Resources
That's Fresh! Newsletter
Read a selection of our past issues.
- 🙌 NumPy 2.0 is almost out!And: Our new data preprocessor with Polars | Interview with S2E at Italy Insurance ForumJune 5, 2024
- 😮 What a month for new LLMs!And: Datacamp webinar with ShaliniMay 22, 2024
- ✨ GenAI true value lies beyond operational enhancementsAnd: The Future of Data Protection | New updates about AI ActApril 24, 2024
- 👁 What are 1-bit Large Language Models?And: Linkedin Live about AI Act | Mastercard's Country Manager interviewed our CEOMarch 6, 2024
- LLaMAntino - Effective Text Generation in ItalianAnd: Creating train and test datasets | Use case: Detecting money muling with the help of synthetic dataFebruary 21, 2024
- 🗞️ The NY Times sues OpenAI and MicrosoftAnd: Can AI work with little data? | La Stampa: AI means developmentJanuary 10, 2024
- Synthetic Data 101 🚨And: Why synthetic data? | New project with Poste ItalianeNovember 8, 2023
- How easy is it for LLM to infer sensitive information?And: Why is data sharing important? | Our new partnership with S2EOctober 25, 2023
- Have you heard of Pythia?And: Data augmentation tutorial | Did you say AI apocalypse?August 30, 2023
- Google's answer to ChatGPTAnd: Generating synthetic data within relational databases. Let's meet at WAICF!February 8, 2023
- Understanding ChatGPT betterAnd: How to deal with imbalanced data. More about our productDecember 14, 2022
- A curated list of failed ML projectsAnd: How to build a data strategy. Clearbox AI and Bearing Point partnership.November 16, 2022
- Our open source library is now on GitHubAnd: Clearbox AI on Cybernews.June 22, 2022
- Discovering DagsterAnd: Quantifying privacy risks. Use case: a synthetic data sandbox to freely share data.June 8, 2022
- Can interaction data be fully anonymized?And: Synthetic Data for privacy preservation: understanding privacy risks. Discover our Enterprise solution.April 6, 2022
- What are GFlow nets?And: Improve models with Synthetic Data. Use case: augment financial time series.March 16, 2022
- The European Commission selected us for Women TechEU pilot project!And: What is Synthetic Data. The new Synthetic Data platform.March 09, 2022
- The EDPS on Synthetic DataAnd: From raw to good quality data. Changelogs: now you can upload unlabeled datasets.February 23, 2022
- 2022 Gartner’s Technology TrendsAnd: How to harness the power of AI in companies. Changelogs: new metrics available for your synthetic dataset.February 09, 2022
FROM THE AI WORLD
Have you been part of the discussion surrounding the introduction of BitNet b1.58, an innovative 1-bit Large Language Model (LLM) developed by Microsoft researchers?
The distinctive feature of this model is its use of ternary values {-1, 0, 1} for parameters, instead of the conventional 16-bit floating-point values.
The research demonstrates that BitNet b1.58 matches the performance of traditional full-precision LLMs in both perplexity and task-specific results while significantly reducing costs associated with latency, memory usage, throughput, and energy consumption.
A notable benefit of this parameter representation approach is the fact that it makes matrix multiplications computable mostly through integer additions, which opens doors to new LLM-optimised hardware possibilities beyond GPUs.
Models like BitNet b1.58 are crucial for enhancing the efficiency and affordability of LLMs, offering a scalable solution that retains high performance but with a substantially lower computational and environmental impact.
New BitNet b1.58 by Microsoft
Recent research is paving the way for a new era of 1-bit Large Language Models. In this paper, researchers introduce the new one by Microsoft.
CLEARBOX AI
Mastercard interviewed our CEO
After 2023 Women & Sustainability Innovation Cup, Michele Centemero, Mastercard Country Manager, meets again our CEO for an insightful conversation.
EVENT
AI Act & Intellectual Property
On March 18th, our CEO Shalini and attorney Luca Egitto will discuss the impact of the AI Act on Intellectual Property matters. Will you be there?