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AI Autonomous Cars And The Challenges Presented By The World’s Most Dangerous Roads 

By Lance Eliot, the AI Trends Insider  Do you prefer driving on roads that are calm, easy to navigate, and present little or no qualms? Or, do you relish roads that are crazy, a wild ride, and for which risk is on your shoulder for each inch driven?  I canvassed some of my colleagues that are […]

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By Lance Eliot, the AI Trends Insider 

Do you prefer driving on roads that are calm, easy to navigate, and present little or no qualms? Or, do you relish roads that are crazy, a wild ride, and for which risk is on your shoulder for each inch driven? 

I canvassed some of my colleagues that are driving daredevils, the type that overtly seeks out these latter kinds of roads and I uncovered some of the key attribute of a list of the most dangerous roads in the world.  

First, the road has to be a road and in one manner or another be passable. This might seem obvious, but the point is that if a road is not really a road and merely a jumble of rocks or a bunch of sand dunes, it doesn’t quite count as a “road” and therefore should not be on a list of the most dangerous ones. You could perhaps place such instances on the most dangerous off-roads or made-up trails, but do not mix them up with actual intended-to-be roads.  

Second, vehicular traffic must travel the road. Once again, this has to do with the notion of whether the road is a road. If there aren’t any cars or trucks or other vehicles that go on the road, it does not seem to be an appropriate candidate. Furthermore, the traffic is actually considered a factor in the dangerousness of the road, namely that it is not just the pavement or asphalt that gets your heart pounding, it is also the other drivers that add to the zany and perilous nature of the journey on the road. 

Third, there should be some disquieting number of car crashes or roadway related deaths and injuries that occur on the road. If the road is truly dangerous, the odds are that car drivers will misjudge and end-up in a ditch, or worse become a casualty of the hazardous road. Now, this can be somewhat misleading or misapplied. Say there is a freeway stretch in a congested city that gets a lot of fatalities, well, it is not necessarily the road per se and perhaps primarily due to the volume of traffic. As such, some suggest using a per-mile metric rather than a raw count of adverse outcomes or otherwise find a means to balance the quantitative numbers against the other factors warranting being considered a most dangerous road. 

Fourth, the roadway design and its placement are likely a significant ingredient in the dangerousness. Generally, roads that weave along a sheer cliff or that try to squeeze between two very tight canyon walls or otherwise present life-threatening pathways are likely considered inherently dangerous, quite obviously due to the apparent risks of driving even just slightly askew. Thus, the roadway design and where the roadway goes are bound to be a vital part of the danger. Just the littlest moment of taking your eyes off the road could lead to a really sour ending of a roadway run. 

Fifth, the dangerous road must have stood the test of time. A washed out bridge or maybe a massive mudslide—though not a good thing and certainly dangerous—is only temporary. In the proper spirit of being a persistent danger, the viewpoint is that the road must have been around for a long time and consistently presented itself as a danger. Sure, there are lots of one-time examples of roads that had a dangerous day, but the all-time list ought to be roads that proudly or imprudently have been enduringly dangerous. 

Sixth, there must be speed involved. One supposes that if you could drive a road at a snail’s pace of say 1 mile per hour, it would seem to knock down the dangerousness factor to some degree. Inching along would make things easier for the driver and allow for moment-to-moment recalibration of the driving effort. On the other hand, if there is the speed involved, perhaps there is other traffic that is desirous of moving at a frenetic pace, this makes the danger come alive, given that you only have a fraction of a second to decide whether the road is curving to the left or the right. 

Seventh, and the last of this set of criteria or considerations, is that opinion matters. The notion underlying this condition is that the road ought to be one that people acknowledge as being dangerous. If a road is on the list and everyone balks at the inclusion, perhaps this implies that the road is not as dangerous as might be claimed. That being said, do not be fooled by those smarmy drivers that will shake their head at any road on such a list and out of the corner of their mouths say that the road is nothing of consequence, and they could drive it blindfolded. There are always those sorts of braggarts or malcontents and are not to be taken at their word as puffery arbitrators of what is dangerous or not.   

With all of those thoughts in mind, let’s take a look at a recently reported list of the Top Ten alleged most dangerous roads in the world (see this link: https://usemypro.com/the-ten-most-dangerous-roads-in-the-world.htm): 

  1. The “Street of Death” Road of North Yungas in Bolivia 
  2. The Road of Jalalabad-Kabul in Afghanistan 
  3. The Highway of James Dalton in Alaska USA 
  4. The Highway of Karakoram in Pakistan 
  5. The Guoliang Tunnel Road in China 
  6. The Pass of Zoji La in India 
  7. The Road of Skippers Canyon in New Zealand 
  8. The Pass of Los Caracoles in Chile 
  9. The Pass of Stelvio in Italy 
  10. The Highway of Sichuan 

How do you feel about the list? If you’ve driven at least one of those roads, pat yourself on the back for having survived to tell the chilling tale. If you’ve driven all ten, one has to ask, do you have a death-wish or are you just that kind of person that loves a good challenge? 

