This presentation was given by Andrew Stephen, University of Pittsburgh, at a pre-conference of the 2015 American Marketing Association Winter Marketing Educators Conference in San Antonio, TX.
On practical philosophy of research in science and technologySeppo Karrila
An attempt to indoctrinate graduate students with some philosophy of science and good practices in their research. Some references are included to disturbing trends known from poor practices that appear common to some fields, to make clear the importance of reliable methods, in particular the Scientific Method. Trigger warning: not trying to be nice to everybody.
On practical philosophy of research in science and technologySeppo Karrila
An attempt to indoctrinate graduate students with some philosophy of science and good practices in their research. Some references are included to disturbing trends known from poor practices that appear common to some fields, to make clear the importance of reliable methods, in particular the Scientific Method. Trigger warning: not trying to be nice to everybody.
Perfect Practices and Perils in Research Project ManagementAMA DocSIG
Presentation given by Vanitha Swaminathan (University of Pittsburgh) and Tom Brown (Oklahoma State University) on February 15, 2015 at the special DocSIG session of the American Marketing Association Winter Educators Conference.
This presentation was given by Leigh McAlister, University of Texas at Austin, at a pre-conference of the 2015 American Marketing Association Winter Marketing Educators Conference in San Antonio, TX.
Coming Up With Relevant Research Ideas - Mary Jo BitnerAMA DocSIG
This presentation was given by Mary Jo Bitner, Arizona State University, at a pre-conference of the 2015 American Marketing Association Winter Marketing Educators Conference in San Antonio, TX.
The 2015 MMA Doctoral Student Teaching Consortium is designed to help future faculty develop innovative approaches to classroom instruction and career planning. The Consortium will be held in conjunction with the MMA Fall Educators’ Conference which has a focus on teaching effectiveness in marketing education. Visit the conference webpage for the Conference Call for Papers, Pre-conference Workshops,
and the Teacher-Scholar Doctoral Student Competition Call for Submissions.
Perfect Practices and Perils in Research Project ManagementAMA DocSIG
Presentation given by Vanitha Swaminathan (University of Pittsburgh) and Tom Brown (Oklahoma State University) on February 15, 2015 at the special DocSIG session of the American Marketing Association Winter Educators Conference.
This presentation was given by Leigh McAlister, University of Texas at Austin, at a pre-conference of the 2015 American Marketing Association Winter Marketing Educators Conference in San Antonio, TX.
Coming Up With Relevant Research Ideas - Mary Jo BitnerAMA DocSIG
This presentation was given by Mary Jo Bitner, Arizona State University, at a pre-conference of the 2015 American Marketing Association Winter Marketing Educators Conference in San Antonio, TX.
The 2015 MMA Doctoral Student Teaching Consortium is designed to help future faculty develop innovative approaches to classroom instruction and career planning. The Consortium will be held in conjunction with the MMA Fall Educators’ Conference which has a focus on teaching effectiveness in marketing education. Visit the conference webpage for the Conference Call for Papers, Pre-conference Workshops,
and the Teacher-Scholar Doctoral Student Competition Call for Submissions.
Sources of Research Questions and Formulation of Hypothesis Psychology Pedia
Research Method -
Research questions, Good research questions, Steps to developing a research question, Sources of research question, Research hypothesis, Characteristics of hypothesis
Making your research and teaching more efficient, transparent and impactfulJay Van Bavel
Science is hard and keeping up with the latest changes in technology and research practices can feel overwhelming. This workshop is designed to increase your productivity by making your research and teaching more efficient, transparent, and impactful. This will introduce you to a wide variety of strategies and technologies that you can employ in your work.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
3. What is a
“hot” topic?
Novelty – to
academics,
practitioners
Important,
relevant,
meaningful
Lots of open
questions
Lots of buzz
about it
4. BUT BE CAREFUL…
• A hot topic worthy of academic research needs to allow
for scholarly theory building and testing
• It is great to listen to marketing practitioners to get a
sense of what’s important to them…but realize that
what’s “hot” about a “hot topic” in their minds might not
be relevant in a scholarly context
– E.g., “big data”
• Be inspired and intrigued by what’s happening “out
there” but bring scholarly thinking and frameworks to
bear on interesting “hot” real-world phenomena
6. THEORY?
• Problem with “hot” topics, particularly the novel ones, is
that you probably don’t have an established theory that
you can use
• Really?
• There’s got to be something out that there that helps
understand/explain the phenomenon of interest
– Prior literature in marketing
– Literature in other fields
– Your own “theories”
8. • Thought of like a virtual
shopping mall
• Shopping mall research on
retail demand externalities
• Apply similar concepts to this
new phenomenon and context
• Find analogies
9. • Mobile display ads – new and
not well understood
• What makes an ad effective?
• Used “old” theory of
persuasion and info
processing (ELM) to explain
differences in effectiveness
• Generate hypotheses based
on existing theory and test
them in the hot/new context
10. • Some studies showed
how using Facebook
boosted self esteem
• Non-social media
literature links self
esteem with lower self
control
• Combine effects
or theories from
prior literature to
understand novel
context
11. FINAL THOUGHTS
• Beauty (hotness) is in the eye of the beholder – almost any
marketing or consumer phenomenon could be construed as a hot
topic or positioned as one
– Don’t do research on hot topics because they are hot
– Work on topics that you care about and that you think are meaningful and
relevant
• Easy to get carried away with novel/new/hot areas (and cool
datasets!) and not pay much attention to the theory side of things
– Most of us have been in this situation
– It is okay to use substantive/practical aspects of a project to inform theory
development
– But you must have theory even if the contribution is substantive – goal is to
explain something, not merely describe it
• Theory can come from a variety of sources
– Prior literature in unrelated contexts that somehow applies (analogies)
– Prior literature on generally related topics (application to new context)
– Prior literature in same/related contexts (combining and extending)
– Or something brand new!