You have all these documents - docs, emails, PDFs, forms, images… They could give you valuable insight into your business and customers to help you make better decisions - but instead most documents are just sitting there, untapped, because it’s difficult to read, compare and understand the relationship between them.

#ArtificialIntelligence

#DocumentAI

#ComputerVision

#NLP

Diverse algorithms have been developed for efficient earthquake signal processing and characterization. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Deep neural networks have been shown to be promising tools for this. We have developed a number of deep learning tools for more efficient processing and characterizing of earthquake signals. In my presentation, I demonstrate the performance of some of these tools applied to seismic data. AI-based techniques have the potential to improve our monitoring ability and as a result, understanding earthquake processes and hazards.

#earthquakemonitoring

#AI4earth

#earthquaketransformer

#scientificmachinelearning

#dataengineering

#management

#datateams

#datascienceoperations

Introduction to brain-computer interfacing (BCI) and AI/ML approaches in this field. - Practical applications of AI/ML for healthcare and neuroscience time-series and multivariate signal processing - dementia/aging biomarkers. Deep learning NLP applications for dementia biomarkers from spontaneous speech. Future challenges in successful AI/ML applications in healthcare.

#AISG

#BCI

#brain

#healthcare

#ML_time_series

AI&Industry: What are we really capable of and what is still out of our reach? What are the real applications in industy? AI&Strategy: How to design a roadmap towards a predictive factory and which are the skills involved and the efforts to be predicted? AI&Ethics: how far can we push automation? What are the potential risks? What is our space for action? Words like algorithm, machine learning and predictive maintenance have entered our daily life. News of the new advances in artificial intelligence is entering our lives at a rapid pace. But how much of this noise corresponds to a real technological advance? What are we really capable of and what is still out of our reach? What are the real examples already in use in our companies? The world of industrial artificial intelligence by describing in a simple but in-depth way which are the industrialized cases of artificial intelligence in Italian industry in particular. How to design a strategy towards the predictive factory, how to choose the use cases, what are the skills to involve and the efforts to predict. What are the impacts that artificial intelligence already has on our lives; how far can we push automation? What are the potential risks? What is our space for action? An engaging lesson open to all those who want to make their contribution to the exciting debate on the use of AI in industry and in the city.

#predictivefactory

#MIPU

#appliedAI

During the session we will focus on the AI Powered Banking with Backbot,  AWS Lex Chatbot for everyday banking and the real world limitations of using AWS lex in banking environments. I will present a prototype I created for Backbase to show how we can leverage AWS Lex for our customers to perform basic tasks like getting your account balance, make transfers between your own accounts dan to your beneficiaries.

The presentation will discuss the real life implementation limitations and constraints when working with AWS Lex

#aws

#lex

#chatbot

#banking

#nlp

Text summarization is a highly useful tool for extracting key information from text, which helps speed up learning, communication and business processes dramatically. With the use of modern attention-based architectures, automatic production of human-like summaries has become easier to achieve than ever. For some of the inputs, however, the quadratic time complexity of transformer encoders results in a specific token limit, acting as a constraint for processing large text sequences. In her talk, Nataliia will show how to produce high quality summaries for large text inputs, guiding you through some of the smart methods to deal with the token limit. She will compare the performance of different state-of-the-art methods on large text sequences on the example of consumer complaint data and show which algorithms ensure producing the best summaries.

#AI

#ML

#IoT

#WebAssembly

#Deployment

#onboardprocessing

#deeplearning

#hyperspectralimageanalysis

#Earthobservation

#satelliteimageanalysis

#imageduplicates

#opensource

#computervision

#python

#AI

Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model while gaining on its accuracy and precision. For managers to control and effectively manage credit risk and for regulators to be convinced with model quality the price to pay is too high. So, it prevents them from using advanced models due to the lack of their interpretability. In this paper, we show how to take credit scoring analytics in to the next level, namely we present comparison of various predictive models (logistic regression, logistic regression with weight of evidence transformations and modern artificial intelligence algorithms) and show that advanced tree based models give best results in prediction of client default. What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, “black box” models like random forests, gradient boosted or extreme gradient boosted trees. All this will be shown on the large dataset obtained from the Polish Credit Bureau to which all the banks and most of the lending companies in the country do report the credit files. This huge extent of data ensures high quality of the model inputs and objectivity of the conclusions.

#XAI

#CreditScoring

#AI

#ML

#Twolayerappraoch

#CombineArtificialandHumanIntelligence

Hortiya develops solutions for the holistic and efficient control of greenhouses in the horticultural industry. To do this, we combine the latest sensor technology with artificial intelligence to enable a completely new plant-centric approach in the horticultural industry. Our first product, the intelligent SVL light, is a coupling of a spectrum adjustable LED growth light with a novel photosynthesis sensor via a self-learning process. This approach enables us to save up to 30% energy compared to conventional LED growth lights and to react adaptively to changes in the environment.

Connecting data to context means to have the right data, use the right algorithms and deliver the right insights in a way they play an integral role in the insurers and customers daily life. The presentation is showing how to support with data products with a focus on lead management in the insurance industry. To go the full journey of a data product the use case, the data and model evaluation, and most important the visualization and implementation will be shown using a real example. In recent years, many companies have begun to invest in AI. However, most AI projects hardly make it beyond a proof of concept and thus do not contribute to increasing competitiveness. This talk shows how to identify AI potentials in companies. We will also discuss which managerial and technical challenges can arise when implementing AI applications. You will learn a number of best practices when implementing AI and how these lead to productive applications that ultimately add financial value.

