Real Talk: What Types of Retail Strategies Work for Small Towns?
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If you live in a small town, you may think retail recruitment is impossible for your community. Learn why it isn't, and how to be proactive in making retail recruitment success your new reality.
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**Introduction**
In the rapidly evolving landscape of artificial intelligence (AI), one of the most groundbreaking advancements has been the development of Large Language Models (LLMs). These AI systems, trained on massive amounts of text data, have demonstrated remarkable capabilities in understanding, generating, and manipulating human language. LLMs have transformed industries, reshaped the way people interact with technology, and raised ethical concerns regarding their usage. This essay delves into the history, development, applications, challenges, and future of LLMs, providing a comprehensive understanding of their significance.
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The foundation of LLMs is built on decades of research in natural language processing (NLP) and machine learning (ML). Early language models were relatively simple and rule-based, relying on statistical methods to predict word sequences. However, the emergence of deep learning, particularly the introduction of neural networks, revolutionized NLP. The introduction of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks in the late 1990s and early 2000s allowed for better sequential data processing.
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2. **Pretraining:** The model learns general language patterns through unsupervised learning, predicting missing words or the next sequence in a text.
3. **Fine-tuning:** Adjusting the model for specific tasks, such as summarization, translation, or question-answering, using supervised learning.
4. **Inference:** The trained model generates text based on user input, leveraging probabilistic predictions to produce coherent responses.
Through these mechanisms, LLMs can perform a wide range of linguistic tasks with human-like proficiency.
**Applications of LLMs**
LLMs have found applications across various domains, including but not limited to:
1. **Content Generation:** LLMs assist in writing articles, blogs, poetry, and even code, helping content creators enhance productivity.
2. **Customer Support:** Chatbots and virtual assistants powered by LLMs provide automated yet contIntroduction to Generative Artificial Intelligence
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This presentation was designed for a broad audience, avoiding technical jargon while addressing the real opportunities and challenges AI brings.
A few key insights from the presentation:
Innovation under constraint: DeepSeek achieved remarkable results at a fraction of competitors' costs.
The productivity paradox: AI may boost output for writers and coders but may reduce job satisfaction for scientists.
The shifting definition: "AI is what computers can't do; once they can, it's just software." Mustafa Suleyman
The GPU Bottleneck: Big Tech is turning to custom chips and nuclear energy to meet AI's soaring computational demands.
誌 Energy-Hungry AI: AI's energy needs are driving investments in dedicated nuclear power for datacenters.
際際滷 decks are meant to support live talks, so they might not capture the full story on their own. If you are curious to dive deeper, feel free to reach outI would be happy to discuss these ideas in a more interactive setting.
Feel free to check out the slides here and my LinkedIn post for further discussion: https://www.linkedin.com/posts/merckel_intro-to-genai-activity-7300095492862930946-rnL9
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9th Edition of International Research Awards |28-29 March 2025 | San Francisco, United States
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In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.
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Cloud computing helps enterprises transform business and technology. Companies have begun to
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- 1. RealTalk:WhatTypes of Retail Strategies Work for SmallTowns?
- 2. Overview Know and ShowYour Market Potential Focus on the Right Retailers Work MultipleApproaches Take Ownership of the Economic Development Process
- 3. Know and ShowYour Market Potential Regardless of the size or location of your community, you must know the market potential you have when communicating with retailers. Take a look at your drive-time trade area. Consumers think in terms of minutes it takes to get to a location, so your trade area may go beyond your city limits.This could help present a more convincing case to retailers and is a helpful strategy for small towns.
- 4. Focus on the Right Retailers Does your community think that a retailer typically located in large cities is a good fit for you? Are you targeting based solely on anecdotal feedback/wish lists? Data insights on your residents, visitor population and current retail mix can help you understand your place in the market and which retailers are a good fit for you.
- 5. RetailersWho Often Locate in SmallTowns
- 6. Work Multiple Approaches Traditional economic development approaches focused only on primary job generators, such as manufacturing. For small communities, growing jobs does help, but is it the only approach? Rather than focusing solely on primary jobs, think holistically and recruit residential, retail, and commercial development simultaneously. Each supports the other and you never know which path will present opportunities first.
- 7. Its easy to get complacent and assume retailers will come to you. As a community, you must be proactive in owning this process and getting the conversation started. When starting conversations with retailers, you should be prepared with data on your community, future and current new developments, aerial photos, etc. Take Ownership of the Economic Development Process