Posts Tagged ‘deeplearning’

Need to make a molecule? Ask this AI for instructions

April 7, 2018

Need to make a molecule? Ask this AI for instructions #DeepLearning to do better #retrosynthesis. Perhaps other things in chemistry could be learned as well!

“The tool, described in Nature on 28 March1, is not the first software to wield artificial intelligence (AI) instead of human skill and intuition. Yet chemists hail the development as a milestone, saying that it could speed up the process of drug discovery and make organic chemistry more efficient.

“What we have seen here is that this kind of artificial intelligence can capture this expert knowledge,” says Pablo Carbonell, who designs synthesis-predicting tools at the University of Manchester, UK, and was not involved in the work. He describes the effort as “a landmark paper”.”

Need to make a molecule? Ask this AI for instructions

April 1, 2018

What to expect in 2018: science in the new year

January 13, 2018

What to expect in ’18: science in the new year Insights from cancer & ancient #genomes. Cures from #CRISPR. Progress in
#OpenAccess. Also, lots on outer space. But nothing on #cryoEM, #DeepLearning, #QuantumComputing or the brain connectome. HT @OBahcall

google released variant calling with deep learning

December 16, 2017

$GOOG Is Giving Away AI That Can Build Your Genome Seq. + GATK creators now doing a tensor-flow version. Release sounded a bit like IBM unveiling Deep Blue decades ago: “Today, we announce…DeepVariant, a #DeepLearning tech…"

Steven Salzberg’s response to deep variant:

QT:{{"On Monday, Google released a tool called DeepVariant that uses deep learning—the machine learning technique that now dominates AI—to identify all the mutations that an individual inherits from their parents.1 Modeled loosely on the networks of neurons in the human brain, these massive mathematical models have learned how to do things like identify faces posted to your Facebook news feed, transcribe your inane requests to Siri, and even fight internet trolls. And now, engineers at Google Brain and Verily (Alphabet’s life sciences spin-off) have taught one to take raw sequencing data and line up the billions of As, Ts, Cs, and Gs that make you you.”

Google Is Giving Away AI That Can Build Your Genome Sequence

The Serial-Killer Detector | The New Yorker

December 9, 2017

The Serial-Killer Detector Journalist finds subtle yet predictive crime patterns with the computer. Wonder if #DeepLearning would be helpful here? Probably not // #CrimeMap

A former journalist, equipped with an algorithm and the largest collection of murder records in the country, finds patterns in crime. “}}

New Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine

November 12, 2017

New Theory Cracks Open the Black Box of #DeepLearning Highlights the importance of a compression phase for generalization

“Then learning switches to the compression phase. The network starts to shed information about the input data, keeping track of only the strongest features — those correlations that are most relevant to the output label. This happens because, in each iteration of stochastic gradient descent, more or less accidental correlations in the training data tell the network to do different things, dialing the strengths of its neural connections up and down in a random walk. This
randomization is effectively the same as compressing the system’s representation of the input data. As an example”

interactive cnn

June 18, 2017

an intuitive example of how CNN works

Journal Club Paper

June 18, 2017

Zhou, J. and Troyanskaya, O.G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12, 931–934.

Predicting (& prioritizing) effects of noncoding variants w. [DeepSEA] #DeepLearning…model Trained w #ENCODE data

By sparring with AlphaGo, researchers are learning how an algorithm thinks

February 26, 2017

With #AlphaGo researchers are learning how an algorithm thinks What images #NNs conjure up for a classification term

-“Tyka was part of the Google team that first published work on DeepDream, a computer-vision experiment that went viral in 2015. The team trained a deep neural network to classify images, i.e. show the network a picture, it tells you what the image depicts. Except instead of asking it to look at pictures, they programmed the network to look at a word and produce what it thought would be an image that represents the word. The deep neural network would then supply its visual “idea” of different words.

And it worked. The team gave the network the word “banana,” for example, and it produced a dizzying fractal of banana-shaped objects. But the experiment also provided insight into how the machine thought about objects. When asked to produce dumbbells, the network generated gray dumbbell shapes with beige protrusions—arms. The neural net correlated arms and dumbbells so highly that they were seen as almost one object.”


The Great A.I. Awakening – The New York Times

December 26, 2016

The Great AI Awakening Quick history of #DeepLearning & its dramatic success in translation. Is med. diagnosis next?

Now flattered to have had 2 Hinton alumni in my lab…!