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¿Por qué Chris Hemsworth reemplazó a Dwayne Johnson en Extraction?

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Chris Hemsworth y Dwayne Johnson posan para un collage de fotos
Medios fijos

¿Has oído hablar del “efecto mariposa”? No, no el vehículo de Ashton Kutcher de 2004, sino el principio asociado con figuras tan diversas como el meteorólogo Edward Norton Lorenz y el autor de ciencia ficción Ray Bradbury, se trata de cómo el más mínimo cambio en las condiciones puede conducir a cambios a gran escala durante un período de tiempo. La historia está llena de ejemplos de este tipo, donde en lugar de acontecimientos obvios que cambian la vida, hay pequeños momentos (o series de momentos) que cambian el futuro de muchas personas. Como ocurre con la mayoría de las cosas en la historia, estas crisis sólo pueden identificarse en retrospectiva y, a veces, resultan sorprendentemente improbables.

Por ejemplo: ¿Quién hubiera pensado que la película de acción de Netflix de 2020 “Extraction” marcaría un punto de inflexión en las carreras de los hermanos Russo, Dwayne Johnson, y su estrella Chris Hemsworth? Para empezar, Extraction en sí era originalmente una película completamente diferente: Cuando se ensambló por primera veziba a ser dirigida por Joe y Anthony Russo y protagonizada por Johnson, así como su título original: la novela gráfica “Ciudad” de los Russo y Ande Parks, con arte de Fernando León González y Eric Skillman. Cuando esta versión inicial de la película no funcionó, la verdadera razón por la que los Russo recurrieron a Hemsworth para reemplazar a Johnson fue la misma razón por la que querían promocionar Sam Hargrave Dirigir: Estaban todos involucrados. Películas realizadas para Marvel Studios.

Los rusos están promocionando a Hemsworth internamente y haciendo de Extraction una joya de acción.


Chris Hemsworth recibe una llamada telefónica desde su celular en el Distrito de Extracción
netflix

“Extraction” comenzó como “Ciudad”, y los rusos mantuvieron el título de su novela gráfica original y, como sugiere el título, la historia se desarrolló originalmente en Brasil y se centró en un mercenario contratado para rescatar a la hija secuestrada de un narcotraficante brasileño. distribuidor. . Originalmente, Johnson estaba programado para interpretar al mercenario, y cuando se anunció la película en 2012, el actor acababa de tener su gran éxito “Fast Five”. “Ciudad” fue una continuación del intento de Johnson de establecerse como la principal estrella de acción de Hollywood de la época, y habría encajado muy bien entre las secuelas de “Fast Saga” y vehículos icónicos como “Snitch”, “Hércules” y ” San Andreas”. Por su parte, los rusos buscaban hacerse un nombre fuera de las comedias y la televisión, y claramente esperaban que “Ciudad”, centrada en la acción, fuera su salida.

Dio la casualidad de que los rusos llamaron la atención de Kevin Feige y Marvel Studios, cuando intervinieron para producir “Capitán América: El Soldado de Invierno” unos meses después de que se anunciara “Ciudad”. Con la salida de los rusos, Ciudad colapsó y Johnson pasó a otros proyectos. Mientras los Russo continuaban su relación con Marvel, pasando a dirigir los enormes capítulos finales de la saga Infinity “Avengers: Infinity War” y “Avengers: Endgame”, los hermanos debieron haber visto potencial en la estrella de “Thor” Chris Hemsworth para dar un paso al frente. El mundo de los roles mercenarios. Joe Russo reescribió “Ciudad” para que se ambientara en Dhaka, antes de “Extraction”. Esta nueva versión se tituló “Dhaka”. En lugar de continuar con la intención de dirigir la película con su hermano, decidió darle a Sam Hargrave, coordinador de especialistas en Infinity War y Endgame (y director de la segunda unidad en esta última película), la oportunidad de dirigir.

