ScanNet (2018) for 3D processing, nuScenes (2019) for multi-sensors driving, SemanticKITTI (2019) for 3D driving scenes, and many others all together allowed a great performance leap. In fact, many researches displayed the benefit from pre-training deep networks on these large public datasets for a large variety of tasks, demonstrating that high-level features can be shared even for tasks of different nature.

Still, the current research line suffers from following this supervised paradigm that requires large datasets (in order of thousands/millions of data) which annotation is both tedious and menial. While supervised learning undoubtedly brings the best performance, the labelling cost will eventually become unbearable since both the dataset size and the number of sensors constantly increase. Not to mention that encompassing all conditions (lightings, traffic scenarios, weathers, etc.) in a single dataset is impractical. For example, not a single available dataset encompasses dangerous driving scenarios. Leveraging semi or unsupervised learning is necessary to ensure scalability of the algorithms to the real outside world, where they ultimately face situations unseen in the training set. The holy Grail of artificial general intelligence is far from our current knowledge but promising techniques in transfer learning allow expanding training done in supervised fashion to new unlabelled datasets, for example with domain adaptation. Exciting experiments in RITS team and other research labs demonstrated the ability to apply such strategy for example to transfer learning to changing lighting conditions (training on day data and testing on night data), weathers (clear to rain driving), or even nature of data (simulator to real driving).

Today, ML is extensively used in the AV field for the perception systems. However, other AI techniques seem as promising as ML, in addition of being easier to interpret. AI certainly paves the way to new research areas and demonstrates great ability to solve long-standing problems crucial for autonomous driving (e.g. semantic labelling of complex outdoor environments).

# RITS

## Robotics & Intelligent Transportation Systems

The project-team RITS is a multidisciplinary project at Inria, working on Robotics for Intelligent Transportation Systems. It seeks in particular to combine artificial intelligence and mathematical modelling to design advanced intelligent robotics systems for autonomous and sustainable mobility.

Among the scientific topics covered:

* Cross-modal techniques for scene understanding from camera, laser data, GPS, etc...,
Unsupervised or weakly supervised training (domain adaptation, distillation),

Low and high level vehicle control,

Decision making for autonomous driving,

Large-scale traffic modelling and simulation,

Control and optimisation of road transport systems,

Development and deployment of automated vehicles (cyber cars, private vehicles,...).

The goal of these studies is to **improve road transportation** in terms of safety, efficiency, and comfort and also to minimize nuisances. The technical approach is based on driver's assistance, going all the way to full driving automation. The project-team provides to the different partner teams some important means such as a fleet of a dozen computer driven vehicles, various sensors and advanced computing facilities including a simulation tool. An experimental system based on fully automated vehicles has been installed on the Inria grounds at Rocquencourt for demonstration purposes.

<figure><img src="image_54.png" /><figcaption>One of the autonomous driving platforms of RITS</figcaption></figure>
## Cooperative and Human-aware Robot Navigation in Dynamic Environments

The overall objective of Chroma is to address fundamental and open issues that lie at the intersection of the emerging research fields called "Human Centred Robotics" [1]. More precisely, the goal is to design algorithms and develop models allowing mobile robots to navigate and cooperate in dynamic and human-populated environments. Chroma is involved in all decision aspects pertaining to single and multi-robot navigation tasks, including perception and motion-planning.

The general objective is to build robotic behaviours that allow one or several robots to operate safely among humans in partially known environments, where time, dynamics and interactions play a significant role. Recent advances in embedded computational power, sensor and communication technologies, and miniaturized mechatronic systems, make the required technological breakthroughs possible (including from the scalability point of view).

Chroma is clearly positioned in the "Artificial Intelligence and Autonomous systems" research theme of the Inria 2018-2022 Strategic Plan. More specifically we refer to the "Augmented Intelligence" challenge (connected autonomous vehicles) and to the "Human centred digital world" challenge (interactive adaptation).

[1] Montreuil, V.; Clodic, A.; Ransan, M.; Alami, R., "Planning human centred robot activities," in Systems, Man and Cybernetics, 2007

<figure><img src="image_55.png" /><figcaption>Mini-UAV Crazyflies 2.0, controlled by ultra wide band (UWB)</figcaption></figure>
## 5.7 Neurosciences and cognition

AI and cognition have a long collaboration history. AI paradigms most often rely on concepts taken from research in cognition, and can in turn contribute to progresses in cognition science e.g. experiencing with large neural networks can be a tool for neuroscientists to check new models of the brain. The intersection between AI, neurosciences and cognition motivated some of the largest research projects undertaken by mankind, such as the Human Brain Project Flagship funded by the European Commission, or the BRAIN Initiative of the NIH in the USA.

An emerging trend in AI is to follow Nobel laureate Daniel Kahneman's proposal to model human thinking as the continuous interaction of two systems, namely System 1 and System 2.

### *From Kahneman's book, Thinking, Fast and Slow:*

*System 1 thinking is FAST, AUTOMATIC, happens UNCONSCIOUSLY and requires MINIMAL EFFORT*

*System 2 thinking is SLOWER, requires EFFORT, and happens CONSCIOUSLY and DELIBERATELY*

Most ML systems using neural networks can be allocated to System 1 e.g. in the case of vision, speech recognition, autonomous driving etc. The question on how to develop System 2 capacities is subject of debate: some authors believe that these capacities can be obtained using more sophisticated models of the brain i.e. more complex neural networks; others are convinced that complementary AI approaches such as semantic and knowledge-based reasoning will be useful for this purpose. Mid-2020, this debate is in its infancy, more research and experimentation is needed and this will take years if not decades.
<figure><img src="image_56.png" /></figure>

Within Inria, a few research teams are at the intersection of AI and neurosciences. Their work can be qualified as contributions to both System 1 and System 2 thinking, even if some of them might be more closely related to one of them.

Their main scientific challenges are the following:

* **Build better models of the brain**

This challenge is shared by all teams in this domain, as it is the most fundamental problem in neurosciences and cognition. It can concern the healthy brain as well as brain diseases. For this purpose, various modelling paradigms are exploited and
matched with diverse data including MRI, EEG and MEG. Models are developed for
individual cells, clusters of cells, connectivity structures as well as activity patterns
stored in dictionaries.

Towards Common sense

Common sense reasoning is an overarching motivation for AI. It remains a distant goal for all approaches even after major investments and years of research such as Doug Lenat's CYC<fnref n="31" /> project in the 1990s. Research in neurosciences and cognition can ultimately contribute new understandings of common sense human reasoning, but our not-so-recent history invites some modesty on the matter.

Access to higher order executive functions/autonomy

Higher executive functions (temporal organization of behaviour, ability to generalize, manipulation of implicit and explicit knowledge, etc.) as well as real autonomy (continuous learning, flexibility, learning with one or few examples) remain major challenges that we are only beginning to address.

ARAMIS

Algorithms, models and methods for images and signals of the human brain

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. A major challenge is now **to build numerical models of brain diseases from multimodal patient data** based on the development of specific data-driven approaches. Such models shall help to deepen our understanding of neurological diseases and to design effective systems to assist in clinical decisions.

The aim of the Inria ARAMIS project team is to design new machine learning and data analysis approaches for modelling brain diseases and decision support systems to assist clinicians. To this end, we develop approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data. A first line of research is devoted to the detection of alterations in brain imaging data and the design of AI systems to assist radiologists [2]. A second thread concerns the analysis of temporal phenomena from longitudinal data. This involves the development of sophisticated mixed effects models using tools from Riemannian geometry [3]. Such models can reconstruct scenarios of disease progression at the individual and population levels. They are implemented in the freely available software tools

<footnote n="31">³¹ https://en.wikipedia.org/wiki/Cyc</footnote>
Leaspy1 and Deformetrica2. A third axis aims to model the functional interactions between distant brain areas that underlie cognitive processes. This is based on approaches that can model the organization of complex brain networks [1]. They are applied to the design of new devices, brain-computer interfaces and neurofeedback, for the rehabilitation of neurological patients. The team devotes many efforts to the transfer of these tools to clinical studies, through the development of the Clinica software platform3. Finally, we also provide guidelines and frameworks for reproducible research in the field. Three team members (N. Burgos, O. Colliot, S. Durrleman) are chairs in the PRAIRIE 3IA Institute.

[1] De Vico Fallani F, Richiardi J, Chavez M, and Achard S, Graph analysis of functional brain networks: practical issues in translational neuroscience., Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 369:1653, 2014.

[2] Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert M-O, Durrleman S, Evgeniou T, and Colliot O, Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data., NeurolImage, 183, 504-521, 2018

[3] Schiratti J-B, Allassonnière S, Colliot O, and Durrleman S, A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations., Journal of Machine Learning Research, 18:133, 1-33, 2017

1 https://gitlab.com/icm-institute/aramislab/leaspy

2 https://www.deformetrica.org/

3 http://www.clinica.run

<figure><img src="image_57.png" /></figure>
# ATHENA

## Computational Imaging of the Central Nervous System

Although exceptional progress has been obtained for exploring the human brain during the past decades, it is still terra-incognita and calls for specific research efforts to better understand its architecture and functioning.

The ATHENA project-team has the overall objective to better understand the human brain structure and function by developing a new generation of computational models and methodological breakthroughs for brain connectivity mapping. To solve the limited view of the brain provided just by one imaging modality, and recover the brain structural and functional connectivity, the models built by the team are solidly grounded on advanced and complementary integrated non invasive and in-vivo imaging modalities: diffusion Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG).

The main research directions of the team are:

1. Develop rigorous mathematical and computational tools for the acquisition, processing and combined analysis of Diffusion MRI and MEG & EEG data.

2. Push forward the state-of-the-art in Computational Brain Connectivity Mapping and Brain Computer Interfaces (BCI).

3. Develop and address, with our collaborators, clinical and BCI applications.

This will greatly help to better understand and reconstruct the structural and functional brain connectivity and to provide a clinical added value to better identify and characterize abnormalities in brain connectivity. While BCI is advocated as a means to communicate and help restore mobility or autonomy for very severe cases of disabled patients, it is also a new tool for interactively probing and training the human brain.

One third of the burden of all the diseases in Europe is due to problems caused by diseases affecting the brain. The objectives of ATHENA represent a fantastic scientific challenge as well as a pressing clinical need that, when solved, will positively impact the unacceptable burden of brain diseases and open new perspectives in neuroscience.
<figure><img src="image_58.png" /></figure>

Brain mapping

## MNEMOSYNE

### Mnemonic Synergy

At the frontier between Artificial Intelligence and Computational Neuroscience, the MNEMOSYNE team proposes to model the main forms of memory and learning in the brain and to study how they are organized and implement complex cognitive functions. In neuroscience, a major dichotomy is reported between explicit (e.g. semantic, episodic) and implicit (e.g. procedural, habitual) memories and learning. Key mechanisms to understand such cognitive functions as reasoning, decision-making, attentional processes and language rely on competition, cooperation and transfer between these different ways to learn and memorize information: they are presently the topic of major progresses in different fields of neuroscience.

