MAROKO133 Breaking ai: OpenAI experiment finds that sparse models could give AI builders t

📌 MAROKO133 Update ai: OpenAI experiment finds that sparse models could give AI bu

OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises with a better understanding of how these models make decisions. 

Understanding how models choose to respond, a big selling point of reasoning models for enterprises, can provide a level of trust for organizations when they turn to AI models for insights. 

The method called for OpenAI scientists and researchers to look at and evaluate models not by analyzing post-training performance, but by adding interpretability or understanding through sparse circuits.

OpenAI notes that much of the opacity of AI models stems from how most models are designed, so to gain a better understanding of model behavior, they must create workarounds. 

“Neural networks power today’s most capable AI systems, but they remain difficult to understand,” OpenAI wrote in a blog post. “We don’t write these models with explicit step-by-step instructions. Instead, they learn by adjusting billions of internal connections or weights until they master a task. We design the rules of training, but not the specific behaviors that emerge, and the result is a dense web of connections that no human can easily decipher.”

To enhance the interpretability of the mix, OpenAI examined an architecture that trains untangled neural networks, making them simpler to understand. The team trained language models with a similar architecture to existing models, such as GPT-2, using the same training schema. 

The result: improved interpretability. 

The path toward interpretability

Understanding how models work, giving us insight into how they're making their determinations, is important because these have a real-world impact, OpenAI says.  

The company defines interpretability as “methods that help us understand why a model produced a given output.” There are several ways to achieve interpretability: chain-of-thought interpretability, which reasoning models often leverage, and mechanistic interpretability, which involves reverse-engineering a model’s mathematical structure.

OpenAI focused on improving mechanistic interpretability, which it said “has so far been less immediately useful, but in principle, could offer a more complete explanation of the model’s behavior.”

“By seeking to explain model behavior at the most granular level, mechanistic interpretability can make fewer assumptions and give us more confidence. But the path from low-level details to explanations of complex behaviors is much longer and more difficult,” according to OpenAI. 

Better interpretability allows for better oversight and gives early warning signs if the model’s behavior no longer aligns with policy. 

OpenAI noted that improving mechanistic interpretability “is a very ambitious bet,” but research on sparse networks has improved this. 

How to untangle a model 

To untangle the mess of connections a model makes, OpenAI first cut most of these connections. Since transformer models like GPT-2 have thousands of connections, the team had to “zero out” these circuits. Each will only talk to a select number, so the connections become more orderly.

Next, the team ran “circuit tracing” on tasks to create groupings of interpretable circuits. The last task involved pruning the model “to obtain the smallest circuit which achieves a target loss on the target distribution,” according to OpenAI. It targeted a loss of 0.15 to isolate the exact nodes and weights responsible for behaviors. 

“We show that pruning our weight-sparse models yields roughly 16-fold smaller circuits on our tasks than pruning dense models of comparable pretraining loss. We are also able to construct arbitrarily accurate circuits at the cost of more edges. This shows that circuits for simple behaviors are substantially more disentangled and localizable in weight-sparse models than dense models,” the report said. 

Small models become easier to train

Although OpenAI managed to create sparse models that are easier to understand, these remain significantly smaller than most foundation models used by enterprises. Enterprises increasingly use small models, but frontier models, such as its flagship GPT-5.1, will still benefit from improved interpretability down the line. 

Other model developers also aim to understand how their AI models think. Anthropic, which has been researching interpretability for some time, recently revealed that it had “hacked” Claude’s brain — and Claude noticed. Meta also is working to find out how reasoning models make their decisions. 

As more enterprises turn to AI models to help make consequential decisions for their business, and eventually customers, research into understanding how models think would give the clarity many organizations need to trust models more. 

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif ai: ChatGPT Group Chats are here … but not for everyone (yet

It was originally found in leaked code and publicized by AI influencers on X, but OpenAI has made it official: ChatGPT now offers Group Chats, allowing multiple users to join the same, single ChatGPT conversation and send messages to each other and the underlying large language model (LLM), online and via its mobile apps.

Imagine adding ChatGPT as another member of your existing group chats, allowing you to text it as you would one of your friends or family members and have them respond as well, and you'll have an idea of the intriguing power and potential of this feature.

However, the feature is only available as a limited pilot for now to ChatGPT users in Japan, New Zealand, South Korea, and Taiwan (all tiers, including free usage).

“Group chats are just the beginning of ChatGPT becoming a shared space to collaborate and interact with others,” OpenAI wrote in its announcement.

This development builds on internal experimentation at OpenAI, where technical staffer Keyan Zhang said in a post on X that OpenAI's team initially considered multiplayer ChatGPT to be “a wild, out-of-distribution idea.”

