Visualized life tools and applications for personal analyticsGene Leybzon
This document discusses tools for personal analytics that can collect data about behaviors, habits, and thoughts in order to gain self-knowledge. It describes collecting data through sensors, devices, and assessments; analyzing the data for patterns and correlations; and visualizing the data to support decision making and behavior changes. The goal is to move from raw data to actionable suggestions through analyzing factors like sleep duration, quality, and correlations with caffeine intake, environment, and activities.
1 night 'auto detect' sleep tracking - group test 19th may 2015Maneesh Juneja
Examining technology that claims to ‘auto detect’ sleep patterns. No user input required. No buttons to press. No need to keep phone on at night.
To compare and contrast the user experience & results of using 4 different devices to track my sleep during 1 night
Devices used in this test: Sense, Basis Peak, Microsoft Band, Mi Band
The way back to normal starts here
We all want to get out of the house. To reopen the economy. To feel secure again. Safe Paths builds tools that help communities flatten the curve of COVID-19 — together. CovidSafePaths.org
This document discusses privacy-aware artificial intelligence and techniques like split learning and federated learning. It notes the tension between utility and privacy with AI systems and proposes approaches like differential privacy, homomorphic encryption, and sharing wisdom rather than raw data to develop private AI. Split learning and federated learning allow models to be trained from distributed data sources without aggregating private information. The goal is to capture precise data to learn and act while respecting privacy through techniques that train models from decentralized and anonymized data.
The document discusses using customer data and connected devices to provide highly personalized services tailored to individual customers, referred to as the "customer of one." It notes that collecting data from sources like wearables, smart home devices, social media, and health records can provide rich insights but people care about how their data is used. The document advocates for balancing data collection with building customer trust in how the data is handled to deliver improved personalized services without overstepping boundaries regarding data sharing and privacy.
This document discusses using personal informatics and sensor technology to track various health and fitness metrics over time. It provides examples of common metrics tracked, such as heart rate, GPS location, weight, sleep, and nutrition. Several popular wearable devices and apps that can be used for tracking are described, including Fitbit, Nike FuelBand, Jawbone Up, and apps like MyFitnessPal. The document also outlines how the data from these devices and apps can be analyzed and visualized over time to gain insights and help achieve health and fitness goals.
Project page: https://splitlearning.github.io/
Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar
Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg
Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf
Distributed learning of deep neural network over multiple agents
https://www.sciencedirect.com/science/article/pii/S1084804518301590
Otkrist Gupta, Ramesh Raskar,
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
This apps is designed for Geology and Geography explorations, where students and staff can plan out a mission. They can take data with sensors that are wireless connected to their phones
Visualized life tools and applications for personal analyticsGene Leybzon
This document discusses tools for personal analytics that can collect data about behaviors, habits, and thoughts in order to gain self-knowledge. It describes collecting data through sensors, devices, and assessments; analyzing the data for patterns and correlations; and visualizing the data to support decision making and behavior changes. The goal is to move from raw data to actionable suggestions through analyzing factors like sleep duration, quality, and correlations with caffeine intake, environment, and activities.
1 night 'auto detect' sleep tracking - group test 19th may 2015Maneesh Juneja
Examining technology that claims to ‘auto detect’ sleep patterns. No user input required. No buttons to press. No need to keep phone on at night.
To compare and contrast the user experience & results of using 4 different devices to track my sleep during 1 night
Devices used in this test: Sense, Basis Peak, Microsoft Band, Mi Band
The way back to normal starts here
We all want to get out of the house. To reopen the economy. To feel secure again. Safe Paths builds tools that help communities flatten the curve of COVID-19 — together. CovidSafePaths.org
This document discusses privacy-aware artificial intelligence and techniques like split learning and federated learning. It notes the tension between utility and privacy with AI systems and proposes approaches like differential privacy, homomorphic encryption, and sharing wisdom rather than raw data to develop private AI. Split learning and federated learning allow models to be trained from distributed data sources without aggregating private information. The goal is to capture precise data to learn and act while respecting privacy through techniques that train models from decentralized and anonymized data.
