Dr. Mikio Braun gave a presentation on his experience transitioning from academia to industry in artificial intelligence over the past decade. He discussed how machine learning has moved from researching problems like image recognition to solving business problems at companies like Zalando. He also compared the exploratory nature of academic research to the need to productize solutions in industry. Throughout, he provided examples of how machine learning is applied at different companies and analyzed whether certain applications truly qualify as artificial intelligence.
Dr. Mikio L. Braun gave a presentation on hardcore data science in practice at StrataConf 2016 in London. He discussed how Zalando, an online fashion retailer operating in 15 countries, heavily uses data science for recommendation engines. Braun covered different recommendation techniques including collaborative filtering, content-based recommendations, and personalized recommendations. He also discussed challenges in moving from static data analysis to production systems that operate in real-time and are frequently updated and monitored. Additionally, Braun addressed collaborations between data scientists and developers who have different coding approaches, and advocated for cross-functional teams and microservices in organizations.
Machine Learning for Time Series, Strata London 2018Mikio L. Braun
The document discusses machine learning techniques for time series analysis. It covers classical time series models, which make strong assumptions about stationarity but provide explicit modeling. General machine learning approaches can be more flexible but require transforming time series into supervised learning problems. Feature engineering can help preprocess time series data for modeling. Deep learning techniques like LSTMs have shown success by automatically learning representations of time series and sequential data. Examples are given of applications at companies like Zalando, Uber, and Amazon for user behavior modeling, demand forecasting, and multi-series predictions.
Smart Markets of Services / ATG meetup TorontoStefan Ianta
Evolutionary machine intelligence in a smart services market. Presentation at Analytics and Technology Group meetup / Ivey Tangerine Centre of Leadership, Toronto, Aug 18, 2016
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
The document discusses bridging the gap between current operating systems and AI-integrated systems, including transforming from process-driven to data-driven enterprises and the challenges of big data science initiatives; it also provides two case studies on using artificial intelligence for subjective analytics on social media and developing chatbots.
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
Enterprise Ready: A Look at Neo4j in Production at Neo4j GraphDay New York CityNeo4j
This document contains the agenda for an enterprise Neo4j training session in New York City on April 18, 2017. The agenda includes sessions on using graphs with Neo4j, working examples of transforming data, and a look at deploying Neo4j in production environments. Lunch is from 12:30-1:30 and a training session runs from 1:30-5:00pm.
Linkurious is a French startup founded in 2013 that provides the first graph visualization platform for Neo4j. It allows users to easily visualize and explore graph data stored in Neo4j to accelerate analysis and improve decision making. Linkurious offers starter, enterprise, and toolkit pricing plans starting at 990 euros per user per year for basic search and exploration of data.
Dr. Mikio L. Braun gave a presentation on hardcore data science in practice at StrataConf 2016 in London. He discussed how Zalando, an online fashion retailer operating in 15 countries, heavily uses data science for recommendation engines. Braun covered different recommendation techniques including collaborative filtering, content-based recommendations, and personalized recommendations. He also discussed challenges in moving from static data analysis to production systems that operate in real-time and are frequently updated and monitored. Additionally, Braun addressed collaborations between data scientists and developers who have different coding approaches, and advocated for cross-functional teams and microservices in organizations.
Machine Learning for Time Series, Strata London 2018Mikio L. Braun
The document discusses machine learning techniques for time series analysis. It covers classical time series models, which make strong assumptions about stationarity but provide explicit modeling. General machine learning approaches can be more flexible but require transforming time series into supervised learning problems. Feature engineering can help preprocess time series data for modeling. Deep learning techniques like LSTMs have shown success by automatically learning representations of time series and sequential data. Examples are given of applications at companies like Zalando, Uber, and Amazon for user behavior modeling, demand forecasting, and multi-series predictions.
Smart Markets of Services / ATG meetup TorontoStefan Ianta
Evolutionary machine intelligence in a smart services market. Presentation at Analytics and Technology Group meetup / Ivey Tangerine Centre of Leadership, Toronto, Aug 18, 2016
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
The document discusses bridging the gap between current operating systems and AI-integrated systems, including transforming from process-driven to data-driven enterprises and the challenges of big data science initiatives; it also provides two case studies on using artificial intelligence for subjective analytics on social media and developing chatbots.
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
Enterprise Ready: A Look at Neo4j in Production at Neo4j GraphDay New York CityNeo4j
This document contains the agenda for an enterprise Neo4j training session in New York City on April 18, 2017. The agenda includes sessions on using graphs with Neo4j, working examples of transforming data, and a look at deploying Neo4j in production environments. Lunch is from 12:30-1:30 and a training session runs from 1:30-5:00pm.
