The document discusses key concepts in probability, including experiments, events, sample spaces, and methods for assigning probabilities. It covers counting rules for determining the number of possible outcomes in experiments. Probability is defined as a numerical measure of the likelihood of an event occurring between 0 and 1. Methods for assigning probabilities include the classical, relative frequency, and subjective approaches. Key probability relationships explored are complements, unions, intersections, and conditional probabilities. Examples are provided to illustrate probability concepts and relationships.
- Prospect theory was developed by Kahneman and Tversky as an alternative to expected utility theory to better explain actual human behavior in decision-making involving risk and uncertainty.
- Key concepts of prospect theory include framing outcomes as gains or losses from a reference point, risk aversion for gains but risk seeking for losses, and greater sensitivity to losses than equivalent gains (loss aversion).
- Experimental evidence showed people make choices that violate expected utility theory but can be explained by prospect theory concepts like loss aversion, reflection effect, and nonlinear probability weighting.
This document discusses chapter 5 of the textbook "Statistics for Business and Economics" which covers discrete probability distributions. It begins with an overview of random variables and discrete probability distributions. It then discusses the binomial and Poisson probability distributions in depth through examples and formulas. Key aspects covered include the probability mass function, expected value, and variance for both the binomial and Poisson distributions. The document uses slides with text, formulas, and diagrams to explain the concepts.
The document discusses statistical methods for making inferences about the difference between two population proportions. It covers interval estimation of p1 - p2 using a normal approximation as well as hypothesis tests to evaluate whether population proportions differ. Examples are provided to illustrate calculating confidence intervals and performing hypothesis tests to determine if an advertising campaign increased brand awareness. The analysis of the example data does not find sufficient evidence to conclude the campaign increased awareness.
This document summarizes key concepts from expected utility theory. It outlines the theory's assumptions that people have rational preferences, maximize utility, and make independent decisions based on information. Preferences are defined as complete and transitive. Expected utility theory says individuals should act to maximize expected utility when making decisions under risk. It provides an example calculation and discusses properties like risk aversion. However, the document notes that expected utility theory has problems accounting for all observed behaviors.
The document appears to be a series of slides covering topics in discrete probability distributions, including random variables, discrete and continuous probability distributions, expected value and variance, and several specific discrete probability distributions such as the binomial, Poisson, and hypergeometric distributions. The slides include definitions, examples, formulas, and calculations related to these probability and statistics concepts.
This document provides an overview and examples of three statistical hypothesis tests: comparing multiple proportions, test of independence, and goodness of fit test. It includes the hypotheses, test statistics, and procedures for each test. An example is provided for comparing customer satisfaction proportions across three home styles. The results of the test show the proportions are not equal. A multiple comparisons procedure identifies where the differences in proportions specifically exist. A second example demonstrates a test of independence between home price and style. The results show price and style are not independent variables.
The document discusses statistical methods for making inferences about population variances from sample data. Specifically, it covers interval estimation of a population variance using a chi-square distribution, developing confidence intervals for the population variance and standard deviation, and hypothesis testing for a population variance using chi-square test statistics. Examples are provided to illustrate computing confidence intervals for a population variance based on temperature readings from different thermostats.
- Prospect theory was developed by Kahneman and Tversky as an alternative to expected utility theory to better explain actual human behavior in decision-making involving risk and uncertainty.
- Key concepts of prospect theory include framing outcomes as gains or losses from a reference point, risk aversion for gains but risk seeking for losses, and greater sensitivity to losses than equivalent gains (loss aversion).
- Experimental evidence showed people make choices that violate expected utility theory but can be explained by prospect theory concepts like loss aversion, reflection effect, and nonlinear probability weighting.
This document discusses chapter 5 of the textbook "Statistics for Business and Economics" which covers discrete probability distributions. It begins with an overview of random variables and discrete probability distributions. It then discusses the binomial and Poisson probability distributions in depth through examples and formulas. Key aspects covered include the probability mass function, expected value, and variance for both the binomial and Poisson distributions. The document uses slides with text, formulas, and diagrams to explain the concepts.
