Blog Post LLM Wars 1: Angry Tweet Rewrite Mistral vs. ChatGPT Author: Dr Ehud Barnea Evaluating creativity on tasks that matter It’s been the same story for a while now. A new open-source model is released, beating all the records. The open source community gets excited, but soon enough, happy tweets disappear – ChatGPT just writes so much better. But in light of recent developments, is this still the case? Tasq.ai set out to find out – and we’re still surprised.  When people try open source LLMs they usually find out quickly enough that they are still no match ..read more

Blog Post Data Split: Training, Validation, and Test Sets Author: Nati Catalan Imagine medical students prepared for their final exams by only studying the exact questions that will be on the test. Obviously no one would want to see that kind of doctor! This metaphor illustrates how using the same dataset for both training and evaluation can lead to a false sense of accuracy, as the model is only learning to perform well on a specific set of data it has already seen, rather than learning to generalize its knowledge to new, unseen data. For models that perform as designed, in real-world e ..read more

Blog Post Unleashing the Power of LLM Fine-Tuning Author: Nati Catalan Introduction They may sound like Sesame Street characters or Imperial vessels from Star Wars, but LLMs from BERT and Ernie to Falcon 40B, Galactica and GPT-4 are changing the way we interact with technology.  However, the true power of LLMs extends beyond their initial training. Fine-tuning, a critical process for enhancing the model’s performance on specific tasks, is what transforms a general-purpose LLM into a specialized tool.  In this article we’ll explore LLM fine-tuning, with a specific focus on leve ..read more

Blog Post Challenges and Solutions in Evaluating Generated Images Author:Tasq Team Introduction Behind the scenes of Generative AI, where algorithms conjure art, realism, and imagination from lines of code, a quest for truth is unfolding. Researchers who once had a high degree of certainty when it came to traditional machine learning, now find themselves grappling with ambiguity and uncertainty over the quality and performance of their generation models. Releasing something into the real world that cannot be vouched for, cannot be controlled, and can do tremendous damage, is the nightma ..read more

Blog Post Image Quality Assessment Author : Yossi Motro The quality of a machine learning model in supervised machine learning is directly correlated with the quality of the data used to train the model. We have powerful machine learning algorithms for many problems that, given enough data, can achieve unprecedented performance. However, having an abundance of versatile and high-quality data is not easily attained. It is especially difficult for problems that require human intervention. Having people directly involved in the data acquisition process is both costly and time consuming. The ..read more

Blog Post AI Ethics – The Hope and the Worry Author: Ella Marlowe AI holds huge potential for facilitation, but it also supplements negative outcomes if data scientists don’t recognize biases in datasets and correct them before model training processing. Incautious data handling leads to damaged and worthless data output (Garbage in – Garbage out). To ensure AI’s future is responsible, we must ask off-putting ethical questions. In this article, we aim to introduce the ethics of AI and explore how AI ethics must align with human ethical principles to be adopted by society at largeToday ..read more

AI holds huge potential for facilitation, but it also supplements negative outcomes if data scientists don’t recognize biases in datasets and correct them before model training processing. Incautious data handling leads to damaged and worthless data output (Garbage in – Garbage out). As AI technology is moving increasingly toward greater integration across all aspects of life, biases are more likely to occur through the complex systems while at the same time, processes of identification and prevention are far slower. But first, let’s define Bias in a valuable and easily understandabl ..read more

The term data catalog could be described as a detailed inventory of all data assets within the organization, designed in order to help data professionals quickly find the most appropriate data for any purpose. A stable data catalog should include: Data compliance– One of the main data catalog goals should be simplifying the compliance process by data asses categorization, automatic classification, and tagging options. Data integration with existing tools related to the company’s privacy policies, data quality rules, business workflow. Enabled environment for deploying (private, publ ..read more

Artificial intelligence (AI) and machine learning (ML) are advancing at an astounding pace, much faster than anyone could have foreseen just a decade-or-so ago. While this is great news for technology, it means that data scientists and ML engineers often find themselves in a situation where they don’t have enough real data that they can use for training and developing their ML models, either because it doesn’t exist or because confidentiality and privacy limitations prevent it. To overcome this problem, they’re more frequently turning to synthetic data. What is synthetic data? Synthetic data i ..read more

It's the battle of the LLMs! Can Mistral take on the mighty ChatGPT? Find out here. The post LLM Wars 1: Angry Tweet Rewrite Mistral vs. ChatGPT appeared first on Tasq.ai ..read more