Linkurious is a French startup founded in 2013 that provides the first graph visualization platform for Neo4j. It allows users to easily visualize and explore graph data stored in Neo4j to accelerate analysis and improve decision making. Linkurious offers starter, enterprise, and toolkit pricing plans starting at 990 euros per user per year for basic search and exploration of data.
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Creating a Data Distribution Knowledge Base using Neo4j, UBSNeo4j
The document describes UBS's effort to create a Knowledge Base to map and manage data flows within the bank using the Neo4j graph database. It discusses how UBS previously distributed reference data through multiple disconnected channels, creating complexity and inefficiencies. The Knowledge Base models entities like datasets, attributes, consumers, and their relationships to answer questions about data distribution and help optimize the system.
GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4jNeo4j
This document discusses graphs and graph databases. It begins with an agenda about graphs 101, the state of graph databases, and the future of graphs. It then provides examples of how graphs can be used for applications like fraud detection and knowledge graphs. The document discusses how the use of graph databases has grown significantly in recent years and is expected to continue growing. It also provides examples of large companies that use graph databases and discusses how graphs can enhance artificial intelligence by providing connections and context.
Neo4j GraphTalks Oslo - Introduction to GraphsNeo4j
This document contains the agenda for a Neo4j graph database conference. It introduces the speakers Fredrik Johansson, Rik Van Bruggen, and Kees Vegter who will be giving presentations on Neo4j introduction, the value of graphs, and next-generation solutions using graph databases. Additional presentations will include graph database case studies. The document provides background on Neo4j and outlines the company's history and adoption as well as the graph platform it provides.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Digital Graph tour Rome: "Connect the Dots, Lorenzo SperanzoniNeo4j
This document provides information about a GraphTour Rome 2020 event hosted by LARUS, including:
- LARUS is an Italian company founded in 2004 that is a leader in graph database development and Neo4j partner.
- The event will discuss how viewing business problems as networks can provide benefits, as well as examples of how customers like banks and telecom companies are using Neo4j for applications like fraud detection and recommendations.
- LARUS' typical customer success roadmap for graph database projects is presented, outlining the assessment, use case identification, prototype, and production phases of a project.
Translating the Human Analog to Digital with GraphsNeo4j
Jeff Morris presented on how graphs can translate human analog activities and relationships to the digital world. Some key points:
1) Graphs can represent people, objects, locations, events and their relationships, capturing information like who, what, where, when, why and how. This models human analog data.
2) Modeling data as graphs allows representing complex relationships that are difficult to uncover with traditional methods. This helps with applications like fraud detection.
3) Graphs are well-suited to power applications like recommendations, smart homes, fraud detection and more by combining diverse data sources and identifying new connections.
"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrig...Data Science Milan
"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrigoni, Senior Data Scientist, Pirelli (pirelli.com)
Abstract:
Pirelli, a global performance tire manufacturer, uses data science in its 20 factories to improve quality and efficiency, and reduce energy consumption. For this “Smart Manufacturing” initiative, Pirelli’s data science team has developed predictive models and analytics tools to monitor processes, machines and materials on the factory floors. In this talk we will show some of the solutions we deploy, demonstrate how we used Domino’s data science platform and Plot.ly to build these solutions, and discuss the next steps in this journey towards predictive maintenance.
Bio:
Alberto Arrigoni is a data scientist at Pirelli, where he works to process sensors and telemetry data for IoT, Smart Factories and connected-vehicle applications.
He works closely with all major business units such as R&D, industrial engineering and BI to develop tailored machine learning algorithms and production systems.
He holds a PhD in biostatistics from the University of Milan Bicocca and prior to joining Pirelli was a staff data scientist at the National Institute of Molecular Genetics (Milan), as well as a Fulbright student at the Santa Clara University and visiting PhD student at Pacific Biosciences (Menlo Park, CA).
Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
1) Pirelli is a leading tire manufacturer that is leveraging data science and analytics to optimize its manufacturing processes.
2) A smart manufacturing team consisting of data scientists, engineers, and software developers is using data from Pirelli's complex tire manufacturing process to develop services around demand forecasting, performance monitoring, and virtualization.
3) Getting started with data science at Pirelli involved focusing on major pain points that could be addressed with available manufacturing data, promoting data exploration tools, and prioritizing descriptive analytics that lead to actionable insights.
GraphTour 2020 - Practical Applications of Neo4j 4.0Neo4j
The document discusses several practical applications of Neo4j 4.0 including fine-grained security, multiple databases, and scalability. Fine-grained security allows restricting queries based on user permissions. Multiple databases enable separating data into logical graphs for multi-tenancy and cleaner models. Scalability focuses on using graphs to power machine learning with insights brought back into the database.