The document discusses statistical methods for making inferences about the difference between two population proportions. It covers interval estimation of p1 - p2 using a normal approximation as well as hypothesis tests to evaluate whether population proportions differ. Examples are provided to illustrate calculating confidence intervals and performing hypothesis tests to determine if an advertising campaign increased brand awareness. The analysis of the example data does not find sufficient evidence to conclude the campaign increased awareness.
This document summarizes key concepts from expected utility theory. It outlines the theory's assumptions that people have rational preferences, maximize utility, and make independent decisions based on information. Preferences are defined as complete and transitive. Expected utility theory says individuals should act to maximize expected utility when making decisions under risk. It provides an example calculation and discusses properties like risk aversion. However, the document notes that expected utility theory has problems accounting for all observed behaviors.
The document appears to be a series of slides covering topics in discrete probability distributions, including random variables, discrete and continuous probability distributions, expected value and variance, and several specific discrete probability distributions such as the binomial, Poisson, and hypergeometric distributions. The slides include definitions, examples, formulas, and calculations related to these probability and statistics concepts.
This document provides an overview and examples of three statistical hypothesis tests: comparing multiple proportions, test of independence, and goodness of fit test. It includes the hypotheses, test statistics, and procedures for each test. An example is provided for comparing customer satisfaction proportions across three home styles. The results of the test show the proportions are not equal. A multiple comparisons procedure identifies where the differences in proportions specifically exist. A second example demonstrates a test of independence between home price and style. The results show price and style are not independent variables.
The document discusses statistical methods for making inferences about population variances from sample data. Specifically, it covers interval estimation of a population variance using a chi-square distribution, developing confidence intervals for the population variance and standard deviation, and hypothesis testing for a population variance using chi-square test statistics. Examples are provided to illustrate computing confidence intervals for a population variance based on temperature readings from different thermostats.
- Probability is a numerical measure of the likelihood of an event occurring, ranging from 0 to 1. Near 0 means unlikely, near 1 means almost certain.
- In statistics, experiments involve probability and outcomes may differ even if the experiment is repeated the same way. Statistical experiments are sometimes called random experiments.
- An experiment's sample space consists of all possible outcomes. The probability of an event is the sum of probabilities of outcomes in that event.
The document outlines guidelines for modeling word problems with equations and provides examples of how to apply the guidelines. It discusses setting up models to solve problems involving interest, area/length, mixtures, time to complete jobs, and distance/rate/time. The examples show identifying relevant variables, translating words to algebraic expressions, setting up equations based on relationships stated in the problem, and solving the equations to answer the original question. Overall, the document introduces how to translate real-world scenarios described in text into mathematical equations that can be used to find unknown values.
The document discusses sampling distributions and point estimation. It begins with definitions of key sampling terms like population, sample, and frame. It then covers topics like selecting samples from finite and infinite populations, including using random numbers. Point estimation is introduced as a way to use sample statistics like the sample mean or proportion to estimate population parameters. An example uses a sample from college applications to estimate the average SAT score and proportion wanting housing in the full population. The sampling distribution of the sample mean is defined, and how it can be used to make statistical inferences about the population mean.
This document contains slides from a lecture on quantitative analysis and decision making. It introduces topics like the decision making process, quantitative analysis, modeling, and cost-benefit analysis. Specifically, it discusses the 7 steps of problem solving, quantitative vs qualitative analysis, different types of models, and examples of using models to represent costs, revenue, and profit for business decisions. The quantitative approach presented in the slides can help structure complex problems, analyze alternatives, and recommend optimal solutions.