“Extraction” de Hargrave ha sido un éxito en Netflix, donde debutó Secuela en 2023 con Actualmente hay una tercera película en camino. Si bien “Ciudad”, protagonizada por Johnson y dirigida por los rusos, probablemente habría sido una buena película, probablemente nos privaría de las grandes películas del MCU realizadas por los rusos. También pudo haber impedido que Johnson ofreciera su mejor actuación en “Pain & Gain” de 2013. Ciertamente significó que nunca hubiéramos obtenido las dos películas de “Extraction” de Hemsworth y Hargrave, dos de las mejores películas de acción jamás realizadas en la última década. Esto no siempre es cierto, pero a veces el destino es lo mejor.



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How to use ChatGPT and LLMs for data extraction

using ChatGPT and large language models for data extraction

Artificial intelligence (AI) has taken huge leaps forward in the last 18 months with the development of sophisticated large language models. These models, including GPT-3.5, GPT-4, and open source LLM OpenChat 3.5 7B, are reshaping the landscape of data extraction. This process, which involves pulling out key pieces of information like names and organizations from text, is crucial for a variety of analytical tasks. As we explore the capabilities of these AI tools, we find that they differ in how well they perform, how cost-effective they are, and how efficiently they handle structured data formats such as JSON and YAML.

These advanced models are designed to understand and process large volumes of text in a way that resembles human cognition. By simply entering a prompt, they can filter through the text and deliver structured data. This makes the task of extracting names and organizations much smoother and allows for easy integration into further data analysis processes.

Data Extraction using ChatGPT and OpenChat locally

The examples below show how to save your extracted data to JSON and YAML files. Because they are easy to read and work well with many programming languages. JSON is particularly good for organizing hierarchical data with its system of key-value pairs, while YAML is preferred for its straightforward handling of complex configurations.

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However, extracting data is not without challenges. Issues like incorrect syntax, unnecessary context, and redundant data can affect the accuracy of the information retrieved. It’s crucial to adjust these large language models carefully to avoid these problems and ensure the responses are syntactically correct.

When we look at different models, proprietary ones like GPT-3.5 and GPT-4 from OpenAI are notable. GPT-4 is the more advanced of the two, with better context understanding and more detailed outputs. OpenChat 3.5 7B offers an open-source option that is less expensive, though it may not be as powerful as its proprietary counterparts.

Data extraction efficiency can be greatly improved by using parallel processing. This method sends multiple extraction requests to the model at the same time. It not only makes the process more efficient but also reduces the time needed for large data extraction projects.

Token Costs

The cost of using these models is an important factor to consider. Proprietary models have fees based on usage, which can add up in big projects. Open-source models can lower these costs but might require more setup and maintenance. The amount of context given to the model also affects its performance. Models like GPT-4 can handle more context, which leads to more accurate extractions in complex situations. However, this can also mean longer processing times and higher costs.

Creating effective prompts and designing a good schema are key to guiding the model’s responses. A well-crafted prompt can direct the model’s focus to the relevant parts of the text, and a schema can organize the data in a specific way. This is important for reducing redundancy and keeping the syntax precise.

Large language models are powerful tools for data extraction, capable of quickly processing text to find important information. Choosing between models like GPT-3.5, GPT-4, and OpenChat 3.5 7B depends on your specific needs, budget, and the complexity of the task. With the right setup and a deep understanding of their capabilities, these models can provide efficient and cost-effective solutions for extracting names and organizations from text.

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Advanced AI data extraction, analysis and transformation

Advanced AI data analysis, extraction and transformation using DatakuAI

In the realm of digital data management, the ability to quickly and accurately convert a jumble of unstructured information into a neatly organized format is more important than ever. Enter DatakuAI, a cutting-edge tool that harnesses the power of artificial intelligence to make data extraction a breeze. This innovative platform is making waves across various industries, from human resources to financial services, by offering a solution that is both powerful and easy to use.

