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In this video, we introduce through 8 questions the Human Rights approach to AI regulation and comment its superior societal performance compared to the regulation outlined in the European Commision white paper on AI. https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf  

ECAI 2020

A wide Human-Rights approach to Artificial Intelligence regulation in Europe

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. As in the case of any study of pedestrian dynamics, adding an affective component implies the rigorous design of experimental protocols and data acquisition sets. The integration of multi-modal signal sources considering both data coming from physical activity and uncontrolled reactions related to affective responses provides new perspectives to study pedestrian dynamics and pedestrian interaction with traditional vehicles as well as with autonomic and autonomous transportation systems. The designed in-vivo experimental protocol devoted to the collection of movement and physiological data as reliable stress indicators during walking and road crossing, and the related analysis will be illustrated.

Experimental approach to study pedestrian dynamics towards affective agents modeling

Artificial Intelligence (AI) is omnipresent today, but, what is AI capable of doing and where are its boundaries? Benchmarks, competitions and challenges are behind much of the recent progress in AI, but the dynamics of rushing breakthroughs at the expense of massive data and compute has led to a more complex AI landscape, in terms of what can be achieved and how. As a result, policy makers and other stakeholders have no way of assessing what AI systems can do today and in the future. In this paper, we identify a series of problems to track and understand what AI is capable of. Then we present the AIcollaboratory, a  data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, which is being developed in the context of AI Watch, the European Commission knowledge service to monitor the development, uptake and impact of AI in Europe.

Tracking the Impact and Evolution of AI: TheAIcollaboratory

Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.

Region Proposal Network for Lung Nodule Detection and Segmentation 

Improved forecasting of blood glucose could aid in the management of diabetes. Recently proposed neural network architectures that use stacked fully connected layers with residual backcasting have achieved state-of-the-art performance on benchmark time-series forecasting tasks. Though promising, previous work ignores opportunities for additional supervision, and the use of fully connected layers fails to account for the temporal nature of the signal. Here, we propose a new architecture that builds on previous work by learning to forecast gradually in stages or blocks. Our updates include replacing the fully connected block structure with a recurrent neural network and adding additional losses to provide auxiliary supervision. In addition, we leverage important context in the form of additional input signals. Applied to the task of glucose forecasting, we find that each of these modifications offers an improvement in performance. On the task of predicting blood glucose values 30 minutes into the future, our proposed approach achieves a mean rMSE of 18.2 mg/dL, versus 21.2 mg/dL achieved by the baseline. These improvements get us closer to predictions that are reliable enough to be used in CGMs or insulin pumps to manage diabetes.

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

"Artificial intelligence-driven Chatbot for Astronauts by
 taking advantage of Natural Language processing with the support of healthcare professionals/ doctors 
These Chatbots created with the expert advice of doctors can learn and evolve over a period of time and answer  medical questions from a pre-trained Database which can be updated periodically
 Astronauts would be able to list down their symptoms and the Chatbots could give medical support by suggesting some medicines or other remedies which helps in diagnosis and treatment of simple medical conditions without much delay
"

Artificial intelligence assisted Chatbot for Astronauts

StaRAI - Semantics and Symmetries in Exact Lifted Inference

Onboard and ground-based automated scheduling for the Mars 2020 Rover Mission

"Sensors are multiplying on machines, networks and living things. Reasoning and extracting knowledge from this huge amount of data is among nowadays challenges. In data- based diagnostic applications, large amounts of data are often available but a key issue is that data remain unlabelled because labelling would require too much time and imply prohibitive costs. The different situations, e.g. normal or faulty, must hence be learned from the data. Clustering methods, also qualified as unsupervised classification methods, can then be used to create groups of samples according to some similarity criterion. The different groups can supposedly be associated to different situations.
Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to qualitative (categorical) data. However, in many application domains, qualitative features are key to properly describe the different situations. This paper presents DyClee-C, an extension of the numeric feature based DyClee algorithm to qualitative data. DyClee-C is applied to two data sets: a soybean data set to diagnose the disease soybean plants and a breast cancer data set to assess the current diagnosis in terms of recurrence events and prognose possible relapse."

DyClee-C: a clustering algorithm for qualitative data based diagnosis

Abstract: Ethics is an ancient matter for human kind, from the origin of civilizations ethics have been related with the most relevant human concerns and determined cultures. Ethics was initially related to religion, politics and philosophy to then be fragmented into specific communities of practice. The undergoing digital revolution enabled by Artificial Intelligence and Data are bringing ethical wicked problems in the social application of these technologies. However, a broader perspective is also necessary. We now face global and highly dynamics challenges that affect groups and individuals, specially those that are most vulnerable. Individual-oriented ethics are not longer sufficient, the new ethic has to consider the several scales in which the current complex society is organized and the interconnections between different systems. Ethics should also give a response to the systemic changes in behavior produced by external factors and threats. Furthermore, AI and digital technologies are global and make us more connected and smart but also more homogeneous, predictable and ultimately controllable. Ethic must take a stand to preserve and keep promoting individuals rights and uniqueness and cultural heterogeneity. Digital technologies have to the foundation for new models of society and help ensure ethical individual and collective values. For these reasons science has to be at the core of the new ethic as it helps understand the complex world. Finally, AI has advanced through the ambition to humanize matter, so we should expect ethics to give a response to the future status of machines and their interactions with humans."

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Next from ECAI 2020

Experimental approach to study pedestrian dynamics towards affective agents modeling

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Learning Adversarially Robust Sparse Networks via Weight Reparameterization

Stay up to date with the latest Underline news!

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