This document discusses various concepts related to investment risk and rates of return. It covers stand-alone risk, portfolio risk, standard deviation as a measure of risk, the benefits of diversification in reducing portfolio risk, and the capital asset pricing model. The key points are: 1) a portfolio's risk is generally lower than the average risk of its individual components due to diversification, especially if the components are not perfectly positively correlated; 2) standard deviation measures total risk while the coefficient of variation allows comparison of risk levels for investments with different returns; and 3) the capital asset pricing model suggests investors should only be compensated for non-diversifiable market risk, not company-specific risk.
This document summarizes research on how behavioral factors can influence managerial decision-making in capital budgeting. It finds that managers may prefer simpler metrics like IRR and payback period due to ease of processing, even if NPV is optimal. Loss aversion can also cause managers to avoid abandoning unprofitable investments. Additionally, experiments show managers are inclined to avoid profitable investments when associated with negative emotions. The document also discusses how overconfidence can lead to excess business entry due to overoptimism about success. Further experiments demonstrate more market entry occurs when payoffs depend on perceived skill rather than luck.
This document summarizes research on how behavioral factors can influence managerial decision-making in capital budgeting. It finds that managers may prefer simpler metrics like IRR and payback period due to ease of processing, even if NPV is optimal. Loss aversion can also cause managers to avoid abandoning unprofitable investments. Additionally, experiments show managers are inclined to avoid profitable investments when associated with negative emotions. The document also discusses how overconfidence can lead to excess business entry due to overoptimism about success. Further experiments demonstrate more market entry occurs when payoffs depend on perceived skill rather than luck.
This document discusses chapter 5 from the textbook "Statistics for Business and Economics (13th edition)" by Anderson, Sweeney, Williams, Camm, and Cochran. The chapter covers discrete probability distributions, including random variables, developing discrete distributions, expected value and variance, and the binomial, Poisson, and hypergeometric distributions. It provides examples and explanations of key concepts such as random variables, discrete probability distributions represented as tables, graphs, and formulas, and expected value.
Online Consumer Panel simulator - First Version demo: Sampling Operations Ana...evaristoc
Extract of a "what if...?" proposal for a tool for operations decision making in the Online Consumer Panels sector based on computational-intensive methods.
A first demo exists at github.com/evaristoc
- Portfolio risk is lower than the weighted average risk of individual securities if their correlations are less than one. The capital asset pricing model relates a security's expected return to its beta, a measure of nondiversifiable risk.
- For markets to be efficient, security prices should quickly incorporate new information and no investors should consistently earn excess returns after accounting for risk and costs. However, the joint hypothesis problem notes that rejections of market efficiency may also reflect issues with the model used to calculate expected returns.
- Agency problems arise in situations where one party (the principal) delegates authority to another (the agent), such as when shareholders hire managers, but their incentives are not perfectly aligned.
This document discusses interest rates and yield curves. It explains that the four main factors affecting interest rate levels are production opportunities, time preferences, risk, and expected inflation. It also defines nominal and real interest rates. The document then presents the determinants of interest rates as the real risk-free rate plus premiums for inflation, default risk, liquidity, and maturity. It constructs a hypothetical yield curve and discusses how corporate bond yield curves relate to Treasury yield curves. Finally, it covers the pure expectations theory of the yield curve.
This document provides slides on interval estimation of population parameters. It introduces concepts such as interval estimates, margin of error, confidence intervals, the t-distribution, and sample size determination. Examples are provided to illustrate how to construct confidence intervals for a population mean when the population standard deviation is both known and unknown.
This document discusses how simulation can be used to model real-world risk in biopharma portfolio optimization. It notes that traditional approaches using expected net present value alone are insufficient. Simulation allows modeling of uncertain variables like clinical trial outcomes, development timelines, and commercial performance. This provides a statistical view of portfolio outcomes over many iterations. It enables analysis of key metrics like the probability of meeting revenue or launch targets. Simulation also facilitates sophisticated tradeoff analysis by characterizing portfolio risk-return and evaluating how changes, like adding new programs, impact overall performance and risk levels.