The document discusses using customer data and connected devices to provide highly personalized services tailored to individual customers, referred to as the "customer of one." It notes that collecting data from sources like wearables, smart home devices, social media, and health records can provide rich insights but people care about how their data is used. The document advocates for balancing data collection with building customer trust in how the data is handled to deliver improved personalized services without overstepping boundaries regarding data sharing and privacy.
This document discusses using personal informatics and sensor technology to track various health and fitness metrics over time. It provides examples of common metrics tracked, such as heart rate, GPS location, weight, sleep, and nutrition. Several popular wearable devices and apps that can be used for tracking are described, including Fitbit, Nike FuelBand, Jawbone Up, and apps like MyFitnessPal. The document also outlines how the data from these devices and apps can be analyzed and visualized over time to gain insights and help achieve health and fitness goals.
Project page: https://splitlearning.github.io/
Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar
Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg
Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf
Distributed learning of deep neural network over multiple agents
https://www.sciencedirect.com/science/article/pii/S1084804518301590
Otkrist Gupta, Ramesh Raskar,
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
This apps is designed for Geology and Geography explorations, where students and staff can plan out a mission. They can take data with sensors that are wireless connected to their phones
The document provides an overview of a Quantified Self club meeting at USC. It discusses what self-quantification is, examples of data that can be tracked, and expectations for the club. Members are expected to do presentations on self-tracking projects or apps. Suggested topics include reviewing a tracking device, sharing an analytics tool, or explaining a quantified self project done by someone else. Common areas of interest to track include health, productivity, and psychological states. The club aims to help members learn data and presentation skills through sharing self-tracking experiences.
Quantified Self - The Human App InstrumentEnola Labs
The quantified self economy features several activity monitoring devices as well as hundreds of applications that can track several aspects of your life. Atomic Axis believes that the problem with these applications is that they are disparate. They function and churn data for an individual independent of the other facets of human existence. Sure, an app can tell you how long you slept last night and can even illustrate your sleep cycle in an impressive visualization and analytical interface—but can it extrapolate that information to tell you how that data will affect your mood, efficiency and behavior throughout the day?
The overall theme of their vision is that organizations are finding interesting ways to gather data and use that data to make predictions. As soon as we are able to find a meaningful way to correlate quantified self data, can we use that data in an effort to solve large scale health issues? Just as Google was able to predict flu trends using aggregated search queries, we can use aggregated health information to make certain predictions that can improve individual and certain demographic’s quality of life, reduce healthcare expenditures by understanding exactly where funds need to be allocated, and aid health professionals in their effort to detect, prevent and remediate any potential large scale health issue.
Business Analytics and Data mining.pdfssuser0413ec
Business analytics involves analyzing large amounts of data to discover patterns and make predictions. It uses techniques like data mining, predictive analytics, and statistical analysis. The goals are to help businesses make smarter decisions, identify trends, and improve performance. Data mining is the process of automatically discovering useful patterns from large data sets. It is used to extract knowledge from vast amounts of data that would otherwise be unknown. Data mining helps businesses gain insights from their data to increase sales, improve customer retention, and enhance brand experience.
TRAQS.me
http://traqs.me
Tools for Reporting and Analysis of the Quantified Self
-- TRAQS is now ActiveOS - a Wearables Insights Platform (http://activeos.com)
Bridge the Gap Between Data and Decisions: Master Data Science Course using Machine Learning
Empower yourself with the in-demand skills of data science and machine learning through our dynamic Applied Hybrid Training program!
This innovative data science course seamlessly blends classroom instruction with online learning, providing a well-rounded foundation for your data science journey. Learn to unlock the power of data and leverage machine learning algorithms to solve real-world challenges.
Uncover the Magic Behind the Data:
Machine Learning Fundamentals: Demystify the concepts of machine learning algorithms and explore their practical applications across various industries.
Python Programming Prowess: Gain hands-on experience with Python, the language of choice for data science. Learn how to leverage its libraries and tools to implement machine learning models effectively.
