Slides for lecture 2 of the course Introduction to Legal Technology at the University of Turku Law School, presented Jan 27 2015.
This lecture presents a brief history and overview of legal technology and legal AI through the 20th century.
Introduction to Legal Technology, lecture 7 (2015)Anna Ronkainen
This document summarizes applications of technology in the legal field, including decision support, predictive analytics, legal automation, and machine translation. It provides examples such as US sentencing guidelines, an Israeli system for criminal sentencing, and the Rechtwijzer platform for marital disputes. Predictive analytics examples include Lex Machina's tools for patent litigation. Legal self-service platforms like LegalZoom are discussed. The role of machine translation in patent applications and the European Patent system is also summarized.
Introduction to Legal Technology, lecture 8 (2015)Anna Ronkainen
Slides for lecture 8 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 24 2015.
This lecture is about various regulatory issues related to legal technology: regulation of the legal procession, data protection issues with regard to automated decision-making, and intellectual property issues for artificial intelligence.
Introduction to Legal Technology, lecture 3 (2015)Anna Ronkainen
Slides for lecture 3 of the course Introduction to Legal Technology at the University of Turku Law School, presented Jan 27 2015.
This lecture presents a number of modern AI technologies which in my opinion are indicative of the direction legal AI is likely to take over the coming decade or two.
Introduction to Legal Technology, lecture 4 (2015)Anna Ronkainen
Slides for lecture 4 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 3 2015.
This lecture combines different perspectives on the role of human factors in legal technology: legal reasoning as cognition and how to model it, and software usability as it relates to legal technology.
Introduction to Legal Technology, lecture 1 (2015)Anna Ronkainen
Slides for lecture 1 of the course Introduction to Legal Technology at the University of Turku Law School, presented Jan 13 2015.
This lecture contains an overall introduction to the course and presents some general legal tech/compsci/AI concepts.
Introduction to Legal Technology, lecture 5 (2015)Anna Ronkainen
Slides for lecture 5 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 10 2015.
This lecture is the first of three lectures on specific legal technology applications: information retrieval, knowledge management, and e-discovery.
Introduction to Legal Technology, lecture 6 (2015)Anna Ronkainen
Slides for lecture 6 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 10 2015.
This lecture is the second of three lectures on specific legal technology applications: case management, online dispute resolution, and access to justice.
Introduction to Legal Technology, lecture 10 (2015)Anna Ronkainen
Slides for lecture 10 of the course Introduction to Legal Technology at the University of Turku Law School, presented Mar 10 2015.
This lecture asks the student to consider different options for the continuation of their career and lists a range of options for different types of new job positions and employers in law. (This presentation was preceded by presentations by the student-driven startup accelerator Boost Turku as well as the Turku-based legal startup Lakivälitys.)
Introduction to Legal Technology, lecture 7 (2015)Anna Ronkainen
This document summarizes applications of technology in the legal field, including decision support, predictive analytics, legal automation, and machine translation. It provides examples such as US sentencing guidelines, an Israeli system for criminal sentencing, and the Rechtwijzer platform for marital disputes. Predictive analytics examples include Lex Machina's tools for patent litigation. Legal self-service platforms like LegalZoom are discussed. The role of machine translation in patent applications and the European Patent system is also summarized.
Introduction to Legal Technology, lecture 8 (2015)Anna Ronkainen
Slides for lecture 8 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 24 2015.
This lecture is about various regulatory issues related to legal technology: regulation of the legal procession, data protection issues with regard to automated decision-making, and intellectual property issues for artificial intelligence.
Introduction to Legal Technology, lecture 3 (2015)Anna Ronkainen
Slides for lecture 3 of the course Introduction to Legal Technology at the University of Turku Law School, presented Jan 27 2015.
This lecture presents a number of modern AI technologies which in my opinion are indicative of the direction legal AI is likely to take over the coming decade or two.
Introduction to Legal Technology, lecture 4 (2015)Anna Ronkainen
Slides for lecture 4 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 3 2015.
This lecture combines different perspectives on the role of human factors in legal technology: legal reasoning as cognition and how to model it, and software usability as it relates to legal technology.
Introduction to Legal Technology, lecture 1 (2015)Anna Ronkainen
Slides for lecture 1 of the course Introduction to Legal Technology at the University of Turku Law School, presented Jan 13 2015.
This lecture contains an overall introduction to the course and presents some general legal tech/compsci/AI concepts.
Introduction to Legal Technology, lecture 5 (2015)Anna Ronkainen
Slides for lecture 5 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 10 2015.
This lecture is the first of three lectures on specific legal technology applications: information retrieval, knowledge management, and e-discovery.
Introduction to Legal Technology, lecture 6 (2015)Anna Ronkainen
Slides for lecture 6 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 10 2015.
This lecture is the second of three lectures on specific legal technology applications: case management, online dispute resolution, and access to justice.
Introduction to Legal Technology, lecture 10 (2015)Anna Ronkainen
Slides for lecture 10 of the course Introduction to Legal Technology at the University of Turku Law School, presented Mar 10 2015.