This document discusses capital budgeting and methods for evaluating investment projects. It covers topics like net present value (NPV), internal rate of return (IRR), profitability index, payback period, and discounted payback. Formulas for NPV and IRR are presented along with examples of calculating these metrics for franchise investment projects. The rationale for using NPV and IRR in capital budgeting decisions is explained. Creating an NPV profile by calculating NPV at different discount rates is also introduced.
With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes.
Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.
This document discusses key concepts related to risk and return in investments. It covers basic return and risk, stand-alone risk measured by standard deviation, portfolio risk, and the relationship between risk and return as described by the Capital Asset Pricing Model. The topics are illustrated with examples of calculating returns, standard deviation, and portfolio returns using historical data for stocks. Risk is shown to be reduced by holding a diversified portfolio rather than individual stocks.
The document discusses techniques for establishing shorter feedback loops between developing features and measuring user behavior, including:
1) Shadow traffic which runs new and old features simultaneously to get early feedback without users noticing a difference.
2) Visual reports which assess the quality of a feature through a report (e.g. HTML page) of key metrics.
3) A/B testing which statistically compares user behavior between a control and test group after exposing each to a different variant of a feature. Sample size considerations and statistical significance are discussed.
The document provides an overview of key concepts related to saving, investment, and the financial system. It discusses how the financial system matches savers with borrowers through financial markets and intermediaries like banks and mutual funds. It explains that in a closed economy, saving must equal investment and explores how budget deficits and surpluses impact public saving. The document also discusses the meaning of private saving and investment and how interest rates adjust in financial markets to equalize the supply and demand of loanable funds. Finally, it notes that budget deficits can "crowd out" private investment and thereby reduce long-run economic growth.
Management Of Business Operation Components PowerPoint Presentation SlidesSlideTeam
Management Of Business Operation Components PowerPoint Presentation Slides is specially-designed for astute business professionals. Use these components of operations management PowerPoint presentation to showcase various categories of infrastructure services as per your project. Elaborate on the global market size for many consecutive years using a bar graph. Represent the key areas of infrastructure investment, key drivers, and technological trends using our PPT slideshow. The key aspects of operations management PowerPoint theme is ideal to demonstrate the asset management process, lifecycle, framework, and condition assessment. Elucidate the deterministic, stochastic, and AI deterioration models to ascertain funding requirements. Take advantage of the state-of-the-art data visualizations to explain infrastructure optimization, and asset management decision journey. Walk the audience through the value-driven decision-making methodology. Employ our PPT layout to represent business infrastructure performance and cost functions. Illustrate critical infrastructure dependencies and interdependencies assessment framework. So download now to outline the key components like the lifecycle of contract management, and showcase the workflow management process. Our Management Of Business Operation Components PowerPoint Presentation Slides are explicit and effective. They combine clarity and concise expression. https://bit.ly/2L3Sc1h
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
- Probability is a numerical measure of the likelihood of an event occurring, ranging from 0 to 1. Near 0 means unlikely, near 1 means almost certain.
- In statistics, experiments involve probability and outcomes may differ even if the experiment is repeated the same way. Statistical experiments are sometimes called random experiments.
- An experiment's sample space consists of all possible outcomes. The probability of an event is the sum of probabilities of outcomes in that event.
The document outlines guidelines for modeling word problems with equations and provides examples of how to apply the guidelines. It discusses setting up models to solve problems involving interest, area/length, mixtures, time to complete jobs, and distance/rate/time. The examples show identifying relevant variables, translating words to algebraic expressions, setting up equations based on relationships stated in the problem, and solving the equations to answer the original question. Overall, the document introduces how to translate real-world scenarios described in text into mathematical equations that can be used to find unknown values.
