New York City–based Rogo is building a chatbot to help junior bankers with time sucks like crunching numbers, preparing presentations and spreadsheets or doing basic research. https://t.co/UVztm7Nacl (Photo: Alexander Karnyukhin for Forbes) #BillionDollarStartups https://t.co/s75oLqTzKb ...

It really does have to think about the profitability of this business in the longer term. That's right. If you think about it.So, you know, I was also recently at a media dinner with Sam Altman when he said we plan to spend trillions on infrastructure in the near future and that economists might call that crazy, but they are marching on. So as open air starts to put this immense, unprecedented amount of capital right toward data center expansion, they're building expertise in that. And I think the long term ...

New York City–based Rogo is building a chatbot to help junior bankers with time sucks like crunching numbers, preparing presentations and spreadsheets or doing basic research. https://t.co/ToceAHwvCH (Photo: Alexander Karnyukhin for Forbes) #BillionDollarStartups https://t.co/iOOcyY1wBI ...

New York City–based Rogo is building a chatbot to help junior bankers with time sucks like crunching numbers, preparing presentations and spreadsheets or doing basic research. https://t.co/JQYdlb9XIB (Photo: Alexander Karnyukhin for Forbes) #BillionDollarStartups https://t.co/soIEnswlXI ...

Company Focus - Rogo, based in New York City, is developing a chatbot for junior bankers [1] Product & Service - The chatbot aims to assist with tasks such as number crunching, presentation and spreadsheet preparation, and basic research [1] Target User - The chatbot is specifically designed for junior bankers [1]

New York City–based Rogo is building a chatbot to help junior bankers with time sucks like crunching numbers, preparing presentations and spreadsheets or doing basic research. https://t.co/gC9wI6DeOC (Photo: Alexander Karnyukhin for Forbes) #BillionDollarStartups https://t.co/ZcueE4N3vO ...

Retrieval System Evaluation - Industry should prioritize fast and inexpensive evaluations (fast evals) using query and document pairs to enable rapid experimentation [7] - Industry can leverage LLMs to generate queries, but should focus on aligning synthetic queries with real-world user queries to avoid misleading results [9][11] - Industry can empirically validate the performance of new embedding models on specific data using fast evals, rather than relying solely on public benchmarks like MTeb [12] - Weights & Biases chatbot analysis reveals that the original embedding model (text embedding three small) performed the worst, while voyage 3 large model performed the best, highlighting the importance of data-driven evaluation [17][18] Output Analysis and Product Development - Industry should extract structured data from user conversations (summaries, tools used, errors, satisfaction, frustration) to identify patterns and inform product development [28][29] - Industry can use extracted metadata to find clusters and identify segments for targeted improvements, similar to how marketing uses user segmentation [29][26] - Cura library enables summarization, clustering, and aggregation of conversations to compare evals across different KPIs, helping to identify areas for improvement [32] - Industry should focus on providing the right infrastructure and tools to support AI agents, rather than solely focusing on improving the AI itself [39] - Industry should define evals, find clusters, and compare KPIs across clusters to make informed decisions on what to build, fix, and ignore [40][41] - Industry should monitor query types and performance over time to understand how the product is being used and identify opportunities for improvement [45]

Microsoft Joins the Crowd Trailing OpenAI in the Chatbot Competition https://t.co/A4Mhp5AVdh ...

Apple (AAPL -1.76%) is one of the most valuable companies in the world, with a market cap of $3.17 trillion. But the stock is down 15% since the beginning of the year (as of July 7). Investors are concerned about the stock's growth potential, as the delayed rollout of artificial intelligence (AI) features for its iPhones could leave the business scrambling to keep up with its rivals.But recently, Apple has been linked to a possible deal to acquire a major AI company. Could this be just what the company need ...

Group 1 - The core viewpoint emphasizes the increasing importance of data infrastructure in driving business innovation and ensuring compliance in the context of digital transformation accelerated by AI and cloud technologies [1] - According to Gartner, global public cloud service end-user spending is projected to reach $723.4 billion by 2025, reflecting a 21.5% increase from 2024, with IaaS and PaaS expected to grow by 24.8% and 21.6% respectively [1] - The rise of data privacy and security regulations, such as China's PIPL and Europe's GDPR, is significantly impacting multinational companies' data strategies, making data localization a critical issue [1] Group 2 - Pfizer's digital delivery head in China highlighted the necessity of a highly scalable, secure, and stable cloud infrastructure as a fundamental consensus for modern data strategies [2] - The challenges of cross-border data transmission and management have become a "lifeline" for companies operating in specific markets due to increasing data sovereignty awareness and regulatory developments [2] - Pfizer recognizes that data localization is not only a regulatory requirement but also essential for stable development in the Chinese market, allowing better adaptation to rapid market changes [2] Group 3 - Pfizer's core needs for data infrastructure include the necessity for global collaboration, agility to respond to market changes, and strong compliance capabilities [3] - The partnership with Amazon Web Services enables Pfizer to build a comprehensive digital ecosystem, enhancing decision-making and business operations through AI capabilities [3] - Pfizer's collaboration with Amazon in biopharmaceutical R&D has led to significant cost savings, estimated between $750 million and $1 billion annually, by leveraging data analysis and machine learning [4] Group 4 - Pfizer aims for a modern data strategy that integrates various aspects, including personnel, technology, and processes, to drive business innovation and process reengineering [4] - Continuous investment in data infrastructure and strategy, combined with AI empowerment, is expected to enhance R&D efficiency, reduce operational costs, and improve market responsiveness for pharmaceutical companies [5] - This approach is anticipated to serve as a reference for other multinational companies looking to implement data strategies in China [5]