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#machinelearning

62 messages54 participants7 messages aujourd’hui
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Petr Nuska

#AcademicJob

AI & Audio Scientist

📍 Universal Music Group (London or remote in EMEA)

Join UMG’s Music and Audio Machine Learning Lab (MAML) to develop cutting-edge AI/ML tools for music. Ideal for those with experience in MIR, generative models, multimodal learning, or music LLMs.

umusic.wd5.myworkdayjobs.com/U

CC @academicjobs

umusic.wd5.myworkdayjobs.comAI & Audio ScientistMusic is Universal It’s the passionate and dedicated team at Universal Music who help make us the world’s leading music company. From A&R to finance, legal to digital, sales to marketing, Universal Music is the place to grow and develop your career within a truly commercial and innovative business that leads in everything it does. Everyone is welcome to apply for our roles, and we are determined to ensure that no applicant or employee receives less favourable treatment because of gender, race, disability, sexual orientation, religion, belief, age, marital status, background, pregnancy, or caring responsibilities. We also recognise the importance of diversity of thought within our teams and are fully committed to embracing the talents of people with autism, dyslexia, ADHD, and other forms of neurocognitive variation. We will always seek to make appropriate adjustments to recruitment, workplaces, and work processes to be fully inclusive to people with different needs and working styles. If you need us to make any reasonable adjustments for you from application onwards, including alternatives to the online form or to disclose a neurocognitive condition, please email UniversalMusicCareers@umusic.com. The A Side: A Day in The Life This role sits within the Music and Audio Machine Learning Lab (MAML) at UMG. Part of the Global Technology organization, the MAML’s mission is to invent and build state of the art AI/ML tools to support and empower Artists, and industry professionals. The MAML covers the full research & development lifecycle, from explorative research to prototypes to industrial scale products. It typically undertakes projects with applications such as, but not limited to, content understanding, creative tools, search, recommendation, etc. We are curious, passionate and committed to push the envelope in AI for music. If you are excited at the idea of working on state-of-the-art music & audio machine learning at industry scale and make an impact, we want to hear from you! The B Side: Skills & Experience Be Technical Design and build state-of-the-art AI/ML algorithms to tackle a variety of tasks in music/audio Explore frontier research in AI/ML for Music/Audio, and related opportunities for UMG Be Collaborative Collaborate with the team and stakeholders to deploy models to production Communicate results and findings to audiences of all levels of expertise Be part of an innovative and dynamic team Person Specification Strong background in AI / Machine Learning / Digital Signal Processing /Computer Science or related technical field 3+ years of hands-on professional and/or research experience in AI/ML or related technical fields applied to audio/music Experience in at least one of: Music Information Retrieval, generative models, multi-modal deep learning, representation learning, music LLMs Up to date knowledge of AI/ML state of the art, including scientific literature, tools, models, best practices etc Proven ability to come up with novel ideas and designs for AI/ML models, systems, pipelines and/or applications, and to implement them successfully Strong software engineering skills Proficiency in Python, ML frameworks (Pytorch, Tensorflow etc.) and usual scientific libraries (e.g. Numpy, Scipy, sklearn) Familiarity with Linux environments Ability to write good, well documented, and reusable code Good communication skills, both oral and written Curious, self-motivated, and proactive Passion for music and/or deep musical understanding Desirable Track record of publication(s) in relevant conferences or journals (e.g. ISMIR, ICASSP, Neurips, ICLR etc) Experience with generative models for audio/music Experience working in industry Experience going all the way from ideation / early research to production Familiarity/experience with cloud computing environments (e.g. AWS, Google Cloud Platform) Experience with distributed model training Experience with containerised software development (e.g. Docker) Bonus Tracks: Your Benefits Group Personal Pension Scheme (between 3% and 9%) Private Medical Insurance 25 paid days of annual leave Interest Free Season Ticket Loan Holiday Purchase scheme Dental and Travel Insurance options Cycle to Work Scheme Salary Sacrifice Cars Subsidised Gym Membership Employee Discounts (Reward Gateway) Just So You Know… The company presents this job description as a guide to the major areas and duties for which the jobholder is accountable. However, the business operates in an environment that demands change and the jobholder's specific responsibilities and activities will vary and develop. Therefore, the job description should be seen as indicative and not as a permanent, definitive, and exhaustive statement. Job Category: Technology We are Universal Music Group, the world’s leading music company. We are the home for music’s greatest artists, innovators and entrepreneurs.
#AI#MIR#MusicTech
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LORIA

[#PODCAST] Dans ce nouvel épisode de "Bienvenue à bord !", rencontrez Guénaël Cabanes, spécialiste en apprentissage automatique pour l'exploration de données et la modélisation de systèmes complexes. Il est maître de conférences #UniversitédeLorraine et chercheur au #Loria depuis cette année. Bienvenue à lui ! 👋

🎧 soundcloud.com/labo-loria/bien

#DataMining #MachineLearning #recherche @cnrs

Podcast Guénaël Cabanes
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scuti🌱
Today, I learned the #Fediverse will never catch on because no one wants to buy user data from a platform where everyone has 2-3 alt and backup accounts.

#joke

I learned how to programmatically make stereotypes of people based on the emails and data I steal from them.

(also a joke.)

I never stole e-mails in bulk, but I would know what to do with them if I ever do. Read my review of an online course about #machinelearning, and check out the final project I wrote for it in #python.

https://scuti.neocities.org/pages/ud120-online-course-review

You can reply to this post to leave a comment on the blog.
My KNN classifier making predictions
My decision tree classifier making predictions.
the data set
Public
Calin Sandu

Hugging Face Transformers is a leading open-source library designed to simplify the use and development of transformer-based models, which have become foundational in natural language processing (NLP) and beyond. This library enables developers to leverage state-of-the-art models for tasks such as text classification, machine translation, question answering, and even computer vision and audio processing.

ml-nn.eu/a1/75.html

ml-nn.euHugging Face Transformers OverviewMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
A répondu dans un fil de discussion
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Mx Verda

ok, tbh, AI language practice does actually sound like one good use of it. languatalk.com/try-langua#pric
No deep understanding or nuance, plus stilted phrasing... but basic errors should be fine, like me forgetting to add が everywhere.

Huge wastage though! AI / LLM / Machine Learning services are massive carbon sources.
Especially with data centers in drought-prone areas, even a good use is still bad.
Hot areas cannot sustain AI: Computer calculations produce heat, which radiates away more slowly into already hot air.

Cooling AI data centers drains water -- especially potable / human-safe sources.
Reservoirs and water tables drop even faster than they would from extras in other human usage, evaporation due to global warming, and whatever third thing probably also happens.
Idk, I'm not a climate scientist. I just have climate grief.

languatalk.comLangua: the world's best AI platform for language learning • LanguaTalkBecome fluent fast through realistic speaking practice, comprehensible input, interactive transcripts, and scientifically-proven flashcard exercises.
#AI#LLM#MachineLearning
Public
Calin Sandu

Markov Decision Processes

MDPs are mathematical frameworks used to model decision-making in environments where outcomes are partly random and partly under the control of a decision maker. They are widely used in various fields, including artificial intelligence, robotics, economics, and operations research, to optimize decisions over time.

ml-nn.eu/a1/49.html

ml-nn.euMarkov Decision Processes (MDPs)Machine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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