The Future of AI: Blockchain and Deep Learning
First point: considering blockchain and deep learning together suggests the emergence of a new class of global network computing system. These systems are self-operating computation graphs that make probabilistic guesses about reality states of the world.
Second point: blockchain and deep learning are facilitating each other’s development. This includes using deep learning algorithms for setting fees and detecting fraudulent activity, and using blockchains for secure registry, tracking, and remuneration of deep learning nets as they go onto the open Internet (in autonomous driving applications for example). Blockchain peer-to-peer nodes might provide deep learning services as they already provide transaction hosting and confirmation, news hosting, and banking (payment, credit flow-through) services. Further, there are similar functional emergences within the systems, for example LSTM (long-short term memory in RNNs) are like payment channels.
Third point: AI smart network thesis. We are starting to run more complicated operations through our networks: information (past), money (present), and brains (future). There are two fundamental eras of network computing: simple networks for the transfer of information (all computing to date from mainframe to mobile) and now smart networks for the transfer of value and intelligence. Blockchain and deep learning are built directly into smart networks so that they may automatically confirm authenticity and transfer value (blockchain) and predictively identify individual items and patterns.
This talk proposes that the future of artificial intelligence is smart networks that have intelligence "baked in" in the form of Blockchain Distributed Ledgers for confirming authenticity and transferring value, and Deep Learning Algorithms for predictive identification. Smart networks are not a far-off possibility but already needed as deep learning systems are going online in connected apps for Autonomous Driving and Drone Delivery, and Human-Robot Interaction. Two high-impact contemporary emerging technologies for the future of AI are Blockchain Distributed Ledgers and Deep Learning Algorithms, and discusses their implications for the future of artificial intelligence.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
A joint report between EY and LSE with contribution from Seldon. This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management.
6 use cases of machine learning in Finance Swathi Young
The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases:
* Chatbots
* RoboAdvisors
* Risk scoring
* Fraud Detection
* Insurance claims
* Underwriting
* regulatory compliance
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
This talk proposes that the future of artificial intelligence is smart networks that have intelligence "baked in" in the form of Blockchain Distributed Ledgers for confirming authenticity and transferring value, and Deep Learning Algorithms for predictive identification. Smart networks are not a far-off possibility but already needed as deep learning systems are going online in connected apps for Autonomous Driving and Drone Delivery, and Human-Robot Interaction. Two high-impact contemporary emerging technologies for the future of AI are Blockchain Distributed Ledgers and Deep Learning Algorithms, and discusses their implications for the future of artificial intelligence.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
A joint report between EY and LSE with contribution from Seldon. This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management.
6 use cases of machine learning in Finance Swathi Young
The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases:
* Chatbots
* RoboAdvisors
* Risk scoring
* Fraud Detection
* Insurance claims
* Underwriting
* regulatory compliance
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Audition ChatGPT Mission IA Commission des LoisAlain Goudey
Mon discours du jour a porté sur un sujet d'actualité extrêmement pertinent : les enjeux des IA génératives sur les entreprises, la société et les individus.
À travers mon intervention, j'ai exploré la manière dont ces IA, dont fait partie la technologie ChatGPT, sont en train de transformer radicalement notre société et notre économie. J'ai examiné comment elles sont utilisées dans le monde des affaires, comment elles affectent nos vies quotidiennes et comment elles ont le potentiel de modeler notre avenir.
J'ai aussi abordé les questions éthiques et législatives associées à ces IA. Comment pouvons-nous garantir une utilisation équitable et sécurisée de ces technologies ? Quel rôle les pouvoirs publics doivent-ils jouer pour réglementer ces outils ? Comment pouvons-nous nous assurer que ces IA bénéficient à tous et ne contribuent pas à accentuer les inégalités existantes ?
J'ai enfin présenté quelques idées d'importance pour les pouvoirs publics, en insistant sur la nécessité d'une politique publique bien pensée et proactive dans ce domaine. J'ai discuté des meilleures pratiques internationales en matière de réglementation de l'IA, et proposera des recommandations sur la manière dont la France pourrait adopter une approche similaire.
Blockchain 3.0, the Encryption of Innovation. This talk looks beyond the immediate economic benefits and risks of distributed ledgers and considers the broader societal innovations implied by blockchain technology. The possibility of innovation and creating and participating in different and multiple self-determined political and economic systems could mobilize how we create ourselves as individuals and societies. Blockchain technology invites the possibility of creating a social world that gives greater weight to the values we apparently care about: freedom, trust, and dignity
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
BBS-248 Artificial Intelligence (AI) for Financial ServicesOzgur Karakaya
• Artificial Intelligence (AI) general info and the AI world market
• AI in financial sector: services that AI can be applied (Investing, Management, Market Research, Blockchain, Fraud Detection, AI Assistants/Bots, etc.)
• AI firms, products and the tech behind.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
This slide shows (1) AI and Accountability , (2) AI Ethics, (2) Privacy Protection. Several AI ethics documents such as IEEE EAD, EC-HELG Ethics Guideline for Trustworthy AI, Social Principles of Human-Centric AI(Japan), focus on AI's transparency, accountability and trust. We follow the discussions of these documents around the above (1),(2) and (3) topics.
***** Blockchain Training : https://www.edureka.co/blockchain-training *****
This Edureka video on "Blockchain Explained" is to guide you through the fundamentals of the new revolutionary technology called Blockchain and its defining concepts. Below are the topics covered in this tutorial:
1. History of blockchain
2. What is Blockchain
3. Traditional Transaction vs Blockchain
4. How Blockchain Works
5. Benefits of Blockchain
6. Blockchain Transaction Demo
Here is the link to the Blockchain blog series: https://goo.gl/DPoAHR
You can also refer this playlist on Blockchain: https://goo.gl/V5iayd
Impact of Generative AI in Cybersecurity - How can ISO/IEC 27032 help?PECB
Generative AI offers great opportunities for innovation in various industries. Hence, by adopting ISO/IEC 27032, you can enhance your cybersecurity resilience and efficiently address the risks associated with generative AI.
Amongst others, the webinar covers:
• AI & Privacy
• Generative AI, Models & Cybersecurity
• AI & ISO/IEC 27032
Presenters:
Christian Grafenauer
Anonymization expert, privacy engineer, data protection officer, LegalTech researcher (GDPR, Blockchain, AI) Christian Grafenauer is an accomplished privacy engineer, anonymization expert, and computer science specialist, currently serving as the project lead for anonymity assessments at techgdpr. With an extensive background as a senior architect in Blockchain for IBM and years of research in the field since 2013, Christian co-founded privacy by Blockchain design to explore the potential of Blockchain technology in revolutionizing privacy and internet infrastructure. As a dedicated advocate for integrating legal and computer science disciplines, Christian’s expertise in anonymization and GDPR compliance enables innovative AI applications, ensuring a seamless fusion of technology and governance, particularly in the realm of smart contracts. In his role at techgdpr, he supports technical compliance, Blockchain, and AI initiatives, along with anonymity assessments. Christian also represents consumer interests as a member of the national Blockchain and DTL standardization committee at din (German standardization institute) in ISO/TC 307.
Akin Johnson
Akin J. Johnson is a renowned Cybersecurity Expert, known for his expertise in protecting digital systems from potential threats. With over a decade of experience in the field, Akin has developed a deep understanding of the ever-evolving cyber landscape.
Akin is an advocate for cybersecurity awareness and frequently shares his knowledge through speaking engagements, workshops, and publications. He firmly believes in the importance of educating individuals and organizations on the best practices for safeguarding their digital assets.
Lucas Falivene
Lucas is a highly experienced cybersecurity professional with a solid base in business, information systems, information security, and cybersecurity policy-making. A former Fulbright scholar with a Master of Science degree in Information Security Policy and Management at Carnegie Mellon University (Highest distinction) and a Master's degree in Information Security at the University of Buenos Aires (Class rank 1st). Lucas has participated in several trainings conducted by the FBI, INTERPOL, OAS, and SEI/CERT as well as in the development of 4 cyber ISO national standards.
Date: July 26, 2023
YouTube Link: https://youtu.be/QPDcROniUcc
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
Smart City Lecture 2 - Privacy in the Smart CityPeter Waher
Privacy is a basic human right that has been heavily eroded on the point of extinction in the current digital age, as the constant reports on security breaches tell us. With the help of the General Data Protection Regulation (GDPR), privacy has been brought back from the dead, and is at least discussed in most enterprises in Europe, and perhaps a large part of the world. This lecture introduces the GDPR and Privacy, as it relates to the Smart City. It presents concepts such as “Data Protection by design and by default”, “Consent”, “Legal Basis”, etc. It also presents technologies that make protecting Privacy more difficult, and why.
These technologies work against the basic principles of privacy by default, so you need to know the details of how they work, to avoid serious pitfalls. There are also technologies that are more Privacy neutral. While not making data protection easier, at least the technology does not work against the basic principles of privacy. Finally, technologies that intrinsically help you protect Privacy are presented. These technologies make it easier to protect Privacy and sensitive data in general.
