Analytics Vidhya
JUNE 16, 2024
Introduction If you are a programmer, I’m sure you have come across the terms “compiler” and “interpreter.” These tools transform human-readable programs into machine code that computers can understand. But what exactly are they, and how do they work? More importantly, how do they differ and how can you know which one to use where?
Marktechpost
JUNE 16, 2024
Artificial intelligence (AI) focuses on creating systems capable of performing tasks requiring human intelligence. Within this field, the development of large language models (LLMs) aims to understand and generate human language, with applications in translation, summarization, and question-answering. Despite these advancements, complex multi-step reasoning tasks, such as solving mathematical problems, still need to be solved for even the most advanced LLMs.
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Analytics Vidhya
JUNE 16, 2024
Introduction Docker is a platform that enables developers to package applications and their dependencies into lightweight, portable containers. They have revolutionized the way software is developed, tested, and deployed across various industries. In this article, we will explore 25 different use cases of Docker across various industries, highlighting some real-world examples.
Artificial Lawyer
JUNE 16, 2024
Generative AI is without doubt the most powerful technology to be applied to the legal sector since the arrival of digital tools, yet it has a serious challenge: accuracy.
Speaker: David Warren and Kevin O'Neill Stoll
Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng
Marktechpost
JUNE 16, 2024
Topological Deep Learning (TDL) advances beyond traditional GNNs by modeling complex multi-way relationships, unlike GNNs that only capture pairwise interactions. This capability is critical for understanding complex systems like social networks and protein interactions. Topological Neural Networks (TNNs), a subset of TDL, excel in handling higher-order relational data and have shown superior performance in various machine-learning tasks.
Artificial Intelligence Zone brings together the best content for AI and ML professionals from the widest variety of thought leaders.
Marktechpost
JUNE 16, 2024
Machine unlearning is a cutting-edge area in artificial intelligence that focuses on efficiently erasing the influence of specific training data from a trained model. This field addresses crucial legal, privacy, and safety concerns arising from large, data-dependent models, which often perpetuate harmful, incorrect, or outdated information. The challenge in machine unlearning lies in removing specific data without the costly process of retraining from scratch, especially given the complex nature
Towards AI
JUNE 16, 2024
Last Updated on June 18, 2024 by Editorial Team Author(s): PromptDervish Originally published on Towards AI. Understanding all of the moving parts and adjusting them for maximum results. There are so many ways to affect Midjourney styles. It can be a bit daunting to understand and use all of the features well. I decided this was a good time to back up just a little and cover familiar ground before diving into how to tinker with all the bits to get them to create the style you want.
Marktechpost
JUNE 16, 2024
In June 2024, Databricks made three significant announcements that have garnered considerable attention in the data science and engineering communities. These announcements focus on enhancing user experience, optimizing data management, and streamlining data engineering workflows. Let’s delve into each of these groundbreaking announcements. 1. The Next Generation of Databricks Notebooks Databricks introduced a major update to their platform with the next generation of Databricks Notebooks.
Machine Learning Research at Apple
JUNE 16, 2024
This paper was accepted in the Industry Track at SIGIR 2024. Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-text) uses query priors, trained solely on text, to distinguish between phonetically confusing alternatives. Hence, the generation of synthetic queries that are similar to existing VA usage can greatly improve upon the VA's abilities-especially for use-cases th
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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
Marktechpost
JUNE 16, 2024
The release of the Tulu 2.5 suite by the Allen Institute for AI marks a significant advancement in model training using Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). The Tulu 2.5 suite comprises diverse models trained on various datasets to enhance their reward and value models. This suite is poised to substantially improve language model performance across several domains, including text generation, instruction following, and reasoning.
Pickl AI
JUNE 16, 2024
Summary: Excel’s COUNT function efficiently tallies numeric entries, enhancing Data Analysis accuracy. Additionally, counting characters using formulas like LEN aids in text analysis. Mastering these count formulas in Excel is crucial for precise data management and boosting productivity in data-driven tasks. Introduction Excel Data Analysis is crucial for making informed decisions based on large datasets.
Marktechpost
JUNE 16, 2024
Large language models (LLMs) like transformers are typically pre-trained with a fixed context window size, such as 4K tokens. However, many applications require processing much longer contexts, up to 256K tokens. Extending the context length of these models poses challenges, particularly in ensuring efficient use of information from the middle part of the context, often referred to as the “Lost-in-the-Middle” problem.
Bugra Akyildiz
JUNE 16, 2024
Apple Intelligence Commentary Apple is playing catchup not only in Generative AI, but overall in AI space. The features were not new or innovative, but rather what are available in Samsung and Google ecosystem for a while. From that perspective, I looked at the news to compare how behind Apple is comparing to industry rather than taking the features as they are.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
Marktechpost
JUNE 16, 2024
A major weakness of current robotic manipulation policies is their inability to generalize beyond their training data. While these policies, trained for specific skills or language instructions, can adapt to new conditions like different object positions or lighting, they often fail when faced with scene distractors or new objects, and need help to follow unseen task instructions.
TheSequence
JUNE 16, 2024
Image Credit: Luma AI Next Week in The Sequence: Edge 405: Our series about autonomous agents starts diving into the topic of memory. We discuss a groundbreaking paper published by Google and Stanford University demonstrating that memory-augmented LLMs are computationally universal. We also provide an overview of the Camel framework for building autonomous agents.
Marktechpost
JUNE 16, 2024
Graph neural networks (GNNs), referred to as neural algorithmic reasoners (NARs), have shown effectiveness in robustly solving algorithmic tasks of varying input sizes, both in and out of distribution. However, NARs are still relatively narrow forms of AI as they require rigidly structured input formatting and cannot be directly applied to problems posed in noisy forms like natural language, even when the underlying problem is algorithmic.
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