Data Wrangling Expertise: Master techniques for handling and manipulating datasets from diverse fields. Understand how to prepare data for optimal use with machine learning algorithms.
Actionable Insights from Algorithms: Discover how to interpret machine learning outputs and translate them into actionable insights that drive real-world results.
Data Communication Mastery: Learn to communicate your data science findings with clarity and impact, effectively presenting the results of your machine learning models.
By the end of this Data Science Course using Machine Learning, you will have enough knowledge and hands-on expertise in Python to use and apply them in the real world around you. Also, you will be able to get prepared for certifications of Data Camp and Cognitive AI.
This document discusses how technology can help people improve their health and fitness. It begins by explaining how tools like websites, apps, and devices empower users to "hack" their wellness by tracking factors like sleep, mood, disease management, fitness, and diet. The rise of wearable technology is described, with shipment of devices like Fitbit and Jawbone projected to greatly increase in coming years. The rest of the document provides examples of popular health and fitness websites, apps, and trackers that allow users to quantify and improve various aspects of their wellness.
Introduction to Routine Health Information System SlidesSaide OER Africa
Introduction to Routine Health Information System was created for undergraduate and postgraduate health science students to introduce them to the concepts and methods of routine health information systems.
The learning objectives are to help users explain the roles of routine health information systems (RHIS) in health service management; examine strategies used to improve routine health information systems; acquaint with skills to carry out the process of improving RHIS performance; discuss three categories of determinants that influence RHIS.
Fitoop.com is the Mint.com of health and fitness data.
Fitoop not only aggregates, and analyzes your data, but also provides you with custom suggestions on how to be a healthier and happier you.
[DigiHealth 22] IT business processes within healthcare organization - Sofija...DataScienceConferenc1
Everyone talks about IT business processes, but it seems like there is a quite some confusion regarding them. On this session, we will talk about IT business processes in Stada IT Solutions, an healthcare organization. First, we will tell you how our data team used to work two and a half years ago, when I joined the company, how we are organized now and all the problems we have met during our improvement journey, and what our future work will be.
Jerry Matczak shares his experiences using the Basis health tracker, discussing features, pros and cons, data available and how it compares with the Fitbit. Presented at the June 2014 Indianapolis Quantified Self Meetup.
The document provides an overview of data science, big data, data mining, and data mining techniques. It defines data science as a multi-disciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. Big data is described as large, diverse datasets that are too large for traditional databases to handle. Common data mining tasks like prediction, classification, clustering and association rule mining are summarized. Finally, specific techniques like decision trees, k-means clustering, and association rule mining are overviewed.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Tech-Savvy Fitness & the Quantified SelfMarc Stephens
Marc used various health tracking technologies like Fitbit, Jawbone Up, and Basis Watch to quantify metrics about his workouts, heart rate, GPS location, weight, body composition, nutrition, blood pressure, sleep, and mood over time. By analyzing trends in the data, Marc gained insights into how his weight, body composition, and other health factors changed when he was consistent with tracking versus not. He also used blogs, websites and other online resources to help educate himself on nutrition, exercise, and staying motivated towards achieving his health and fitness goals.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Introduction to Digital Life (Social Media, Reputation Management, and Altmet...KR_Barker
This document provides an introduction to digital life and reputation management. It discusses how digital life has become possible through technologies like the internet, smartphones, and internet of things devices. It also covers digital identity, how Google works to determine search rankings, and the importance of reputation management online. Altmetrics are introduced as new ways to measure the impact of scholarly work beyond traditional citations. Issues with altmetrics and how researchers can start tracking their online impact are also summarized.
The document provides instructions for registering an account and setting up devices for the L4L activity tracking program. It discusses entering registration information, reviewing terms and policies, completing additional questions, and setting privacy settings. It also describes how to set up the Fitlinxx Actiped motion sensor device by entering the serial number into your profile. The software agent is then installed to upload movement data wirelessly to your activity page.
About
Evolution of Data, Data Science , Business Analytics, Applications, AI, ML, DL, Data science – Relationship, Tools for Data Science, Life cycle of data science with case study,
Algorithms for Data Science, Data Science Research Areas,
Future of Data Science.
