This presentation was given at the Empowerment Summit and is about the current of regulation with respect to Articifical Intelligence (AI) and contains some prospective insights about the elements that AI regulation should take into account
1. New Patent Development Opportunity Analysis
2. New Patent Preparation & Prosecution Strategy
3. Strategic Patent Development Exploiting Existing Patents
4. Monetization Exploiting Strategically Packaged Patent Portfolio
5. Development of Strategically Packaged Patent Portfolio Best Practice
6. Methodology for Developing Strategically Packaged Patent Portfolio
Introduction to ENT (Entity Network Translation)ENT Technologies
Want to eliminate hacks of critical infrastructure, vehicles and military systems? Do you think patients should exclusively own and control access to their medical records? Want to eliminate counterfeiting on digital and physical goods? Want to be able to exclusively own, sell, transfer, buy or lease your data and digital assets like physical property?
ENT makes all these things possible - TODAY.
ENT (Entity Network Translation) is a fully decentralized, next-generation trust infrastructure that replaces passwords, PKI, blockchain, and centralized data stores. ENT is a radical innovation in core enabling trust technology & networked systems.
+ Trusted micro-networking between entities of any kind: humans, devices, data and files, software processes, physical objects, concepts like corporations and currencies, and groups of any/all of these;
+ Exponentially increases security and privacy because entities are individually protected and connected directly without any middleman or central management;
+ Built on a patent-pending fundamental advancement in asymmetric key models called Relational Key Infrastructure (RKI) that eliminates central authorities and key management - the first key infrastructure innovation in 30 years;
+ Fully decentralized and owner-driven;
+ Useful for any purpose: Internet of Things, government, healthcare, finance, manufacturing, retail, etc;
+ Useful in any environment: scales from embedded components up to complex global systems;
+ Open standard for transparent governance, high usability across industries and wide adoption.
New IoT Product/Service Development
Even though the IoT is getting a huge attention recently the concept of interconnected billions of devices is not new and has been under development for over 10 years. Thus, there are a large number of related patented technologies that can be exploited for developing new products/services, and thus, new business for the emerging IoT market.
1. New Patent Development Opportunity Analysis
2. New Patent Preparation & Prosecution Strategy
3. Strategic Patent Development Exploiting Existing Patents
US20150227118 illustrates the IoT Cloud Big Data AI system for facilitating automatic control of the smart home devices based on past device behavior, current device events, sensor data, and server-sourced data. Cloud-based big data analytics is accessible via a server system for analyzing data associated with persons or buildings in a geographic region about the building, such as local news and weather information and data pertaining to appliances within the geographic region, such as a neighborhood, zip code, and so on. The analyzed data is used to develop the control rules to control smart home devices automatically.
IoT and machine learning - Computational Intelligence conferenceAjit Jaokar
Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities
The document discusses topics related to Internet of Things (IoT) and machine learning, including:
- Definitions and brief history of IoT and how it works by connecting devices to the cloud.
- Examples of common IoT applications and devices in various industries.
- The relationship between machine learning and IoT, where machine learning is used to analyze vast amounts of data collected by IoT sensors.
- Popular tools and platforms for developing IoT and machine learning solutions, along with online courses for further learning.
- Predictions about the growing role of IoT and technologies like 5G, artificial intelligence, and smart cities in the future.
1. New Patent Development Opportunity Analysis
2. New Patent Preparation & Prosecution Strategy
3. Strategic Patent Development Exploiting Existing Patents
4. Monetization Exploiting Strategically Packaged Patent Portfolio
5. Development of Strategically Packaged Patent Portfolio Best Practice
6. Methodology for Developing Strategically Packaged Patent Portfolio
Introduction to ENT (Entity Network Translation)ENT Technologies
Want to eliminate hacks of critical infrastructure, vehicles and military systems? Do you think patients should exclusively own and control access to their medical records? Want to eliminate counterfeiting on digital and physical goods? Want to be able to exclusively own, sell, transfer, buy or lease your data and digital assets like physical property?
ENT makes all these things possible - TODAY.
ENT (Entity Network Translation) is a fully decentralized, next-generation trust infrastructure that replaces passwords, PKI, blockchain, and centralized data stores. ENT is a radical innovation in core enabling trust technology & networked systems.