The MNEMOSYNE team designs models of the underlying neuronal structures and circuits under this functional view of brain organization and dynamics. Models are based on different kinds of neural architectures (feedforward, recurrent, convolutional, generative) with the challenge of mimicking the loops between the prefrontal cortex and the basal ganglia, and their interactions with the sensory cortex, hippocampus, amygdala and other cerebral structures, reported to be the substratum for the targeted cognitive functions. These models are the bases for collaborations of the team with the neuroscience and medical communities; they are also the ground for its original positioning in Machine Learning, towards Artificial General Intelligence. The team considers it a major challenge to propose computational models, embodied into virtual or real agents interacting on-line with the environment and able to autonomously extract structures to build a distributed model of the world, flexibly select the best strategy to reach internal and external goals and learn from their errors.

Recent topics of investigation concern language acquisition and the extraction of
syntax, goal encoding in motivated behaviour, transfer from goal-directed to habitual behaviour, planning and reasoning with a working memory and retrospective and prospective deliberation. These models are built in tight interaction with neuroscientists, in association with experimental protocols; they are exploited to consider pathological cases in the medical domain. They are also transferred to the socio-economic world with industrial applications and their impact in social science and humanities is also actively investigated, particularly in joint projects with educational science, linguistics, economics and philosophy.

## PARIETAL

**Modelling brain structure, function and variability based on high-field MRI data.**

Artificial intelligence is a multi-faceted field, and the study of the brain through brain imaging offers an almost unique opportunity to explore these different facets. The Parietal team, member of the largest French brain-imaging platform, Neurospin, explores the links between brain, imaging, and cognition.

First, data acquired on the brain is provided as **signals** (electrophysiology recordings) or **images**, such as those acquired in Magnetic Resonance Imaging. Correctly exploiting these data involves large-scale estimation and statistical problems, which are nowadays solved by optimisation and statistical learning methods (machine learning), one of the areas of AI. For example, reconstructing brain electrical activity from measurements of electromagnetic fields taken at the scalp surface requires the solution of an **ill-posed inverse problem**, for which large-scale regression tools offer optimal solutions. The Parietal team has developed particularly efficient models and algorithms for parsimonious regression. Similarly, reconstructing a MRI image of the brain from a limited number of measurements to reduce the acquisition time amounts to solving a formally similar inverse problem. For these two problems, Parietal's researchers develop methods based on deep learning, leading to faster solvers for large-scale analysis.

On the other hand, it is sometimes necessary to extract patterns present in the brain activity data to build much simpler models of the data based on these patterns. The Parietal team has developed **dictionary learning techniques** and, by working on the structure of the estimators, they have developed very efficient algorithms that can analyse millions of images of the brain in a reasonable amount of time. The same method also allows extraction of patterns from time series.

On other methodological aspects, work on the analysis of **statistical guarantees** is ongoing: when one asserts that the activity of a brain region predicts a person's behaviour, how to guarantee that this is the case, and that this is not an erroneous interpretation? It is difficult to prove that a given region plays a role in the
prediction when many other areas could have the same effect. Parietal researchers develop techniques to find confidence intervals to establish that the statistical relationships highlighted in the images are indeed credible.

Functional images of the brain represent activation when the subject performs particular tasks, such as watching a movie. But while describing in detail the mental operations that follow one another when watching a movie or listening to a story is complicated, we now have artificial neural networks that do it as well as or even better than humans. It is therefore exciting to study **whether certain regions of the brain could react like artificial neurons**. Parietal's researchers have shown that certain areas of the visual cortex behave like successive layers of a deep neural network! We are now studying whether modern language processing systems can explain the response observed in the brain when listening to a story.

Knowledge of the brain does not stop with image and signal processing: experiments produce results that need to be integrated into **knowledge bases**, so that they can be incorporated in unifying theories or can be reused to better analyse new data. Until now, this work has been done by reading publications in the field. Parietal's recent research contributed to automate the acquisition and use of knowledge from publications (neuroquery.org), but also to test the results of several dozens of cognitive neuroscience experiments in order to integrate them into a model. In this way, we can synthesize the experimental information collected into a model of the brain's organization, which becomes more precise as more data is added. In addition, to make it possible to question the role, the structure and relationships between different parts of the brain, Parietal's researchers have created a domain-specific language Neurolang that allows data sets to be queried to automatically identify brain structures in a new brain image. This language has formal guarantees, and allows probabilistic information to be produced with a limited degree of certainty.

<figure><img src="image_59.png" /><figcaption>Functional connectivity between brain regions</figcaption></figure>
## New models of human learning

Teams in this domain study how machines can acquire knowledge models by interacting with their environment, pushed by artificial curiosity mechanisms (otherwise called *developmental robotics*). This is an important challenge connected to the question of sustainability of AI, by learning with a small set of examples as opposed to the huge datasets currently used by deep learning systems with the now well-known consequences in terms of computing resources and energy consumption.

# FLOWERS

## Flowing Epigenetic Robots and Systems

FLOWERS studies models of open-ended development and learning. These models are used as tools to help us understand better how children learn, as well as to build developmental machines that learn like children, with applications in robotics, human-computer interaction and educational technologies.

A major scientific challenge in artificial intelligence and cognitive sciences is to understand **how humans and machines can efficiently acquire world models, as well as open and cumulative repertoires of skills over an extended time span.** Processes of sensorimotor, cognitive and social development are organised along ordered phases of increasing complexity, and result from the complex interaction between the brain/body with its physical and social environment.

To advance the fundamental understanding of mechanisms of development, the FLOWERS team has developed computational models that leverage advanced machine learning techniques such as **intrinsically motivated deep reinforcement learning**, in strong collaboration with developmental psychology and neuroscience. In particular, the team has focused on models of intrinsically motivated learning and exploration (also called curiosity-driven learning), with mechanisms enabling agents to learn to represent and generate their own goals, self-organizing a learning curriculum for efficient learning of world models and skill repertoire under limited resources of time, energy and compute. The team also studies how autonomous learning mechanisms can enable humans and machines to acquire **grounded language skills, using neuro-symbolic architectures for learning structured representations and handling systematic compositionality and generalization**.

Beyond leading to new theories and new experimental paradigms to understand human development in cognitive science, as well as new fundamental approaches to **developmental machine learning**, the team has also explored how such models can find applications in robotics, human-computer interaction and educational technologies. In robotics, the team has shown how artificial curiosity combined with imitation learning can provide essential building blocks allowing
robots to acquire multiple tasks through natural interaction with naive human users, for example in the context of assistive robotics. The team also showed that **models of curiosity-driven learning can be transposed in algorithms for intelligent tutoring systems**, allowing educational software to incrementally and dynamically adapt to the particularities of each human learner, and proposing personalised sequences of teaching activities. In human-computer interaction, the team has shown how incremental learning algorithms can be used to remove the calibration phase in certain brain-computer Interfaces.

<figure><img src="image_60.png" /><figcaption>Poppy torso: curiosity driven learning</figcaption></figure>
## Cognitive Machine Learning

The general aim of CoML is to bridge the gap in cognitive flexibility between humans and machines learning in language processing and commonsense reasoning by reverse engineering how young children between 1 and 4 years of age learn from their environment. CoML conducts work along two axes: the first one, **Developmental AI** is focused on building infant-inspired machine learning algorithms. The second axis, **Quantitative studies of human learning**, uses these algorithms to conduct large scale quantitative analyses of human infants learning in the wild across diverse environments.

Developmental AI rests on the idea that it might be simpler to build a machine that learns as an infant than to build an adult one (A. Turing, 1950). Developmental research shows that infants spontaneously and autonomously learn language, social cognition, and common sense from limited uncurated and unlabelled multimodal data, and in most cultures, with only sparse direct adult supervision. We study how **self-supervised** or **weakly supervised** algorithms can discover representations or discrete units like phonemes or words from the raw acoustic signal, without any expert label (zero resource speech learning). We explore the **inductive biases** of neural systems by studying the conditions of language emergence (zero data language learning). We establish metrics and datasets for unsupervised/self-supervised systems and put together **benchmarks and challenges** in order to help building an international community in this general area.

<figure><img src="image_61.png" /><figcaption>The Zero Resource Challenge Series: learning speech and language representations by self-supervision from raw audio (www zerospeech com).</figcaption></figure>
In quantitative studies of human learning, we analyse naturalistic longform recordings of infants-parents interactions to provide **upper and lower bounds** on the data that can yield successful language learning through self- or weak supervision (for instance, a 4 year old requires between only 2k and 5k hours of directed speech to learn a functioning spoken language dialogue system). We construct **causal models** of language growth that predict infant vocabulary given their input. We also model second language acquisition in adults. The team develops a hardware and software **platform** to help with data collection, annotation, and analysis on a large scale while preserving privacy and security (BeHive project).

## Exploratory actions (AEx)

### AEx- ORIGINS - Grounding Artificial Intelligence in the origins of human behaviour

Project team: FLOWERS

One of the most ambitious goal in Artificial Intelligence (AI) is the realisation of a so- called Artificial General Intelligence (AGI), i.e. an AI that is not limited to the realisation of a predefined set of tasks but is able to generalise its capabilities to any cognitive task that can be solved by human intelligence. However, although AGI is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capacities during the evolution of the species. The AEx ORIGINS will address this gap by extracting computational principles from the literature in Human Behavioural Ecology and applying them in AI to improve the acquisition of complex behaviour in artificial agents.

### AEx - ODiM - Computerised tools to assist the diagnosis of mental illness

Project team: SEMAGRAMME

ODIM is an interdisciplinary project at the interface between psychiatry- psychopathology, linguistics, formal semantics and digital sciences. It aims to develop novel approaches to help diagnose and screenning of psychotic disorders by broadening the long-term methods used in psychiatry. Production of tools is planned so that a maximum number of users from to the Mental Health Sector (doctors-psychiatrists, psychologists, speech therapists...) are able to use them.

Other project-team in this domain: NEUROSYS (Nancy)
5.8 Optimisation

The turn of the century has seen the development of optimisation technology in the
industry and the corresponding scientific field, at the border of Constraint
Programming, Mathematical Programming, Local Search and Numerical Analysis.
Optimisation technology is now assisting public sector, companies and people to
some extent for making decisions that use resources better and match specific
requirements in an increasingly complex world. Indeed, computer aided decision and
optimisation is becoming one of the cornerstones for aiding all kinds of human
activities.

