Friday, June 28, 2019

Energy and Exploitation: AIs dirty secrets

A couple of days ago I gave a short 15 minute talk at an excellent 5x15 event in Bristol. The talk I actually gave was different to the one I'd originally suggested. Two things prompted the switch: one was seeing the amazing line up of speakers on the programme - all covering more or less controversial topics - and the other was my increasing anger in recent months over the energy and human costs of AI. So it was that I wrote a completely new talk the day before this event.

But before I get to my talk I must mention the amazing other speakers: we heard Phillipa Perry speaking on child parent relationships, Hallie Rubenhold on the truth about Jack the Ripper's victims,  Jenny Riley speaking very movingly about One25's support for Bristol's (often homeless) sex workers, and Amy Sinclair introducing her activism with Extinction Rebellion.

Here is the script for my talk (for the slides go to the end of this blog post).


Artificial Intelligence and Machine Learning are often presented as bright clean new technologies with the potential to solve many of humanity's most pressing problems.

We already enjoy the benefit of truly remarkable AI technology, like machine translation and smart maps. Driverless cars might help us get around before too long, and DeepMind's diagnostic AI can detect eye diseases from retinal scans as accurately as a doctor.

Before getting into the ethics of AI I need to give you a quick tutorial on machine learning. The most powerful and exciting AI today is based on Artificial Neural Networks. Here [slide 3] is a simplified diagram of a Deep Learning network for recognizing images. Each small circle is a *very* simplified mathematical model of biological neurons, and the outputs of each layer of artificial neurons feed the inputs of the next layer. In order to be able to recognise images the network must first be trained with images that are already labelled - in this case my dog Lola.

But in order to reliably recognise Lola the network needs to be trained not with one picture of Lola but many. This set of images is called the training data set and without a good data set the network will not work at all or will be biased. (In reality there will need to be not 4 but hundreds of images of Lola).

So what does an AI ethicist do? Well, the short answer is worry. I worry about the ethical and societal impact of AI on individuals, society and the environment. Here are some keywords on ethics [slide 4], reflecting that we must work toward AI that respects Human Rights, diversity and dignity, is unbiased and sustainable, transparent, accountable and socially responsible.

But I do more than just worry. I also take practical steps like drafting ethical principles, and helping to write ethical standards for the British Standards Institute and the IEEE Standards Association. I lead P7001: a new standard on transparency in of autonomous systems based on the simple ethical principle that it should always be possible to find out why an AI made a particular decision. I have given evidence in parliament several times, and recently took part in a study of AI and robotics in healthcare and what this means for the workforce of the NHS.

Now I want to share two serious new worries with you.

The first is about the energy cost of AI. In 2016 Go champion Lee Sedol was famously defeated by DeepMind's AlphaGo. It was a remarkable achievement for AI. But consider the energy cost. In a single two hour match Sedol burned around 170 kcals: roughly the amount of energy you would get from an egg sandwich.  Or about the power of an LED night light -  1 Watt. In the same two hours the AlphaGo machine reportedly consumed 50,000 times more energy than Sedol. Equivalent to a 50 kW generator for industrial lighting. And that's not taking account of the energy used to train AlphaGo.

Now some people think we can make human equivalent AI by simulating the human brain. But the most complex animal brain so far simulated is that of c-elegans – the nematode worm. It has 302 neurons and about 5000 synapses - these are the connections between neurons. A couple of years ago I worked out that simulating a neural network for a simple robot with only a 10th the number of neurons of c-elegans costs 2000 times more energy than the whole worm.

In a new paper that came out just a few days ago we have for the first time estimates of the carbon cost of training large AI models for natural language processing such as machine translation [1]. The carbon cost of simple models is quite modest, but with tuning and experimentation the carbon cost leaps to 7 times the carbon footprint of an average human in one year (or 2 times if you're an American).

And the energy cost of optimising the biggest model is a staggering 5 times the carbon cost of a car over its whole lifetime, including manufacturing it in the first place. The dollar cost of that amount of energy is estimated at between one and 3 million US$. (Something that only companies with very deep pockets can afford.)

These energy costs seem completely at odds with the urgent need to halve carbon dioxide emissions by 2030. At the very least AI companies need to be honest about the huge energy costs of machine learning.

Now I want to turn to the human cost of AI. It is often said that one of the biggest fears around AI is the loss of jobs. In fact the opposite is happening. Many new jobs are being created, but the tragedy is that they are not great jobs, to say the least. Let me introduce you to two of these new kinds of jobs.

The first is AI tagging. This is manually labelling objects in images to, for instance, generate training data sets for driverless car AIs. Better (and safer) AI needs huge training data sets and a whole new outsourced industry has sprung up all over the world to meet this need. Here [slide 9] is an AI tagging factory in China.

Conversational AI or chat bots also need human help. Amazon for instance employs thousands of both full-time employees and contract workers to listen to and annotate speech. The tagged speech is then fed back to Alexa to improve its comprehension. And last month the Guardian reported that Google employs around 100,000 temps, vendors and contractors: literally an army of linguists working in "white collar sweatshops" to create the handcrafted data sets required for Google translate to learn dozens of languages. Not surprisingly there is a huge disparity between the wages and working conditions of these workers and Google's full time employees.

AI tagging jobs are dull, repetitive and in the case of the linguists highly skilled. But by far the worst kind of new white collar job in the AI industry is content moderation.

These tens of thousands of people, employed by third-party contractors, are required to watch and vet offensive content: hate speech, violent pornography, cruelty and sometimes murder of both animals and humans for Facebook, YouTube and other media platforms [2]. These jobs are not just dull and repetitive they are positively dangerous. Harrowing reports tell of PTSD-like trauma symptoms, panic attacks and burnout after one year, alongside micromanagement, poor working conditions and ineffective counselling. And very poor pay - typically $28,800 a year. Compare this with average annual salaries at Facebook of ~$240,000.

The big revelation to me over the past few months is the extent to which AI has a human supply chain, and I am an AI insider! The genius designers of this amazing tech rely on both huge amounts of energy and a hidden army of what Mary Gray and Siddhartha Suri call Ghost Workers.