+ Trusted micro-networking between entities of any kind: humans, devices, data and files, software processes, physical objects, concepts like corporations and currencies, and groups of any/all of these;
+ Exponentially increases security and privacy because entities are individually protected and connected directly without any middleman or central management;
+ Built on a patent-pending fundamental advancement in asymmetric key models called Relational Key Infrastructure (RKI) that eliminates central authorities and key management - the first key infrastructure innovation in 30 years;
+ Fully decentralized and owner-driven;
+ Useful for any purpose: Internet of Things, government, healthcare, finance, manufacturing, retail, etc;
+ Useful in any environment: scales from embedded components up to complex global systems;
+ Open standard for transparent governance, high usability across industries and wide adoption.
New IoT Product/Service Development
Even though the IoT is getting a huge attention recently the concept of interconnected billions of devices is not new and has been under development for over 10 years. Thus, there are a large number of related patented technologies that can be exploited for developing new products/services, and thus, new business for the emerging IoT market.
1. New Patent Development Opportunity Analysis
2. New Patent Preparation & Prosecution Strategy
3. Strategic Patent Development Exploiting Existing Patents
US20150227118 illustrates the IoT Cloud Big Data AI system for facilitating automatic control of the smart home devices based on past device behavior, current device events, sensor data, and server-sourced data. Cloud-based big data analytics is accessible via a server system for analyzing data associated with persons or buildings in a geographic region about the building, such as local news and weather information and data pertaining to appliances within the geographic region, such as a neighborhood, zip code, and so on. The analyzed data is used to develop the control rules to control smart home devices automatically.
IoT and machine learning - Computational Intelligence conferenceAjit Jaokar
Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities
The document discusses topics related to Internet of Things (IoT) and machine learning, including:
- Definitions and brief history of IoT and how it works by connecting devices to the cloud.
- Examples of common IoT applications and devices in various industries.
- The relationship between machine learning and IoT, where machine learning is used to analyze vast amounts of data collected by IoT sensors.
- Popular tools and platforms for developing IoT and machine learning solutions, along with online courses for further learning.
- Predictions about the growing role of IoT and technologies like 5G, artificial intelligence, and smart cities in the future.
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.
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The document provides an overview of the EU's new AI Act, which aims to address risks posed by certain AI systems while harnessing opportunities. It establishes a risk-based regulatory framework for AI, defining high-risk and unacceptable risk AI uses. For high-risk AI, it imposes obligations like conformity assessments and transparency. It also regulates biometric identification and addresses general-purpose AI models that could pose systemic risks. The rules are designed to protect fundamental rights while ensuring trust and adoption of beneficial AI technologies.
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The document summarizes key topics around artificial intelligence, autonomous vehicles, and regulation. It discusses what AI is, including narrow and general AI, as well as machine learning approaches. It also covers applications of AI like autonomous vehicles and issues around regulation, fairness, and safety. New regulators like an EU agency for robotics and AI are proposed to help govern emerging technologies.
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- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
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Algorithms are taking control of our information rich world. As the twin sibling to Big Data, increasingly they decide how society views us via constructed profiles (as criminals? as terrorists? as rich or poor consumers?); what we see as important, newsworthy, cool or profitable (eg Twitter trending topics, automated stock selling, Amazon recommendations, BBC website top news topics etc); and indeed what we see at all as algorithms are increasingly used to filter our illegal or undesirable content as tools of public policy. Algorithms are peceived by virtue of their automation as neutral, objective and fair, unlike human decision makers - yet evidence increasingly shows the opposite - eg a series of legal complaints assert that Google games its own search results to promote its own economic interests and demote those of competitors or annoyances; while in the defamation field, French, German and Italian courts have decided that algorithmically generated autosuggests in search can be libellous (eg "Bettina Wolf prostitute"). . This paper asks if any legal remedies do or should exist to *audit* proprietary algorithms , given their importance, and asks if one way forward might be via existing and future subject access rights to personal data in EU data protection law. The transformation of these rights as proposed in the draft Data Protection Regulation is not however hopeful.
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The document discusses a research project that aims to map values in AI governance by studying how value attributions take form in human and computational ecologies. It proposes moving beyond focusing on ideal norms and values or trying to directly understand legal and computational commands, and instead "encircling" the topic by analyzing mundane practices. The researchers argue this assemblage perspective is needed to understand the interactions that constitute systems' viability and better inform academics, practitioners, regulators and judges.
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This document discusses the need for and challenges of regulating artificial intelligence and robotics development. It outlines several proposals and principles that could guide regulation, including ensuring humans maintain control over AI systems, developing emotional connections between humans and robots, implementing data protection and privacy by design, establishing a system for certifying advanced robots, and creating an EU agency to oversee and regulate AI. The document argues that proactive, cautious regulation is needed to provide predictable business conditions and ensure the EU maintains control over standards, while not stifling innovation.
