Decentralized AI Draper
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Draper Accelerator Talk Slides - convering convergence of of AI and Blockchain and how it solves challenges for IoT, Ai@Edge and Data Ethics and User Data Monetization.
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With both Facebook and Google's recent shift in direction towards a "Future is Private" world, learn how you too can train and deploy your AI models in a privacy-preserving way, with Decentralized AI and a combination of AI and Blockchain. These techniques will become even more rampant as we move into a world where users will own their own data and companies will start using “ethically sourced data” and move towards a path for Ethical AI for the IoT space.
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This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
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Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
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Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
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In this talk I will explain how I solved this by integrating a conversational assistant, built with Rasa, with a knowledge graph, built with Grakn. Together, these open source libraries help me understand what my bot’s users are talking about.
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As a polyglot developer, specialising in Java/JVM tech, I have explored GraalVM, an enhanced JVM with runtime performance being its primary goal.
My first instinct was to run Grakn on GraalVM and then run GraknLabs’ benchmark suite to measure runtime performance against both the traditional JVM and GraalVM.
We will see how we can utilize and speak Graql just like we would be speaking to another human. We will explore English-to-Graql and vice-versa, using natural language to communicate with this novel graph engine called Grakn (this all started from a meetup and a few Github issues).
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The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
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2) LOV4IoT provides an HTML user interface and web services to automatically compute statistics about the projects in its dataset, such as the number per domain.
3) The goal of LOV4IoT is to extract reusable domain knowledge from the referenced ontologies and datasets, such as a dictionary to unify IoT data and rules to interpret sensor data, to help developers design semantic-based IoT applications.
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Unveiling the knowledge in knowledge graphs
- Davide Mottin is an assistant professor in the Department of Computer Science at Aarhus University who researches graph mining.
- His talk discusses unveiling knowledge in knowledge graphs through personalized summarization techniques. Knowledge graphs contain entities and relationships between them.
- He describes an approach for generating personalized summaries of a knowledge graph based on a user's query history. The algorithm aims to find a subgraph that maximizes the probability of answering future queries, subject to a size limit.
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This document discusses the convergence of blockchain and AI through decentralized AI approaches. It outlines challenges with centralized AI models regarding privacy, influence, economics and transparency. Decentralized solutions proposed include federated learning, blockchain, homomorphic encryption, and data marketplaces. Blockchain provides an open, trustless network to replace centralized authorities and enable applications like data exchanges, AI marketplaces and distributed machine learning across devices. Overall the goal is to democratize AI and data through user ownership and control.
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Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
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This document discusses how security teams are overwhelmed by large volumes of data from security alerts and indicators. It proposes that graph algorithms can help identify related alerts and events that should be investigated together, such as those targeting the same users or part of the same attack. The document provides examples of how community detection, centrality analysis, and other graph algorithms run on preprocessed security data can help prioritize work and generate new threat indicators.
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How does a conversational assistant understand questions like “What is the most expensive transaction on food I have made?” and where does it get the data to answer this kind of questions correctly? Or imagine a conversation with a bot that helps you manage your bank accounts. A person might ask questions like “On which of those accounts do I have more money?” or “What is the IBAN of the second account you just mentioned?”. How do we give our bot access to the relevant domain knowledge?
In this talk I will explain how I solved this by integrating a conversational assistant, built with Rasa, with a knowledge graph, built with Grakn. Together, these open source libraries help me understand what my bot’s users are talking about.
Industry of Things World - Berlin 19-09-16
Dr. Boris Adryan gave a talk on the impact of IoT analytics on development budgets. He discussed that IoT data problems are often not as complex as perceived and do not necessarily require "big data" solutions or specialists. Basic data storage and processing can often be done cost-effectively using standard tools. True challenges lie in extracting useful insights, which may require specialized machine learning approaches. Not all analytics need to be real-time. The appropriate solution depends on the use case and desired insights.
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The cyber-security threat landscape is a faceless whirlwind of deliberate and persistent attempts to compromise individual and organizational data toward nefarious ends. We might not know the direction of the next threat, but we certainly know it is inevitable. All organizations are faced with the massive problem of evolving threat; however, due to the current drought of cyber-security and data science professionals this problem can be prohibitively expensive to solve in isolation. In the absence of shared intelligence, best efforts can lead to ineffective ad-hoc security systems which are only token in nature. The dichotomy is as such: hackers and cyber-criminals can share intelligence for their purposes; how can we leverage and share intelligence about these common threats without compromising the data integrity of each organization? How do we detect advanced persistent threats without violating the privacy rights of individual users? In this talk, we discuss the state of the science in privacy-preserving threat analytics scaled to massive data sets and propose a solution to this dichotomy.