This document discusses an index for tracking companies involved in 5G technology. It describes the index's semi-annual rebalancing process and criteria for selecting companies, including a minimum market capitalization and liquidity, membership or participation in 5G standards organizations, and scoring based on 5G patents, consortium involvement, and financial metrics. The index is aimed at maximizing returns from dividend payments by reinvesting dividends.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Neo4j is a graph database platform for connected data. The document introduces Neo4j and discusses how connected data and relationships between data are increasingly important for business value. It provides examples of how Neo4j is used by organizations for applications like fraud detection, personalization, and network analysis. The document also summarizes Neo4j's capabilities like real-time transaction processing, analytics, and visualization and highlights its native graph architecture and performance advantages over traditional databases. Finally, it briefly describes Neo4j's key architecture components and how it can be used for common data architecture patterns.
Neo4j GraphTalks Oslo - Next Generation Solutions built on NeoejNeo4j
The document discusses next generation solutions built on Neo4j graph database. It provides an agenda for the talk including solutions using Neo4j, recommendations, GDPR, and conclusions. It discusses how graph-based solutions with Neo4j enable flexibility, intuitiveness, and high performance for connected data scenarios. It also provides examples of using Neo4j for recommendation engines in retail, logistics, fraud detection and more. Case studies describe how Walmart and eBay improved recommendations and routing with Neo4j.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
Democratizing Intelligence - Sri Ambati, CEO & Co-Founder, H2O.aiSri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/ZrlJQqNaSMI.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://www.twitter.com/h2oai.
This document discusses graph data science and Neo4j's capabilities. It describes how Neo4j can help simplify graph data science through its native graph database, graph data science library, and data visualization tool. Example use cases are also provided that demonstrate how Neo4j has helped companies with fraud detection, customer journey analysis, supply chain management, and patient outcomes.
Don’t Choose One Database Choose Them All!, CapgeminiNeo4j
This document discusses using multiple database technologies together to unlock more value from data. It argues that no single database is optimal for all use cases, and that combining databases like SQL, graph, and NoSQL can provide a richer set of insights. The document provides an example of analyzing car insurance fraud risk by querying different types of applicant data stored in different databases, and then combining the results. It acknowledges costs of this approach but suggests starting with one database and iteratively adding more as needed to address bottlenecks.
The document summarizes a presentation given by Dr. Mikio Braun on architecting AI applications. It discusses the history and approaches of artificial intelligence, including classical, machine learning, and deep learning methods. It also provides examples of applying AI to autonomous driving, chatbots, recommendations, games and more. Finally, it outlines common elements of AI applications and design patterns for aspects like core machine learning, serving models, preprocessing data, automation, and integrating machine learning components.
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Creating a Data Distribution Knowledge Base using Neo4j, UBSNeo4j
The document describes UBS's effort to create a Knowledge Base to map and manage data flows within the bank using the Neo4j graph database. It discusses how UBS previously distributed reference data through multiple disconnected channels, creating complexity and inefficiencies. The Knowledge Base models entities like datasets, attributes, consumers, and their relationships to answer questions about data distribution and help optimize the system.
GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4jNeo4j
This document discusses graphs and graph databases. It begins with an agenda about graphs 101, the state of graph databases, and the future of graphs. It then provides examples of how graphs can be used for applications like fraud detection and knowledge graphs. The document discusses how the use of graph databases has grown significantly in recent years and is expected to continue growing. It also provides examples of large companies that use graph databases and discusses how graphs can enhance artificial intelligence by providing connections and context.
Neo4j GraphTalks Oslo - Introduction to GraphsNeo4j
This document contains the agenda for a Neo4j graph database conference. It introduces the speakers Fredrik Johansson, Rik Van Bruggen, and Kees Vegter who will be giving presentations on Neo4j introduction, the value of graphs, and next-generation solutions using graph databases. Additional presentations will include graph database case studies. The document provides background on Neo4j and outlines the company's history and adoption as well as the graph platform it provides.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Digital Graph tour Rome: "Connect the Dots, Lorenzo SperanzoniNeo4j
This document provides information about a GraphTour Rome 2020 event hosted by LARUS, including:
- LARUS is an Italian company founded in 2004 that is a leader in graph database development and Neo4j partner.
- The event will discuss how viewing business problems as networks can provide benefits, as well as examples of how customers like banks and telecom companies are using Neo4j for applications like fraud detection and recommendations.
- LARUS' typical customer success roadmap for graph database projects is presented, outlining the assessment, use case identification, prototype, and production phases of a project.
Translating the Human Analog to Digital with GraphsNeo4j
Jeff Morris presented on how graphs can translate human analog activities and relationships to the digital world. Some key points:
1) Graphs can represent people, objects, locations, events and their relationships, capturing information like who, what, where, when, why and how. This models human analog data.