Article started one year ago, obtains far more relevancy these days. Its meaning stays the same however: "Without laws and regulations would be chaos affecting our freedom and human nature."
CIA concepts of confidentiality, integrity, and availability are essential information security principles. Risk management includes identifying risks, reducing risks to acceptable levels through controls and standards like ISO 27005, and ensuring budgets allow for sufficient security. Control frameworks provide standardized, measurable approaches to examining security comprehensively and modularly. Due care and due diligence require organizations to take reasonable steps to understand and address security threats.
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3. Algorithm
Set of instructions within computer programs that determine how these
programs read, collect, process, and analyze data to generate some readable
form of analysis or output
(Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2009).
Introduction to algorithms (3rd ed.). Cambridge, Mass.: MIT Press. p. 5)
3
4. Instructions
Every algorithm must satisfy the following criteria:
input;
output;
definiteness;
finiteness;
effectiveness.
4
5. Algorithmic bias
Algorithmic bias
Bias can be introduced to an algorithm :
- during the assemblage of a database;
- as a result of algorithm design.
5
6. < 2 > Technological Singularity
The technological singularity (also, simply, the singularity) is the
hypothesis that the invention of artificial superintelligence (ASI)
will abruptly trigger runaway technological growth, resulting in
unfathomable changes to human civilization.
6
8. Where are we?
AlphaZero (2017)
Deep Neural Networks
General Reinforcement Learning Algorithm
Google Brain
-> develops AI that allegedly builds AI better and faster than humans can
8
9. Dystopian Future
9
Utopian describes a society that's
conceived to be perfect.
Dystopian is the exact opposite
— it describes an imaginary
society that is as dehumanizing
and as unpleasant as possible.
11. Challenges
AI's moral maze and the infinite scenarios that are still to be addressed;
Exponential speed of technological progress;
International/transnational/global regulation required
11
12. Legal questions
AI will raise legal questions in three areas:
Rights: Are/Should algorithms ever be protected by IP Rights?
Could algorithms create IP rights?
Accountability: Who or what should be liable if A.I causes harm ?
Ethics: How can we create and enforce moral codes for AI?
12
14. Copyright
Idea/ expression dichotomy
Balance between fostering
creativity/innovation and public good
Originality
Author = physical person (Art. 6
Copyright Act)
14
15. Copyright
15
Copyright protects the “literal expression” of computer
programs (source and object code), it does not protect
the “ideas”underlying the computer program.
Art. 2 para. 3 Swiss Copyright Act: Computer programs
are literary works.
-> not applicable to algorithms
16. Open Source
a type of computer software whose source code is released
under a license in which the copyright holder grants users
the rights to study, change, and distribute the software to
anyone and for any purpose
Relates to the copyrights in and to the computer software
Increased transparency and allowed collaborative software
development
16
17. Patents
17
To be eligible for patent protection, an invention must meet several
criteria:
(i) the invention must consist of patentable subject matter;
(ii) the invention must be capable of industrial application (or, in certain
countries, be useful);
(iii) it must be new (novel);
(iv) it must involve an inventive step (be non-obvious); and
(v) the disclosure of the invention in the patent application must meet
certain formal and substantive standards.
18. Patents
18
Abstract algorithms cannot be patented: the organization and
ordering of a series of math equations is not human invention.
However, a patent can be granted on an application of an abstract
idea
Dividing line between unpatentable abstract ideas or algorithms and
patentable inventions, which include processes.
19. Patents
19
-> Protection during 20 years of a business method
or process encapsulated into an algorithm
-> Disclosure but full control by the patent owner
-> Not suitable for algorithms
20. Trade secrets
20
Broadly speaking, any confidential business information
which provides an enterprise a competitive edge may be
considered a trade secret. Trade secrets encompass
manufacturing or industrial secrets and commercial
secrets.
.
21. Trade secrets
21
Trade secret means information which meets all of the
following requirements:
- secret in the sense that it is not, as a body or in the
precise configuration and assembly of its components,
generally known among or readily accessible to
persons within the circles that normally deal with the
kind of information in question;
- commercial value because it is secret;
- it has been subject to reasonable steps under the
circumstances, by the person lawfully in control of the
information, to keep it secret.
-> Algorithms can be protected by trade secrets
22. Trade secrets
22
Trade secret law lacks any expressly delineated justice
balancing mechanism between the rights holder and the
public, unlike patents and copyright.
But:
-> Privatisation of algorithms by companies that developed
them for a unlimited period in time.
