Market Research & Consumer Behavior - Large language models (LLMs) can mirror human purchase intent with near-survey accuracy [1] - The study suggests a future where synthetic shoppers could replace real ones in market research [1]

Experimenting with the math and data behind large language models helped me understand how AI “thinks.” I wish everyone had the chance to do the same, writes WSJ software engineer John West. https://t.co/9ldAeQiRib ...

Core Insights - Large Language Models (LLMs) like ChatGPT are being misused in professional settings, leading to inefficiencies and risks [1][2] Mistakes and Solutions Mistake 1: Treating LLMs Like Search Engines - LLMs are not fact-checking tools and can produce fabricated information, leading to serious consequences [3][4] Mistake 2: The "Copy, Paste, Send" Disaster - Using LLM output without human review can perpetuate biases and require more time to correct than creating original content [4][5] - Example incidents include a law firm submitting fake legal cases and Air Canada being forced to honor a non-existent policy generated by a chatbot [5] Fix for Mistake 1 and 2 - LLMs should be used to create first drafts, with human expertise added for finalization [6] Mistake 3: Sharing Sensitive Information - A significant 77% of employees admit to inputting confidential data into public LLMs, risking data breaches and regulatory violations [7][8] Fix for Mistake 3 - Organizations should establish clear policies against entering confidential data into public LLMs and invest in secure AI solutions [9] Mistake 4: Using LLMs for All Tasks - LLMs are not suitable for complex reasoning or specialized tasks, which can lead to decreased productivity [10][11] Fix for Mistake 4 - It is essential to use the right tools for specific tasks, recognizing the limitations of LLMs [11] Conclusion - LLMs should be viewed as powerful assistants that require human oversight to maximize their potential and minimize risks [12]

Technology & AI Understanding - The article discusses how experimenting with the mathematics and data behind large language models can help individuals understand how AI "thinks" [1] - The author, a WSJ software engineer, suggests that everyone should have the opportunity to explore the inner workings of AI [1]

Model Style Definition & Importance - Model style encompasses values (what models should/shouldn't do), traits (curiosity, warmth, conciseness), and flare (emojis, m-dashes), which together form demeanor [8] - Style matters because it shapes user experience, influencing how people perceive and trust the model, shifting usage from simple search to collaboration [9][10][11] Model Style Development - Model style is primarily set by pre-training (corpus defining knowledge and voice), refined by fine-tuning (adding tone, guardrails), and shaped by user prompts and app settings [12][13][16] - User prompts significantly influence model response style, with personalization features like memory further tailoring the style over time [14][15] Challenges & Considerations - Consistency in style is a major challenge because large language models approximate patterns rather than execute rules, making alignment difficult [27][28][31] - The company balances maximizing user autonomy and freedom with minimizing harm, setting default behaviors that users and developers can override within safety policies [23][24][25] - There is no single style that works for all users; the company aims to provide choice and flexibility for models to adapt to different contexts and needs [26][27] Future Directions - The company is focused on steerability, aiming to improve how well models follow customization requests for managing traits and flare [34][35] - The company aims to improve contextual awareness, enabling models to shift tone appropriately based on the user's context [36] - The company prioritizes AI literacy and accessibility, striving to make style management simple and intuitive for all users [37]

Group 1 - U.S. GDP growth in the first half of 2025 was primarily driven by investment in data centers and information processing technology, with a mere 0.1% growth when excluding these sectors [1][2] - The dollar value contributed to GDP growth by AI data-center buildout surpassed U.S. consumer spending for the first time, highlighting the significance of technology investment [2] - Investment in information-processing equipment and software constituted only 4% of U.S. GDP but accounted for 92% of GDP growth in the first half of 2025, indicating a disproportionate impact on economic expansion [3] Group 2 - Major tech companies like Microsoft, Google, Amazon, Meta, and Nvidia have invested tens of billions into data centers to meet the rising demand for AI and large language models [4] - Hyperscaler capital expenditures on data centers have increased fourfold, approaching $400 billion annually, with the top 10 spenders responsible for nearly a third of this spending [5] - Data center-linked spending is estimated to be adding approximately 100 basis points to U.S. real GDP growth, underscoring its economic significance [5] Group 3 - The surge in technology-led growth occurs amidst broader economic sluggishness, with job creation slowing and concerns that the economy could have faced recession without technology investment [5]

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):JSON prompting for LLMs, clearly explained: ...

AI Security Concerns - AI系统难以被训练成只执行“好”的指令 [1] - 大型语言模型存在难以修复的安全漏洞 [1] Industry Implication - 行业需关注大型语言模型的安全性问题 [1]

Company Focus - The company specializes in AI consulting services for businesses [1] - The company is also involved in content creation and education, particularly online educational content related to AI [1] AI Education and Training - The company educates audiences about AI and offers workshops for companies [2] - The company assists in upskilling future AI engineers [2] Industry Trends and NVIDIA's Role - NVIDIA is recognized as a key player in the AI industry [2] - Generative AI and large language models are identified as current hot topics [3] - NVIDIA certifications cover various AI topics, including multimodal AI and accelerated data science [4] Future Outlook - The company is interested in exploring NVIDIA's offerings further [4] - The company anticipates future releases from NVIDIA [4]

Unlike most software, large language models are probabilistic. A deterministic approach to safety is thus inadequate. A better way forward is to copy engineers in the physical world https://t.co/COsTN2tSGf ...