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Introducing Android Push Notification In A New Light

There was a time when SMSs and emails used to rule the market. They were used to be considered as a prime marketing strategy for any business. But with the advent of technology push notifications starts to show their presence. Within a short span of time Push notifications in the form of React native push […]

The post Introducing Android Push Notification In A New Light appeared first on AIIOT – Artificial Intelligence | Internet of Things | Technology.

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There was a time when SMSs and emails used to rule the market. They were used to be considered as a prime marketing strategy for any business.

But with the advent of technology push notifications starts to show their presence. Within a short span of time Push notifications in the form of React native push notifications for Android mobile applications have marked their huge presence. This presence was so effective that businesses from all around the world are forced to adopt push notifications.

Push notifications work on the principle of the following quote by Richard Bach that says

“If you love someone, set them free. If they come back they are yours; If they don’t, they never were” 

This is the power of push notifications where a user is given control to receive push notifications. It let them come to your platform at their own pace.

Well, if you are unaware of the power of Android push notifications, we are introducing Android Push notification in a new light for you.

What are Android Push notifications?

Android Push notifications are messages that pop up on Android mobile phones. As far as the Android platform is concerned, a Notification manager is provided for this purpose.

Push notifications are rich media messages and are composed of text, images, graphics, videos, gifs, emojis, etc. They are especially sent with a purpose to create an urge in the mind of the user to take action.

Push notifications can be timed and organized as per requirement. This is the reason why these are known for providing effective results. It doesn’t matter, which part of the world your audience resides. They only demand a single click to reach any corner of the world.

That’s why any marketing strategy is considered insufficient without their effective implementation.

Benefits of Android push notifications.

According to “businessofapps.com”, the opt-in rate of Android push notifications is 91.1%. It means users find push notifications interesting.

Moreover, according to a report posted in “infographics”,

  • Push notifications carry the ability to boost app engagement by up to 88%.
  • Push notifications can lift the app retention rate by 3-10X.
  • When push notifications were enabled 65% of users open the app within 30 days.
  • In-store purchase on receiving push notifications was 48% in the case of mobile users.

These stats are capable enough to prove the power of push notifications. To give you more detail on the benefits of Android push notifications let us dig some deeper.

Communication: Push notifications provides easy and effective two-way communication with users. They are one of the fastest ways to reach the users, present in any corner of the world. It just takes a single click to reach them.

Moreover, the users are also provided with an opportunity to directly reach you or your products and services. It makes them feel special by providing good user experience. As a result, it helps you to earn customer loyalty and free advocacy for your business.

Engagement and retention: In most cases, the users download the app, opens it once or twice, and never show up again. In another case, a user downloads an app but never completes the registration process or never opens it. In these circumstances, push notifications are very effective.

Automated push notifications motivate the user on regular time intervals to engage with the app. It not only helps to engage users but also lets them feel that they are valuable users. As a result, it helps to retain users for a long period of time.

Personalization and segmentation: According to “Statista” currently there are around 3.5 billion smartphone users worldwide. Among these 74.13% are Android smartphone users. It means a variety of people from whole over the world. As a result, you cannot sell a single product or service to all users.

Here Push notifications can help you a lot. Push notifications can be segmented and personalized. It means you can send push notifications on the base of gender, age, region, interests, etc. This will provide you with an opportunity to target the right users. As a result, the probability of a sale will increase exponentially.

Advertising: You start up your business or are looking for expanding it worldwide. Which will be the rapid and cost-effective source to reach billions of people around the world?

Obviously push notifications are the best source. You don’t require any personal information from your audience. You need to spend a lot of money in making and running video ads on Television. This era is an era of internet where most of the audience can be accessed easily through mobile phones and social media platforms.

“You can make effective use of the React native push notifications and Android push notifications to promote your brand or business.” 

Push notifications give you an opportunity to reach every corner of the world instantly. It will take some moments and you are done with branding and advertising of your business.

Conclusion: Android push notifications have earned the top spot in any marketing strategy. It is due to their ability to provide effective results. Moreover, the majority of the population around the world uses Android phones and tablets. Hence it is easy to target the audience.

But when it comes to leaving an impact in the mind of the user, push notifications with their ability to carry rich media stands apart. Moreover, they can be used for multiple purposes at an economical price. This is the reason for its popularity.

Source: https://www.aiiottalk.com/business/android-push-notifications/

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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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