In the more or less near future, quantum computing is expected to revolutionise the
field of optimisation, making it possible to solve problems that are intractable today.
<figure><img src="image_62.png" /></figure>

OPTIMISATION AND MACHINE LEARNING

Machine Learning relies on numerical optimisation for the adjustment of model parameters (billions of them in the case of deep learning), therefore close links have been established for decades between both paradigms. The use of ML as a component of optimisation is a more recent trend, where machine learning models – usually neural networks, thanks to their differentiability properties, allow an end-to-end optimization using simple gradient methods, provided enough data is available. Some challenges are at the intersection of both approaches.
## Scaling up

Models and data continue to grow exponentially as problem sizes increase. It is mandatory to design methods and algorithms able to cope with larger and larger problems without using exponentially increasing computer resources. This is true for all kinds of optimisation paradigms i.e. continuous, discrete or hybrid and for all machine learning approaches.

## Complex structures

ML and optimisation deal with complex objects i.e. not only 1-D to 3-D signal (sound, images, videos etc.) but also structures like graphs, trees, semantic networks etc. Even if in many cases these complex structures can be represented by vectors thanks to the development of specialised embeddings, this is not true for all structures, in particular working directly with graphs can be particularly useful, but this remains a challenging question.

## Proofs, confidence

When dealing with real-world applications, all elements supporting confidence in the AI/optimisation systems used are welcome. In the beginning of this chapter, we addressed the generic question of trust and confidence in AI – in particular in the case of ML. There is a need to produce proofs of convergence or confidence intervals for optimisation systems within a reasonable amount of resources used or computing time.

## Proper use of surrogate models

The first historical use of ML within an optimisation framework, still widely used and profoundly useful, has been to provide a surrogate model for the complex system at hand, which can be used efficiently and faithfully instead of running the real system – which in some cases is not even thinkable. The use of such surrogate models implies to develop tools and methods providing guarantees that the model is close enough to reality so that the results can be put into use.

## OPIS

### Optimisation for large Scale biomedical data

OPIS is a new Inria-Saclay project that aims at addressing challenges raised by **advanced optimisation methods for processing large scale biomedical data**. Optimisation methods are at the core of many recent advances in artificial intelligence since one of the main brain functionalities is to provide optimal responses to problems we face. OPIS seeks optimisation methods able to tackle data with both a large sample-size ("big N" e.g., N=109) and/or many measurements ("big P" e.g., P=104). The methodologies to be explored will be grounded on nonsmooth functional analysis, fixed point theory, parallel/distributed strategies, and neural networks. The new optimisation tools that will be developed will be set in the general framework of graph signal processing, encompassing both regular graphs (e.g., images) and non-regular graphs (e.g., gene regulatory networks).

More precisely, OPIS is working on three fronts:
1. New algorithms are designed for solving high-dimensional problems (sometimes involving up to billions of variables) that are encountered in inverse problems e.g., image reconstruction or restoration, for medical applications.

2. Novel strategies are proposed to address data mining problems that are formulated over graphs. Graph structures allow us to capture complex system interactions such as those existing in biological networks.

3. Deep learning methods are investigated by putting emphasis on robustness guarantees and the ability to account for prior information. Proposing better neural network models is of crucial importance in the context of the diagnosis or prognosis of diseases from medical images.

<figure><img src="image_63.png" /></figure>

Digital Breast Tomosynthesis reconstruction based on machine learning techniques to increase the detectability of microcalcifications (collaboration with GE Healthcare)
The RandOpt team at Inria's Saclay - Ile-de-France research centre, joint team with the CMAP at Ecole Polytechnique, deals with the analysis, development and implementation of randomized blackbox optimisation methods in the continuous domain. RandOpt is in particular focusing on CMA-ES type methods and are interested in benchmarking.

The specificity in black-box optimisation is that methods are intended to solve problems characterized by a non-property—non-convex, non-linear, non-smooth. This contrasts with gradient-based optimisation and poses on the one hand some challenges when developing theoretical frameworks but also makes it compulsory to complement theory with empirical investigations.

RandOpt ultimate goal is to provide software that is useful for practitioners. They see that theory is a means for this end (rather than an end in itself) and it is also RandOpt's firm belief that parameter tuning is part of the designer's task.

This shapes, on the one hand, four main scientific objectives:

1. **develop novel theoretical frameworks** for guiding (a) the design of novel black-box methods and (b) their analysis, allowing to

2. **provide proofs off-key features** of stochastic adaptive algorithms including the state-of-the-art method CMA-ES: linear convergence and learning of second order information.

3. **develop stochastic numerical black-box algorithms** following a **principled design** in domains with a strong practical need for much better methods namely **constrained, multiobjective, large-scale and expensive optimisation**. Implement the methods such that they are easy to use. And finally, to

4. **set new standards in scientific experimentation, performance assessment and benchmarking** both for optimisation on continuous or combinatorial search spaces. This should allow in particular to advance the state of **reproducibility of results of scientific papers** in optimisation.

# OPTIMISATION AND PERFORMANCE

In terms of the design of effective Artificial Intelligence techniques dealing with complex tasks and optimisation problems, the main challenges are:

(1) gaining a more fundamental understanding of what makes a task/problem difficult to solve,
(2) accommodating the broad range of complex tasks/problems with respect to the broad range of specialized solving techniques in an abstract, flexible and efficient manner,

(3) cross-fertilizing the knowledge from other disciplines, such as HPC, operation research, etc, for an increased accuracy and efficiency,

(4) Dealing with large scale and computationally expensive tasks/problems,

(5) Incorporating the multi-objective nature of many practical tasks/problems, and scaling on (ultra-scale) modern supercomputers.

## BONUS

### Big Optimisation aNd Ultra Scale computing

BONUS is a joint research team between Inria Lille - Nord Europe, CRISTAL (UMR 9189, Univ Lille, CNRS, EC Lille) and the University of Lille. The team addresses big optimisation problems, defined by a large number of parameters, of decision variables, and/or many computationally expensive objective functions. The focus is on the design of effective solving techniques from computational intelligence (stochastic local search, evolutionary computation) and exact combinatorial search (branch-and-bound) following three research lines:

1. **Decomposition-based optimisation:** Given the particularly large scale of big optimisation problems in terms of variables and objectives, BONUS develops new decomposition techniques by breaking up the original target problem into smaller subproblems that are easier to solve, and loosely coupled or independent. Solving these subproblems simultaneously and cooperatively is essential to address the curse of dimensionality.

2. **Machine learning-assisted optimisation:** When dealing with high-dimensional problems and objective(s) coming from simulations or other black-box systems, BONUS is coupling computational intelligence techniques with surrogate meta-models and other machine learning algorithms in order to speed-up the convergence of the optimisation process and to cope with the computationally expensive nature of big optimisation problems.

3. **Ultra-scale optimisation:** In order to benefit from the massive parallelism offered by modern supercomputers, BONUS relies on ultra-scale computing for the effective resolution of big optimisation problems, such as handling the large amount of subproblems generated by decomposition, or the parallel evaluation of simulation-based objectives and meta-models.

From the software standpoint, BONUS objective is to integrate the approaches BONUS will develop in ParadisEO [3] (ParadisEO: http://paradiseo.gforge.inria.fr/) framework in order to allow their reuse inside and outside the Bonus team. The
major challenge will be to extend ParadisEO in order to make it more collaborative with other software including machine learning tools, other (exact) solvers and simulators.

<figure><img src="image_64.png" /></figure>

BONUS closely collaborates with international researchers from the University of Mons (Belgium), the University of Coimbra (Portugal), Shinshu University (Japan), City University (Hong Kong), Monash University, and University of Luxembourg in an effort to reflect the strong synergy between optimisation, computational intelligence and parallel computing.

## NEO

### Network Engineering and Operations

NEO is positioned at the intersection of Operations Research and Network Science. NEO researchers model situations arising in several application domains, involving networking and distributed systems in one way or the other, with the goal to take (possibly) optimal decisions using the tools of Stochastic Operations Research. Modern AI is also involved with decision, taken (or suggested) by machines based upon some data (machine learning). Quite naturally then, distributed AI has become one of NEO research topics along the following axes:

1. Semi-supervised learning on graph structures and its distributed implementations.

2. Design of Internet-scale distributed machine learning systems, both for training and inference, with a focus on the trade-off between performance and economic and environmental costs.

3. Multi-agent learning models based on game theory. This includes evolutionary game theory whose equilibrium consists of restpoints of Darwinian-type dynamics, dynamic non-cooperative games in which
cooperation may be induced by threats and punishments, and matching
games that have been applied for recommendation networks.

4. Analysis of the fundamental limits of the influence of information-provisioning policies (recommender systems, media, social networks, etc.) on decision takers involved in competitive interactions (markets, shared-resource systems).

The team collaborates on these topics with many industrial partners, including Qwant, Nokia, Accenture, MyDataModels, Azursoft.

Other related NEO research topics are: resource allocation in communication networks, social networks, green computing and communications, and sustainable development.

POLARIS

Performance analysis and Optimisation of LARge Infrastructures and Systems

The goal of the POLARIS project is to contribute to the understanding (from the
observation, modeling and analysis to the actual optimisation through adapted
algorithms) of the performance of very large-scale distributed systems such as
supercomputers, cloud infrastructures, wireless networks, smart grids,
transportation systems, or even recommendation systems.

A first line of research is devoted to the use statistical learning techniques (Bayesian inference) to model the expected performance of distributed systems to build aggregated performance views, to feed simulators of such systems, or to detect anomalous behaviours.

In a distributed context it is also essential to design systems that can
seamlessly adapt to the workload and to the evolving behaviour of its
components (users, resources, network). Obtaining faithful information on the
dynamic of the system can be particularly difficult, which is why it is generally
more efficient to design systems that dynamically learn the best actions to play
through trial and errors. A key characteristic of the work in the POLARIS project
is to leverage regularly game-theoretic modeling to handle situations where
the resources or the decision is distributed among several agents or even
situations where a centralised decision maker has to adapt to strategic users.

The POLARIS members are thus particularly interested in the design and
analysis of adaptive learning algorithms for multi-agent systems, i.e. agents
that seek to progressively improve their performance on a specific task (see
Figure). The resulting algorithms should not only learn an efficient (Nash)
equilibrium but they should also be able of doing so quickly (low regret), even
when facing the difficulties associated to a distributed context (lack of
coordination, uncertain world, information delay, limited feedback, ...).