I delivered a talk on application of Artificial Intelligence in Fintech to the visiting students of University of Applied Sciences, Wurzburg-Schweinfurt, Germany at Christ University
Audition ChatGPT Mission IA Commission des LoisAlain Goudey
Mon discours du jour a porté sur un sujet d'actualité extrêmement pertinent : les enjeux des IA génératives sur les entreprises, la société et les individus.
À travers mon intervention, j'ai exploré la manière dont ces IA, dont fait partie la technologie ChatGPT, sont en train de transformer radicalement notre société et notre économie. J'ai examiné comment elles sont utilisées dans le monde des affaires, comment elles affectent nos vies quotidiennes et comment elles ont le potentiel de modeler notre avenir.
J'ai aussi abordé les questions éthiques et législatives associées à ces IA. Comment pouvons-nous garantir une utilisation équitable et sécurisée de ces technologies ? Quel rôle les pouvoirs publics doivent-ils jouer pour réglementer ces outils ? Comment pouvons-nous nous assurer que ces IA bénéficient à tous et ne contribuent pas à accentuer les inégalités existantes ?
J'ai enfin présenté quelques idées d'importance pour les pouvoirs publics, en insistant sur la nécessité d'une politique publique bien pensée et proactive dans ce domaine. J'ai discuté des meilleures pratiques internationales en matière de réglementation de l'IA, et proposera des recommandations sur la manière dont la France pourrait adopter une approche similaire.
Blockchain 3.0, the Encryption of Innovation. This talk looks beyond the immediate economic benefits and risks of distributed ledgers and considers the broader societal innovations implied by blockchain technology. The possibility of innovation and creating and participating in different and multiple self-determined political and economic systems could mobilize how we create ourselves as individuals and societies. Blockchain technology invites the possibility of creating a social world that gives greater weight to the values we apparently care about: freedom, trust, and dignity
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
BBS-248 Artificial Intelligence (AI) for Financial ServicesOzgur Karakaya
• Artificial Intelligence (AI) general info and the AI world market
• AI in financial sector: services that AI can be applied (Investing, Management, Market Research, Blockchain, Fraud Detection, AI Assistants/Bots, etc.)
• AI firms, products and the tech behind.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
This slide shows (1) AI and Accountability , (2) AI Ethics, (2) Privacy Protection. Several AI ethics documents such as IEEE EAD, EC-HELG Ethics Guideline for Trustworthy AI, Social Principles of Human-Centric AI(Japan), focus on AI's transparency, accountability and trust. We follow the discussions of these documents around the above (1),(2) and (3) topics.
***** Blockchain Training : https://www.edureka.co/blockchain-training *****
This Edureka video on "Blockchain Explained" is to guide you through the fundamentals of the new revolutionary technology called Blockchain and its defining concepts. Below are the topics covered in this tutorial:
1. History of blockchain
2. What is Blockchain
3. Traditional Transaction vs Blockchain
4. How Blockchain Works
5. Benefits of Blockchain
6. Blockchain Transaction Demo
Here is the link to the Blockchain blog series: https://goo.gl/DPoAHR
You can also refer this playlist on Blockchain: https://goo.gl/V5iayd
Impact of Generative AI in Cybersecurity - How can ISO/IEC 27032 help?PECB
Generative AI offers great opportunities for innovation in various industries. Hence, by adopting ISO/IEC 27032, you can enhance your cybersecurity resilience and efficiently address the risks associated with generative AI.
Amongst others, the webinar covers:
• AI & Privacy
• Generative AI, Models & Cybersecurity
• AI & ISO/IEC 27032
Presenters:
Christian Grafenauer
Anonymization expert, privacy engineer, data protection officer, LegalTech researcher (GDPR, Blockchain, AI) Christian Grafenauer is an accomplished privacy engineer, anonymization expert, and computer science specialist, currently serving as the project lead for anonymity assessments at techgdpr. With an extensive background as a senior architect in Blockchain for IBM and years of research in the field since 2013, Christian co-founded privacy by Blockchain design to explore the potential of Blockchain technology in revolutionizing privacy and internet infrastructure. As a dedicated advocate for integrating legal and computer science disciplines, Christian’s expertise in anonymization and GDPR compliance enables innovative AI applications, ensuring a seamless fusion of technology and governance, particularly in the realm of smart contracts. In his role at techgdpr, he supports technical compliance, Blockchain, and AI initiatives, along with anonymity assessments. Christian also represents consumer interests as a member of the national Blockchain and DTL standardization committee at din (German standardization institute) in ISO/TC 307.
Akin Johnson
Akin J. Johnson is a renowned Cybersecurity Expert, known for his expertise in protecting digital systems from potential threats. With over a decade of experience in the field, Akin has developed a deep understanding of the ever-evolving cyber landscape.
Akin is an advocate for cybersecurity awareness and frequently shares his knowledge through speaking engagements, workshops, and publications. He firmly believes in the importance of educating individuals and organizations on the best practices for safeguarding their digital assets.
Lucas Falivene
Lucas is a highly experienced cybersecurity professional with a solid base in business, information systems, information security, and cybersecurity policy-making. A former Fulbright scholar with a Master of Science degree in Information Security Policy and Management at Carnegie Mellon University (Highest distinction) and a Master's degree in Information Security at the University of Buenos Aires (Class rank 1st). Lucas has participated in several trainings conducted by the FBI, INTERPOL, OAS, and SEI/CERT as well as in the development of 4 cyber ISO national standards.
Date: July 26, 2023
YouTube Link: https://youtu.be/QPDcROniUcc
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
Smart City Lecture 2 - Privacy in the Smart CityPeter Waher
Privacy is a basic human right that has been heavily eroded on the point of extinction in the current digital age, as the constant reports on security breaches tell us. With the help of the General Data Protection Regulation (GDPR), privacy has been brought back from the dead, and is at least discussed in most enterprises in Europe, and perhaps a large part of the world. This lecture introduces the GDPR and Privacy, as it relates to the Smart City. It presents concepts such as “Data Protection by design and by default”, “Consent”, “Legal Basis”, etc. It also presents technologies that make protecting Privacy more difficult, and why.
These technologies work against the basic principles of privacy by default, so you need to know the details of how they work, to avoid serious pitfalls. There are also technologies that are more Privacy neutral. While not making data protection easier, at least the technology does not work against the basic principles of privacy. Finally, technologies that intrinsically help you protect Privacy are presented. These technologies make it easier to protect Privacy and sensitive data in general.
I delivered a talk on application of Artificial Intelligence in Fintech to the visiting students of University of Applied Sciences, Wurzburg-Schweinfurt, Germany at Christ University
Invited Talk for the SIGNLL Conference on Computational Natural Language Learning 2017 (CoNLL 2017) Chris Dyer (DeepMind / CMU) 3 Aug 2017. Vancouver, Canada
One weekend software hack called "Movie Hack Attack".
Video content is played and is analyzed realtime for sentiment, emotions, and more.
Sentiment shown in chart below, emotions+objects of attention to the left with random picture grabbed from google search, persons/locations/organizations to the right with random picture grabbed from google search.
Construisons ensemble le chatbot bancaire dedemain !LINAGORA
Retrouvez les slides réalisées pour notre Meetup collaboratif du jeudi 9 novembre 2017 : "Construisons ensemble le chatbot bancaire de demain !"
Après la publication de son étude sur les chatbots de l'écosystème bancaire "ChatBots et intelligence artificielle arrivent dans les banques : y êtes-vous préparé(e) ?", LinDA, l'agence digitale du groupe LINAGORA, à réaliser un atelier de co-conception du chatbot bancaire de demain.
Cet atelier gratuit d'idéation fut l'occasion d'imaginer, avec plusieurs participants du monde bancaire, la meilleure solution d'agent conversationnel pour leur banque.
Nos animateurs, Christophe Clouzeau (UX Digital Strategist) et Jean-Philippe Mouton (Head of digital consulting), ont appliqué des méthodes de conception UX, utilisées avec nos clients et par les startups innovantes.
Economics, broadly defined, is concerned with the description and analysis of the production, distribution, and consumption of goods and services. Also related is how individuals and groups make choices about these goods and services, and the consequences of their decisions. Decisions might be explicitly in regard to money and resources, but the same principles pertain to any kind of decision. The general form of the problem is that wants are bigger than resources, and even if two choices are both free, there is an opportunity cost in terms of deploying resources or focus into one area and not another. The same structure of decision-making among multiple options, with there being an opportunity cost to the road not taken, may persist regardless of domain, whether in classical economics or distributed ledger economics.
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
Blockchain Smartnetworks: Bitcoin and Blockchain ExplainedMelanie Swan
Beyond digitalizing money, payments, economics, and finance, and governance, smart property and smart contracts, blockchains secure automated fleet coordination
The implications could be an orderly transition to the automation economy and trust-rich digital smartnetwork societies of the future
Deep Qualia: Philosophy of Statistics, Deep Learning, and Blockchain
Deep learning: What is it, why is it important, and what do I need to know?
