Diane Kim (x.ai) spoke about "Designing Intelligent Agents and a new class of Perceived Errors". This talk covers new research in UI and how we can take advantage of NLP and AI in general, and change the way we interact with technology dramatically. Diane discusses how the standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.
By Diane Kim (AI Interaction Designer, x.ai)
@_DianeKim
part of NYAI #19: AI & UI on Tues, 27 Feb 2018 at Capital One Labs
nyai.co
Hiring the Right People to Avoid Disasters In Social MediaMelissaFach
Explains what to look for, what to ask in interviews, creating company protocols and setting up a work environment that will work for your social team. Presentation for Pubcon SFIMA 2017.
Women in AI Social: Fall Edition (NYAI x Aggregate Intellect x AI Geeks)Maryam Farooq
These slides are from our Women in AI Fall Social event presented by NYAI, Aggregate Intellect, and AI Geeks.
On September 15th, 2020 we provided a space for women-identified folks and allies in the AI community to get together in a relaxed, social environment, and learn from each other’s journeys. People of all genders were welcomed at event, and we heard from expert thought leaders in the AI space.
Guests:
Marilyn Ma - Co-Founder at Quali AI
Catherine Havasi - CEO at Dalang Technologies
Ideshini Naidoo - Chief Technology Officer at Wave HQ
Vicki Saunders - Founder at SheEO
Linda McIver - Executive Director at Australian Data Science Education Institute
AI & COVID19: Ethics & Data Rights (NYAI x AISC)Maryam Farooq
This was a joint event with AISC (Aggregate Intellect) on Thurs, Apr 30th 2020. We had attendees from NYC, Toronto, Ottawa, California, Nebraska, Georgia, Florida, South Africa, Denmark, Argentina, and more!
Special thank you to our partners AISC & our speakers Joe Toscano, Brittany Kaiser, Stuart Culpepper, Jennifer L. Williams, and Tiffany Johnson. We talked about questions like:
-Is it worth giving up your privacy to insure your safety from disease, or violence?
-Is it worth giving up your privacy for money? How much would/should it cost?
-Where do ethics come in? - What tools / tech consumers & companies can utilize?
-Risks of Privacy Erosion from AI
-Disparity of how covid19 affects different communities?
-How can we as an AI community come together to leverage our knowledge & skills to bridge this disparity?
What are your thoughts on this topic? Watch the video here: https://youtu.be/DjCtHFkgkwI
Hiring the Right People to Avoid Disasters In Social MediaMelissaFach
Explains what to look for, what to ask in interviews, creating company protocols and setting up a work environment that will work for your social team. Presentation for Pubcon SFIMA 2017.
Women in AI Social: Fall Edition (NYAI x Aggregate Intellect x AI Geeks)Maryam Farooq
These slides are from our Women in AI Fall Social event presented by NYAI, Aggregate Intellect, and AI Geeks.
On September 15th, 2020 we provided a space for women-identified folks and allies in the AI community to get together in a relaxed, social environment, and learn from each other’s journeys. People of all genders were welcomed at event, and we heard from expert thought leaders in the AI space.
Guests:
Marilyn Ma - Co-Founder at Quali AI
Catherine Havasi - CEO at Dalang Technologies
Ideshini Naidoo - Chief Technology Officer at Wave HQ
Vicki Saunders - Founder at SheEO
Linda McIver - Executive Director at Australian Data Science Education Institute
AI & COVID19: Ethics & Data Rights (NYAI x AISC)Maryam Farooq
This was a joint event with AISC (Aggregate Intellect) on Thurs, Apr 30th 2020. We had attendees from NYC, Toronto, Ottawa, California, Nebraska, Georgia, Florida, South Africa, Denmark, Argentina, and more!
Special thank you to our partners AISC & our speakers Joe Toscano, Brittany Kaiser, Stuart Culpepper, Jennifer L. Williams, and Tiffany Johnson. We talked about questions like:
-Is it worth giving up your privacy to insure your safety from disease, or violence?