2) Modeling data as graphs allows representing complex relationships that are difficult to uncover with traditional methods. This helps with applications like fraud detection.
3) Graphs are well-suited to power applications like recommendations, smart homes, fraud detection and more by combining diverse data sources and identifying new connections.
"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrig...Data Science Milan
"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrigoni, Senior Data Scientist, Pirelli (pirelli.com)
Abstract:
Pirelli, a global performance tire manufacturer, uses data science in its 20 factories to improve quality and efficiency, and reduce energy consumption. For this “Smart Manufacturing” initiative, Pirelli’s data science team has developed predictive models and analytics tools to monitor processes, machines and materials on the factory floors. In this talk we will show some of the solutions we deploy, demonstrate how we used Domino’s data science platform and Plot.ly to build these solutions, and discuss the next steps in this journey towards predictive maintenance.
Bio:
Alberto Arrigoni is a data scientist at Pirelli, where he works to process sensors and telemetry data for IoT, Smart Factories and connected-vehicle applications.
He works closely with all major business units such as R&D, industrial engineering and BI to develop tailored machine learning algorithms and production systems.
He holds a PhD in biostatistics from the University of Milan Bicocca and prior to joining Pirelli was a staff data scientist at the National Institute of Molecular Genetics (Milan), as well as a Fulbright student at the Santa Clara University and visiting PhD student at Pacific Biosciences (Menlo Park, CA).
Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
1) Pirelli is a leading tire manufacturer that is leveraging data science and analytics to optimize its manufacturing processes.
2) A smart manufacturing team consisting of data scientists, engineers, and software developers is using data from Pirelli's complex tire manufacturing process to develop services around demand forecasting, performance monitoring, and virtualization.
3) Getting started with data science at Pirelli involved focusing on major pain points that could be addressed with available manufacturing data, promoting data exploration tools, and prioritizing descriptive analytics that lead to actionable insights.
GraphTour 2020 - Practical Applications of Neo4j 4.0Neo4j
The document discusses several practical applications of Neo4j 4.0 including fine-grained security, multiple databases, and scalability. Fine-grained security allows restricting queries based on user permissions. Multiple databases enable separating data into logical graphs for multi-tenancy and cleaner models. Scalability focuses on using graphs to power machine learning with insights brought back into the database.
This document discusses an index for tracking companies involved in 5G technology. It describes the index's semi-annual rebalancing process and criteria for selecting companies, including a minimum market capitalization and liquidity, membership or participation in 5G standards organizations, and scoring based on 5G patents, consortium involvement, and financial metrics. The index is aimed at maximizing returns from dividend payments by reinvesting dividends.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Neo4j is a graph database platform for connected data. The document introduces Neo4j and discusses how connected data and relationships between data are increasingly important for business value. It provides examples of how Neo4j is used by organizations for applications like fraud detection, personalization, and network analysis. The document also summarizes Neo4j's capabilities like real-time transaction processing, analytics, and visualization and highlights its native graph architecture and performance advantages over traditional databases. Finally, it briefly describes Neo4j's key architecture components and how it can be used for common data architecture patterns.
Neo4j GraphTalks Oslo - Next Generation Solutions built on NeoejNeo4j
The document discusses next generation solutions built on Neo4j graph database. It provides an agenda for the talk including solutions using Neo4j, recommendations, GDPR, and conclusions. It discusses how graph-based solutions with Neo4j enable flexibility, intuitiveness, and high performance for connected data scenarios. It also provides examples of using Neo4j for recommendation engines in retail, logistics, fraud detection and more. Case studies describe how Walmart and eBay improved recommendations and routing with Neo4j.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
Democratizing Intelligence - Sri Ambati, CEO & Co-Founder, H2O.aiSri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/ZrlJQqNaSMI.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://www.twitter.com/h2oai.
This document discusses graph data science and Neo4j's capabilities. It describes how Neo4j can help simplify graph data science through its native graph database, graph data science library, and data visualization tool. Example use cases are also provided that demonstrate how Neo4j has helped companies with fraud detection, customer journey analysis, supply chain management, and patient outcomes.
Don’t Choose One Database Choose Them All!, CapgeminiNeo4j
This document discusses using multiple database technologies together to unlock more value from data. It argues that no single database is optimal for all use cases, and that combining databases like SQL, graph, and NoSQL can provide a richer set of insights. The document provides an example of analyzing car insurance fraud risk by querying different types of applicant data stored in different databases, and then combining the results. It acknowledges costs of this approach but suggests starting with one database and iteratively adding more as needed to address bottlenecks.
The document summarizes a presentation given by Dr. Mikio Braun on architecting AI applications. It discusses the history and approaches of artificial intelligence, including classical, machine learning, and deep learning methods. It also provides examples of applying AI to autonomous driving, chatbots, recommendations, games and more. Finally, it outlines common elements of AI applications and design patterns for aspects like core machine learning, serving models, preprocessing data, automation, and integrating machine learning components.
