Jacob Ajwani

P̳i̳c̳k̳ ̳A̳ ̳T̳r̳i̳b̳e̳ * Specialize, Don't Generalize Now, I know the temptation to be a jack-of-all-trades is strong, but trust me, that's a surefire way to end up a master of none. Instead, I encourage you to dive deep into a specific technique or vendor. *Become an In-Demand Expert Think about it this way – when companies are looking to level up their AI game, they're not just looking for generalists. They want the real deal, the specialists who can hit the ground running and deliver results. By choosing a tribe and becoming an expert in that field, you're setting yourself up for success.

𝐂𝐨𝐧𝐟𝐥𝐢𝐜𝐭 = Strength: Embrace, learn, unite. 🫂🫂 ---- 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐂𝐨𝐧𝐟𝐥𝐢𝐜𝐭'𝐬 𝐒𝐨𝐮𝐫𝐜𝐞: Pinpoint tension causes - mismatched expectations, miscommunication, or misalignment. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐎𝐩𝐞𝐧𝐥𝐲: Gather, listen with empathy, not blame. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐂𝐥𝐞𝐚𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Define roles, set processes, and regular check-ins. 𝐅𝐨𝐬𝐭𝐞𝐫 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐂𝐮𝐥𝐭𝐮𝐫𝐞: View conflicts as growth opportunities. Encourage dialogue, celebrate wins, and value everyone.

𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐀𝐈-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 *Involve data scientists early to align goals and processes between product and model teams. *Consider parallel development, with product and model teams working concurrently but coordinating closely. *Conduct regular meetings and training to align on goals, processes, and responsibilities. Clear communication is crucial. *Establish feedback loops to address issues proactively and build trust between teams. *Validate the collaboration through field experiments to understand real-world impacts. *Develop policies to ensure AI systems are transparent, explainable, and respect human values.

🐙 Consider incorporating neuro-symbolic approaches when choosing an AI decision-making framework. Neuro-symbolic AI combines neural networks with symbolic reasoning, leveraging the strengths of both to enhance decision-making capabilities. By integrating neural networks' ability to learn from data with symbolic reasoning's interpretability and logical inference, neuro-symbolic approaches offer promising potential for more robust and transparent decision-making in complex AI systems.

𝐖𝐡𝐞𝐧 𝐜𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐚𝐧 𝐀𝐈 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 𝐀𝐬𝐬𝐞𝐬𝐬 𝐍𝐞𝐞𝐝𝐬: Carefully evaluate requirements, considering decision complexity, data volume/sensitivity, and ethical implications. 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 Options: Explore rule-based systems, machine learning models, or hybrid approaches. Weigh factors like flexibility, cost, and expertise. 𝐓𝐞𝐬𝐭 𝐚𝐧𝐝 𝐈𝐭𝐞𝐫𝐚𝐭𝐞: Validate performance with sample data, refine based on real-world results, and prioritize ongoing improvement. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Maintain focus on fairness, transparency, and accountability. Engage stakeholders to mitigate risks.

Some key factors to assess include: * Business objectives and the specific problems you're trying to solve with AI * Data availability, quality, and sensitivity *Key performance metrics like accuracy, speed, scalability, and explainability * Level of customization and control needed for the AI model * Cost, timeline, and expertise required to implement the solution * Regulatory and ethical compliance requirements