Applied AI Innovation
Tuesday, October 26, 2021
PRO WORKSHOP (AI): Making Apps Listen and React with LUIS (Language Understanding Intelligent Service)
Language Understanding Intelligence Service (LUIS) is part of Azure's Cognitive Services. It's built on the interactive machine learning and language understanding research from Microsoft Research. Luis provides the capability to understand a person’s natural language and respond with actions specified by application code. In this session we'll examine how this powerful feature can be integrated into applications, offering a more natural interaction with a device.
Wednesday, October 27, 2021
Through an innovative project, reducing CO2 emissions and all other air pollution induced by the mobility in cities by 30% by deploying a solution for a real-time automatic emission-based road traffic micro-regulation, we managed to use the best of AI technologies. Indeed, AI is the key enabler addressing the complexity of real-time analysis of mobility in crossroads and local air pollution with the trend predictions that leads to recommendation to how to regulate road traffic to decrease air pollution and apply these recommendations directly to traffic lights. Using embedded AI at local camera level was instrumental to allow detecting the different road users (vehicle, public transportation, pedestrian, cyclist…) in real-time, while respecting privacy and GDPR, in order to apply mobility strategies for the optimal mobility management with minimum pollution impact. This last part is combining two AI engines with 5 models. This project, [AI]Roads, is an European awarded project and the outcome is tested in some major cities in EU. Beyond technical challenge, we will share some key advantages of combining AI and embedded AI, which might become the mainstream for some applications, and how we offered a scalable solution to a complex problem: the automatic and best trade-off between air pollution and mobility.
PRO TALK (AI): Social Media Data for AI Applications: Unprecedented Opportunities and Ethical Considerations
Social media platforms like Twitter, Instagram, and Facebook are a hotbed of activity and therefore provide unprecedented opportunities for the gathering of big data. While there are certainly ethical and privacy considerations, the availability of these large quantities of data, in both imagery and text forms, allow for the use of artificial intelligence approaches for analytics. Natural language processing techniques can yield insights into sentiment analysis around important topics such as breaking news and disease diagnoses. Computer vision-based methods can harness the wide range of photos posted on social networks to inform disaster relief pipelines in crisis situations and analyze the change in urban areas over time. In any case, the unique quality of social media as vehicles for the widespread and easily facilitated dissemination of data means that there will continue to be exciting AI-driven innovations in the future. These innovations will help to save lives, optimize energy and create a more sustainable society, and enable solutions that tackle mental health issues. Of course, the challenges associated with this work include the privacy of the data posted on the platforms and legal issues regarding web scraping. Another challenge is deploying certain social media analytics technologies into the real world for use by everyday folks. Making sure that the technology is accessible and interpretable is key, especially when talking about typical "black box" models like convolutional neural networks (CNNs). Regardless, social media's applications in the field of AI are exciting, and we discuss current and future ramifications in this session.
The quest for a practical AI solution for automated ECG diagnostic is motivated by desire to reduce the human and financial resources required for patient monitoring and to enable more ubiquitous remote outpatient monitoring. Today, Deep Neural Networks (DNN) are considered the building blocks of all AI solutions. Yet, DNNs are not widely adopted in hospitals for automatic diagnostic for the following reasons. First, doctors do not have the time nor the desire to be “mechanical Turks” who label row-by-row millions of ECG patient records for model training. Second, doctors do not trust black box diagnostic predictions as humans need reasons to support deliberate actions. Third, “the right-to-know” regulation included in GDPR requires organizations to provide to stakeholders explanations for any automatic decision making. We overcomes these challenges with an innovative, patent-pending variant of the Neural Networks. The LNN by Trendalyze in cooperation with LaTrobe University (Australia) and St. Ekaterina University Hospital (Bulgaria). It achieved the best performance of 100% within-patient accuracy in recognizing atrial fibrillation in 12-lead ECG recordings and showed robustness with respect to the wide variations of ECG patterns among different patients.
Microsoft Azure Cognitive Services offers various commoditized yet powerful and sophisticated services to work with visual content through Computer Vision.Currently those services can- detect and recognize Faces,- classify images or find objects in an image- run spatial analytics on video, - understand rich information in an image,- process whole videos and get insights from the video- recognize handwriting and ink in application - provide OCR (Optical Character Recognition) on documents - automatically parse a scan of a paper form This fast paced session will introduce the currently available set of services and explain through sample code and demonstrations how to use a selection of them.