-Is it worth giving up your privacy for money? How much would/should it cost?
-Where do ethics come in? - What tools / tech consumers & companies can utilize?
-Risks of Privacy Erosion from AI
-Disparity of how covid19 affects different communities?
-How can we as an AI community come together to leverage our knowledge & skills to bridge this disparity?
What are your thoughts on this topic? Watch the video here: https://youtu.be/DjCtHFkgkwI
NLP Community Conference - Dr. Catherine Havasi (ConceptNet/MIT Media Lab/Lum...Maryam Farooq
Dr. Catherine Havasi's keynote talk from the AI Community Conference on Natural Language Processing (by NYAI.co) on Thurs, Jun 27th 2019 at Moody's Analytics.
Sponsored by Moody's Analytics, NYU Tandon Future Lab, NYAI.co
For more information & the full talk video, please visit nyai.co
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...Maryam Farooq
For more AI talks, visit: nyai.co
These slides are from NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catherine Havasi, which took place Tues, 12/18/19 at Kirkland & Ellis NYC.
[Speaker Bio] Dr. Catherine Havasi is a technology strategist, artificial intelligence researcher, and entrepreneur. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, the first crowd-sourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects and will be turning 20 next year. She has started several companies commercializing AI research, including Luminoso where she acts as Chief Strategy Officer. She is currently a visiting scientist at the MIT Media Lab where she works on computational creativity and previously directed the Digital Intuition group.
[Abstract] People who build everything from entertainment experiences to financial management face a dilemma: how can you scale what you’re building for broader consumption, yet maintain the personalization that makes it special? A fundamental tension exists between building something individualized, and scaling it to consumers such as visitors at a theme park, or gamers exploring the latest Zelda adventure. True disruption happens when we overcome the idea that one must sacrifice personalization to achieve mass production — like it has in advertising, recommendations, and web search.
Artificial Intelligence practitioners, especially in natural language understanding, dialogue, and cognitive modeling, face the same issue: how can we personalize our models for all audiences without relying on unscalable efforts such as writing specific rules, building dialogue trees, or designing knowledge graphs? Catherine Havasi believes we can remove this dichotomy and achieve “mass personalization.” In this session we’ll discuss how to understand domain text and build believable digital characters. We’ll talk about how adding a little common sense, cognitive architectures, and planning is making this all possible.
nyai.co
NYAI #26: Federated Learning: Machine Learning on Edge Devices w/ Alice Albre...Maryam Farooq
Federated learning enables us to build machine learning models using data collected by edge devices like smartphones and IoT devices, without moving data off the device. This minimizes concerns about privacy, data regulation, bandwidth, and storage, while providing similar results as centralized models. Examples include predictive text on cell phones, a person’s engagement with their own photos, and machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and ML on low-powered field devices in energy, agriculture and logistics.
The principles of data minimization established by the GDPR and the prevalence of smart sensors makes these use cases more common, and the advantages of federated learning more compelling. In this talk we’ll cover the algorithmic solutions and the product opportunities.
This talk was presented by Alice Albrecht (Research Engineer, Cloudera) at NYAI #26 on Tues, 11/28 at Capital One Labs.
nyai.co/nyai-26
NYAI #23: Using Cognitive Neuroscience to Create AI (w/ Dr. Peter Olausson)Maryam Farooq
Dr. Peter Olausson started his career as a cognitive neuroscientist and spent over a decade at Yale University researching how our memories, motivation and cognitive control together affect decision-making. Before starting COGNITUUM, Peter was focusing on new breakthroughs in the information solutions that shape the human experience, including cognitive computing, data analytics, neuromanagement, and knowledge networks. Peter received his PhD in neuropharmacology at the University of Gothenburg in Sweden and his postdoctoral training at Yale University.
COGNITUUM has developed a general intelligence framework that provides a viable pathway towards human-level machine intelligence. The platform features continuous and real-time learning from any data source.
