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Disney Joins Global Facebook Boycott

You might have missed it in the news, but during recent weeks and months, there’s been a global protest movement happening with respect to Facebook and advertising revenue. A loosely-aligned alliance of some of the biggest and most respected brands and businesses on the planet has taken the decision to either temporarily or permanently stop […]

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You might have missed it in the news, but during recent weeks and months, there’s been a global protest movement happening with respect to Facebook and advertising revenue. A loosely-aligned alliance of some of the biggest and most respected brands and businesses on the planet has taken the decision to either temporarily or permanently stop spending money on Facebook’s advertising platforms in protest against what they feel is a lack of action against hate speech on the social media platform. After weeks of sitting on the fence, Disney has now joined the ranks of the protesters – and that could have significant implications for Facebook.

Disney joins Facebook advertising boycott

The boycott has been going on since early June and is thought to have cost Mark Zuckerberg’s company several million dollars thus far, but if the Facebook CEO was worried about it, he wasn’t in the mood to show that concern to investors or the media. As recently as the beginning of July, he dismissed the protest and stated his belief that the companies “would be back soon,” which he may live to regret. His perceived arrogance about the situation only appears to have increased the determination of the protesters to be heard, and as July draws to a close, he’s now looking at a bigger hole in his advertising revenue figures than he was when the month began. 

Businesses and companies from almost every major industry are involved in the protest, including Cola-Cola, Starbucks, Microsoft, Target, and Unilever. Without the combined funds that each of those companies spent with Facebook on a weekly basis, there was always likely to be a tangible difference to Facebook’s revenue picture, but Disney poses a unique problem to Zuckerberg’s stance on the matter. As the world’s largest entertainment company, they’re not just another advertising client. Until they made the decision to cut their funding, they were the single biggest client Facebook dealt with. Disney deciding to join in with the boycott means that Zuckerberg has lost his biggest customer. In almost any other business, losing your biggest customer would be a trigger to stand down or resign. There’s no sign that Zuckerberg is likely to do that just yet, but he may finally have to re-examine his reluctance to change the company’s policies on policing content posted by users.

There might be more than one reason that Disney has decided to sever advertising ties with Facebook temporarily. While the company has always been child-focused, in recent years, we’ve seen Disney take significant steps away from most forms of adult content. One such example is the range of Marvel online slots that used to be available at online slots websites such as Rose Slots, all of which have been pulled. Facebook has semi-recently become involved with the online slots industry itself, having developed an in-house slots page. Those in charge of Disney clearly don’t wish to be associated with online slots even if the figures tell them that they can make money from doing so, and so the combination of a perceived hate-speech problem coupled with a new online slots service might have been the final straw as far as Disney’s decision-makers are concerned. 

It’s understood that Disney’s withdrawal goes for Instagram as well as the main Facebook website, and will also result in the removal of adverts for Hulu as well as Disney Plus and other core Disney products. It’s thought that some Disney products and services will still be advertised, but at a far lower spending rate than Facebook has become accustomed to receiving from the company. That will negatively impact Facebook’s earnings projections for the remainder of the year, and that can only be deemed as bad news for the company’s shareholders. There has already been more than one half-hearted attempt to remove Zuckerberg from his position by irritated Facebook shareholders. Should the value of those shares now drop dramatically, it’s likely that calls for the founder’s head will resume. Zuckerberg has thus far managed to cling on to full control of his company throughout every negative publicity storm it’s endured in recent years. Only he and his shareholders know whether this will be a case of one time too many.

Statistics that have been made available by the Wall Street Journal show that Disney spent in excess of two hundred million dollars on advertising through Facebook during the first six months of 2020. The combined income that Facebook made from advertising during that period was several billion dollars, but there are now more than one thousand companies – large and small – involved in the boycott. No single one of them would be capable of causing harm to a company of Facebook’s means by stopping their spend, but the cumulative effect of so many – and especially so many large companies – has to be significant. No business in the world would easily be able to shake off the sudden loss of two hundred million dollars, and the unexpected announcement will likely send shockwaves through Facebook’s head office. While Facebook claims to be making progress on social issues and hate speech, an independent team of auditors appointed by the company completed a report on July 8th and returned a damning verdict. According to the auditors, Facebook has not only failed to take action on hate speech but has also taken decisions that have allowed incidents of hate speech to occur and to go unchecked. Facebook promised to take the feedback on board and make changes, but the findings of the report have only served to amplify the concerns that many companies already have about who is allowed to post on Facebook, and what they’re allowed to say and do with that platform. While Zuckerberg has repeatedly reaffirmed his commitment to ensuring that everybody has a right to freedom of speech on his platform, there comes the point where free speech becomes hate speech – and becomes a crime in the process. If the auditors are correct, Facebook is yet to come up with a consistent way of defining where that line is.

Source: https://1reddrop.com/2020/08/03/disney-joins-global-facebook-boycott/

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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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