- Wednesday, March 1st Baltimore, MD — In March 2016 Insilico Medicine initiated a research collaboration with Life Extension to apply advanced bioinformatic methods and deep learning algorithms to screen for naturally occurring compounds that may slow down or even reverse the cellular and molecular mechanisms of aging.
- Today Life Extension (LE) launched a new line of nutraceuticals called GEROPROTECTTM, and the first product in the series called Ageless CellTM combines some of the natural compounds that were shortlisted by Insilico Medicine’s algorithms and are generally recognized as safe (GRAS).
- The first research results on human biomarkers of aging and the product will be presented at the Re-Work Deep Learning in Healthcare Summit in London 28.02−01.03, 2017, one of the popular multidisciplinary conferences focusing on the emerging area of deep learning and machine intelligence.
- “We salute Life Extension on the launch of GEROPROTECTTM: Ageless Cell, the first combination of nutraceuticals developed using our artificial intelligence algorithms.
- Also, LE also has a unique mail order blood test service that allows US customers to perform comprehensive blood tests to help identify potential health concerns and to track the effects of the nutraceutical products,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Artificial intelligence enters the nutraceutical industry
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- Kelly’s word of choice is cognification, and he uses it to describe ‘smart’ things.
- The industrial revolution saw a large-scale switch from the agricultural world—where everything that was made was made by muscle power—to the mechanized world, where gasoline, steam engines, and electricity applied artificial power to everything.
- We made a grid to deliver that power, so we could have it on-demand anytime and anywhere we wanted, and everything that used to require natural power could be done with artificial power.
- Kelly gives the example of a car, which is simple but compelling: you summon the power of 250 horses just by turning a key.
- The next step is to take that same car that already has the artificial power of 250 horses and add the power of 250 artificial minds.
What’s the first thing that comes to mind when you hear ‘artificial intelligence’? For those raised on a steady diet of big budget Hollywood sci-fi, the answer to that question is something along the lines of “evil robots and all-knowing computers that are going to destroy humanity.”
Continue reading “How AI Is Like Electricity—and Why That Matters”
- I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
- Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
- Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
- More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
- It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.
Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.
Continue reading “An Overview of Python Deep Learning Frameworks”
- TSFRESH frees your time spend on building features by extracting them automatically.
- TSFRESH automatically extracts 100s of features from time series.
- Those features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic.
- The set of features can then be used to construct statistical or machine learning models on the time series to be used for example in regression or classification tasks.
- To avoid extracting irrelevant features, the TSFRESH package has a built-in filtering procedure.
tsfresh – Automatic extraction of relevant features from time series:
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- A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that!
- You can actually achieve great results in a lot of areas even with a relatively small data set, argue the guests on this podcast, if you build the right product on top of it.
- So how do you go about building the right product (beyond machine-learning algorithms in academic papers)?
- Because, observes moderator (and a16z board partner) Steven Sinofsky, “To achieve product market fit, there’s a whole bunch of stuff beyond a giant corpus of data, and the latest deep learning algorithm.”
- Machine learning is an ingredient, part of a modern software-as-a-service company; going beyond the hype, it’s really about figuring out the problem you’re trying to solve… and then figuring out where machine learning fits in (as opposed to the other way around).
Stream a16z Podcast: The Product Edge in Machine Learning Startups by a16z from desktop or your mobile device
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