No disclosure of the underlying instructions, criteria -> no
possibility to discover potential algorithmic bias.
23. Antitrust Law
Algorithms may foster tacit collusion, adversely affect consumer choice, even pose a threat to
pluralism.
Holders of algorithms as trade secrets could be in a dominant position.
The spreading of “static”, non-learning algorithms may facilitate collusion in markets that were not
prone to such conduct.
Dynamic, deep-learning algorithms may, once they are broadly implemented, require an
adjustment of competition law concepts such as causality, awareness and intent.
23
24. Data Protection
Algorithms treat data -> consent required for such treatment.
Art 71 Recitals / Art. 22 para 3 GDPR:
Right not to be subject to a decision, which may include a measure, evaluating personal aspects relating to him or her
which is based solely on automated processing , such as automatic refusal of an online credit application or e-
recruiting practices without any human intervention.
Such processing should be subject to suitable safeguards, which should include specific information to the data subject
and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision
reached after such assessment and to challenge the decision.
right to explanation is a right to be given an explanation for an output of the algorithm.
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25. Open Algorithms
Open Algorithms: open source for algorithms
Google claims to license the algorithms on Open source licenses
Initiatives:
- OPAL (for "Open Algorithms") is a non-profit socio-technological innovation developed by a group of partners
aiming to unlock the potential of private sector data for public good purposes by “sending the code to the data” in
a safe, participatory, and sustainable manner (https://www.opalproject.org/)
- Open marketplace for algorithms in a range of domains (https://algorithmia.com/algorithms).
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28. Five core principles
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Responsibility: For any algorithmic system, there needs to
be a person with the authority to deal with its adverse
individual or societal effects in a timely fashion
Explainability: any decisions produced by an algorithmic
system should be explainable to the people affected by
those decisions.
Accuracy: sources of error and uncertainty throughout an
algorithm and its data sources need to be identified,
logged, and benchmarked
29. Five core principles
29
Auditability: algorithms should be developed to enable
third parties to probe and review the behavior of an
algorithm.
Fairness: all algorithms making decisions about
individuals should be evaluated for discriminatory effects.
The results of the evaluation and the criteria used should
be publicly released and explained.
30. Robots or AI as
“electronic
person” ?
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Sophism
33. Isaac Asimov's "Three Laws of Robotics"
A robot may not injure a human being or, through inaction, allow a human being to come to
harm.
A robot must obey orders given by human beings except where such orders would conflict with
the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First
or Second Law.
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34. Oren Etzioni’s proposal
AI system must be subject to the full gamut of laws that apply to its human operator;
AI system must clearly disclose that it is not human;
AI system cannot retain or disclose confidential information without explicit approval from the
source of that information.
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35. Google guidelines
Any set of rules for AI should focus on predicting harm, mitigating risk, and ensuring safety is a priority
Avoiding Negative Side Effects: How can we ensure that an AI system will not disturb its environment in
negative ways while pursuing its goals?
Avoiding Reward Hacking: How can we avoid gaming of the reward function?
Scalable Oversight: How can we efficiently ensure that a given AI system respects aspects of the
objective that are too expensive to be frequently evaluated during training?
Safe Exploration: How do we ensure that an AI system doesn’t make exploratory moves with very
negative repercussions?
Robustness to Distributional Shift: How do we ensure that an AI system recognizes, and behaves
robustly, when it’s in an environment very different from its training environment?
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36. EU Ethical Guidelines on AI
As with any transformative technology, artificial intelligence may raise new ethical and legal
questions, related to liability or potentially biased decision-making. New technologies should not
mean new values.
The Commission will present ethical guidelines on AI development by the end of 2018, based on
the EU's Charter of Fundamental Rights, taking into account principles such as data protection
and transparency
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37. Working Group
1. Should we regulate AI? If yes, at which level?
2. Should algorithms be protected by IP rights (trade secrets)?
3. Should companies have an obligation to disclose algorithms?
4. What should be the guiding principles of regulation?
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39. Guiding principles (European Group on
Ethics)
Justice, equality and solidarity
Democracy
Rule of law and accountability
39
40. Guiding principles (European Group on
Ethics)
40
Security, safety, bodily and mental integrity
Data protection and privacy
Sustainability
41. Merci pour votre attention!
Florian Ducommun
Avocat ; LL.M
Av. Auguste Tissot 2bis
1006 Lausanne
021/310.73.10
ducommun@hdclegal.ch
41
Editor's Notes
input: there are zero or more quantities which are externally supplied;
output: at least one quantity is produced;
definiteness: each instruction must be clear and unambiguous;
finiteness: if we trace out the instructions of an algorithm, then for all cases the algorithm will terminate after a finite number of steps;
effectiveness: every instruction must be sufficiently basic that it can in principle be carried out by a person using only pencil and paper. It is not enough that each operation be definite, but it must also be feasible.
their actions are often no longer intelligible, and no longer open to scrutiny by humans. This is the case because, first, it is impossible to establish how they accomplish their results beyond the initial algorithms. Second, their performance is based on the data that have been used during the learning process and that may no longer be available or accessible.
biases and errors that they have been presented with in the past become engrained into the system.
