New York Tech Journal
Tech news from the Big Apple

Structured and Scalable Probabilistic Topic Models

Posted on March 24th, 2017

Data Science Institute Colloquium

03/23/2017 Schapiro Hall (CEPSR), Davis Auditorium @Columbia University

John Paisley, Assistant Professor of Electrical Engineering, spoke about models to extract topics and the their structure from text. He first talked about topic models in which global variables (in this case words) were extracted from documents. In this bag-of-words approach, the topic proportions were the local variables specific to each document, while the words were common across documents.

Latent Dirichlet Analysis captures the frequency of each word. John also noted that #LDA can be use for things other than topic modeling.

  1. Capture assumptions with new distributions – is the new thing different?
  2. Embedded into more complex model structures

Next he talked about moving beyond the “flat” LDA model in which

  1. No structural dependency among the topics – e.g. not a tree model
  2. All combinations of topics are a prior equally probable

To a Hierarchical topics model in which words are placed as nodes in a tree structure with more general topics are in the root and inner branches. He uses #Bayesian inference to start the tree (assume an infinite number of branches coming out of each node) with each document a subtree within the overall tree. This approach can be further extended to a Markov chain which shows the transitions between each pair of words.

He next showed how the linkages can be computed using Bayesian inference to calculate posterior probabilities for both local and global variables: The joint likelihood of the global and local variables can be factors into a product which is conditional on the probabilities of the global variables.

He next compared the speed-accuracy trade off for three methods

  1. Batch inference – ingest all documents at once, so its very slow, but eventually optimal
    1. optimize the probability estimates for the local variables across documents (could be very large)
    2. optimize the probability estimates for the global variables.
    3. Repeat
  2. Stochastic inference – ingest small subsets of the documents
    1. optimize the probability estimates for the local variables across documents (could be very large)
    2. take a step toward to improve the probability estimates for the global variables.
    3. Repeat using the next subset of the documents
  3. MCMC, should be more accurate, but #MCMC is incredibly slow, so it can only be run on a subset

John showed that the stochastic inference method converges quickest to an accurate out-sample model.

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Building an #AI #AutonomousAgent using #SupervisedLearning with @DennisMortensen

Posted on March 23rd, 2017

#NYAI

03/21/2017 @ Rise, 43 West 23rd Street, NY, 2nd floor

In mid 2013, dennis@human.x.ai started x.ai to employ machine learning to set up meetings. After an introduction to the software, Dennis talked about the challenges for creating a conversational agent to act as your assistant setting up business meetings.

He talked about the 3 processes within the agent: NLU + reasoning + NLG

Natural Language Understanding needs to define the universe – what is it we can do and what is it that we cannot do and will not do?

Natural Language Understanding (NLU) Challenges

  1. Define intents then hire AI trainers. Need to get the intents right since it’s expensive to change to a different scheme
  2. What data set do we align to? What are the guidelines for labeling? Coders need to learn and remember the rules defining all the intents. Need to keep it compact, but not too much so
  3. They have 101 AI trainers full time. On what software do they label the words? Need a custom-built annotation platform. Spent 2 years building it.

Reasoning challenges

  1. How do people want the agent to behave? Manually determine what is supposed to happen. This will create a new intent, but this often requires changes in the coding of the NLU
  2. Some of the things humans want to do are very complicated. Especially common sense
    1. don’t do meeting after 6:00, but if there is one at 6:15, there is a reason for this happening.
    2. a 6:30 PM call to Singapore might be a good idea.
    3. When to have a meeting and when to have a phone call

Natural Language Generation (NLG) challenges

  1. They have 2 interaction designers
  2. Need to inject empathy if it’s appropriate. For instance if there is a change in schedule, we need to respond appropriately: understanding initially and more assertive if the change needs to be unchanged. Also need to honor requests to speak in a given language.

They evaluate the performance of the software when being used by a client by

  1. customer-centric metrics, such as the number of schedule changes
  2. is the customer happy?

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NYAI#7: #DataScience to Operationalize #ML (Matthew Russell) & Computational #Creativity (Dr. Cole)

Posted on November 22nd, 2016

#NYAI

11/22/2016 Risk, 43 West 23rd Street, NY 2nd floor

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Speaker 1: Using Data Science to Operationalize Machine Learning – (Matthew Russell, CTO at Digital Reasoning)

Speaker 2: Top-down vs. Bottom-up Computational Creativity  – (Dr. Cole D. Ingraham DMA, Lead Developer at Amper Music, Inc.)

