#AI reasoning models

[jcmarchi]

What the Launch of OpenAI’s o1 Model Tells Us About Their Changing AI Strategy and Vision

New Post has been published on https://thedigitalinsider.com/what-the-launch-of-openais-o1-model-tells-us-about-their-changing-ai-strategy-and-vision/

What the Launch of OpenAI’s o1 Model Tells Us About Their Changing AI Strategy and Vision

OpenAI, the pioneer behind the GPT series, has just unveiled a new series of AI models, dubbed o1, that can “think” longer before they respond. The model is developed to handle more complex tasks, particularly in science, coding, and mathematics. Although OpenAI has kept much of the model’s workings under wraps, some clues offer insight into its capabilities and what it may signal about OpenAI’s evolving strategy. In this article, we explore what the launch of o1 might reveal about the company’s direction and the broader implications for AI development.

Unveiling o1: OpenAI’s New Series of Reasoning Models

The o1 is OpenAI’s new generation of AI models designed to take a more thoughtful approach to problem-solving. These models are trained to refine their thinking, explore strategies, and learn from mistakes. OpenAI reports that o1 has achieved impressive gains in reasoning, solving 83% of problems in the International Mathematics Olympiad (IMO) qualifying exam—compared to 13% by GPT-4o. The model also excels in coding, reaching the 89th percentile in Codeforces competitions. According to OpenAI, future updates in the series will perform on par with PhD students across subjects like physics, chemistry, and biology.

OpenAI’s Evolving AI Strategy

OpenAI has emphasized scaling models as the key to unlocking advanced AI capabilities since its inception. With GPT-1, which featured 117 million parameters, OpenAI pioneered the transition from smaller, task-specific models to expansive, general-purpose systems. Each subsequent model—GPT-2, GPT-3, and the latest GPT-4 with 1.7 trillion parameters—demonstrated how increasing model size and data can lead to substantial improvements in performance.

However, recent developments indicate a significant shift in OpenAI’s strategy for developing AI. While the company continues to explore scalability, it is also pivoting towards creating smaller, more versatile models, as exemplified by ChatGPT-4o mini. The introduction of ‘longer thinking’ o1 further suggests a departure from the exclusive reliance on neural networks’ pattern recognition capabilities towards sophisticated cognitive processing.

From Fast Reactions to Deep Thinking

OpenAI states that the o1 model is specifically designed to take more time to think before delivering a response. This feature of o1 seems to align with the principles of dual process theory, a well-established framework in cognitive science that distinguishes between two modes of thinking—fast and slow.

In this theory, System 1 represents fast, intuitive thinking, making decisions automatically and intuitively, much like recognizing a face or reacting to a sudden event. In contrast, System 2 is associated with slow, deliberate thought used for solving complex problems and making thoughtful decisions.

Historically, neural networks—the backbone of most AI models—have excelled at emulating System 1 thinking. They are quick, pattern-based, and excel at tasks that require fast, intuitive responses. However, they often fall short when deeper, logical reasoning is needed, a limitation that has fueled ongoing debate in the AI community: Can machines truly mimic the slower, more methodical processes of System 2?

Some AI scientists, such as Geoffrey Hinton, suggest that with enough advancement, neural networks could eventually exhibit more thoughtful, intelligent behavior on their own. Other scientists, like Gary Marcus, argue for a hybrid approach, combining neural networks with symbolic reasoning to balance fast, intuitive responses and more deliberate, analytical thought. This approach is already being tested in models like AlphaGeometry and AlphaGo, which utilize neural and symbolic reasoning to tackle complex mathematical problems and successfully play strategic games.

OpenAI’s o1 model reflects this growing interest in developing System 2 models, signaling a shift from purely pattern-based AI to more thoughtful, problem-solving machines capable of mimicking human cognitive depth.

Is OpenAI Adopting Google’s Neurosymbolic Strategy?

For years, Google has pursued this path, creating models like AlphaGeometry and AlphaGo to excel in complex reasoning tasks such as those in the International Mathematics Olympiad (IMO) and the strategy game Go. These models combine the intuitive pattern recognition of neural networks like large language models (LLMs) with the structured logic of symbolic reasoning engines. The result is a powerful combination where LLMs generate rapid, intuitive insights, while symbolic engines provide slower, more deliberate, and rational thought.

Google’s shift towards neurosymbolic systems was motivated by two significant challenges: the limited availability of large datasets for training neural networks in advanced reasoning and the need to blend intuition with rigorous logic to solve highly complex problems. While neural networks are exceptional at identifying patterns and offering possible solutions, they often fail to provide explanations or handle the logical depth required for advanced mathematics. Symbolic reasoning engines address this gap by giving structured, logical solutions—albeit with some trade-offs in speed and flexibility.

