JULY 29 - 30, 2020: ML Conference Online Edition –
There’s no doubt that a revolution is coming. As mixed mode, customer contact through Amazon Alexa and Google Assistant becomes ubiquitous and enterprises create their own voice apps and chatbots, how will they ensure brand identity and differentiation from their direct competitors?Enterprises are coming to understand that these voice and text enabled services can be valuable new channels and a direct connection with the consumer. But will brand identity be diluted by these MetaBots? How do brands avoid disintermediation? There’s an opportunity and a process for delivering extraordinary, branded, A.I. driven customer experiences through these channels. In this keynote, we’ll discuss how.
Learn from successful customer examples of how to apply machine learning in practice. Hear how customers have automated their business processes using a design-led approach, and intelligent services with SAP’s Leonardo Machine Learning portfolio.
With the total number of Alexa skills surpassing 50000 worldwide, companies now face the challenge how to make themselves heard in the voice universe. In this session we take a closer look at how companies can combine the strengths of existing brands with a user-centric approach to conversational design in order to create voice experiences that manage to stick out no matter the size of the competition.
People always wanted to know what the future holds. After all, forewarned is forearmed. For businesses, although often undermined, forecasting is pivotal.
Classical statistical forecasting has been utilized since the first half of the previous century yielding decent results with relatively few data.
However, “data hungry” machine learning algorithms are revolutionizing many areas of our lives, among them forecasting.
What are the advantages and disadvantages of modern vs. classical methods. How is one to decide which to choose?
And where to turn when you need good predictions for your business KPIs.
This talk explores the genres and types that attract the attention of professional voice developers, as well as the business models that have been tried, established, and brought to fruition. This includes ‘gaming’ - Amazon’s developer reward program, highly engaging games with premium content, voice apps built for branding and marketing purposes, and assistants for board or video games.
One notable example we’ll be investigating is Sensible Object’s ‘When In Rome’. This is a board game that comes with its own Skill, in which Alexa introduces the rules, keeps track of scores, and moderates the game’s trivia questions. We’ll be looking at how well voice is integrated into this traditional medium, and if it has the potential to impact customers’ expectations towards tabletop games in general.
After this fine example of a voice-enabled game, we will see how much value Alexa & Co can provide in voice-assisted games where it serves as an optional modality. One such example from contemporary computer games is Destiny 2, where Alexa assumes the role of an in-game character and manages parts of the player’s inventory and clan communications.
In the past year, machine learning has made another great step forward and is about to become pervasive in enterprise software. Learn more about a wide spectrum of new capabilities intended to provide intelligent solutions for an even broader variety of business challenges.
It has not been long since the moment when ML transferred from a purely academic discipline, to a technology that is actively being implemented in business. Because of that, it is a commonsituation when specialists encounter problems they did not have while doing research or participating in competitions (e.g. Kaggle). At the same time, companies that want to introduce ML in their business process, often do not realize what is needed for that to happen, what difficulties might occur and how to estimate the outcome of a project. We will look at two projects implemented in production: carotid artery examination and vehicle behavior analysis. Based on those examples we will cover some of the problems you might encounter in your projects including the fields of data processing, algorithm selection, communication with the customer and other.We will go through the most significant challenges faced during the development, how they were dealt with and what are the general options. In your future projects, you will probably encounter these and other problems, but this talk will make you a little more prepared for them, whether you are a developer or an entrepreneur.
In comparison to other Voice Assistants, Alexa already has a shopping function for the Amazon store. However, the Amazon Store -and the rest of the internet, as a matter of fact- is not prepared to support this function.
The content and search option must be transformed into "natural language" to fit the users' needs and to make a great Voice-User-Interface possible.
Robert C. Mendez from Internet of Voice (Cologne) offers some Dos and Don'ts, as well as some hints as to what vendors can do on Amazon to make their content findable with Alexa.
Recent viral articles on the internet revealed that a number of purchases done through smart speakers is lower than expected and customers tend to avoid shopping via voice.
Does this mean that voice shopping is just an utopia? A promise that cannot be fulfilled?
During the talk we’ll find out the truth behind voice shopping. We’ll take a look at some success stories as well as harsh failures to discover common misconceptions and what the secret of voice commerce is. Attendees will better understand how to design and develop voice applications that really make sense for e-commerce from strategy to user experience. We'll uncover the utopia and discover the business sense in creating for voice purchasing.
Echo Buttons are the first gadgets of their kind with a lot of potential for developers. They allow an additional contextual user input for your skills - physically and visually.
When developing for the Buttons, there are more things to consider on the technical and the conceptual side as if you are developing for an echo device alone.
Mario Johansson will show you his best practices, techniques and a few tips to consider when building for the echo buttons in this interactive session. Learn how to use the Gadgets Controller and Game Engine API to build great skills for this revolutionary input device.
Voice Games are one of the fastest growing categories in the Amazon Alexa Skill Store and casual gaming is currently experiencing a revolution with Voice Assistants. This year even a board game was released that interacts with Amazon Alexa. Why “voice” and “games” are a perfect match and how you can transform a game concept from “mobile screen” to a “voice-first” experience will be revealed in their talk.Tim Kahle, one of the co-founders of 169 Labs, and one of the three German Alexa Champions, is on the jury for the current Alexa Skills Kit Challenge with prizes worth a total of EUR 50,000. He and Dominik Meissner will talk about the agency’s latest voice game project (to be published in October 2018). They have now brought one of the most famous quiz games (even with an own TV show) on Amazon Alexa.
In this talk Matthias will present an overview of the best banking voice applications for Alexa and Google Assistant. He will also look at the banking skill of Sparkasse Bremen in more detail and will pay special attention to topics like optimization for Echo Show and gamification.
