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Concept

Singularité technologique

Résumé

vignette|redresse=1.75|La frise chronologique de Ray Kurzweil (2005) date le moment où un superordinateur sera plus puissant qu'un cerveau humain, et pourra alors le simuler : il s'agit d'une méthode envisagée pour créer une intelligence artificielle, utilisée notamment dans le projet Blue Brain. La singularité technologique (ou simplement la Singularité) est l'hypothèse selon laquelle l'invention de l'intelligence artificielle déclencherait un emballement de la croissance technologique qui induirait des changements imprévisibles dans la société humaine. Au-delà de ce point, le progrès ne serait plus l’œuvre que d’intelligences artificielles, ou « supraintelligence » qui s’auto-amélioreraient, de nouvelles générations de plus en plus intelligentes apparaissant de plus en plus rapidement, créant une « explosion d'intelligence » créant finalement une puissante superintelligence qui dépasserait qualitativement de loin l'intelligence humaine. Le risqu

Source officielle

https://fr.wikipedia.org/wiki/Singularit%C3%A9_technologique

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Ce cours a pour objectif de présenter la fonction logistique de l'entreprise dans le cadre d'une approche globale, de la logistique au supply chain management, en insistant sur l'importance de la demande "client" tirant tous processus de la chaîne d'ajout de valeur du produit ou du service

This course provides an introduction to the topic of entrepreneurship, the process of new firm creation and the commercialization of technologies. The course integrates theories of entrepreneurship with a practical, process-based project in which students develop a business concept in teams.

The course will cover fundamental concepts and recent developments in the field of ultrafast spectroscopy and introduce the basic theory to understand ultrafast (10-16 - 10-9 s) phenomena in chemistry, biology and condensed matter physics.

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Many real-world systems are intrinsically nonlinear. This thesis proposes various algorithms for designing control laws for input-affine single-input nonlinear systems. These algorithms, which are based on the concept of quotients used in nonlinear control design, can break down a single-input system into cascade of smaller subsystems of reduced dimension. These subsystems are well defined for feedback-linearizable systems. However, approximations are required to handle non-feedback-linearizable systems. The method proceeds iteratively and consists of two stages. During the forward stage, an equivalence relationship is defined to isolate the states that are not directly affected by the input, which reduces the dimension of the system. The resulting system is an input-affine single-input system controlled by a pseudo-input which represents a degree of freedom in the algorithm. The pseudo-input is a complementary state required to complete the diffeomorphism. This procedure is repeated (n − 1) times to give a one-dimensional system, where n is the dimension of the system. The backward stage begins with the one-dimensional system obtained at the end of the forward stage. It iteratively builds the control law required to stabilize the system. At every iteration, a desired profile of the pseudo-input is computed. In this next iteration, this desired profile is used to define an error that is driven asymptotically to zero using an appropriate control law. The quotient method is implemented through two algorithms, with and without diffeomorphism. The algorithm with diffeomorphism clearly depicts the dimension reduction at every iteration and provides a clear insight into the method. In this algorithm, a diffeomorphism is synthesized in order to obtain the normal form of the input vector field. The pseudo-input is the last coordinate of the new coordinate system. A normal projection is used to reduce the dimension of the system. For the algorithm to proceed without any approximation, it is essential that the last coordinate appears linearly in the projection of the transformed drift vector field. Necessary and sufficient conditions to achieve linearity in the last coordinate are given. Having the pseudo-input appearing linearly enables to represent the projected system as an input-affine system. Hence, the whole procedure can be repeated (n−1) times so as to obtain a one-dimensional system. In the second algorithm, a projection function based on the input vector field is defined that imitates both operators, the push forward operater and the normal projection operator of the previous algorithm. Due to the lack of an actual diffeomorphism, there is no apparent dimension reduction. Moreover, it is not directly possible to separate the drift vector field from the input vector field in the projected system. To overcome this obstacle, a bracket is defined that commutes with the projection function. This bracket provides the input vector field of the projected system. This enables the algorithm to proceed by repeating this procedure (n−1) times. As compared with the algorithm with diffeomorphism, the computational effort is reduced. The mathematical tools required to implement this algorithm are presented. A nice feature of these algorithms is the possibility to use the degrees of freedom to overcome singularities. This characteristic is demonstrated through a field-controlled DC motor. Furthermore, the algorithm also provides a way of approximating a non-feedback-linearizable system by a feedback-linearizable one. This has been demonstrated in the cases of the inverted pendulum and the acrobot. On the other hand, the algorithm without diffeomorphism has been demonstrated on the ball-on-a-wheel system. The quotient method can also be implemented whenever a simulation platform is available, that is when the differential equations for the system are not available in standard form. This is accomplished numerically by computing the required diffeomorphism based on the data available from the simulation platform. Two versions of the numerical algorithm are presented. One version leads to faster computations but uses approximation at various steps. The second version has better accuracy but requires considerably more computational time.

EPFL2012

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Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Mohammad Ali Shaeri, Mahsa Shoaran

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain machine interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.

IEEE2022

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Mission Design and GNC for In-Orbit Demonstration of Active Debris Removal Technologies with CubeSats

Federico Belloni, Volker Gass, Christophe Dominique Paccolat, Camille Sébastien Pirat, Muriel Richard, Reto Wiesendanger

Within the frame of the ESA's General Support Technology Programme, two In-Orbit Demonstration (IOD) missions using CubeSat technologies were studied. These IODs aim at alleviating the technical risk inherent to new technologies required for Active Debris Removal (ADR) of large space objects, by using small and low-cost CubeSat systems. Rendezvous and docking with uncooperative debris has been only partially demonstrated and still raises technological issues. To test technologies such as the navigation and Rendezvous (RV) sensors or capture systems, our studies show that CubeSat missions are appropriate. Already, Guidance, Navigation and Control (GNC), communications and power technologies have successfully been miniaturised and the corresponding equipments are now available to the CubeSat community. This fact extends the range of feasible CubeSat missions from the initial flight of simple sensors to more complex systems. This paper presents two CubeSat ADR experiments and demonstrates how mission design and GNC can serve the verification or navigation sensors performances as well as the validation of uncooperative debris capture using a net. Each mission is composed of a Chaser and a Target. The former being an 8 Units (8U) CubeSat and the latter a 4U, launched together in a 12U deployer. Both satellites are 3-axis attitude controlled. The Chaser has in addition 3 Degrees of Freedom (DoF) translation capability using 1 mN cold gas thrusters. Both CubeSats will carry GNSS receivers to assist in the determination of range and relative-velocity. The global positioning and attitude data or the Target will be transmitted to the Chaser using an inter-satellite link having the additional capacity to measure the corresponding range. This system provides a reference validation for the RV sensors. The relative position and velocity to be controlled are fully observable. Thus a linear quadratic regulator is appropriate to ensure robust and optimal control. Based on the mission design, various close inspection configurations are demonstrated. To emphasise the feasibility of such missions, a system approach will be briefly addressed. Both missions are analysed using a 6 DoF simulator. The performances and absolute errors of the GNC as well as fuel consumption are provided. Power consumption, Telecom capability and thermal aspects will be shown for sake of completeness. Current issues and limitations of the CubeSat GNC will be discussed, as well as conclusions regarding the feasibility of such missions.

Elsevier2016

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EPFL2012

Mission Design and GNC for In-Orbit Demonstration of Active Debris Removal Technologies with CubeSats

Federico Belloni, Volker Gass, Christophe Dominique Paccolat, Camille Sébastien Pirat, Muriel Richard, Reto Wiesendanger

Elsevier2016