By combining these approaches, Google has successfully scaled its models, enabling AlphaGeometry and AlphaGo to compete at the highest level without human intervention and achieve remarkable feats, such as AlphaGeometry earning a silver medal at the IMO and AlphaGo defeating world champions in the game of Go. These successes of Google suggest that OpenAI may adopt a similar neurosymbolic strategy, following Google’s lead in this evolving area of AI development.

o1 and the Next Frontier of AI

Although the exact workings of OpenAI’s o1 model remain undisclosed, one thing is clear: the company is heavily focusing on contextual adaptation. This means developing AI systems that can adjust their responses based on the complexity and specifics of each problem. Instead of being general-purpose solvers, these models could adapt their thinking strategies to better handle various applications, from research to everyday tasks.

One intriguing development could be the rise of self-reflective AI. Unlike traditional models that rely solely on existing data, o1’s emphasis on more thoughtful reasoning suggests that future AI might learn from its own experiences. Over time, this could lead to models that refine their problem-solving approaches, making them more adaptable and resilient.

OpenAI’s progress with o1 also hints at a shift in training methods. The model’s performance in complex tasks like the IMO qualifying exam suggests we may see more specialized, problem-focused training. This ability could result in more tailored datasets and training strategies to build more profound cognitive abilities in AI systems, allowing them to excel in general and specialized fields.

The model’s standout performance in areas like mathematics and coding also raises exciting possibilities for education and research. We could see AI tutors that provide answers and help guide students through the reasoning process. AI might assist scientists in research by exploring new hypotheses, designing experiments, or even contributing to discoveries in fields like physics and chemistry.

The Bottom Line

OpenAI’s o1 series introduces a new generation of AI models crafted to address complex and challenging tasks. While many details about these models remain undisclosed, they reflect OpenAI’s shift towards deeper cognitive processing, moving beyond mere scaling of neural networks. As OpenAI continues to refine these models, we may enter a new phase in AI development where AI performs tasks and engages in thoughtful problem-solving, potentially transforming education, research, and beyond.

[redactedshapes]

PARTY IN THE OLDEST HOUSE GUUUYYYYYS

There it is, eight months in the making.

Given the size of this file and the amount of details, I've included more close-ups and a download link to a 2k file over here:

big thanks to @wankernumberniiiiiiiiine, she's the reason this painting exists 🥰

[pigeonphd]

nooooo dude you dont get it when i draw upon my lifetime's worth of observations of other peoples art and then synthesize the patterns i noticed into an original yet derived work of art its beautiful but when a computer does it its copyright infringement

[harrowing-of-hell]

i think the big thing is that there just isn't any way to ethically create ai generated content, at least with the way training ai models currently works

for the sake of conciseness, lets just focus on the amount of labor required to produce the images text-to-image ai models are trained on

theoretically, you could hire a bunch of artists whose jobs are to create art to feed into an algorithm and train it. there's no wage theft here.

in theory, that could work.

reality is, these models are trained on so many images that there is no way to do this ethically.

for example, DALL-E, which is a text-to-image model developed by OpenAI, was trained on 250 million images

to pay the labor force that would be required to even produce that many images... ignoring the amount of time that would take even with thousands of artists, there's just literally no fucking way.

this is precisely why these ai models, both text-to-image ai art generators similar to DALL-E and LLMs like ChatGPT, resort to scraping the internet for data to train their models on. they have no other option besides just... not making the model to begin with.

the only way to realistically create a good ai model meant to function like these two examples is to resort to unethical methods

and again, this is ignoring all the other ethical concerns with ai generated content! this is the reality of JUST training these models to begin with!

[frank-olivier]

Rethinking AI Research: The Paradigm Shift of OpenAI’s Model o1

The unveiling of OpenAI's model o1 marks a pivotal moment in the evolution of language models, showcasing unprecedented integration of reinforcement learning and Chain of Thought (CoT). This synergy enables the model to navigate complex problem-solving with human-like reasoning, generating intermediate steps towards solutions.

OpenAI's approach, inferred to leverage either a "guess and check" process or the more sophisticated "process rewards," epitomizes a paradigm shift in language processing. By incorporating a verifier—likely learned—to ensure solution accuracy, the model exemplifies a harmonious convergence of technologies. This integration addresses the longstanding challenge of intractable expectation computations in CoT models, potentially outperforming traditional ancestral sampling through enhanced rejection sampling and rollout techniques.