The aim of this talk is to discuss deep learning as an advanced computational method and its philosophical implications. Computing is a fundamental model by which we are understanding more about ourselves and the world. We think that reality is composed of patterns, which can be detected by machine learning methods.
Deep learning is a complexity optimization technique in which algorithms learn from data by modeling high-level abstractions and assigning probabilities to nodes as they characterize the system and make predictions. An important challenge in deep learning is that these methods work in certain domains (image, speech, and text recognition), but we do not have a good explanation for why, which impedes a wider application of these solutions.
Another recent advance in computational methods is blockchain technology which allows the secure transfer of assets and information, and the automated coordination of operations via a trackable remunerative ledger and smart contracts (automatically-executing Internet-based programs).
This talk looks at how deep learning technology, particularly as coupled with blockchain systems, might be used to produce a new kind of global computing platform. The goal is for blockchain deep learning systems to address higher-dimensional computing challenges that require learning and dynamic response in domains such as economics and financial risk, epidemiology, social modeling, public health (cancer, aging), dark matter, atomic reactions, network-modeling (transportation, energy, smart cities), artificial intelligence, and consciousness.
Future of AI: Blockchain & Deep LearningMelanie Swan
Future of AI: intelligence “baked in” to smart networks, blockchains to confirm authenticity and transfer value, and Deep Learning algorithms for predictive identification. This talk presents two high-impact contemporary emerging technologies: big data and deep learning algorithms, and blockchain distributed ledgers, and discusses their implications for the future of artificial intelligence.
State of Blockchain 2017: Smartnetworks and the Blockchain EconomyMelanie Swan
Blockchain is a fundamental IT for secure value transfer over networks. For any asset registered in a cryptographic ledger, the whole Internet is a VPN for its confirmation, assurity, and transfer. Blockchain reinvents economics and governance for the digital age. The long-tail structure of digital networks allows personalized economic and governance services. Smartnetworks are a new form of automated global infrastructure for large-scale next-generation projects.
Distributed ledgers imply peer-banking services offered by every network node to others for a small fee. Money becomes an accounting ledger running on a distributed computer network, a transaction, credit, and payment graph. Digitized money and payments, and activity possibly being securely forward-committed in payment contracts, suggests that the economy could settle on the basis of net rather than gross transfers. A net-clearings contracts-for-difference economy could enable us to rethink debt, replacing crippling monolithic capital structures with streaming money disgorged in smaller chunks that are more closely tied to costs and repayment possibilities. Pre-paid consumption and 30-60-90 day vendor credit terms models could be offset to facilitate a directed payment graph economy of just-in-time money. A wide slate of contemporary economic challenges might be addressed including health care price rationalization, global energy management, entitlements, and the automation economy.
Blockchain Economics
http://timreview.ca/article/1109
Blockchain Philosophy
http://onlinelibrary.wiley.com/doi/10.1111/meta.2017.48.issue-5/issuetoc
Blockchain Investing: Economics Implications of Distributed LedgersMelanie Swan
The investment market for cryptocurrencies is becoming increasingly institutional. In July 2017 (in the wake of the “ICO dotcom bubble”), the SEC signaled its stance on ICOs. “Stock-like” ICOs are likely to be deemed securities, and as such, would need to be registered offerings, which by implication, would target institutional investors. Also in July 2017, the CFTC granted a derivatives clearing license to New York-based LedgerX for cryptocurrency derivatives, and options listings may appear on the CBOE later in 2017. Since derivatives markets are already part of the institutional ecosystem, this means that cryptocurrency derivatives might be a more accessible, liquid, and large-scale means of obtaining exposure to crypto asset classes than investing in the underlying cryptocurrencies themselves. Finally, there is greater emphasis on institutional liquidity aggregation platforms for large-size cryptocurrency trading (i.e. $20+ million positions), with Genesis Trading, Cumberland Mining, Circle, and Project Omni.
Smart Networks: Blockchain, Deep Learning, and Quantum ComputingMelanie Swan
Considering high-impact emerging technologies (AI machine learning and blockchain) together suggests the emergence of a new class of global computational infrastructure: smart networks. Smart networks are intelligent self-operating computation networks such as deep learning neural nets, blockchains, UAV fleets, industrial robotics cloudminds
Digital Transformation:
Business process re-engineering with digital technologies
Technology used to make existing work more efficient, now technology is transforming the work itself
Example: single shared item lookup process in blockchain supply chain
Productivity gains
Capital investment in technology
Data centers
Blockchain as a Service, Deep Learning nets
Skilled work force development
Train 1000 software developers
Hyperledger, Ethereum, Corda
Machine Learning, AI, Deep Learning
Scale efficiencies
Natural resources, regional strength, large companies
Manage global trade supply chain with blockchain/deep learning
Blockchain: a Singularity-class technology - No other technology has the power to
pull 2 billion people out of poverty overnight (with intermediary-free international remittances), produce a safe and orderly transition to the automation economy (with humans and machines in collaboration, and enacting friendly artificial intelligence), and fundamentally transform the only remaining sectors not yet re-engineered for the Internet era: economics and politics. There are growing classes of activities for smartnetwork execution, moving up the stack, pushing different qualitative states through the Internet pipes, building future smartnetworks. The smartnetworks thesis is that complex future operations will involve automated fleet coordination of “quantized” items via smartnetworks, using some kind of technology like blockchains with algorithmically-derived trust.
Blockchain distributed ledger technology is evolving from the hype phase into one of greater maturity and long-term value creation. This graduate course overview examines how blockchains, networks, and social interaction patterns are related.
There’s a lot of hype right now about blockchain, the technology that underpins the Bitcoin virtual currency, with speculation that it could transform just about every aspect of our lives. In this webinar I’ll consider possible blockchain applications in research and education, and do a little myth-busting about when and where it makes sense to use blockchain.
The cryptographic asset market turns institutional with regulated ICOs, exchanges, and options. The retail market allows the long tail of personalized economic services to meet in “eBay for money” digital marketplaces and global financial inclusion. Digitized money and payments, and activity possibly being securely forward-committed in payment contracts, implies that the economy could settle on the basis of net rather than gross flows. A net-clearings contracts-for-difference economy could rethink crippling monolithic debt structures with streaming money disgorged in much smaller chunks that are more closely tied to costs and repayment possibilities. Pre-paid consumption and 30-60-90 day vendor credit terms models could be offset to facilitate a directed payment graph economy of just-in-time money.
This talk provides a speculative contemplation of philosophical topics that might arise with brain-machine interface technology and explores the new ways that individuals and society might self-enact as a result. Brain-machine interfaces that could be pervasive, continuous, and widely-adopted suggest interesting new possibilities for our future selves. From a philosophical perspective, these possibilities concern the definition of what it is to be human, our current existence and interaction with reality, and how all of this could be dramatically different in a scenario of digitally-linked cloudmind collaborations. This talk looks at some of the foundational ontological questions of how the progression of the existence of the classic human might evolve. Perhaps the most pressing question that currently-minded potential adopters have is how to avoid getting irreparably pulled into a groupmind. To protect against this, there could be an expansion and letting go of the term and concepts of personal identity, and humans as a unit of organization, in favor of instead self-relying on a decentralized permissioning structure like blockchain technology for managing empowered and resilient crowdmind participations.
Blockchain in research and education - UKSG Webinar - September 2017Martin Hamilton
There’s a lot of hype right now about blockchain, the technology that underpins the Bitcoin virtual currency, with speculation that it could transform just about every aspect of our lives. In this talk for UKSG I consider possible blockchain applications in research and education, and do a little myth-busting about when and where it makes sense to use blockchain.
eyond digitalizing money, payments, economics, and finance, blockchains are a singularity-class technology that enables the secure, trackable, automated coordination of very large-scale projects, fleets, and swarms
The implications could be an orderly transition to the automation economy and trust-rich human-machine collaboration in the digital smartnetwork societies of the future
Beyond digitalizing money, payments, economics, and finance, blockchains are a singularity-class technology that enables the secure, trackable, automated coordination of very large-scale projects, fleets, and swarms
The implications could be an orderly transition to the automation economy and trust-rich human-machine collaboration in the digital smartnetwork societies of the future
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Blockchain insider | Chapter 3 : Smart MoneyKoh How Tze
What we have now is truly borderless, programmable money
backed by immutable computer systems based on pure logic & mathematics.
3.1 ABCDs That Are Changing The World
3.2 A Century of Technology Innovation
3.3 Two Monetary Worlds
3.4 Three Phases of Cryptocurrencies
Corporate Currency
CBDC, Central Bank-issued Digital Currency
The Money Flower
Money Trees
3.5 The Creation of Capital In Its Simplest Form
3.6 Incentivizing Good Behaviour
Smart Mobility - Ethical Driving and Data Sharing
Resilient City - Impactful Positive Behaviors
Social Contributions - Datanomics
3.7 Bringing Down Borders
Assets Backed Tokens
Security Token Offering
Do We Need A Nation-State Backed Crypto Exchange?