1945: Isaac Asimov: Escape!
1950s : John von Neumann was both fascinated and alarmed by “the ever-accelerating progress of technology and changes in the modes of human life, which gives the appearance of some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue”
1993 : Vernor Vinge coined the term technological singularity, as «a hypothetical scenario in which generating a superhuman AI results in runaway, uncontrollable technological advance with unpredictable outcome».
their actions are often no longer intelligible, and no longer open to scrutiny by humans. This is the case because, first, it is impossible to establish how they accomplish their results beyond the initial algorithms. Second, their performance is based on the data that have been used during the learning process and that may no longer be available or accessible.
biases and errors that they have been presented with in the past become engrained into the system.
Idea/ expression dichotomy:
Art 9(2) of the TRIPS convention : ‘Copyright protection shall extend to expression and not to ideas, procedures, and methods of operation or mathematical concepts as such.'
Originality
The threshold of originality is a concept in copyright law that is used to assess whether a particular work can be copyrighted. It is used to distinguish works that are sufficiently original to warrant copyright protection from those that are not.
In Europe, European Patent Office policy is that a program for a computer is not patentable if it does not have the potential to cause a "technical effect" which is by now understood as a material effect (a "transformation of nature").
In the US, patents granted for business methods, which could include processes -> patents have been granted in the US on algorithms under certain conditions
In the United States, the policy on trade secrets states that they consist of information that may include a formula, pattern, compilation, program, device, method, technique or process
EU Trade Secrets Directive covers the unlawful acquisition, use and disclosure of trade secrets.
July 2018: the French and German competition authorities (Autorité de la concurrence and Bundeskartellamt) have started a joint project on algorithms and their effects on competition.
-> The project is intended to analyze the relevance of algorithms for competition and to develop a conceptual approach to dealing with potential issues.
Human dignity – understood as the recognition of the inherent human state of being worthy of respect, must not be violated by ‘autonomous’ technologies.
Autonomy – the principle of autonomy implies the freedom of the human being. This translates into human responsibility and thus control over and knowledge about ‘autonomous’ systems as they must not impair freedom of human beings to set their own standards and norms and be able to live according to them.
Responsibility – ‘autonomous’ systems should only be developed and used in ways that serve the global social and environmental good, as determined by outcomes of deliberative democratic processes
Justice, equality and solidarity – AI should contribute to global justice and equal access to the benefits and advantages that AI, robotics and ‘autonomous’ systems can bring. Discriminatory biases in data sets used to train and run AI systems should be prevented or detected, reported and neutralised at the earliest stage possible.
Democracy – key decisions on the regulation of AI development and application should be the result of democratic debate and public engagement. A spirit of global cooperation and public dialogue on the issue will ensure that they are taken in an inclusive, informed, and farsighted manner.
Rule of law and accountability – rule of law, access to justice and the right to redress and a fair trial provide the necessary framework for ensuring the observance of human rights standards and potential AI specific regulations. This includes protections against risks stemming from ‘autonomous’ systems that could infringe human rights, such as safety and privacy.
Security, safety, bodily and mental integrity – Safety and security of ‘autonomous’ systems materialises in three forms: (1) external safety for their environment and users, (2) reliability and internal robustness, e.g. against hacking, and (3) emotional safety with respect to human-machine interaction. All dimensions of safety must be taken into account by developers.
Data protection and privacy – In an age of ubiquitous and massive collection of data through digital communication technologies, the right to protection of personal information and the right to respect for privacy are crucially challenged. Both physical AI robots as part of the Internet of Things, as well as AI softbots that operate via the World Wide Web must comply with data protection regulations and not collect and spread data or be run on sets of data for whose use and dissemination no informed consent has been given.
Sustainability – AI technology must be in line with the human responsibility to ensure the basic preconditions for life on our planet, continued prospering for mankind and preservation of a good environment for future generations.