2016 FS-ISAC Annual Summit (Miami) - Developing Effective Encryption Strategies
Data protection is at the center of a mature organizational information security strategy. Encryption plays an important role in that strategy to effectively protect data, even after other lines of defense have been compromised.
Unfortunately, there are many factors complicating the when, where and how of successfully using encryption technologies:
Getting Productive my Journey with Grakn and Graql
Over many weeks and months, I have been learning about Grakn Labs and the two core components i.e. Grakn and Graql.
As a polyglot developer, specialising in Java/JVM tech, I have explored GraalVM, an enhanced JVM with runtime performance being its primary goal.
My first instinct was to run Grakn on GraalVM and then run GraknLabs’ benchmark suite to measure runtime performance against both the traditional JVM and GraalVM.
We will see how we can utilize and speak Graql just like we would be speaking to another human. We will explore English-to-Graql and vice-versa, using natural language to communicate with this novel graph engine called Grakn (this all started from a meetup and a few Github issues).
2018 learning approach-digitaltrends
The document provides an overview of various digital technologies including AI, IoT, cloud computing, data analytics, and more. It discusses the "apples" or fundamental technologies in these areas like AR, VR, AI, IoT, and cloud computing. It then outlines several learning paths one could take to understand these technologies, beginning with foundations in areas like probability, statistics, computer science, and communications. It provides recommendations for books and courses to learn about each technology from roots to more advanced concepts. Finally, it discusses bringing all the pieces together using design thinking.
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The document discusses designing cross-domain semantic web of things applications. It introduces challenges including how to interpret IoT data, combine data from different domains, and reuse domain knowledge. The proposed M3 framework addresses these challenges through components like a SWoT generator template, M3 language and ontology, sensor-based linked open rules, and linked open vocabularies for IoT. Evaluations show the framework helps developers build semantic applications and interprets data efficiently while reusing interoperable domain knowledge. The framework has potential applications in domains like health, tourism and transportation.
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This document discusses graph visualization tools and approaches. It describes two main approaches to graph visualization - the global approach which provides an overview first before allowing zooming and filtering for details, and the centered approach which allows searching first before showing context and expanding on demand. Several graph visualization tools are described and it is noted which approach they best support. Linkurious is presented as a tool that can visualize any graph database and supports the centered approach through search, context, and expansion.
Log Data Mining
This chapter is devoted to log mining or log knowledge discovery - a different type of log analysis, which does not rely on knowing what to look for. This takes the “high art” of log analysis to the next level by breaking the dependence on the lists of strings or patterns to look for in the logs.
CarolinaCon Presentation on Streaming Analytics
Presentation by Rob Weiss and John Eberhardt on using streaming analytics and immersive visualization to model network data
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This document provides an overview of Think Big Analytics, an analytics consulting firm. It discusses their services portfolio including data engineering, data science, analytics operations and managed services. It also highlights their global delivery model and successful projects with over 100 clients. The document then discusses their approach to artificial intelligence and deep learning, including applications across industries like banking, connected cars, and automated check processing. It emphasizes the need for a phased implementation approach to AI and challenges around technology, data, and deployment.
Understanding the "Intelligence" in AI
In this presentation Raffael Marty, VP of Research of Intelligence, Forcepoint X-Labs, explores the topic of Artificial Intelligence (AI) in cyber security. What is AI and how do we get to real intelligence in a cyber context? Raffael outlines some of the dangers of the way we are using algorithms (AI, Machine Learning) today and what that leads to. We then explore how we can add real intelligence through export knowledge to the problem of finding attackers and anomalies in our applications and networks.
Presented at AI 4 Cybersecurity in NYC on April 30, 2019
AI-SDV 2021: Francisco Webber - Efficiency is the New Precision
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
FiCloud2016 lov4iot extended
1) LOV4IoT is an extension of the Linked Open Vocabularies (LOV) catalogue that references over 300 ontology-based Internet of Things projects across numerous domains to encourage reuse of existing domain knowledge.
2) LOV4IoT provides an HTML user interface and web services to automatically compute statistics about the projects in its dataset, such as the number per domain.
3) The goal of LOV4IoT is to extract reusable domain knowledge from the referenced ontologies and datasets, such as a dictionary to unify IoT data and rules to interpret sensor data, to help developers design semantic-based IoT applications.
