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Stop, look and listen to the people you want to help

I like to take things slow. Take it slowly and get it right first time, one participant said, but was quickly countered by someone else around the table: But Im impatient, I want to see the benefits now. This exchange neatly captures many of the conversations I heard at DeepMind Healths recent Collaborative Listening Summit. It also represents, in laymans terms, the debate that tech thinkers and policy-makers are having right now about the future of artificial intelligence.The Collaborative Listening Summit brought together members of the public, patient representatives and stakeholder, and was facilitated by Ipsos MORI. The objective of the Summit: to explore how principles, co-created in earlier events with the public, patients and stakeholders, should govern DeepMind Healths operating practices and engagement with the NHS. These principles ranged from the technical for example, how evidence should inform DeepMinds practice to the societal for example, operating in the best interests of society.The challenge of how technology companies and the NHS should interact has had many of us, including myself, cautious about the risk of big technology firms leveraging their finance and power over an NHS that is under seemingly endless pressure.

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‘I like to take things slow. Take it slowly and get it right first time,’ one participant said, but was quickly countered by someone else around the table: ‘But I’m impatient, I want to see the benefits now.’ This exchange neatly captures many of the conversations I heard at DeepMind Health’s recent Collaborative Listening Summit. It also represents, in layman’s terms, the debate that tech thinkers and policy-makers are having right now about the future of artificial intelligence.

The Collaborative Listening Summit brought together members of the public, patient representatives and stakeholder, and was facilitated by Ipsos MORI. The objective of the Summit: to explore how principles, co-created in earlier events with the public, patients and stakeholders, should govern DeepMind Health’s operating practices and engagement with the NHS. These principles ranged from the technical – for example, how evidence should inform DeepMind’s practice – to the societal – for example, operating in the best interests of society.

The challenge of how technology companies and the NHS should interact has had many of us, including myself, cautious about the risk of big technology firms leveraging their finance and power over an NHS that is under seemingly endless pressure. Despite our desire to see the NHS become more agile and innovative, the ‘move fast and break things’ mentality of big tech is something that the public and policy-makers are rightly wary of when lives are at stake.

These fears usually manifest themselves in headlines about protecting patient data, but the issues run far deeper than this. For example, algorithms have an increasingly large part to play in our everyday lives, deciding which film we might like to watch next or the adverts we see online, but undergo relatively little scrutiny. In health care specifically, algorithms are beginning to augment existing services, with NHS England predicting that algorithms will soon handle one in three NHS 111 enquiries. Algorithms currently in place are relatively simple, and they need to be tested to ensure they are safe and effective. As the technology acquires greater decision-making capability, testing will become more challenging and more important. Frankly, regulators and policy-makers are still getting up to speed with these issues, so a cautious approach to regulation is sensible.

There is evidently a need for policy-makers to further deepen their understanding of what rules of engagement should be for tech companies. Initiatives like the government’s new Centre for Data Ethics and Innovation, intended to promote ethical data innovation, provide a platform for this, as do the trailblazing efforts of the biomedical research community. However, if recent public trust traps like GM foods and care.data and even the Royal Free/DeepMind data-sharing agreement are to be avoided big tech companies must themselves take responsibility for understanding what their relationship with NHS organisations should be.

DeepMind’s past attempts to engage have been subject to some criticism and show how challenging, yet important, it is to get dialogue with the public right. However, I was reassured that the Summit was designed to impart understanding; researching what people think about an issue where DeepMind staff themselves are on uncertain ground. Of course, for this to make any difference, the research needs to be applied – and that means practising the principles in the real world.

Principles such as ‘do what is best for society’ might seem trite and obvious especially as, in the real world, contracts may be put above ethics, as participants at the event rightly pointed out. So, if DeepMind and other big tech companies are to hold themselves to ethical principles around testing, transparency and societal benefit, they must show that these principles translate into ethical business practice. This will mean not working with NHS organisations where they feel they cannot hold themselves to these principles.

And this will get harder as time goes on – currently there is more than enough interest in machine learning to keep DeepMind and its competitors busy. But as these technologies come on-stream and there is market share to be gained, it will become more and more tempting for DeepMind to break these principles, especially as other technology companies eye up the NHS for size. This is where policy re-enters the picture: data ethics initiatives need to catch up with this work and encourage other companies to undertake robust research with the public. After all, the NHS cannot become a world leader in artificial intelligence if doctors and managers cannot look patients in the eye and assure them that their data is not being used in ways that are contrary to their own values.

Participants at the workshop were cautious, especially around the need to safeguard their data, but they could also see the benefits that new technologies could have in terms of saving time and lives. Tech companies should not be scared of engaging with the public – but they must listen to the people whose lives they claim to be bettering.

Source: https://deepmind.com/blog/article/harry-evans-collaborative-listening

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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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