Generative AI Application Development using LangChain and LangFlowGene Leybzon
LangChain and LangFlow are tools for developing applications using large language models (LLMs). LangChain provides libraries, templates, and tools to facilitate building context-aware systems using LLMs from prototype to production. It includes components, chains to process data, and LangSmith for debugging models. LangFlow is a GUI for LangChain. The presentation demonstrates LangChain's chat capabilities and use of tools/agents. It discusses building applications with LangChain and deploying them via LangServe APIs. LangChain aims to enhance LLM utility by making them more reasoning and context-aware.
🚀 What Are GPTs?
GPTs are tailor-made ChatGPT versions that you can craft to suit your specific needs. Whether it's for learning new skills, aiding in education, or assisting in unique work tasks, these custom GPTs are designed to be versatile and incredibly user-friendly.
✨ Create Your Own AI Assistant - No Coding Required! The best part? You don't need to be a tech wizard to create your GPT. The process is as simple as starting a conversation - guiding the AI, feeding it extra knowledge, and choosing its capabilities, like web searching, crafting images, or data analysis.
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The document provides an overview of a Quantified Self club meeting at USC. It discusses what self-quantification is, examples of data that can be tracked, and expectations for the club. Members are expected to do presentations on self-tracking projects or apps. Suggested topics include reviewing a tracking device, sharing an analytics tool, or explaining a quantified self project done by someone else. Common areas of interest to track include health, productivity, and psychological states. The club aims to help members learn data and presentation skills through sharing self-tracking experiences.
Quantified Self - The Human App InstrumentEnola Labs
The quantified self economy features several activity monitoring devices as well as hundreds of applications that can track several aspects of your life. Atomic Axis believes that the problem with these applications is that they are disparate. They function and churn data for an individual independent of the other facets of human existence. Sure, an app can tell you how long you slept last night and can even illustrate your sleep cycle in an impressive visualization and analytical interface—but can it extrapolate that information to tell you how that data will affect your mood, efficiency and behavior throughout the day?
The overall theme of their vision is that organizations are finding interesting ways to gather data and use that data to make predictions. As soon as we are able to find a meaningful way to correlate quantified self data, can we use that data in an effort to solve large scale health issues? Just as Google was able to predict flu trends using aggregated search queries, we can use aggregated health information to make certain predictions that can improve individual and certain demographic’s quality of life, reduce healthcare expenditures by understanding exactly where funds need to be allocated, and aid health professionals in their effort to detect, prevent and remediate any potential large scale health issue.
Business Analytics and Data mining.pdfssuser0413ec
Business analytics involves analyzing large amounts of data to discover patterns and make predictions. It uses techniques like data mining, predictive analytics, and statistical analysis. The goals are to help businesses make smarter decisions, identify trends, and improve performance. Data mining is the process of automatically discovering useful patterns from large data sets. It is used to extract knowledge from vast amounts of data that would otherwise be unknown. Data mining helps businesses gain insights from their data to increase sales, improve customer retention, and enhance brand experience.
TRAQS.me
http://traqs.me
Tools for Reporting and Analysis of the Quantified Self
-- TRAQS is now ActiveOS - a Wearables Insights Platform (http://activeos.com)
Bridge the Gap Between Data and Decisions: Master Data Science Course using Machine Learning
Empower yourself with the in-demand skills of data science and machine learning through our dynamic Applied Hybrid Training program!
This innovative data science course seamlessly blends classroom instruction with online learning, providing a well-rounded foundation for your data science journey. Learn to unlock the power of data and leverage machine learning algorithms to solve real-world challenges.
Uncover the Magic Behind the Data:
Machine Learning Fundamentals: Demystify the concepts of machine learning algorithms and explore their practical applications across various industries.
Python Programming Prowess: Gain hands-on experience with Python, the language of choice for data science. Learn how to leverage its libraries and tools to implement machine learning models effectively.
