Spain

&quot;This tutorial teaches the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® and the NUMBA compilers. Each participant will have access to a GPU workstation in the cloud on which to complete the many example programs used to demonstrate the concepts. Participants will work through dozens of hands-on coding exercises, and at the end of the training, implement a new workflow to accelerate an example program originally designed for CPUs, observing impressive performance gains.
On completion of this tutorial, participants will have additional resources to help them create new GPU-accelerated applications on their own.  Moreover, upon successful completion of the final assessment, participants will receive an NVIDIA Deep Learning Institute (DLI) certificate to recognize their subject matter competency and support professional career growth.
The content for this tutorial has been developed by NVIDIA’s Deep Learning Institute (https://www.nvidia.com/en-us/deep-learning-ai/education/)

The tutorial covers 3 main topics (note that if not all these topics can be covered in the time allocated for the workshop, participants can complete them through self-study thereafter):

1.	Introduction to CUDA Python with Numba: 
a.	Begin working with the Numba compiler and CUDA programming in Python.
b.	Use Numba decorators to GPU-accelerate numerical Python functions.
c.	Optimize host-to-device and device-to-host memory transfers.
2.	Create CUDA kernels in Python with Numba:
a.	Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
b.	Launch massively parallel custom CUDA kernels on the GPU.
c.	Utilize CUDA atomic operations to avoid race conditions during parallel execution.
3.	Multi-dimensional grids and shared memory for CUDA Python with Numba:
a.	Learn support for GPU-accelerated Monte Carlo methods.
b.	Learn multidimensional grid creation and how to work in parallel on 2D matrices.
c.	Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.

The tutorial is aimed at anyone wishing to accelerate Python applications. By making use of the Numba (just-in-time) compiler, CPU-based applications can be easily modified to execute on a GPU with possible performance gains. 
Prerequisites include: 

1.	Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations. 
2.	NumPy competency, including the use of ndarrays and ufuncs. 
Note that no previous knowledge of CUDA programming is required.
Please note that prior to the start of the tutorial, all participants should have created an account on the NVIDIA developer site:  courses.nvidia.com/join 
&quot;

ECAI 2020

Fundamentals of Accelerated Computing with CUDA Python

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


<figure class="video_container">
  <iframe src="https://s3.amazonaws.com/pf-user-files-01/u-59356/uploads/2022-03-22/hk13mxo/NAML_ReceptionWithtitle.mp4" width=826  height=526  frameborder="0" allowfullscreen="true"> </iframe>
</figure>

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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



"This tutorial teaches the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® and the NUMBA compilers. Each participant will have access to a GPU workstation in the cloud on which to complete the many example programs used to demonstrate the concepts. Participants will work through dozens of hands-on coding exercises, and at the end of the training, implement a new workflow to accelerate an example program originally designed for CPUs, observing impressive performance gains.
On completion of this tutorial, participants will have additional resources to help them create new GPU-accelerated applications on their own.  Moreover, upon successful completion of the final assessment, participants will receive an NVIDIA Deep Learning Institute (DLI) certificate to recognize their subject matter competency and support professional career growth.
The content for this tutorial has been developed by NVIDIA’s Deep Learning Institute (https://www.nvidia.com/en-us/deep-learning-ai/education/)

The tutorial covers 3 main topics (note that if not all these topics can be covered in the time allocated for the workshop, participants can complete them through self-study thereafter):

1.	Introduction to CUDA Python with Numba: 
a.	Begin working with the Numba compiler and CUDA programming in Python.
b.	Use Numba decorators to GPU-accelerate numerical Python functions.
c.	Optimize host-to-device and device-to-host memory transfers.
2.	Create CUDA kernels in Python with Numba:
a.	Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
b.	Launch massively parallel custom CUDA kernels on the GPU.
c.	Utilize CUDA atomic operations to avoid race conditions during parallel execution.
3.	Multi-dimensional grids and shared memory for CUDA Python with Numba:
a.	Learn support for GPU-accelerated Monte Carlo methods.
b.	Learn multidimensional grid creation and how to work in parallel on 2D matrices.
c.	Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.

The tutorial is aimed at anyone wishing to accelerate Python applications. By making use of the Numba (just-in-time) compiler, CPU-based applications can be easily modified to execute on a GPU with possible performance gains. 
Prerequisites include: 

1.	Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations. 
2.	NumPy competency, including the use of ndarrays and ufuncs. 
Note that no previous knowledge of CUDA programming is required.
Please note that prior to the start of the tutorial, all participants should have created an account on the NVIDIA developer site:  courses.nvidia.com/join 
"

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.

StaRAI - Semantics and Symmetries in Exact Lifted Inference

Mathematical Morphology and Logics: In propositional logics, considering the lattice of formulas, morphological operators will act on formulas (and on their models). In modal logics, dilation and erosion can define modal operators. Such examples will be described in several logics. Then we will show how they can be used to define revision operators satisfying the AGM postulates, merging operators, or explanatory relations.

Mathematical Morphology and Artificial Intelligence

Advancing Towards the SDGS Artificial Intelligence for a Fair, Just and Equitable World, AI4EQ (AI4EQ)

Cognitive Logics: Mechanisms Predicting Human Inference Patterns

The tutorial introduces the audience to the concept of referring expressions, formulae that can be used to communicate identities of otherwise abstract objects. The formalism provides foundations for a successful and unambiguous exchange of information about individuals between agents sharing common knowledge about such individuals, a task that is indispensable in most modern applications of knowledge representation and semantic technologies.

Referring Expressions in Knowledge Representation System

The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases.

In this tutorial, the first 35 years of this development are summarized.

The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, and criticism of the model.

Belief Change: From 1985 to present days

The internet of things (IoT) has been expanding through an increased number of IoT devices and the vast deployment of sensors. Simultaneously, there have been significant advancements in artificial intelligence (AI), in particular machine learning (ML), due to the increased availability of data and computation capabilities. We consider that IoT enhanced with AI would enrich our lives in the future. This tutorial focuses on combining IoT and ML for what we call situation classification. Situation classification targets recognizing real-world events and generating insights for an accurate understanding of the environment's dynamics.

This tutorial's particular theme is the situation classification for future smart cities and smart mobility IoT applications, where we generate insights for relevant events happening in these domains. For instance, we may detect situations such as accidents, emergencies, and traffic congestions in smart cities in real-time for the responsiveness of the city to such incidents. Moreover, IoT data captured from various sensors may enable us for more efficient crowd management and improved public safety. Lastly, autonomous systems such as autonomous driving vehicles, robots, and unmanned aerial vehicles would undoubtedly benefit from situation classification by accurately understanding their environments and planning decisions based on their enhanced perspectives.

"

Combining IoT and ML for situation classification

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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