NYAI #25: Evolution Strategies: An Alternative Approach to AI w/ Maxwell ReboMaryam Farooq
NYAI #25: Evolution Strategies: An Alternative Approach to AI w/ Maxwell Rebo
at Capital One Labs on Tues, 10/23/18
Join us for what's sure to be an awesome night in AI! This month's event is focused Evolution Strategies, and will touch on many themes discussed here (https://blog.openai.com/evolution-strategies/).
Maxwell Rebo is a machine learning founder working on a stealth project in ML-powered simulation engine.
A class of heuristic search algorithms have been shown to be viable alternatives to reinforcement learning as well as other ML tasks. These methods can be parallelized on arbitrary numbers of CPUs and do not require GPUs to be effective. To increase explicability, it is possible to create attribution mechanisms within these methods.
Maxwell is the former founder of Machine Colony, and enterprise AI platform company, and a founding member of NYAI. A machine learning developer and three-time founder, he has been doing ML at massive scale since 2010. He has previously spoken at venues such as the Ethereal conference in NYC and the joint Asian Leadership/HelloTomorrow conference in Seoul.
NYAI #24: Developing Trust in Artificial Intelligence and Machine Learning fo...Maryam Farooq
NYAI #24: Developing Trust in Artificial Intelligence and Machine Learning for High-Stakes Applications with Dr. Kush Varshney (Principal Research Manager, IBM Research AI).
Check out the the IBM AI Fairness 360 open source toolkit: https://www.ibm.com/blogs/research/2018/09/ai-fairness-360/
nyai.co
NYAI #19: AI & UI - "AI + Emotion: It's all about Trust" by Steph Hay (VP Des...Maryam Farooq
Steph Hay (VP of Design @ Capital One) shares on AI + Emotion - why it's all about trust.
part of NYAI #19: AI & UI on Tues, 27 Feb 2018 at Capital One Labs
nyai.co
NYAI #18: Team Alignment for Human-Centered AI (Chris Butler - Director of AI...Maryam Farooq
Through our recent Design in AI survey we found that on AI projects there is frequently a lack of alignment between technical and non-technical team members. During this talk, we will share the results of our report and then talk about specific methods to build alignment. You will learn how two of our favorite workshops, Empathy Mapping for the Machine and Confusion Mapping, can build stronger teams and better products. You will walk away with a better idea of the nuances required in product and design practice for AI systems.
by Chris Butler (Director of AI, Philosophie)
at NYAI #18: AI & UX on Tues, 27 Feb 2018 at Capital One Labs
nyai.co
NYAI #18: Designing for AI (Rob Strati & Jesse Schifano of ECHO)Maryam Farooq
Understanding emotions is becoming more important as technology is expected to respond to each individual based on their tastes. AI is the technology that is powering this expectation.
We will talk about how, using emotional research and design methodologies, it is possible to gather not only what people think about using a system, but also how they feel. Doing emotional research to gain insights and catalogue them is one of the first steps. From there designers can leverage these findings and translate the feelings into design conventions. These conventions can then provide the machine learning with the signal it can use to generate more refined and meaningful results based on a person's preferences. These emotionally based features can then be quantifiably measured to prove out the effectiveness of the process.
By using this process with machine learning technologies we can create systems that go from being simply useful to something that is a joy to use.
by Rob Strati and Jesse Schifano (Co-Founders, ECHO)
part of NYAI #18: AI & UX on Tues, 27 Feb 2018 at Capital One Labs.
nyai.co
"Understanding Humans with Machines" (Arthur Tisi)Maryam Farooq
At NYAI #16, Arthur Tisi explores deep neural networks that dominate advanced approaches to pattern recognition. Today neural networks transcribe our speech, recognize our pets, understand linguistics and fight our trolls. Recent advances by Geoff Hinton and the introduction of capsule networks only ups the ante. But despite the results, we have to wonder… why do they work so well?