This lecture asks the student to consider different options for the continuation of their career and lists a range of options for different types of new job positions and employers in law. (This presentation was preceded by presentations by the student-driven startup accelerator Boost Turku as well as the Turku-based legal startup Lakivälitys.)
Introduction to Legal Technology, lecture 9 (2015)Anna Ronkainen
Slides for lecture 9 of the course Introduction to Legal Technology at the University of Turku Law School, presented Mar 3 2015.
This lecture is about legal startups, containing an overview of the most central startup-related concepts in general, a detailed presentation of all legal startups in Finland and a categorized listing of a representative selection of legal startups across the world.
The document discusses the history and challenges of using AI for legal research and decision-making. It describes early attempts in the 1950s and 1960s to build systems to help with legal tasks, and the open questions and uncertainties that remained. It then outlines the author's work building prototypes in the 2000s to model legal concepts like trademark similarity using techniques like fuzzy logic. The author reflects on lessons learned from this research commercialization, noting the difficulties of translating academic research directly into viable products. The document concludes by arguing that successful commercialization is more likely when problems, rather than solutions, are the starting point and when legal practitioners are involved from the beginning.
General introduction to legal technology and legal AI, presented at the inaugural Helsinki Legal Tech Meetup on 2016-03-17 (for a more thorough overview, please see my Introduction to Legal Technology slides for lectures 1–10, also on SlideShare)
From Research to Innovative Legal Tech ProductsAnna Ronkainen
Keynote presentation about legal tech and innovation held 2016-05-26 at the Legal Technology Conference organized by University of Tartu and the Estonian chapter of Legal Hackers.
Ethical machines: data mining and fairness – the optimistic viewAnna Ronkainen
Introductory remarks to a seminar on algorithms and discrimination arranged by the Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences at the University of Helsinki, 2016-05-02.
Finnish Legal Tech Forum launch presentationAnna Ronkainen
Legal technology refers to technology used by lawyers or to perform legal work conventionally done by lawyers. The Finnish Legal Tech Forum is a new organization promoting legal tech in Finland with around 40 members. It plans to take over the Helsinki Legal Tech Meetup and hold events to discuss topics like online dispute resolution and legal tech startups. The presentation provided an overview of legal tech areas and upcoming conferences and meetups for people to learn more.
AI in legal practice – the research perspectiveAnna Ronkainen
1) The document discusses AI in legal practice from a research perspective.
2) It defines AI as using computers to do things that people can do easily but computers cannot, such as legal tasks like document review, due diligence, and trademark search and clearance.
3) The author notes that AI and law is an established research field since the 1980s, and legal applications of AI research like natural language processing are now a wake-up call for the field.
Modeling meaning and knowledge: legal knowledgeAnna Ronkainen
Guest lecture on modelling legal knowledge held 2016-04-25 at the course Modeling meaning and knowledge at the University of Helsinki, Department of Modern Languages.
This document discusses electronic discovery (e-discovery) and the evolution of technology and legal demands in the 21st century. It defines e-discovery and electronically stored information (ESI), outlines the common forms and sources of ESI, and describes the stages of the electronic discovery reference model including identification, preservation, collection, processing, review, analysis, and production. It emphasizes the importance of preservation in e-discovery and explains why e-discovery problems involving volume and sources of electronic information are challenging. Finally, it pitches partnering with Pye Legal for e-discovery solutions that contain costs, reduce risks, and avoid sanctions.
Commercializing legal AI research: lessons learnedAnna Ronkainen
Presentation about research commercialization (general points and some examples), held 2016-05-19 at the CodeX Legal Innovation Workshop in connection with the FutureLaw conference at Stanford University.
HBS seminar 3/26/14: Dark Markets, Bad Patents, No DataBrian Kahin
This document summarizes Brian Kahin's presentation on challenges in the digital economy related to institutional and information failures. Some key points discussed include: the disruptive effects of digital technology on economic policy domains; the slow pace of change for institutions and policies compared to firms; examples of complex public-private systems like patents; and the need for multidisciplinary approaches to analyze such systems. The document also discusses fundamental aspects of the digital economy like exponential growth and combinatorial effects, and implications for policy areas like intellectual property.
Ai and applications in the legal domain studium generale maastricht 20191101jcscholtes
November 20, 2019, it was my great pleasure to present a special lecture on Artificial Intelligence and Application in the Legal Domain. In this lecture I discuss how the development of machines that can learn, reason and act intelligently – Artificial Intelligence (AI) – is advancing rapidly in the legal domain. In some areas, machine intelligence have even already surpassed the limits of what the brightest human minds are capable of achieving, especially in the field of eDiscovery and Legal Review of large data set.
In others, machines still struggle with seemingly basic tasks. Nonetheless, breakthroughs in AI already have profound impact on the legal profession. AI is set to improve our world now and will continue to do so in the future. At the same time, there is the fear of losing control.
