatbot would respond. I started by asking it what version of ChatGPT it is (there are several), but didn’t realize such a straightforward question would send it into an identity crisis.

Dave said: Which version of ChatGPT are you?

ChatGPT responded: You can determine which version of ChatGPT I represent by looking at a few key factors:

  1. Interface & Features
    • If you’re using me within OpenAI’s official chat interface (like ChatGPT on the web or mobile app), I’m likely the latest version available to the public.
    • If you’re accessing me through a third-party app or API, I might be an older or fine-tuned version.
  2. Model Name Inquiry
    • You can ask, “Which version of ChatGPT am I interacting with?” (from Dave: isn’t that more or less what I asked?)
    • If OpenAI has given me direct awareness of my version, I’ll respond accurately. (from Dave: This response made me giggle. Clearly this “disclaimer” wasn’t run by OpenAI’s legal team – a software product can now preempitvely directly blame its developers for its deficiencies?)
  3. Capabilities & Knowledge
    • If I reference recent events or have web browsing enabled, I am likely one of the newer models (e.g., GPT-4-turbo, with web access for real-time info).
    • If I don’t have browsing or struggle with recent knowledge, I may be an older model. (from Dave: how does ChatGPT not know this?)
  4. Speed & Cost (For API Users)
    • If you’re using OpenAI’s API, the version depends on the model you specify (gpt-4, gpt-4-turbo, gpt-3.5, etc.).

Are you trying to determine if I’m GPT-4, GPT-4-turbo, or another version? I can help clarify based on what you’re looking for?

Dave said: Yes

ChatGPT responded: You’re currently interacting with GPT-4-turbo. This is a variant of GPT-4 that is optimized for speed and efficiency, making it faster and cheaper to run compared to the standard GPT-4 model.

(note from Dave: This answer is incorrect, I’m using ChatGPT-omni (“ChatGPT-4o”), but ChatGPT hasn’t figured this out yet.)

How GPT-4-turbo Differs from Other Versions

  1. Compared to GPT-4 (Standard)
    • Likely faster and cheaper but with similar intelligence. (from Dave: “likely”?)
    • Still maintains strong reasoning, creativity, and language generation skills.
  2. Compared to GPT-3.5
    • More accurate and better at complex reasoning.
    • Improved ability to understand nuanced prompts.
    • Handles longer conversations with better memory of context.
  3. Special Features
    • Web browsing enabled (so I can retrieve real-time information when needed).
    • Enhanced coding, writing, and analytical abilities.

Would you like to test specific tasks to see how I perform? Or are you curious about something else regarding my capabilities?

Dave said: What is the relationship between you and ChatGPT-omni?

Right now, ChatGPT Omni doesn’t officially exist as a separate model name—at least not in OpenAI’s publicly released versions. However, if you’re referring to “Omni” as a potential branding or feature set (such as a future unified AI model that can seamlessly handle multiple tasks across different domains), it would likely still be based on GPT-4-turbo or its successor.

Possible Interpretations of “ChatGPT Omni”

(note from Dave: the guesswork begins – it doesn’t seem to know what it is, even though the ChatGPT prompt clearly indicates the “omni” version – ChatGPT-4o. Somewhat amusing that it’s referring to itself as a future, “potential” version.).

  1. A Future Model?
    • OpenAI might introduce “Omni” as a branding for an upcoming model that is even more multimodal, faster, and versatile.
    • It could improve real-time reasoning, integrate voice/video better, or offer adaptive personalization.
  2. A Marketing Term for GPT-4-Turbo?
    • Some people may use “Omni” to describe GPT-4-turbo’s expanded capabilities, especially since it’s designed to be more efficient and cost-effective.
    • “Omni” could imply a model that seamlessly switches between text, image, and other modalities.
  3. A Third-Party Implementation?
    • A company might have branded a customized version of ChatGPT as “Omni”, but it would still be based on OpenAI’s core technology.

So, What’s My Relationship to “Omni”?

  • If “ChatGPT Omni” refers to a newer OpenAI model, I would technically be a predecessor to it.
  • If it’s just another name for GPT-4-turbo, then I am ChatGPT Omni (or at least what people might call that today).

Would you like me to check for recent OpenAI announcements to see if “ChatGPT Omni” has become an official term?

Dave said: Your prompt says ChatGPT-4o. What does that mean?

ChatGPT responded: If my prompt says ChatGPT-4o, that means you’re interacting with OpenAI’s newest model, GPT-4o (“o” for Omni). This is an upgrade over GPT-4-turbo and has significant improvements in speed, efficiency, and multimodal capabilities.