An important research direction in POLARIS is thus centered on reinforcement learning (Multi-armed bandits, Q-learning, online learning) and active learning in environments with one or several of the

following features:

* Feedback is limited (e.g., gradient or even stochastic gradients are not available, which requires for example to resort to stochastic approximations);

* Multi-agent setting where each agent learns, possibly not in a synchronised way (i.e., decisions may be taken asynchronously, which raises convergence issues);

* Delayed feedback (avoid oscillations and quantify convergence degradation);

* Non stochastic (e.g., adversarial) or non stationary workloads (e.g., in presence of shocks);

* Systems composed of a very large number of entities, that we study through mean field approximation (mean-field games and mean field control). As a side effect, many of the gained insights can often be used to dramatically improve the scalability and the performance of the implementation of more standard machine or deep learning techniques over supercomputers.

## KAIROS

### Temps Logique Multiforme pour Conception de Systèmes Cyber-Physiques

Machine Learning (ML) techniques (e.g. Deep Neural Networks) have benefited from efficient implementation platforms (GPUs and TPUs) and from compilation methods developed by the High Performance Computing (HPC) community to gain practical feasibility and recognition.

Meanwhile, Safety-Critical (often Real-Time) Embedded systems identified themselves as a place of choice for real-life ML applications (e.g. automated driving, digital twin models). Therefore it becomes tempting and profitable to combine both domains, and in particular to federate:
1. the optimized compilation methods for data parallel specifications, developed in the HPC/ML community, and

2. the methods developed in the embedded real-time community to provide worst-case resource consumption guarantees for task parallel specifications.

Based on the deep proximity between intermediate formalisms of HPC/ML compilers (MLIR/SSA) and formalisms used in real-time design (Lustre), the Kairos team explores methods for the specification and (safe and efficient) implementation of ML-friendly high-performance embedded applications.

Other project-team in this domain: REALOPT (Bordeaux)

## 5.9 AI and Human-Computer Interaction (HCI)

Humans can now delegate tasks such as driving a car or piloting a plane, and AI systems are regularly touted to be "better than humans" at various high-level tasks. AI systems are not perfect however, and humans have been kept or put "in the loop" of many AI-based safety-critical systems to protect against unexpected system behaviours. Unfortunately, this arrangement has led to some dire consequences, as exemplified by recent accidents such as the crashes of two Boeing 737 Max commercial planes, where the anti-stall system made the planes nose-drop twenty-six times in a row in less than ten minutes without giving the pilots the necessary information and control to save the plane. Such accidents are the consequence of an unfettered trust in technology over human skills, and a shift from situations where humans delegate tasks but remain in control to those where the computer treats the human as a source of input to an algorithm. The "human in the loop" is essentially a cog in the machine, who takes the blame when things go wrong. Such systems do not take optimal advantage of human talent and system abilities, but rather assume that the computer can always compute an optimal solution. *Thus, a major challenge for both AI and HCI is to create a better division of labor between humans and computers, harnessing their respective powers and capabilities while acknowledging their limitations and weaknesses.*

Another strand that interweaves AI and HCI relates to the massive quantities of personal data analysed by powerful machine learning algorithms. Our interaction with the digital world has been fundamentally redefined — our decisions are monitored, nudged and often manipulated, which threatens not only our privacy but also democracy and basic human rights. Here too, human control over computer processes has been traded for computer control over human behaviour. A second major challenge is how to bring true transparency and explainability to AI systems through appropriate user interfaces and visualizations.
Current applications of AI techniques to fields such as medical diagnostic, justice sentencing or automated driving tend to deskill expert users: by automating tasks once performed by humans, it may be possible to improve productivity for "normal" situations. But computers are extremely bad at handling exceptional cases, and it is illusory to think that a "better" AI will significantly change this situation. Humans, on the other hand, are very good at handling exceptional cases, as long as they can stay trained, but are notoriously bad at monitoring activities. A *third major challenge is how to combine interactive and AI systems so that each takes advantage of the other's strengths at the appropriate time, while minimizing each other's limitations*.

Modern AI systems are becoming so complex that engineers require new tools simply to monitor and manage their development, evolution, debugging, and generally understand what is happening "under the hood". For example, large ML environments come with sophisticated tools to design and program them<fnref n="32" />. Most steps involved in AI systems require tools to assess quality of data, features, training, and decisions; to understand the behaviour of an AI system at any particular point; to monitor and improve its quality; to discover biases and uncertainty in the results; and to deliver the results to target users in a meaningful way. A *fourth major challenge is to create better, more user-centred tools for experts who create and evaluate AI systems*.

## HCI to Improve AI

In addition to tools to improve AI, HCI should also help create more transparent AI systems so they can be assessed by experts in their application domains. For example, bank loan management is more and more assisted by AI tools and has a direct impact on the life of citizens. Some automated decisions have been subject to structural biases difficult to foresee by AI engineers but certainly detectable by loan experts<fnref n="33" />. However, addressing these biases require communication tools between the two kinds of experts to find-out the causes and agree on remedies. For the loans, causes have been found in faulty proxy measures used to score people, and in unbalanced training data misrepresenting women or minorities. Discovering these biases requires human judgement, and can be very different in kind.

Transparency is also more than explaining decisions or showing the machinery, it also consists in explaining or taking into account the capabilities of a system and its limitations. Self-driving cars are good in some standard situations but unreliable in others. They should provide a warning to the driver to take back the control when needed, which requires AI systems to be aware of their own level or reliability (something they rarely do), and to gracefully hand the control over to humans, something that is notoriously difficult and will require more research.

<footnote n="32">³² K. Wongsuphasawat et al., "Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow," in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 1-12, Jan. 2018,</footnote>

<footnote n="33">³³ C. O'Neil, *Weapons of Math Destruction*, Crown Publishing, 2016</footnote>
Finally, novel machine learning systems try to learn continuously from humans through interaction with them to complete their knowledge. A system such as Google search improves its precision by monitoring the rank of the results that the user reads (clicks on) after a search query. This method is only effective at improving the "precision" of the search engine, but not its recall (if a result is not shown, it cannot be ranked). Finding methods to learn interactively and measure the increase in quality and usability remains a complex problem needing more research.

## Aviz
Analysis and Visualization

Aviz is a multidisciplinary project that seeks to improve visual exploration and analysis of large, complex datasets by tightly integrating analysis methods with interactive visualization.

Our work has the potential to affect practically all human activities for and during which data is collected and managed and subsequently needs to be understood. Often data-related activities are characterized by access to new data for which we have little or no prior knowledge of its inner structure and content. In these cases, we need to interactively explore the data first to gain insights and eventually be able to act upon the data contents. Interactive visual analysis is particularly useful in these cases where automatic analysis approaches fail and human capabilities need to be exploited and augmented.

Within this research scope Aviz focuses on five research themes:

- Methods to visualize and smoothly navigate through large datasets;

- Efficient analysis methods to reduce huge datasets to visualisable size;

- Visualization interaction using novel capabilities and modalities;

- Evaluation methods to assess the effectiveness of visualization and analysis methods and their usability;

- Engineering tools for building visual analytics systems that can access, search, visualize and analyze large datasets with smooth, interactive response.

In collaboration with the TAU project-team, Aviz visualizes the HAL repository, containing all the publications of public French research institutions, using multidimensional projections to create a "map", resulting from Natural Language Processing analysis (topic modelling), clustering to collect thematic regions over the map and find meaningful labels. All these techniques related to AI are gathered using a web-based user interface to let researchers of any domain explore the publications around topics or authors, allowing complex AI techniques to be explored by a large audience of users. See [Philippe Caillou, Jonas Renault, Jean-Daniel Fekete, Anne-Catherine Letournel, Michèle Sebag. Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. IEEE CG&A 2020, to appear] and https://cartolabe.fr.
<figure><img src="image_65.png" /><figcaption>Cartolabe visualizing HAL, with 208984 authors (red) and 827156 articles (blue)</figcaption></figure>

Aviz is also working on network analysis and visualization, to let network researchers such as historians, sociologists, or brain researchers incorporate their prior knowledge into ensemble clustering methods [Alexis Pister, Paolo Buono, Jean-Daniel Fekete, Catherine Plaisant, Paola Valdivia. Integrating Prior Knowledge in Mixed Initiative Social Network Clustering. IEEE TVCG 2021, to appear]. With PK-Clustering, users with little understanding of the clustering algorithms can still introduce some of their prior knowledge to better select or steer algorithms, instead of blindly believing the results of one particular algorithm.

<figure><img src="image_66.png" /><figcaption>PK-Clustering, showing the results of nine clustering algorithms shown as columns of dots on the left (each cluster has a colour), applied to the network on the right, and consolidated on the rightmost column against prior knowledge.</figcaption></figure>
LOKI envisions computers as tools that could ultimately empower people, focusing on how such tools can be designed and engineered. By better understanding phenomena that occur at each level of interaction and their relationships, we gather the necessary knowledge and technological bricks to reconcile the way interactive systems are engineered for, around, and with human abilities. Our scope of research encompasses a broad set of interactive environments (desktop computers, mobile devices, VR, BCI...) and borrows its methods from fields as varied as psychology and neuroscience, AI, or design and engineering.

In our goals to better understand users and to design systems that adequately respond to their abilities, we frequently make use of recent AI contributions, notably machine learning and optimization. We played an instrumental role in the design of the new French keyboard layout standard [NF Z 71-300. http://norme-azerty.fr/) commissioned by the French Ministry of Culture, using state-of-the-art combinatorial optimization methods [A. Feit et al., Élaboration de la disposition AZERTY modernisée. 2018. https://hal.inria.fr/hal-01826476]. In collaboration with Aalto University and the Max Planck Institute, we developed a workflow that allowed non-technical typography and linguistics experts to iterate and evaluate layout ideas with an optimizer. That optimizer was in turn able to express the consequences of these ideas in understandable terms of ergonomics and typing performance [A. Feit et al., AZERTY améliore: Computational Design on a National Scale. In Communications of the ACM (In press)].

Using a different approach, AI methods can also be leveraged to dynamically adapt user interfaces depending on the user's profile, context of interaction, or needs. As an example, with colleagues from University College London, we used hierarchical clustering methods to adapt displayed content to user's profile, in the context of mobile news reading [Constantinides et al., Exploring mobile news reading interactions for news app personalisation https://hal.inria.fr/hal-01252631]. We also plan to explore the use of computational methods to dynamically anticipate users' needs in the context of rich-software interaction and help them discover novel features they are not yet aware of (ANR project DISCOVERY).