I would like to leave you with a question: how can we, as ethical consumers, justify continuing to make use of unsustainable and unethical AI technologies?





References:

[1] Emma Strubell, Ananya Ganesh, Andrew McCallum (2019) Energy and Policy Considerations for Deep Learning in NLP, arXiv:1906.02243
[2] Sarah Roberts (2016) Digital Refuse: Canadian Garbage, Commercial Content Moderation and the Global Circulation of Social Media’s Waste, Media Studies Publications. 14.

Thursday, May 30, 2019

My top three policy and governance issues in AI/ML


In preparation for a meeting of the WEF global AI council today, we were asked the question:

What do you think are the top three policy and governance issues that face AI/ML currently? 

Here are my answers.

1.     For me the biggest governance issue facing AI/ML ethics is the gap between principles and practice. The hard problem the industry faces is turning good intentions into demonstrably good behaviour. In the last 2.5 years there has been a gold rush of new ethical principles in AI. Since Jan 2017 at least 22 sets of ethical principles have been published, including principles from Google, IBM, Microsoft and Intel. Yet any evidence that these principles are making a difference within those companies is hard to find – leading to a justifiable accusation of ethics-washing – and if anything the reputations of some leading AI companies are looking increasingly tarnished.

2.     Like others I am deeply concerned by the acute gender imbalance in AI (estimates of the proportion of women in AI vary between ~12% and ~22%). This is not just unfair, I believe it too be positively dangerous, since it is resulting in AI products and services that reflect the values and ambitions of (young, predominantly white) men. This makes it a governance issue. I cannot help wondering if the deeply troubling rise of surveillance capitalism is not, at least in part, a consequence of male values.

3.     A major policy concern is the apparently very poor quality of many of the jobs created by the large AI/ML companies. Of course the AI/ML engineers are paid exceptionally well, but it seems that there is a very large number of very poorly paid workers who, in effect, compensate for the fact that AI is not (yet) capable of identifying offensive content, nor is it able to learn without training data generated from large quantities of manually tagged objects in images, nor can conversational AI manage all queries that might be presented to it. This hidden army of piece workers, employed in developing countries by third party sub contractors and paid very poorly, are undertaking work that is at best extremely tedious (you might say robotic) and at worst psychologically very harmful; this has been called AI’s dirty little secret and should not – in my view – go unaddressed.

Thursday, April 18, 2019

An Updated Round Up of Ethical Principles of Robotics and AI

This blogpost is an updated round up of the various sets of ethical principles of robotics and AI that have been proposed to date, ordered by date of first publication.

I previously listed principles published before December 2017 here; this blogpost appends those principles drafted since January 2018 (plus one in October 2017 I had missed). The principles are listed here (in full or abridged) with links, notes and references but without critique.

Scroll down to the next horizontal line for the updates.

If there any (prominent) ones I’ve missed please let me know.

Asimov’s three laws of Robotics (1950)
  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
I have included these to explicitly acknowledge, firstly, that Asimov undoubtedly established the principle that robots (and by extension AIs) should be governed by principles, and secondly that many subsequent principles have been drafted as a direct response. The three laws first appeared in Asimov’s short story Runaround [1]. This wikipedia article provides a very good account of the three laws and their many (fictional) extensions.

Murphy and Wood’s three laws of Responsible Robotics (2009)
  1. A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics. 
  2. A robot must respond to humans as appropriate for their roles. 
  3. A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws. 
These were proposed in Robin Murphy and David Wood’s paper Beyond Asimov: The Three Laws of Responsible Robotics [2].

EPSRC Principles of Robotics (2010) 
  1. Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security.
  2. Humans, not Robots, are responsible agents. Robots should be designed and operated as far as practicable to comply with existing laws, fundamental rights and freedoms, including privacy.
  3. Robots are products. They should be designed using processes which assure their safety and security.
  4. Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.
  5. The person with legal responsibility for a robot should be attributed.
These principles were drafted in 2010 and published online in 2011, but not formally published until 2017 [3] as part of a two-part special issue of Connection Science on the principles, edited by Tony Prescott & Michael Szollosy [4]. An accessible introduction to the EPSRC principles was published in New Scientist in 2011.

Future of Life Institute Asilomar principles for beneficial AI (Jan 2017)

I will not list all 23 principles but extract just a few to compare and contrast with the others listed here:
6. Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
7. Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
8. Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
9. Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
10. Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.
11. Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
12. Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.
13. Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
14. Shared Benefit: AI technologies should benefit and empower as many people as possible.
15. Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
An account of the development of the Asilomar principles can be found here.

The ACM US Public Policy Council Principles for Algorithmic Transparency and Accountability (Jan 2017)
  1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society.
  2. Access and redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions.
  3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results.
  4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts.
  5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process.
  6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected.
  7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results.
See the ACM announcement of these principles here. The principles form part of the ACM’s updated code of ethics.

Japanese Society for Artificial Intelligence (JSAI) Ethical Guidelines (Feb 2017)
  1. Contribution to humanity Members of the JSAI will contribute to the peace, safety, welfare, and public interest of humanity.
  2. Abidance of laws and regulations Members of the JSAI must respect laws and regulations relating to research and development, intellectual property, as well as any other relevant contractual agreements. Members of the JSAI must not use AI with the intention of harming others, be it directly or indirectly.
  3. Respect for the privacy of others Members of the JSAI will respect the privacy of others with regards to their research and development of AI. Members of the JSAI have the duty to treat personal information appropriately and in accordance with relevant laws and regulations.
  4. Fairness Members of the JSAI will always be fair. Members of the JSAI will acknowledge that the use of AI may bring about additional inequality and discrimination in society which did not exist before, and will not be biased when developing AI.
  5. Security As specialists, members of the JSAI shall recognize the need for AI to be safe and acknowledge their responsibility in keeping AI under control.
  6. Act with integrity Members of the JSAI are to acknowledge the significant impact which AI can have on society.
  7. Accountability and Social Responsibility Members of the JSAI must verify the performance and resulting impact of AI technologies they have researched and developed.
  8. Communication with society and self-development Members of the JSAI must aim to improve and enhance society’s understanding of AI.
  9. Abidance of ethics guidelines by AI AI must abide by the policies described above in the same manner as the members of the JSAI in order to become a member or a quasi-member of society.
An explanation of the background and aims of these ethical guidelines can be found here, together with a link to the full principles (which are shown abridged above).