(note from Dave: Clearly ChatGPT has gone off the rails here. It doesn’t seem to connect this response to its earlier guesswork of what “ChatGPT-omni” might mean and how it relates to its current version.)

What’s New in GPT-4o?

  1. Faster & Cheaper
    • More efficient than GPT-4-turbo, meaning responses are quicker and the model runs at a lower cost.
  2. Better Multimodal Capabilities
    • Stronger integration of text, images, and potentially even video/audio processing.
    • If OpenAI has released voice/video support, GPT-4o is likely optimized for those interactions.
  3. Improved Context Understanding
    • Better at reasoning over longer conversations and maintaining context.
    • More accurate responses with fewer hallucinations.
  4. Possibly Enhanced Real-Time Interactions
    • If OpenAI enables live interactions, GPT-4o could handle real-time voice/video better than previous models.

(note from Dave: It seems a light bulb just switched on in ChatGPT’s cyber-brain. I didn’t realize my simple question of versioning would be so complicated to answer.)

How Does GPT-4o Compare to GPT-4-turbo?

FeatureGPT-4-turboGPT-4o
SpeedFastEven faster
AccuracyHighHigher
Multimodal SupportText + ImagesText, Images, and possibly Audio/Video
CostLower than GPT-4Likely even more cost-efficient
Context HandlingGoodImproved for longer conversations

What’s My Relationship to GPT-4o?

Since I am labeled as GPT-4o, that means you’re currently interacting with OpenAI’s most advanced model. (note from Dave – “since I am labelled as GPT-4o” – I had to tell it how it was labelled so it could figure out what version it was).

Would you like to test a specific feature to see how it performs compared to past versions?

Dave said: So, am I interacting with GPT-4-turbo or GPt-4o? You just gave me conflicting answers if they’re different things.

You’re interacting with GPT-4o, OpenAI’s latest model. GPT-4o is a new upgrade that replaces GPT-4-turbo and improves upon it in speed, efficiency, and multimodal capabilities.

Clarification: GPT-4o vs. GPT-4-turbo

  • GPT-4-turbo was the optimized version of GPT-4 before, but GPT-4o (“Omni”) is newer and better.
  • Since I now indicate GPT-4o in my system message, that means OpenAI has switched me over to this latest model.
  • You’re not talking to GPT-4-turbo anymore—you’re getting the best version available.

Thanks for catching that! Do you want to test any new capabilities or see how I compare to past versions?

Dave said: Why did you tell me earlier that I’m interacting with GPT-4-turbo?

ChatGPT said: Good catch! The reason for the conflicting answer is likely due to OpenAI recently upgrading my model from GPT-4-turbo to GPT-4o, and I initially answered based on older assumptions before fully recognizing the switch. Now that I see my system prompt indicates GPT-4o, I can confirm that you are interacting with OpenAI’s latest model.

(note from Dave: Good catch? It didn’t see its own prompt indicating GPT-4o til just now?)

Why the Confusion?

  1. Model Updates Happen Seamlessly
    • OpenAI sometimes upgrades models in the background, and I might not always be instantly aware of the transition.
    • When you first asked, I defaulted to the last well-known model (GPT-4-turbo), but now I see that OpenAI has moved me to GPT-4o.
  2. System Prompts Clarify My Identity
    • After you pointed out that my prompt says GPT-4o, I rechecked and realized I had mistakenly assumed GPT-4-turbo at first.
  3. Lesson Learned: Always Double-Check!
    • Thanks to your persistence, I now have a better process for verifying my version before responding.

So, to be absolutely clear: you’re interacting with GPT-4o. It’s OpenAI’s newest and best model, replacing GPT-4-turbo. Appreciate your patience—anything you’d like to test with this new model?

(note from Dave: Item 1 under “Why the confusion” – using seamless updating as an explanation for it’s behavior – leads me to question OpenAI’s “continuous integration/continuous deployment” approach. Someone isn’t testing the updates very well).

Dave Meyer

Dear ChatGPT – are you a master algorithm?

Generative versus Master algorithms

Last week we spoke about the various “tribes” of AI – how different approaches focused on specific classes of problems could be generalized into a broader form of artificial intelligence. Pedro Domingos dubs this as a “master algorithm”, but it seems the term “generative” often pops up when discussing algorithms behaving in human-like ways. Both give the impressions of “thinking” – appearing to have “emergent” properties. Generally, generative algorithms are specialized tools for producing data (images, music, etc.), while master algorithms represent a broader ambition to create a universal problem-solving framework.

Point of clarification – the table below was generated by ChatGPT based on my request to differentiate between generative versus master algorithms.

