Dijous va ser un dia mogut a la UdG… en una reunió del vicerector de personal amb els directors de departament, es va anunciar que a partir de dilluns el personal podria teletreballar, i que no es faria activitat presencial, durant unes dues setmanes, per als estudiants. Divendres mateix, a la tarda, hi havia companys que gravaven classes. Per si de cas, vaig agafar material, ordinador, pantalla, etc… I des demà estarem confinats. No sabem pas quant durarà, esperem que no pas gaire. La veritat és que, malgrat que sabíem que podia passar, l’evolució de tot plegat ha estat molt ràpida. Així doncs, cap a casa!
Demà hauria d’haver anat al Col·legi Lestonnac de Tarragona per fer-hi matemàgia (en ocasió del Dia de les Matemàtiques/Dia de Pi, ahir). Ja m’havia preparat jocs que no demanéssin els alumnes de tocar res, que permetéssin estar separats… Doncs divendres mateix em van avisar de la suspensió d’aquesta activitat. Suposo que s’aniran suspenenet totes, i també els cogressos i activitats acadèmiques presencials. Què hi farem! De fet, abans d’ahir s’havia de fer Ciencia en Redes al Cosmocaixa de Barcelona. Suspès també.
Dimarts vaig participar tot el matí en una jornada de formació sobre #artint i Machine Learning, orientada al món del màrketing i l’empresa, amb la Montse Peñarroya (n’hi ha una entrada en aquest blog). Ben interessant… llàstima que la segona part, demà passat, no es podrà fer.
Aquesta setmana he estat fent tutories personalitzades al meu grup d’estudiants del PAT (Programa d’Acció Tutorial) de la Facultat de Ciències. Cada persona és un món, i més quan es tracta d’estudiant de darrer curs de grau, com és aquest el cas (tots, de Química).
Precisament aquesta setmana estic fent un curs per aprendre a fer anar l’assistent Watson d’IBM, mitjançant Intel·ligència Artificial. És un tema apassionant! I divertit, francament.
La imatge de la setmana
Segurament que és la foto que vaig fer a la Sílvia i en Pedro, tot gravant una classe, a l’únic espai accessible amb pissarra. Perquè totes les aules van ser clausurades divendres.
Navegant per la Internet
Una weeknote ha de recollir allò interessant, novedós o curiós vist per la xarxa al llarg de set dies. Aquesta setmana no n’és pas cap excepció. Suposo que en les properes setmanes, gairebé tot el que es digui a la xarxa estarà relacionat amb la pandèmia.
#artint
Bona guia per entendre #artint, bàsica: A beginner’s guide to artificial intelligence, machine learning, and cognitive computing. Esmenta al final de la pàgina:
This article covered just a fraction of AI’s history and the latest in neural network and deep learning approaches. Although AI and machine learning have had their ups and downs, new approaches like deep learning and cognitive computing have significantly raised the bar in these disciplines. A conscious machine might still be out of reach, but systems that help improve people’s lives are here today.
Relacionat més aviat amb l’ètica de la intel·ligència artificial, he vist a Wired l’entrada AI is an Ideology, Not a Technology que subtitula At its core, “artificial intelligence” is a perilous belief that fails to recognize the agency of humans. Per pensar-hi!
“AI” is best understood as a political and social ideology rather than as a basket of algorithms. The core of the ideology is that a suite of technologies, designed by a small technical elite, can and should become autonomous from and eventually replace, rather than complement, not just individual humans but much of humanity. Given that any such replacement is a mirage, this ideology has strong resonances with other historical ideologies, such as technocracy and central-planning-based forms of socialism, which viewed as desirable or inevitable the replacement of most human judgement/agency with systems created by a small technical elite.
#quantcomp
He vist el Manifest de Talavera sobre Computació Quàntica. A la foto d’aquest web, malauradament, cap dona, i setze homes! Amb principis, compromisos i crida a l’acció.
This manifesto collects some principles and commitments about the quantum software engineering and programming field, as well as some calls for action. This is the result of the discussion and different viewpoints of academia and industry practitioners who joined at the first International Workshop on QuANtum SoftWare Engineering & pRogramming (QANSWER) promoted by aQuantum.
We believe Quantum Software Engineering (QSE) is a necessary contribution to the success of quantum computing. We feel that the time has come to take care of producing quantum software by applying knowledge and lessons learned from the software engineering field. This implies to apply or adapt the existing soft-ware engineering processes, methods, techniques, practices and principles for the development of quantum software (or it may imply creating new ones).
We recognize that there is a rapidly-increasing awareness of the need for quantum computing applications, and there is a great desire to produce quantum software in an industrial, controlled manner. However, this is ineffective unless we come to un-derstand how software engineering can help.
#artint + #quantcomp
He vist a Slashdot que l’empresa D-Wave esmenta que la #artint i la computacio quàntica lliguen molt bé:
Following D-Wave’s announcement of Leap 2, a new version of its quantum cloud service for building and deploying quantum computing applications, VentureBeat had the opportunity to sit down with Murray Thom, D-Wave’s VP of software and cloud services. We naturally talked about Leap 2, including the improvements the company hopes it will bring for businesses and developers. But we also discussed the business applications D-Wave has already seen to date. Thom explained that D-Wave has seen success particularly with optimization and machine learning use cases. And he has the data to back it up: D-Wave’s customer applications are about 50% optimization, 20% AI and ML, 10% materials science, and 20% other.
Thom believes quantum computing and machine learning are “extremely well matched. The features the technology has and the needs of the field are very close.”
“It’s something I think is going to be a very productive use of the technology in the future because there’s so many aspects of what the quantum computers can do in terms of the probabilistic sampling,” Thom continued. “For optimization, the probabilistic sampling is like ‘oh, I can do robust optimization with that.’ But for machine learning it’s essential for what you need to do. It’s very hard to reproduce that with a classical computer and you get it natively from the quantum computer. So those features can’t be accidental. It’s just that it’s going to take time for the community to find the right methods for incorporating it and then for the technology to insert into that space productively.”
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