In this session, Arthur Tisi, CEO and Founder of MeaningBot, will share some extremely remarkable results in applying deep neural networks to natural language processing (NLP), particularly in the areas of determining human traits in the areas of leadership, team building, personality, consumption preferences and more. Arthur will cite real world examples and share some of the math and science behind these advances including different variants of artificial neural networks, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory (LSTM), sequence-to-sequence model, and shallow neural networks including word2vec for word embeddings.
NYAI #13: "Designing AI by Learning from Enterprise" - Nicholas Borge (Impart...Maryam Farooq
"Designing AI by Learning from Enterprise" - Nicholas Borge (Impartial.ai)
Presented by New York Artificial Intelligence at Rise New York on Tues, 6/20/17.
NYAI #13: "AI and Business Transformation" - Josh SuttonMaryam Farooq
"AI & Business Transformation" - Josh Sutton (Global Head of Data & AI, Publicis.Sapient)
Presented at NYAI #13 - AI & Enterprise on Tues, 6/20/17 at Rise New York.
Presented by New York Artificial Intelligence (NYAI).
NYAI #10: Building an AI Autonomous Agent Using Supervised Learning with Denn...Maryam Farooq
NYAI #10 - Tuesday, 21 March 2017 @ Rise NY
This talk covers the key questions & challenges to consider if you’re involved in designing artificially intelligent agents, based on those faced by x.ai in building their AI assistants (Amy & Andrew).
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).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
NLP Community Conference - Dr. Catherine Havasi (ConceptNet/MIT Media Lab/Lum...Maryam Farooq
Dr. Catherine Havasi's keynote talk from the AI Community Conference on Natural Language Processing (by NYAI.co) on Thurs, Jun 27th 2019 at Moody's Analytics.
Sponsored by Moody's Analytics, NYU Tandon Future Lab, NYAI.co
For more information & the full talk video, please visit nyai.co
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...Maryam Farooq
For more AI talks, visit: nyai.co
These slides are from NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catherine Havasi, which took place Tues, 12/18/19 at Kirkland & Ellis NYC.
[Speaker Bio] Dr. Catherine Havasi is a technology strategist, artificial intelligence researcher, and entrepreneur. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, the first crowd-sourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects and will be turning 20 next year. She has started several companies commercializing AI research, including Luminoso where she acts as Chief Strategy Officer. She is currently a visiting scientist at the MIT Media Lab where she works on computational creativity and previously directed the Digital Intuition group.
[Abstract] People who build everything from entertainment experiences to financial management face a dilemma: how can you scale what you’re building for broader consumption, yet maintain the personalization that makes it special? A fundamental tension exists between building something individualized, and scaling it to consumers such as visitors at a theme park, or gamers exploring the latest Zelda adventure. True disruption happens when we overcome the idea that one must sacrifice personalization to achieve mass production — like it has in advertising, recommendations, and web search.
Artificial Intelligence practitioners, especially in natural language understanding, dialogue, and cognitive modeling, face the same issue: how can we personalize our models for all audiences without relying on unscalable efforts such as writing specific rules, building dialogue trees, or designing knowledge graphs? Catherine Havasi believes we can remove this dichotomy and achieve “mass personalization.” In this session we’ll discuss how to understand domain text and build believable digital characters. We’ll talk about how adding a little common sense, cognitive architectures, and planning is making this all possible.
nyai.co
NYAI #26: Federated Learning: Machine Learning on Edge Devices w/ Alice Albre...Maryam Farooq
Federated learning enables us to build machine learning models using data collected by edge devices like smartphones and IoT devices, without moving data off the device. This minimizes concerns about privacy, data regulation, bandwidth, and storage, while providing similar results as centralized models. Examples include predictive text on cell phones, a person’s engagement with their own photos, and machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and ML on low-powered field devices in energy, agriculture and logistics.
The principles of data minimization established by the GDPR and the prevalence of smart sensors makes these use cases more common, and the advantages of federated learning more compelling. In this talk we’ll cover the algorithmic solutions and the product opportunities.