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
ITCamp 2019 - Andy Cross - Business Outcomes from AIITCamp
Andy Cross, Director of Elastacloud, Microsoft Regional Director, Azure MVP and all round good guy, gives a session on how to successfully build or transform a business using AI technologies.
Over the last years, Elastacloud have delivered analytics projects to a variety of customers. The greatest challenges around AI are both technical and organisational. The existing landscape of process and strategy doesn't solve these challenges in combination, and the gap between causes friction and the failure of AI projects.
When modelling the outcome of actions that were informed by AI, possibly enacted by AI, the standard risk modelling approaches need to be transformed to include a factor that can change over time to represent the effectiveness of the AI solutions. Given that we should accept errors as part of the AI solution, and that errors are reinforcing of better future decisions, we need to project risk as a decreasing vector over time.
Innovation is Key to Growth - Big ideas - 2019 - ARK InvestPaulo Ratinecas
Why Invest in Disruptive Innovation?
ARK believes that disruptive innovation is key to growth.
We aim to identify large-scale investment opportunities
by focusing on public companies that are the leaders,
enablers, and beneficiaries of disruptive innovation.
Opportunities resulting from disruptive innovation are
often undiscovered or misunderstood by traditional
investment managers who are focused on sectors,
indexes, short-term earnings and price movements.
ARK’s research spans across sectors, industries, and
markets to gain a deeper understanding of the
convergence, market potential, and long-term impact
of disruptive innovation.
DISRUPTIVEINNOVATION
ARK defines ‘‘disruptive innovation’’ as the introduction
of a technologically enabled new product or service
that should transform economic activity by creating
simplicity and accessibility while driving down costs.
Adaptive Upskill As The Future Of The WorkforceMichal Juhas
This document discusses the future of the workforce and the need for adaptive upskilling. It notes that exponential technologies are changing jobs rapidly and that by 2020 over 40% of the US workforce will consist of freelance workers. The nature and lifespan of jobs is changing, requiring lifelong learning and serial careers. It advocates for adaptive upskilling that is personalized and delivered through micro-learning, gamification, video content and collective learning experiences like online academies and bootcamps. The key takeaway is that companies and individuals must upskill continuously to prepare for jobs of the future.
BIM For Free_28May2013_CaseyRutland_BIM for beginnersBIM Academy
This document provides an introduction to Building Information Modeling (BIM) for beginners. It discusses key BIM acronyms and concepts, common myths and pitfalls to avoid, and benefits of BIM for projects and companies. It also reviews various BIM tools and the importance of developing a BIM culture and integrating it as a business process rather than just a software add-on. The document encourages readers to start adopting BIM now through small, manageable projects and continuing education on BIM best practices.
BIM For Free_28May2013_CaseyRutland_BIM for beginnersBIM Academy
This document provides an introduction to Building Information Modeling (BIM) for beginners. It discusses key BIM acronyms and concepts, common myths and pitfalls to avoid, and benefits of BIM for projects and companies. It also reviews various BIM tools and the importance of developing a BIM culture and integrating it as a business process rather than just a software add-on. The document encourages readers to start adopting BIM now through small, manageable projects and continuing education on BIM best practices.
#OSSPARIS19 - Overcoming open source challenges in reinforcement learning - W...Paris Open Source Summit
#IA Track - Practical applications
Reinforcement learning is a rapidly growing branch of artificial intelligence that has achieved super-human performance in board games such as Go and chess and video games such as Starcraft. Research papers and open code in this field are widely available.
However, unlike other fields of machine learning, open code and research has so far largely failed to translate into real world applications.
In this talk, we leverage the indust.ai team's experience in developing their own reinforcement learning activity to discuss the challenges involved. These include poor reproducibility, varying code quality, prohibitive computation and data requirements, the difference in mindset between traditional machine learning and reinforcement learning, and the difficulty of finding the skills required to transfer academic research to the real world. We will also present some of our approaches for overcoming these issues.
GraphTour - Crédit Agricole: Making the Most of Public Information on our Cli...Neo4j
PanOptes is a monitoring solution that uses machine learning and collective intelligence to sort through public information and provide relevant insights about clients. It automatically classifies news articles and recommends readings based on users' actions. The solution draws on diverse internal expertise and uses technologies like Elastic Search, Event Store, and Neo4j to analyze and store data semantically. PanOptes started as an intrapreneurship initiative and grew as an internal startup by collaboratively defining users' needs and validating value.
My Slides for #IAD18
The rigidity of some processes is typical in manufacturing sector.