Data Wrangling Expertise: Master techniques for handling and manipulating datasets from diverse fields. Understand how to prepare data for optimal use with machine learning algorithms.
Actionable Insights from Algorithms: Discover how to interpret machine learning outputs and translate them into actionable insights that drive real-world results.
Data Communication Mastery: Learn to communicate your data science findings with clarity and impact, effectively presenting the results of your machine learning models.
By the end of this Data Science Course using Machine Learning, you will have enough knowledge and hands-on expertise in Python to use and apply them in the real world around you. Also, you will be able to get prepared for certifications of Data Camp and Cognitive AI.
This document discusses how technology can help people improve their health and fitness. It begins by explaining how tools like websites, apps, and devices empower users to "hack" their wellness by tracking factors like sleep, mood, disease management, fitness, and diet. The rise of wearable technology is described, with shipment of devices like Fitbit and Jawbone projected to greatly increase in coming years. The rest of the document provides examples of popular health and fitness websites, apps, and trackers that allow users to quantify and improve various aspects of their wellness.
Introduction to Routine Health Information System SlidesSaide OER Africa
Introduction to Routine Health Information System was created for undergraduate and postgraduate health science students to introduce them to the concepts and methods of routine health information systems.
The learning objectives are to help users explain the roles of routine health information systems (RHIS) in health service management; examine strategies used to improve routine health information systems; acquaint with skills to carry out the process of improving RHIS performance; discuss three categories of determinants that influence RHIS.
Fitoop.com is the Mint.com of health and fitness data.
Fitoop not only aggregates, and analyzes your data, but also provides you with custom suggestions on how to be a healthier and happier you.
[DigiHealth 22] IT business processes within healthcare organization - Sofija...DataScienceConferenc1
Everyone talks about IT business processes, but it seems like there is a quite some confusion regarding them. On this session, we will talk about IT business processes in Stada IT Solutions, an healthcare organization. First, we will tell you how our data team used to work two and a half years ago, when I joined the company, how we are organized now and all the problems we have met during our improvement journey, and what our future work will be.
Jerry Matczak shares his experiences using the Basis health tracker, discussing features, pros and cons, data available and how it compares with the Fitbit. Presented at the June 2014 Indianapolis Quantified Self Meetup.
The document provides an overview of data science, big data, data mining, and data mining techniques. It defines data science as a multi-disciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. Big data is described as large, diverse datasets that are too large for traditional databases to handle. Common data mining tasks like prediction, classification, clustering and association rule mining are summarized. Finally, specific techniques like decision trees, k-means clustering, and association rule mining are overviewed.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Tech-Savvy Fitness & the Quantified SelfMarc Stephens
Marc used various health tracking technologies like Fitbit, Jawbone Up, and Basis Watch to quantify metrics about his workouts, heart rate, GPS location, weight, body composition, nutrition, blood pressure, sleep, and mood over time. By analyzing trends in the data, Marc gained insights into how his weight, body composition, and other health factors changed when he was consistent with tracking versus not. He also used blogs, websites and other online resources to help educate himself on nutrition, exercise, and staying motivated towards achieving his health and fitness goals.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Introduction to Digital Life (Social Media, Reputation Management, and Altmet...KR_Barker
This document provides an introduction to digital life and reputation management. It discusses how digital life has become possible through technologies like the internet, smartphones, and internet of things devices. It also covers digital identity, how Google works to determine search rankings, and the importance of reputation management online. Altmetrics are introduced as new ways to measure the impact of scholarly work beyond traditional citations. Issues with altmetrics and how researchers can start tracking their online impact are also summarized.
The document provides instructions for registering an account and setting up devices for the L4L activity tracking program. It discusses entering registration information, reviewing terms and policies, completing additional questions, and setting privacy settings. It also describes how to set up the Fitlinxx Actiped motion sensor device by entering the serial number into your profile. The software agent is then installed to upload movement data wirelessly to your activity page.
About
Evolution of Data, Data Science , Business Analytics, Applications, AI, ML, DL, Data science – Relationship, Tools for Data Science, Life cycle of data science with case study,
Algorithms for Data Science, Data Science Research Areas,
Future of Data Science.