This talk was presented by Alice Albrecht (Research Engineer, Cloudera) at NYAI #26 on Tues, 11/28 at Capital One Labs.
nyai.co/nyai-26
NYAI #23: Using Cognitive Neuroscience to Create AI (w/ Dr. Peter Olausson)Maryam Farooq
Dr. Peter Olausson started his career as a cognitive neuroscientist and spent over a decade at Yale University researching how our memories, motivation and cognitive control together affect decision-making. Before starting COGNITUUM, Peter was focusing on new breakthroughs in the information solutions that shape the human experience, including cognitive computing, data analytics, neuromanagement, and knowledge networks. Peter received his PhD in neuropharmacology at the University of Gothenburg in Sweden and his postdoctoral training at Yale University.
COGNITUUM has developed a general intelligence framework that provides a viable pathway towards human-level machine intelligence. The platform features continuous and real-time learning from any data source.
NYAI #25: Evolution Strategies: An Alternative Approach to AI w/ Maxwell ReboMaryam Farooq
NYAI #25: Evolution Strategies: An Alternative Approach to AI w/ Maxwell Rebo
at Capital One Labs on Tues, 10/23/18
Join us for what's sure to be an awesome night in AI! This month's event is focused Evolution Strategies, and will touch on many themes discussed here (https://blog.openai.com/evolution-strategies/).
Maxwell Rebo is a machine learning founder working on a stealth project in ML-powered simulation engine.
A class of heuristic search algorithms have been shown to be viable alternatives to reinforcement learning as well as other ML tasks. These methods can be parallelized on arbitrary numbers of CPUs and do not require GPUs to be effective. To increase explicability, it is possible to create attribution mechanisms within these methods.
Maxwell is the former founder of Machine Colony, and enterprise AI platform company, and a founding member of NYAI. A machine learning developer and three-time founder, he has been doing ML at massive scale since 2010. He has previously spoken at venues such as the Ethereal conference in NYC and the joint Asian Leadership/HelloTomorrow conference in Seoul.
NYAI #24: Developing Trust in Artificial Intelligence and Machine Learning fo...Maryam Farooq
NYAI #24: Developing Trust in Artificial Intelligence and Machine Learning for High-Stakes Applications with Dr. Kush Varshney (Principal Research Manager, IBM Research AI).
Check out the the IBM AI Fairness 360 open source toolkit: https://www.ibm.com/blogs/research/2018/09/ai-fairness-360/
nyai.co
NYAI #19: AI & UI - "AI + Emotion: It's all about Trust" by Steph Hay (VP Des...Maryam Farooq
Steph Hay (VP of Design @ Capital One) shares on AI + Emotion - why it's all about trust.
part of NYAI #19: AI & UI on Tues, 27 Feb 2018 at Capital One Labs
nyai.co
NYAI #18: Team Alignment for Human-Centered AI (Chris Butler - Director of AI...Maryam Farooq
Through our recent Design in AI survey we found that on AI projects there is frequently a lack of alignment between technical and non-technical team members. During this talk, we will share the results of our report and then talk about specific methods to build alignment. You will learn how two of our favorite workshops, Empathy Mapping for the Machine and Confusion Mapping, can build stronger teams and better products. You will walk away with a better idea of the nuances required in product and design practice for AI systems.
by Chris Butler (Director of AI, Philosophie)
at NYAI #18: AI & UX on Tues, 27 Feb 2018 at Capital One Labs
nyai.co
NYAI #18: Designing for AI (Rob Strati & Jesse Schifano of ECHO)Maryam Farooq
Understanding emotions is becoming more important as technology is expected to respond to each individual based on their tastes. AI is the technology that is powering this expectation.
We will talk about how, using emotional research and design methodologies, it is possible to gather not only what people think about using a system, but also how they feel. Doing emotional research to gain insights and catalogue them is one of the first steps. From there designers can leverage these findings and translate the feelings into design conventions. These conventions can then provide the machine learning with the signal it can use to generate more refined and meaningful results based on a person's preferences. These emotionally based features can then be quantifiably measured to prove out the effectiveness of the process.
