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AI, Machine Learning Playing Important Role in Fighting COVID-19

By AI Trends Staff AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads. […]

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Researchers are employing AI to track the spread of COVID-19; a French startup is distributing accurate information to the public using a chatbot. (GETTY IMAGES)

By AI Trends Staff

AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads.

Chatbots employing AI are speeding up communication around the pandemic. One example is from Clevy.io, a French startup that launched a chatbot to make it easier for people to find official government communications about COVID-19, according to an account from the World Economic Forum.

The bot is getting realtime information from the French government and the World Health Organization, to help relay known symptoms and answer questions about government policies. Some three million messages had been sent through mid-May, with questions ranging from recommended exercises to an evaluation of COVID-19 risks. French cities including Strasbourg, Orleans and Nanterre are using the chatbot to help distribute accurate information, according go the report author, Swami Sivasubramanian, VP of Machine Learning for  Amazon Web Services (AWS).

Swami Sivasubramanian, VP of Machine Learning for Amazon Web Services (AWS)

Researchers at the Chan Zuckerberg Biohub in California are working towards an early warning system for COVID-19. They are analyzing great volumes of data to help forecast the virus’ spread, how it mutates as it spreads, estimate the number of undetected infections, and determine the public health consequences. They have divided the world into 12 regions for the work.

In March, a group of volunteer professionals led by former White House Chief Data Scientist DJ Patil, worked on a scenario-planning tool that would help hospitals to plan for how many beds would be needed for COVID-19 patients. In a partnership of AWS and Johns Hopkins Bloomberg School of Public Health, the group moved the model to the cloud, enabling them to run multiple scenarios in hours, and to roll out the model to all 50 states.

Another startup is working to limit the spread of COVID-19 to vulnerable populations. Startup ClosedLoop.ai is using its expertise in healthcare data to identify those at the highest risk of severe complications from COVID-19. The company has developed and open-sourced a COVID vulnerability index, an AI-based predictive model. The ‘C-19’ Index is being used by healthcare systems, care management organizations and insurance companies to identify high-risk individuals. The company then calls these individuals to discuss handwashing, social distancing and whether they need food and other essentials delivered so they can stay at home.

“I’m inspired and encouraged by the speed at which these organizations are applying machine learning to address COVID-19,” stated author Sivasubramanian.

Mount Sinai Health System Gets Grant from Microsoft

Elsewhere in COVID-19 and AI news, the Mount Sinai Health System in New York has received a grant for an undisclosed amount to support the work of a new data science center dedicated to COVID-19 research. The Mount Sinai COVID Informatics Center (MSCIC) will bring together leaders from hospital units including the Hasso Plattner Institute for Digital Health, the Department of Genetics and Genomic Sciences, and the BioMedical Engineering and Imaging Institute.

“This partnership with Microsoft provides us with cloud resources that will accelerate our discovery, translation and implementation of digital tools in the fight against COVID-19,” stated Robbie Freeman, MSN, RN, Vice President of Clinical Innovation at The Mount Sinai Hospital, in a press release. “Through this collaboration with AI for Health, we are leveraging the expertise of the Mount Sinai Health System in delivering world-class patient care and the Azure cloud to bring our AI-enabled products from bench to bedside.”

MSCIC represents expertise in health care delivery, health sciences, biomedical and digital engineering, machine learning, and artificial intelligence. The center seeks to develop digital health projects that incorporate realtime data used to improve health. A recent study called Warrior Watch followed hundreds of health care workers to monitor biometrics such as heart rate variability, sleep disruption and physical activity through an Apple Watch. This was complemented by surveys to better understand the level of stress and anxiety health care workers face on the front lines.

Prediction Spread Model Being Developed at Binghamton University

Researchers at the Thomas J Watson School of Engineering and Applied Science at Binghamton University, New York, are working on a COVID-19 spread prediction model incorporating AI. Using data collected from around the world by Johns Hopkins University, Arti Ramesh and Anand Seetharam, both assistant professors in the Department of Computer Science, have built several prediction models.

Arti Ramesh, Assistant Professors, Computer Science, Binghamton University, New York

Machine learning allows the algorithms to learn and improve without being explicitly programmed. The models examine patterns from 50 countries with high coronavirus infection rates, including the US. The model can predict within a 10% margin of error the likely spread for the next three days based on data for the past 14 days. The initial study included infection numbers through April 30, allowing a view into how predictions played out through May.

Certain anomalies can pose challenges. For instance, data from China was not included because of concerns about government transparency regarding COVID-19. Also, with health resources often taxed to the limit, tracking the virus’ spread sometimes wasn’t the priority.

“We have seen in many countries that they have counted the infections but not attributed it on the day they were identified,” stated Ramesh in a press release. “They will add them all on one day, and suddenly there’s a shift in the data that our model is not able to predict.”

As the virus continues to spread, the researchers are updating their model, hoping to have it become more accurate over time and continue to be useful. The model is posted online for interested researchers.

“Each data point is a day, and if it stretches longer, it will produce more interesting patterns in the data,” Ramesh stated. “Then we will use more complex models, because they need more complex data patterns. Right now, those don’t exist — so we’re using simpler models, which are also easier to run and understand.”

Read the source articles at the World Economic Forum, in a press release from the Mount Sinai Hospital, and in a press release from Binghamton University.

Source: https://www.aitrends.com/ai-research/ai-machine-learning-playing-important-role-in-fighting-covid-19/

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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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