This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Blogged at: http://socialoptic.com/2013/05/big-human-data/ from the ChinwagPsych event in London - the era of Human Big Data - some challenges and opportunities.
New Developments in Machine Learning - Prof. Dr. Max WellingTextkernel
Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam.
At the end of this slide deck, you can also find the YouTube recording.
Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important:
1) Learning to interact with the world through reinforcement learning,
2) Learning while respecting everyone's privacy, and
3) Learning the causal relations in data (as opposed to discovering mere correlations).
Together, they represent the "power tools" of the future machine learner.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Blogged at: http://socialoptic.com/2013/05/big-human-data/ from the ChinwagPsych event in London - the era of Human Big Data - some challenges and opportunities.
New Developments in Machine Learning - Prof. Dr. Max WellingTextkernel
Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam.
At the end of this slide deck, you can also find the YouTube recording.
Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important:
1) Learning to interact with the world through reinforcement learning,
2) Learning while respecting everyone's privacy, and
3) Learning the causal relations in data (as opposed to discovering mere correlations).
Together, they represent the "power tools" of the future machine learner.
My public presentation as delivered to the Public Interest Declassification Board (PIDB) trying to determine the best way to declassify and release over 400M classified documents.
This is a presentation I gave at the Fluoro Safety Conference 2015
The talk explores where data can help detect human behavior that may help identify early interventions before mental health issues become a risk factor
From Information to Insight: Data Storytelling for OrganizationsThinking Machines
What kind of stories are best told with data? How do you take raw numbers and turn them into an engaging, meaningful story? Thinking Machines' content strategist Pia Faustino delivered this presentation on the data storytelling process at the "Humans + Machines: Using Artificial Intelligence to Power Your People" conference on February 19, 2016 in Bonifacio Global City, Taguig, Philippines.
Big Data, Inteligência Artificial, Machine Learning e o que Hollywood não vai...Bruno Henrique - Garu
Terra, 2020. O mundo sofreu uma grande mudança com o poder que as máquinas obtiveram. Elas passaram a interferir no dia a dia das pessoas, dão opiniões em decisões, extraem informações dos seres humanos para uso próprio e tudo parece acabado para a sobrevivência da nossa espécie. Esse poderia muito bem ser a sinopse de um filme B de Hollywood, talvez um blockbuster (Transcendence prova isso). Eles gostam muito desse tom dramático. Embora seja uma visão interessante, não é única. Eu tenho uma visão mais otimista sobre o assunto. Segundo estudos do International Data Corporation (IDC), em 2020 chegaremos a 40 mil exabytes, o equivalente a 100 milhões de vezes a quantidade de livros já escritos hoje. Não precisamos esperar chegar em 2020 para tirarmos proveito do que chamamos de Big Data. Essas duas palavras acabam servindo de guarda-chuva para uma série de outras que estão mudando a forma de nos relacionarmos com o mundo. Nessa palestra eu pretendo mostrar alguns insights de como já podemos tirar proveito de coisas como inteligência artificial e machine learning e o que precisamos entender para lidar com tudo isso.
How Machine Learning is easing (or disrupting?) the Human life.
Would machines take up ALL of the human jobs 50 years from now ?
Posted at a part of course presentation at MBA Program at The University of Connecticut School of Business.
#Leonar
Machine Learning: Understanding the Invisible Force Changing Our WorldKen Tabor
Readers will gain an appreciation for machine learning, and take away valuable strategies including:
• What is machine learning.
• How it’s changing the world.
• Who the major players are.
• How you can control it.
Machine learning. It’s in the news. It’s discussed in corporate boardrooms. It’s on your mind. ML algorithms seem to be at once everywhere, yet nowhere. Can we possibly understand how this invisible force is shaping our world? How will it reform your industry, and change your job?
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportuni...Junaid Qadir
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportunities, and Pitfalls
Accompanying Paper Available at:
Caveat Emptor: The Risks of Using Big Data for Human Development
IEEE Technology and Society Magazine 38(3):82-90
DOI: 10.1109/MTS.2019.2930273
September 2019
https://www.researchgate.net/publication/335745617_Caveat_Emptor_The_Risks_of_Using_Big_Data_for_Human_Development
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of In...John Liu
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems.
Strata Conference NY: The Accidental Chief Privacy OfficerJim Adler
Strata Conference
New York
September 23, 2011
http://strataconf.com/stratany2011/public/schedule/detail/21484
http://youtu.be/PKUI9iz0l9g
The first generation of chief privacy officers were typically attorneys, charged with the formulation and enforcement of privacy policies. Times have changed. Given the speed and complexity of technology, the privacy policy is necessary but hardly sufficient. Because we live much of our lives in public, both online and offline, the Internet is transforming the anonymity of our cities into the familiarity of small towns. Privacy is deeply ingrained within the technology that manages this personal data. The products and services driving this transformation must consider privacy from the earliest design sessions.