At the heart of DatakuAI lies a set of advanced large language models, which give the tool an uncanny ability to understand and process complex data with impressive accuracy. This means that whether you’re dealing with text, images, or tables, DatakuAI can handle it all. Imagine the time saved when you can quickly sift through massive CSV files and pull out the information you need without breaking a sweat.

Easily extract data

One of the standout features of DatakuAI is its proficiency in extracting data from tables within documents. This is particularly useful when dealing with invoices, where the tool can accurately identify and extract key details such as dates, amounts, and vendor information. This level of precision is invaluable for businesses that need to process large volumes of documents on a regular basis.

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AI data analysis

Ease of use is a cornerstone of DatakuAI’s design. The platform is built to be intuitive, allowing even those who are new to data extraction to get started without a hitch. Users can choose to customize their extraction settings or take advantage of pre-set templates for common data types, making the tool accessible to a wide range of skill levels.

But DatakuAI isn’t just about accuracy and user-friendliness; it’s also a cost-effective solution. By reducing the need for manual data entry, which is often time-consuming and prone to errors, businesses can save on labor costs and minimize mistakes. Additionally, the tool is scalable, capable of handling large-scale data processing tasks without a drop in performance.

Structure unstructured data

The versatility of DatakuAI extends to multiple sectors. In human resources, for example, it can quickly parse through resumes, streamlining the recruitment process. In the financial sector, it can organize financial documents, providing essential insights for analysis.

DatakuAI is designed to offer a comprehensive suite of solutions tailored to meet the diverse needs of modern businesses and organizations. By leveraging advanced data extraction and analysis techniques, DatakuAI provides a powerful toolset for enhancing operational efficiency, understanding market dynamics, and improving customer engagement. The key features of DatakuAI, as derived from the provided text, include:

  • Resume Extraction: This feature is aimed at streamlining the recruitment process. By automating the sorting of resume data, DatakuAI enables quicker candidate evaluation, significantly reducing the time and effort HR departments or recruiters spend on manual screening. This not only accelerates the hiring process but also helps in identifying the most suitable candidates based on objective data criteria.
  • Review Insights: Understanding customer sentiments and feedback is crucial for any business aiming to improve its products and services. DatakuAI’s review insights feature decodes customer feedback, providing actionable insights that can drive product and service enhancements. This capability allows businesses to directly address customer needs and preferences, fostering a more customer-centric approach to development and improvement.
  • Customer Data Utilization: Personalizing customer experiences is a key driver of customer loyalty and retention. DatakuAI leverages customer interaction data to personalize experiences effectively. This feature enables businesses to build deeper connections with their customers by tailoring interactions, offers, and communications to meet individual preferences and behaviors.
  • Market Trends Analysis: Staying ahead in a competitive market requires a keen understanding of market dynamics. DatakuAI’s market trends feature helps businesses identify and capitalize on market opportunities by providing insights into current trends. This enables strategic decision-making regarding product launches, marketing strategies, and other business developments.
  • Financial Analysis: For strategic decision-making, in-depth financial analysis is indispensable. DatakuAI empowers businesses with the tools to conduct comprehensive analyses of financial documents. This feature supports better investment decisions, risk management, and overall financial planning by providing a detailed understanding of financial health and prospects.

DatakuAI is committed to making its technology accessible to all. The platform offers free access to its core extraction features, schema history management, and community support. This ensures that even those with limited resources can benefit from the tool’s capabilities. DatakuAI is a versatile and efficient AI-based data extraction tool that stands out for its customizable features and advanced language models. It offers a high level of accuracy and cost-effectiveness, making it an ideal choice for processing invoices, extracting resume data, or analyzing financial documents.

With its scalable solution, DatakuAI is ready to meet a wide range of data processing needs. The platform’s free access to essential features and extra benefits for subscribers make it an attractive option for anyone looking to streamline their data extraction and processing tasks. Here are some other articles you may find of interest on the subject of data analysis :

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