By using this process with machine learning technologies we can create systems that go from being simply useful to something that is a joy to use.
by Rob Strati and Jesse Schifano (Co-Founders, ECHO)
part of NYAI #18: AI & UX on Tues, 27 Feb 2018 at Capital One Labs.
nyai.co
"Understanding Humans with Machines" (Arthur Tisi)Maryam Farooq
At NYAI #16, Arthur Tisi explores deep neural networks that dominate advanced approaches to pattern recognition. Today neural networks transcribe our speech, recognize our pets, understand linguistics and fight our trolls. Recent advances by Geoff Hinton and the introduction of capsule networks only ups the ante. But despite the results, we have to wonder… why do they work so well?
In this session, Arthur Tisi, CEO and Founder of MeaningBot, will share some extremely remarkable results in applying deep neural networks to natural language processing (NLP), particularly in the areas of determining human traits in the areas of leadership, team building, personality, consumption preferences and more. Arthur will cite real world examples and share some of the math and science behind these advances including different variants of artificial neural networks, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory (LSTM), sequence-to-sequence model, and shallow neural networks including word2vec for word embeddings.
NYAI #13: "Designing AI by Learning from Enterprise" - Nicholas Borge (Impart...Maryam Farooq
"Designing AI by Learning from Enterprise" - Nicholas Borge (Impartial.ai)
Presented by New York Artificial Intelligence at Rise New York on Tues, 6/20/17.
NYAI #13: "AI and Business Transformation" - Josh SuttonMaryam Farooq
"AI & Business Transformation" - Josh Sutton (Global Head of Data & AI, Publicis.Sapient)
Presented at NYAI #13 - AI & Enterprise on Tues, 6/20/17 at Rise New York.
Presented by New York Artificial Intelligence (NYAI).
NYAI #10: Building an AI Autonomous Agent Using Supervised Learning with Denn...Maryam Farooq
NYAI #10 - Tuesday, 21 March 2017 @ Rise NY
This talk covers the key questions & challenges to consider if you’re involved in designing artificially intelligent agents, based on those faced by x.ai in building their AI assistants (Amy & Andrew).
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).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
6. Perceived Error: Not what I requested
WHEN
Thursday, February 03, 2018 4:00pm – 4:30pm EST
WHERE
200 Broadway, New York, NY | Irving Farms
WHO
Diane Kim, Host
Tanya Rose, Guest
Amy Ingram, Assistant to Diane
Hi Tanya,
Looking forward to getting together like we
talked about! Wednesday is my preferred
day, Amy will help us find a time.
,
Dennis
Tanya, DianeTanya
Diane
Let’s get coffee Wednesday!
Tanya, Diane | Coffee
7. “But I said to schedule on Wednesday!”
Except Tanya was out of town until Thursday.
8. Perceived Error: Outside my preferences
WHEN
Friday, March 16, 2018 9:00am – 9:30am EST
WHERE
Maryam to call Diane at 719-284-5634, PIN 36812
WHO
Diane Kim, Host
Maryam Farooq, Guest
Amy Ingram, Assistant to Diane
Hi Maryam,
Looking forward to speaking with you! Amy
can help us find time for our prep call.
Diane
Maryam, DianeMaryam
Diane
Connecting before NYAI
Maryam, Diane | Call
Abstract: As we move to the conversational UI and take advantage of NLP and AI in general, we change the way we interact with technology dramatically. The standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.
Diane will talk through where x.ai has encountered error perception issues as we seek to develop frictionless software, how we thought about the problem, and the communication strategies we’re exploring to resolve it.
Traditional UX
“I click crop in Photoshop and the image is cropped to the specs I asked for”
Options are black and white.
User is fully in control of every outcome (but receives no decision assists from the software).