Draft principles of The Future Society’s Science, Law and Society Initiative (Oct 2017)
  1. AI should advance the well-being of humanity, its societies, and its natural environment.
  2. AI should be transparent.
  3. Manufacturers and operators of AI should be accountable.
  4. AI’s effectiveness should be measurable in the real-world applications for which it is intended.
  5. Operators of AI systems should have appropriate competencies.
  6. The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society.
This article by Nicolas Economou explains the 6 principles with a full commentary on each one.

Montréal Declaration for Responsible AI draft principles (Nov 2017)
  1. Well-being The development of AI should ultimately promote the well-being of all sentient creatures.
  2. Autonomy The development of AI should promote the autonomy of all human beings and control, in a responsible way, the autonomy of computer systems.
  3. Justice The development of AI should promote justice and seek to eliminate all types of discrimination, notably those linked to gender, age, mental / physical abilities, sexual orientation, ethnic/social origins and religious beliefs.
  4. Privacy The development of AI should offer guarantees respecting personal privacy and allowing people who use it to access their personal data as well as the kinds of information that any algorithm might use.
  5. Knowledge The development of AI should promote critical thinking and protect us from propaganda and manipulation.
  6. Democracy The development of AI should promote informed participation in public life, cooperation and democratic debate.
  7. Responsibility The various players in the development of AI should assume their responsibility by working against the risks arising from their technological innovations.
The Montréal Declaration for Responsible AI proposes the 7 values and draft principles above (here in full with preamble, questions and definitions).

IEEE General Principles of Ethical Autonomous and Intelligent Systems (Dec 2017)
  1. How can we ensure that A/IS do not infringe human rights?
  2. Traditional metrics of prosperity do not take into account the full effect of A/IS technologies on human well-being.
  3. How can we assure that designers, manufacturers, owners and operators of A/IS are responsible and accountable?
  4. How can we ensure that A/IS are transparent?
  5. How can we extend the benefits and minimize the risks of AI/AS technology being misused?
These 5 general principles appear in Ethically Aligned Design v2, a discussion document drafted and published by the IEEE Standards Association Global Initiative on Ethics of Autonomous and Intelligent Systems. The principles are expressed not as rules but instead as questions, or concerns, together with background and candidate recommendations.

A short article co-authored with IEEE general principles co-chair Mark Halverson Why Principles Matter explains the link between principles and standards, together with further commentary and references.

Note that these principles have been revised and extended, in March 2019 (see below).

UNI Global Union Top 10 Principles for Ethical AI (Dec 2017)
  1. Demand That AI Systems Are Transparent
  2. Equip AI Systems With an Ethical Black Box
  3. Make AI Serve People and Planet
  4. Adopt a Human-In-Command Approach
  5. Ensure a Genderless, Unbiased AI
  6. Share the Benefits of AI Systems
  7. Secure a Just Transition and Ensuring Support for Fundamental Freedoms and Rights
  8. Establish Global Governance Mechanisms
  9. Ban the Attribution of Responsibility to Robots
  10. Ban AI Arms Race
Drafted by UNI Global Union‘s Future World of Work these 10 principles for Ethical AI (set out here with full commentary) “provide unions, shop stewards and workers with a set of concrete demands to the transparency, and application of AI”.

Updated principles

Intel’s recommendation for Public Policy Principles on AI (October 2017)
  1. Foster Innovation and Open Development – To better understand the impact of AI and explore the broad diversity of AI implementations, public policy should encourage investment in AI R&D. Governments should support the controlled testing of AI systems to help industry, academia, and other stakeholders improve the technology.
  2. Create New Human Employment Opportunities and Protect People’s Welfare – AI will change the way people work. Public policy in support of adding skills to the workforce and promoting employment across different sectors should enhance employment opportunities while also protecting people’s welfare.
  3. Liberate Data Responsibly – AI is powered by access to data. Machine learning algorithms improve by analyzing more data over time; data access is imperative to achieve more enhanced AI model development and training. Removing barriers to the access of data will help machine learning and deep learning reach their full potential.
  4. Rethink Privacy – Privacy approaches like The Fair Information Practice Principles and Privacy by Design have withstood the test of time and the evolution of new technology. But with innovation, we have had to “rethink” how we apply these models to new technology.
  5. Require Accountability for Ethical Design and Implementation – The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms.
These principles were announced in a blog post by Naveen Rao (Intel VP AI) here.

Lords Select Committee 5 core principles to keep AI ethical (April 2018)
  1. Artificial intelligence should be developed for the common good and benefit of humanity.
  2. Artificial intelligence should operate on principles of intelligibility and fairness.
  3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.
  4. All citizens have the right to be educated to enable them to flourish mentally, emotionally and economically alongside artificial intelligence.
  5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.
These principles appear in the UK House of Lords Select Committee on Artificial Intelligence report AI in the UK: ready, willing and able? published in April 2019. The WEF published a summary and commentary here.

AI UX: 7 Principles of Designing Good AI Products (April 2018)
  1. Differentiate AI content visually – let people know if an algorithm has generated a piece of content so they can decide for themselves whether to trust it or not.
  2. Explain how machines think – helping people understand how machines work so they can use them better
  3. Set the right expectations – especially in a world full of sensational, superficial news about new AI technologies.
  4. Find and handle weird edge cases – spend more time testing and finding weird, funny, or even disturbing or unpleasant edge cases.
  5. User testing for AI products (default methods won’t work here).
  6. Provide an opportunity to give feedback.
These principles, focussed on the design of the User Interface (UI) and User Experience (UX), are from Budapest based company UX Studio.