This lecture was part of a larger series on AI organized by our department of data science and knowledge engineering: https://www.maastrichtuniversity.nl/events/artificial-intelligence.
More information can be found here: https://textmining.nu
October 29, 2019, I was invited to present the keynote of the LegalTech Alliance meeting on eDiscovery and Big Data, in which 11 law departments from the Universities of Applied Sciences in the Netherlands participate.
eDiscovery is more and more important than ever. Future legal professionals must be able to deal with large electronic data sets so they can:
- Take decisions based on facts and not based on guesses and assumptions;
- Answer information requests timely, accurately and complete;
- Avoid high cost, reputation damage, regulatory measures, business disruption and stress!
It is great that the LegalTech Alliance understands that need and that they embed eDiscovery in their educational programs.
Attached are slides of the workshop were we presented the course eDiscovery (including the hands-on with ZyLAB) which we developed together with the University of Applied Sciences in Amsterdam
Digital Survival Skills: A Course for TalTech EmployeesKaido Kikkas
The document describes a digital skills course for employees at Tallinn University of Technology (TalTech) called Digital Security Skills or DigiTarkus. The course aims to improve everyday digital knowledge and skills as society increasingly relies on technology but basic skills are often assumed. It has six modules covering topics such as basics of IT, information work, office software, programming, security and legislation. Each module contains multiple topics that end with a test. The course was launched in Estonian in 2018 and English in 2019 and is designed to raise awareness on important digital issues in a practical way using multiple platforms and open-source software. As of December 2019, over 500 employees had completed parts of the course.
The document summarizes changes to USPTO fees and procedures effective January 1, 2014 or December 18, 2013. Key changes include decreases to various issue fees, elimination of the assignment recordation fee, relaxed filing date requirements, longer deadlines to respond to certain notices, and extension of a missing parts pilot program through 2014. The document also summarizes recent case law related to functional language, multiple embodiments in design patents, "and/or" claim language, and inequitable conduct.
Creating products that lawyers love (sic!) – design in legal technologyAnna Ronkainen
The document discusses usability in legal technology products. It defines usability and its key attributes of learnability, efficiency, memorability, errors, and satisfaction. It describes different levels of usability representation from mental models to implementation. The document advocates for implementing usability through evaluation, field studies, prototypes, iterative development, and user testing. Good usability can increase productivity, reduce costs, and improve legal processes and decisions. However, more research on usability is still needed in the legal field.
Introduction to legal design: Product & project managementAnna Ronkainen
Guest lecture on product and project management in legal design, held on the course Introduction to Legal Design at Stanford University’s d.school 2015-05-07.
Information technology and law and traiHimanshu Jawa
The document discusses key aspects of information technology and telecommunications law in India. It provides an overview of the Information Technology Act 2000 and the Telecom Regulatory Authority of India (TRAI) Act 1997. The IT Act aims to provide a legal framework for e-commerce and digital transactions. TRAI was established to regulate the telecom sector and ensure consumer interests are protected. The document outlines important sections of the IT Act regarding cybercrimes and data privacy. It also explains the purpose and functions of TRAI in regulating tariffs and resolving disputes in the telecom industry.
Bommarito Presentation for University of Houston Computational Law Conferencemjbommar
Law ? Computation: The past, present, and future relationship
In this talk, I will present the set of frames through which I view the relationship between law and computation: "law as computation," "computation on law," and "law and computation." By distinguishing these frames and understanding their context, I hope to increase clarity in our discussions, summarize current research, and suggest future avenues for both academic and commerical effort. This talk will include a number of original examples that highlight current possibilities at the forefront of law and computation.
Mr. Bommarito is consultant, currently employed in the hedge fund industry, who specializes in collecting, processing, and analyzing information from financial, political, and legal systems. His publications range from graph theory to the Supreme Court to algorithmic trading, and can be found in Quantitative Finance, Physica A, and various law reviews. He holds three degrees from the University of Michigan, including an MSE in Financial Engineering. Outside of academia, Mr. Bommarito’s contributions include co-founding the Computational Legal Studies blog, maintenance of the World Treaty Index, and press coverage on Seeking Alpha, the Financial Times, the New York Times, Zero Hedge, Abnormal Returns, Marginal Revolution, and Wired Magazine.
Introduction to Legal Technology, lecture 9 (2015)Anna Ronkainen
Slides for lecture 9 of the course Introduction to Legal Technology at the University of Turku Law School, presented Mar 3 2015.
This lecture is about legal startups, containing an overview of the most central startup-related concepts in general, a detailed presentation of all legal startups in Finland and a categorized listing of a representative selection of legal startups across the world.
The document discusses the history and challenges of using AI for legal research and decision-making. It describes early attempts in the 1950s and 1960s to build systems to help with legal tasks, and the open questions and uncertainties that remained. It then outlines the author's work building prototypes in the 2000s to model legal concepts like trademark similarity using techniques like fuzzy logic. The author reflects on lessons learned from this research commercialization, noting the difficulties of translating academic research directly into viable products. The document concludes by arguing that successful commercialization is more likely when problems, rather than solutions, are the starting point and when legal practitioners are involved from the beginning.