Shifting gears, consider what the future will be like when we have AI-based true self-driving cars on our roadways.   

Here’s today’s intriguing question: Will AI-based true self-driving cars be able to drive on dangerous roads, and if so, how will they fare in that treacherous endeavor?   

Let’s unpack the matter and see.   

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/ 

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/ 

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/ 

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/ 

The Levels Of Self-Driving Cars 

True self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task. 

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there.   

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some point out).   

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable).   

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that in spite of those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car. 

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/ 

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/ 

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/ 

Self-Driving Cars And Dangerous Roads 

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. ll occupants will be passengers. The AI is doing the driving. 

In discussing the handling of highly dangerous roads, keep in mind the earlier articulated criteria for what constitutes a dangerous road. This is worthy of a reminder for several crucial reasons. The most notable reason involves a quite significant matter that surprises many people about the nature of self-driving cars, including startling those that purport to know a lot about self-driving cars.   

In the classification used to rate self-driving cars, the aspects of being able to have the AI drive off-road is considered off-the-table. This means that the levels of self-driving do not encompass off-road driving. The standard has nothing to say particularly about off-road driving and considers off-roading to be outside the purview of the existing standard. 

That’s a shock to some. Why wouldn’t the standard include off-road driving, many ask incredulously?   

Generally, the thinking is that off-roading is so varied and open-ended that it made more sense to focus the standard toward on-road driving (and, some would assert that we need self-driving for on-road driving, but don’t necessarily “need” self-driving for going off-roading, though this is a debatable contention).  

Keep in mind that there isn’t anything that precludes a standard that does focus on off-road, and nor does it preclude the existing standard from being later extended to add off-road driving aspects. 

Anyway, in short, there is no requirement in the standard that an AI driving system has to drive off-road, at least in terms of meeting the standardized levels of semi-autonomous and autonomous driving. 

Automakers and self-driving tech firms can decide if they want to encompass off-road driving or not do so. One small irony, some suggest, stems from the fact that the early days of self-driving were initially all about doing off-road kinds of driving, such as competitions in the desert, partially to ensure that no one would get hurt by using desolate areas for doing tryouts and experimentation. 

This lack of an off-road stipulation does not seemingly factor into today’s question about the dangerous roads, since please recall that the suggested criteria emphasized that the road has to be a road and be somehow reasonably passable as a road. 

Back to the matter at hand and the pondering of whether AI-based true self-driving cars could handle dangerous roads, including for example the reported Top Ten such roads.   

The answer is somewhat amorphous because it comes down to the driver, namely, the AI system, and whether the AI has been appropriately readied for coping with the conditions and situations of a dangerous road. 

Let’s delve into that facet. 

For many of today’s roadway tryouts, the AI has been shaped to deal with normal and routine driving conditions. The AI is dealing with driving in quiet neighborhoods, or on conventional highways, or on well-kept freeways, etc. Dealing with a winding road that has severe potholes and makes its way along sheer cliffs, well, that’s not especially what the AI driving systems are yet crafted to do.   

Furthermore, recall the point about speed. 

If you were to have a self-driving car proceed at 1 mile per hour, the chances of successfully navigating a dangerous road are going to be a lot higher. Speed for AI is about as daunting as speed is for humans, in the sense that the faster the car is going, the harder the driving task becomes, simply due to the need to make split-second decisions and also be aware of the roadway status with little time to figure out what to do next.   

Here’s an additional twist. Would the AI self-driving car have any human passengers in it? 

You might be perplexed about why the aspect of having riders inside the self-driving car would be a consideration. 

The reason is rather straightforward. The AI has presumably been programmed to keep the car within the allowable limits of what the human body can deal with. For the AI system, making a super-fast and sharp turn is no problem for the AI and nor the car, but the human passenger might get injured, even if wearing a seatbelt (due to a whiplash effect).   

You could say that AI is hampered by the inclusion of human passengers. 

That is obviously the case if the vehicle was being driven by a human, the same limitations would exist.   