The Toronto Declaration on equality and non-discrimination in machine learning systems (May 2018)The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems does not succinctly articulate ethical principles but instead presents arguments under the following headings to address concerns “about the capability of [machine learning] systems to facilitate intentional or inadvertent discrimination against certain individuals or groups of people”.
  1. Using the framework of international human rights law The right to equality and non-discrimination; Preventing discrimination, and Protecting the rights of all individuals and groups: promoting diversity and inclusion
  2. Duties of states: human rights obligations State use of machine learning systems; Promoting equality, and Holding private sector actors to account
  3. Responsibilities of private sector actors human rights due diligence
  4. The right to an effective remedy

Google AI Principles (June 2018)
  1. Be socially beneficial.
  2. Avoid creating or reinforcing unfair bias.
  3. Be built and tested for safety.
  4. Be accountable to people.
  5. Incorporate privacy design principles.
  6. Uphold high standards of scientific excellence.
  7. Be made available for uses that accord with these principles.
These principles were launched with a blog post and commentary by Google CEO Sundar Pichai here.

IBM’s 5 ethical AI principles (September 2018)
  1. Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes.
  2. Value alignment: AI should be designed to align with the norms and values of your user group in mind.
  3. Explainability: AI should be designed for humans to easily perceive, detect, and understand its decision process, and the predictions/recommendations. This is also, at times, referred to as interpretability of AI. Simply speaking, users have all rights to ask the details on the predictions made by AI models such as which features contributed to the predictions by what extent. Each of the predictions made by AI models should be able to be reviewed.
  4. Fairness: AI must be designed to minimize bias and promote inclusive representation.
  5. User data rights: AI must be designed to protect user data and preserve the user’s power over access and uses
For a full account read IBM’s Everyday Ethics for Artificial Intelligence here.

Microsoft Responsible bots: 10 guidelines for developers of conversational AI (November 2018)
  1. Articulate the purpose of your bot and take special care if your bot will support consequential use cases.
  2. Be transparent about the fact that you use bots as part of your product or service.
  3. Ensure a seamless hand-off to a human where the human-bot exchange leads to interactions that exceed the bot’s competence.
  4. Design your bot so that it respects relevant cultural norms and guards against misuse.
  5. Ensure your bot is reliable.
  6. Ensure your bot treats people fairly.
  7. Ensure your bot respects user privacy.
  8. Ensure your bot handles data securely.
  9. Ensure your bot is accessible.
  10. Accept responsibility.
Microsoft’s guidelines for the ethical design of ‘bots’ (chatbots or conversational AIs) are fully described here.

CEPEJ European Ethical Charter on the use of artificial intelligence (AI) in judicial systems and their environment, 5 principles (February 2019)
  1. Principle of respect of fundamental rights: ensuring that the design and implementation of artificial intelligence tools and services are compatible with fundamental rights.
  2. Principle of non-discrimination: specifically preventing the development or intensification of any discrimination between individuals or groups of individuals.
  3. Principle of quality and security: with regard to the processing of judicial decisions and data, using certified sources and intangible data with models conceived in a multi-disciplinary manner, in a secure technological environment.
  4. Principle of transparency, impartiality and fairness: making data processing methods accessible and understandable, authorising external audits.
  5. Principle “under user control”: precluding a prescriptive approach and ensuring that users are informed actors and in control of their choices.
The Council of Europe ethical charter principles are outlined here, with a link to the ethical charter istelf.

Women Leading in AI (WLinAI) 10 recommendations (February 2019)
  1. Introduce a regulatory approach governing the deployment of AI which mirrors that used for the pharmaceutical sector.
  2. Establish an AI regulatory function working alongside the Information Commissioner’s Office and Centre for Data Ethics – to audit algorithms, investigate complaints by individuals,issue notices and fines for breaches of GDPR and equality and human rights law, give wider guidance, spread best practice and ensure algorithms must be fully explained to users and open to public scrutiny.
  3. Introduce a new Certificate of Fairness for AI systems alongside a ‘kite mark’ type scheme to display it. Criteria to be defined at industry level, similarly to food labelling regulations.
  4. Introduce mandatory AIAs (Algorithm Impact Assessments) for organisations employing AI systems that have a significant effect on individuals.
  5. Introduce a mandatory requirement for public sector organisations using AI for particular purposes to inform citizens that decisions are made by machines, explain how the decision is reached and what would need to change for individuals to get a different outcome.
  6. Introduce a ‘reduced liability’ incentive for companies that have obtained a Certificate of Fairness to foster innovation and competitiveness.
  7. To compel companies and other organisations to bring their workforce with them – by publishing the impact of AI on their workforce and offering retraining programmes for employees whose jobs are being automated.
  8. Where no redeployment is possible, to compel companies to make a contribution towards a digital skills fund for those employees
  9. To carry out a skills audit to identify the wide range of skills required to embrace the AI revolution.
  10. To establish an education and training programme to meet the needs identified by the skills audit, including content on data ethics and social responsibility. As part of that, we recommend the set up of a solid, courageous and rigorous programme to encourage young women and other underrepresented groups into technology.
Presented by the Women Leading in AI group at a meeting in parliament in February 2019, this report in Forbes by Noel Sharkey outlines both the group, their recommendations, and the meeting.

The NHS’s 10 Principles for AI + Data (February 2019)
  1. Understand users, their needs and the context
  2. Define the outcome and how the technology will contribute to it
  3. Use data that is in line with appropriate guidelines for the purpose for which it is being used
  4. Be fair, transparent and accountable about what data is being used
  5. Make use of open standards
  6. Be transparent about the limitations of the data used and algorithms deployed
  7. Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision
  8. Generate evidence of effectiveness for the intended use and value for money
  9. Make security integral to the design
  10. Define the commercial strategy
These principles are set out with full commentary and elaboration on Artificial Lawyer here.