Blockchaining Sukuk
3.8 Summary
Programmable Money for Effective Resources Distribution
Similar to Future of AI: Blockchain and Deep Learning (20)
AI Health Agents: Longevity as a Service in the Web3 GenAI Quantum RevolutionMelanie Swan
Health Agents are a form of Math Agent as the concept of a personalized AI health advisor delivering “healthcare by app” instead of “sickcare by appointment.” Mobile devices
can check health 1000 times per minute as opposed to the standard one time per year doctor’s office visit, and model virtual patients in the digital twin app. As any AI agent, Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their operation. Health Agents could facilitate the ability of physicians to oversee the health of thousands of individuals at a time. This could ease overstressed healthcare systems and contribute to physician well-being and the situation that (per the World Health Organization) more than half of the global population is still not covered by essential health services.
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
This work introduces “quantum intelligence” as a concept of intelligence for operating in the quantum realm may help in a potential AI-Quantum Computing convergence (~2030e), and towards the realization of SRAI for well-being (economics, health, energy, space). “Scale-free intelligence” is formulated as a generic capacity for learning.
AI did not spring onto the scene with chatGPT, but is in an ongoing multi-year adoption. A transition may be underway from an information society to a knowledge society (one tempered and specifically using knowledge to improve the human condition). AI is a dual-use technology with both significant risk and upleveling possibilities.
SRAI for well-being is a social objective, and also a technological objective. SRAI is part of AI development and within the technological trajectory of harnessing all scales of physical reality ranging from quantum materials to space exploration.
Conceptually, thinking in quantum and relativistic terms expands the physical worldview, and likewise the social worldview of entities inhabiting the larger world. Practically, SRAI may be realized in phases: short-term regulation and registries, medium-term agents learning to implement human values with internal reward functions, and long-term responsible human-AI entities acting in partnership in a future of SRAI for well-being.
The Human-AI Odyssey: Homerian Aspirations towards Non-labor IdentityMelanie Swan
The visionary progression in The Odyssey from shipbuilding to seafaring to advanced civilization informs contemporary tension in the human-AI relation forcing a broader articulation of human-identity beyond labor-identity. Edith Hall analyzes why one of the earliest known literatures, The Odyssey, remains a central cultural trope with numerous references in the storytelling vernacular of all eras, ranging from 1860s British theater to a highly-watched 1990 episode of The Simpsons. The argument is that The Odyssey provides a constant aspirational reference for human identity – who we think we are and where we are going on the epic journey of life, especially at the current crossroad in our relationship with technology.
The contemporary moment finds humanity, and the humanities, experiencing an identity crisis in the relationship with technology. Information science is having an ever more pervasive role in academia, and the machine economy continues to offload vast classes of tasks to labor-saving technology giving rise to two questions. First, at the level of labor-identity, humans wonder who they are as they have long defined their sense of self through their professional participation in the economy. Second, at the level of human-identity, with AI now performing cognitive labor in addition to physical labor, humans wonder if there is anything that remains uniquely human.
The effect of The Odyssey is to provide world-expanding imaginaries to change the way we see ourselves as subjects; in this way, Homer is an early modernist in reconfiguring our self-concept.
This work applies a philosophy (of literature)-aided information science method to discuss how Homer’s Odyssey persists as a literary imaginary to help us think through potential futures of human-AI flourishing as rapid automation continues to impact humanity. The intensity of the human-AI relation is likely to increase, which invites thought leadership to steward the transition to a potential AI abundance economy with fulfilling human-technology collaboration.
The shipbuilding-seafaring-advanced civilization progression in The Odyssey identifies that the human-AI relation is not one of the labor-identity-crisis of “robots stealing our jobs,” but rather one of the more difficult challenge of envisioning who we can be in the new larger world of human-AI partnership addressing a larger set of planetary-scale problems. Towards this new configuration of human-AI relation, the longer-term may hold radically different notions of identity, as we become physical-virtual hybrids, augmented post-disease entities in the health-faring, space-civilizing, energy-marshalling post-scarcity cultures of the future.
AdS Biology and Quantum Information ScienceMelanie Swan
Quantum Information Science is a fast-growing discipline advancing many areas of science such as cryptography, chemistry, finance, space science, and biology. In particular AdS/Biology, an interpretation of the AdS/CFT correspondence in biological systems, is showing promise in new biophysical mathematical models of topology (Chern-Simons (solvable QFT), knotting, and compaction). For example, one model of neurodegenerative disease takes a topological view of protein buildup (AB plaques and tau tangles in Alzheimer’s disease, alpha-synuclein in Parkinson’s disease, TDP-43 in ALS). AdS/Neuroscience methods are implicated in integrating multiscalar systems with different bulk-boundary space-time regimes (e.g. oncology tumors, fMRI + EEG imaging), entanglement (correlation) renormalization across scales (MERA, random tensor networks, melonic diagrams), entropy (possible system states), entanglement entropy (interrelated fluctuations and correlations across system tiers), and non-ergodicity (implied efficiency mechanisms since biology does not cycle through all possible configurations per temperature (thermotaxis), chemotaxis, and energy cues); Maxwell’s demon of biology (partition functions), conservation across system scales (biophysical gauge symmetry (system-wide conserved quantity)), and the presence of codes (DNA, codons, neural codes). A multiscalar AdS/CFT correspondence is mobilized in 4-tier ecosystem models (light-plankton-krill-whale and ion-synapse-neuron-network (AdS/Brain)).
Humanity’s constant project is expanding the range of attainable geography. Melville’s romance of the sea gives way to Kerouac’s romance of the road, and now the romance of space. In expanding into new geographies, markets (commerce) is the driving impulse, entailing a legal and judiciary system to order the new larger continuous marketplace, which brings a bigger overall scope of world under our control, and hence a new idea of who we are as subjects in this bigger domain.
Space Humanism is a concept of humanism based on the principles of inclusion, progress, and equity posited as a condition of possibility for a potential large-scale human movement into space. A philosophy of literature approach is used to contextualize Space Humanism, first through Melville-Foucault to articulate the mind-frame of extra-planetary geographies as one of human expansion, and second through posthuman philosophy extending from Shakespeare’s Renaissance humanism to contemporary enhancement-based theories of subjectivation.
Historical imaginaries outline subjectivation moments that have changed the whole notion who we are as humanity. Four examples are: the concept of the “new world” in Hegel’s philosophy, von Humboldt’s infographic maps, Baudelaire as the Painter of Modern Life, and Keats’s seeing the world in a new way upon reading an updated translation of Homer.
The reach to beyond-Earth geographies is a two-cultures project involving both arts and science. Technical competence is necessary to realize the aspirational, explorational, and survivalist aims of humanity pushing beyond planetary limits. Space was once a fantastic dream that is becoming quotidian with fourteen U.S. spaceports, six completed Blue Origin space tourist missions, and SpaceX having over 155 successful rocket launches including human space flights to and from the International Space Station. The notion of Space Human articulated through Shakespeare, Moby-Dick, and neuroenhancement informs the project of our reach to awaiting beyond-Earth geographies.
Quantum Information Science and Quantum Neuroscience.pptMelanie Swan
Mathematical advance in quantum information science is proceeding quickly and applies to many fields, particularly the complexities of neuroscience (here focusing on image-readable physical behaviors such as neural signaling, as opposed to higher-order operations of cognition, memory, and attention). Quantum mathematical models are extensible to neuroscience problem classes treating dynamical time series, diffusion, and renormalization in multiscalar systems. Approaches first reconstruct wavefunctions observed in EEG and fMRI scans. Second, single-neuron models (Hodgkin-Huxley, integrate-and-fire, theta neurons) and collective neuron models (neural field theories, Kuramoto oscillators) are employed to model empirical data. Third, genome physics is used to study time series sequence prediction in DNA, RNA, and proteins based on 3d+ complex geometry involving fields, curvature, knotting, and information compaction. Finally, quantum neuroscience physics is applied in AdS/Brain modeling, Chern-Simons biology (topological invariance), neuronal gauge theories, network neuroscience, and the chaotic dynamics of bifurcation and bistability (to explain epileptic and resting states). The potential benefit of this work is an improved understanding of disease and pathology resolution in humans.