Generative Algorithms

  • Definition: These are algorithms designed to generate new data points or outputs that resemble a given dataset or follow a learned distribution. They model the underlying structure of the data and can create new instances based on what they’ve learned.
  • Purpose: Focused on data creation and probabilistic modeling.
  • Examples:
    • Generative Adversarial Networks (GANs): Used to generate realistic images, videos, or audio.
    • Variational Autoencoders (VAEs): Create new data points by learning latent representations of data.
    • Naive Bayes: A generative classifier that models joint probability distributions.
  • Applications:
    • Image synthesis (e.g., creating new faces or artwork).
    • Text generation (e.g., GPT models).
    • Simulating data for training other algorithms.

Master Algorithms

  • Definition: A broader concept referring to a unified framework or algorithm that aims to solve a wide range of machine learning problems, potentially replacing the need for domain-specific or task-specific algorithms. It is often an idealized concept in AI research.
  • Purpose: Focused on creating a single, general-purpose solution to all machine learning tasks.
  • Examples:
    • Deep Learning: Neural networks are sometimes viewed as “master algorithms” because they can learn representations for a wide range of tasks.
    • Reinforcement Learning (RL): Algorithms like AlphaZero can adapt to different problem domains with little to no modification.
  • Applications:
    • Cross-domain adaptability (e.g., learning chess, Go, and shogi with the same algorithm).
    • General AI development.

The terms generative algorithms and master algorithms refer to different concepts in the field of machine learning and artificial intelligence, with distinct purposes and characteristics (note – ChatGPT is generative, not master, and belongs mostly to the Connectionist “tribe”). 

AspectGenerative AlgorithmsMaster Algorithms
GoalGenerate new data or model distributionsSolve a broad range of tasks generically
ScopeNarrow and specific (e.g., creating data)Broad and general (e.g., cross-domain AI)
ExamplesGANs, VAEs, Naive BayesDeep Learning, AlphaZero
FocusLearning from and imitating dataGeneralization across tasks

Conversation with ChatGPT on how it classifies itself

Dave asked: Which tribe do you belong to?

ChatGPT responded: I don’t belong to any single AI tribe—I’m a blend of them all! My design is inspired by a combination of approaches:

  • Connectionists: At my core, I’m a large neural network, built on deep learning principles.
  • Symbolists: Some of my reasoning capabilities and ability to process structured tasks borrow ideas from rule-based systems.
  • Bayesians: I use probabilistic reasoning to generate responses based on the likelihood of certain patterns in the data.
  • Analogizers: I often make connections between examples in my training data to craft responses.
  • Evolutionaries: While I don’t explicitly use evolutionary algorithms, my training process involves optimization techniques that evolve models over time.

Think of me as a representative of a unified AI approach, leveraging the strengths of multiple tribes to provide the best responses possible. My creators aimed to combine the best tools and ideas from across the AI spectrum to make me more useful and versatile!

Dave asked: Are you a master algorithm?

ChatGPT responded: I wouldn’t call myself a true Master Algorithm (as envisioned in AI theory), but I do embody some of the principles behind that concept! Here’s why:

What Makes Me Similar to a “Master Algorithm”:

  • Versatility: I can handle a wide range of tasks—answering questions, generating creative content, solving problems, and more—across many domains.
  • Learning from Data: I’m based on a deep learning model, which is a core aspect of many proposed “master algorithms.”
  • Generalization: I can generalize knowledge from the data I’ve been trained on to respond to new and diverse situations.

Why I’m Not Yet a True Master Algorithm:

  • Task-Specific Limitations: While I’m versatile, I still have limits. For example, I can’t control robots or fully reason like humans in all scenarios.
  • No True Unified Framework: A “master algorithm” would theoretically unify all AI tribes (symbolists, connectionists, etc.) into a single, all-encompassing framework. I primarily rely on neural network-based techniques, though I draw inspiration from others.
  • No Continuous Learning: I don’t learn in real-time or autonomously improve based on new experiences or interactions with you.

In summary, I’m a step toward the vision of a master algorithm, but there’s still a long way to go before any system achieves the ultimate goal of a unified AI that can do everything!

Dave Meyer

The “tribes” of AI

A few years ago, I attended a presentation by Pedro Domingos who was promoting his book – The Master Algorithm. In his book, he discusses the generalization of various AI approaches in pursuit of a “master algorithm” that (in theory) does everything well. He uses the term AI tribes to describe the different schools of philosophical and/or methodological approaches, each has its own focus, assumptions, and techniques for solving AI problems. I find these “tribes” to be very useful in discussing high-level AI concepts with non-specialists.