The document discusses sampling distributions and point estimation. It begins with definitions of key sampling terms like population, sample, and frame. It then covers topics like selecting samples from finite and infinite populations, including using random numbers. Point estimation is introduced as a way to use sample statistics like the sample mean or proportion to estimate population parameters. An example uses a sample from college applications to estimate the average SAT score and proportion wanting housing in the full population. The sampling distribution of the sample mean is defined, and how it can be used to make statistical inferences about the population mean.
This document contains slides from a lecture on quantitative analysis and decision making. It introduces topics like the decision making process, quantitative analysis, modeling, and cost-benefit analysis. Specifically, it discusses the 7 steps of problem solving, quantitative vs qualitative analysis, different types of models, and examples of using models to represent costs, revenue, and profit for business decisions. The quantitative approach presented in the slides can help structure complex problems, analyze alternatives, and recommend optimal solutions.
This document discusses various concepts related to investment risk and rates of return. It covers stand-alone risk, portfolio risk, standard deviation as a measure of risk, the benefits of diversification in reducing portfolio risk, and the capital asset pricing model. The key points are: 1) a portfolio's risk is generally lower than the average risk of its individual components due to diversification, especially if the components are not perfectly positively correlated; 2) standard deviation measures total risk while the coefficient of variation allows comparison of risk levels for investments with different returns; and 3) the capital asset pricing model suggests investors should only be compensated for non-diversifiable market risk, not company-specific risk.
This document summarizes research on how behavioral factors can influence managerial decision-making in capital budgeting. It finds that managers may prefer simpler metrics like IRR and payback period due to ease of processing, even if NPV is optimal. Loss aversion can also cause managers to avoid abandoning unprofitable investments. Additionally, experiments show managers are inclined to avoid profitable investments when associated with negative emotions. The document also discusses how overconfidence can lead to excess business entry due to overoptimism about success. Further experiments demonstrate more market entry occurs when payoffs depend on perceived skill rather than luck.
This document summarizes research on how behavioral factors can influence managerial decision-making in capital budgeting. It finds that managers may prefer simpler metrics like IRR and payback period due to ease of processing, even if NPV is optimal. Loss aversion can also cause managers to avoid abandoning unprofitable investments. Additionally, experiments show managers are inclined to avoid profitable investments when associated with negative emotions. The document also discusses how overconfidence can lead to excess business entry due to overoptimism about success. Further experiments demonstrate more market entry occurs when payoffs depend on perceived skill rather than luck.
This document discusses chapter 5 from the textbook "Statistics for Business and Economics (13th edition)" by Anderson, Sweeney, Williams, Camm, and Cochran. The chapter covers discrete probability distributions, including random variables, developing discrete distributions, expected value and variance, and the binomial, Poisson, and hypergeometric distributions. It provides examples and explanations of key concepts such as random variables, discrete probability distributions represented as tables, graphs, and formulas, and expected value.
Online Consumer Panel simulator - First Version demo: Sampling Operations Ana...evaristoc
Extract of a "what if...?" proposal for a tool for operations decision making in the Online Consumer Panels sector based on computational-intensive methods.
A first demo exists at github.com/evaristoc
- Portfolio risk is lower than the weighted average risk of individual securities if their correlations are less than one. The capital asset pricing model relates a security's expected return to its beta, a measure of nondiversifiable risk.
- For markets to be efficient, security prices should quickly incorporate new information and no investors should consistently earn excess returns after accounting for risk and costs. However, the joint hypothesis problem notes that rejections of market efficiency may also reflect issues with the model used to calculate expected returns.
- Agency problems arise in situations where one party (the principal) delegates authority to another (the agent), such as when shareholders hire managers, but their incentives are not perfectly aligned.
This document discusses interest rates and yield curves. It explains that the four main factors affecting interest rate levels are production opportunities, time preferences, risk, and expected inflation. It also defines nominal and real interest rates. The document then presents the determinants of interest rates as the real risk-free rate plus premiums for inflation, default risk, liquidity, and maturity. It constructs a hypothetical yield curve and discusses how corporate bond yield curves relate to Treasury yield curves. Finally, it covers the pure expectations theory of the yield curve.
