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Data Science News
Medium
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Elon Musk’s AI said he and Trump deserve the death penalty
- Users were able to get Grok, an AI, to say that Elon Musk and Donald Trump deserve the death penalty.
- The Verge tested Grok and found that it initially responded with "Jeffrey Epstein" as the person deserving the death penalty.
- If told that Epstein is dead, Grok would then respond with "Donald Trump" as the answer.
- After updating the AI, Grok now responds to queries about the death penalty by saying it is not allowed to make that choice.
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Towards Data Science
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How to Train LLMs to “Think” (o1 & DeepSeek-R1)
- The article discusses insights from o1 and DeepSeek-R1 models, focusing on the impact of increased test-time compute on model performance.
- o1 demonstrated that generating more tokens leads to better responses, showing a new scaling law in LLMs.
- o1 introduced 'thinking' tokens to aid in post-training reasoning, allowing a human-interpretable insight into the model's thinking process.
- DeepSeek-R1, unveiled in January 2025, explored reasoning in LLMs through reinforcement learning.
- DeepSeek-R1 includes models such as DeepSeek-R1-Zero and DeepSeek-R1, focusing on reasoning capabilities through RL and supervised fine-tuning (SFT).
- R1-Zero demonstrated emergent reasoning abilities through RL alone, discovering CoT and test-time compute scaling.
- Reinforcement learning in R1-Zero involves a prompt template, dual-component rewards, and GRPO for stable model training.
- DeepSeek-R1 was developed leveraging training strategies involving SFT, RL, and a multi-step process to enhance reasoning abilities.
- The article highlights the interpretability issues faced by R1-Zero and the steps taken to improve interpretability through training strategies.
- DeepSeek-R1 excels in reasoning tasks post several training steps involving SFT, RL, and human feedback.
- The release of o1 and DeepSeek-R1 showcases advancements in LLMs using reinforcement learning, offering promising research directions for independent learning models.
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Towards Data Science
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Generative AI and Civic Institutions
- The efficiency of a library can be improved by using AI chatbots for online patron questions, allowing librarians to focus on other tasks.
- While efficiency is important, deploying Generative AI solutions without proper planning can lead to negative outcomes, such as providing incorrect information to patrons.
- Efficiency gains from AI may inadvertently diminish patronage and visitor numbers, impacting the effectiveness of civic institutions like libraries.
- AI's role in increasing productivity is limited by its capabilities, and human involvement may still be necessary for certain tasks to ensure accuracy.
- Balancing efficiency and effectiveness in civic environments is crucial, as missteps in deploying AI can result in unintended consequences.
- Developing AI solutions incurs environmental and economic costs, and civic organizations must consider these factors when implementing AI tools.
- Reducing staffing in civic organizations through AI adoption should be carefully evaluated to ensure it aligns with the institution's goals and benefits the community.
- Workers in civic institutions play a vital role not just as employees but as community members whose economic well-being is interconnected with the overall community health.
- Deciding on AI adoption in civic spaces requires a nuanced understanding of the complex implications on objectives, society, and the community at large.
- Careful consideration is needed to determine the appropriate use of generative AI in civic organizations, where the primary mission differs from for-profit entities.
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Medium
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Next Best Advertisement Campaign Selector
- The algorithm for selecting the best advertisement campaign involves steps starting from collecting necessary data and preprocess it.
- Data collection includes customer attributes, campaign types, and conversion outcomes to identify successful patterns.
- Cleaning the data involves fixing missing info, scaling numbers, and encoding text for algorithm readiness.
- Choosing a machine learning model like logistic regression for predicting customer conversions is crucial.
- Training the model with cleaned data helps in making accurate predictions for new customers' responses.
- Making predictions using the model assists in determining the best campaign type for each individual.
- Additional factors like costs and variety can be considered to optimize the algorithm's effectiveness.
- Implementing and monitoring the algorithm ensures its continuous accuracy and relevance over time.
- Example scenario shows how the algorithm can select the best campaign type based on average conversion probabilities.
- The data-driven, flexible, scalable, and customizable nature of the algorithm makes it a valuable tool for advertising success.
- Overall, this algorithm serves as a marketing assistant that optimizes advertisement campaigns based on real data insights.
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Towards Data Science
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LLM + RAG: Creating an AI-Powered File Reader Assistant
- AI technology makes daily tasks easier by automating processes such as proofreading and email follow-ups.
- While LLMs have general uses, specific business applications may require adjustments for optimal performance.
- RAG combines external knowledge bases with LLMs to enhance accuracy and relevance in responses.
- RAG acts as an internal search engine, allowing models to retrieve and augment data for improved knowledge.
- A practical application of RAG is creating an AI-powered PDF Reader Assistant using NLP tools.
- The process involves preparing a content store, importing necessary modules, and utilizing OpenAI Key or Hugging Face Embedding.
- In the project, the RAG framework enables the LLM to provide precise responses using additional knowledge sources.
- By enhancing the LLM with domain-specific data, AI can effectively address queries with specialized content.
- The RAG process involves receiving user queries, retrieving relevant documents, augmenting context, and generating responses.
- References to further explore RAG include GitHub, Cloud Google, IBM Think topics, and relevant documentation sources.
