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AI Ethics Guidelines from Diverse Groups: The Consensus?

We are yet to see a holistic framework for the ethical development of artificial intelligence applications that can be applied to every industry in every country around the world. A lot of work is being done by corporate entities as well as academia, not to mention special-interest groups that warn of the dangers of uncontrolled […]

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We are yet to see a holistic framework for the ethical development of artificial intelligence applications that can be applied to every industry in every country around the world. A lot of work is being done by corporate entities as well as academia, not to mention special-interest groups that warn of the dangers of uncontrolled AI proliferation. Nevertheless, we’re still a long way from a consensus on what it should involve.

This snapshot of the views of various entities with regard to AI principles could offer a clue to what is really missing in our quest for AI governance of the future.

Google

Despite the company disbanding its Advanced Technology External Advisory Council (ATEAC) after only one week due to internal controversy, Google already has the recipe for proper AI research. As listed in their blog, here are the key points:

“We will assess AI applications in view of the following objectives. We believe that AI should:

  • Be socially beneficial.
  • Avoid creating or reinforcing unfair bias.
  • Be built and tested for safety.
  • Be accountable to people.
  • Incorporate privacy design principles.
  • Uphold high standards of scientific excellence.
  • Be made available for uses that accord with these principles.”

While there are a lot of good points in there, a lot of questions crawl out of the woodwork when you talk about weaponizing AI. For example, where is their stand against using AI in warfare or other harmful acts like cyber attacks? As part of that last point above, the blog does say that they will evaluate the “primary purpose and use” and see if it is “related to or adaptable to a harmful use,” but little more than that.

Microsoft

Microsoft has a slightly different set of beliefs, some of which align with Google’s, but only in the broader sense. Again, there’s nothing that directly mentions ways to tackle the AI weaponization issue.

“Designing AI to be trustworthy requires creating solutions that reflect ethical principles that are deeply rooted in important and timeless values.

  • Fairness: AI systems should treat all people fairly
  • Inclusiveness: AI systems should empower everyone and engage people
  • Reliability & Safety: AI systems should perform reliably and safely
  • Transparency: AI systems should be understandable
  • Privacy & Security: AI systems should be secure and respect privacy
  • Accountability: AI systems should have algorithmic accountability”

To be fair, neither company has the ability to control what happens at the international level, so it’s understandable that their AI tenets are limited to positive applications. Not condonable, but understandable. So let’s see where the European Union stands on AI principles.

European Union

The EU’s stance is a lot more inclusive in the press release it issued earlier this month, and it does account for various aspects including how AI can and cannot be applied.

“AI should respect all applicable laws and regulations, as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:

  • Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.
  • Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.
  • Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.
  • Transparency: The traceability of AI systems should be ensured.
  • Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.
  • Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.
  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.”

This looks a lot closer to what we all want to see, and the very first item covers the misuse of AI, albeit in a very generic way. However, it does bring up “societal and environmental well-being”, which is clearly an allusion to not using AI to disrupt social and environmental balances. It looks like the EU has mulled over this for a longer time than Google or Microsoft.

But it’s the Future of Life Institute that clearly outlines and addresses the dangers of uncontrolled AI development.

Future of Life Institute’s Asilomar Principles

These guidelines have been in place for the past two years, and so far offer the only viable base for a framework of any sort.

“Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.

Ethics and Values

  • Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
  • Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
  • Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
  • 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.
  • 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.
  • Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
  • 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.
  • Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
  • Shared Benefit: AI technologies should benefit and empower as many people as possible.
  • Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
  • Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.
  • Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.
  • AI Arms Race: An arms race in lethal autonomous weapons should be avoided.”

As you can see, this is a lot more comprehensive, and it looks like we’re getting there. The only thing that’s missing is the involvement of the government, which is crucial for any of this to work. This is addressed by the guidelines arrived at by attendees of the New Work Summit that was hosted by The New York Times earlier this year.

New Work Summit

“Attendees at the New Work Summit, hosted by the New York Times, worked in groups to compile a list of recommendations for building and deploying ethical artificial intelligence:

  • Transparency: Companies should be transparent about the design, intention and use of their A.I. technology.
  • Disclosure: Companies should clearly disclose to users what data is being collected and how it is being used.
  • Privacy: Users should be able to easily opt out of data collection.
  • Diversity: A.I. technology should be developed by inherently diverse teams.
  • Bias: Companies should strive to avoid bias in A.I. by drawing on diverse data sets.
  • Trust: Organizations should have internal processes to self-regulate the misuse of A.I. Have a chief ethics officer, ethics board, etc.
  • Accountability: There should be a common set of standards by which companies are held accountable for the use and impact of their A.I. technology.
  • Collective governance: Companies should work together to self-regulate the industry.
  • Regulation: Companies should work with regulators to develop appropriate laws to govern the use of A.I.
  • “Complementarity”: Treat A.I. as tool for humans to use, not a replacement for human work.

After looking at this final list of guidelines, we’re still seeing a gap in how these issues will be addressed at various levels. The New Work Summit does cover collective governance and regulations, but fails to mention that regulatory bodies need a proper framework by which to guide the development of AI. What’s missing is that nobody is telling the government what it needs to do, and that’s the weakest link in the chain right now.

The America AI Initiative executive order signed by Trump earlier this year are as lacking of government accountability as the EU’s guidelines. Everybody seems to love telling everybody else what they should do, but offer very vague support for these initiatives. Trump’s order mentions nothing of where government agencies will get additional funding, but rather encourages them to reallocate spending. Not an easy pill for bureaucracy to swallow.

Governments in countries like the United States should be the ones taking the first step. They’re the ones who should be taking this bull by the horns and wrestling it to the ground. If AI is to remain subservient to humans, this is where it starts.

Unfortunately, that would require a tectonic shift in government policy itself, so don’t hold your breath. We’ll continue to muddle through for the next few years until a serious transgression by an AI entity brings everything to the forefront and makes it an urgent matter of international interest.

The question is, are we going to repeat history by waiting for something bad to happen before we react? To analogize, do we need a major global incident like WWII in order to set up a NATO? Can’t we be more proactive and setup a failsafe now when AI is still in its nascency?

These are the hard questions governments must answer because such a massive initiative requires financial and other resources that only governments can provide and control. There won’t be any lack of participants, but the participants cannot host the show.

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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