transcranial

Hence why time tends to slow down while living in the moment rather than reminiscing or dwelling on the past.

It does say "temporarily offered at a discount" when you hover over the model in the dropdown.

As a collective endeavor to seek out higher truth, maybe some amount of fraud is necessary to train the immune system of the collective body, so to speak, so that it's more resilient in the long-term. But too much fraud, I agree, could tip into mistrust of the entire system. My fear is that AI further exacerbates this problem, and only AI itself can handle wading through the resulting volume of junk science output.

As a former radiology resident, I totally agree. That's why we're building exactly this: https://md.ai/product/reporting/.

MD.ai | Full-stack and Devops/Infrastructure Engineers | New York, NY or REMOTE (US only) | Full-Time | https://md.ai

MD.ai helps doctors, scientists, and engineers build medical AI that have potential to improve patient care and outcomes. Our overarching goal is to accelerate medical AI development, deployment, and validation. We provide software tools to enable large-scale collaborative dataset curation and annotation as well as model deployment and federated clinical validation, with a particular focus on medical imaging. Our software platform is used by top academic medical institutions as well as large pharmaceutical and healthcare companies.

We're looking for talented full-stack and infrastructure/devops engineers interested in ML/AI and healthcare to join our growing team. You'll have an opportunity to take on significant ownership over product and code, help drive our engineering culture, and really make an impact. Our tech stack includes: React, TypeScript, WebGL, PostgreSQL, Redis, Python, PyTorch, Kubernetes, Terraform, AWS/GCP/Azure.

Full-Stack Software Engineer | https://boards.greenhouse.io/mdai/jobs/4011987005

Lead Infrastructure Engineer | https://boards.greenhouse.io/mdai/jobs/4011991005

MD.ai | Summer Interns (for Front-End/Full-Stack Engineering) | Remote

We are a medical AI development platform (https://www.md.ai), currently focused on medical imaging (radiology). We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We currently have availability for summer interns with experience with JavaScript/TypeScript/React/GraphQL/DICOM.

Please email us directly at jobs@md.ai.

MD.ai | Summer Interns (for Front-End/Back-End/Machine Learning Engineering) | Remote We are a medical AI development platform (https://www.md.ai), currently focused on radiology/pathology/dermatology. We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We currently have availability for summer interns with skills in React, GraphQL, Kubernetes, Docker, Terraform, GCP/AWS/Azure, TensorFlow/PyTorch, or medical imaging.

Please email us directly at jobs@md.ai.

Same here. Poetry has been a joy, after many bouts of frustration with pipenv.

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We are a medical AI development platform (https://www.md.ai), currently focused on radiology/pathology/dermatology. We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We are currently looking for front-end developers (React, GraphQL) and software engineers experienced in devops/cloud technologies (Kubernetes, Docker, Terraform, GCP/AWS/Azure).

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DeBakey performed his last surgery at age 90 [1], pioneered numerous procedures -- including one performed on himself at age 97 [2], and continued to practice medicine until his death at age 99. Remarkable person, but obviously an outlier. Just thought it would be interesting to bring him up in this thread.

[1] https://www.telegraph.co.uk/news/obituaries/2403698/Michael-...

[2] https://www.nytimes.com/2006/12/25/health/25surgeon.html

MD.ai | Front-end / Full-stack Engineer | New York, NY (NYC) | Full-time | ONSITE | https://www.md.ai

We are a medical machine learning platform helping doctors and researchers build medical AI. Our focus is on creating high-quality labeled datasets for training and clinical validation, and building tools for model development, training, deployment and validation. Some of our unique challenges include: operating in HIPAA-compliant environments, managing huge medical imaging/text/genomic datasets, distributed data processing and machine learning model training, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We're currently looking for highly motivated front-end or full-stack engineers (React/Vue/GraphQL) to join our growing team.

Please email us directly at jobs@md.ai.

MD.ai | Front-end Engineer, Summer Interns | New York, NY | Full-time | ONSITE or REMOTE | https://www.md.ai

We are a medical machine learning platform helping doctors and researchers build medical AI, with the ultimate goal of improving patient care. We help build high-quality labeled datasets for both training and clinical validation, as well as tools for model deployment and execution. Some of our unique challenges include: operating in HIPAA-compliant environments, managing huge medical imaging/text/genomic datasets, distributed data processing and machine learning model training, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We're looking for awesome front-end engineers (React/Vue/GraphQL), maybe that's you? Experience with devops (Docker/Kubernetes/GCP/AWS), machine learning (Tensorflow/Keras), and anything healthcare-related are definite pluses.

Please email us directly at jobs@md.ai.

