💡Datasets used to build various ML bases

Iphone dataset - a set of datasets on the basis of which more than 40 thousand dynamic and more than 100 thousand static Gaussians, 20 SE(3) bases were built using Shape of Motion

The training time on 1xGPU A100 using the Adam optimizer with a resolution of 960x720 was just over 2 hours at a rendering speed of 40 frames per second.

According to the results of tests during the training process, Shape of Motion showed good results in the quality and consistency of scene construction.
However, the method still requires optimization for each specific scene and cannot handle significant changes in camera angle. There is also a critical dependence on precise camera parameters and user input to create a moving object mask.

🌎TOP DS-events all over the world in August
Aug 2-4 - MLMI 2024 - Osaka, Japan - https://mlmi.net/
Aug 3-9 - International Joint Conference on Artificial Intelligence (IJCAI) - Jeju, South Korea - https://ijcai24.org/
Aug 5-6 - ICASAM 2024 - Vancouver, Canada - https://waset.org/applied-statistics-analysis-and-modeling-conference-in-august-2024-in-vancouver
Aug 7-8 - CDAO Chicago - Chicago, United States - https://da-metro-chicago.coriniumintelligence.com/
Aug 12-14 - AI4 2024 - Las Vegas, United States - https://ai4.io/vegas/
Aug 16-17 - Machine Learning for Healthcare 2024 - Toronto, Canada - https://www.mlforhc.org/
Aug 19-20 - Artificial Intelligence and Machine Learning - Toronto, Canada - https://www.scitechseries.com/artificial-intelligence-machine
Aug 19-22 - The Bioprocessing Summit - Boston, USA - https://www.bioprocessingsummit.com/
Aug 25-29 - ACM KDD 2024 - Barcelona, Spain - https://kdd2024.kdd.org/
Aug 27 - Azure AI Summer Jam -
Aug 27-29 - ITCN Asia 25th - Karachi, Pakistan - https://itcnasia.com/karachi/
Aug 31 - DATA SATURDAY #52 - Oslo, Norway - https://datasaturdays.com/Event/20240831-datasaturday0052

💡😎A startup that revolutionized the way we process data

CRAM is a new memory technology that can reduce energy consumption when processing AI data by 1000 times.

Researchers from the University of Minnesota have developed a new technology, Computational Random-Access Memory (CRAM), that can reduce energy consumption when processing data. Unlike traditional solutions, where data moves between memory and the processor, CRAM allows data to be processed directly in memory cells.

This is achieved through the use of a high-density and reconfigurable spintronic structure embedded in memory cells. Thus, the data does not leave the memory, which minimizes response delays and energy consumption associated with the transfer of information.

With CRAM, data never leaves memory, but is instead processed entirely within the computer’s memory array. This allows a system running an AI computing application to reduce power consumption by “about 1,000 times compared to a state-of-the-art solution,” according to the research team.

💡😎Interesting Caldera Dataset

The Caldera dataset is an open source scene dataset containing much of the geometry found in the game Call of Duty®: Warzone™.

This includes geometry that can be visualized, as well as some alternate, usually unseen representations used in other calculations. For example, the developers have included volumes here to aid in lighting calculations or simple shapes for collision detection. Excluded are many single-point entities, such as character spawn locations or complex noscript-based models. As the developers note, they decided not to include textures and materials in this release. That would have added complexity and size to an already heavy scene. They focused on the many connections between spatial elements that can be found in this set, rather than an accurate visual representation.

💡😎The book "PostgreSQL 16 from the inside" is now freely available

The Postgres Professional DBMS developer has released a new book "PostgreSQL 16 from the inside". The electronic version of the textbook is freely available. The author of the book is Egor Rogov, Director of Educational Program Development at Postgres Professional.

The first edition of this textbook, based on version 14 of PostgreSQL, was released in March 2022 and updated to version 15. Due to great reader interest, the company translated the book into English. It later became the most popular thematic publication of 2023 according to Postgres Weekly and was included in the list of professional literature on the official website of the PostgreSQL community.

The current edition of the book "PostgreSQL 16 from the Inside" takes into account readers' comments, corrects typos, and reflects changes that occurred in the PostgreSQL 16 version. Postgres Professional has also updated the localized documentation for PostgreSQL 16.