Quantum information science enables a new tier of scientific problem-solving as exemplified in early-adopter fields, foundational tools in quantum cryptography, quantum machine learning, and quantum chemistry (molecular quantum mechanics), and advanced applications in quantum space science, quantum finance, and quantum biology
Grammatology and Performativity: A Critical Theory of Silence: Silence is a crucial device for subversion, opposition, and socio-political commentary, the theoretical underpinnings of which are just starting to be understood. This work illuminates another position in the growing field of critical silence studies, theorizing silence as an asset whose ontological value has been lost in a world of literal and figurative noise. Part 1 philosophizes silence as a continuation of Derrida’s grammatology project. Such a grammatology of silence valorizes silent thinking over noisy speaking, and identifies the deconstructive binary pairing not as silence-speaking, but rather as silence-noise. Noise has a simultaneous physical-virtual existence as Shannon entropy calculates signal-to-noise ratios in modern communications networks. Part 2 employs the philosophy of noise to assess what is conceptually necessary to overcome noise in a critical theory of silence. Malaspina draws from Simondon to argue that noise is a form of individuation, essentially a living thing with unstoppable growth potential, not defined by a binary on-off switch but as a matter of gradation. Hence different theory resources are required to oppose it. Part 3 then develops a critical theory of silence to oppose noise in both its physical and virtual instantiations, with the two arms of a deeply human positive performativity (Szendy, Bennett) and a beyond-computational posthumanism (Puar). The result is a novel critical theory of silence as positive performativity that destabilizes noise and recoups the ontological status of silence as not merely an empty post-modern reification but a meaningful actuality.
Philosophy-aided Physics at the Boundary of Quantum-Classical Reality The philosophical themes of truth-knowledge and appearance-reality are used to interrogate the contemporary situation of the quantum-classical boundary, and more broadly the quantum-classical-relativistic stratification of physical scale boundaries. The contemporary moment finds us at breakneck pace in the industrial information revolution, digitizing remaining matter-based industries into a seamless exchange between physical-digital reality. Digitized news is giving way to digitized money and perhaps in the farther future, digitized mindfiles (such as personalized connectome files for precision medicine, autologous (own-DNA) stem cell therapies, and CRISPR for Alzheimer’s disease prevention). Our technologies are allowing us control over vast new domains, the relativistic with GPS and space-faring, and the quantum with quantum computing, harnessing the properties of superposition, entanglement, and interference. Philosophy provides critical thinking tools that can help us understand and master these rapid shifts in science and technology to avoid an Adornian instrumental reality (subsuming humanity under societal structures) and to maintain a Heideggerian backgrounded and enabling relation with technology (versus technology enframing us into mindless standing reserve).
The philosophical theme underlying the investigation of the scales of planets, persons, and particles is the relationship between truth and knowledge (or appearance and reality). The truth-knowledge problem is whether knowledge of the truth, true knowledge, the reality under the appearance, is even possible. Three salient moments in the history of the truth-knowledge problem are examined here. These are the German idealism of Kant and Hegel, the deconstructive postmodernism of Foucault and Derrida, and the unclear leanings of the current moment. The German idealism lens incorporates the self-knowing subject as agent into the truth and knowledge problem. The postmodernist view breaks with the subject and emphasizes the hidden opposites in the formulations, the constant reinterpretation of meaning, and porous boundaries. The contemporary moment wonders whether truth-knowledge boundaries still hold, in a Benjaminian view of non-identity between truth and knowledge, and truth increasingly being seen as a Foucauldian biopolitical manufactured quantity. Contemporaneity has a bimodal distribution of the subject: the hyperself (the constantly digitally represented selfie self) and the alienated post-subject subject.
These moments in the truth and knowledge debate inflect into the scale considerations of relativity, classicality, and quantum mechanics. Whereas general relativity and quantum mechanics are domains of universality, totality, and multiplicity, everyday classical reality is squeezed in as a belt between the two multiplicities as the concretion of drawing a triangle or tossing a ball. Recasting truth and k
Comprehensive philosophical programs arise within a historical context (for Hegel and Derrida in the democracy-shaping moments of the French Revolution (1789) and the student-worker protests (1968) in which French politics serve as a global harbinger of contemporary themes). In the Derrida-Hegel relationship, there is more rapprochement concerning core notions of difference, history, and meaning-assignation than may have been realized. In particular, Hegel’s philosophy, despite being assumed to be a totalizing system, in fact indicates precisely some of the same kinds of revised metaphysics-of-presence formulations that Derrida exhorts, namely those that are flexible, expansive, and include non-identity and identity.
A crucial Derrida-Hegel interchange is that of différance and difference. Derrida develops the notion directly from Hegel (“Différance,” “The Pit and the Pyramid”), but only draws from the Encyclopedia, not Hegel’s masterwork, the Phenomenology of Spirit. For Derrida, the “A” in différance is inspired by the form of the pyramid in the capitalized letter and in Hegel’s comparing the sign “to the Egyptian Pyramid” (“Différance,” p. 3). Derrida invokes the symbolism of the pyramid, antiquity, and Egyptian hieroglyphics as an early semiotic system. However, when considering Hegel’s central definition of difference in the dialectical progression of thesis-antithesis-synthesis in the Phenomenology of Spirit (§§159-163), the articulations of différance and difference are remarkably aligned.
Parallel formulations are also seen in history as a series of reinterpretable events, and indexical wrappers as a mechanism for meaning assignation. The thinkers examine the universal and the particular by exploring regulative mechanisms such as law (natural and social). In Glas, Derrida highlights not the singular-universal relation, but the law of singularity and the law of universality relation as being relevant to Hegel’s Antigone interpretation (Glas, p. 142a), a theme continued in “Before the Law.” Finally (time permitting), there is a question whether the most valid critiques of Hegel (Nietzsche’s unreason and Benjamin’s non-synthesis), as alternatives to Hegelian dialectics, are visible in Derrida’s thought.
The upshot is that the two thinkers produce similar formulations, derived from different trajectories of philosophical work; a situation which points to the potential universality of fundamental solution classes to open-ended philosophical problems, including the future of democracy.
Quantum Moreness: Kantian Time and the Performative Economics of Multiplicity
There is no domain with greater moreness than that of the quantum. A philosophy-aided physics approach (postmodernism and Continental philosophy) examines the contemporary situation of quantum moreness (more time and space dimensions than are available classically). Quantum moreness is configured by quantum reality being probabilistic; a multiplicity of outcomes all co-existing in superposition until collapsed in measurement. The quantum mindset uses quantum moreness to solve problems by thinking in terms of the greater scalability afforded in time and space with the quantum properties of superposition, entanglement, and interference. Quantum studies fields proliferate in arts and sciences, raising the Levi-Straussian raw-cooked dilemma of how “traditional humanities” are to be named alongside “digital humanities” and “quantum humanities.” Kant facilitates the conceptualization of quantum moreness by insisting on the dual nature of time as transcendentally ideal and empirically real. Kant’s moreness is allness, the absolute totality and multiplicity of time at the ideal level. Each faculty (sensibility, understanding, reason) has its own species of the a priori synthetic unity of ideal time that precedes and conditions the operation of the faculty. Each faculty also has a concretized formulation of empirically-real time as the time series, which is the basis for the faculties to interoperate to perform the conception of any empirical object. Kant’s achievement of time interoperability has potential extensibility to other areas of temporal incompatibility such as the scales of general relativity, Newtonian mechanics (human-scale), and quantum mechanics. The quantum moreness mindset with which Kant connects the ideal-real is visible in the domain of economics, itself too an ideal-real construction. The quantum moreness of money configures the postmodern abstraction of global cryptocurrencies and smart contract pledges, the implicative hope of which is a post-debt capital world that restores the human esprit in the face of an increasingly intense technologized reality.
Blockchain Crypto Jamming: Subverting the Instrumental Economy
The ultimate subversion is money, refusing the pecuniary resources of the state. This project applies a philosophical and critical theory lens to examine the use of nomenclature in one of the most radical longitudinal transformations in contemporary times, the shift away from state-run monetary resources towards cryptocurrencies and smart contracts in citizen-determined decentralized financial networks.
A Cryptoeconomic Theory of Social Change is presented in which linguistic progression serves as a tracking mechanism. The steps to lasting change have their own vocabulary (Brandom). First, there is the social critique, the complaint about what is wrong, the negative side (Adorno and Horkheimer highlight instrumental reason and the empty culture industry). Second, there is the antidote, an alternative that can overcome the complaint, the positive side. Third, the solution becomes the new reality, and as a consequence, the whole of reality is now seen in this context, adopting its vocabulary (“fiat health” system for example, referring to the antiquated method). The social movement graduates from language game (Wittgenstein) to form of life (Jaeggi).
Blockchains are Occupy with teeth, notable in the level of personal responsibility-taking by individuals to steward their own financial resources. The crypto citizen is not merely trading CryptoKitties and Bored Ape Yacht Club tokens, but getting blocktime loans through DeFi liquidity pools instead of fiat banks, earning labor income in crypto, and shifting all economic activity to blockchain networks. The artworld signals mainstream acceptance with Christie’s non-fungible token digital artwork auctioned from Beeple for $61 million. At the global level, coin communities constitute a new form of Kardashev-level (planetary-scale) democracy. Blockchains emerge as a robust smart network automation technology for super-class projects ranging from space-faring to quantum computing and thought-tokening. The further stakes of this work are having a language-based theory of social change with broad applicability to social transformation.
