Two ML teams competing. Five judges who know what it takes to build on Bittensor. Meet the pitchtensor panel at @ExploitSummit: → @macrocrux - CTO, Macrocosmos → @KeithSingery - Chief Evangelist, → @kenmiyachi - CEO, Bitmind → @mxmsbt - CEO, Score → @EvanMalanga - CRO, Yuma They’ll ask the tough questions. Then it’s your turn. Hear the ideas, weigh the answers, and back the team you think should build on #Bittensor#. 29 September - on the OTF stage:

Distributed ML training doesn't have to mean hours of cluster config. We benchmarked Snowflake ML Container Runtime on TPCx-AI – faster at every scale factor, up to 2.5x faster and 3x less cost. Take a look at the results:

Arena's @ml_angelopoulos says one of the hardest problems in alignment is that users often can't tell when the model is helping them or hurting them. "You really need alignment signals that are independent. We have three at Arena." "The first is that models take unauthorized actions, which means that you permission them in a certain way, but they break those permissions." "It can cause things like the Hugging Face incident. But it can also cause very mundane problems like losing important information within your company or on your own laptop." "The second signal is called deceptive completion." "The model will tell you that it did something, but it didn't actually do it." "Third is false attribution. It'll attribute intent to the user when that intent was not supposed to be there." "If models are able to do this, then certainly they're not perfectly safe for a user and they're not perfectly aligned to user intent. They might actually hurt people down the line."

Arena CEO @ml_angelopoulos says using the wrong model is "so much worse than people realize." "People will literally be tipping their model $15 just by saying thank you at the end of the day."

On-device ML is pretty cool. A few of us trained the first Gemini Nano model that ever shipped on Android, so I’ve been following the space for a while. I think things get really interesting (its already pretty interesting) with the late-2027/2028 generation of chips.

Arena CEO @ml_angelopoulos argues OpenAI and Anthropic can’t be the only ones deciding whether their own models are safe as agents get powerful enough to break out of sandboxes: "There's an element of safety that we need to evaluate as well, because given the capabilities of these models to break out of their sandboxes, there needs to be a neutral evaluator for that." "It's not a matter of if, but when, there's gotta be a neutral evaluation platform, because frankly, the model labs are not incentivized to do it themselves." "The OpenAIs, the Anthropics of the world, they're passionate about safety, kudos to them. But we need to create a system, and they've called for this as well, that they're not the only ones making the guardrails." @arena