⚡️📊OpenAI now provides normal structured JSON with data

I would like to remind you that the JSON mode has been working for about a year, but the outputs of the models corresponded to the declared format in less than half of the cases.

However, there is great news for developers who need good data markup. The updated version gpt-4o-2024-08-06 no longer has this problem: 100% of tests have no errors in the format.

The code and tutorial on using the feature are here.

⚠️Attention! Spark = Pandas + Big Data support

Be careful when applying your Pandas knowledge to Spark!!!

Of course, Pandas and Spark operate on the same data type — tables. However, the way they interact with them is significantly different.
For example, the main difference is that Pandas runs in a single process on a single machine and loads all the data into memory, while Spark is designed to work with large distributed data sets and can process terabytes and petabytes of data without loading it entirely into the memory of a single node

However, unfortunately, many programmers often transfer their knowledge from Pandas to Spark, assuming similar architectures, which leads to performance bottlenecks.

You can learn more about solving this problem from this article

⚡️A Scalable Dataset for Tuning Instructions in Software Mathematical Reasoning

The Mathematical Reasoning pipeline emphasizes separating numbers from mathematical problems to synthesize number-independent programs, enabling efficient and flexible scaling while minimizing dependence on specific numerical values.

As the authors note in their paper, experiments in fine-tuning open-source language and code models such as Llama2 and CodeLlama demonstrate the practical benefits of the InfinityMATH dataset.

In addition, these models have shown high reliability on the GSM8K+ and MATH+ benchmarks, which are improved versions of the benchmarks with minor changes to the numerical values.

📊Dataset
📖Research paper

🧐What is the difference between DICOM and NIfTI medical image formats

Before we look at the differences between DICOM and NIfTI, let's take a closer look at what each of these formats is individually

🤔What is the DICOM standard?

The DICOM standard — Digital Imaging and Communications in Medicine (DICOM) — is used to exchange images and information, it has been popular for more than a decade. Today, almost every device used in radiology (including CT, MRI, ultrasound and radiography) is equipped with support for the DICOM standard. According to the information from the standard developer (), DICOM allows you to transfer medical images in an environment of devices from different manufacturers and simplify the development and expansion of image archiving and communication systems.

🤔What is the NIfTI standard?

The Neuroimaging Informatics Technology Initiative (NIfTI) was created to work with users and manufacturers of medical devices to address some of the problems and shortcomings of other imaging standards. NIfTI was specifically designed to address these issues in the field of neuroimaging, with a focus on functional magnetic resonance imaging (fMRI). According to the NIfTI definition, the primary mission of NIfTI is to provide coordinated, targeted services, education, and research to accelerate the development and usability of neuroimaging informatics tools. NIfTI consists of two standards, NIfTI-1 and NIfTI-2, the latter being a 64-bit enhancement of the former. It does not replace NIfTI-1, but is used in parallel and supported by a wide range of medical neuroimaging devices and operating systems.

❓What is the difference between DICOM and NIfTI?

1. NIfTI files have less metadata: An NIfTI file does not require as many tags to be filled in as a DICOM image file. There is much less metadata to inspect and analyze, but this is in some ways a disadvantage because DICOM provides users with different layers of image and patient data.

2. DICOM files are often bulkier: DICOM data transfer is governed by strict formatting rules that ensure that the receiving device supports SOP classes and transfer syntaxes, such as the file format and encryption used to transfer the data. When transferring DICOM files, one device talks to another. If one device cannot process the information that the other is trying to send, it will inform the requesting device so that the sender can roll back to a different object (e.g. a previous version) or send the information to a different receiving end. Therefore, NIfTI files are usually easier and faster to process, transfer, read, and write than DICOM image files.

3. DICOM works with 2D layers, while NIfTI can display 3D details: NIfTI files store images and other data in a 3D format. It is specifically designed to overcome the spatial orientation issues of other medical imaging file formats. DICOM image files and associated data are made up of 2D layers. This allows for viewing different sections of an image, which is especially useful when analyzing the human body and different organs. However, with NIfTI, neurosurgeons can quickly identify objects in images in 3D, such as the right and left hemispheres of the brain. This is invaluable when analyzing images of the human brain, which is extremely difficult to evaluate and annotate.

4. DICOM files can store more information: As mentioned above, DICOM files allow medical professionals to store more information in different layers. Structured reports can be created and even images can be frozen so that other clinicians and data scientists can clearly see what the opinion/recommendation is based on.