This work argues that the emerging understanding of time in quantum information science can be articulated as a philosophical theory of change. Change and time are interrelated, and one can be used to interrogate the other, namely, a theory of change can be derived from a theory of time. What is new in quantum science is time being regarded as just another property to be engineered. At the quantum scale, time is reversible in certain ways, which is quite different from the everyday experience of time whose unidirectional arrow does not allow a dropped egg to reassemble. At the quantum scale of atoms, though, a particle retains the history of its trajectory, which may be retraced before collapsed in measurement.
Quantum scientists evolve systems backward and forward in time, controlling phase transitions with Floquet engineering. Quantum systems are entangled in time and space, with temporal correlations exhibiting greater multiplicity than spatial correlations. The chaotic time regimes of ballistic spread followed by saturation are implemented in quantum walks for faster search and heightened cryptosecurity. In quantum neuroscience, seizure may be explained by chaotic dynamics and normal resting state by Floquet-like periodic cycles. Time is revealed to have the same kinds of repeating structures as space (described by entanglement, symmetry, and topology), differently instantiated and controlled.
The quantum understanding of time can be propelled into a macroscale-theory of change through its connotation of a more flexible, malleable, probabilistic interface with reality. Change becomes less rigid. Probability is the lever of change, but notoriously difficult for humans to grasp, as we think better in storylines than statistics. The idea of manipulating quantum system properties in which time, space, dynamics (change), are all just parameters, is an empowering frame for the acceptance of change. The quantum mindset affords greater facility with probability-driven events (change).
Blockchains in Space: Non-Euclidean Spacetime and Tokenized Thinking - Two requirements for the large-scale beyond-terrestrial expansion of human intelligence into the universe are the ability to operate in diverse spatiotemporal regimes and to instantiate thinking in various formats. Newtonian mechanics describe everyday reality, but Einsteinian physics is needed for GPS and the orbital technologies of telescopes and spacecraft. Space agencies already integrate the Earth-day and the slightly-longer Martian-sol. A more substantial move into space requires facility with non-Euclidean spacetimes. One challenge is that general relativity and quantum mechanics are non-interoperable. However, the theories can be formulated together when considering black holes and quantum computing since geometric theories and gauge theories are both field-based. Quantum blockchains instantiate blockchain logic in quantum computational environments. Blockchains have their own temporal regime (blocktime: the number of blocks for an event to occur), and hence quantum blocktime is a non-classical functionality for operating in diverse spatiotemporal regimes. Thinking is a rule-based activity that is unrestricted by medium. Central to thinking is concepts, which are referenced by words. Word-types include universals, particulars, and indexicals which can be encoded into a formal system as thought-tokens, and registered to blockchains. Blockchains are contemplated as an automation technology for asteroid mining and space settlement construction, and thought-tokening adds an intelligence layer. Time and tokenized thinking come together in the idea of smart networks in space. In blockchain quantum smart networks, spatiotemporal regimes and thought-tokens are simply different value types (asset classes) coordinated with blockchain logic, towards the aim of extending human capabilities into the farther reaches of space.
Cryptography, entanglement, and quantum blocktime: Quantum computing offers a more scalable energy-efficient platform than classical computing and supercomputing, and corresponds more naturally to the three-dimensional structure of atomic reality. Blockchains are a decentralized digital economic system made possible by the 24-7 global nature of the internet.
Quantum Neuroscience: CRISPR for Alzheimer’s, Connectomes & Quantum BCIsMelanie Swan
This talk provides an introduction to quantum computing and how it may be deployed to study the human brain and its diseases of pathology and aging. Refined to its present state over centuries, the brain is one of the most complex systems known, with 86 billion neurons and 242 trillion synapses connected in intricate patterns and rewired by synaptic plasticity. Research continues to illuminate the mysteries of the brain. Quantum computing provides a more capacious architecture with greater scalability and energy efficiency than current methods of classical computing and supercomputing, and more naturally corresponds to the three-dimensional structure of atomic reality. The vision for quantum neuroscience is to model the nature of the brain exactly as it is, in three-dimensional atomically-accurate representations. Neuroscience (particularly genetic disease modeling, connectomics, and synaptomics) could be the “killer application” of quantum computing. Implementations in other industries are also important, including in quantum finance, quantum cryptography using Shor’s factoring algorithm (“the Y2K of Crypto”), Grover’s search, quantum chemistry, eigensolvers, quantum machine learning, and continuous-time quantum walks. Quantum computing is a high-profile worldwide scientific endeavor with platforms currently available via cloud services (IBM Q 27-qubit, IonQ 32-qubit, Rigetti 19Q Acorn) and is in the process of being applied in various industries including computational neuroscience.
Art Theory: Two Cultures Synthesis of Art and ScienceMelanie Swan
Thesis: Aesthetic resources contribute broadly to the human endeavor of progress, self-understanding, and science, beyond the immediate experience of art. Aesthetic Resources are frameworks, concepts, and modes of expression in art, literature, and philosophy that capture the imagination and the intellect through the senses. The role of art is to inspire the future: the romance of the sea, the open road, space.
The arts are a hallmark of civilization, but can their benefit be crystallized as aesthetic resources that can be mobilized to new situations? How can aesthetic resources help in moments of crisis?
A worldwide social identity crisis has been provoked by pandemic recovery, politics, equity, and environmental sustainability. Philosophical and aesthetic resources can help. Understanding art as a reflection of who we are as individuals and groups, this talk explores conceptualizations of art, with examples, in different periodizations from the 1800s to the present. A marquis definition as to what constitutes an artwork is Adorno’s, for whom the work must promulgate its own natural law and engage in novel materials manipulation. For many theorists, art is the pressing of our self-concept into concrete materiality (whether pyramids, sculpture, or painting). What do contemporary periodizations of art mean to our current and forward-looking self-concept? Recent eras include the neo-avant-gardes of 1945, the conceptual art of the 1960s, and post-conceptual art starting in the 1970s, produced generatively with found materials, the digital domain, and audience interactivity. What is the now-current idea of art? Is today’s Baudelairian flâneur and Balzacian modern hero incarnated in the quantum aesthetic imaginary and the digital cryptocitizen? Far from an “end of art” thesis sometimes attributed to Hegel, aesthetic practices are more relevant than ever. Individually and societally, we are reinventing creative energy and productive imagination in venues from science, technology, health, and biology to the arts.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Future of AI: Blockchain and Deep Learning
1. World Future Society
Scottsdale AZ, November 9, 2017
Slides: http://slideshare.net/LaBlogga
The Future of Artificial Intelligence
Blockchain & Deep Learning
Melanie Swan
Philosophy, Purdue University
melanie@BlockchainStudies.org
2. 9 Nov 2017
Blockchain
Discussion Questions
1. Probability humans will extinct
ourselves by mistake? _____%
2. How much are automated algorithms
changing your workplace or everyday
life? _____%
3. Would you prefer a mortgage that
corresponds to your specific needs, or
is standard (for the same cost)?
4. Would you like to make a digital backup
of your mind?
1
?
??
3. 9 Nov 2017
Blockchain 2
Melanie Swan, Technology Theorist
Philosophy Department, Purdue University,
Indiana, USA
Founder, Institute for Blockchain Studies
Singularity University Instructor; Institute for Ethics and
Emerging Technology Affiliate Scholar; EDGE invited
contributor; FQXi Advisor
Traditional Markets Background
Economics and Financial
Theory Leadership
New Economies research group
Source: http://www.melanieswan.com, http://blockchainstudies.org
https://www.facebook.com/groups/NewEconomies
4. 9 Nov 2017
Blockchain
Agenda
Artificial Intelligence
Blockchain Technology
Deep Learning Algorithms
Future of Artificial Intelligence
3
5. 9 Nov 2017
Blockchain 4
Considering blockchain and deep learning
together suggests the emergence of a new
class of global network computing system.
These systems are self-operating
computation graphs that make probabilistic
guesses about reality states of the world.
Future of AI Smart Network thesis
6. 9 Nov 2017
Blockchain
What are we running on networks?
5
Value (Money)
Intelligence (Brains)
Information
2010s-2020s
2050s(e)
1980s
Thought-
tokening
Value-
tokening
7. 9 Nov 2017
Blockchain
Future of AI: Smart Networks
6
Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html
Fundamental Eras of Network Computing
8. 9 Nov 2017
Blockchain
What is Artificial Intelligence?
Artificial intelligence
(AI) is a computer
performing tasks
typically associated
with intelligent beings
-Encyclopedia Britannica
7
Source: https://www.britannica.com/technology/artificial-intelligence
Ke Jie vs. AlphaGo AI Go player, Future of
Go Summit, Wuzhen China, May 2017
9. 9 Nov 2017
Blockchain
“Creeping Frontier” of Technology
8
Source: https://www.britannica.com/technology/artificial-intelligence
Achievements are quickly forgotten
AI = “whatever we can’t do yet”
Innovation Frontier
10. 9 Nov 2017
Blockchain
What is the AI problem?