IEEE General Principles of Ethical Autonomous and Intelligent Systems (A/IS) (March 2019)
  1. Human Rights: A/IS shall be created and operated to respect, promote, and protect internationally recognized human rights.
  2. Well-being: A/IS creators shall adopt increased human well-being as a primary success criterion for development.
  3. Data Agency: A/IS creators shall empower individuals with the ability to access and securely share their data to maintain people’s capacity to have control over their identity.
  4. Effectiveness: A/IS creators and operators shall provide evidence of the effectiveness and fitness for purpose of A/IS.
  5. Transparency: the basis of a particular A/IS decision should always be discoverable.
  6. Accountability: A/IS shall be created and operated to provide an unambiguous rationale for all decisions made.
  7. Awareness of Misuse: A/IS creators shall guard against all potential misuses and risks of A/IS in operation.
  8. Competence: A/IS creators shall specify and operators shall adhere to the knowledge and skill required for safe and effective operation.
These amended and extended general principles form part of Ethical Aligned Design 1st edition, published in March 2019. For an overview see pdf here.

Ethical issues arising from the police use of live facial recognition technology (March 2019)
9 ethical principles relate to: public interest, effectiveness, the avoidance of bias and algorithmic justice, impartiality and deployment, necessity, proportionality, impartiality, accountability, oversight, and the construction of watchlists, public trust, and cost effectiveness.

Reported here the UK government’s independent Biometrics and Forensics Ethics Group (BFEG) published an interim report outlining nine ethical principles forming a framework to guide policy on police facial recognition systems.

Floridi and Clement Jones’ five principles key to any ethical framework for AI (March 2019)
  1. AI must be beneficial to humanity.
  2. AI must also not infringe on privacy or undermine security.
  3. AI must protect and enhance our autonomy and ability to take decisions and choose between alternatives.
  4. AI must promote prosperity and solidarity, in a fight against inequality, discrimination, and unfairness
  5. We cannot achieve all this unless we have AI systems that are understandable in terms of how they work (transparency) and explainable in terms of how and why they reach the conclusions they do (accountability).
Luciano Floridi and Lord Tim Clement Jones set out, here in the New Statesman, these 5 general ethical principles for AI, with additional commentary.

The European Commission’s High Level Expert Group on AI Ethics Guidelines for Trustworthy AI (April 2019)
  1. Human agency and oversight AI systems should support human autonomy and decision-making, as prescribed by the principle of respect for human autonomy.
  2. Technical robustness and safety A crucial component of achieving Trustworthy AI is technical robustness, which is closely linked to the principle of prevention of harm.
  3. Privacy and Data governance Closely linked to the principle of prevention of harm is privacy, a fundamental right particularly affected by AI systems.
  4. Transparency This requirement is closely linked with the principle of explicability and encompasses transparency of elements relevant to an AI system: the data, the system and the business models.
  5. Diversity, non-discrimination and fairness In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle.
  6. Societal and environmental well-being In line with the principles of fairness and prevention of harm, the broader society, other sentient beings and the environment should be also considered as stakeholders throughout the AI system’s life cycle.
  7. Accountability The requirement of accountability complements the above requirements, and is closely linked to the principle of fairness
For more detail on each of these principles follow the links above.

Published on 8 April 2019, the EU HLEG AI ethics guidelines for trustworthy AI are detailed in full here.

Draft core principles of Australia’s Ethics Framework for AI (April 2019)
  1. Generates net-benefits. The AI system must generate benefits for people that are greater than the costs.
  2. Do no harm. Civilian AI systems must not be designed to harm or deceive people and should be implemented in ways that minimise any negative outcomes.
  3. Regulatory and legal compliance. The AI system must comply with all relevant international, Australian Local, State/Territory and Federal government obligations, regulations and laws.
  4. Privacy protection. Any system, including AI systems, must ensure people’s private data is protected and kept confidential plus prevent data breaches which could cause reputational, psychological, financial, professional or other types of harm.
  5. Fairness. The development or use of the AI system must not result in unfair discrimination against individuals, communities or groups. This requires particular attention to ensure the “training data” is free from bias or characteristics which may cause the algorithm to behave unfairly.
  6. Transparency & Explainability. People must be informed when an algorithm is being used that impacts them and they should be provided with information about what information the algorithm uses to make decisions.
  7. Contestability. When an algorithm impacts a person there must be an efficient process to allow that person to challenge the use or output of the algorithm.
  8. Accountability. People and organisations responsible for the creation and implementation of AI algorithms should be identifiable and accountable for the impacts of that algorithm, even if the impacts are unintended.
These draft principles are detailed in Artificial Intelligence Australia’s Ethics Framework A Discussion Paper. This comprehensive paper includes detailed summaries of many of the frameworks and initiatives listed above, together with some very useful case studies.

References

[1] Asimov, Isaac (1950): Runaround, in I, Robot, (The Isaac Asimov Collection ed.) Doubleday. ISBN 0-385-42304-7.
[2] Murphy, Robin; Woods, David D. (2009): Beyond Asimov: The Three Laws of Responsible Robotics. IEEE Intelligent systems. 24 (4): 14–20.
[3] Margaret Boden et al (2017): Principles of robotics: regulating robots in the real world
Connection Science. 29 (2): 124:129.
[4] Tony Prescott and Michael Szollosy (eds.) (2017): Ethical Principles of Robotics, Connection Science. 29 (2) and 29 (3).

Wednesday, March 20, 2019

The Tea test of robot intelligence

Here's a test for a general purpose robot: to pass the test it must go into someone's kitchen (previously unseen) and make them a cup of tea. When I give talks I surprise people by explaining that despite remarkable progress there isn't a robot on the planet that could walk (or roll) into your kitchen and make you a cup of tea. It's my Tea test of robot intelligence; no robot would pass the test (and I'll wager) will not for some time. It seems like such a straightforward thing; most humans over the age of 12 can do it.

Of course there are quite a few YouTube videos of robots making tea. But like this one from 30 years ago, they pretty much all require the everything to be in exactly the right place for the robot.



So why is it so hard?

To understand, imagine yourself in a house you've never been in before. Maybe it's a neighbour's house and she's unwell - so you call round to help out. Perhaps she's ill in bed. Let's assume that you know where the kitchen is.

The first thing you need to do is locate the kettle. Not so hard for a human because you know in general what kettles look like, and even if you've never seen your neighbour's kettle before there's a pretty good chance you will find it. Of course you have other important prior knowledge - you know that (at least in British kitchens) kettles are used all the time and generally kept on a work surface -not hidden away in a cupboard.