General introduction to legal technology and legal AI, presented at the inaugural Helsinki Legal Tech Meetup on 2016-03-17 (for a more thorough overview, please see my Introduction to Legal Technology slides for lectures 1–10, also on SlideShare)
From Research to Innovative Legal Tech ProductsAnna Ronkainen
Keynote presentation about legal tech and innovation held 2016-05-26 at the Legal Technology Conference organized by University of Tartu and the Estonian chapter of Legal Hackers.
Ethical machines: data mining and fairness – the optimistic viewAnna Ronkainen
Introductory remarks to a seminar on algorithms and discrimination arranged by the Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences at the University of Helsinki, 2016-05-02.
Finnish Legal Tech Forum launch presentationAnna Ronkainen
Legal technology refers to technology used by lawyers or to perform legal work conventionally done by lawyers. The Finnish Legal Tech Forum is a new organization promoting legal tech in Finland with around 40 members. It plans to take over the Helsinki Legal Tech Meetup and hold events to discuss topics like online dispute resolution and legal tech startups. The presentation provided an overview of legal tech areas and upcoming conferences and meetups for people to learn more.
AI in legal practice – the research perspectiveAnna Ronkainen
1) The document discusses AI in legal practice from a research perspective.
2) It defines AI as using computers to do things that people can do easily but computers cannot, such as legal tasks like document review, due diligence, and trademark search and clearance.
3) The author notes that AI and law is an established research field since the 1980s, and legal applications of AI research like natural language processing are now a wake-up call for the field.
Modeling meaning and knowledge: legal knowledgeAnna Ronkainen
Guest lecture on modelling legal knowledge held 2016-04-25 at the course Modeling meaning and knowledge at the University of Helsinki, Department of Modern Languages.
This document discusses electronic discovery (e-discovery) and the evolution of technology and legal demands in the 21st century. It defines e-discovery and electronically stored information (ESI), outlines the common forms and sources of ESI, and describes the stages of the electronic discovery reference model including identification, preservation, collection, processing, review, analysis, and production. It emphasizes the importance of preservation in e-discovery and explains why e-discovery problems involving volume and sources of electronic information are challenging. Finally, it pitches partnering with Pye Legal for e-discovery solutions that contain costs, reduce risks, and avoid sanctions.
Commercializing legal AI research: lessons learnedAnna Ronkainen
Presentation about research commercialization (general points and some examples), held 2016-05-19 at the CodeX Legal Innovation Workshop in connection with the FutureLaw conference at Stanford University.
HBS seminar 3/26/14: Dark Markets, Bad Patents, No DataBrian Kahin
This document summarizes Brian Kahin's presentation on challenges in the digital economy related to institutional and information failures. Some key points discussed include: the disruptive effects of digital technology on economic policy domains; the slow pace of change for institutions and policies compared to firms; examples of complex public-private systems like patents; and the need for multidisciplinary approaches to analyze such systems. The document also discusses fundamental aspects of the digital economy like exponential growth and combinatorial effects, and implications for policy areas like intellectual property.
Ai and applications in the legal domain studium generale maastricht 20191101jcscholtes
November 20, 2019, it was my great pleasure to present a special lecture on Artificial Intelligence and Application in the Legal Domain. In this lecture I discuss how the development of machines that can learn, reason and act intelligently – Artificial Intelligence (AI) – is advancing rapidly in the legal domain. In some areas, machine intelligence have even already surpassed the limits of what the brightest human minds are capable of achieving, especially in the field of eDiscovery and Legal Review of large data set.
In others, machines still struggle with seemingly basic tasks. Nonetheless, breakthroughs in AI already have profound impact on the legal profession. AI is set to improve our world now and will continue to do so in the future. At the same time, there is the fear of losing control.
This lecture was part of a larger series on AI organized by our department of data science and knowledge engineering: https://www.maastrichtuniversity.nl/events/artificial-intelligence.
More information can be found here: https://textmining.nu
October 29, 2019, I was invited to present the keynote of the LegalTech Alliance meeting on eDiscovery and Big Data, in which 11 law departments from the Universities of Applied Sciences in the Netherlands participate.
eDiscovery is more and more important than ever. Future legal professionals must be able to deal with large electronic data sets so they can:
- Take decisions based on facts and not based on guesses and assumptions;
- Answer information requests timely, accurately and complete;
- Avoid high cost, reputation damage, regulatory measures, business disruption and stress!
It is great that the LegalTech Alliance understands that need and that they embed eDiscovery in their educational programs.
Attached are slides of the workshop were we presented the course eDiscovery (including the hands-on with ZyLAB) which we developed together with the University of Applied Sciences in Amsterdam
Digital Survival Skills: A Course for TalTech EmployeesKaido Kikkas
The document describes a digital skills course for employees at Tallinn University of Technology (TalTech) called Digital Security Skills or DigiTarkus. The course aims to improve everyday digital knowledge and skills as society increasingly relies on technology but basic skills are often assumed. It has six modules covering topics such as basics of IT, information work, office software, programming, security and legislation. Each module contains multiple topics that end with a test. The course was launched in Estonian in 2018 and English in 2019 and is designed to raise awareness on important digital issues in a practical way using multiple platforms and open-source software. As of December 2019, over 500 employees had completed parts of the course.