Entire knowledge base built over the last 20 years of interacting with software through GUI
Examples of traditional errors: can’t find the function (“where is the crop button”, the software doesn’t have the feature/functionality (“there is no crop button”)
Traditional UX
“I click crop in Photoshop and the image is cropped to the specs I asked for”
Options are black and white.
User is fully in control of every outcome (but receives no decision assists from the software).
Entire knowledge base built over the last 20 years of interacting with software through GUI
Examples of traditional errors: can’t find the function (“where is the crop button”, the software doesn’t have the feature/functionality (“there is no crop button”)
Traditional UX
“I click crop in Photoshop and the image is cropped to the specs I asked for”
Options are black and white.
User is fully in control of every outcome (but receives no decision assists from the software).
Entire knowledge base built over the last 20 years of interacting with software through GUI
Examples of traditional errors: can’t find the function (“where is the crop button”, the software doesn’t have the feature/functionality (“there is no crop button”)
Concept definition: perceived error
If the given output does not match my expected result, I might initially feel like this is a bug - something's not right.
“I click crop in Photoshop and it crops to the dimensions it thinks are best - but I might perceive that decision as “wrong””
Software is working to make the best choice for the user but not always understood why, or may be counter to what they intended—in reality, the software is already a few steps ahead to meet my original request/need, exceeding my expectations for what it is capable of doing. And that is the aha!/wow moment
It’s actually a few steps ahead, exceeding what I thought it was capable of.
Upon explanation people understand why a decision was made that way and actually LIKE the thought process
This is NOT the same as an error in input (12:30am vs. tomorrow errors)
For example, in the future, I ask restaurant booking-robot to book me a table for Tuesday dinner at "Taco King". Instead, they booked me Tuesday dinner at "Taqueria Diana". Perceived error initially, but the bot then explains that Taco King is fully booked all week, so it did the next best thing and booked a table at the closest restaurant that had availability, in closest vicinity, with the most similar menu items/cuisine, etc.
Example #1
Request: schedule me for wednesday
Response: thursday invite
Guest was out of town
The software actually made the CORRECT decision - except it was not what you originally asked for.
Example #2
Request: schedule me with so-and-so
Response: great, it’s at 9am next week, even though you asked me not to schedule anything before 9:30
Flex scheduling hours
Sometimes a meeting is important enough to warrant changes to your regular schedule, so we spent a lot of time and energy training Amy + Andrew to understand flexibility in time preferences.
Got many perceived errors in return! Users were concerned that a meeting was being held at a time that was outside their explicit preferences. BUT if Amy + Andrew stuck to the preferences, no meeting would have been scheduled.
Flex scheduling hours
Sometimes a meeting is important enough to warrant changes to your regular schedule, so we spent a lot of time and energy training Amy + Andrew to understand flexibility in time preferences.
Got many perceived errors in return! Users were concerned that a meeting was being held at a time that was outside their explicit preferences. BUT if Amy + Andrew stuck to the preferences, no meeting would have been scheduled.
When building a conversational or "invisible" interface, you lose a lot of those touch points along the experience for the user to have visibility, confirmation, and comfort in knowing what steps and actions have been taken. The main advantage for this type of interface is that you simply shoot off a request --> you get your result, as opposed to having to click "OK" on 5 pop ups, check off input boxes, select + drag, every micro-step of the way to accomplish your task.
The ideal happy medium is removing the burden for the user to have to complete all of those micro-steps, but still communicating enough relevant information of how the agent went from Request --> result
Simple concept (harder to execute) - communicate more!
[first image] Add explanation to decision
Amy explains why as she goes. “You might have something on the calendar that’s affecting this”, asking permission before going outside scheduling hours
[second image] Readbacks
“Here is what I understood you asked me to do”
Positive feedback, no numbers yet on how they’re helping.
Improved onboarding:
More calls, more explanation.
We don’t have all the answers yet - still working through these issues.
Potential solutions / issues we’ll see?
