Trying something new, everyone is guaranteed an interview! Open interviews! For a limited time no one will be skipped (except for clear cases of abuse).
So we still have about 10 more 100% remote positions to hire for full-time market-fair positions here at QOTO/CleverThis.
100% remote, work from anywhere, even the beach, market-fair offers. Ethics first, we treat our people like family.
We have an urgent need for Machine learning experts with a background in NLP and Deep Learning (Natural Language Processing and Neural Networks). There is a focus on Knowledge Graphs, Mathematics, Java, C, looking for Polyglots.
We are an open-source first company, we give back heavily to the OSS community.
We need everything from jr to sr, data scientist to programmer. If your IT and your good, you might be a fit.
I will personally be both your direct boss, and hiring manager. I am also the founder and inventor.
The NLP position can be found at this link, other positions can be found on the menu bar on the left:
If you would like to submit yourself for an interview, which for a limited time I am guaranteeing you will get a first stage interview, then you can submit your application here, and even schedule your interview as you apply, instantly!
If you use Slack for work, your messages and DMs to friends and colleagues are now being used to train the company’s machine learning features — and everyone is opted in by default.
A quiet update to the company’s policy suggests messages, data and files sent by users are helping Slack to improve its in-app features like channel recommendations, search results and emoji suggestions, reports @PCMag. Individual users can’t opt out either, something critics have called a “privacy mess.”
Hello Mastodon, I know that a lot of you discuss the high environmental cost (such as energy use and water use) of AI and I hope that some of you could reply with authoritative publications/links regarding this problem! I want to try to convince an environmental science colleague #climatechange#AI#chatgpt#energy#technology#machinelearning#llm
Before I head off on a trip to various parts of not-Barcelona, I thought I’d share a somewhat provocative paper by David Hogg and Soledad Villar. In my capacity as journal editor over the past few years I’ve noticed that there has been a phenomenal increase in astrophysics papers discussing applications of various forms of Machine Leaning (ML). This paper looks into issues around the use of ML not just in astrophysics but elsewhere in the natural sciences.
The abstract reads:
Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology – in which only the data exist – and a strong epistemology – in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics
arXiv:2405.18095
P.S. The answer to the question posed in the title is probably “yes”.
I was so, SO happy to leave Slack behind. And, remembering how that last group of people used it: YEESH.
“If you use Slack for work, your messages and DMs are now being used to train the company’s machine learning features — and everyone is opted in by default.
“A quiet Individual users can’t opt out either, something critics have called a “privacy mess.”
So… Big Tech is allowed to blatantly steal the work, styles and therewith the job opportunities of thousands of artists and writers without being reprimanded, but it takes similarity to the voice of a famous actor to spark public outrage about AI. 🤔
First and foremost, we had more than 20 wonderful participants at #YoMos2024, ranging from Bachelor- to #PhD- level #students all modelling #ecological systems. Everyone had the chance to present their projects and methods and we had vivid question+discussion rounds afterwards. Although #YoMos seems like a niche group already, we heard about a bandwidth of methods including mechanistic #eco(-evo)-models, #SDMs, #climate and vegeation models, network models, #Machinelearning, #AI and much more.
#AI and #MachineLearning models are shifting a number of core assumptions on which the various Web stakeholders have been relying on for years.
In this presentation at the @w3c member meeting in #Hiroshima 🇯🇵, @dontcallmeDOM reviews the systemic impact and possible mitigation the Web community should consider to ensure the long term prosperity of the #Web in the face of these changes.
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.
I was looking for examples of interactive data visualization for a gradient descent algorithm, and I found this app by Lili Jiang. This desktop app is based on C++ and enables simulation and visualization of different gradient descent algorithms, such as momentum, AdaGrad, RMSProp, and Adam. The app enables to compare different methods simultaneously.
I am excited to present at the Dev AI conference in Paris on June 19!
I am going to run a workshop about the deployment and monitoring of ML pipelines with free and open-source tools. This includes using tools such as GitHub Actions and Pages, Docker, Python, Quarto, etc.
If you are looking for a #PhD and are interested in working on #OpticalComputing for #MachineLearning (and to spend some time in the UK and some time in Australia), contact me!
Got the funding but the official advert is not out yet. Will update when it is (but the deadlines are going to be short).
(1/2) I am excited to present at the useR!2024 conference on July 2nd!
I am going to run a virtual workshop about deployment and monitoring data and ML pipelines using free and open-source tools. This includes setting pipelines using GitHub Actions, Docker 🐳, R, and Quarto 🚀.
Andrej Karpathy released today a tutorial for reproducing GPT-2 from scratch. OpenAI released GPT -2 in 2019, and it is a 124M parameters model. This four-hour tutorial covers setting up the GTP-2 network and then training and optimizing its parameters.
It looks like a really cool tutorial; I hope to get the bandwidth to watch it in the coming weeks!