Taking this idea to another realm, consider what the AI driving system could do if it didn’t need to worry about human passengers. The self-driving car can be completely empty and have no humans present at all, thus, in that use case, it can proceed to drive to the extreme limits allowed by the physics of the car). 

Would an unencumbered AI driving system do better on a dangerous road than a human-driven car? 

That’s hard to say, since the nature of the road, the speed of travel, and other factors all come into play, along with however the AI itself has been primed for the driving. 

This brings up another important aspect. For some of the existing public roadway tryouts, the roads being used have been pre-mapped and oftentimes have been pre-driven to allow the AI system to get up-to-speed about the roads. This can be handy for the use of Machine Learning (ML) and Deep Learning (DL), allowing the AI to figure out what the road is like and aim to do better on future travels of the road. 

One nuance that might not be apparent is that if you have an AI self-driving car in a fleet and it traverses a particular road for the first time, in theory, the result can be shared with all other AI systems of the fleet, thus being able to get those other self-driving cars ready to drive the road too. 

For humans, you cannot especially do the same trick, since having one person drive a road might be somewhat handy for others when the person explains what they did, but this is assuredly not the same as being able to transmit the moment-to-moment and detailed driving nuances involved.   

In the use case of the dangerous roads, we ought to consider whether a human driver has had a chance to preview the road by driving it perhaps slowly one time and then increasing speed for later journeys. The same aspect can be considered in the case of AI self-driving cars.   

For more details about ODDs, see my indication at this link here: https://www.aitrends.com/ai-insider/amalgamating-of-operational-design-domains-odds-for-ai-self-driving-cars/ 

On the topic of off-road self-driving cars, here’s my details elicitation: https://www.aitrends.com/ai-insider/off-roading-as-a-challenging-use-case-for-ai-autonomous-cars/ 

I’ve urged that there must be a Chief Safety Officer at self-driving car makers, here’s the scoop: https://www.aitrends.com/ai-insider/chief-safety-officers-needed-in-ai-the-case-of-ai-self-driving-cars/ 

Expect that lawsuits are going to gradually become a significant part of the self-driving car industry, see my explanatory details here: https://aitrends.com/selfdrivingcars/self-driving-car-lawsuits-bonanza-ahead/ 

  

Conclusion 

Another factor to contemplate involves the risk threshold of the driver. We all know of human drivers that are willing to take great risks while driving, weaving in and out of traffic and making chancy moves that increase the odds of getting into a car crash or other adverse result.   

For AI self-driving cars, there is an ongoing debate about the threshold of risks that the AI should be allowed to undertake. While traveling on a dangerous road, what should the risk setting be for the AI system? 

Presumably, if you dial down the acceptable risk, the AI is going to drive more slowly and cautiously. If you push up the risk meter higher in terms of risk tolerance, the AI will drive the car with greater speed and aim toward the brink of calamity.   

As a final quick thought on this topic, consider what human passengers might do when regularly able to go for a drive in AI self-driving cars. 

Suppose you are late for work. You urge the AI to push the pedal to the floor and get a move on. Essentially, the human rider is seeking to increase the risks of the driving act. 

Should an AI driving system allow for the human riders to make such changes?   

In theory, some believe that the AI will and should always drive in the same relatively low-risk way, regardless of the interests or desires of the passengers.   

But, right away there are apparent exceptions, such as a passenger that is about to give birth and has to be rushed to the hospital or someone suffering from a gunshot wound or other emergencies that might require taking a riskier driving approach. 

Let’s return back to the dangerous road topic.   

Will we have AI self-driving cars that will allow us to take a wild ride on a dangerous road, doing so by telling the AI to maximally take risks on such roads, giving the humans quite a thrill (one presumes)?   

For now, the automakers and self-driving tech firms have their hands full with getting self-driving cars to safely take people to the local grocery store, and thus this inquisitiveness about coping with especially dangerous roads is considered an edge or corner case (not something to be dealt with right now). 

In the future, don’t be surprised if you start to see advertising for brands of AI self-driving cars that showcase they can readily drive on scandalously dangerous roads, which might become a marketing pitch to differentiate one AI driving system from another. 

You’d certainly seek out to take an AI self-driving car to get over to downtown for work if that AI driving system was known to have nimbly and safely handled death-defying roads of grand peril, enough so that you might even take a short catnap on the way to the office. 

  

Copyright 2020 Dr. Lance Eliot  

This content is originally posted on AI Trends. 

 

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/ and http://ai-selfdriving-cars.libsyn.com/website] 

Source: https://www.aitrends.com/ai-insider/ai-autonomous-cars-and-the-challenges-presented-by-the-worlds-most-dangerous-roads/

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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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