Today’s engineer CPO, and I’m one, must deeply involve themselves with the technology and product design process to bake-in privacy. This new breed of CPO is comfortable in an engineering scrum, product focus group, reviewing pending regulations, or analyzing A/B test results. They have the historical awareness, frontier spirit, regulatory caution, technical chops, and innovator’s curiosity to work through the toughest data issues. The promise of the engineer CPO is that products, not only safeguard privacy, but compete on it.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
Slides from today's presentation in Brussels at LT-Accelerate. The short talk covered the core ideas underpinning a recent patent for a method known colloquially as CoderRank. I focus on the challenge of human annotation, the role of measurement, and the tools we have built, both free/open source and commercial, to make it easier for teams to create gold standard annotation sets quickly and accurately.
My public presentation as delivered to the Public Interest Declassification Board (PIDB) trying to determine the best way to declassify and release over 400M classified documents.
This is a presentation I gave at the Fluoro Safety Conference 2015
The talk explores where data can help detect human behavior that may help identify early interventions before mental health issues become a risk factor
From Information to Insight: Data Storytelling for OrganizationsThinking Machines
What kind of stories are best told with data? How do you take raw numbers and turn them into an engaging, meaningful story? Thinking Machines' content strategist Pia Faustino delivered this presentation on the data storytelling process at the "Humans + Machines: Using Artificial Intelligence to Power Your People" conference on February 19, 2016 in Bonifacio Global City, Taguig, Philippines.
Big Data, Inteligência Artificial, Machine Learning e o que Hollywood não vai...Bruno Henrique - Garu
Terra, 2020. O mundo sofreu uma grande mudança com o poder que as máquinas obtiveram. Elas passaram a interferir no dia a dia das pessoas, dão opiniões em decisões, extraem informações dos seres humanos para uso próprio e tudo parece acabado para a sobrevivência da nossa espécie. Esse poderia muito bem ser a sinopse de um filme B de Hollywood, talvez um blockbuster (Transcendence prova isso). Eles gostam muito desse tom dramático. Embora seja uma visão interessante, não é única. Eu tenho uma visão mais otimista sobre o assunto. Segundo estudos do International Data Corporation (IDC), em 2020 chegaremos a 40 mil exabytes, o equivalente a 100 milhões de vezes a quantidade de livros já escritos hoje. Não precisamos esperar chegar em 2020 para tirarmos proveito do que chamamos de Big Data. Essas duas palavras acabam servindo de guarda-chuva para uma série de outras que estão mudando a forma de nos relacionarmos com o mundo. Nessa palestra eu pretendo mostrar alguns insights de como já podemos tirar proveito de coisas como inteligência artificial e machine learning e o que precisamos entender para lidar com tudo isso.
How Machine Learning is easing (or disrupting?) the Human life.
Would machines take up ALL of the human jobs 50 years from now ?
Posted at a part of course presentation at MBA Program at The University of Connecticut School of Business.
#Leonar
Machine Learning: Understanding the Invisible Force Changing Our WorldKen Tabor
Readers will gain an appreciation for machine learning, and take away valuable strategies including:
• What is machine learning.
• How it’s changing the world.
• Who the major players are.
• How you can control it.
Machine learning. It’s in the news. It’s discussed in corporate boardrooms. It’s on your mind. ML algorithms seem to be at once everywhere, yet nowhere. Can we possibly understand how this invisible force is shaping our world? How will it reform your industry, and change your job?
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportuni...Junaid Qadir
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportunities, and Pitfalls
Accompanying Paper Available at:
Caveat Emptor: The Risks of Using Big Data for Human Development
IEEE Technology and Society Magazine 38(3):82-90
DOI: 10.1109/MTS.2019.2930273
September 2019
https://www.researchgate.net/publication/335745617_Caveat_Emptor_The_Risks_of_Using_Big_Data_for_Human_Development
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of In...John Liu
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems.
Strata Conference NY: The Accidental Chief Privacy OfficerJim Adler
Strata Conference
New York
September 23, 2011
http://strataconf.com/stratany2011/public/schedule/detail/21484
http://youtu.be/PKUI9iz0l9g
The first generation of chief privacy officers were typically attorneys, charged with the formulation and enforcement of privacy policies. Times have changed. Given the speed and complexity of technology, the privacy policy is necessary but hardly sufficient. Because we live much of our lives in public, both online and offline, the Internet is transforming the anonymity of our cities into the familiarity of small towns. Privacy is deeply ingrained within the technology that manages this personal data. The products and services driving this transformation must consider privacy from the earliest design sessions.