Many interactive contexts could benefit from a synergy between user input and system intelligence. One of our hypotheses is that users are more likely to accept a solution suggested by an AI when they have directly contributed to the development of that solution (e.g., through occasional explicit inputs), while the AI provides "honest" feedback that acknowledges its possible imprecision. We are exploring this question in the context of archival of old handwritten documents, which currently combines document scanning with manual or automatic transcription in a sequential manner. Following the same Human-AI partnership paradigm, we are currently
exploring with colleagues from the University of Waterloo how users rely on AI-suggested words when typing text. We investigate how users manage the trade-off between typing words with a virtual keyboard and using the suggestions proposed by the AI, depending on the accuracy of the suggestions and the efficiency of the interface. This will help inform the design of interactive systems by providing ways to automate the user's task [Roy et al. under review CHI 2021].

<figure><img src="image_67.png" /></figure>

Interacting with a system in real time requires the ability to gather and interpret continuous data streams that can be noisy or that can lack semantics. AI allows us to better leverage these rich signals and to solve known interface issues in novel and efficient ways. Latency for instance, whether noticeable or not [R. Jota et al., How Fast is Fast Enough? A Study of the Effects of Latency in Direct-touch Pointing Tasks. In Proc. of ACM CHI '13], is a scourge of interaction performance. Up until recently, its only cure was to wait for hardware to improve — which is however inevitably followed by more demanding software, bringing latency back to where it started. We tried another, more hardware-independent approach: we applied state-of-the-art optimization and estimation techniques to tune an algorithm capable of accurately predicting cursor movements in the near future, which we used to visually compensate end-to-end latency for relative pointing [M. Nancel et al., Next-Point Prediction for Direct Touch Using Finite-Time Derivative Estimation. In Proc. of ACM UIST '18. https://hal.inria.fr/hal-01893310]. Also using optimization algorithms, and in collaboration with Aalto University and KAIST, we designed a tool able to adapt in real time the acceleration profile of a cursor to the user's pointing skills and habits, be it controlled by a mouse, a trackpad, or even by hand gestures in mid-air [B. Lee et al. AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization. In Proc. ACM CHI '20. https://hal.inria.fr/hal-02918581].

<figure><img src="image_68.png" /></figure>
Early thinkers such as J.C.R. Licklider and D. Engelbart have put forward the concept of “human-machine symbiosis”<fnref n="34" /> or the vision of “augmenting human intellect”<fnref n="35" /> where computer systems use AI to serve, rather than replace, human intelligence and expertise. Creating such successful *human-AI partnerships* is key when combining AI and HCI.

Human-Computer Interaction focuses on the interaction between the user and a system, which we assume is a dynamic relationship that changes over time. When we deal with intelligent systems, both the user and the system can have agency. One of the key interaction design challenges is how to manage this shared agency, ideally leaving the user in control of the interaction, but at least giving them ‘informed consent’ as to what is happening. The standard ‘human-in-the-loop’ perspective treats the human user as input to the algorithm, and success is defined in terms of creating faster, higher performing algorithms. While creating better algorithms remains a desirable goal, it is critical that we also take a human-centered perspective that defines success in more qualitative, user-oriented terms, which includes increased human performance, but also increased human capabilities and satisfaction. This perspective also colors how we view mixed-initiative approaches. Instead of trying to replace the human user with an algorithm, they emphasize the on-going role of the user within the interaction. Most of today’s mixed-initiative research still focuses on the algorithm, rather than enhancing human skills. Human-AI partnerships seek to leverage the best characteristics of human users and intelligent systems, where the combination exceeds what can be accomplished by either alone.

ExSitu
Extreme Situated Interaction

ExSitu explores the limits of interaction — how extreme users interact with
technology in extreme situations. We are particularly interested in creative
professionals, artists and designers who rewrite the rules as they create new works,
and scientists who seek to understand complex phenomena through creative
exploration of large quantities of data. Studying these advanced users today will not
only help us to anticipate the routine tasks of tomorrow, but to advance our
understanding of interaction itself.

<footnote n="34">³⁴ http://memex.org/licklider.pdf</footnote>

<footnote n="35">35 https://www.dougengelbart.org/pubs/papers/scanned/Doug_Engelbart-AugmentingHumanIntellect.pdf</footnote>
In creative practices, *human-centred machine learning* facilitates the workflow for creatives to explore new ideas and possibilities. We have compiled recent research and development advances in human-centred machine learning and AI in creative industries [B. Caramiaux et al. *AI in the media and creative industries*, New European Media (NEM), April 2019, pp. 1-35. https://hal.inria.fr/hal-02125504]. We have also explored the use of Deep Reinforcement Learning in the context of sound design by comparing manual exploration versus exploration by reinforcement. We showed that an algorithmic sound explorer learning from human preferences enhances the creative process by allowing holistic and embodied exploration as opposed to the analytic exploration afforded by standard interfaces.

We are also interested in designing effective *human-computer partnerships*, in which expert users control their interaction with technology. Rather than treating human users as the 'input' to a computer algorithm, we explore human-centered machine learning, where the goal is to use machine learning and other techniques to increase human capabilities. Our specific goal is to create *co-adaptive systems* that are discoverable, appropriable and expressive for the user. The CREATIV ERC Advanced project developed this approach and created a series of prototypes designed to increase the user's power of expression on mobile devices: *CommandBoard* [J. Alvina et al. *CommandBoard: Creating a General-Purpose Command Gesture Input Space for Soft Keyboards*. Proc. UIST 2017. http://hal.inria.fr/hal-01679137], *FieldWard* [J. Malloch et al. *Fieldward and Pathward: Dynamic Guides for Defining Your Own*. Proc. CHI 2017. http://hal.inria.fr/hal-01614267], *Expressive Keyboard* [J. Alvina et al. *Expressive Keyboards: Enriching Gesture-Typing on Mobile Devices*. Proc. UIST 2016. http://hal.inria.fr/hal-01437054] (figure below).
<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

Expressive Keyboard

rich output

word

mapping

recognizer:
what word?

feature
analysis

rich gesture

CommandBoard (left) lets users enter complex commands with gestures; Fieldward (center) lets users define their own gestures while ensuring that they are recognizable by the system; and Expressive Keyboard (right) extracts expressive characteristics of the user's gesture to generate rich, expressive output, including dynamically modifying color, font characteristics and even emoji expressions.

When we work with creative professionals, we focus not on trying to make them more creative — they are already creative — but rather on providing tools that support their own, personal creative process. Such tools include the use of interactive paper to support composers [Musink, Polyphony] and designers [StickyLines, Enact]. We have also explored how mood board designers and intelligent systems can effectively share agency according to their in-the-moment needs with Semantic Collage [J. Koch et al. (2020) Semantic Collage. In Proc. DIS'20. https://dl.acm.org/doi/10.1145/3357236.3395494] and ImageSense: [J. Koch et al. (2020) ImageSense: An Intelligent Collaborative Ideation Tool to Support Diverse Human-Computer Partnerships. In Proc. ACM on Human Computer Interaction, Issue CSCW. https://hal.archives-ouvertes.fr/hal-02867303], joint with Aalto University.
In the *Bayesian Information Gain* (BIG) project, joint with Telecom Paris, we use a technique based on Bayesian Experimental Design where the criterion is to maximize the information-theoretic concept of mutual information: rather than simply interpret user commands, BIG uses user input to update its knowledge about the user's intended goal and provides an output that maximizes the expected information gain from the next input. In other words, the system challenges the user in order to make interaction more efficient. We have applied BIG to multiscale navigation [W. Liu et al. BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation. Proc. CHI 2017. http://hal.inria.fr/hal-01677122] and to file retrieval [W. Liu et al. BIGFile: Bayesian Information Gain for Fast File Retrieval. Proc. CHI 2018. http://hal.inria.fr/hal-01791754] and demonstrated performance gains of up to 40% compared to conventional navigation techniques.

## ILDA

### Interacting with Large Data

ILDA designs data-centric interactive systems that provide users with the right data at the right time and enable them to effectively manipulate and share these data. Our work focuses on the design, development and evaluation of novel interaction and visualization techniques to empower users in both mobile and stationary contexts involving a variety of display devices, including: smartphones and tablets, augmented reality headsets, desktop workstations, table tops, ultra-high-resolution wall-sized displays. Our research themes include novel forms of input and display for both groups and individuals, as well as novel ways to interact with novel data models that enable diverse structuring and querying strategies, give machine-processable semantics to the data and ease their interlinking. We investigate ways to leverage this richness from the users' perspective, designing interactive systems adapted to the specific characteristics of data models and data semantics, with a focus on mission critical systems and the exploratory analysis of scientific data.

With colleagues from Paris-Descartes and the ExSitu team we investigated human-Al partnerships in the domain of neuroscience and time series analysis (EEG signals). We first explored how to aid expert neuroscientists evaluate epileptiform patterns found in EEG signals, by combining visualization and automated processing in the form of similarity search algorithms. We examined how using different visualizations can affect the similarity perception in EEG signals, and how different visualizations can better match similarity measures [A.Gogolou, et al. Comparing Similarity Perception in Time Series Visualizations. IEEE TVCG 2019 (Proc InfoVis 2018), https://hal.inria.fr/hal-01845008]. We thus showed that the notion of similarity is visualization-dependent, and the need to match automated processes with appropriate visual representations. Other work also helps experts query massive data series collections (such as EEG databases) within interaction times. We provided
progressive similarity search results on large time series collections (100 GB) and showed how these can cut waiting times for users, as we observed that high-quality approximate answers are found very early, e.g., in less than a second [A.Gogolou et al. *Progressive Similarity Search on Time Series Data*. Proc BigVis 2019, https://hal.inria.fr/hal-02103998v1]. Nevertheless, it is important for users to be able to determine the quality of these early answers and to decide if they need to wait further for better matches. To this end, we have worked on providing probabilistic distance and error bounds, to help analysts evaluate the quality of their progressive results [A.Gogolou et al. *Data Series Progressive Similarity Search with Probabilistic Quality Guarantees*. Proc ACM SIGMOD 2020, https://hal.inria.fr/hal-02560760v1].

<figure><img src="image_73.png" /></figure>

Three time series visualizations compared in order to understand if we perceive similarity differently with each one (Line Chart left, Horizon Graph middle, Colorfield right).

We also have a long-lasting collaboration with colleagues from INRAe, where we combine visual exploration with evolutionary computation to help guide experts in exploring large multi-dimensional datasets. Our framework (Evolutionary Visual Exploration - EVE), uses an interactive evolutionary algorithm to steer the exploration of multidimensional datasets towards two dimensional projections that are of interest to the analyst [N.Boukhelifa et al. *Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search Evolutionary Computation*, In Evolutionary Computation, MIT Press, 2018]. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. This work has led to a prototype application that has been used by domain experts in different fields to formulate interesting hypotheses and reach new insights when exploring freely [N.Boukhelifa et al. *Evolutionary Visual Exploration: Evaluation With Expert Users*. In Computer Graphics Forum 2013, https://hal.inria.fr/hal-02005699v1]; has acted as a collaborative platform for teams of researchers to explore trade-offs [N.Boukhelifa et al. *An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts*. Proc ACM CHI 2019, https://hal.inria.fr/hal-02005699v1]; and has initiated investigations about how to best test and evaluate frameworks such as EVE, which incorporate human and artificial intelligence that work together to reach decisions.