This document provides slides on interval estimation of population parameters. It introduces concepts such as interval estimates, margin of error, confidence intervals, the t-distribution, and sample size determination. Examples are provided to illustrate how to construct confidence intervals for a population mean when the population standard deviation is both known and unknown.
This document discusses how simulation can be used to model real-world risk in biopharma portfolio optimization. It notes that traditional approaches using expected net present value alone are insufficient. Simulation allows modeling of uncertain variables like clinical trial outcomes, development timelines, and commercial performance. This provides a statistical view of portfolio outcomes over many iterations. It enables analysis of key metrics like the probability of meeting revenue or launch targets. Simulation also facilitates sophisticated tradeoff analysis by characterizing portfolio risk-return and evaluating how changes, like adding new programs, impact overall performance and risk levels.
This document discusses capital budgeting and methods for evaluating investment projects. It covers topics like net present value (NPV), internal rate of return (IRR), profitability index, payback period, and discounted payback. Formulas for NPV and IRR are presented along with examples of calculating these metrics for franchise investment projects. The rationale for using NPV and IRR in capital budgeting decisions is explained. Creating an NPV profile by calculating NPV at different discount rates is also introduced.
With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes.
Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.
This document discusses key concepts related to risk and return in investments. It covers basic return and risk, stand-alone risk measured by standard deviation, portfolio risk, and the relationship between risk and return as described by the Capital Asset Pricing Model. The topics are illustrated with examples of calculating returns, standard deviation, and portfolio returns using historical data for stocks. Risk is shown to be reduced by holding a diversified portfolio rather than individual stocks.
The document discusses techniques for establishing shorter feedback loops between developing features and measuring user behavior, including:
1) Shadow traffic which runs new and old features simultaneously to get early feedback without users noticing a difference.
2) Visual reports which assess the quality of a feature through a report (e.g. HTML page) of key metrics.
3) A/B testing which statistically compares user behavior between a control and test group after exposing each to a different variant of a feature. Sample size considerations and statistical significance are discussed.
The document provides an overview of key concepts related to saving, investment, and the financial system. It discusses how the financial system matches savers with borrowers through financial markets and intermediaries like banks and mutual funds. It explains that in a closed economy, saving must equal investment and explores how budget deficits and surpluses impact public saving. The document also discusses the meaning of private saving and investment and how interest rates adjust in financial markets to equalize the supply and demand of loanable funds. Finally, it notes that budget deficits can "crowd out" private investment and thereby reduce long-run economic growth.
Management Of Business Operation Components PowerPoint Presentation SlidesSlideTeam
Management Of Business Operation Components PowerPoint Presentation Slides is specially-designed for astute business professionals. Use these components of operations management PowerPoint presentation to showcase various categories of infrastructure services as per your project. Elaborate on the global market size for many consecutive years using a bar graph. Represent the key areas of infrastructure investment, key drivers, and technological trends using our PPT slideshow. The key aspects of operations management PowerPoint theme is ideal to demonstrate the asset management process, lifecycle, framework, and condition assessment. Elucidate the deterministic, stochastic, and AI deterioration models to ascertain funding requirements. Take advantage of the state-of-the-art data visualizations to explain infrastructure optimization, and asset management decision journey. Walk the audience through the value-driven decision-making methodology. Employ our PPT layout to represent business infrastructure performance and cost functions. Illustrate critical infrastructure dependencies and interdependencies assessment framework. So download now to outline the key components like the lifecycle of contract management, and showcase the workflow management process. Our Management Of Business Operation Components PowerPoint Presentation Slides are explicit and effective. They combine clarity and concise expression. https://bit.ly/2L3Sc1h
Similar to Introduction to Prob theory _Summary_.pptx (20)
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."