machine-learning   16825

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A16Z AI Playbook
Andreesen Horowitz team has put together a great resource for considering a business in the AI (machine learning) space.
Also, resources for figuring out how to implement an AI (machine learning) system.
machine-learning 
yesterday by sisterical
Tensor Processing Unit
The future of software performance is hardware customization & Google has their own hardware for running machine learning algorithms.

By James Hamilton (Principle Engineer on AWS)
machine-learning 
yesterday by sisterical
Being a Data Scientist: My Experience and Toolset
Experience report from a data scientist. Talks about what he does, the value he adds, the tools he uses, and the differences between data science and machine learning.

By Jefferson Heard
machine-learning 
yesterday by sisterical
DeepTraffic
MIT's deep learning traffic simulation. A tool for understanding & experimenting with self driving cars.
machine-learning 
yesterday by sisterical
Is Google Hyping it? Why Deep Learning cannot be Applied to Natural Languages Easily
talks in detail about problems that machine learning & neural nets are good for, and those that aren’t.

By Riza C. Berkan, Ph.D
machine-learning 
yesterday by sisterical
A Guide to Solving Social Problems with Machine Learning
If a decision is being made that already depends on a prediction, why not help inform this decision with more accurate predictions? The law already requires bond court judges to make pre-trial release decisions based on their predictions of defendant risk. Decades of behavioral economics and social psychology teach us that people will have trouble making accurate predictions about this risk – because it requires things we're not always good at, like thinking probabilistically, making attributions, and drawing inferences. The algorithm makes the same predictions judges are already making, but better.

But many social-sector decisions do not hinge on a prediction. Sometimes we are asking whether some new policy or program works – that is, questions that hinge on understanding the causal effect of something on the world. The way to answer those questions is not through machine learning prediction methods. We instead need tools for causation, like randomized experiments. In addition, just because something is predictable, that doesn't mean we are comfortable having our decision depend on that prediction. For example we might reasonably be uncomfortable denying welfare to someone who was eligible at the time they applied just because we predict they have a high likelihood to fail to abide by the program's job-search requirements or fail a drug test in the future.


policymakers need to know when they should override the algorithm. For people to know when to override, they need to understand their comparative advantage over the algorithm – and vice versa. The algorithm can look at millions of cases from the past and tell us what happens, on average. But often it's only the human who can see the extenuating circumstance in a given case, since it may be based on factors not captured in the data on which the algorithm was trained.

From Harvard Business Review
machine-learning 
yesterday by sisterical
Building Jarvis
Mark Zuckerberg takes on using machine learning to automate things at home.

Speech recognition systems have improved recently, but no AI system is good enough to understand conversational speech just yet. Speech recognition relies on both listening to what you say and predicting what you will say next, so structured speech is still much easier to understand than unstructured conversation.

"Another interesting limitation of speech recognition systems -- and machine learning systems more generally -- is that they are more optimized for specific problems than most people realize. For example, understanding a person talking to a computer is subtly different problem from understanding a person talking to another person. If you train a machine learning system on data from Google of people speaking to a search engine, it will perform relatively worse on Facebook at understanding people talking to real people."
machine-learning 
yesterday by sisterical
The Great A.I Awakening
Long read from the New York Times on how AI (really, machine learning) became better than existing methods at Google. A collection of 3 stories about the formation & impact of the Google Brain team.

"We did hundreds of experiments," Schuster told me, "until we knew that we could stop the training after one week. You're always saying: When do we stop? How do I know I'm done? You never know you're done. The machine-learning mechanism is never perfect. You need to train, and at some point you have to stop. That's the very painful nature of this whole system. It's hard for some people. It's a little bit an art — where you put your brush to make it nice. It comes from just doing it. Some people are better, some worse."
machine-learning 
yesterday by sisterical
This AI Boom Will Also Bust
The bottom line here is that while some see this new prediction tech as like a new pipe tech that could improve all pipes, no matter their size, it is actually more like a tech only useful on very large pipes. Just as it would be a waste to force a pipe tech only useful for big pipes onto all pipes, it can be a waste to push advanced prediction tech onto typical prediction tasks. And the fact that this new tech is mainly only useful on rare big problems suggests that its total impact will be limited. It just isn’t the sort of thing that can remake the world economy in two decades. To the extend that the current boom is based on such grand homes, this boom must soon bust.

From Overcoming Bias
machine-learning 
yesterday by sisterical
A.I. Experiments
Experiments by Google engineers with machine learning. Some fun examples.
machine-learning 
yesterday by sisterical

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