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Amazon Personalize improvements reduce model training time by up to 40% and latency for generating recommendations by up to 30%

We’re excited to announce new efficiency improvements for Amazon Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables you to build applications with the same machine learning […]

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We’re excited to announce new efficiency improvements for Amazon Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up to 30%.

Amazon Personalize enables you to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations—no ML expertise required. Amazon Personalize provisions the necessary infrastructure and manages the entire ML pipeline, including processing the data, identifying features, using the best algorithms, and training, optimizing, and hosting the models.

When serving recommendations, minimizing the time your system takes to generate and serve a recommendation improves conversion. A 2017 Akamai study shows that every 100-millisecond delay in website load time can hurt conversion rates by 7%.[1] All other things being equal, lower latency is better. Our efficiency improvements have generated latency reductions of up to 30% for user recommendations across the full range of item catalogs supported in Amazon Personalize.

As your datasets grow and your users’ behavior changes, regular retraining is needed to keep your recommendations relevant. Solution training is one of the three cost drivers when using Amazon Personalize and can be a significant portion of your overall cost of ownership for Amazon Personalize. Improved training efficiency in Amazon Personalize reduces the cost of training solutions and increases the speed at which you can deploy new recommendation solutions for your users. New solution versions ensure that your Amazon Personalize model includes the most recent user events and that new items in your catalog are included in your personalized recommendations. The relative popularity of items changes as user preferences shift and when your catalog changes. Now, you can maintain the relevance of your recommendations at a lower cost and in less time.

The following sections walk you through how to use Amazon Personalize.

Creating dataset groups and datasets

When you get started with Amazon Personalize, the first step is to create a dataset group and import data about your users, your item catalog, and your users’ interaction history with those items. Each dataset group contains three distinct datasets: user-item interaction data, item, data, and user data. If you don’t have historical data or if you want to ensure you generate the most relevant recommendations based on in-session behavior, real-time user-item interactions (events) can be recorded using the putEvents API. New items and user records can be added incrementally to your item and user datasets using the putItems and putUsers APIs, allowing you to update not only your model’s recent user actions but also ensure the most current item and user data is available when updating or retraining your solutions.

Creating an interaction dataset

Use the Amazon Personalize console to create an interaction dataset, with the following schema and import the file bandits-demo-interactions.csv, which is a synthetic movie rating dataset:

{ "type": "record", "name": "Interactions", "namespace": "com.amazonaws.personalize.schema", "fields": [ { "name": "USER_ID", "type": "string" }, { "name": "ITEM_ID", "type": "string" }, { "name": "EVENT_TYPE", "type": "string" }, { "name": "EVENT_VALUE", "type": ["null","float"] }, { "name": "TIMESTAMP", "type": "long" }, { "name": "IMPRESSION", "type": "string" } ], "version": "1.0"
}

Creating an item dataset

You follow similar steps to create an item dataset and import your data using bandits-demo-items.csv, which has metadata for each movie. We use an optional reserved keyword CREATION_TIMESTAMP for the item dataset, which helps Amazon Personalize compute the age of the item and adjust recommendations accordingly.

If you don’t provide the CREATION_TIMESTAMP, the model infers this information from the interaction dataset and uses the timestamp of the item’s earliest interaction as its corresponding release date. If an item doesn’t have an interaction, its release date is set as the timestamp of the latest interaction in the training set and it is considered a new item with age 0.

Our dataset for this post has 1,931 movies, of which 191 have a creation timestamp marked as the latest timestamp in the interaction dataset. These newest 191 items are considered cold items and have a label number higher than 1800 in the dataset.

Create your dataset and import the data with the following item dataset schema:

{ "type": "record", "name": "Items", "namespace": "com.amazonaws.personalize.schema", "fields": [ { "name": "ITEM_ID", "type": "string" }, { "name": "GENRES", "type": ["null","string"], "categorical": true }, { "name": "TITLE", "type": "string" }, { "name": "CREATION_TIMESTAMP", "type": "long" } ], "version": "1.0"
}

Training a model

After the dataset import jobs are complete, you’re ready to train a model.

  1. On the Amazon Personalize console, in the navigation pane, choose Solutions.
  2. Choose Create solution.
  3. For Solution name, enter your name.
  4. For Recipe, choose aws-user-personalization.

This recipe combines deep learning models (RNNs) with bandits to provide you more accurate user modeling (high relevance) while also allowing for effective exploration of new items.

  1. Leave the Solution configuration section at its default values and choose Next.

  1. On the Create solution version page, choose Finish to start training.

When the training is complete, you can navigate to the Solution Version Overview page to see the offline metrics.

Creating a campaign

In this step, you create a campaign using the solution created in the previous step.

  1. On the Amazon Personalize console, choose Campaigns.
  2. Choose Create Campaign.
  3. For Campaign name, enter a name.
  4. For Solution, choose user-personalization-solution.
  5. For Solution version ID, choose the solution version that uses the aws-user-personalization recipe.

Retraining and updating campaigns

To update a model (solutionVersion), you can call the createSolutionVersion API with trainingMode set to UPDATE. This updates the model with the latest item information for the item in the dataset used to train the solution previously and adjusts the exploration according to implicit feedback from the users. This is not equivalent to training a model, which you can do by setting trainingMode to FULL. Full training should be done less frequently, typically one time every 1–5 days depending on your use case.

When the new solutionVersion is created, you can update the campaign using the UpdateCampaign API or on the Amazon Personalize console to get recommendations using it.

Conclusion

Product and content recommendations are only one part of an overarching personalization experience. End-to-end latency budgets require fast responses, and unnecessary latency decreases the impact and value of personalization for your users and business. The reduced latency of recommendations generated by Amazon Personalize has improved the speed at which you can generate recommendations for your users. Additionally, the improved efficiency of training Amazon Personalize ensures that your recommendations maintain relevance at a lower cost. For more information about training and deploying personalized recommendations for your users with Amazon Personalize, see What Is Amazon Personalize?

[1] https://www.akamai.com/us/en/multimedia/documents/report/akamai-state-of-online-retail-performance-2017-holiday.pdf


About the Authors

Deepesh Nathani is a Software Engineer with Amazon Personalize focused on building the next generation recommender systems. He is a Computer Science graduate from New York University. Outside of work he enjoys water sports and watching movies.

Venkatesh Sreenivas is a Senior Software Engineer at Amazon Personalize and works on building distributed data science pipelines at scale. In his spare time, he enjoys hiking and exploring new technologies.

Matt Chwastek is a Senior Product Manager for Amazon Personalize. He focuses on delivering products that make it easier to build and use machine learning solutions. In his spare time, he enjoys reading and photography.

Source: https://aws.amazon.com/blogs/machine-learning/amazon-personalize-improvements-reduce-model-training-time-by-up-to-40-and-latency-for-generating-recommendations-by-up-to-30/

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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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