🌎TOP DS-events all over the world in September
Sep 2-4 - CDAO Melbourne - Melbourne, Australia - https://cdao-mel.coriniumintelligence.com/
Sep 6-7 - Big Data Conference 2024 - Harvard, USA - https://cmsa.fas.harvard.edu/event/bigdata_2024/
Sep 7 - Platzi Conf 2024 - Bogota, Colombia - https://platzi.com/conf/
Sep 9-11 - ECDA 2024 - Sopot, Poland - https://www.ecda2024.pl/
Sep 10-11 - Civo Navigate Europe 2024 - Berlin, Germany - https://www.civo.com/navigate/europe
Sep 11-13 - RTC.ON 2024 - Krakow, Poland - https://rtcon.live/
Sep 18-19 - THE UK’S LEADING DATA, ANALYTICS & AI EVENT - London, UK - https://www.bigdataldn.com/
Sep 23-25 - Data Makers Fest - Alfândega do Porto, Portugal - https://www.datamakersfest.com/
Sep 24 - hayaData 2024 - Tel Aviv, Israel - https://www.haya-data.com/
Sep 24 - APAC Data 2030 Summit 2024 - Singapore, Singapore - https://apac.data2030summit.com/
Sep 24-26 - ICBDE 2024 - London, UK - https://www.icbde.org/
Sep 25-26 - BIG DATA & ANALYTICS MONTRÉAL SUMMIT 2024 - Montreal, Canada - https://bigdatamontreal.ca/
Sep 26-27 - Big Data Conference 2024 - Smaland, Sweden - https://lnu.se/en/meet-linnaeus-university/current/events/2024/conferences/big-data-2024---26-27-sep/
Sep 28-30 - GovAI Summit 2024 - Arlington, United States - https://www.govaisummit.com/
Sep 30-Oct 2 - Ray Summit 2024 - San Francisco, United States - https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/eventsite

⚠️Text2SQL is no longer enough

I recently came across an article in which the authors describe in detail the innovative TAG approach.

Table Augmented Generation (TAG) is a unified general-purpose paradigm for answering natural language questions using databases. The essence of this approach is that we have a model that accepts a natural language query, processes it, and returns a natural language answer.

Thus, Text2SQL only represents the spectrum of interactions between LM and the database. The very essence of these interactions is described using TAG.

📚 Article with a detailed denoscription
🛠 Implementation of the approach

😎Universal database with embeddings

✅txtai is a universal database of embeddings designed for semantic search, orchestration of large language models (LLM), and management of machine learning workflows. This platform allows you to efficiently process and extract information, use semantic search for text search, and organize and automate tasks related to training and applying machine learning models.

Key features of txtai:

— Includes vector search using SQL, object storage, graph analysis, and multimodal indexing
— Supports embeddings for various data types, including text, documents, audio, images, and videos
— Allows you to build pipelines based on language models to perform various tasks, such as generating suggestions for LLM, answering questions, labeling data, trannoscription, translation, summarization, and more

🖥 GitHub
🟡 Documentation

😎3 useful tools for working with SQL tables

SQL Fiddle - A tool for simple testing, debugging and sharing SQL fragments. Add text to the panel, and SQL Fiddle turns it into a noscript for creating the necessary table. Suitable for both working with databases and practicing SQL skills.

SQL Database Modeler - can create the structure of new tables and relationships between them, connect to existing databases and design changes to them. And all this in a nice graphical interface and with a link to GitHub.

SQLFlow - a simple tool for visualizing SQL queries and displaying dependencies. Allows you to track data lineage and transformations in data when executing queries.

🤔Conducting a data quality assessment at Airbnb

✅Airbnb is an online platform for posting and searching for short-term rentals of private housing around the world.

I recently came across an article, where the author describes the process of developing and implementing a data quality assessment methodology, as well as the principles, criteria, and parameters used for this assessment.

