The fundamental act of reading is essential in today's knowledge society. Reading is not just about decoding words. Reading requires understanding vocabulary in context, analyzing structure, inferring meaning, and synthesizing information—complex skills that many struggle with. The time is now to eliminate the gap between the critical importance of reading comprehension in our society and the challenges that individuals encounter in their reading journeys. Goal: Deliver a thoughtful, AI-empowered approach to significantly enhance how billions of people read, learn, and access information. In May 2023, Anthropic expanded Claude’s context window from 9K to 100K tokens. There are many opportunities for using this capability! Let’s start with reading! "Reading Boost," is an AI-empowered process that individualizes reading comprehension for students, educators, professionals, and individuals with reading or learning difficulties. The process has four steps – plan, implement, review, and improve. AI’s role is to augment each step, maintain metacognitive knowledge, and produce counterfactual strategies to improve reading tasks.

Objective: Address the needs of marketing professionals for a succinct, efficient, and effective CV Profile - a concise encapsulation of their work activities and workstyle. Utilize AI21's generative AI technology for this task. Develop a unique prompt using a chain-of-thought approach, allowing marketing professionals to quickly create a personalized CV profile. This solution streamlines the updating process of a CV profile. As professionals grow and their skill set diversifies, they can edit their work activities and work style, paste the prompt, and swiftly generate an updated profile. The key advantage of our solution is speed and efficiency. It removes the tedious process of data sourcing, cleaning, and repetitive trial-and-error prompt creation, providing a high-quality output quickly.

Whatever work you do, there is one question that all of us face: How will AI impact my job?! Societally, we are not answering this question very well. The majority of messages are fear-based and vague. Our objective was to figure out how to focus on any job and recognize tasks that can be automated and augmented by generative AI. Within the machine learning workflow, we focused on prompt design, hyperparameter tuning, training and experiment tracking. Though our knowledge harvesting team didn’t include any software engineers, we found that the Generative AI Studio of Google Vertex AI was a powerful way to achieve No-Code AI. So, flip the paradigm! Don’t dwell on data, notebooks, and APIs! Prompt well!

Egregore: An implementation of LangChain and Claude for Accelerated Competitive Analysis Our Knowledge Harvesting team aimed to create an AI solution for a crucial stage of the product development process: Analyzing competition. Understanding the competition is vital to decipher a market and assess market attractiveness. Accurate knowledge of the competition helps product and marketing managers communicate product functions, features, and benefits effectively. We explored techniques to structure information for LangChain and opted to use prompts, testing them in GPT-4 and Claude 2. Additionally, we experimented with semantic triples to drive agent behaviors. Triples and knowledge graphs are powerful and interpretable ways to structure and curate data. In a real-world application, we focused on five ultrasound devices, enabling rich comparisons, including regulatory requirements. We delved into FDA processes for medical devices. Though we didn't fully structure this complex data, we captured the necessary elements to make it AI-digestible. With a 55-page Integrated Product Management Process as reference, we converted its step-by-step guidance into prompts and agents. While we encountered some uncompleted tests to assess AI configurations, we successfully developed workflows, data structures, and prompts to leverage LangChain's potential. Although more work lies ahead, we established a strong foundation for further exploration. Our team's experience in using AI for a real-world scenario was good! In just 3 days, we covered significant ground, demonstrating the tremendous potential of AI to augment human intelligence. Our project's ambition affirmed the possibilities of utilizing AI to enhance product management and innovation.

In light of COP 28's emphasis on accelerated climate action and energy transition, the challenge for managers and executives in the upstream and downstream petroleum sectors is to innovatively and responsibly navigate the industry's current role in meeting global energy demands while actively transitioning towards more sustainable energy sources. This requires balancing immediate energy security with long-term environmental and societal commitments, necessitating a strategic, collaborative, and transparent approach to both energy production and climate responsibility. We delivered three agents (custom GPTs) for helping professionals: - Make the transition from fossil fuels to renewable energy, with advice tailored to their transferable skills and the renewable job market. - Explore specific skills and opportunities for learning and development in the renewable sector. - Identify key renewable energy standards, certifications, and practices that are essential for a career in this field.