The document summarizes changes to USPTO fees and procedures effective January 1, 2014 or December 18, 2013. Key changes include decreases to various issue fees, elimination of the assignment recordation fee, relaxed filing date requirements, longer deadlines to respond to certain notices, and extension of a missing parts pilot program through 2014. The document also summarizes recent case law related to functional language, multiple embodiments in design patents, "and/or" claim language, and inequitable conduct.
Creating products that lawyers love (sic!) – design in legal technologyAnna Ronkainen
The document discusses usability in legal technology products. It defines usability and its key attributes of learnability, efficiency, memorability, errors, and satisfaction. It describes different levels of usability representation from mental models to implementation. The document advocates for implementing usability through evaluation, field studies, prototypes, iterative development, and user testing. Good usability can increase productivity, reduce costs, and improve legal processes and decisions. However, more research on usability is still needed in the legal field.
Introduction to legal design: Product & project managementAnna Ronkainen
Guest lecture on product and project management in legal design, held on the course Introduction to Legal Design at Stanford University’s d.school 2015-05-07.
Information technology and law and traiHimanshu Jawa
The document discusses key aspects of information technology and telecommunications law in India. It provides an overview of the Information Technology Act 2000 and the Telecom Regulatory Authority of India (TRAI) Act 1997. The IT Act aims to provide a legal framework for e-commerce and digital transactions. TRAI was established to regulate the telecom sector and ensure consumer interests are protected. The document outlines important sections of the IT Act regarding cybercrimes and data privacy. It also explains the purpose and functions of TRAI in regulating tariffs and resolving disputes in the telecom industry.
Bommarito Presentation for University of Houston Computational Law Conferencemjbommar
Law ? Computation: The past, present, and future relationship
In this talk, I will present the set of frames through which I view the relationship between law and computation: "law as computation," "computation on law," and "law and computation." By distinguishing these frames and understanding their context, I hope to increase clarity in our discussions, summarize current research, and suggest future avenues for both academic and commerical effort. This talk will include a number of original examples that highlight current possibilities at the forefront of law and computation.
Mr. Bommarito is consultant, currently employed in the hedge fund industry, who specializes in collecting, processing, and analyzing information from financial, political, and legal systems. His publications range from graph theory to the Supreme Court to algorithmic trading, and can be found in Quantitative Finance, Physica A, and various law reviews. He holds three degrees from the University of Michigan, including an MSE in Financial Engineering. Outside of academia, Mr. Bommarito’s contributions include co-founding the Computational Legal Studies blog, maintenance of the World Treaty Index, and press coverage on Seeking Alpha, the Financial Times, the New York Times, Zero Hedge, Abnormal Returns, Marginal Revolution, and Wired Magazine.
Electronic Surveillance of Communications 100225Klamberg
The document discusses electronic surveillance of communications and legislation around signal intelligence. It provides context on changes in technology and threats that created demands for new legislation. It describes how signal intelligence works, including intercepting messages and metadata, as well as traffic analysis and social network analysis. Legislation in Sweden and other countries regulates agencies conducting signal intelligence and their mandates, clients, and oversight. Key aspects of Swedish law include the Defence Radio Establishment's mandate for surveillance, its clients and review mechanisms, methods like traffic analysis, and the scope of interception and data collection.
This material is for PGPSE / CSE students of AFTERSCHOOOL. PGPSE / CSE are free online programme - open for all - free for all - to promote entrepreneurship and social entrepreneurship PGPSE is for those who want to transform the world. It is different from MBA, BBA, CFA, CA,CS,ICWA and other traditional programmes. It is based on self certification and based on self learning and guidance by mentors. It is for those who want to be entrepreneurs and social changers. Let us work together. Our basic idea is that KNOWLEDGE IS FREE & AND SHARE IT WITH THE WORLD
This document discusses various threats to information security and safeguards organizations can implement. The three main sources of threats are human error, malicious human activity, and natural disasters. Some key threats include hacking, viruses, unauthorized data disclosure through actions like phishing. Technical safeguards include identification & authentication like passwords, encryption, firewalls, malware protection. Human safeguards involve policies, training, account management and monitoring. Senior management must establish security policies, assess risks, and ensure all necessary safeguards are in place to protect the organization's information systems and data. The organization should also have an incident response plan to deal with security breaches when they do occur.
Introduction to information technology lecture 1adpafit
The document provides an overview of computers and information technology. It discusses how IT uses computing and communication to spread information. It then defines computers and their basic components of hardware and software. The document outlines the different generations of computers and some early calculating devices. It also describes input/output devices, common computer components, and uses of computers in various fields.