Text classification can be very important in businesses. Some tasks involve a lot of repetitive, prone to errors processes that could be automated. One of those could be whether certain unstructured text data belongs in which category. One example of a list of categories on one side and unstructured text on the other side is a script like Star Wars.
We will look at the characters of Star Wars in python with a Jupyter Notebook. How can you analyze and visualize questions like "Which characters use similar words?", using popular open source libraries? The talk will cover the prediction of which character most probably said a given text with different algorithms, from classical machine learning to neural networks using tensorflow.
Modern deep learning architectures are getting more and more computationally demanding which has started hurting hyperparameter tuning and experimentation speed. GPUs are getting stronger and cheaper, but vertical scaling is too slow to keep up with professional demand; we need to go horizontal, multi-GPU and multi-machine.
But what is distributed learning? Should it be used? How is it used? Data parallelism, model parallelism, federated learning, what, what, WHAT?
In this talk, I'll present bottlenecks that various distributed learning approaches solve, so you learn when to start looking at distributed learning if you encounter the presented hindrances. I'll also highlight the differences between different distributed learning technologies, e.g., TensorFlow Parameter Servers and Horovod.
Building a voice application for Amazon Alexa requires the Voice First approach. But with the growing device family with displays like the Echo Spot, the Echo Show, or the Fire TV, you are able to support your voice experience with photos, illustrations, or videos. This session concentrates on how to build a Multi-Modal application with Amazon Alexa. We will have a closer look on the best-practices as well as some tools and techniques to help you to create richer voice applications.
Machine learning enables customized conversations between man and machine that can result in buying decisions. Marketing experts should include artificial intelligence purposefully in their strategic thinking. It is important to build a bridge for the customer from the source of inspiration to your own content. Kathleen Jaedtke and Tina Nord explain how this can be achieved through the use of dialogue-oriented technologies.
Jan König is one of the founders of Jovo (http://www.jovo.tech ), the first open source framework that enables developers to build voice apps for both Amazon Alexa and Google Assistant. In this session, Jan will walk through the essentials of building for Alexa and Google, talk about important differences of both platforms, and show practical examples of successful voice apps.
As a consumer, commodities are great. Companies will compete for the lowest possible price because every product is almost the same. For the companies on the other hand, this is a cut throat business. Margins are low and differentiating yourself is nearly impossible. Building materials, vegetables, cars and even smartphones have become (near) commodities. But will AI ever become a commodity? And what are the hurdles we need to overcome to (not) get there?
(by the way the URL seems to be for sale for an outrageous amount of money)
Machine Learning has given us amazing new technologies: Image recognition, Speech-to-text, machine translation,.. And yet, having a robotic arm build a simple lego house remains one of the most challenging problems in the field. Why is such a trivial task (which children learn without effort) so hard even for state-of-the-art machine learning techniques? In this talk I will give a general introduction to Reinforcement Learning, an overview of the most challenging problems in the field and an outlook on what we can expect in the near future of intelligent robotics.
Make your devices smarter by embedding the Google Assistant into your own device using the Assistant SDK. You could build your own voice-driven interactions without requiring your users to also have their own voice assistant device (like Google Home). It is available for you to tinker with on Raspberry Pi devices and it's easy to get started!
This would be a dive into the foundations of setting up and using the Assistant SDK as seen here https://developers.google.com/assistant/sdk/.
In 2015 I won the national data science bowl on kaggle. In this talk I will go over some of the tricks we used to build the best possible plankton classifier we could come up with.
The nitty gritty details can be found here: http://benanne.github.io/2015/03/17/plankton.html
A hands on approach to developing a Google Action with Dialogflow and Google protocol buffers. After a quick introduction of the tools, we are going to do a hands on coding session to create a Google Action with a python webhook. In the session you will learn how to create your own Google Action and still have access to all the powerful machine learning tools python has to offer.
Learn how to create your own voice interfaces using the Google Actions platform. We'll look at the technologies involved, how to plan for a conversation, and then build a voice interaction together. With the rise of voice assistants, voice is becoming another surface area for users to interact with your product or service. We can now start to blend this new technology with our existing offerings to improve user experience, engagement, and satisfaction.
In this workshop, we'll learn about the Google Actions platform and how it works to provide you with all the tools you need to build your own conversational interfaces. Throughout the workshop, you'll also build your own Action and see how to extend it for deeper integration with your application.We'll also spend time looking at how to design a conversation interface, including thinking through the various phases of dialog and sketching out expected flows.Finally, we'll look at how to review and improve your Action by using the analytics and AI training tools available from Google.
Learn the technical fundamentals of building voice actions quickly, as well as the social and human considerations for its design.
Machine Learning is often hyped, but how does it work? We will show you hands-on how you can do data inspection, prediction, build a simple recommender system and so on. Using realistic datasets and partially programmed code we will make you accustomed to machine learning concepts such as regression, classification, over-fitting, cross-validation and many more. This tutorial is accessible for anyone with some basic Python knowledge who's eager to learn the core concepts of Machine Learning. We make use of an iPython/Jupyter Notebook running on a dedicated server, so nothing but a laptop with an internet connection is required to participate.
In this workshop, we will dive into the world of Deep Learning. After introducing the basics of Google’s Deep Learning library ‘TensorFlow’ we will continue with hands on coding exercises. You will learn how to create a neural network that is able to classify images of handwritten digits (MNIST) into their respective classes (Image Classification). While starting with a very basic, shallow network, we gradually add depth and introduce convolutional layers and regularization to improve the performance of our model.