Computer capabilities can grow faster than
human capabilities
Therefore, one day computers might
become vastly more capable than humans
(i.e. superintelligent)
And willfully or inadvertently present a
danger to humans
9
Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind-
ethics-society/research/AI-morality-values/
“Pessimistic”
“Optimistic”
11. 9 Nov 2017
Blockchain
Global Existential Risk
10
Source: Sandberg, A. & Bostrom, N. (2008): “Global Catastrophic Risks Survey”, Technical Report #2008-1, Future of Humanity
Institute, Oxford University: pp. 1-5.
Percent chance of different types of disaster before 2100
Method: Informal
survey of
participants,
Global
Catastrophic
Risk Conference,
Oxford, July
2008
12. 9 Nov 2017
Blockchain
Standard AI Ethics Modules?
Roboethics (how the machine behaves)
Facebook AI bots create own language
OpenAI self-play bot defeats top Dota2 player
Instagram “nice” filter eliminates hate speech
Criminal justice algorithms discriminate
Robotiquette (how the machine interacts)
11
Facebook AI bots OpenAI Dota2 Victory
Source: Swan. M. In review. Toward a Social Theory of Dignity: Hegel’s Master-Slave Dialectic and Essential Difference in the
Human-Robot Relation. In Robots, Power, Relationships. Eds. J. Carpenter, F. Ferrando, A. Milligan.
14. 9 Nov 2017
Blockchain
Technological Unemployment
Challenge: facilitate an orderly transition to
Automation Economy
Half (47%) of employment is at risk of automation in the
next two decades – Carl Frey, Oxford, 2015
Why are there still so many jobs in a world that could be
automating more quickly? – David Autor, MIT, 2015
13
Source: Swan, M. (2017). Is Technological Unemployment Real? Abundance Economics. In Surviving the Machine Age: Intelligent
Technology and the Transformation of Human Work. Hughes & LaGrandeur, Eds. London: Palgrave Macmillan. 19-33.
15. 9 Nov 2017
Blockchain
Future of “Work”?
14
http://www.robotandhwang.com/attorneys
“Work” = meaningful
engagement of human
capacities
16. 9 Nov 2017
Blockchain
What is important for our Future?
15
Maslow’s hierarchy of needs
Survive
Flourish &
Thrive
Source: Swan, M. (2017). Cognitive Easing: Human Identity Crisis in a World of Technology,
http://ieet.org/index.php/IEET/more/Swan20170107.
Enable human potential, Maslow’s self-actualization
Freed from obligatory work, who will we be?
Aspirational
Needs
Material
Needs
17. 9 Nov 2017
Blockchain
Privacy Pendulum:
Swinging back to more privacy
16
Historically: lots of privacy; Surveillance era: strange
logic of few bad apples so insecure surveillance of all;
centralized (Equifax) cybersecurity does not work
Future era: swing back to privacy; restore checks &
balances
Institutionally-
specified Reality
Self-determined
Reality
More Privacy
18. 9 Nov 2017
Blockchain
Our AI Future: high-impact emerging tech
17
Big Data &
Deep Learning
Blockchain CRISPR &
Bioprinting
19. 9 Nov 2017
Blockchain 18
Top disruptors: Deep Learning & Blockchain
Source: https://www.ipe.com/reports/special-reports/securities-services/securities-services-blockchain-a-beginners-
guide/10014058.article
20. 9 Nov 2017
Blockchain
Job Growth Skills in Demand
1. Robotics/automation/data science/deep learning
2. Blockchain/Bitcoin
19
Source: https://www.computerworld.com/article/3235972/financial-it/blockchains-explosive-growth-pushes-job-
skills-demand-to-no-2-spot.html
21. 9 Nov 2017
Blockchain
Future of AI: Smart Networks
20
Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html
Fundamental Eras of Network Computing
Future of AI: intelligence “baked in” to smart networks
Blockchains to confirm authenticity and transfer value
Deep Learning algorithms for predictive identification
22. 9 Nov 2017
Blockchain
Species of Networks
21
Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind-
ethics-society/research/AI-morality-values/
Social Networks
Transportation
Communications
Information
Biological
Superorganisms
Ecosystems
Organisms
Plants
Finance, credit, payment
Deep Learning
Superorganisms: Trans-individual, Trans-national
23. 9 Nov 2017
Blockchain
Agenda
Artificial Intelligence
Blockchain Technology
Deep Learning Algorithms
Future of Artificial Intelligence
22
25. 9 Nov 2017
Blockchain 24
Conceptual Definition:
Blockchain is a software protocol;
just as SMTP is a protocol for
sending email, blockchain is a
protocol for sending money
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
What is Blockchain/Distributed Ledger Tech?
26. 9 Nov 2017
Blockchain 25
Technical Definition:
Blockchain is the tamper-resistant
distributed ledger software underlying
cryptocurrencies such as Bitcoin, for
recording and transferring data and assets
such as financial transactions and real
estate titles, via the Internet without needing
a third-party intermediary
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
What is Blockchain/Distributed Ledger Tech?
27. 9 Nov 2017
Blockchain
How does Bitcoin work?
Use eWallet app to submit transaction
26
Source: https://www.youtube.com/watch?v=t5JGQXCTe3c
Scan recipient’s address
and submit transaction
$ appears in recipient’s eWallet
Wallet has keys not money
Creates PKI Signature address pairs A new PKI signature for each transaction
28. 9 Nov 2017
Blockchain
P2P network confirms & records transaction
27
Source: https://www.youtube.com/watch?v=t5JGQXCTe3c
Transaction computationally confirmed
Ledger account balances updated
Peer nodes maintain distributed ledger
Transactions submitted to a pool and miners assemble
new batch (block) of transactions each 10 min
Each block includes a cryptographic hash of the last
block, chaining the blocks, hence “Blockchain”
29. 9 Nov 2017
Blockchain
How robust is the Bitcoin p2p network?
28
p2p: peer to peer; Source: https://bitnodes.21.co, https://github.com/bitcoin/bitcoin
11,690 global nodes run full Bitcoind (11/17); 160 gb
Run the software yourself:
30. 9 Nov 2017
Blockchain
What is Bitcoin mining?
29
Mining is the accounting function to record
transactions, fee-based
Mining ASICs “find new blocks” (proof of work)
Network regularly issues random 32-bit nonces
(numbers) per specified cryptographic parameters
Mining software constantly makes nonce guesses
At the rate of 2^32 (4 billion) hashes (guesses)/second
One machine at random guesses the 32-bit nonce
Winning machine confirms and records the
transactions, and collects the rewards
All nodes confirm the transactions and append the
new block to their copy of the distributed ledger
“Wasteful” effort deters malicious players
Sample
code:
Run the software yourself:
Fast because ASICs
represent the hashing
algorithm as hardware
31. 9 Nov 2017
Blockchain
Distributed Networks
30
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
Decentralized
(based on hubs)
Centralized Distributed
(based on peers)
Radical implication: every node is a peer who can
provide services to other peers
32. 9 Nov 2017
Blockchain
P2P Network Nodes provide services
31
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
Centralized bank tracks
payments between clients
“Classic”
Banking
Peer
Banking
Nodes deliver services to others, for a small fee
Transaction ledger hosting (~11,960 Bitcoind nodes)
Transaction confirmation and logging (mining)
News services (“decentralized Reddit”: Steemit, Yours)
Banking services (payment channels (netting offsets))
Network nodes store transaction
record settled by many individuals
33. 9 Nov 2017
Blockchain
Public and Private Distributed Ledgers
32
Source: Adapted from https://www.linkedin.com/pulse/making-blockchain-safe-government-merged-mining-chains-tori-adams
Private: approved users
(“permissioned”)
Identity known, for enterprise
Approved credentials
Controlled access
Public: open to anyone
(“permissionless”)
Identity unknown, for individuals
Ex: Zcash zero-knowledge proofs
Open access
Transactions logged
on public Blockchains
Transactions logged
on private Blockchains
Any user Financial Inst, Industry
Consortia, Gov’t Agency
Examples:
Bitcoin
Ethereum
Examples:
R3
Hyperledger
34. 9 Nov 2017
Blockchain
Blockchain Applications Areas
33
Source: http://www.blockchaintechnologies.com
Smart Property
Cryptographic
Asset Registries
Smart Contracts
IP Registration
Money, Payments,
Financial Clearing
Identity
Confirmation
Impacting all industries
because allows secure
value transfer in four
application areas
35. 9 Nov 2017
Blockchain
Agenda
Artificial Intelligence
Blockchain Technology
Deep Learning Algorithms
Future of Artificial Intelligence
34
36. 9 Nov 2017
Blockchain
Global Data Volume: 40 EB 2020e
Scientific, governmental, corporate, and personal
Big Data…is not Smart Data
Source: http://www.oyster-ims.com/media/resources/dealing-information-growth-dark-data-six-practical-steps/
35
35
37. 9 Nov 2017
Blockchain
Big Data requires Deep Learning
36
Older algorithms cannot keep up with the growth in
data, need new data science methods
Source: http://blog.algorithmia.com/introduction-to-deep-learning-2016
38. 9 Nov 2017
Blockchain
Broader Computer Science Context
37
Source: Machine Learning Guide, 9. Deep Learning
Within the Computer Science discipline, in the field of
Artificial Intelligence, Deep Learning is a class of
Machine Learning algorithms, that are in the form of a
Neural Network
39. 9 Nov 2017
Blockchain 38
Conceptual Definition:
Deep learning is a computer program that can
identify what something is
Technical Definition:
Deep learning is a class of machine learning
algorithms in the form of a neural network that
uses a cascade of layers (tiers) of processing
units to extract features from data and make
predictive guesses about new data
Source: Swan, M., (2017)., Philosophy of Deep Learning, https://www.slideshare.net/lablogga/deep-learning-explained
What is Deep Learning?