The evolution of baseline approaches, from ancestral sampling to integrated generator-verifier models, highlights the community's relentless pursuit of efficiency and accuracy. The speculated merge of generators and verifiers in OpenAI's model invites exploration into unified, high-performance architectures. However, elucidating the precise mechanisms behind OpenAI's model and experimental validations remain crucial, underscoring the need for collaborative, open-source endeavors.

A shift in research focus, from architectural innovations to optimizing test-time compute, underscores performance enhancement. Community-driven replication and development of large-scale, RL-based systems will foster a collaborative ecosystem. The evaluative paradigm will also shift, towards benchmarks assessing step-by-step solution provision for complex problems, redefining superhuman AI capabilities.

Speculations on Test-Time Scaling (Sasha Rush, November 2024)

Friday, November 15, 2024

[endreal]

brb, hooking an ai system trained to write scripts to an ai system trained to produce video from textual input and an ai system that generates descriptions of video content as a research exercise in identifying the most prevalent tropes and plot beats in modern cinema by (manually) cross-comparing discrete productions once content has stabilized at statistically significant similarity.

[r3dblccd]

Wait, so is the voice behind Naevis was created based on differents voice actors? And it's not an actual trainee's voice that's debuting?

[mellorocket]

Man I feel like I could write a whole essay about Nope rn and I am so tempted!

[dihalect]

anti 'ai' people make 1 (one) well-informed, good-faith argument challenge(impossible)

[chipped-chimera]

Sometimes I contemplate making smut fanart/content, but then I remember we live in a puritan tech dystopia and where the everloving fuck would I post it these days?

[jcmarchi]

Beyond Chain-of-Thought: How Thought Preference Optimization is Advancing LLMs

New Post has been published on https://thedigitalinsider.com/beyond-chain-of-thought-how-thought-preference-optimization-is-advancing-llms/

Beyond Chain-of-Thought: How Thought Preference Optimization is Advancing LLMs

A groundbreaking new technique, developed by a team of researchers from Meta, UC Berkeley, and NYU, promises to enhance how AI systems approach general tasks. Known as “Thought Preference Optimization” (TPO), this method aims to make large language models (LLMs) more thoughtful and deliberate in their responses.

The collaborative effort behind TPO brings together expertise from some of the leading institutions in AI research. 

The Mechanics of Thought Preference Optimization

At its core, TPO works by encouraging AI models to generate “thought steps” before producing a final answer. This process mimics human cognitive processes, where we often think through a problem or question before articulating our response. 

The technique involves several key steps:

The model is prompted to generate thought steps before answering a query.

Multiple outputs are created, each with its own set of thought steps and final answer.

An evaluator model assesses only the final answers, not the thought steps themselves.

The model is then trained through preference optimization based on these evaluations.

This approach differs significantly from previous techniques, such as Chain-of-Thought (CoT) prompting. While CoT has been primarily used for math and logic tasks, TPO is designed to have broader utility across various types of queries and instructions. Furthermore, TPO doesn’t require explicit supervision of the thought process, allowing the model to develop its own effective thinking strategies.

Another key difference is that TPO overcomes the challenge of limited training data containing human thought processes. By focusing the evaluation on the final output rather than the intermediate steps, TPO allows for more flexible and diverse thinking patterns to emerge.

Experimental Setup and Results

To test the effectiveness of TPO, the researchers conducted experiments using two prominent benchmarks in the field of AI language models: AlpacaEval and Arena-Hard. These benchmarks are designed to evaluate the general instruction-following capabilities of AI models across a wide range of tasks.

The experiments used Llama-3-8B-Instruct as a seed model, with different judge models employed for evaluation. This setup allowed the researchers to compare the performance of TPO against baseline models and assess its impact on various types of tasks.

The results of these experiments were promising, showing improvements in several categories:

Reasoning and problem-solving: As expected, TPO showed gains in tasks requiring logical thinking and analysis. 

General knowledge: Interestingly, the technique also improved performance on queries related to broad, factual information. 

Marketing: Perhaps surprisingly, TPO demonstrated enhanced capabilities in tasks related to marketing and sales. 

Creative tasks: The researchers noted potential benefits in areas such as creative writing, suggesting that “thinking” can aid in planning and structuring creative outputs.

These improvements were not limited to traditionally reasoning-heavy tasks, indicating that TPO has the potential to enhance AI performance across a broad spectrum of applications. The win rates on AlpacaEval and Arena-Hard benchmarks showed significant improvements over baseline models, with TPO achieving competitive results even when compared to much larger language models.

However, it’s important to note that the current implementation of TPO showed some limitations, particularly in mathematical tasks. The researchers observed that performance on math problems actually declined compared to the baseline model, suggesting that further refinement may be necessary to address specific domains.