The challenges behind Industry 4.0 and IoT are not only the Digital Transformation and the arrive of data in the cloud but the opening of Service, Big Data and Machine Learning channels, this requires to rethink some processes already consolidated. The ideation of a service depends by the final users feedback and this feedback usually comes when the machinery are already operative and in production.
And if a Gantt exists before starting a new project ... what happen?
The Next Generational Shift In Enterprise Infrastructure Has Arrived. If SlideShare is broken, please download report here: https://www.scribd.com/document/352452857/2017-Enterprise-Almanac
Applying deep learning tools to data available at the banking industry level....Data Driven Innovation
Deep learning tools can be applied to data in the banking industry to drive innovation. Some potential use cases include interventions to improve business models and customer services, as well as optimizing business processes. However, these opportunities must be balanced with risks. Artificial intelligence approaches are available to companies of all sizes. Data quality is important both as a starting point and ongoing target for AI systems. Combining multiple techniques such as computer vision and deep learning can provide benefits. Model training may eventually replace some data collection needs.
The document discusses how APIs allow companies to focus on their core business by outsourcing non-core functions. It provides examples of companies in different industries that use APIs to improve their business model, including a telecommunications company, a cosmetics company, and a banking company. It also discusses how coding and programming skills are becoming more important and how various initiatives are making it easier for more people to learn how to code.
1. The document discusses several manufacturing technology topics for 2014, including newshoring expanding national economies but dampening global trade, emerging economies leapfrogging centralized manufacturing through distributed digital manufacturing, and the blurring lines between hardware, software, and materials with advances in 3D printing and materials.
2. It also touches on 3D printing enabling more automated "last mile" assembly in factories, the impact of algorithmic design on product design careers, and potential early applications of quantum computing despite major challenges in developing usable quantum algorithms and hardware.
3. Specific examples and perspectives are provided for each topic from sources like CNN, Bloomberg, IT World, and Re/Code to further illustrate trends and issues in these developing areas
This presentation discusses human-machine collaboration in industrial automation. It covers the need for human-machine interaction, handling uncertainty through a lifecycle approach using digital twins, and progressing towards more autonomous systems through a step-by-step approach. The overarching goal is a symbiotic relationship between humans and machines by leveraging their respective strengths to maximize benefits.
DataDevOps: A Manifesto for a DevOps-like Culture Shift in Data & AnalyticsDr. Arif Wider
A talk by Sebastian Herold & Dr. Arif Wider at TDWI 2018 Munich.
Abstract:
More and more companies migrate their monolithic applications to a microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: huge amounts of structured and unstructured data, and hundreds of data sources.
Furthermore, data-driven product development multiplies the analytics requirements: every product team needs constantly updated and specially tailored metrics which often combine product specific data with company wide data.
Having a centralized data team does not scale in this setting as it becomes the bottleneck between data producers and data consumers.
We created a Manifesto based on five general themes which break with traditional separation of roles and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDev: a culture shift similar to DevOps in which application developers own their data and take over responsibilities for data & analytics.
Learn about our experiences and best practices with facilitating this cultural transformation at Zalando, one of Europe's largest online fashion platforms.
These are the slides of the tutorial presented at the 17th International Conference on Business Process Management (BPM 2019), Wien, Austria, 2--6 September 2019.
Talk given by Francesco Leotta, Andrea Marrella and Massimo Mecella
Cite them as:
Leotta F., Marrella A., Mecella M. (2019) IoT for BPMers. Challenges, Case Studies and Successful Applications. In: Hildebrandt T., van Dongen B., Röglinger M., Mendling J. (eds) Business Process Management. BPM 2019. Lecture Notes in Computer Science, vol 11675. Springer, Cham.
Similar to Academia to industry looking back on a decade of ml (20)
Bringing ML To Production, What Is Missing? AMLD 2020Mikio L. Braun
This document discusses key considerations for bringing machine learning to production. It addresses identifying suitable problems for ML, architectures for ML systems, and organizing teams and data platforms for ML. Specifically, it provides examples of recommender systems and preprocessing patterns. It emphasizes that the ML problem must address the underlying business problem and have different metrics. Architectures include serving patterns, preprocessing in feature stores, and integrating multiple ML models. The document also discusses effective collaboration between data scientists and developers and organizing data science teams within companies.
Data flow vs. procedural programming: How to put your algorithms into FlinkMikio L. Braun
The document discusses the differences between procedural and data flow programming paradigms, using Apache Flink as an example data flow system. Data flow programming uses sets of data as basic building blocks and operations on these sets, rather than variables and control flow. It describes translating algorithms like computing a sum or mean, least squares regression, and vector/matrix operations into data flow operations. Broadcast variables are introduced as a way to combine intermediate results in data flow programming.