MD.ai | Front-end/Full-stack/DevOps Engineers | New York, NY | Full-time | ONSITE or REMOTE | https://www.md.ai

We are a medical machine learning platform helping doctors and researchers build medical AI, with the ultimate goal of improving patient care. We help build high-quality labeled datasets for both training and clinical validation, as well as tools for model deployment and execution. Some of our unique challenges include: operating in HIPAA-compliant environments, managing huge medical imaging/text/genomic datasets, distributed data processing and machine learning model training, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

We're currently hiring front-end engineers (React/Vue/GraphQL) and full-stack/devops engineers (Docker/Kubernetes/GCP/AWS). Experience with machine learning (Tensorflow/Keras) is definitely a plus. Experience in medicine or healthcare is preferred.

Please email us directly at jobs@md.ai.

MD.ai | Front-End Engineer | New York, NY (NYC) | Full-time or Part-time | ONSITE or REMOTE | https://www.md.ai

We are a medical machine learning platform helping doctors and researchers build medical AI, with the ultimate goal of improving patient care. We help build high-quality labeled datasets for both training and clinical validation, as well as tools for model deployment and execution. Some of our unique challenges include: operating in HIPAA-compliant environments, handling of huge medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.

Looking for: experienced React/Vue/GraphQL developers, or junior developers eager to learn. Experience or interest in medical imaging, medical informatics, or machine learning is definitely a plus but not a requirement.

Please email us directly at jobs@md.ai.

Here's a recent follow-up paper [1] testing exactly that on many diverse datasets, positing that scale-free (power-law distributed) networks are actually not that common in the real world. Quanta has a nice article about it [2].

[1] https://arxiv.org/abs/1801.03400

[2] https://www.quantamagazine.org/scant-evidence-of-power-laws-...

MD.ai | Machine Learning Engineer, Front-End / Full-Stack Engineer | New York, NY (NYC) | Full-time or Part-time | ONSITE

We're building a medical machine learning platform encompassing the entire model development cycle, from the creation of high-quality labeled datasets to model training/validation/deployment. We want to help enable clinicians and researchers to more easily and efficiently build medical AI, ultimately with the goal of improving patient care. Some of our challenges include operating in HIPAA-compliant environments, handling of huge medical imaging datasets, managing ML training workloads, building complex web applications with UI/UX appealing to both doctors and ML engineers alike.

Our stack: Python, JavaScript, React, Vue, GraphQL, Postgres, Docker, Kubernetes, TensorFlow, Keras

If interested, please introduce yourself at jobs@md.ai.

Here's an old Nautilus article talking about exactly your point about the social element: http://nautil.us/issue/28/2050/why-futurism-has-a-cultural-b...

See https://webgl2fundamentals.org/webgl/lessons/webgl2-whats-ne... for an overview of new (well, to WebGL at least) features.

Direct texel lookups and the expanded texture formats has been amazing for using WebGL 2 for GPGPU purposes.

I recently discovered this awesome presentation on generative music: https://teropa.info/loop

Yeah, my first thought was the latter interpretation. Got excited for a moment that we may someday be able to cure sleep (kidding, of course -- sleep is glorious).

GraphQL has been a breath of fresh air -- you can tell a lot of thought went into its design and implementation.

Totally, I think people have this weird sense of entitlement when it comes to high-quality datasets without the commensurate respect for how they're created or the level of effort that goes into them.

Fei-Fei Li gives a good sense for this in her history of ImageNet [1][2].

[1] https://qz.com/1034972/the-data-that-changed-the-direction-o...

[2] http://image-net.org/challenges/talks_2017/imagenet_ilsvrc20...

But it's already diverged enough IMO to warrant enjoying them separately. Might as well get the full experience... Plus without the source material to work from I'm skeptical as to just how great the TV ending will be. But I guess we all take what we can get at this point.

A tremendous amount of trust is certainly involved. The brain itself doesn't actually have any pain receptors!

This is not at all uncommon:

https://www.washingtonpost.com/news/speaking-of-science/wp/2...

https://www.bustle.com/articles/35791-watch-inspiring-violin...

I've watched a few myself in person. Sometimes it's crucial to monitor preservation of function -- this is how it's done. Awake brain surgery isn't as crazy as it may seem.

Great video on the power of play as a driver of innovation/invention:

https://ww2.kqed.org/mindshift/2017/04/13/how-play-is-at-the...

I just started reading the Nexus trilogy by Ramez Naam (http://rameznaam.com/nexus/). It's quite good so far, and explores some of these concepts from a sci-fi bent.

This is really exciting! Chris et al: have you guys seen Keras.js (https://github.com/transcranial/keras-js)? It could probably be useful for certain interactive visualizations or papers.

There are definitely measurable population-level effects, like increased traffic accidents [1] and heart attacks [2]. And given that its purported benefits are completely unsubstantiated, it's amazing that DST still exists.

[1] https://www.aeaweb.org/articles?id=10.1257/app.20140100

[2] http://www.nejm.org/doi/full/10.1056/NEJMc0807104

IBM bought Merge Healthcare for $1B, presumably to gain access to vast amounts of their imaging data. Would love to see some developments on the deep learning front from them, but sadly I doubt it'll ever break through the fog of marketing/PR BS that always seems to surround Watson.