The document provides an overview of the history and operation of the internet and communication technologies. It discusses early communication technologies like the slide rule and telephone and how they evolved over time. It then covers the basics of how the internet works, including physical connections, data transmission speeds, protocols, IP addresses, and applications. It also summarizes key technologies that enabled the development of the modern internet like radio, computers, and the world wide web.
This document provides an introduction to cyber law. It discusses how the internet and technology have evolved over time to connect people to information and each other. It also notes some of the risks that have emerged with technology, such as cyber crimes. The document then defines cyberspace and discusses some key aspects of it, such as its lack of boundaries and potential for anonymity. It concludes by outlining some penalty provisions under cyber law for offenses related to unauthorized access and harming computers/networks.
This document provides an overview of formal methods for software engineering. It begins with an introduction to formal methods, including the use of mathematically rigorous techniques to specify, design, and verify software systems. Notations for formal methods include first-order logic and set theory. Tools can be used for specification and verification. Verification tools may prove properties like the absence of race conditions. Formal methods can provide high confidence that software meets critical requirements by proving properties will "always" or "never" hold. The document discusses the history of logic and mathematical reasoning leading to formal methods. It also outlines limitations of formal methods established by Gödel's incompleteness theorems and the halting problem.
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Mikial Singh Nijjar is explaining the Definition of Computer Science. Mikial Singh Nijjar is a magnificent and skilled IT manager in Oakland, California.
1. The document discusses artificial intelligence and machine learning concepts including problem solving techniques in AI. It covers topics like knowledge representation, problem types, search algorithms, and formulating problems.
2. Key advances in AI are explained as increased computing power, availability of big data, and developments in deep learning algorithms. Applications of AI span many domains from medical to manufacturing.
3. Challenges in AI include dealing with large, complex, interdependent problems and developing practical solutions while addressing issues like costs and software development difficulties. Problem solving in AI involves defining problems, analyzing, planning, executing, and evaluating solutions.
Machine Intelligence - Part 3 of Piero Scaruffi's class "Thinking about Thoug...piero scaruffi
Machine Intelligence - Part 3 of Piero Scaruffi's class "Thinking about Thought" at UC Berkeley (2014), excerpted from http://www.scaruffi.com/nature I keep updating these slides at www.scaruffi.com/ucb.html
Information technology refers to the use of computers and telecommunications equipment to store, retrieve, transmit, and manipulate data. It involves several industries including hardware, software, electronics, internet, and computer services. Key developments include the earliest use of writing in Sumerian civilization in 3000 BC, the modern definition of IT in a 1958 article, and four phases of development based on storage and processing technologies: pre-mechanical, mechanical, electromechanical, and electronic. Telecommunication technologies have also advanced from early visual and audio messages to modern electrical networks like radio, fiber optics, and the internet. The amount of information exchanged through telecommunication networks has grown enormously in recent decades. Computer technology has similarly progressed from early mechanical devices to modern
Expert systems are computer programs that emulate human experts by using knowledge about a domain to solve complex problems. They are divided into a knowledge base containing facts and rules, and an inference engine that applies rules to deduce new facts. Early expert systems were developed in the 1970s and proliferated in the 1980s, becoming some of the first truly successful forms of artificial intelligence software. They were used for applications like medical diagnosis, molecular identification, and configuring computer systems. While interest grew in the 1980s, expert systems declined as a standalone technology in the 1990s as their capabilities were integrated into broader business applications using tools like rule engines.
The development of mathematics led to tools for computation. Blaise Pascal built the first calculating machine in the 17th century. Charles Babbage invented the analytical engine, the first computer, in the 1820s based on mechanical gears. Herman Hollerith used punch cards to help classify information for the 1890 US Census. The transistor was invented in 1943, greatly reducing the size and cost of computers. ENIAC, the first general-purpose electronic digital computer, was developed in 1946. Integrated circuits in the 1960s further drove down costs and size. Information technologies have progressed through premechanical, mechanical, electromechanical, and electronic stages of development.
Susskind, 'A Manifesto for AI in the Law' ICAIL 2017, London, 2017Richard Susskind
Keynote address at the 16th International Conference on Artificial Intelligence and Law, June, London, including 15-point manifesto for the AI/Law research community.
Invitation to Computer Science 8thEd Ch 1 (1).pptxkalyank35
Computer science is the study of algorithms and their formal properties, hardware realizations, linguistic realizations, and applications. Key events in the history of computing included the development of programmable looms and calculators, Babbage's analytical engine, the first general-purpose electronic computers in the 1940s, and advances like integrated circuits that led to personal computers. The document outlines the levels that will be covered in the textbook from algorithms to applications and social issues.
International journal of engineering issues vol 2015 - no 1 - paper3sophiabelthome
This document summarizes a paper on modeling evolving complex software systems as cyber-physical systems using principles from physics and mathematics. It discusses how software systems can be viewed as complex automatons with mathematical foundations in areas like complex numbers and Fourier transforms. Cybernetics provides tools to model human behaviors and interactions in these systems. The paper also discusses how analog computers were early models of physical phenomena, and how infinitesimals and differentials from calculus can model continuously changing aspects of cyber-physical systems, within the limits imposed by physical reality.