The document discusses scalable machine learning techniques for analyzing large datasets. It explains that while parts of the machine learning pipeline like data preparation are easily parallelizable, training steps involving gradient descent are more difficult to parallelize. However, there are approaches for scalable training such as stochastic gradient descent, parameter servers, and feature hashing that approximate the model to make distributed optimization feasible. The key aspects of scalable machine learning involve faster learning algorithms, approximating the optimization problem and features, and asynchronous distributed techniques rather than just relying on parallelization alone.
Talk I gave at StratHadoop in Barcelona on November 21, 2014.
In this talk I discuss the experience we made with realtime analysis on high volume event data streams.
This document provides an introduction to Cassandra, a distributed database management system. It begins with an overview of Cassandra and how it compares to traditional databases. Key aspects discussed include that Cassandra uses a simple query language, scales out through clustering rather than up on larger servers, does not require a fixed database schema, and is eventually consistent. The document then covers Cassandra's data model, architecture, configuration, usage, performance considerations and tuning. Real-world experiences with Cassandra in a Twitter analytics application are also shared.
Mein Talk, den ich auf dem LinuxTag 2011 gegeben habe. Ich geben ein Übersicht über Cassandra und erzähle von Erfahrungen, die wir mit Cassandra gemacht haben, als wir es zur Echtzeitanalyse von Twitterdaten verwendet haben.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
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."
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!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
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
3. 3
FROM THINKING MACHINES… TO DETECTING CATS AND DOGS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
4. 4
MACHINE LEARNING: RESEARCH & INDUSTRY
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
5. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20185
ZALANDO
• 17 Countries
• 7 locations in Europe
• 4.5 bn € revenue 2017
• ~15.000 employees
• IPO Oct 2014
https://geschaeftsbericht.zalando.de/2017/geschaeftsbericht/zahlen-daten-und-fakten/
6. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20186
SEARCH PLATFORM
7. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20187
WAS I RIGHT?
8. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20188
WAS IT WHAT I EXPECTED?
🤔
10. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201810
• What is out there: AI Hierarchy of needs + methods + tools = AI?
SO HOW DO YOU “DO AI?”
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007
11. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201811
ORGANIZATION OF WORK
12. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201812
HOW?
14. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201814
• Customer has a Job to be
done.
• Business has to give a
Solution that adds value.
• Solution consists of
Activities, and some of that
activities might be solved
with ML.
• ML: solve a task by learning
from examples.
FROM BUSINESS PROBLEMS TO MACHINE LEARNING PROBLEMS
15. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201815
MACHINE LEARNING: LEARN FROM EXAMPLES
16. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201816
EXAMPLE: RECOMMENDATIONS
17. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201817
Typical problem for machine learning:
• Hard to specify what exactly means “similar.”
• A lot of example data is available.
• Recommendations have to change based on new articles
frequently.
EXAMPLE: RECOMMENDATION
18. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201818
Typical algorithms:
• Collaborative
filtering,
• Content based
recommendation,
• Predicting customer‘s
next action.
EXAMPLE: RECOMMENDATIONS
20. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201820
Data Scientists and Developers
21. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201821
THE SCIENTIFIC METHOD
22. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201822
Very different approaches to
coding…
← developers
data scientists →
DS&D: Coding
23. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201823
• What is the most
productive way?
• Ideally, interface on
code, not just
documentation
• Production logs often
become data analysis
input!
DS&D: Collaboration
24. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201824
ADDING ML TO THE MIX
25. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201825
ADDING ML TO THE MIX
26. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201826
ORGANIZING DATA SCIENCE
32. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201832
33. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201833
MARVIN MINSKY: RECOGNIZING SIMPLE PICTURES
http://web.media.mit.edu/~minsky/papers/PR1971.html
34. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201834
AI TIMELINE PAST 20 YEARS (that’s 1998 till 2018)
35. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201835
• Initially approached AI like any
other problem computers could
solve.
• Alternatively, using an approach
inspired by human biology.
• Machine Learning added a
statistical approach to the mix.
• Recently, Deep Learning has led to
impressive improvements.
APPROACHES IN ARTIFICIAL INTELLIGENCE
36. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201836
• Classical approach is to specify
what the input/output relation is,
then devise programs to solve
that.
• Machine Learning replaces that
with examples (+ a cost
function).
• Training then means to infer a
model that generalizes well on
future data.
BIRD’S EYE VIEW OF MACHINE LEARNING
37. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201837
• Artificial Intelligence is the overarching goal or challenge.
• Machine Learning is one approach that has proven very successful if the problem
itself cannot be specified easily.
ARTIFICIAL INTELLIGENCE VS. MACHINE LEARNING
38. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201838
• Many reasons why:
convenience, security,
disrupting mobility.