40. 9 Nov 2017
Blockchain
Deep Learning & AI
System is “dumb” (i.e. mechanical)
“Learns” with big data (lots of input examples) and trial-and-error
guesses to adjust weights and bias to identify key features
Creates a predictive system to identity new examples
AI argument: big enough data is what makes a
difference (“simple” algorithms run over large data sets)
39
Input: Big Data (e.g.;
many examples)
Method: Trial-and-error
guesses to adjust node weights
Output: system identifies
new examples
41. 9 Nov 2017
Blockchain
Sample task: is that a Car?
Create an image recognition system that determines
which features are relevant (at increasingly higher levels
of abstraction) and correctly identifies new examples
40
Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
42. 9 Nov 2017
Blockchain
Supervised and Unsupervised Learning
Supervised (classify
labeled data)
Unsupervised (find
patterns in unlabeled
data)
41
Source: https://www.slideshare.net/ThomasDaSilvaPaula/an-introduction-to-machine-learning-and-a-little-bit-of-deep-learning
43. 9 Nov 2017
Blockchain
Early success in Supervised Learning (2011)
YouTube: user-classified data
perfect for Supervised Learning
42
Source: Google Brain: Le, QV, Dean, Jeff, Ng, Andrew, et al. 2012. Building high-level features using large scale unsupervised
learning. https://arxiv.org/abs/1112.6209
44. 9 Nov 2017
Blockchain
Machine learning: human threshold
43
Source: Mary Meeker, Internet Trends, 2017, http://www.kpcb.com/internet-trends
All apps voice-activated and conversational?
45. 9 Nov 2017
Blockchain
2 main kinds of Deep Learning neural nets
44
Source: Yann LeCun, CVPR 2015 keynote (Computer Vision ), "What's wrong with Deep Learning" http://t.co/nPFlPZzMEJ
Convolutional Neural Nets
Image recognition
Convolve: roll up to higher
levels of abstraction in feature
sets
Recurrent Neural Nets
Speech, text, audio recognition
Recur: iterate over sequential
inputs with a memory function
LSTM (Long Short-Term
Memory) remembers
sequences and avoids
gradient vanishing
46. 9 Nov 2017
Blockchain
3 Key Technical Principles of Deep Learning
45
Reduce combinatoric
dimensionality
Core computational unit
(input-processing-output)
Levers: weights and bias
Squash values into
Sigmoidal S-curve
-Binary values (Y/N, 0/1)
-Probability values (0 to 1)
-Tanh values 9(-1) to 1)
Loss FunctionPerceptron StructureSigmoid Function
“Dumb” system learns by
adjusting parameters and
checking against outcome
Loss function
optimizes efficiency
of solution
Non-linear formulation
as a logistic regression
problem means
greater mathematical
manipulation
What
Why
47. 9 Nov 2017
Blockchain
How does the neural net actually learn?
System varies the
weights and biases
to see if a better
outcome is obtained
Repeat until the net
correctly classifies
the data
46
Source: http://neuralnetworksanddeeplearning.com/chap2.html
Structural system based on cascading layers of
neurons with variable parameters: weight and bias
48. 9 Nov 2017
Blockchain
Backpropagation
Problem: Inefficient to test the combinatorial
explosion of all possible parameter variations
Solution: Backpropagation (1986 Nature paper)
Backpropagation of errors and gradient descent are
an optimization method used to calculate the error
contribution of each neuron after a batch of data is
processed
47
Source: http://neuralnetworksanddeeplearning.com/chap2.html
49. 9 Nov 2017
Blockchain
Agenda
Artificial Intelligence
Blockchain Technology
Deep Learning Algorithms
Future of Artificial Intelligence
48
50. 9 Nov 2017
Blockchain
Future of Artificial Intelligence
49
Source: https://www.slideshare.net/lablogga/deep-learning-explained
Blockchain & Deep Learning
Next-gen global computing network
technology
Computation graphs
Self-operating state engines
Make probabilistic guesses about
reality states of the world
51. 9 Nov 2017
Blockchain
Future of AI: Smart Networks
50
Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html
Fundamental Eras of Network Computing
Future of AI: intelligence “baked in” to smart networks
Blockchains to confirm authenticity and transfer value
Deep Learning algorithms for predictive identification
52. 9 Nov 2017
Blockchain
Deep Learning Chains: cross-functionality
Deep Learning Applications for Blockchain
TensorFlow for Fee Estimation
Predictive pattern recognition for security
Fraud, privacy, money-laundering
Deep Learning techniques (backpropagations of errors,
gradient descent, loss curves) to optimize financial graphs
Formulate debt-credit-payment problems as sigmoidal
optimizations to solve with machine learning
Blockchain Applications for Deep Learning
Secure automation, registry, logging, tracking + remuneration
functionality for deep learning systems as they go online
BaaS for network operations (LSTM is like a payment channel)
Blockchain P2P nodes provide deep learning network services:
security (facial recognition), identification, authorization
51
53. 9 Nov 2017
Blockchain
Deep Learning Chains: App #1
Autonomous Driving & Drone Delivery, Social Robotics
Deep Learning (CNNs): identify what things are
Blockchain: secure automation technology
Track arbitrarily-many units, audit, upgrade
Legal liability, accountability, remuneration
52
54. 9 Nov 2017
Blockchain
Deep Learning Chains: App #2
53
Source: https://www.illumina.com/science/technology/next-generation-sequencing.html
Big Health Data
Large-scale secure predictive analysis of big health
data to understand disease prevention
Population
7.5 bn
people
worldwide
55. 9 Nov 2017
Blockchain
Deep Learning Chains: App #3
Leapfrog technology for human potential
Financial Inclusion
2 bn under-banked, 1.1 bn without ID
70% lack access to land registries
Health Inclusion
400 mn no access to health services
Does not make sense to build out brick-
and-mortar bank branches and medical
clinics to every last mile in a world of
digital services
eWallet banking and deep learning medical
diagnostic apps
54
Source: Pricewaterhouse Coopers. 2016. The un(der)banked is FinTech's largest opportunity. DeNovo Q2 2016 FinTech ReCap
and Funding ReView., Heider, Caroline, and Connelly, April. 2016. Why Land Administration Matters for Development. World Bank.
http://www.who.int/mediacentre/news/releases/2015/uhc-report/en/
Digital health wallet
56. 9 Nov 2017
Blockchain
Deep Learning Chains: App #4
55
Enact Friendly AI
Digital intelligences running on
consensus-managed smart
networks (not in isolation)
Good reputational standing required
to conduct operations
Transactions to access resources
(like fund-raising), provide services,
enter into contracts, retire
Smart network consensus only
validates and records bonafide
transactions from ‘good’ agents
Sources: http://cointelegraph.com/news/113368/blockchain-ai-5-top-reasons-the-blockchain-will-deliver-friendly-ai,
http://ieet.org/index.php/IEET/more/swan20141117
57. 9 Nov 2017
Blockchain
Deep-thinkers Registry
Register deep learners with
blockchains and monitor with
deep learning algorithms
Secure tracking
Remuneration
Examples
Autonomous lab robots
On-chain IP discovery tracking
Roving agriculture bots
Manufacturing bots
Intelligent gaming
Go-playing algorithms
56
Source: Swan, M. Future of AI Thinking: The Brain as a DAC. Neural Turing Machines: https://arxiv.org/abs/1410.5401.
IPFS (Benet): https://medium.com/@ConsenSys/an-introduction-to-ipfs-9bba4860abd0#.bgig18cgp
Deep Learning Chains: App #5
58. 9 Nov 2017
Blockchain
Conclusion
Deep learning chains: needed for
next-generation challenges
Financial inclusion, big health data,
global energy markets, and space
Smart networks: a new form of
automated global infrastructure
Identify (deep learning)
Validate, confirm, and route
transactions (blockchain)
Future of AI is smart networks
Running value
Running intelligence
Possible answer to AI worries
57
59. World Future Society
Scottsdale AZ, November 9, 2017
Slides: http://slideshare.net/LaBlogga
The Future of Artificial Intelligence
Blockchain & Deep Learning
Melanie Swan
Philosophy, Purdue University
melanie@BlockchainStudies.org