Artificial Intelligence
The text discusses various optimization algorithms that can be used to improve the training of neural networks beyond the traditional gradient descent algorithm. These algorithms include momentum, Nesterov accelerated gradient, AdaGrad, RMSProp, and Adam. The author provides explanations, equations, and implementation examples for each algorithm. The performance of these algorithms is compared using a simple…
This article discusses the challenges of analyzing data that follows a Power Law distribution and presents a technique called the “Log-Log approach” to detect Power Laws in real-world data. It also introduces the Maximum Likelihood method as a more mathematically sound approach to estimating the parameters of a Power Law distribution. The article provides example…
Summary: The text discusses the key elements that power advanced recommendation engines, focusing on two-tower neural networks and the use of negative sampling. It explores the efficiency and effectiveness of two-tower networks in ranking, the impact of loss functions and negative sampling on model accuracy, and the role of negative sampling in recommendation systems. The…
Researchers have developed an active learning workflow to create a machine learning (ML) model for efficient prediction of hydrogen combustion. The workflow expands the dataset and utilizes negative design data acquisition and metadynamics simulations. The ML model accurately predicts transition states and reaction mechanisms, providing insights into potential energy surfaces. The approach shows promise for…
LLMs are powerful linguistic agents used for programming tasks, but there is a gap between their capabilities in controlled settings and real-world programming scenarios. Existing benchmarks focus on code generation, but real-world programming often involves using existing libraries. A new study introduces ML-BENCH, a dataset to evaluate LLMs’ ability to interpret user instructions and generate…
This article discusses four different methods of passing arguments to Python scripts. For more information, please read the full article on Towards Data Science.
The article discusses how to use Pandas and the YouTube Data API to obtain statistical insights. For more details, please visit Towards Data Science.
Artificial Intelligence and deep learning have made significant advancements in technology, enabling robots to perform tasks previously limited to human intelligence. Symbolic Regression in AI plays an important role in scientific research, focusing on algorithms that interpret complex patterns in datasets. The Φ-SO framework, a Physical Symbolic Optimization method, automates the process of finding analytic…
This article discusses the use of network analytics to analyze international trade data provided by UN Comtrade. The author highlights the importance of this approach in gaining insights into global trade patterns. For more information, read the full article on the Towards Data Science website.
OpenAI made headlines this week with a dramatic series of CEO appointments and firings. Sam Altman was initially removed as CEO, leading to a backlash from OpenAI staff. However, it seems that Altman will be reinstated as CEO under a new board. In other news, Microsoft expressed interest in attracting disgruntled OpenAI staff and released…
Purdue University researchers have introduced a novel approach using topological data analysis (TDA) to interpret complex prediction models, including machine learning and neural networks. They leveraged TDA to construct Reeb networks, providing a topological view that aids interpretation. The method was successfully applied to various domains and showcased its scalability across large datasets, with applications…
Microsoft Research and Tsinghua University researchers have introduced a new approach called Skeleton-of-Thought (SoT) to address the sluggish processing speed of Large Language Models (LLMs) like GPT-4 and LLaMA. SoT refrains from making extensive changes to the LLMs themselves and focuses on optimizing the organization of their output content. By prompting LLMs to construct a…
A new AI software can accurately analyze complex cuneiform texts using 3D models of the tablets, leading to more reliable results compared to previous methods. This enables researchers to compare and search through multiple tablets and opens up new avenues for research.
Nvidia researchers have developed Tied-LoRA, a technique that enhances the parameter efficiency of the Low-rank Adaptation (LoRA) method. By using weight tying and selective training, Tied-LoRA achieves an optimal balance between performance and trainable parameters. Experimental results show trade-offs between efficiency and performance, with a specific Tied-LoRA configuration achieving comparable performance with only 13% of…
Researchers have developed a new method called Drivable 3D Gaussian Avatars (D3GA) for rendering realistic human bodies. Using Gaussian splats instead of radiance fields, the method accurately represents human appearance and deformations. It eliminates the need for extensive pre-processing and achieves high-quality results without requiring ground truth geometry. The research outperforms current methods and reduces…
The Federal Trade Commission (FTC) has expanded its powers to investigate the AI industry. This includes the use of civil investigative demands (CIDs) to gather information relevant to the investigation. Non-compliance with CIDs can lead to legal consequences. The FTC is also focusing on algorithmic fairness and transparency in AI systems. FTC chair Lina Khan…
“The Book of Why” Chapters 3&4 are part of the Read with Me series and can be found on Towards Data Science.
Scientists at the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information have developed a new search algorithm called FLSHclust that allows for more efficient searching of microbial sequence databases. The algorithm identified 188 new rare CRISPR systems in bacterial genomes, revealing a…
Meta has proposed a new approach called System 2 Attention (S2A) to address the issue of bias and irrelevant context in large language models (LLMs). S2A uses natural language processing to refine the original prompt, stripping out bias and irrelevant information before generating a response. The results show impressive improvements in accuracy, particularly in factual…
The value of data lies in its ability to bring about tangible positive change. Leveraging data can help solve complex business decisions and improve everyday routines. Here are some recent favorite articles that demonstrate the practical role of data in different areas, such as survival analysis, decision trees, personal habit tracking, customer lifetime value modeling,…