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How Science Fiction Shapes Tomorrow’s Tech
23 May

How Science Fiction Shapes Tomorrow’s Tech

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The below is a summary of the first episode of my Synthetic Minds podcast. Think science fiction is just for entertainment? Think again. It's time for businesses to read today's sci-fi to shape tomorrow's reality. In the debut episode of the Synthetic Minds podcast, Dr. Mark van Rijmenam chats with Karl Schroeder, a science fiction author and strategic foresight consultant. They delve into how sci-fi narratives, like Schroeder's “Stealing Worlds” and “Lady of Mazes,” provide valuable insights for navigating future technological landscapes. These stories blend AI, blockchain, and mixed reality to imagine radical shifts in governance and personal freedoms, offering…
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Unveiling the Leaders in Data and AI: The 2024 Finalists for the Databricks Data Visionary Award
23 May

Unveiling the Leaders in Data and AI: The 2024 Finalists for the Databricks Data Visionary Award

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The Data Team Awards annually recognize the indispensable roles of enterprise data teams across industries, celebrating their resilience and innovation from around the world.With more than 200 nominations, the awards showcase the behind-the-scenes successes in data science and artificial intelligence. We look forward to highlighting these forward-thinking clients across six categories at the Data + AI Summit in June.The Data and AI Visionary Award is presented to an executive innovator who has spearheaded the integration of data, analytics, and AI into their company’s strategic initiatives. These visionaries exemplify unparalleled foresight and inventiveness, charting new paths for data's role in predictive…
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Instruction Tuning GPT2 on Alpaca Dataset
23 May

Instruction Tuning GPT2 on Alpaca Dataset

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Fine-tuning language models to follow instructions is a major step in making them more useful. In this article, we will train the GPT2 model for following simple instructions. Instruction tuning GPT2 on the Alpaca dataset will reveal how well very small language models perform at following instructions. Figure 1. Instruction tuned GPT2 on Alpaca dataset inference result. In particular, we will train the GPT2 base model which contains just 124 million parameters. This is much smaller than what the industry considers as SLMs (Small Language Models), which us typically 7 bllion (7B) parameters. In fact, any language model below 3…
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Breaking Down Silos, Building Up Insights: Implementing a Data Fabric 
23 May

Breaking Down Silos, Building Up Insights: Implementing a Data Fabric 

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(amiak/Shutterstock) Data is the lifeblood of modern business, but for commercial-sized companies, managing and leveraging data can feel like navigating a maze. But what if there was a way to simplify the journey and unlock the full potential of a company’s data? Read on to learn how a data fabric can add value by maximizing the value of a company’s data infrastructure. Large, global enterprises have massive data teams set up to transfer and manage their data, using approaches like a data mesh. But commercial-sized companies are also dealing with more and more complex data landscapes, and finding that a…
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Data Machina #251
23 May

Data Machina #251

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Six Nerdy AI Activities for the Long W/E. I’ve just read that lots of AI engineers in the US are running the rate race, feeling burnout. Here in the European AI scene things are innately a bit more relaxed.Aah… A long bank holiday in London; so much stuff to do in this amazing city! But if you are feeling the AI FOMO kick and can’t survive a long weekend IRL, here are six AI activities for you:Generate comics with AI. I gave it a go, generated a few short comics, and having fun so far. The AI team at Bytedance…
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Optimizing Databricks LLM Pipelines with DSPy
23 May

Optimizing Databricks LLM Pipelines with DSPy

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If you’ve been following the world of industry-grade LLM technology for the last year, you’ve likely observed a plethora of frameworks and tools in production. Startups are building everything from Retrieval-Augmented Generation (RAG) automation to custom fine-tuning services. Langchain is perhaps the most famous of all these new frameworks, enabling easy prototypes for chained language model components since Spring 2023. However, a recent, significant development has come not from a startup, but from the world of academia. In October 2023, researchers working in Databricks co-founder Matei Zaharia’s Stanford research lab released DSPy, a library for compiling declarative language model calls into…
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Hugging Face Autotrain – Getting Started
23 May

Hugging Face Autotrain – Getting Started

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Autotrain is a no-code platform from Hugging Face to train, evaluate, and deploy machine learning and deep learning models. In this article, we will use Hugging Face Autotrain to train a Small Language Model (SLM). The Hugging Face Autotrain platform offers several functionalities for training: Computer Vision models Machine Learning models And LMs & LLMs However, in this article we will focus on training a language model for instruction following using the Autotrain platform. Although we can directly access Autotrain from their platform, we will use local installation. So, in way, we will use some code rather than the no-code…
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Self-Driving Cars vs. Coding Copilots
23 May

Self-Driving Cars vs. Coding Copilots

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Back in the mid-2010s, the world of autonomous vehicles was making great progress, and it seemed that we would soon be ushered around in cars that drove themselves, leaving us free to spend our time how we wanted. That obviously hasn’t happened, but instead, we’ve been treated to a form of AI we weren’t expecting: generative AI-powered copilots. Following the launch of ChatGPT in late 2022, the world of generative AI has been on a tear. Every company seems to be investing in large language models (LLMs) to build one of the two most visible forms of GenAI: chatbots and…
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Data Machina #252
23 May

Data Machina #252

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Diffusion, FM & Pre-Trained AI models for Time-Series. DeepNN-based models are starting to match or even outperform statistical time-series analysis & forecasting methods in some scenarios. Yet, DeepNN-based models for time-series suffer from 4 key issues: 1) complex architecture 2) enormous amount of time required for training 3) high inference costs, and 4) poor context sensitivity.Latest innovative approaches. To address those issues, a new breed of foundation or pre-trained AI models for time-series is emerging. Some of these new AI models use hybrid approaches borrowing from NLP, vision/ image, or physics modelling, like: transformers, diffusion models, KANs and state space…
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