Signal+AI as input to HCI

Interactive systems increasingly take advantage of sensors that capture rich user input such as voice, gaze, gestures or brain activity. HCI uses AI techniques, particularly machine learning, to analyse, recognize and/or classify these signals. The context of interaction creates specific constraints that push the limits of current AI
techniques: processing must occur in real time, at the scale of the human perception-action loop (typically under 100ms and sometimes much less); models often need to be trained with very few examples, e.g. a user is only willing to show a gesture once or twice and expect the system to robustly recognize it from then on; the model must adapt to changes in user behaviour over time. In many cases, recognition must occur progressively, as the signal arrives, so that the system can provide real-time feedback and feed-forward, as exemplified by the Octopocus dynamic guide for gesture input<fnref n="36" />. In addition, continuous input, e.g. movement data from a Kinect sensor, must be segmented in real-time in addition to the segments being recognized. Interactive Machine Learning, Reinforcement Learning, Active Learning and Online Learning all provide potential approaches to address these problems.

## PERVASIVE

The Inria project PERVASIVE INTERACTION develops theories and models for context aware, sociable interaction with systems and services that are composed from ordinary objects that have been augmented with abilities to sense, act, communicate and interact with humans and with the environment (smart objects). The ability to interconnect smart objects makes it possible to assemble new forms of systems and services in ordinary human environments.

Pervasive Interaction explores the use of situation models as a foundation for situated behaviour by smart objects. Research is driven by experiments with situated interaction with people, with environments, and with pervasive computing.

The research program addresses the question: can situation modelling provide a theory for situated behaviour by smart objects? The program is driven by the following four research questions:

* Q1: What are the most appropriate computational techniques for acquiring and using situation models for situated behaviour by smart objects?

* Q2: What perception and action techniques are most appropriate for situated smart objects?

* Q3: Can we use situation modelling as a foundation for sociable interaction with smart objects?

* Q4: Can we use situated smart objects as a form of immersive media?

It is organized as four interacting research areas responding to these research questions:

* RA1. Acquiring and Using Situation Models (Q1)

* RA2. Perception of People, Activities and Emotions (Q2)

<footnote n="36">³⁶ O.Bau & W. Mackay. OctoPocus: A Dynamic Guide for Learning Gesture-Based Command Sets. UIST 2008. http://dl.acm.org/citation.cfm?id=1449724].</footnote>
## Explainable AI

Explainable AI is usually characterized in terms of explaining to users how an algorithm works. However, a true human-computer interaction perspective shifts the focus, arguing that users rarely care about the details of how the *algorithm* works, and instead are more concerned with how such algorithms may *affect* them personally as well as on their ability to accomplish the task at hand. Thus, the key challenge of user-centred explainable AI is how to reveal information to the user in terms that users understand. Users must be able to visualize how the AI system is currently interpreting and reacting to their behaviour, as well as what decisions it is making and why. Users should be able to intervene in the process, not simply to discover how and why the AI performed a particular interaction, but also have easy ways to inform the AI when those decisions are incorrect and suggest better solutions. Systems such as Fieldward and Pathward<fnref n="37" /> provide both visual feedback and progressive feedforward as the user draws a proposed new gesture command. The AI dynamically interprets the gesture as it is drawn and provides a continuous classification that is revealed via a changing coloured heatmap or gesture continuations. This shows the user both how the AI has interpreted the gesture as of that instant and suggests alternative strategies for successfully generating a new, unique command.

## Cognitive Biases, Ethics, and Legal Issues

Fairness, explainability and accountability are critical properties for the acceptability of AI systems in a wide range of domains. These properties, however, must be assessed from a human perspective, not just from a system perspective. For example, Tversky and Kahneman's seminal experiments in behavioral economics show that human perception of fairness is not always rational and depends heavily on contextual information such as how the question is asked. More generally, many cognitive biases are known to affect human decision making and reasoning, such as confirmation bias and anchoring. This implies that we need to adopt HCI-centric experimental methods that involve participants rather than relying solely on the simulations and measurements common in AI research. However this also raises ethical questions about whether and how AI systems should account for human biases, either by reproducing them or, on the contrary, combating them.

Another type of bias involves the training sets for intelligent systems. Recent studies have shown that face detection algorithms are extremely accurate for white men (over 98%), less accurate for white women, and less than 30% accurate for black

<footnote n="37">³⁷ J. Malloch et al. Fieldward and Pathward: Dynamic Guides for Defining Your Own. Proc. CHI 2017. http://hal.inria.fr/hal-01614267]</footnote>
women. When young, white male engineers select training sets of people who look like them, the result will be biased when applied to the general population, such as using this data for identifying potential criminals or potential job candidates.

Delegating tasks and decisions to AI systems raises additional ethical and legal questions, in particular about accountability and responsibility. While there seems to be consensus that humans should ultimately be responsible for the decisions made by AI systems, the temptation is to blame the user rather than the system designer, as exemplified by the accident that killed the driver of an autonomous car. A key question here is whether the interface to the AI system provided the user with sufficient information to avoid the accident, and whether it accounted for human traits and behavior. Assuming that users will always remain in a high state of alert after hours of accident-free driving is a fundamentally poor design decision, not a fault of the human user. Ethical issues must be addressed within the larger socio-technical environment in which the system operates.
# 6. European and international collaboration on AI at Inria

## COLLABORATIONS IN AI: INRIA'S VISION

Inria's European and international cooperation actions aim to promote exchange between Inria and the most dynamic geographical areas, whilst upholding European values for a human-centric AI<fnref n="38" />. The context is well known: the race for investment in certain areas of the world, the role of China and the United States in AI, the race for talent by prestigious foreign academic institutions and by private actors in AI.

This context encourages the institute to reinforce collaborations that are likely to boost the quality of Inria's work, to guarantee the visibility and positioning of teams at the best European and international level, but also to enrich the institute's debate on the impact of AI on our societies.

In addition to the links that are naturally established between researchers through informal collaborations and exchanges, Inria, as a national public institute on digital technology, builds its international policy through targeted agreements with partners, taking into account the orientations of France's international strategy, the specific constraints it faces, and the European framework.

## CONTRIBUTION TO EUROPEAN R&I EFFORTS IN AI

Europe's strengths lie in the quality of its researchers and engineers, training and applications. Aware of the challenges of sovereignty, the EU has adopted a human-centred strategy, advocating ethical principles<fnref n="39" />. Inria's involvement in European AI efforts relies on three dimensions: integration into networks, participation in large-scale projects and a solid contribution to exploratory research, notably through ERC-funded projects.

### (i) Integration into networks

Inria is a member of BDVA (Big Data Value Association) and EU Robotics, which are European associations bringing together industrial and academic partners active in the fields of data and robotics, respectively coordinating the corresponding Public-Private Partnerships (PPPs). Moreover, INRIA participates in the AI/Data/Robotics PPP proposal to be submitted to the European Commission in 2021.

In addition, a number of academic oriented networks emerged in Europe that include:

* networks at the initiative of scientific communities, such as, CLAIRE (Confederation of Artificial Intelligence Research Laboratories in Europe) and, ELLIS (European laboratory for learning and intelligent systems). Inria

<footnote n="38">³⁸ The Ethics Guidelines for Trustworthy Artificial Intelligence (AI), AI HLEG, April 2019</footnote>

<footnote n="39">³⁹ White Paper on Artificial Intelligence: a European approach to excellence and trust, EC, February 2020</footnote>
institutionally supports the CLAIRE initiative, but acknowledges support to the ELLIS initiative from part of its researchers;

* networks at the initiative of the European Commission to help structure the various AI communities and stimulate dialogue and convergence between them.

With respect to the network supported by the European Commission through the Horizon 2020 programme, Inria is involved in three projects that started on Sept. 1st, 2020: the TAILOR and HumanAI R&I projects and the VISION support and coordination action. These projects lay the foundations for a world-class European research and innovation ecosystem, to implement safe, reliable AI that respects the values advocated by the European Union. Some Inria researchers are also members of the ELISE project.

TAILOR aims to reinforce links between academic, public and industrial research actors to develop the scientific basis for trusted AI. It does so, by combining learning, optimisation and reasoning to produce AI systems that guarantee the requirements of reliability, safety, transparency and respect for human activities, and optimising the expected benefits while reducing possible harm.

HumanAINet is to develop an AI that is safe, reliable, and capable of adapting to real environments and interact appropriately in complex social contexts. The objective is to promote AI systems that enhance human capabilities and provide support to individuals and society as a whole, while respecting human autonomy and self-determination.

ELISE gathers the best European research in machine learning to create a network of artificial intelligence. Where ELISE starts from machine learning as current core technology of AI, the network is inviting all ways of reasoning, considering all types of data, applicable for almost all sectors of science and industry.

VISION intends to coordinate the activity of the four European networks of excellence in AI (TAILOR, HumanAINet, ELISE and AI4Media), to help position European research as a major player in AI. This requires overcoming the fragmentation of the AI community in Europe, and stimulate synergies for the emergence of the next generation of reliable AI tools and systems, based on methods covering a wider range of AI techniques.

## (ii) Collaborations through large research projects

Large-scale projects complement and extend the work carried out at Inria:

AI4EU is the project that aims to build the European on demand platform, which is to render AI technology accessible to all, and as such reduce barriers to innovation, stimulate technology transfer and facilitate the growth of start-ups and SMEs in all economic sectors.
TRUST-AI and ALMA are two fundamental research projects that seek to advance human-centric AI. More precisely, TRUST-AI aims to integrate the notion of explicability into the learning phase of "black box" models, without compromising their performance. ALMA relies on the Algebraic Machine Learning (AML) paradigm, which produces generalizing models from the semantic integration of data into discrete algebraic structures, which has a number of advantages over statistical learning models.

### (iii) Scientific excellence promoted by ERC

Since the launch of the ERC (European Research Council) in 2007, Inria has obtained 59 individual grants (Starting, Consolidator, Advanced), 2 Synergy grants and 9 Proof of Concept (PoC) grants. In the field of AI, Inria has 17 ERC laureates, one of whom obtained a PoC funding in addition to his individual grant (see table below and list in appendix).