Today’s engineer CPO, and I’m one, must deeply involve themselves with the technology and product design process to bake-in privacy. This new breed of CPO is comfortable in an engineering scrum, product focus group, reviewing pending regulations, or analyzing A/B test results. They have the historical awareness, frontier spirit, regulatory caution, technical chops, and innovator’s curiosity to work through the toughest data issues. The promise of the engineer CPO is that products, not only safeguard privacy, but compete on it.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
Slides from today's presentation in Brussels at LT-Accelerate. The short talk covered the core ideas underpinning a recent patent for a method known colloquially as CoderRank. I focus on the challenge of human annotation, the role of measurement, and the tools we have built, both free/open source and commercial, to make it easier for teams to create gold standard annotation sets quickly and accurately.
Similar to Why Your Big Data Project Will Fail, and How to Avoid It (20)
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
13. People don’t always agree with rules
of the game for example
Super Bowl XL
Scott Steinmann
14. A Quiz for you…
On the next slide, I want you to tell
me what these four types of data
have in common
Raise your hand when you get the
answer…
(don’t worry, I won’t call on anyone)
15. “A computer would deserve to be called
intelligent if it could deceive a human into
believing that it was human.”
16. Did you get it right?
Alan Turing
The more data types we have
The harder the classification
17. Classification Cracked The Enigma Code
158,962,555,217,826,360,000
possibilities
Turing used Classification of the data to
narrow the problem set
1st A letter can never be itself
2nd Known Phrases - The weather report
Big Data and Classification – why more than ever, classification and good data architecture is critical to providing confidence in analytical outcomes for your big data project.
Paul Balas
25 + years of data architecture and data-centric implementations from sourcing all types of data, to sourcing, mastering, modeling, and sharing
Implemented numerous content architectures for fortune 500 companies and the Kingdom of Saudi Arabia – KAPSARC
Chair of the Big Data in Denver LinkedIn Group.
He never imagined a world with so much data that information would be obscured simply by the volume and variety of data.
If you can’t categorize your data, you can’t analyze it. If you aren’t performing data profiling on your big data as a first step to your analysis, you’ve already failed.
The earliest known system of classification is that of Aristotle, who attempted in the 4th cent. B.C. to group animals according to such criteria as mode of reproduction and possession or lack of red blood. Aristotle's pupil Theophrastus classified plants according to their uses and methods of cultivation. Little interest was shown in classification until the 17th and 18th cent., when botanists and zoologists began to devise the modern scheme of categories. The designation of groups was based almost entirely on superficial anatomical resemblances.
Machine driven classification can assist in human analysis and refinement of classification systems, but as it stands, without human context, machine driven classification is limited.
Well-known classification systems such as GAAP-based accounting or plant taxonomies provide a common language that is widely accepted and therefor trusted. Common classification systems facilitate understanding and knowledge sharing.
A new Big Data phenomenon is the ‘Data Lake’. I like to call it the ‘Data Swamp’ as the information added to the lake is useless until it’s classified. The excitement around Hadoop and other NoSQL technologies is it allows you to defer classfication, cleansing, and standardization post-load and on the fly, thus making the ingestion process and certain types of analytical workloads much faster
Billions of dollars and tens of thousands of person-years effort has been spent on search technologies all with the focus of classifying data on-the-fly to help people locate precise information. Most of this effort has been driven by the internet search engines and firms trying to capitalize on e-commerce.
Bad categorization of a population has the effect of completely misleading results and creating controversy
NASA:
Ninety-seven percent of climate scientists agree that climate-warming trends over the past century are very likely due to human activities,1and most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.
The Wall Street Journal
The Myth of the Climate Change '97%'
What is the origin of the false belief—constantly repeated—that almost all scientists agree about global warming?
By
JOSEPH BAST And
ROY SPENCER
Ms. Oreskes's definition of consensus covered "man-made" but left out "dangerous"—and scores of articles by prominent scientists such as Richard Lindzen, John Christy, Sherwood Idso and Patrick Michaels, who question the consensus, were excluded. The methodology is also flawed. A study published earlier this year in Nature noted that abstracts of academic papers often contain claims that aren't substantiated in the papers.
According to IBM, In 2015 Global Data Volume is about 8,000 exabytes
Most of it is sensor and social media data
By 2020 some predict a 5x growth to 40,000 exabytes
Even though he was already ejected from the game, Scott Steinmann continues to argue with the umpires call.
Why were there so many controversial calls in Super Bowl XL? Where the rules for each penalty applied fairly? The outcome of that game was hotly debated.
What was easy for those of you who knew the answer, is exponentially difficult for machines. Each data type has to be parsed and a common taxonomy applied as metadata to the data itself, then correlated to find the commonalities in each data source.
That is 158 Quintillion if you wanted to know.
Was the chad in favor of Bush or Gore?
The risk of the o-ring failure wasn’t correctly classified based on the temperatures it would encounter.
Credit default swap risk wasn’t correctly categorized and risky financial decisions ensued.