- This page provides status information on the services that are part of Google Cloud Platform.
- Check back here to view the current status of the services listed below.
- If you are experiencing an issue not listed here, please contact Support.
- Learn more about what's posted on the dashboard in this FAQ.
- For additional information on these services, please visit cloud.google.com.
GitHub is down
- GitHub is a development platform inspired by the way you work.
- From open source to business, you can host and review code, manage projects, and build software alongside millions of other developers.
- GitHub brings teams together to work through problems, move ideas forward, and learn from each other along the way.
- The conversations and code reviews that happen in Pull Requests help your team share the weight of your work and improve the software you build.
- On GitHub, project management happens in Issues and Projects, right alongside your code.
- Integrate the tools you already use or discover new favorites to create a happier, more efficient way of working.
- Developers use GitHub for personal projects, from experimenting with new programming languages to hosting their life’s work.
- Businesses of all sizes use GitHub to support their development process and to securely build software.
The 8 smartest things I did when I started my new job
- But once I relaxed into my new role and saw what other people on my team were doing, it became clear to me what types of questions were helpful and which were just me being too much in my own head.
- Finding the right phrases and keywords, by paying attention at orientation as well as asking my manager and employees who'd worked at the company for a while, helped me access the information I needed on the wiki.
- I bookmarked pages I'd need to access on a regular basis, like the office floorplan, and vital information about my team, which turned out to be enormously helpful, too.
- So even though I was a bit self-conscious about my work, I forced myself to consistently ask for feedback from my editor.
Amazon AI predicts users’ musical tastes based on playback duration
- Engineers at Amazon have developed a novel way to learn users’ musical tastes and affinities with artificial intelligence: by using song duration as an “implicit recommendation system.” Bo Xiao, a machine learning scientist and lead author on the research, today described the method in a blog post ahead of a presentation at the Interspeech 2018 conference in Hyderabad, India.
- Users don’t often rate songs played back through Alexa and other voice assistants, and playback records don’t necessarily provide insight into musical taste.
- In a paper (“Play Duration based User-Entity Affinity Modeling in Spoken Dialog System”), Xiao and colleagues reasoned that people will cancel the playback of songs they dislike and let songs they enjoy continue to play, providing a dataset on which to train a machine learning-powered recommendation engine.
The Future of Machine Learning at the Edge
- The edge is advantageous for machine learning for a number of reasons, but a key benefit is minimized latency, which leads to faster data processing and real time, automated decision-making.
- In the case of self-driving cars, machine learning applications are being trained both locally in the car itself and at the edge to cut back on bandwidth and latency to process data, which can rack up to about 4,000 GB a day – equivalent to 3 billion peoples' worth of data, in real-time.
- Not to mention the life-safety factor required; the ability for these vehicles to process data instantly is critical and can be life-saving as automated decision-making based on road conditions or unexpected instances can keep passengers out of harm’s way.
- As the fusion of machine learning and edge continues to evolve, we’ll see it drive business efficiency, automation, predictive capabilities and decision-making on a greater scale.
Debate about science at organizations like Google Brain/FAIR/DeepMind
- Google can beat University of Kansas for the sole reason that they can hire ten times more graduate students per researcher.
- But if you want, at some point in your flourishing career, with your mind and your soul, to join the two-thousand year old parade of intellectual progress, you are not going to do it at Google.
- In my particular field (ML + medicine) research, we've had a solid 3+ decade parade of "intellectual progress" in the form of academic papers about ML + medicine.
- Hot take: It seems like what's really going on in this thread (and similar threads about ML scholarship) is hype envy - people see other work getting a bunch of attention and think "why not me" and then get all strident like "what I'm doing is the *real* science and this other stuff is just bullshit/engineering/alchemy/PR".
The 5 Clustering Algorithms Data Scientists Need to Know
- K-Means has the advantage that it’s pretty fast, as all we’re really doing is computing the distances between points and group centers; very few computations!
- Mean shift clustering is a sliding-window-based algorithm that attempts to find dense areas of data points.
- DBSCAN is a density based clustered algorithm similar to mean-shift, but with a couple of notable advantages.
- With GMMs we assume that the data points are Gaussian distributed; this is a less restrictive assumption than saying they are circular by using the mean.
- Then we can proceed on to the process of Expectation–Maximization clustering using GMMs. There are really 2 key advantages to using GMMs. Firstly GMMs are a lot more flexible in terms of cluster covariance than K-Means; due to the standard deviation parameter, the clusters can take on any ellipse shape, rather than being restricted to circles.
- With so many possible paths, it’s so easy to feel overwhelmed to the point that I start trying to do several different things.
- Often times I realize that my initial breakdown was not simple enough and I need to break it down even further (i.e. do one thing that I already know how to do or Google and find a point of reference quickly).
- One way I’ve become better at KISS is by paying attention to other developers and putting new things I learn in my toolbox.
- At this stage of the developer game, it’s much simpler to learn/accomplish one thing really well before moving on to something else (as opposed to trying to become good at 7 different things at one time).
- I’m going to close out by saying that once I started keeping KISS as one of my goals, it has helped to keep me focused on a smaller scale.
Slime Molds Remember—But Do They Learn?
- Starting in 2015, Dussutour and her team obtained samples of slime molds from colleagues at Hakodate University in Japan and tested their ability to habituate.
- In a follow-up study, her team showed that “naïve,” non-habituated slime molds can directly acquire a learned behavior from habituated ones via cell fusion.
- Research into the behavior of protozoa such as the slime mold Physarum polycephalum (especially the work of Toshiyuki Nakagaki at Hokkaido University in Japan) suggests that these seemingly simple organisms are capable of complex decision-making and problem-solving within their environments.
- Chris Reid and his colleague Simon Garnier, who heads the Swarm Lab at the New Jersey Institute of Technology, are working on the mechanism behind how a slime mold transfers information between all of its parts to act as a kind of collective that mimics the capabilities of a brain full of neurons.