Towards Data Science

MARCH 17, 2023

Take advantage of the distributive power of Apache Spark and concurrently train thousands of auto-regressive time-series models on big data Photo by Ricardo Gomez Angel on Unsplash 1. How should we train and manage thousands of models? What if we need to create this forecast relatively frequently? or even in real time?

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Google Research AI blog

JANUARY 18, 2023

I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Language Models The progress on larger and more powerful language models has been one of the most exciting areas of machine learning (ML) research over the last decade. Let’s get started!

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Machine Learning Mastery

OCTOBER 12, 2024

Feature engineering and model training form the core of transforming raw data into predictive power, bridging initial exploration and final insights. This guide explores techniques for identifying important variables, creating new features, and selecting appropriate algorithms.

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Google Research AI blog

MAY 15, 2023

In general, models’ success at in-context learning is enabled by: Their use of semantic prior knowledge from pre-training to predict labels while following the format of in-context examples (e.g., Flipped-label ICL uses flipped labels, forcing the model to override semantic priors in order to follow the in-context examples.

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The Verge

MARCH 12, 2021

Today, Facebook announced a new initiative that it hopes will give it an edge in this consequential work: training its AI on Facebook users’ public videos. The resulting machine learning models will be used to create new content recommendation systems and moderation tools, says Facebook, but could do so much more in the future.

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TechCrunch

APRIL 19, 2023

Stability AI , the startup behind the generative AI art tool Stable Diffusion , today open-sourced a suite of text-generating AI models intended to go head to head with systems like OpenAI’s GPT-4. But Stability AI claims it created a custom training set that expands the size of the standard Pile by 3x. make up) facts.

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.orgSource

AUGUST 6, 2024

Perhaps your organization is one of those tradition-bound groups with a history that has been a decades-long cast iron model for culture, governance, and operations. AI streamlines training by allowing employees to move through information at their own pace. And, if you asked why I made that choice, I would say “AI.”

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Google Research AI blog

MAY 19, 2023

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. These models achieve remarkable performance partially due to the abundance of available training data.

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Kaggle Blog

SEPTEMBER 9, 2020

Every ML challenge ended with new knowledge, code, and model weights. The student trains a model, writes a paper. After it is accepted to the conference, pipelines are abandoned, training artifacts deleted and student moves on. How to start training How to perform inference. In the end, all of them were deleted.

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Recode by Vox

MARCH 13, 2025

The newest reasoning models from top AI companies are already essentially human-level, if not superhuman, at many programming tasks , which in turn has already led new tech startups to hire fewer workers. Fast AI progress, slow robotics progress If youve heard of OpenAI, youve heard of its language models: GPTs 1, 2, 3, 3.5,

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Machine Learning Mastery

JUNE 21, 2024

If you are a machine learning student, researcher, or practitioner, it is crucial for your career growth to have a deep understanding of how each algorithm works and the various techniques to enhance model performance.

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Google Research AI blog

NOVEMBER 10, 2022

Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team The field of natural language processing (NLP) has been revolutionized by language models trained on large amounts of text data. Overall, we present dozens of examples of emergent abilities that result from scaling up language models.

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Google Research AI blog

FEBRUARY 23, 2023

Pre-training on diverse datasets has proven to enable data-efficient fine-tuning for individual downstream tasks in natural language processing (NLP) and vision problems. So, we ask the question: Can we enable similar pre-training to accelerate RL methods and create a general-purpose “backbone” for efficient RL across various tasks?

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Nonprofit Tech for Good

OCTOBER 22, 2020

She provides proven fundraising strategies, tactics, and tools including coaching, training, and content for fundraising success. What happens online must follow the same offline prospecting to identification to qualification to cultivation to solicitation to stewardship model.

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TechCrunch

APRIL 12, 2021

Cybersecurity training startup Hack The Box , which emerged originally from Greece, has raised a Series A investment round of $10.6 Started in 2017, Hack The Box specializes in using “ethical hacking” to train cybersecurity techniques. Hack The Box is using a SaaS business model.

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TechSpot

MARCH 25, 2022

Nvidia's AI research arm has been working on inverse rendering and developed a Neural Radiance Field it calls Instant NeRF because it can render the 3D scene up to 1,000-times faster than other NeRF techniques. The AI model only needs a few seconds to train on a few dozen stills.

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InfoWorld

DECEMBER 25, 2023

Here the rapid pace of innovation in model quantization, a technique that results in faster computation by improving portability and reducing model size, is playing a pivotal role. To read this article in full, please click here

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TechCrunch

JULY 19, 2022

Even shielded behind an API, hackers can attempt to reverse-engineer the models underpinning these services or use “adversarial” data to tamper with them. In fact, at HiddenLayer, we believe we’re not far off from seeing machine learning models ransomed back to their organizations.”

