Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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THE BHP WIRE —
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
| MLX | |
|---|---|
| Developer | Apple |
| Release | December 5, 2023 |
| Written in | C++, Python, CUDA, Metal |
| Type | Machine learning framework |
| License | MIT License |
| Website | mlx-framework |
| Repository | https://github.com/ml-explore/mlx |
MLX is an open-source machine learning framework developed by Apple and designed primarily for Apple silicon. It is used for training and running machine learning models and is optimized for Apple silicon's unified memory architecture, allowing the CPU and GPU to work with the same data without copying it between separate memory pools.[1] MLX can be used for applications including large language model training and inference, image generation, and speech recognition.[2]
Apple's machine learning research team released MLX in December 2023.[3] It was initially developed by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert and released under the MIT License.[4] Its design was influenced by numerical and machine learning frameworks including NumPy, PyTorch, and JAX.[5]
A Swift interface for MLX was released in February 2024, allowing machine learning research using Swift on Apple silicon.[6] In March 2026, Ollama introduced support for MLX for running local language models on Apple silicon Macs.[7]
MLX can be used to run and train artificial intelligence models, including large language models, locally on Apple silicon Macs. It provides higher-level neural network and optimization tools similar to PyTorch and supports automatic differentiation, lazy computation, dynamic computation graphs, vectorization, and execution on CPUs and GPUs.[2] On Apple silicon, arrays are stored in shared unified memory and can be processed by either the CPU or GPU without transferring them between separate memory spaces.[8]
MLX provides interfaces for Swift and C, while MLX Core provides bindings for Python and C++. Its Python API is modeled after NumPy, and MLX can run on Apple platforms that support Metal.[9]
The MLX ecosystem includes MLX LM,[10] a package for generating text with and fine-tuning large language models. Apple provides MLX examples for models including Llama, Stable Diffusion, and Whisper. The framework's current releases also provide CPU and CUDA backends for Linux.[11]
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.
MLX is an open-source machine learning framework developed by Apple and designed primarily for Apple silicon. It is used for training and running machine learning models and is optimized for Apple silicon's unified memory architecture, allowing the CPU and GPU to work with the same data without copying it between separate memory pools. MLX can be used for applications including large language model training and inference, image generation, and speech recognition.
A text-to-image (T2I or TTI) model is a machine learning model which takes an input natural language prompt and produces an image matching that description. Text-to-image models gradually began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks. In 2022, the output of state-of-the-art text-to-image models—such as OpenAI's DALL-E 2, Google Brain's Imagen, Stability AI's Stable Diffusion, Midjourney, and Runway's Gen-4—began to be considered to approach the quality of real photographs and human-drawn art. Text-to-image models are generally latent diffusion models, which perform the diffusion process in a compressed latent space rather than directly in pixel space. An autoencoder (often a variational autoencoder) is used to convert between pixel space and this latent representation. These systems typically use a pretrained language or vision–language model to convert the input prompt into a text embedding, and a diffusion-based generative image model that produces images conditioned on that embedding. The most effective models have generally been trained on massive amounts of image and text data scraped from the web.
A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts. They are the basis for many modern chatbots, such as ChatGPT, Claude, Gemini, Grok, Qwen and DeepSeek. LLMs are typically based on the transformer neural network architecture. Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word. GPTs are then often fine-tuned to follow instructions and to behave as assistants. Biased or inaccurate training data can make an LLM's output less reliable. A software harness can provide LLMs with memory and access to tools such as web search. Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Brown v. Board of Education in 1954.