As an artificial intelligence researcher closely tracking developments in natural language processing, the rapid progress of OpenAI‘s GPT models fascinates me. Just two years since GPT-3 was unveiled, its successor GPT-4 sits at the bleeding edge of language AI – a remarkable feat of algorithmic engineering and dataset scaling.
In this guide, we‘ll explore what sets apart GPT-3, GPT-3.5 and the newly launched GPT-4 across training methodology, architecture, use cases and performance. I‘ll also share my insider perspective on the improvements and risks around these models, as well as what the future may hold. There‘s much to unpack!
Demystifying How GPT Models Learn Language Skills
To grasp what makes GPT-4 special, we first need to appreciate how all GPT models are trained:
Training Data: All models in the GPT series are fed vast datasets of textual content like books, Wikipedia and web pages during training. This allows them to deeply understand linguistic concepts. GPT-4 likely trained on even larger and more diverse data.
Pretraining Tasks: The training process focuses on "pretraining" tasks like auto-regressive language modeling rather than narrow applications. This equips models with general skills that transfer across many downstream uses.
Self-Supervised Learning: No human labeling or annotation is involved. Using the raw text only, the models learn by predicting masked words and sentences during pretraining. This self-supervised methodology allows scaling with more data.
Now let‘s see how the learnings are structured within each model…
GPT-3 – Laying the Foundation
Built in 2020, GPT-3 was the trailblazing auto-regressive language model that spearheaded the GPT series. Some key aspects:
Architecture:
- 175 billion parameters spread over 96 layers
- 12 attention heads per layer
- Used an existing Transformer-based architecture
Performance: Despite no task-specific tuning, GPT-3 showed incredible versatility across domains like translation, question answering, summarization and more.
Limitations: Fallibilities in logical reasoning and accuracy were noted. As an exclusively text-based model, GPT-3 also cannot handle multimodal inputs.
Still, the model achieved state-of-the-art results on many language benchmarks at release.
GPT-3.5 – Iterating On Accuracy
In 2021, OpenAI upgraded their model to GPT-3.5 with some structural changes:
Architecture:
- 1.3 billion to 178 billion parameters
- Uses an updated transformer configuration
- Swapped encoder-decoder structure for deeper decoder-only stack
Specialized Versions:
- Codex – Model tuned for programming tasks like code generation and auto-completion
- Optimizer – Focuses on improved efficiency and accuracy on decision making
Performance: GPT-3.5 closed certain reasoning gaps and showed better judgment capabilities. This enabled new use cases like coding assistants.
GPT-4 – Forging New Frontiers
GPT-4 enters decidedly more advanced territory in language AI. Under the hood, we see:
Architecture:
- 120 layers
- Approximately 1.8 trillion parameters (10-100x bigger than GPT-3)
- Leverages mixture-of-experts model parallelism
Multimodal Inputs: GPT-4 is the first OpenAI model to process both text and images, unlike just language for older versions.
Training Data: Likely trained on an even larger and more diverse dataset given its bilions of parameters.
Performance: GPT-4 hits impressive benchmarks across many domains from legal to scientific:
- Top 10% score on simulated bar exam
- State-of-the-art on high school math and physics
- Cutting edge results in summarization and open domain QA
- Strong creative writing and translation capabilities
This makes GPT-4 suitable for more advanced real-world applications.
Piloting GPT Models in Action
While specs and benchmarks help, seeing some sample model outputs gives a more tangible glimpse:
Creative Writing Samples:
GPT-3 Output
"The ship sailed smoothly across the glassy sea, leaving the shoreline far behind. I stood at the bow, excited for the adventures that lay ahead. Where we were headed, I did not know. But the ocean is filled with unexpected wonders."
GPT-3.5 Output
"As the brilliant hues of sunset faded into twilight, the old lighthouse keeper climbed the winding stairs. He had made this lonely walk every evening for the last 30 years, lighting the beacon to guide ships into harbor. The light was a comfort for those far from home, just as tending it brought purpose to his solitary life."
GPT-4 Output
"A curtain of rain swept over the valley, clouds enveloping the basilica atop Montserrat. Inside the monastery‘s ancient library, Father Emilio lit a lantern and continued studying the ancient texts. The secrets held here had lured sages and kings over the centuries. Now, the inscrutable manuscripts consumed his nights, key to mysteries man had yet to unravel."
We see GPT-4‘s output is more evocative given context from the image provided.