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TechCrunch

JULY 13, 2022

Companies face several hurdles in creating text-, audio- and image-analyzing AI models for deployment across their apps and services. Cost is an outsize one — training a single model on commercial hardware can cost tens of thousands of dollars, if not more. Geifman proposes neural architecture search (NAS) as a solution.

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InfoWorld

FEBRUARY 15, 2024

Whether it’s large-scale, public large language models (LLM) like GPT or small-scale, private models trained on company content, developers need to find ways of including those models in their code. That means finding ways to test that code, without pushing it to production servers.

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The Verge

OCTOBER 15, 2020

Google is adding a new “ hum to search ” feature to its search tools today that will let you hum (or whistle, or sing) the annoying song that’s stuck in your head, and then use machine learning techniques to try to identify it.

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Google Research AI blog

FEBRUARY 7, 2023

In the last 10 years, AI and ML models have become bigger and more sophisticated — they’re deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning.

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The AI Alignment Forum

MARCH 13, 2025

Published on March 13, 2025 7:18 PM GMT We study alignment audits systematic investigations into whether an AI is pursuing hidden objectivesby training a model with a hidden misaligned objective and asking teams of blinded researchers to investigate it. As a testbed, we train a language model with a hidden objective.

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TechCrunch

MAY 5, 2023

Daily Crunch: New AI model DeepFloyd IF offers ‘advanced text-to-image generation techniques’ by Christine Hall originally published on TechCrunch . — Christine and Haje The TechCrunch Top 3 The ABCs of AI : Kyle reports about how, with DeepFloyd, artificial intelligence finally learns to draw text on images.

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Beth's Blog: How Nonprofits Can Use Social Media

FEBRUARY 11, 2014

Here are some techniques you can incorporate into your training and staff meetings that will help with learning and retention. 2. Walk and Talk: If you have taken a training with me , you know that won’t be sitting in your chair for long. Training Design' Here’s some examples.

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TechCrunch

JANUARY 25, 2023

Tanmay Chopra Contributor Share on Twitter Tanmay Chopra works in machine learning at AI search startup Neeva , where he wrangles language models large and small. Last summer could only be described as an “AI summer,” especially with large language models making an explosive entrance. Let’s start with buying.

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Beth's Blog: How Nonprofits Can Use Social Media

APRIL 1, 2014

Last month I had the pleasure of taking the Luma Institute Train the Trainers workshop where I got a chance to immerse in practicing facilitation techniques based on human centered design principles. The workshop instructor Peter Maher is founder and CEO, of Luma Institute , and a Jedi Master.

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The AI Alignment Forum

MARCH 11, 2025

Published on March 11, 2025 3:57 PM GMT TL;DR Large language models have demonstrated an emergent ability to write code, but this ability requires an internal representation of program semantics that is little understood. In this work, we study how large language models represent the nullability of program values.

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Google Research AI blog

MARCH 2, 2023

This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy. This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy.

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Forum One

APRIL 30, 2024

The introduction of Retrieval-Augmented Generation, or RAG, now allows AI applications to enhance the capabilities of machine learning models by integrating them with a retrieval component. Avoid Vendor and Model Lock-in: The AI space is still evolving rapidly.

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Google Research AI blog

AUGUST 24, 2023

Posted by Hattie Zhou, Graduate Student at MILA, Hanie Sedghi, Research Scientist, Google Large language models (LLMs), such as GPT-3 and PaLM , have shown impressive progress in recent years, which have been driven by scaling up models and training data sizes. manipulating symbols based on logical rules).

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NVIDIA AI Blog

NOVEMBER 15, 2023

Like a good judge, large language models ( LLMs ) can respond to a wide variety of human queries. But to deliver authoritative answers that cite sources, the model needs an assistant to do some research. What’s more, the technique can help models clear up ambiguity in a user query. That builds trust.

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The Verge

OCTOBER 20, 2020

To achieve these effects, Adobe is harnessing the power of generative adversarial networks — or GANs — a type of machine learning technique that’s proved particularly adept at generating visual imagery. Eventually, when even the GAN is getting confused trying to tell the difference between the two, the training process is complete.

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Google Research AI blog

JUNE 13, 2023

Modeling human attention (the result of which is often called a saliency model) has therefore been of interest across the fields of neuroscience, psychology, human-computer interaction (HCI) and computer vision. Attention-guided image editing Human attention models usually take an image as input (e.g.,

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OpenAI

JANUARY 27, 2022

We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research.

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DataRobot

AUGUST 25, 2021

In a recent blog, we talked about how, at DataRobot , we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model , trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business process.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. We provided a model-based taxonomy that unified many graph learning methods. In addition, we discovered insights for GNN models from their performance across thousands of graphs with varying structure (shown below).

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