As the author notes, the assessment is based on the following principles:

1. Full coverage is an assessment method that can be applied to all data from an entire array, ensuring analysis and processing of information without omissions or limitations. This principle allows for a more complete and accurate study of data, covering the entire set, regardless of its volume or complexity.
2. Automation is a process in which the collection of input data required for the assessment is fully automated, without the need for manual intervention. This principle ensures high speed, accuracy and efficiency in collecting and processing data, which improves the quality of analysis and reduces the time for decision-making.
3. Actionable is a characteristic that means that the data quality assessment is easily accessible and understandable for both producers and consumers of data. This ensures transparency and ease of use of the assessment results, which contributes to more effective interaction and increased trust between all parties.
4. Multidimensionality is a property of the assessment that allows it to be decomposed into various basic components of data quality. This helps to analyze in detail individual aspects affecting the overall quality, such as accuracy, completeness, relevance and consistency, providing a deeper understanding and the ability to target improvement of each component.
5. Evolvability is a characteristic of the assessment, meaning that the criteria and their definitions can adapt and change over time. This flexible approach allows the assessment to remain relevant and effective in the face of changing requirements, new data and technological advances.

💡🤖😎10 AI Terms and Aspects That Everyone Needs to Understand and Be Aware of Today
🧐Today, we’ll look at 10 aspects that most broadly cover the field of AI in its various manifestations:
✅ Reasoning/Planning: Modern AI systems can solve problems by using patterns they’ve learned from historical data to understand the information, which is similar to the process of reasoning. The most advanced systems can go further, tackling more complex problems by creating plans and determining a sequence of actions to achieve a goal.
✅ Learning/Inference: There are two stages to creating and using an AI system: learning and inference. Learning can be compared to the process of educating an AI, where it’s given a set of data and it learns to perform tasks or make predictions based on that data.
Inference is the process by which an AI uses learned patterns and parameters to, for example, predict the price of a new home that will soon go on sale.
✅ Small Language Models (SLMs): Compact versions of Large Language Models (LLMs). Both of these types use machine learning techniques to recognize patterns and relationships, allowing them to generate realistic and natural language responses. However, unlike LLMs, which are huge and require a lot of computing power and memory, SLMs like Phi-3 are trained on smaller, curated datasets and have fewer parameters.
✅ Grounded: Generative AI systems can create stories, poems, jokes, and answer research questions. However, they sometimes have difficulty separating fact from fiction or use outdated data, leading to erroneous answers called “hallucinations.” Developers aim to make AI interactions with the real world more accurate through a process called grounding, where the model is connected to current data and specific examples to improve accuracy and produce more relevant results.
✅ Retrieval Augmented Generation (RAG): When developers give AI access to external data sources to make it more accurate and relevant, a technique called Retrieval Augmented Generation (RAG) is used. This approach saves time and resources by adding new knowledge without having to retrain the AI.
✅ Orchestration: AI programs perform many tasks when processing user requests, and an orchestration layer manages their actions in the right order to get the best response. The orchestration layer can also follow the RAG pattern, searching the web for fresh information and adding context.
✅ Memory: Modern AI models technically do not have memory. However, they may have orchestration instructions that help them “remember” information by performing specific steps with each interaction.
✅ Transformers and Diffusion Models: Humans have been training AI systems to understand and generate language for decades, but one of the breakthroughs that has accelerated progress is the Transformer model. Among generative AIs, Transformers are the ones that understand context and nuance the best and fastest.
Diffusion models are typically used to generate images. These models continue to make small adjustments until they create the desired output.
✅ Frontier Models: Frontier models are large-scale systems that push the boundaries of AI and can perform a wide range of tasks with new and advanced capabilities. They are becoming key tools for a variety of industries, including healthcare, finance, scientific research, and education.
✅ GPU: A graphics processing unit is a powerful computing unit. Initially created to improve the graphics in video games, they have now become the real “muscles” of the computing world. And since AI essentially deals with a huge number of computational problems in order to understand language and recognize images or sounds, GPUs are indispensable for AI both at the training stage and when working with finished models.

💡Creating recommendations for applications with minimal complexity using vector databases

This data not only trains AI systems, but is also the final output that you continue to work with. That's why it's so important to use "good" data. No matter how powerful the model is, if the input is bad data, the output will be the same.

This article is about an example of using the Weaviate database in Streamlit format to simplify working with vector databases. The authors believe that this will allow you to create a powerful search and recommendation system taking into account technical and cost factors.

📚For information, it is worth noting that:
✅Weaviate is an open-source vector database that allows users to store data objects and vector data from machine learning models and easily scales to billions of data objects. .

✅Streamlit is a Python framework. It contains a set of software tools that allow you to transfer a machine learning model to a website. The written "smart" program with this framework can be quickly turned into web applications.