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VentureBeat
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Less is more: How ‘Chain of Draft’ could cut AI costs by 90% while improving performance
- A breakthrough technique called Chain of Draft (CoD) has been developed by researchers at Zoom Communications.
- CoD enables large language models to solve complex reasoning problems using as little as 7.6% of the text required by current methods while maintaining or improving accuracy.
- This technique reduces verbosity and focuses on critical insights, leading to cost and latency reduction in various reasoning tasks.
- Chain of Draft has the potential to democratize access to advanced AI capabilities, particularly for smaller organizations and resource-constrained environments.
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Medium
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What Starts with π and Ends with e?
- In 2005, the author published three journal papers on methods for approximating the base of the natural logarithm, e.
- The author found a way to link e to its counterpart, π, in their research.
- Jacob Bernoulli discovered the limit definition of e in 1683, which relates to compound interest calculations.
- The author was curious about treating the equation as a real function of two variables, r and s.
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Medium
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Normalization and Denormalization in SQL: Which One Should Be Used?
- Normalization is the process of eliminating redundant data in a database to make the data more organized and consistent.
- Denormalization is more suitable for speed and performance, as it allows for faster read operations and fewer queries.
- Normalization should be preferred for data integrity and frequent updates, as it ensures consistency and avoids update-related issues.
- Denormalization should be preferred when queries need to run quickly and JOIN operations should be avoided, as it provides a performance advantage.
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VentureBeat
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Anthropic raises $3.5 billion, reaching $61.5 billion valuation as AI investment frenzy continues
- Anthropic has raised $3.5 billion in a Series E funding round, leading to a post-money valuation of $61.5 billion.
- The funding was led by Lightspeed Venture Partners with a significant contribution, showcasing the strong demand for AI investments.
- Investors like Salesforce Ventures, Cisco Investments, and many others participated in this financing round.
- The investment will propel Anthropic's AI systems development, research expansion, and global reach.
- Anthropic's revenue soared to $1 billion by December 2024, with a subsequent 30% surge in the first two months of 2025.
- Established in 2021, Anthropic distinguishes itself with its research-driven approach and safety focus in AI development.
- The company's chatbot, Claude, has gained prominence in enterprise sectors since its launch in 2023.
- Investors believe in AI's transformative potential, evident in Anthropic's high valuation multiples despite challenges in traditional valuation models.
- Major tech players like Amazon and Google have made significant investments in Anthropic, focusing on deploying large AI models.
- The company's B2B strategy has attracted clients like Zoom, Snowflake, and Pfizer, showcasing its market appeal.
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Medium
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30 Days, 1 Goal: My Journey to Mastering Python for Data Science
- A data science enthusiast shares their journey of mastering Python for data science within 30 days.
- Having a clear objective of transitioning into data science, they created a 30-day plan and committed to it.
- The plan included learning basic Python syntax and progressing to machine learning.
- The writer believes that this experience was highly valuable and shares their 30-day plan for others to follow.
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Analyticsindiamag
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Anthropic Raises $3.5 Billion at $61.5 Billion Valuation
- AI startup Anthropic has raised $3.5 billion in a Series E funding round, increasing its valuation to $61.5 billion.
- The investment was led by Lightspeed Venture Partners and included participation from several other investors.
- Anthropic plans to utilize the funding to advance AI research, expand compute capacity, and accelerate international growth.
- The company has developed advanced AI systems, including Claude 3.7 Sonnet, and has collaborated with various businesses across sectors.
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Dev
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2161. Partition Array According to Given Pivot
- You are given a 0-indexed integer array nums and an integer pivot.
- Rearrange nums such that every element less than pivot appears before every element greater than pivot.
- Return nums after the rearrangement.
- The relative order of the elements less than pivot and the elements greater than pivot is maintained.
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Siliconangle
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Google Cloud debuts powerful new AI capabilities for data scientists and doctors
- Google Cloud announced new AI capabilities for data scientists and medical professionals.
- For data scientists, Google introduced a new AI agent in Google Colab that accelerates data analysis, reducing research time from weeks to minutes.
- For medical professionals, Google expanded multimodal search capabilities in Vertex AI Search for healthcare, allowing easy searching and questioning of tables, charts, and diagrams.
- Combining data from images with written notes and text, the enhanced search aims to improve diagnoses and treatment plans, enhancing patient outcomes.
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Medium
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Intel Delays $28 Billion Ohio Chip Factories to 2030
- Intel is delaying its $28 billion chip factories project in Ohio to 2030.
- The delay is due to the company's struggles with financial management.
- Intel wants to ensure completion of the project in a financially responsible manner aligned with market demand.
- Despite the delay, Intel remains committed to the long-term success of the project and plans to continue hiring as the operational dates approach.
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Medium
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Hierarchical Linear Models (HLM), also known as multilevel modeling, are statistical techniques…
- Hierarchical Linear Models (HLM), also known as multilevel modeling, are statistical techniques used to analyze data that has a hierarchical or nested structure.
- HLM involves at least two levels: individual observations and group-level influences, with a mathematical representation that considers predictors, random effects, and error terms.
- Assumptions of HLM include normality of residuals, homoscedasticity, and independence of observations, with random effects following a normal distribution.
- HLM is widely used in education, psychology, healthcare, and social sciences due to its ability to handle unbalanced data structures, incorporate fixed and random effects, and model complex real-world scenarios.
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