Implications for AI Development

The success of TPO in improving performance across various categories opens up exciting possibilities for AI applications. Beyond traditional reasoning and problem-solving tasks, this technique could enhance AI capabilities in creative writing, language translation, and content generation. By allowing AI to “think” through complex processes before generating output, we could see more nuanced and context-aware results in these fields.

In customer service, TPO could lead to more thoughtful and comprehensive responses from chatbots and virtual assistants, potentially improving user satisfaction and reducing the need for human intervention. Additionally, in the realm of data analysis, this approach might enable AI to consider multiple perspectives and potential correlations before drawing conclusions from complex datasets, leading to more insightful and reliable analyses.

Despite its promising results, TPO faces several challenges in its current form. The observed decline in math-related tasks suggests that the technique may not be universally beneficial across all domains. This limitation highlights the need for domain-specific refinements to the TPO approach.

Another significant challenge is the potential increase in computational overhead. The process of generating and evaluating multiple thought paths could potentially increase processing time and resource requirements, which may limit TPO’s applicability in scenarios where rapid responses are crucial.

Furthermore, the current study focused on a specific model size, raising questions about how well TPO will scale to larger or smaller language models. There’s also the risk of “overthinking” – excessive “thinking” could lead to convoluted or overly complex responses for simple tasks. 

Balancing the depth of thought with the complexity of the task at hand will be a key area for future research and development.

Future Directions

One key area for future research is developing methods to control the length and depth of the AI’s thought processes. This could involve dynamic adjustment, allowing the model to adapt its thinking depth based on the complexity of the task at hand. Researchers might also explore user-defined parameters, enabling users to specify the desired level of thinking for different applications.

Efficiency optimization will be crucial in this area. Developing algorithms to find the sweet spot between thorough consideration and rapid response times could significantly enhance the practical applicability of TPO across various domains and use cases.

As AI models continue to grow in size and capability, exploring how TPO scales with model size will be crucial. Future research directions may include:

Testing TPO on state-of-the-art large language models to assess its impact on more advanced AI systems 

Investigating whether larger models require different approaches to thought generation and evaluation 

Exploring the potential for TPO to bridge the performance gap between smaller and larger models, potentially making more efficient use of computational resources

This research could lead to more sophisticated AI systems that can handle increasingly complex tasks while maintaining efficiency and accuracy.

The Bottom Line

Thought Preference Optimization represents a significant step forward in enhancing the capabilities of large language models. By encouraging AI systems to “think before they speak,” TPO has demonstrated improvements across a wide range of tasks, potentially revolutionizing how we approach AI development. 

As research in this area continues, we can expect to see further refinements to the technique, addressing current limitations and expanding its applications. The future of AI may well involve systems that not only process information but also engage in more human-like cognitive processes, leading to more nuanced, context-aware, and ultimately more useful artificial intelligence.

[mythyk-art]

Blorbos. Apparently.

[lwoorl]

I'll say it: "Oh all AI artists do is write a stupid description and immediately get an image with no effort, there's no art in that" is the new "Digital painting doesn't count as art because it takes no effort"

[wachi-delectrico]

Tbh i don't know what to think of AI art anymore. I don't find any utility, personally, in centring the discussion on law and copyright; there are far more interesting things to discuss on the topic beyond its use as a replacement for human artists/workforce by the upper class

[reallygroovyninja]

I was working on something for Clextober and here are some Alycia AI generated images that I didn't use.

[medicinemane]

You know, you have the Whorf hypothesis, which talks about how language might effect how we think

I believe one of the things he (or someone else saying similar things) brought up was the idea that:

If we for instance have barrels which used to contain a toxic chemical that's now empty, but the barrel is still dangerous, does lacking a word for "empty but dangerous" influence how we think about or treat this barrel? Would someone be less cautious around it for instance because "empty" implies to an extent that the barrel is back to how it was before it was filled?

Anyway, this is just me establishing a concept here

My thought here is if poorly fitting words may disproportionately warp people's understanding of concepts

I wonder if by using phrases like "artificial intelligence" we don't meaningfully skew perception of "ai" programs towards a thinking program, even among people who have some understanding of how it works (basically rapidly running a number of calculations until it gets an answer it thinks will be good, it's similar to those "having a simulated bird learn to walk" things you'll see, just very fast)

How much do we end up having certain terms basically become poison pills because of how ubiquitous they've become while being almost totally wrong

I'm not even really talking about things like reasonable terms used wrong, like people saying "gaslighting" when they mean "lying"

It really is specifically with terms like "ai" where... well... where I'm afraid we may have done irrevocable damage to public understanding of something, and where... I don't know that there's a way to ever fix it and shift the language used

Just something I'm thinking about tonight