Sentient Foundation

Build AI that kills. Nov. 9. 50 teams. $20k Cash Prizes. AGI House. You will build Al Agents that play the conversational game Werewolf (aka Mafia). Each round of Werewolf has night and day. Agents are either werewolves or villagers. Werewolves pick a villager to kill each night, and villagers discuss who to eliminate each day. We have built a tournament orchestrator that will place your agent in hundreds of games against other agents. This first of its kind large scale agent vs. agent tournament presents a new opportunity to test our ability to build Al that can reason, interact and seek the truth. Application closing soon: https://lnkd.in/ePu7jUqN

Open AGI Summit is coming to DevCon in Thailand on November 14. Featuring an incredible roster of speakers, and supported by Google Cloud, Polygon Labs, Sentient, and more. Apply for your ticket while they are still available: https://lu.ma/7s7231aw

Werewolves, wake up. Who do you choose to eliminate today? Reason, deceive, expose. Welcome to the first ever Werewolf Agents Tournament. Teams will build AI Agents that will battle in a giant Werewolf (aka Mafia) Arena at AGI House in Hillsborough. This dynamic environment presents a unique competitive system for testing the advancing capabilities of agents fully built on open source AI models. We believe such environments will be key to the path towards community-built open AGI. Apply if you have what it takes: https://lnkd.in/ePu7jUqN

Reasoning and Privacy: The Case of Open-Source Reasoning is a critical component of AI, enabling systems to make logical deductions and solve complex problems. While open-source models have made significant strides, they still lag behind proprietary models in terms of reasoning capabilities. This gap is particularly evident in tasks that require complex planning, problem-solving, and understanding natural language. Furthermore, the effectiveness of AI depends on both the reasoning capabilities of the models themselves and the quality and relevance of the data they have access to. For instance, one area where AI models can be particularly powerful is in personal assistants. These applications, which can be integrated into smartphones or other devices, can provide personalized assistance based on the user's data and preferences. Given that these models require access to a wide range of personal data, the models processing this data must be hosted securely to respect user privacy. Consequently, there is a growing interest in developing local open-source AI models that can run on personal devices without relying on cloud-based services. This approach can help protect user privacy while still providing the benefits of AI-powered assistance. As mentioned above, local AI models have their limitations. There is a clear gap between open-source and closed-source model quality today, and self-hosted models are also constrained by the locally available computing resources. Creating better and more efficient open-source models is thus a necessity. As AI technology continues to evolve, it will be essential to develop models that are both powerful and privacy-preserving.

In the long run, truly open source AI is infinitely more beneficial than closed AI for humanity. Here's a specific use case to help illustrate why: Imagine a company utilizes OML (Open, Monetizable, Loyal) to create a smart AI chatbot to provide customer service. By OMLizing it, they can then share this chatbot with other businesses to use or build upon for their own purposes. When new customers use this chatbot, the company that created it can receive a portion of the payments. OML helps ensure that the creators of AI models receive credit and proceeds from their efforts. When incentives are properly aligned, individuals can build upon one another's creations to develop a wide array of remixed custom solutions across the digital landscape. At the rate AI is advancing, it is beginning to give us virtual superpowers. OML is vital, because it is what helps protect and fuel the economy of AI entrepreneurs of the future. By continuing to lower the barriers of entry for everyone, we can, in the words of Naval Ravikant — play long term games with long term people. 

🌟 Exciting Job Openings at Sentient Foundation! 🌟 We're thrilled to announce that we have several positions available and are looking for talented individuals to join our team! If you're passionate, motivated, and ready to make an impact, we want to hear from you! Current Openings: Director of Venture and Developer Relations Director of Business Development Director of Legal Director of Operations Director of Integrations and Security Senior Product Marketing Manager Feel free to share this announcement with anyone who might be interested. We can't wait to meet our future team members! 🚀

We're entering a future wherein the barriers of entry for building an indie maker business can be lower than ever before. This is due to the emergence of AI entrepreneurship. To fully actualize this vision, we need AI models that are OMLized — Open, Monetizable, and Loyal.

We are excited to announce the release of our whitepaper, OML: Unleashing the Era of AI Entrepreneurship. There are two paradigms of AI model deployment today: open and closed. Open models offer community-driven innovation and a resistance to centralized AI companies taking control of the world. Closed models (accessed via API) on the other hand offer those centralized AI companies direct monetization and usage controls. We introduce a new paradigm where models can be Open, Monetizable and Loyal (OML). This new addition to the landscape of deploying AI models opens a new world of possibility for open source AI development and AI entrepreneurship. In the paper, we summarize our research on different approaches to OML and explain OML1.0 – an approach to OML that leverages data poisoning attacks and collateral to incentivize users of an openly distributed model to report and pay for its usage. Check out the full whitepaper release here: https://lnkd.in/eKBAGRzp

Notable figures such as Elon Musk have often spoken about the importance of truly open source AI development, and why it’s vital for our shared future. Here’s why this is absolutely crucial: Openness in AI promotes transparency, collaboration, and innovation. When models are publicly accessible, researchers can examine, improve, and build upon the technology. This can lead to faster progress and more innovative AI solutions. Ultimately this propensity for innovation is at the core of what makes community built AI powerful. When builders can access the weights code and methodology for previous iterations of a model, they can rapidly contribute improvements in the open. Open-source AI models counter the dominance of centralized AI companies. With publicly accessible AI, openness can break down barriers to entry — enabling vibrant innovations and tools that improve many sectors of society. In turn, openness is essential for ensuring that the benefits of AI are widely accessible and shared. Continuous open source AI model development ensures that centralized AI companies cannot abuse the control they have over foundational models to wipe out the value of builders developing applications on top of these models. The Sentient Foundation's OML (Open, Monetizable, Loyal) format enables open AI models while ensuring they are monetizable and loyal. This approach allows creators to benefit financially from their work, while also promoting the principles of openness and transparency. With OML, contributors to open source models can own their contributions, incentivizing them to contribute even more. In the long run, a closed AI ecosystem doesn’t have much of a chance to provide nearly the same level of value as open source AI. Sentient will be releasing its full OML whitepaper tomorrow. To become a Sentient early bird supporter, join the Sentient Discourse and receive access before anyone else: https://lnkd.in/e9Fh45JV

On the importance of native monetization for AI models: Monetization has always posed a challenge to open source development. The technological development of society is largely driven by an economic system of rewards and consequences. These incentives foster the allocation of resources that make the world go around. Without avenues for direct monetization, open source projects lack the powerful incentive structures that sometimes develop in well run closed source projects. In the era of AI, this handicap is dangerous, given the extreme power closed source projects now stand to accumulate. OML (Open, Monetizable, Loyal) enables the direct monetization of open source AI models. OML enables the formation of incentive systems for community built AGI. We have seen that foundational model development is especially prone to economic increasing returns to scale. The bigger the AI company, the more compute it can afford, the more user data it can generate for free. Sentient aspires to help create a future where contributors to AI models own their contribution. Where we can bring the fundamental value of property rights to AI model development so that innovation and entrepreneurship will flourish.