1. The document discusses sensors and actuators used in robotics including collision sensors, interaction sensors, proximity/distance sensors, inertial motion sensors, position sensors, localization sensors, and 2D cameras.
2. It describes the perception-action loop where robots sense the environment, plan actions, and act in the environment.
3. The history of robotics is reviewed from early automata in ancient times to modern humanoid, service, and research robots.
The presentation at MCIS Corfu (look for the title in AIS library, for the full paper). "EU-Wide Legal Text Mining using Big Data Processsing Infrastructures"
This document is an introduction to discrete mathematics submitted by five students to their lecturer. It begins with an overview of discrete vs continuous mathematics, noting that discrete mathematics considers objects that change in discrete steps, like digital watches, while continuous mathematics models smoothly changing phenomena. It then provides examples of uses of discrete mathematics, including number theory and public key cryptography, graph theory and networks, and probability and chance. The goals of the discrete mathematics course are then outlined as introducing mathematical tools and building skills in rigorous thinking, writing and proof.
This document provides an introduction to artificial intelligence (AI) including:
1. Definitions of AI from four perspectives: systems that think like humans, act like humans, think rationally, and act rationally.
2. A brief history of AI from its origins in the 1940s through recent advances in neural networks, hidden Markov models, and intelligent agents.
3. An overview of key periods in AI's development including the initial enthusiasm in the 1950s-60s, a move to realism in the 1960s-70s, the rise of knowledge-based systems and expert systems in the 1970s-80s, and recent progress in neural networks.
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Introduction to Legal Technology, lecture 2 (2015)
1. TLS0070 Introduction to
Legal Technology
Lecture 2
Artificial intelligence and
law: the 20th century
University of Turku Law School 2015-01-27
Anna Ronkainen @ronkaine
anna.ronkainen@onomatics.com
3. Vaucanson automata
- Jacques Vaucanson (1709–1782), France
- 1737: The Flute Player
- 1738: The Tambourine Player, The Digesting
Duck
- 1745: the first completely automated loom
4. The Jacquard loom
- Joseph Marie Jacquard (1752–1834), France
- developed Vaucanson’s loom further by
making it programmable
- exchangeable weaving patterns input using
punched cards
5. Babbage Analytical Engine
- Charles Babbage (1791–1871), England
- designed the Difference Engine for
tabulating polynomial functions
- based on it, designed the Analytical Engine,
a mechanical general-purpose computer
- none were built at the time
6. The first programmer
- Ada Lovelace (1815–1852), England
- wrote the first programs written specifically
for the Analytical Engine (which did not exist
at the time), generally considered the first
computer algorithms
7. Algorithm for computing Bernoulli Numbers on the Analytical Engine (page 1), Ada Lovelace, 1843
8. Hollerith tabulating machines
- Herman Hollerith (1860–1929), USA
- generalized the use of punched cards into increasingly
general-purpose mechanical data processing (1886-)
- founded the Tabulating Machine Company (-> IBM)
- widespread use of Hollerith machines across many
applications through the 1st half of 1900s
- downside: Hollerith machines facilitated the Holocaust
(and afterwards gave rise to data protection legislation)
9. ENIAC and the end of the mechanical
age
- the first electronic general-purpose
computer
- built at Penn in 1943–1946, commissioned
by the US Army
- initial use: calculating artillery trajectory
tables
- 17468 vacuum tubes, 65 m3, 150 kW
- data input/output with punch cards,
programmed with rewiring
10. Things start getting smaller:
On to semiconductors
- transistor developed in 1947 by Bardeen,
Brandain and Shockley (Bell Labs)
- first transistorized computer built in
Manchester 1953
- first integrated circuit constructed in 1958:
Jack Kilby (Texas Instruments)
- first microprocessors in 1971 (Garrett CADC,
TI TMS 1000, Intel 4004)
12. The first search-and-replace ever:
s/retarded child/exceptional child/g
- terminology change in the Pennsylvania health
code in the late 1950s
- legislative technique required all instances of
textual changes to be enumerated individually
- the legislature turned to prof Horty at Penn
- first tried to solve this manually, too unreliable
- solution: input text into computer, index the
position of each word to find all occurrences of
the word in question
- obviously generalizable into textual information
retrieval in general
13. Next steps
- M.U.L.L. (later Jurimetrics) journal 1959–
- case law retrieval experiments by Colin Tapper
(Oxford) through the 1960s
- Centre d’études pour le traitement de
l'information juridique (IRETIJ, Montpellier)
1965
- CREDOC (Belgium) 1967
- OBAR (Ohio) 1964 -> LEXIS 1970
- NORIS (Norway) 1970
- Westlaw 1975
14. First expert systems: mid-1980s
- inspired by systems from other fields (e.g.
