New OpenAI Model 'Imminent' and AI Stakes Get Raised (plus Med Gemini, GPT 2 Chatbot and Scale AI)
AI ExplainedKey Moments
OpenAI's new model is 'imminent,' GPT-2 chatbot caused confusion, Med-Gemini shows AI medical prowess.
Key Insights
OpenAI is reportedly preparing to release a new model, likely GPT-4.5, soon, with a full GPT-5 expected later.
A mysterious GPT-2 chatbot briefly surfaced, causing confusion but its performance was similar to GPT-4 Turbo, suggesting data quality over architecture.
The Scale AI paper highlights data contamination in benchmarks and suggests larger models generalize better, indicating compute and data scale can drive performance.
Google's Med-Gemini demonstrates significant AI capabilities in medical diagnosis, even outperforming doctors in some areas and showing potential as a clinical assistant.
The development in AI models underscores the critical role of high-quality data and significant compute resources in achieving state-of-the-art performance.
Benchmark quality is a growing concern, with issues identified in math reasoning tests (GSM 8K) and potential for models to overfit to benchmark-style data.
OPENAI'S UPCOMING MODEL RELEASE
Insider reports and government officials suggest OpenAI is on the verge of releasing a new model, potentially named GPT-4.5, optimized for reasoning and planning. This imminent release is inferred from OpenAI's commitment to safety testing and Sam Altman’s confident remarks about release dates. Unlike a potential GPT-5, which is predicted for late 2024 or early 2025, GPT-4.5 is expected to be an iterative update, aligning with OpenAI’s philosophy of gradual deployment to avoid societal surprises and allow for user influence on AI systems.
THE MYSTERY OF THE GPT-2 CHATBOT
A recent surge of confusion was fueled by a mysterious GPT-2 chatbot that appeared and was quickly withdrawn. While initially sparking speculation about it being a new OpenAI model like GPT-4.5, testing revealed its performance was comparable to GPT-4 Turbo. The chatbot's ability to generate specific images, like a unicorn, highlighted the quality of its training data, suggesting that advanced performance can be achieved through data rather than solely through architectural innovations, especially for smaller models.
DATA CONTAMINATION AND BENCHMARK RELIABILITY
A paper by Scale AI reveals significant issues with data contamination in AI benchmarks, particularly in mathematical reasoning (GSM 8K). Many models may have encountered benchmark questions during training, skewing their performance metrics. The paper highlights that larger models tend to generalize better, even if exposed to training data. It also points to potential flaws in benchmarks themselves and the possibility that models are inadvertently optimized for benchmark-style data over real-world application.
THE SCALING HYPOTHESIS: DATA AND COMPUTE
The prevailing theme is that significant advancements in AI performance are increasingly driven by the scale of compute power and the quality of training data, rather than just architectural novelty. As demonstrated by Meta's LLaMA 3, and echoed by OpenAI's own insights, brute-forcing performance with vast datasets and computational resources is becoming a viable path to state-of-the-art results. This shifts the focus towards which companies can sustain the massive financial investment required for such scaling.
MED-GEMINI: A BREAKTHROUGH IN MEDICAL AI
Google's Med-Gemini paper showcases impressive AI capabilities in the medical domain. The model demonstrates performance competitive with, and in some areas superior to, doctors in diagnosing diseases. Innovations include confidence scoring, using search to resolve uncertainties, and a novel fine-tuning loop. Med-Gemini's ability to process extensive electronic health records and its potential use in real-time surgical assistance highlight its transformative potential in healthcare, aiming to reduce medical errors and improve patient outcomes.
COMPETITION AND ETHICAL CONSIDERATIONS IN MEDICAL AI
The development of Med-Gemini and Microsoft's Med-Prompt reveals a competitive landscape aimed at advancing medical AI. Google and Microsoft are publicly contrasting their approaches, each claiming superiority. As AI models like Med-Gemini become increasingly capable, questions arise about the ethical implications of not deploying them to assist clinicians, especially given the high rates of medical errors worldwide. The potential for AI to augment or even surpass human medical expertise necessitates careful consideration of equitable and safe deployment.
THE ENERGY AND INFRASTRUCTURE CHALLENGE
The exponential growth in AI model development is placing immense pressure on computational resources, particularly GPUs. While supply constraints are easing, the industry is approaching significant energy constraints. Data center capacity and sustainable energy sources are becoming critical bottlenecks. The continuous demand for more powerful models necessitates substantial investment not only in hardware but also in the underlying infrastructure required to power and cool these increasingly complex AI systems.
FUTURE DIRECTIONS AND OPEN-SOURCE DEBATES
While Med-Gemini represents a significant leap, its proprietary nature raises questions about accessibility and commercial interests. The ongoing debate around open-sourcing AI models versus keeping them proprietary impacts research dissemination and wider societal benefit. As AI progresses, the balance between commercial advantage and the ethical imperative to leverage these powerful tools for global good, particularly in critical fields like medicine, will continue to be a central point of discussion.
Mentioned in This Episode
●Products
●Software & Apps
●Companies
●Organizations
●Studies Cited
●Concepts
●People Referenced
Benchmark Performance Comparison: New vs. Old Questions
Data extracted from this episode
| Model Family | Performance on Original Benchmark | Performance on New Benchmark |
|---|---|---|
| Mistral and Phi | High | Notably Lagged |
| GPT-4 and Claude | High | Same or Better |
Med QA Benchmark Performance
Data extracted from this episode
| Model/Clinician | Performance (with search) | Performance (with search, errors removed) |
|---|---|---|
| Doctor Pass Rate | ~60% | N/A |
| Med Gemini | Outperformed Expert Clinicians | ~93% |
Common Questions
Insider reports suggest an imminent release of new OpenAI models, possibly a GPT-4.5 version optimized for reasoning and planning, with GPT-5 predicted for late 2024 or early 2025.
Topics
Mentioned in this video
A mystery OpenAI model showcased and then withdrawn, tested by the author, whose performance was found to be similar to GPT-4 Turbo.
The location of the AI Safety Summit in Southern England.
Mentioned as an example of a model whose release was significantly delayed, contrasting with the expected imminent release of new OpenAI models.
A model from Mistral that performed the same on a benchmark as Mistral Instruct, despite potentially having seen the benchmark questions.
A small model (3.8 billion parameters) that performed comparably to GPT-4 Turbo on a new benchmark, highlighting the importance of high-quality data.
A summit held in Bletchley where AI companies like Meta and OpenAI promised the UK government they would allow safety testing of their models.
A model from Mistral that underperformed on a new benchmark, suggesting it may not generalize as well as larger models.
Lead author of the Phi series of models, who stated that even smaller models can perform well with high-quality data.
Google's new AI model series that is highly competitive with doctors in providing medical answers and can assist in complex areas like surgery.
A benchmark that assesses an AI's ability to diagnose diseases, where Med Gemini achieved state-of-the-art performance.
A news publication that reported on insider information regarding upcoming OpenAI models and government safety testing.
Of OpenAI, stated that behavior is determined by the data set and that scale compute can lead to state-of-the-art performance.
A benchmark for AI mathematical reasoning designed for high schoolers, found to have errors in its original design.
A previous model from Google, mentioned in the context of a past controversy where it was claimed to be GPT-4.
Microsoft's approach to medical AI, contrasted with Google's Med Gemini approach.
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