Similar to How to gain insight on yourself? Tools for personal analytics. (20)
Generative AI Application Development using LangChain and LangFlowGene Leybzon
LangChain and LangFlow are tools for developing applications using large language models (LLMs). LangChain provides libraries, templates, and tools to facilitate building context-aware systems using LLMs from prototype to production. It includes components, chains to process data, and LangSmith for debugging models. LangFlow is a GUI for LangChain. The presentation demonstrates LangChain's chat capabilities and use of tools/agents. It discusses building applications with LangChain and deploying them via LangServe APIs. LangChain aims to enhance LLM utility by making them more reasoning and context-aware.
🚀 What Are GPTs?
GPTs are tailor-made ChatGPT versions that you can craft to suit your specific needs. Whether it's for learning new skills, aiding in education, or assisting in unique work tasks, these custom GPTs are designed to be versatile and incredibly user-friendly.
✨ Create Your Own AI Assistant - No Coding Required! The best part? You don't need to be a tech wizard to create your GPT. The process is as simple as starting a conversation - guiding the AI, feeding it extra knowledge, and choosing its capabilities, like web searching, crafting images, or data analysis.
Generative AI Use cases for Enterprise - Second SessionGene Leybzon
This document provides an overview of generative AI use cases for enterprises. It begins with addressing concerns that generative AI will replace jobs. The presentation then defines generative AI as AI that generates new content like text, images or code based on patterns learned from training data.
Several examples of generative AI outputs are shown including code, text, images and advice. Potential use cases for enterprises are then outlined, including synthetic data generation, code generation, code quality checks, customer service, and data analysis. The presentation concludes by emphasizing that people will be "replaced by someone who knows how to use AI", not AI itself.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
Non-fungible tokens (NFTs) are unique digital assets that are verified on a blockchain network, allowing for the creation and ownership of one-of-a-kind digital items, such as artwork, music, videos, and other types of digital content. They are important because they provide a way for digital creators to monetize their work and establish ownership, scarcity, and authenticity of their creations. NFTs have also gained popularity as a form of investment and collectible item, with some NFTs selling for millions of dollars.
This slide deck includes the following sections:
Introduction: Provide a brief overview of what NFTs are and their significance in the digital world.
How NFTs work: Explain the process of creating and verifying NFTs on a blockchain network, including the use of smart contracts and cryptographic hashing.
Types of NFTs: Describe the various types of NFTs that can be created, such as digital artwork, music, videos, and other types of digital content.
Benefits of NFTs: Highlight the benefits of NFTs, including the ability to establish ownership, scarcity, and authenticity of digital assets, as well as their potential as a new source of revenue for creators.
Market trends: Provide an overview of the current state of the NFT market, including recent sales and trends in various industries, such as art, sports, and gaming.
Potential use cases: Discuss potential use cases for NFTs beyond the current market, such as in the areas of identity verification, supply chain management, and digital voting.
Challenges and risks: Acknowledge the challenges and risks associated with NFTs, such as environmental concerns related to blockchain networks and the potential for fraudulent activity.
Conclusion: Summarize the key takeaways of the presentation and emphasize the growing importance of NFTs in the digital world.
Introduction to Solidity and Smart Contract Development (9).pptxGene Leybzon
Here is a suggested learning path for getting started with blockchain and smart contracts development:
1. Learn the fundamentals of blockchain technology - how it works, key components, types of blockchains.
2. Understand cryptography basics - hashes, digital signatures, public/private key encryption.
3. Learn the Solidity programming language for writing Ethereum smart contracts.
4. Build simple smart contracts and deploy them to testnets.
5. Learn how to develop decentralized applications (dApps) using smart contracts.
6. Explore blockchain development platforms like Ethereum, Hyperledger, etc.
7. Learn frontend libraries like Web3.js for interacting with blockchains.
8.