While looking for the kettle you will have also found the sink, and next you do something really difficult (for a robot): you pick up and take the kettle to the sink, open its lid, position it under the cold water tap, then turn on the tap. You don't leave the tap running for long because you don't want to overfill the kettle, but luckily you're a pretty good judge of how much water is enough.

Then while waiting for the kettle to boil you will do something even more remarkable: you will look for a cup and a tea bag. Again your prior knowledge helps here. You know what cups look like and generally where they are stored - there may even be one on the draining board by the sink. You also know what kind of jar or packaging tea bags are found in, and you have the considerable dexterity to take one tea bag and place it in the cup near the kettle.

Breaking the task down like this really brings home the point that this is an extraordinarily difficult thing for a robot to do. And of course there's more: the robot must safely and carefully pour the boiling water from the kettle into the cup - and importantly sense when the cup is full enough to leave room for milk*.

And even then the task is by no means complete. A robot would then need to locate the fridge, open its door, identify and take out the milk (which might be either a carton, glass or plastic bottle) and bring the milk to the work surface. The robot must then judge how long to leave the tea bag in (you of course will have asked your neighbour whether she prefers her tea weak or strong). 

The robot then needs to do another difficult thing: find and pick up a tea spoon and carefully extract the tea bag from the cup. Then add the milk - which of course requires first opening whatever the milk is contained in - especially hard is a cardboard carton where the robot might have to make an opening if it isn't already open (sometimes not so easy for humans). And unscrewing the top of a plastic carton isn't much easier. Pouring a dash of milk isn't easy either.

I could go on and explain the difficulty a robot then faces of picking up and carrying the tea to your neighbour.

One of the hard lessons of Artificial Intelligence that the things we thought would be difficult 60 years ago have turned out to be (relatively) easy, like playing chess, but the things we thought would be easy - like making a cup of tea - are still far from solved. Chess playing AIs are examples of narrow (single function) AI, whereas making a cup of tea in an unknown kitchen requires a wider set of general skills.

*I am British after all!

Thursday, February 21, 2019

First automated robot assembly

This month saw the first important milestone toward Autonomous Robot Evolution: the Bristol and York team demonstrated automated assembly of a complete working robot, from evolved and 3D printed parts. In essence we demonstrated one robot assembling another.

Our evolved robots consist of 3 elements:

* pre-designed modules which we call organs (for sensors, actuators, controllers, etc),
* an evolved and 3D printed skeleton, and
* cables (with 3.5mm jack plugs) to connect the organs and the controller.

Note that the organs are not evolved but hand designed; the rationale for this approach is outlined here.

Here are 3 basic organs:

On the left is a sensor, in the middle a controller and on the right a motor + wheel assembly.









And here are screenshots from the video showing the steps involved:











Step 1 shows the skeleton in the process of 3D printing. In step 2 the skeleton has been manually moved from the print bed onto the assembly area: note the organ and cable bank at the back of the assembly area. Step 3 shows the robot arm inserting the organs into the skeleton. Step 4 shows the robot arm connecting the cables. Step 5 shows the wheels being manually added, and in step 6 the robot is complete. Step 7 shows the assembled robot powered and running.

And here is the complete video:



Our aim is of course to automate the whole process and right now the team are working on the two problems of (1) how to remove the 3D printed skeleton from the print bed ready for transfer to the assembly area, and (2) how best to secure the skeleton in the assembly area ready for the processes outlined above.



Related blog posts:

Autonomous Robot Evolution: from cradle to grave (July 2018)
Autonomous Robot Evolution: first challenges (Oct 2018)

Sunday, January 27, 2019

When Robots Tell Each Other Stories: The Emergence of Artificial Fiction

When I wrote about story-telling robots nearly 7 years ago I had no idea how we could actually build robots that can tell each other stories. Now I believe I do, and my paper setting out how has just been published in a new volume called Narrating Complexity. You can find a pdf online here.

The book emerged from a hugely interesting series of workshops, led by Richard Walsh and Susan Stepney, which brought together several humanities disciplines including narratology, with complexity scientists, systems biologists and a roboticist (me). It was at one of those workshops that I realised that simulation-based internal models - the focus of much of my recent work - could form the basis for story-telling.

To recap: a simulation-based internal model is a computer simulation of a robot and its environment, including other robots, inside itself. Like animals all robots have a set of next possible actions, but unlike animals (and especially humans) robots have only a small repertoire of actions. With an internal model a robot can predict what might happen (in its immediate future) for each of those next possible actions. I call this model a consequence engine because it gives the robot a powerful way of predicting the consequences of its actions, for both itself and other robots.

So, how can we use the consequence engine to make story-telling robots?

When the robot runs it's consequence engine it is asking itself a 'what if' question; 'what if I turned left?' or, 'what if I just stand here?'. Some researchers have called a simulation-based internal model a 'functional imagination' and it's not a bad metaphor. Our robot 'imagines' what might happen in different circumstances. And when the robot has imagined something it has a kind of internal narrative: 'if I turn left I will likely crash into the wall'. In a way the robot is telling itself a story about something that might happen (in Dennett's conceptual Tower-of-Generate-and-Test the robot is a Popperian creature).

Now consider the possibility that the robot converts that internal narrative into speech, and literally speaks it out loud. With current speech synthesis technology that should be relatively easy to do. Here is a diagram showing this.

The blue box on the left is a simplified version of the consequence engine; it's the cognitive machinery that allows the robot to predict the consequences of a particular action. For an outline of how it works there's a description in the paper.

Another robot (B) is equipped with exactly the same cognitive machinery as robot A, and - as shown below robot B listens to robot A's 'story' (using speech recognition), interprets that story as an action and a consequence, which it 'runs' in its consequence engine. In effect robot B 'imagines' robot A's story. It 'imagines' turning left and crashing into the wall - even though it might not be standing near a wall to its left.