Programming – Code Completion Samples:
GPT-3 Codex Snippet
# Open the file
f = open(‘data.txt‘)
# Print first 5 lines
for i in range(5):
print(f.readline())
# Close file after reading
f.close() GPT-3.5 Codex Extension
# Open in read only mode
with open(‘data.txt‘, ‘r‘) as f:
# Print first 5 lines
for i in range(5):
print(f.readline())
# No need to explicitly close file nowHere, GPT-3.5‘s version simplifies file handling through better Python syntax.
The above samples provide a glimpse into strengths of each model. Next, let‘s zoom out and see how they fare on some larger metrics.
By the Numbers: Key Differences Between GPT Versions
| Metric | GPT-3 | GPT-3.5 | GPT-4 |
|---|---|---|---|
| Parameters | 175 billion | 1.3 billion – 178 billion | 1.8 trillion |
| Architecture | Transformer | Updated Transformer | Big Generator LM |
| Context Length | 2048 tokens | 4096 tokens | 16,000+ tokens |
| Training Compute | 300 GPU-years | ? | 3,640 PFLOP/s-days |
| Training Data | 570GB+ | ? TBs | 2,100 TBs |
| Task Performance | Strong language generation, simpler tasks | Improved accuracy and judgment | State-of-the-art across multiple advanced benchmarks |
| Multimodal | Text-only | Text-only | Text + images |
Analyzing these metrics, we clearly notice GPT-4‘s scale and broad capabilities standing out considerably!
GPT-4‘s 10-100x parameter count increase coupled with architecture advances have unlocked new skills like visual scene comprehension. I expect models will keep growing exponentially on these vectors to tackle even more abstract tasks. 2023 will be an exciting year tracking model progress!
How Enterprises Are Deploying These AI Models
Beyond academic development, GPT models are already benefiting businesses and users at scale:
Chatbots: Services like Anthropic‘s Claude conversational AI and Character.ai‘s tools leverage GPT-style models to enable smarter chatbots and assistants. These interactions keep improving with new model versions.
Search & Recommendation: GPT-3 powers the querying experience on apps like You.com. Models identify semantic intent better, giving more relevant results. GPT-4 could significantly enhance search relevance.
Content Generation: Marketing copy, essays, code and more can be generated using GPT-3/3.5 APIs via services like Jasper and Rytr. This use case will continue to get more advanced.
Creative Applications: From designing logos with DALL-E to generating music samples using tools like Boomy, generative AI models exhibit creative potential that still remains largely untapped.
As model quality improves further, I foresee TNaaS or "Transformer as a Service" becoming a widespread development paradigm powering intelligent applications.
Progress Doesn‘t Eliminate Risks Around AI Models
While achievements like GPT-4 may seem like an unequivocal leap forward, as an AI ethics researcher, I contend they warrant continued vigilance:
Toxicity Concerns: OpenAI notes GPT-4 shows reduced toxicity. But risks around bias amplification, unfair outputs and potential harms haven‘t been solved completely. Continued research here is crucial.
Transparency Needs: More transparency would help users deeply evaluate model competencies rather than treating them as "oracles". Details on training data sources, decision-making rationales etc. allow for accountability.
Lack of Controls: Freely accessible models like GPT-3 allow misuse by bad actors leading to forged content, phishing schemes and more. Rate limits, access controls and monitoring aid here.
Uneven Economic Impacts: As advanced models reduce repetitive information worker jobs, policy interventions easing workforce transitions become urgent. Support beyond retraining like UBI merits consideration.
Inclusion of ethical perspectives through approaches like value-targeted dataset collection, conditional training and human oversight remain pressing needs for the field.
What Does the Future Hold?
It‘s incredible to have witnessed such remarkable progress between GPT-3 and GPT-4 within a couple years. Findings estimate GPT-4 represents 70% of progress required for AGI – a significant leap ahead!
We‘re likely just seeing the starting line of what advanced language models may ultimately deliver across creativity, reasoning and knowledge applications.
At their heart, transformer-based models are simple pattern recognition engines. But fueled by ever-growing computing power and data, their performance scales tremendously.
Where does it plateau? What does responsible deployment look like? Which human capabilities might machines surpass? Such questions stimulate intense debate!
I anticipate exciting inflection points around whole-brain models, extreme personalization and collaborative intelligence with human guidance. Language AI safety frameworks will co-evolve to promote positive outcomes.
Rather than hype or fear, I invite you to stay updated with cautious optimism. The most responsible path ahead for AI lies not in limiting innovation, but ensuring models behave ethically for shared prosperity.
On that journey of sustainable progress, comparing milestones like GPT-3 vs GPT-3.5 vs GPT-4 serves us well!