Ethereum and other blockchains are finding their way into the enterprise world. We look into common use cases, blockchains, and standard approaches to deploy and access enterprise blockchains
This document discusses rentable non-fungible tokens (NFTs) and the ERC-4907 standard. It begins with an overview of NFTs and common standards like ERC-721. It then introduces the concept of renting NFTs and outlines the rental experience. The ERC-4907 standard is presented as enabling risk-free NFT rentals by allowing contracts to set users and expiration dates for rented NFTs. Code examples and next steps are provided to implement rentable NFTs using this standard.
The document discusses decentralized governance and smart contracts, providing examples of DAO governance models and OpenZeppelin governor contract functionality. It defines DAOs as organizations represented by rules encoded as a computer program and controlled by members, not a central authority. Notable DAOs like Dash, The DAO, Augur, and Uniswap are examined. Yearn Finance's multi-DAO governance structure using YFI tokens is explained in detail. Finally, examples are provided for deploying a MeetupToken contract, MeetupGovernor contract, and creating a DAO using these contracts and on-chain voting functionality.
Smart contracts and NFTs call for a revised approach to store data. In these slides, 3 options for distributed and fault-tolerant data storage are presented:
IPFS
Filecoin
Arweave
Demonstrating how to create an end-to-end Web-based application that uses blockchain for user authentication, read, and write access to the data stored on the blockchain
Instantly tradeable NFT contracts based on ERC-1155 standardGene Leybzon
The document discusses the ERC-1155 token standard which allows for both fungible and non-fungible tokens to be transferred together in a single transaction, providing benefits over existing standards like ERC-20 and ERC-721. It provides an overview of the standard's functions and events as well as examples of how it can be implemented using OpenZeppelin's ERC-1155 contract. The presentation also covers how to publish an ERC-1155 based NFT collection on the OpenSea marketplace.
Non-fungible tokens. From smart contract code to marketplaceGene Leybzon
This document provides an overview of non-fungible tokens (NFTs), including their history from colored coins in 2012 to recent growth in 2021. Key concepts covered include the differences between fungible and non-fungible assets, common NFT use cases like art and collectibles, and technical standards like ERC-721 and ERC-1155. The document demonstrates how to create, mint, and list an NFT for sale using OpenSea and the Ethereum blockchain.
This document provides an overview of non-fungible tokens (NFTs). It defines NFTs and discusses their history from colored coins in 2012 to recent growth in 2021. Common use cases for NFTs like art, games, and collectibles are described. The ERC-721 and ERC-1155 token standards are explained. Hands-on examples are provided for creating an ERC-721 contract from scratch and minting/selling an NFT on OpenSea.
Chainlink is a decentralized oracle network that allows smart contracts to securely access external data and APIs. It provides smart contracts with data from outside sources through oracle nodes that query, verify, and authenticate external data feeds. Chainlink has built-in price feeds and adapters that allow smart contracts to request data from nodes paid in LINK tokens. The document demonstrates a smart contract using Chainlink to request ETH price data from an API and receive the response once fulfilled.
The document discusses creating an ERC-20 token on the Binance Smart Chain using OpenZeppelin and Remix IDE. It provides an overview of ERC-20 standards, describes how to create an ERC-20 token contract using OpenZeppelin, edit the code in Remix, deploy it to BSC testnet, and check that the token was successfully created and transferred between accounts.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
How to gain insight on yourself? Tools for personal analytics.
1. How to get insight on yourself?
Tools for personal analytics
By Gene Leybzon
2. Personal Informatics
“Personal informatics is a class of tools that
help people collect personally relevant
information for the purpose of self-reflection
and self-monitoring“
[from http://www.personalinformatics.org/]
3. What we want to know?
• Common behavior
• Habits
• Thoughts
• Feelings
4. Why? (Motivation)
• To improve health
– Cure or manage health condition
– Achieve a specific goal
– Find triggers
• To improve quality of life
– Maximize performance
– Mindfulness
• To find new experiences
6. Data Collection
• Sensors (Fitbit, WiFi-scale, heart rate monitor)
• Spreadsheets
• Applications (including mobile apps)
• Pen and paper
• Web sites