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¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
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Decentralized AI Draper
- 1. D E C E N T R A L I Z E D A I : C O N V E R G E N C E O F B L O C K C H A I N + A I A P R I L , 2 0 1 9 G E E TA C H A U H A N , C TO
- 2. RESILIENT ANTI-FRAGILE PLATFORMS • Data Center as a Computer • Microservices • Elastic Scaling • Self healing • Chaos Monkey • Canary Deployments • A/B Testing • Continuous Learning • Continuous Monitoring • Software Defined Networking • 17 Platforms, Countless apps, 1Bil customers… • Decentralized Cloud • Crowd-sourcing Platform • SaaS for Chip Design • AI/Deep Learning Pipelines, Recommendation Engines • Data Platforms for Telecom • Own Cloud Platform • IoT Analytics • Mobile Device Management • Social Media Monitoring Platform • Web Monitoring Platform • Speech Analytics Platform • Developer Platforms w/ Real-time Debugger
- 3. C E N T R A L I Z E D A I I S L I K E T H E C L O S E D S O U R C E O F T H E 1 9 9 0 S
- 4. CHALLENGES? Privacy Problem Can entities train model without disclosing data? Influence problem Can 3rd parties contribute towards behavior of AI model in a way that is quantifiably influential? Economic Problem Can 3rd parties be correctly incentivized to contribute to knowledge & quality of AI models? Transparency Problem Can the activity of behaviour of AI model be transparently available to all parties without a trusting middleman? Latency Problem Centralized AI is inappropriate for use-cases where AI needs to interact in real time with the real world
- 5. DECENTRALIZED AI Federated Learning Blockchain Homomorphic Encryption Data Exchanges Marketplaces
- 6. FEDERATED AI • Subset of devices selected, each downloads the model • Train model with local data • Model updates – gradients – sent back to server • Server aggregates • Cancer treatment centers training models
- 7. WHAT IS BLOCKCHAIN? • An Immutable record of digital events shared peer to peer between different parties • Distributed Ledger, AuditTrail • Open +Trust + Secure • Smart Contracts → dApps, DAO • Non-FungibleTokens, SecurityTokens • Fully Democratize Internet Information Age → Internet ofValue Source: Economist.com
- 8. W H A T I S H O M O M O R P H I C E N C R Y P T I O N ?
- 9. OPENMINED
- 10. DATA EXCHANGE • Blockchain for Data Provenance • User Owned Data • Time expiry for data • Ethically Sourced Data – Transparency – Fairness – Privacy
- 11. ETHICAL AI • Personal Data Rights & Individual Access Controls • Well-being metrics • Awareness of Misuse • Respect for Privacy • Governance of AI Autonomy • Accountability,Transparency • IEEE Ethical Design, Europe Trustworthy AI • California “Data Dividend” proposal https://cnn.it/2N9KEay
- 12. AI MARKETPLACE • Data Competition each week • Encrypted data released • Crowdsource Data Science Model • Data Scientists retain IP – encrypted models • Participants paid in Bitcoin based on accuracy of their guesses, payouts to top 60 • Originality paid extra
- 13. OTHER PLAYERS SingularityNET Smart Contracts for Decentralized AI Microservices Ocean Protocol Ecosystem for Sharing Data and Services Effect.AI Decentralized Mturk, Human in the loop AI Distributed ML Blockchain agnostic Runtime to run ML models across devices
- 14. LATENCY CHALLENGE • Slow Inference problem • Real-time scenarios • AI needs to interact in real-time with real-world • Need compute on / close to edge devices Democratizing cloud computing for cloud resource providers and application developers
- 15. LATENCY CHALLENGE FOR IOT • Slow Inference problem • Real-time scenarios • AI needs to interact in real-time with real-world • Need compute on / close to edge devices for your apps / microservices
- 17. REFERENCES • Google Research: Federated Learning: https://arxiv.org/abs/1610.05492 • Homomorphic Encryption: https://en.wikipedia.org/wiki/Homomorphic_encryption • Openmined https://www.openmined.org/ • IEEE Ethically Aligned Design • EUTrustworthy AI • California Data Dividend Proposal: https://cnn.it/2N9KEay • Numer.ai https://numer.ai/ • SingularityNET https://singularitynet.io/ • Ocean Protocol https://oceanprotocol.com/ • Effect.AI http://effect.ai/ • DML https://decentralizedml.com/ • DeepCloud AI https://www.deepcloudai.com/