MYCIN)
- Latent Damage Law (Susskind and Capper)
- British Nationality Act (Bench-Capon and
Sergot)
- SHYSTER (Popple)
15. Where did all the lawyers go?
- the PC revolution (1980s) and the launch of
the commercial Internet (1993) ->
computer-related legal problems
everywhere!
- expert systems were considered a failure –
not just in law – for good reason -> the AI
winter of late 1980s
- leaving the field to computer scientists and
legal theorists made AI & law
17. Information retrieval (1-st gen)
- normal database search (exact match or
wildcard characters)
- Boolean search operators
- modest practical advances since the 1980s
(with some recent exceptions)
- legal AI contributions negligible
18. Administrative automation
- has been with us since the 1960s (or 1890s if you count
the use of Hollerith machines for the US census...)
- an absolute must for effective administration on a large
scale
- works well if the rules to be applied are straightforward
enough (rather hopeless with discretionary rules)
- seems that implementing new rules in these kinds of
systems is still a major PITA
- (also an occasional subject of doctrinal work in
administrative law, rule-of-law issues etc., e.g. Kuopus
1988)
19. Expert systems
- a big thing in AI in the 1980s
- basic idea pretty straightforward:
- you take an expert in some domain (e.g.
some area of law)
- make them turn their domain expertise into
computable rules
- add a reasoning engine
- and voilá, you have a computer giving
expert advice or making expert decisions
20. Example: British Nationality Act
1-[1] A person born in the United Kingdom after commencement shall be a British
Citizen, if a t the time of birth his father or mother is:
a) a British Citizen, or
b) settled in the United Kingdom
Represented as
Rule 1: X acquires British citizenship on date Y
IF X was born in the UK
AND X was born on date Y
AND X is after or on commencement of the act
AND X has a parent who qualifies under 1.1 on date
Rule 2: X has a parent who qualifies under 1.1 on date Y
IF X has a parent Z
AND Z was a British citizen on date Y
Rule 3: X has a parent who qualifies under 1.1 on date Y
IF X has a parent Z
AND Z was settled in the UK on date Y
21. Expert systems work (sort of)
- if the legal rules are straightforward enough:
- no ambiguity or vagueness regarding the inputs
- clarity about which rule applies in each situation
- even in the best case, formalization of rules is far
from trivial (knowledge-acquisition bottleneck)
- also requires expertise on what to model and what
to leave out (and how to make sure the system isn’t
used beyond its design limits)
- how much of the expertise really lies in the system
and how much in the user?
- in a sense, expert systems are doing just fine, it’s
mainly the term that’s fallen into disuse...
22. Case-based reasoning
- one possible approach: analyze legal cases in
terms of factors (very common in US
doctrine)
- use factors to find best match for case at
hand
- map factors into a network to find
23. Soft computing: Fuzzy logic and
neutral networks
- both highly fashionable in AI in the 1980s
- also some experiments within legal AI in the
early 1990s
- fuzzy logic was also popular among legal
theorists (mostly on a metaphorical level)
since Reisinger 1972
‘We suggest that fuzzy logic is no more than (over)sophistication of the approximation
approach, that it may give good results in some very special applications, but its
philosophical basis is uncertain generally and very uncertain when applied to open-
textured legal concepts. Both the appearance of precision and the appearance of
generality are spurious.’ (Bench-Capon and Sergot 1985/1988)
25. Ontologies
- the philosophical meaning of ontology: the
study of the nature of being (what is and
isn’t)
- in computer science: a way of formalizing
entities in an universe of discourse (concepts
and their relationships etc.)
- the Semantic Web (Berners-Lee et al 2001)
- Cyc 1984– (OpenCyc 2002–)
- WordNet 1985–
26. Ontologies contain (in very general
terms)
- individual entities
- classes of entities
- attributes for entities
- relations between entities
- function terms
- restrictions
- rules
- axioms
- events (changes to entities)
27. Ontologies in law
- Valente’s functional ontology (1995):
- norms (normative knowledge)
- things, events, etc. (world knowledge)
- obligations (responsibility knowledge)
- legal remedies (reactive knowledge: penalties,
compensation)
- rules of legal reasoning (meta-legal knowledge,
e.g. lex specialis)
- legal powers (creative knowledge)
- (and several others)
30. Use of ontologies
- always exist in a specific context, built for that
(no Begriffshimmel and no point in aiming for
one)
- can be generated by hand or by machine
- two very different ontologies can work just as
well (no Right Answer!)
- very useful for information retrieval (find similar
things that are called something else)
- can also be used e.g. for similarity metrics
- categorization hierarchy also interesting from a
cognitive perspective (basic-level concepts etc.)
32. Argumentation frameworks
(Dung 1995)
- a set of arguments, and attack relations
between pairs of arguments (A attacks B)
- general semantics for argument trees
- plus specific rules for finding which attack
relation dominates (in case of conflict)
33. Pros and cons
- argument maps can illustrate how things are
made (and sometimes also show that some
valid arguments are actually always ignored)
- easier as a theoretical than a practical
exercise
- a lot easier when you already have a
decision and have to find a matching
argument scheme
34. Questions?
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