Thematic distribution:

<table><tr><td>Machine Learning & its applications</td><td>Francis Bach, Julien Mairal, Alessandro Rudi, George Drettakis (application)</td></tr><tr><td>Computer Vision & Signal-Image Processing</td><td>Cordelia Schmid, Ivan Laptev, Josef Sivic, Jean Ponce, Rémi Gribonval, Radu Horaud, Alexandre Gramfort, Emilie Chouzenoux</td></tr><tr><td>Medical imagery</td><td>Nicolas Ayache, Stanley Durrleman, Rachid Deriche</td></tr><tr><td>Robotics</td><td>Pierre-Yves Oudeyer, Jean-Baptiste Mouret</td></tr></table>

## INRIA'S INTERNATIONAL PARTNERSHIPS IN AI

Since 2017, we observe an increase in public policies and national strategies on AI that are issued by national authorities and often include an international dimension. This gives rise to multiple demands. These contacts can thus generate agreements to explore the opportunities and challenges of collaboration, in a top-down approach.

For example, through Inria Chile<fnref n="40" />, the institute is participating in actions and projects in the field of AI or its applications. Inria Chile, in partnership with local institutions, contributes to the definition of Chilean AI policy conducted by the Ministry of Science, Technology, Knowledge and Innovation and the Senate.

In addition, Inria supports international collaborations, in a bottom-up approach, thanks to ad hoc incentives (Inria International Labs, Associated Teams, mobility programmes), which enable Inria to remain responsive to cooperation opportunities.

Finally, as AI advances come largely from the private sector, Inria sometimes chooses to establish collaborations with international industrial players with significant R&D

<footnote n="40">⁴⁰ https://www.inria.fr/fr/centre-inria-chile</footnote>
capacities (cf. Inria - Fujitsu long-term research program on AI and big data processing).

In addition to this international watch policy, Inria is currently focusing its collaborative efforts in the field of AI on three geographical areas: bilateral Europe, Asia and North America.

## BILATERAL EUROPE

### Inria-DFKI Partnership

Following the Treaty of Aachen of 22 January 2019 signed between Germany and France promoting joint efforts in the field of AI, Inria and the DFKI concluded a memorandum of understanding in January 2020, in which they commit to implement a joint research and innovation programme. This programme covers the areas of AI for industry 4.0, AI for portable technologies, AI and cybersecurity, and human-robot cooperation. The Memorandum of Understanding is also part of a joint commitment within the CLAIRE network.

### Inria-University College of London partnership

Signed at the end of 2019, the agreement between Inria and University College of London (UCL) formalizes the collaboration between Inria and UCL. This collaboration is set to grow and expand to include other London partners.

## ASIA

Two countries are now considered to be a priority for the Institute in establishing cooperation in artificial intelligence in Asia: Japan and Singapore.

### Japan

Many similarities exist between the Japanese and French (and European) visions of AI: the Japanese "human-centric AI" approach echoes the French strategy's AI for humanity concept, and the secure sharing of data and resources between trusted partners is considered to gain competitiveness.

Furthermore, in both national strategies, the mobility and health sectors are identified as priority sectors for the application of AI. Finally, the two countries also converge on the use of AI to improve productivity, the consideration of environmental issues and the need to train more talent in the field.

In June 2019, Inria signed a four-year Memorandum of Understanding with the Department of Information Technology and Human Factors of the National Institute for Advanced Science and Industrial Science and Technology-AIST, which gathers eight research centres, including the Artificial Intelligence Research Centre (AIRC). This agreement aims to strengthen Inria-AIST cooperation, particularly in the field of
AI and robotics, through the development of scientific exchanges and joint research projects.

## Singapore

A cooperation agreement was signed in 2018 between the National University of Singapore (NUS), as operator of the AI Singapore plan, and Inria, the CNRS and INSERM. This agreement aims to promote the development of joint activities in AI and intelligent digital technologies, in the areas of cooperation in AI and Health; explainable AI; federated learning; automatic natural language processing; and confidentiality, security and responsibility in data sharing.

## NORTH AMERICA

Following long-term cooperation between the Inria project-teams and North American researchers in the field of AI, the Institute has for several years been formalizing partnerships with highly visible players on the international scene and renowned researchers in the field, mainly in the field of fundamental methods and tools for learning and data analysis.

## United States

The Centre for Data Science and Courant Institute of Mathematical Sciences is strongly involved in the New York University - Inria agreement signed in May 2017 for a period of 5 years. The joint programme has made it possible to fund collaborative projects and visits by researchers and doctoral students and the long-term stay of an Inria senior researcher (Jean Ponce).

## Canada

Inria and CIFAR (Canadian Institute for Advanced Research) signed an agreement in January 2015, which is currently being renewed. Inria is involved in the "Neural Computing and Adaptive Perception" program, now called "Machine Learning, Biological Learning". This program is co-coordinated by Yann Le Cun (NYU & Facebook) and Yoshua Bengio (Université de Montréal). The WILLOW and SIERRA project-teams participate in the activities of this group. Its main objective is to understand the principles underlying natural and artificial intelligence, and to elucidate the mechanisms by which learning can lead to the emergence of intelligence.

In addition to these two partnerships, five collaborations are supported within the framework of Inria's Associated Teams programme:

* Carnegie Mellon University (GAYA Associate Team on Semantic and Geometric Models for Video Interpretation);

* University of Southern California (LEGO Associate Team on Automatic Language Processing);
* Stanford University (Meta&Co Associate Team on Machine Learning and Automatic Language Processing for Meta-Analysis of Neuro-Cognitive Associations

* and Geomstat Associate Team on algorithmic anatomy - application of learning methods in neuroscience);

* the Argonne National Laboratory (UNIFY Associate Team on AI aspects as a complement to optimize hybrid workflows coupling computationally intensive simulation and massive data analysis).

## LATIN AMERICA

### Brazil

Inria and LNCC, the Brazilian National Scientific Computing Laboratory, have a long history of scientific cooperation. A partnership agreement was signed in 2020 on several research fields, including AI.
7. INRIA REFERENCES: NUMBERS

Over the 2013-2019 period, Inria researchers published more than **450 AI journal articles and more than 1800 AI conference papers** in the following list of journals and conferences. Indeed, Inria is among the top 20 entities in the 2019 AI Research Ranking. The 2019 edition of the AI Research Ranking analyzed publications at the Annual conference of Neural Information Processing Systems (NeurIPS) and the International Conference on Machine Learning (ICML). Using the 2019 conference proceedings, they went into each of the 2200 accepted papers, compiled the list of authors and their affiliated organizations and released the ranking of the top countries and organizations. Inria comes 16th in the overall ranking of the public research organizations. Only 3 other European public entities appear in the list (Oxford University, ETH and EPFL).
# 8. Other references for further reading

This section contains other references identified to be relevant for further reading, grouped in categories. It does not claim to be exhaustive but simply gives some reading additional to those mentioned in the previous chapters and to the publications of Inria project teams.

## Generic AI

One Hundred Year Study on Artificial Intelligence (AI100), Stanford University, August 2016, https://ai100.stanford.edu.

AI for humanity. French strategy for AI. https://www.aiforhumanity.fr/en/

Alan Turing. *Intelligent Machinery, a Heretical Theory*. Philosophia Mathematica (1996) 4 (3): 256-260. Original article from 1951.

Yves Caseau et al., Renouveau de l'Intelligence artificielle et de l'apprentissage automatique, Commission technologies de l'information et de la communication, Rapport de l'Académie des technologies, 2018

Ernest Davis and Gary Marcus. *Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence*. Communications Of The ACM Vol. 58 No. 9. 2015

Olivier Ezratty, Les usages de l'intelligence artificielle, 2020 edition, downloadable at
http://www.oezratty.net/

Michael A. Goodrich and Alan C. Schultz. *Human-Robot Interaction: A Survey. Foundations and Trends® in Human-Computer Interaction Vol. 1, No. 3 (2007) 203-275

Jonathan Grudin. *AI and HCI: Two Fields Divided by a Common Focus*. AI magazine, 30(4), 48-57. 2008

Kevin Kelly. *The Three Breakthroughs That Have Finally Unleashed AI On The World*. http://www.wired.com/2014/10/future-of-artificial-intelligence. 2014

Yang Li, Ranjitha Kumar, Walter S. Lasecki, Otmar Hilliges. *Artificial Intelligence for HCI: A Modern Approach*. CHI, 2020.
Pierre Marquis, Odile Papine, Henri Prade (eds). *Panorama de l'Intelligence Artificielle. ses bases méthodologiques, ses développements*. 3 vols. Cepaduès. 2014.

Raymond Perrault, Yoav Shoham, Erik Brynjolfsson, Jack Clark, John Etchemendy, Barbara Grosz, Terah Lyons, James Manyika, Saurabh Mishra, and Juan Carlos Niebles, *The AI Index 2019 Annual Report*, AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December 2019.

Stuart Russell and Peter Norvig. *Artificial Intelligence: A Modern Approach*. http://aima.cs.berkeley.edu/

Terry Winograd. *Shifting viewpoints: Artificial intelligence and human-computer interaction*. Artificial Intelligence 170(18):1256-1258. 2006.

## Debates about AI

Dario Amodei, Chris Olah et al, Concrete Problems in AI Safety, arXiv:1606.06565v2, 2016

Ronald C. Arkin. *The Case for Ethical Autonomy in Unmanned Systems*. Journal of Military Ethics 12/2010; 9(4)

Anne Bouverot, Thierry Delaporte et al., Algorithmes: contrôle des biais, S.V.P., Institut Montaigne, 2020

Bertrand Braunschweig and Malik Ghallab, editors, Reflections on AI for Humanity, book to be published, Springer, 2020

Erik Brynjolfsson, Daniel Rock and Chad Syverson, Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics Working Paper 24001 http://www.nber.org/papers/w24001

Samuel Butler. *Erewhon*. Free eBooks at Planet eBook.com, 1872.

Lettre du CICDE N°10. Emploi opérationnel de l'intelligence artificielle. April 2018. https://www.irsem.fr/data/files/irsem/documents/document/file/2934/20180412-NP-CICDE-Lettre-CICDE-AVRIL-2018.pdf

Kate Crawford, Roel Dobbe, Theodora Dryer et al. *AI Now 2019 Report*. AINow Institute, 2019, https://ainowinstitute.org/AI_Now_2019_Report.html

Dominique Cardon. *A quoi rêvent les algorithmes*. Seuil, 2015.
Dominique Cardon, Jean-Philippe Cointet and Antoine Mazières, La revanche des neurones, L'invention des machines inductives et la controverse de l'intelligence artificielle, La Découverte «Réseaux» 2018/5 n° 211, pp 173-220, 2018

Thomas G. Dietterich and Eric J. Horvitz. *Rise of Concerns about AI: Reflections and Directions*. Communications of the ACM | October 2015 Vol. 58 No. 1

Virginia Dignum, Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way, Springer, 2019.