According to Zhang, the model’s performance in those early tests demonstrated far more potential than existing interfaces typically allow.

The move follows OpenAI investor yet competitor Microsoft's update of its Copilot AI assistant to allow group chats last month, as well as Anthropic's introduction of shareable context and chat histories from its Claude AI models through its Projects feature introduced summer 2024, though this is not a simultaneous, realtime group chat in the same way.

Collaborative functionality integrated into ChatGPT

Group chats function as shared conversational spaces where users can plan events, brainstorm ideas, or collaborate on projects with the added support of ChatGPT.

These conversations are distinct from individual chats and are excluded from ChatGPT’s memory system—meaning no data from these group threads is used to train or personalize future interactions.

Users can initiate a group chat by selecting the people icon in a new or existing conversation. Adding others creates a copy of the original thread, preserving the source dialogue. Participants can join via a shareable link and are prompted to create a profile with a name, username, and photo. The feature supports 1 to 20 participants per group.

Each group chat is listed in a new section of the ChatGPT interface, and users can manage settings like naming the group, adding or removing participants, or muting notifications.

Powered by GPT-5.1 with expanded tools

The new group chat feature runs on GPT-5.1 Auto, a backend setting that chooses the optimal model based on the user’s subscription tier and the prompt.

Functionality such as search, image generation, file upload, and dictation is available inside group conversations.

Importantly, the system applies rate limits only when ChatGPT is producing responses. Direct messages between human users in the group do not count toward any plan’s message cap.

OpenAI has added new social features to ChatGPT in support of this group dynamic. The model can react with emojis, interpret conversational context to decide when to respond, and personalize generated content using members’ profile photos—such as inserting user likenesses into images when asked.

Privacy by default, controls for younger users

OpenAI emphasized that privacy and user control are integral to group chat design. The feature operates independently of the user’s personalized ChatGPT memory, and no new memories are created from these interactions.

Participation requires an invitation link, and members are always able to see who is in a chat or leave at any time.

Users under the age of 18 are automatically shielded from sensitive content in group chats. Parents or guardians can disable group chat access altogether via built-in parental controls.

Group creators retain special permissions, including immunity from being removed by others. All other participants can be added or removed by group members.

A testbed for shared AI experiences

OpenAI frames group chats as an early step toward richer, multi-user applications of AI, hinting at broader ambitions for ChatGPT as a shared workspace. The company expects to expand access over time and refine the feature based on how early users engage with it.

Keyan Zhang’s post suggests that the underlying model capabilities are far ahead of the interfaces users currently interact with. This pilot, in OpenAI’s view, offers a new “container” where more of the model’s latent capacity can be surfaced.

“Our models have a lot more room to shine than today’s experiences show, and the current containers only use a fraction of their capabilities,” Zhang said.

With this initial pilot focused on a limited set of markets, OpenAI is likely monitoring both usage patterns and cultural fit as it plans for broader deployment. For now, the group chat experiment offers a new way for users to interact with ChatGPT—and with each other—in real time, using a conversational interface that blends productivity and personalization.

Developer access: Still unclear

OpenAI has not provided any indication that Group Chats will be accessible via the API or SDK. The current rollout is framed strictly within the ChatGPT product environment, with no mention of tool calls, developer hooks, or integration support for programmatic use. This absence of signaling leaves it unclear whether the company views group interaction as a future developer primitive or as a contained UX feature for end users only.

For enterprise teams exploring how to replicate multi-user collaboration with generative models, any current implementation would require custom orchestration—such as managing multi-party context and prompts across separate API calls, and handling session state and response merging externally. Until OpenAI provides formal support, Group Chats remain a closed interface feature rather than a developer-accessible capability.

Here is a standalone concluding subsection tailored for the article, focusing on what the ChatGPT Group Chat rollout means for enterprise decision makers in both pilot regions and globally:

Implications for enterprise AI and data leaders

For enterprise teams already leveraging AI platforms—or preparing to—OpenAI’s group chat feature introduces a new layer of multi-user collaboration that could shift how generative models are deployed across workflows. While the pilot is limited to users in Japan, New Zealand, South Korea, and Taiwan, its design and roadmap offer key signals for AI engineers, orchestration specialists, and data leads globally.

AI engineers managing large language model (LLM) deployments can now begin to conceptualize real-time, multi-user interfaces not just as support tools, but as collaborative environments for research, content generation, and ideation. This adds another front in model tuning: not just how models respond to individuals, but how they behave in live group settings with context shifts and varied user intentions.

For AI orchestration leads, the ability to integrate ChatGPT into collaborative flows without exposing private memory or requiring custom builds may reduce …

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🔗 Sumber: venturebeat.com