The new idea here is that the listener robot (B) converts the story it has heard into a 'what if' question, then 'runs' it in its own consequence engine. In a sense A has invited B to imagine itself in A's shoes. Although compared with the stories we humans tell each other, A's story is trivial, it does I suggest have all the key elements. And of course A and B are not limited to fictional stories: A could - just as easily - recount something that has actually happened to it, like 'I turned right to avoid crashing into the wall'.

You may be wondering 'ok but where is the meaning? Surely B cannot really understand A's simple stories..?' Here I am going to stick my neck out and suggest that the process of re-imagining is what understanding is. Of course you and I can imagine a vast range of things, including situations that no human has ever (or perhaps could ever) experience; Roy Batty's famous line "I've seen things you people wouldn't believe. Attack ships on fire off the shoulder of Orion..." comes to mind.

In contrast our robots have a profoundly limited imagination; their world (both real and imagined) contains only the objects and hazards of their immediate environment and they are capable only of imagining next possible actions and the immediate consequences of those actions. And that limited imagination does have the simple physics of collisions built in. But I contend that - within the constraints of that very limited imagination - our robots can properly be said to 'understand' each other.

But perhaps I'm getting ahead of myself, given that we haven't actually run the experiments yet.


Friday, October 26, 2018

Autonomous Robot Evolution: first challenges

Just spent an exciting two days at the first 'all hands meeting' of our new EPSRC funded project: Autonomous Robot Evolution (ARE): from cradle to grave (read here for an introduction). There are eleven of us in total: 4 postdocs (one from each partner university), 2 PhD students, 1 technician, and the four seniors (co-investigators).


Much of the meeting was spent discussing the fundamental (and tough) questions of (1) how we design the genotype, and the mapping between genotype and phenotype, and (2) how exactly we will physically create the robots. 

Let me outline where we are going with these two questions.

1. Genotype-phenotype mapping. As I explained here most evolutionary robotics research has, to date, used a direct mapping approach, in which each parameter of a robot's genome specifies one feature of the real robot (phenotype). For the robot's controller those parameters might be the weights of the robot's artificial neural network, and for the robot's body they might each specify some physical characteristic of the body (such as the length of leg segments in the illustration here). Of course in biology the mapping is indirect; to put it very simply, the genome determines how an organism develops, rather than the organism itself. And because the expression of genes is affected by the environment in which the organism is developing, identical genotypes give rise to non-identical phenotypes (albeit very similar as with identical twins); this is called phenotypic plasticity.

Because we are looking for both biological plausibility and phenotypic plasticity in this project, we have decided on an indirect mapping from genotype to phenotype. Exactly how this will work is still to be figured out, but I feel sure the genotype will need to be split into two parts: one for the robot's controller and the other for its body, and I rather suspect the mapping will be different for those two parts.

2. How to create the robots. In ARE we will adopt the engineering approach 'in which the process is embodied but takes place in a kind of evolution factory'. Now, in theory we could evolve every part of a robot's hardware, listed below.


But in practice this would be impossible; evolving any one of these subsystems would be a research project in its own right, and we're not attempting to re-run the whole of evolution in this project. Instead we will be designing and fabricating discrete modules for sensing, signalling, actuation and control, that we call 'organs'. So what will we actually evolve? It will be:
  • the number, type and position of sensing, signalling and actuation subsystems, and
  • the 3D shape of the robot’s physical structure or chassis.
At this point you're probably thinking: hang on a minute - if you're designing the organs then what's left to evolve? It's a fair question, but in fact evolution will still have huge freedom to choose which and how many organs and where to position them in the body. And when we bear in mind that we will be co-evolving the robot's controller then the space of all possible phenotypes is vast. Of course we may need to introduce some constraints: for instance that there must be at least one controller. But in general we want as few constraints as possible so that evolution is free to explore the phenotypic space to find the best robots, bearing in mind that we will be breeding robots to be able to operate in challenging environments.

And I would argue that in specifying and designing organs we have not compromised on biological plausibility at all. Biological evolution is, after all, highly modular. Most of the organs (and systems of organs) in your body were evolved long before hominids: livers, hearts, eyes, noses, vascular systems, digestive systems, central nervous systems; all of those evolved in early vertebrates (with some repurposing along the way*). Architecturally humans have a huge amount in common with all mammals. My dog is not so different from me (and in some aspects superior: her senses of hearing and smell are much better); our key differences are in morphology and intelligence. These are the two properties that we will be exploring through co-evolution in this project.

So, in the coming few months we have some big mechanical and electronic engineering challenges in this part of the project. Here are just a few:
  • experiment with 3D printing materials and print heads,
  • specify, design and prototype the organs (including their packaging and interconnects),
  • decide on how to power the organs (i.e. a single central power organ, or a battery per organ) and figure out how to re-charge the batteries,
  • determine how to connect the organs with the controller and each other (i.e. with wires or wirelessly), and
  • work out the best way of picking and placing organs within the robot as it is 3D printed.
Challenging? For sure, but we have a wonderful team.

*See Neil Shubin's wonderful book Your Inner Fish.

Related blog posts:

Friday, September 28, 2018

Experiments in Artificial Theory of Mind

Since setting out my initial thoughts on robots with simulation-based internal models about 5 years ago - initially in the context of ethical robots - I've had a larger ambition for these models: that they might provide us with a way of building robots with artificial theory of mind - something I first suggested when I outlined the consequence engine 4 years ago.

Since then we've been busy experimentally applying our consequence engine in the lab, in a range of contexts including ethics, safety and imitation, giving me little time to think about theory of mind. But then, in January 2017 I was contacted by Antonio Chella, inviting me to submit a paper to a special issue on Consciousness in Humanoid Robots. After some hesitation on my part and encouragement on Antonio's I realised that this was a perfect opportunity.

Of course theory of mind is not consciousness but it is for sure deeply implicated. And, as I discovered while researching the paper, the role of theory of mind in consciousness (or, indeed of consciousness in theory of mind) is both unclear and controversial. So, this paper, written in the autumn of 2017, submitted January 2018, and - after tough review and major revisions - accepted in June 2018, is my first (somewhat tentative) contribution to the machine consciousness literature.