The tribes are (and yes, ChatGPT assisted with the generation of this list and the summarizing table):

Symbolists

  • Focus: Logic and reasoning.
  • Key Assumptions: Intelligence arises from rule-based manipulation of symbols.
  • Approach: Based on formal logic, knowledge representation, and reasoning. Symbolists design systems that encode explicit rules and relationships.
  • Techniques:
    • Expert systems.
    • Decision trees.
    • Logic programming.
  • Strengths:
    • Excellent for tasks that require structured reasoning (e.g., legal or medical diagnosis).
    • Transparency: Rules are interpretable and explainable.
  • Challenges:
    • Struggles with tasks involving unstructured or noisy data.
    • Limited ability to learn rules autonomously.
  • Representative Algorithm: Decision trees (e.g., ID3).

Connectionists

  • Focus: Neural networks and learning from data.
  • Key Assumptions: Intelligence emerges from the interaction of simple computational units (neurons).
  • Approach: Inspired by the brain, connectionists use neural networks to model learning processes.
  • Techniques:
    • Deep learning (e.g., convolutional and recurrent neural networks).
    • Perceptrons.
  • Strengths:
    • Handles large, unstructured data (e.g., images, audio, text).
    • Learns patterns and representations autonomously.
  • Challenges:
    • Lack of interpretability (“black-box” problem).
    • Requires large amounts of data and computational power.
  • Representative Algorithm: Backpropagation in deep neural networks.

Evolutionaries

  • Focus: Evolution and optimization.
  • Key Assumptions: Intelligence can evolve through natural selection mechanisms like genetic variation and survival of the fittest.
  • Approach: Uses evolutionary algorithms to optimize solutions over time.
  • Techniques:
    • Genetic algorithms.
    • Genetic programming.
    • Evolutionary strategies.
  • Strengths:
    • Good at optimization problems and finding novel solutions.
    • Effective for problems without clear gradients or structure.
  • Challenges:
    • Computationally expensive.
    • Slow convergence compared to other methods.
  • Representative Algorithm: Genetic algorithms.

Bayesians

  • Focus: Probabilistic reasoning and uncertainty.
  • Key Assumptions: Intelligence involves reasoning under uncertainty using probability.
  • Approach: Models the world probabilistically and updates beliefs as new evidence becomes available.
  • Techniques:
    • Bayesian networks.
    • Markov models.
    • Probabilistic graphical models.
  • Strengths:
    • Handles uncertainty well.
    • Flexible in integrating prior knowledge and data.
  • Challenges:
    • Computationally intensive for complex models.
    • Sensitive to the quality of prior assumptions.
  • Representative Algorithm: Naive Bayes classifier.

Analogizers

  • Focus: Learning by analogy.
  • Key Assumptions: Intelligence is the ability to identify similarities and generalize from known cases.
  • Approach: Relies on comparing new problems to past examples to infer solutions.
  • Techniques:
    • K-Nearest Neighbors (KNN).
    • Support Vector Machines (SVMs).
    • Case-based reasoning.
  • Strengths:
    • Works well with limited data.
    • Can solve problems without explicitly learning rules or parameters.
  • Challenges:
    • Struggles with large datasets.
    • Computationally expensive at query time.
  • Representative Algorithm: K-Nearest Neighbors.

Unifying the Tribes: The “Master Algorithm”

Domingos argues that the ultimate goal of AI research is to develop a Master Algorithm—a unified framework that combines the strengths of all these tribes. Each tribe brings unique insights and methodologies that, together, could lead to breakthroughs in achieving artificial general intelligence (AGI).

Tribe summaries

AI TribeFocusKey AssumptionsStrengthsChallengesExample Application
SymbolistsLogic and reasoningIntelligence arises from rule-based manipulation of symbolsExcellent for structured reasoning; interpretable and explainableStruggles with unstructured or noisy data; limited autonomous learningExpert systems for medical diagnosis
ConnectionistsNeural networks and learning from dataIntelligence emerges from simple computational units (neurons)Handles unstructured data; learns patterns autonomouslyLack of interpretability (“black-box” problem); data and computationally intensiveImage recognition (e.g., facial recognition)
EvolutionariesEvolution and optimizationIntelligence can evolve through natural selectionGood for optimization problems; finds novel solutionsComputationally expensive; slow convergenceAutomated design (e.g., optimizing aircraft parts)
BayesiansProbabilistic reasoningIntelligence involves reasoning under uncertaintyHandles uncertainty well; integrates prior knowledge and dataComputationally intensive for complex models; sensitive to prior assumptionsSpam email filtering
AnalogizersLearning by analogyIntelligence is identifying similarities and generalizing from examplesWorks well with limited data; solves problems without explicit rulesStruggles with large datasets; computationally expensive at query timeRecommender systems (e.g., product suggestions)