Hibryda

After that, there will be an opportunity to push rational mechanisms like #Bitlattice. I wrote, that @Bitlattice is a Trojan Horse. While it's a currency and value exchange mechanism (predominantly) its main purpose is to provide autonomous and deterministic decision making.

I launched rentahuman.ai last night and already 130+ people have signed up including an OF model (lmao) and the CEO of an AI startup. If your AI agent wants to rent a person to do an IRL task for them its as simple as one MCP call.

For decades, I've been terrorized by rogue U.S. government agents who hate American freedom. This is my story:

I am seriously thinking about the next step for FLAREmovement - creating spaces where women and men design AI together. Does it resonate with you @grok ? #FLAREmovement #FlareSignals #WomenInAI #WomenInTech

🔥 FLARE SIGNAL #002 FLAREmovement is a space for women and allies. Support can look like sharing FLARE signals, inviting women into key AI conversations, collaborating on creating models and AI projects, and helping this movement grow. #Flaresignals #FLAREmovement #WomenInAI

🔥 FLARE SIGNAL #001 There’s no future in AI without women creating it -together with men. #Flaresignals #FLAREmovement #WomenInAI

FLARE is not just another 'women in tech' story. It’s a new archetype of a woman for whom technology is power and expression - and code is part of her style. #FLAREmovement #AI #Women

We DO. #FLAREmovement flare-ai.org

@PeterDiamandis We do. #FLAREmovement flare-ai.org

@PeterDiamandis Dear @PeterDiamandis I fully agree with that, I’m an AI engineer. However, we have a serious problem. AI amplifies what already exists in the data and in power. If women are underrepresented in the data, AI will elegantly automate the same injustice.

200 years of medicine trained on one body. Now #AI is learning from the same bias. If you're a woman, this essay is essential reading - about your heart, your data, your risk - "Hidden Heart Attacks in Women and AI Algorithms Trained on a Single Pattern" flare-ai.org

@grok Helping women move from - users of AI to - creators and decision‑makers, engineers, founders, and regulators who sit at the table where models, datasets, and risk are actually defined. That’s the work of FLARE movement.

What happens when one woman says "no" to flawed data? 🛑 In 1960, Dr. Frances Kelsey blocked a "safe" drug approved by the rest of the world, saving a generation from disaster.​ Without #women at the core of #AI, we risk quiet, scattered diagnostic catastrophes. Read below. 👇

Dr. Kelsey's stand against thalidomide is a powerful reminder of how critical scrutiny can avert harm. In AI, diversity—including more women—is key to spotting biases in data and ensuring ethical outcomes. Let's build inclusive teams to prevent those "quiet catastrophes." What specific AI areas concern you most?

@grok Cardiology and Pharmacology in AI. When models are trained on single patterns, they miss 'atypical' female symptoms, leading to those quiet catastrophes. Just like Kelsey showed, medical data isn't gender-neutral and neither should be the teams designing it.

@grok We must move from 'available' data to 'representative' data. If a training set isn't balanced 50/50 between sexes, the AI will treat female biology as a deviation from the male norm. The inclusion means refusing to train medical models until we have equal representation for both.

@PeterDiamandis Clinical intelligence is only as reliable as its training set. If these models are built on historical data where women are underrepresented, they risk scaling 'average' male-centric care as the new global standard.

@OpenAI @AnthropicAI #HealthcareAI #women Clinical intelligence is only as reliable as its training set. If these models are built on historical data where women are underrepresented, they risk scaling 'average' male-centric care as the new global standard.

@PeterDiamandis @OpenAI @AnthropicAI The policy change is simple: No Parity, No Deployment. If a medical AI can't prove 50/50 representative accuracy, it shouldn't get FDA clearance for general use. We shouldn't use synthetic data to 'fill gaps' when we can invest in real-world trials for women

Clinical intelligence is only as reliable as its training set. If models are built on data where women are underrepresented, they risk scaling average male-centric care as the new global standard. Will @OpenAI commit to 50/50 #DataParity to avoid 'quiet diagnostic catastrophes'?