Experiments in Artificial Theory of Mind: From Safety to Story-Telling, advances the hypothesis that simulation-based internal models offer a powerful and realisable, theory-driven basis for artificial theory of mind.

Here is the abstract
Theory of mind is the term given by philosophers and psychologists for the ability to form a predictive model of self and others. In this paper we focus on synthetic models of theory of mind. We contend firstly that such models—especially when tested experimentally—can provide useful insights into cognition, and secondly that artificial theory of mind can provide intelligent robots with powerful new capabilities, in particular social intelligence for human-robot interaction. This paper advances the hypothesis that simulation-based internal models offer a powerful and realisable, theory-driven basis for artificial theory of mind. Proposed as a computational model of the simulation theory of mind, our simulation-based internal model equips a robot with an internal model of itself and its environment, including other dynamic actors, which can test (i.e., simulate) the robot’s next possible actions and hence anticipate the likely consequences of those actions both for itself and others. Although it falls far short of a full artificial theory of mind, our model does allow us to test several interesting scenarios: in some of these a robot equipped with the internal model interacts with other robots without an internal model, but acting as proxy humans; in others two robots each with a simulation-based internal model interact with each other. We outline a series of experiments which each demonstrate some aspect of artificial theory of mind.
For an outline of the work of the paper see the slides below, presented at the SPANNER workshop in York a few weeks ago.



In fact all of the experiments outlined here have been described in some detail in previous blog posts (although not in the context of artificial theory of mind):
  1. The Corridor experiment 
  2. The Pedestrian experiment
  3. The Ethical robot experiments: with e-puck robots and with NAO robots
  4. Experiments on rational imitation (the imitation of goals)
  5. Story-telling robots**
The thing that ties all of these experiments together is that they all make use of a simulation-based internal model (which we call a consequence engine), which allows our robot to model and hence predict the likely consequences of each of its next possible actions, both for itself and for the other dynamic actors it is interacting with. In some of the experiments those actors are robots acting as proxy humans, so those experiments (in particular the corridor and ethical robot experiments) are really concerned with human-robot interaction.

Theory of mind is the ability to form a predictive model of ourselves and others; it's the thing that allows us to infer the beliefs and intentions of others. Curiously there are two main theories of mind: the 'theory theory' and the 'simulation theory'. The theory theory (TT) holds that one intelligent agent’s understanding of another’s mind is based on innate or learned rules, sometimes known as folk psychology. In TT these hidden rules constitute a 'theory' because they can be used to both explain and make predictions about others’ intentions.  The simulation theory (ST) instead holds that “we use our own mental apparatus to form predictions and explanations of someone by putting ourselves in the shoes of another person and simulating them” (Michlmayr, 2002).

When we hold our simulation-based internal model up against the simulation theory of mind, the two appear to mirror each other remarkably well. If a robot has a simulation of itself inside itself then it can explain and predict the actions of both itself, and others like itself by using its simulation-based internal model to model them. Thus we have an embodied computational model of theory of mind, in short artificial theory of mind.

So, what properties of theory of mind (ToM) are demonstrated in our five experiments?

Well, the first thing to note is that not all experiments implement full ST. In the corridor, pedestrian and ethical robot experiments robots predict their own actions using the simulation-based internal model, i.e. ST, but use a much simpler TT to model the other robots; we use a simple ballistic model for those other robots (i.e. by assuming the robot will continue to move at the speed and direction it is currently moving). Thus I describe these experiments as ST (self) + TT (other), or just ST+TT for short. I argue that this hybrid form of artificial ToM is perfectly valid, since you and I clearly don't model strangers we are trying to avoid in a crowded corridor as anything other than people moving in a particular direction and speed. We don't need to try and intuit their state of mind, only where they are going.

The rational imitation and story-telling experiments do however, use ST for both self and other, since a simple TT will not allow an imitating robot to infer the goals of the demonstrating robot, nor is it sufficient to allow a listener robot to 'imagine' the story told by the storytelling robot.

The table below summarises these differences and highlights the different aspects of theory of mind demonstrated in each of the five experiments.

*Theory Mode: ST (self) + TT/ST (other)

An unexpected real-world use for the approach set out in this paper, is to allow robots to explain themselves. I believe explainability will be especially important for social robots, i.e. robots designed to interact with people. Let me explain by quoting two paragraphs from the paper.

A major problem with human-robot interaction is the serious asymmetry of theory of mind. Consider an elderly person and her care robot. It is likely that a reasonably sophisticated near-future care robot will have a built-in (TT) model of an elderly human (or even of a particular human). This places the robot at an advantage because the elderly person has no theory of mind at all for the robot, whereas the robot has a (likely limited) theory of mind for her. Actually the situation may be worse than this, since our elderly person may have a completely incorrect theory of mind for the robot, perhaps based on preconceptions or misunderstandings of how the robot should behave and why. Thus, when the robot actually behaves in a way that doesn’t make sense to the elderly person, her trust in the robot will be damaged and its effectiveness diminished.

The storytelling model proposed here provides us with a powerful mechanism for the robot to be able to generate explanations for its actual or possible actions. Especially important is that the robot’s user should be able to ask (or press a button to ask) the robot to explain “why did you just do that?” Or, pre-emptively, to ask the robot questions such as “what would you do if I fell down?” Assuming that the care robot is equipped with an autobiographical memory, the first of these questions would require it to re-run and narrate the most recent action sequence to be able to explain why it acted as it did, i.e., “I turned left because I didn’t want to bump into you.” The second kind of pre-emptive query requires the robot to interpret the question in such a way it can first initialize its internal model to match the situation described, run that model, then narrate the actions it predicts it would take in that situation. In this case the robot acts first as a listener, then as the narrator (see slide 18 above). In this way the robot would actively assist its human user to build a theory-of-mind for the robot.


**This one remains, for the time-being, a thought experiment.

Reference:

Michlmayr, M. (2002). Simulation Theory Versus Theory Theory: Theories Concerning the Ability to Read Minds. Master’s thesis, Leopold-Franzens- Universität Innsbruck.