Jessica Fjeld, Nele Achten et al., Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI, https://cyber.harvard.edu/publication/2020/principled-ai, 2020

Carl Benedikt Frey and Michael A. Osborne, The future of employment: how susceptible are jobs to computerisation ?, 2013

Malik Ghallab, Responsible AI: Requirements and Challenges, by request to the author, LAAS-CNRS, University of Toulouse, malik.ghallab@laas.fr, 2020

Thilo Hagendorff. The Ethics of AI Ethics -- An Evaluation of Guidelines. Minds & Machines, 2020.

High Level Expert Group on AI. Ethics guidelines for trustworthy AI. 2019. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres. Quantifying the Carbon Emissions of Machine Learning. 2019 https://arxiv.org/abs/1910.09700

OECD (2019); Deliberations of the Expert Group on Artificial Intelligence at the OECD (AIGO); available at https://www.oecd-ilibrary.org/Stuart Russell. *Human compatible, AI and the problem of control*. Penguin books, 2019.

Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni. Green AI. 2019 https://arxiv.org/abs/1907.10597

Ion Stoica, Dawn Song, Raluca Ada Popa, David A. Patterson, Michael W. Mahoney, Randy H. Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph Gonzalez, Ken Goldberg, Ali Ghodsi, David E. Culler and Pieter Abbeel. *A Berkeley View of Systems Challenges for AI*. EECS Department, University of California, Berkeley, 2017.

UNESCO (2019); Preliminary Study on the Ethics of Artificial Intelligence. SHS/COMEST/EXTWG-ETHICS-AI/2019/1; Available on https://unesdoc.unesco.org/
Moshe Vardi. *On Lethal Autonomous Weapons*. Communications of the ACM, December 2015 vol. 58 no. 12.

Machine learning

Martin Abadi et al. *Large-Scale Machine Learning on Heterogeneous Distributed Systems*. Software available from tensorflow.org. 2015.

Nicholas Ayache. AI and Healthcare: towards a Digital Twin?. *MCA 2019 - 5th International Symposium on Multidisciplinary Computational Anatomy, 2019*
https://issuu.com/univ-cotedazur/docs/ayache-ai-summit-2018-v10-uca

Alejandro Barredo Arrieta and Natalia Díaz-Rodríguez and Javier Del Ser and Adrien Bennetot and Siham Tabik and Alberto Barbado and Salvador García and Sergio Gil-López and Daniel Molina and Richard Benjamins and Raja Chatila and Francisco Herrera. *Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI*. Information fusion, 2020.

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, et al., Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach, Hal-02506409, 2020

Tarek R. Besold et al., Neural-Symbolic Learning and Reasoning: a Survey and Interpretation, arXiv:1711.03902v1, 2017

Christopher Bishop. *Pattern Recognition and Machine Learning*. Springer, 2006.

Léon Bottou: *From machine learning to machine reasoning: an essay*, Machine Learning, 94:133-149, January 2014.

Mathieu Causse, Cameron James, Mohamed Masmoudi and Houcine Turki, Parsimonious Neural Networks, Adagos company, 2019

Pedro Domingos. *The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World*. Penguin books, 2015.

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. *A Survey of Methods for Explaining Black Box Models*. ACM Comput. Surv. 2018.

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal. *Explaining Explanations: An Overview of Interpretability of Machine Learning*. 2019
https://arxiv.org/abs/1806.00069
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield and Matthew Botvinick, Neuroscience-Inspired Artificial Intelligence, Neuron 95, pp. 245-258, 2017

Michael I. Jordan and Tom M. Mitchell. *Machine learning: Trends, perspectives, and prospects*. Science, Vol 349 Issue 6245. 2015.

Peter Kairouz, H. Brandan MacMahan et al., Advances and Open Problems in Federated Learning, arXiv:1912.04977v1, 2019

Nan Rosemary Ke et al., Learning neural causal models from unknown interventions, arXiv:1910.01075v1, 2019

Yann Le Cun. *The Unreasonable Effectiveness of Deep Learning*. Facebook AI Research & Center for Data Science, NYU. http://yann.lecun.com, 2015

Yann Le Cun. Quand la machine apprend, La révolution des neurones artificiels et de l'apprentissage profond (French). Odile Jacob, 2019.

Volodymyr Mnih et al. *Human-level control through deep reinforcement learning*. Nature 518, 529-533. 2015

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, et al. Scikit-learn: Machine Learning in Python. *Journal of Machine Learning Research*, Microtome Publishing, 2011.

Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms, MIT Press, 2017

David Rolnick et al., Tackling Climate Change with Machine Learning, arXiv:1906.05433v1, 2020

Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke. *Explainable Machine Learning for Scientific Insights and Discoveries*. IEEE Access, 2020.

Bernhard Schölkopf, Causality for machine learning, arXiv:1911.10500v1, 2019

Michèle Sebag. *A tour of Machine Learning: an AI perspective*. AI Communications, IOS Press, 2014, 27 (1), pp.11-23.

Thomas Serre. *Deep Learning: The Good, the Bad, and the Ugly*. Annual Reviews, 2019
Emma Strubell Ananya Ganesh Andrew McCallum, Energy and Policy Considerations for Deep Learning in NLP, arXiv:1906.02243v1, 2019

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz, PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, arXiv:1709.02371v2, 2017

Neil C. Thompson et al, The Computational Limits of Deep Learning, arXiv:2007.05558v1, 2020

## Vision

Nicholas Ayache. *Des images médicales au patient numérique*, Leçons inaugurales du Collège de France. Collège de France / Fayard, March 2015.

Yasutaka Furukawa, Jean Ponce. *Accurate, Dense, and Robust Multiview Stereopsis*. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.

Sancho McCann, David G. Lowe. *Efficient Detection for Spatially Local Coding*. Lecture Notes in Computer Science Volume 9008 pp 615-629. 2015.

Farhood Negin, Serhan Cosar, Michal Koperski, François Bremond. *Generating Unsupervised Models for Online Long-Term Daily Living Activity Recognition*. Asian conference on pattern recognition (ACPR 2015), 2015.

A. Rosenfeld, R. Zemel, J.K. Tsotsos, *The Elephant in the Room*, 2018
https://arxiv.org/abs/1808.03305

Oriol Vinyals, Alexander Toshev, Samy Bengio & Dumitru Erhan. *Show and Tell: A Neural Image Caption Generator*, 2015. https://arxiv.org/pdf/1502.03044.2015

## Knowledge representation, semantic web, data

Bettina Berendt, Fabien Gandon, Susan Halford, Wendy Hall, Jim Hendler, Katharina Kinder-Kurlanda, Eirini Ntoutsi, and Steffen Staab. *Web Futures: Inclusive, Intelligent, Sustainable, The 2020 Manifesto for Web Science*, Dagstuhl Manifesto, pp. 1–44, issn:2193-2433 https://www.webscience.org/wp-content/uploads/sites/117/2020/07/main.pdf

Tim Berners-Lee, James Hendler and Ora Lassila. *The Semantic Web*. Scientific American, May 2001.
Fabien Gandon. *A Survey of the First 20 Years of Research on Semantic Web and Linked Data*. Revue des Sciences et Technologies de l'Information - Série ISI : Ingénierie des Systèmes d'Information, Lavoisier, 2018.

Fabien Gandon. *The three 'W' of the World Wide Web call for the three 'M' of a Massively Multidisciplinary Methodology*. Valérie Monfort; Karl-Heinz Krempels. 10th International Conference, WEBIST 2014, Barcelona, Spain. Springer International Publishing, 226, Web Information Systems and Technologies. 2014

Janowicz, K.; Hitzler, P.; Hendler, J.; and van Harmelen, F. *Why the Data Train Needs Semantic Rails*. AI Magazine, 36(5-14). 2015

Antonella Poggi et al. *Linking Data to Ontologies*. Journal on data semantics X Pages 133-173. Springer-Verlag Berlin, Heidelberg. 2008

## Robotics and self-driving cars

Safety First for Automated Driving – a new cross-industry white paper, 2019. https://www.bmwgroup.com/en/company/bmw-group-news/artikel/Safety-First-for-Automated-Driving.html

Jean-François Bonnefon, Iyad Rahwan, and Azim Shariff. *The social dilemma of autonomous vehicles*. Science (2016), 352 (6293). p. 1573-1576.J.

Antoine Cully, Jeff Clune, Danesh Tarapore & Jean-Baptiste Mouret. *Robots that can adapt like animals*. Nature Vol 521 503-507. 2015.

Ethics Commission of the Federal Ministry of Transport and Digital Infrastructure of Germany, Automated and connected driving report, 2017

Christian Gerdes, Sarah M. Thornton. *Implementable Ethics for Autonomous Vehicles*. Autonomes Fahren: Technische, rechtliche und gesellschaftliche Aspekte. Springer, Berlin. 2015.

Pierre-Yves Oudeyer. *Developmental Robotics*. Encyclopaedia of the Sciences of Learning, N.M. Seel ed., Springer References Series, Springer. 2012.

## AI and cognition

Stanislas Dehaene, Apprendre !: Les talents du cerveau, le défi des machines (French). Odile Jacob sciences, 2018
Jacqueline Gottlieb, Pierre-Yves Oudeyer, Manuel Lopes and Adrien Baranes.
*Information-seeking, curiosity, and attention: computational and neural
Mechanisms*. Trends in Cognitive Science (2013) 1-9. 2013.

Douglas Hofstadter & Emmanuel Sander. *L'analogie, cœur de la pensée*. Ed. Odile Jacob, 2013.

Daniel Kahneman. *Thinking, Fast And Slow*. New York: Farrar, Straus And Giroux, 2011

Luc Steels. *Self-organization and selection in cultural language evolution*. In Luc Steels (Ed.), *Experiments in Cultural Language Evolution, 1 – 37*. Amsterdam: John Benjamins. 2012.

## Natural language, speech, audio

Daniel Adiwardana et al., Towards a Human-like Open-Domain Chatbot, arXiv:2001.09977v1, 2020

Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, et al.. *CamemBERT: a Tasty French Language Model*. 2019.

Kenneth Church. *A Pendulum Swung Too Far*. Linguistic Issues in Language Technology – LiLT. Volume 2, Issue 4. 2007

G. Hinton, L. Deng, D. Yu, G.E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T.N. Sainath, B. Kingsbury, *Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups*. IEEE Signal Processing Magazine, 29(6):82-97, 2012.

Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. *Improving Language Understanding by Generative Pre-Training*. OpenAI, 2018. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

Stephen Roller et al., Recipes for building an open-domain chatbot, arXiv:2004.13637v2, 2020

Ashish Vaswani et al., Attention Is All You Need, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, arXiv:1706.03762v5, 2017
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