• Current approaches are a mix
of many systems, some of
which make heavy use of
machine learning.
• Deep Learning very successful
for computer vision and image
analysis.
AUTONOMOUS DRIVING
39. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201839
Autonomous driving is based on a mix of sensors with quite
different capabilities to improve reliability.
• Sonar/Radar: Cheap, low resolution, works well under
extreme weather and in darkness, can estimate velocity.
• Camera: Cheap, very high resolution, similar to what we
humans see.
• Lidar (light detection and ranging): expensive, very
accurate depth maps.
SENSORS IN AUTONOMOUS DRIVING
40. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201840
• Companies like Waymo do extensive data
collection and simulation to evaluate and tune
the system.
• Not just for training ML methods, also for overall
systems testing.
ML inspired approach to defining the problem, but mix
of ML and explicit solutions.
(Waymo lecture at MIT)
DATA-DRIVEN APPROACH TO AUTONOMOUS DRIVING
41. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
• Not that much ML in
there (at least right
now).
• Dialog is done through
Frames that capture a
piece of information
required and an
analysis part that maps
user input to fields.
• ML used for
understanding
speech2text, named
entity recognition,
analysis
41
CHATBOTS
Example 1:
A: “I’d like to book a flight tomorrow”
B: “From where to where do you want to fly?”
A: “From London to Berlin.”
B: “With how many passengers?”
A: “Just me.”
B: “Okay, so I have one passenger for a flight
from London to Berlin tomorrow. Is that
correct?”
A: “Yes.”
B: …
Example 2:
A: “I’d like to book a flight for me
tomorrow from London to Berlin.”
B: …
Booking a flight:
Frame:
- when: Date
- start, end: Location
- how many persons: Number
42. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201842
• Machine Learning
used especially on
“perception” part.
• Core is rule based
system.
• Potential to improve
those based on
examples, too, same
for text2speech.
CHATBOT SYSTEM OVERVIEW
43. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201843
Recommendations as an AI problem:
• Understand what the user is
looking for right now. What is his
intent, what is in his mind?
• Technically, predict next action.
• Quite involved, dealing with real-
time data, etc.
RECOMMENDATIONS
44. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201844
• From computer science’s point
of view, strategy games are
“easy” if you know the value of
each state.
• Cleverly simulating “plausible
actions” leads to speedup
(Monte Carlo tree search)
ALPHA GO AND OTHER STRATEGY GAMES
45. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201845
ALPHA GO: CONVOLUTIONAL NEURAL NETWORKS FOR POLICY AND VALUE PREDICTION
46. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201846
• Machine Learning used as part of the system
• Otherwise lots of engineering and “classical approaches”
• Main difference IMHO is “data driven” vs. “spec driven” approach.
SOME MODERN “AI” SYSTEMS
47. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201847
• Image recognition almost on human level performance.
• Outperforms humans on strategy games.
• Able to learn features by itself.
• Can act intelligently and autonomously in the real world.
YES!
48. What is Deep Learning - StrataData New York, 2017 - Mikio Braun48
• From raw data to symbolic representations?
• Capability to self reflect?
• What happens inside the machine? Is it just imitating?
• Learning from single examples.
NO!
50. This presentation and its contents are strictly confidential. It may not, in
whole or in part, be reproduced, redistributed, published or passed on to
any other person by the recipient.
The information in this presentation has not been independently verified. No
representation or warranty, express or implied, is made as to the accuracy
or completeness of the presentation and the information contained herein
and no reliance should be placed on such information. No responsibility is
accepted for any liability for any loss howsoever arising, directly or
indirectly, from this presentation or its contents.
DISCLAIMER
50
Editor's Notes
- Always interested in AI and Model of the Mind.
- As soon as I could => ML
Just about prediction
But that is actually great
=> what is the core of ML?
- Name
- Position
- Story of Academia to Industry and what I learned about ML
Fulfillment center == warehouse
135M visits/month =~ 450K visits/day
Active Customer: Anyone who has placed an order in the past 12 months
⅓ of 9000 in Berlin
Spend some time
Explore
Explore islands, look for loops
Global system to facilitate: peer reviewed publications focussed on novelty
=> something is missing.
Startup on the side
Pilot projects
Two engs, didn’t take off => industry
ML at the core
Layer of infrastructure to deliver product
Automate what was one off
Research still there, at product level
=> so how do we compare?
research explores / industry builds
Simple stuff works best, because research is often exploring
Similar to toolsmiths, but overlooked because everything is software
=> Leaps: Deep Learning
This is eniac. Ever since people built the first computers, the idea was to build a machine that thinks, to understand the mechanics of mental work, but we didn’t really know what this is.
Famously, Marvin Minsky in 1966 posed this as a summer project, and years later it wasn’t solved.