#predictiveanalytics

#conversionmodeling

#leadpriorisation

#dataproductimplementation

#visualization

The problem: How food waste negatively affects our climate
The solution: “Tech for Good” - Using an AI driven distribution platform to match time-critical perishable food oversupply with the demand
The approach: Meeting and understanding the needs of supply and demand partners
The impact: Using existing food resources using a circular economy approach, reducing unnecessary CO2 emissions (environmental impact), supporting people in need and NGOs (social impact)

#techforgood

#reducefoodwaste

#climateaction

#circulareconomy

#SDGs

#data

#engineering

#dataengineering

#modenstack

#scaleteams

A classified website is an online marketplace where people can sell and buy new and used items from a wide selection of categories. The vast majority of user actions on these platforms is done between buyers and sellers without any real payment transaction. Therefore, as eBay Classified Group (eCG), we had to come up with an operational definition of conversion: if a buyer sends a message to a seller or if a seller posts a new listing on the platform, we refer to them both as "converted". The eCG is an umbrella company managing 14 different classified platforms from all across the world. Conversion numbers for the group company are tracked by a central team named 'Global Growth', which is tasked with increasing the number of active (recently converted) users on the platforms. They turned to the Data Science Team with a question: "Who will have a conversion soon?" The initial purpose of this model was to let them know in advance who is likely to convert soon, so that the marketing team would be able to take actions on users beforehand. The idea is straightforward: focus only on users who are unlikely to convert soon and try to find different ways to convince them to get back on our platform again (also: avoid allocating resources on users who will convert soon anyway, without requiring any extra targeting). This personalised marketing strategy was the main objective of this project. This is an article about an ML model developed to accomplish this objective by predicting user likelihood of conversion in the near future based on the users' past actions on a platform.

#MachineLearning

#BigData

#PredictiveModeling

#ConversionRateOptimization

#PersonalisedMarketing

Data is not just a technical topic. It touches much more aspects of large enterprises such as Skill Development, Culture and creating Business Value. In order to get it right, it is very important to not create a silo with experts but to work inclusive in the enterprise. During the session, we will focus on AI Powered Banking with Backbot, AWS Lex Chatbot for everyday banking, Real world limitations of using AWS lex in banking environments.

The WiTraPres project (conducted by the data science lab at the FH Südwestfalen, Germany) aims at implementing a predictive maintenance module to a fully automated warm reshaping plant (which produces parts for vehicle interiors) using machine learning methods. More precisely, insights gained from data are to be combined with expert knowledge in order to achieve predictions that are as accurate as possible. In the data science component, new predictive maintenance methods are developed and applied using process data and are then compared with conventional methods. One approach aims at implementing Bayesian-LSTM-Autoencoders for anomaly detection. Here, Bayesian methods are coupled with time-resolving deep learning methods to achieve the highest possible predictive power of the model with high precision. The predictions made by this model can be effectively combined with expert knowledge. Our approach shows promising results for this case specifically and is able to compete with state of the art predictive maintenance methods.

#machinelearing

#deeplearning

#predictivemaintenance

#automotivemanufacturing

#kubeflow

#pipelines

#bert

#nlp

#sagemaker

Deep learning approaches have proven powerful in modelling the volatility of financial stocks and other assets, as they are able to capture non-linearities in sequential data. I will present an analysis that helps asset managers to select, forecast and analyse different optimal asset portfolios over multiple backtest iterations. - The analysis includes the preselection of stocks using an autoencoder model, a new way to clean the sample covariance matrix that describes the risk of a portfolio, a ten day forecast using recurrent neural networks and the final portfolio optimization.

#DeepLearning

#QuantativeFinance

#Autoencoders

#PortfolioOptimization

In this presentation, I describe the challenge of tax deviation - what promised to you at the end of the process vs what you have received. I will talk about the challenges of data collection, target and problem definition, model selection and deployment.

#Prediction

#FinTech

#B2C

#FeatureEngineering

#CustomerSatisfaction

How the AI Team in Krakow operates, with focus on structure and ways of working, which we believe become fundamental to deliver. Overview of the use-cases we have. Particular case study in the deep learning area in detailed look.

#ai

#artificialintelligence

#deeplearning

#productteam

#dl

Everybody talks about artificial intelligence and machine learning these days, but how many of the solutions mentioned genuinely involve a **machine** that is **learning**? There are multiple results being published about e.g. DeepMind beating Starcraft - and most Atari games have been mastered by RL algos a while ago - but about actual business applications? In this talk, I will give an overview of how you can apply associative reinforcement learning to a real problem: online content personalisation (and stay within budget, because open source).

Creating the future of remote maintenance is strongly connected with aerial applications of drones. In the industries of energy, construction and facility management, drones are a fully established tool for various tasks. FlyNex enables companies to automatically generate and collect data. By managing the data within the FlyNex Platform companies can analyze them with a chosen (AI-based) software, which can be fully integrated into the platform.

#aerialremotemaintenance

#drones

#AI

#FlyNex

Germany is at the forefront of Machine Learning (ML) research in Europe whether it is published papers or issued patents in the past decade. As a consequence, theoretically you would expect a lot of ML in our products across all industries and a certain maturity of the field in Germany. The reality is however different. ML is a “Neuland” for many companies here and we are not good at taking state-of-the-art research results into production. In this talk, I will present why this is the case and then show some recipes on how to solve this problem.