Matthew Russell @DigitalReasoning  spoke about understanding language using NLP,  relationships among entities, and temporal relationship. For human language understanding he views technologies such as knowledge graphs and document analysis is becoming commoditized. The only way to get an advantage is to improve the efficiency of using ML: KPI for data analysis is the number of experiments (tests an hypothesis) that can be run per unit time. The key is to use tools such as:

  1. Vagrant – allow an environmental setup.
  2. Jupyter Notebook – like a lab notebook
  3. Git – version control
  4. Automation –

He wants highly repeatable experiments. The goal is to speed up the number of experiments that can be conducted per unit time.

He then talked about using machines to read medical report and determine the issues. Negatives can be extracted, but issues are harder to find. Uses an ontology to classify entities.

He talked about experiments on models using ontologies. The use of a fixed ontology depends on the content: the ontology of terms for anti-terrorism evolves over time and needs to be experimentally adjusted over time. Medical ontology is probably most static.

In the second presentation, Cole D. Ingraham @Ampermusic talked about top-down vs bottom-up creativity in the composition of music. Music differs from other audio forms since it has a great deal of very large structure as well as the smaller structure. ML does well at generating good audio on a small time frame, but Cole thinks it is better to apply theories from music to create the larger whole. This is a combination of

Top-down: novel&useful, rejects previous ideas – code driven, “hands on”, you define the structure

Bottom-up: data driven – data driven, “hands off”, you learn the structure

He then talked about music composition at the intersection of Generation vs. analysis (of already composed music) – can do one without the other or one before the other

To successfully generate new and interesting music, one needs to generate variance. Composing music using a purely probabilistic approach is problematic as there is a lack of structure. He likes the approach similar to replacing words with their synonyms which do not fundamentally change the meaning of the sentence, but still makes it different and interesting.

It’s better to work on deterministically defined variance than it is to weed out undesired results from nondeterministic code.

As an example he talked about Wavenet (google deepmind project) which input raw audio and output are raw audio. This approach works well for improving speech synthesis, but less well for music generation as there is no large scale structural awareness.

Cole then talked about Amper, as web site that lets users create music with no experience required: fast, believable, collaborative

They like a mix of top-down and bottom-up approaches:

  1. Want speed, but neural nets are slow
  2. Music has a lot of theory behind it, so it’s best to let the programmers code these rules
  3. Can change different levels of the hierarchical structure within music: style, mood, can also adjust specific bars

Runtime written in Haskell – functional language so its great for music

posted in:  AI, Big data, data analysis, Data science, NewYorkAI, Programming    / leave comments:   No comments yet

NYAI#5: Neural Nets (Jason Yosinski) & #ML For Production (Ken Sanford)

Posted on August 24th, 2016

#NYAI, New York #ArtificialIntelligence

08/24/2016 @Rise 43 West 23rd Street, NY, 2nd floorPreview Changes

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Jason Yosinski@GeometricTechnology spoke about his work on #NeuralNets to generate pictures. He started by talking about machine learning with feedback to train a robot to move more quickly and using feedback to computer-generate pictures that are appealing to humans.

Jason next talked about AlexNet, based on work by Krizhevsky et al 2012, to classify images using a neural net with 5 convolutional layers (interleaved with max pooling and contrast layers) plus 3 fully connected layers at the end. The net with 60 million parameters was training on ImageNet which contains over 1mm images. His image classification Code is available on http://Yosinski.com.

Jason talked about how the classifier thinks about categories when it is not being trained to identify that category. For instance, the network may learn about faces even though there is no human category since it helps the system detect things such as hats (above a face) to give it context. It also identifies text to give it context on other shapes it is trying to identify.

He next talked about generating images by inputting random noise and randomly changing pixels. Some changes will cause the goal (such as a ‘lions’) to increase in confidence. Over many random moves, the goal increases in its confidence level. Jason showed many random images that elicited high levels of confidence, but the images often looked like purple-green slime. This is probably because the network, while learning, immediately discards the overall color of the image and is therefore insensitive to aberrations from normal colors.  (See Erhan et al 2009)

[This also raises the question of how computer vision is different from human vision. If presented with a blue colored lion, the first reaction of a human might be to note how the color mismatches objects in the ‘lion’ category. One experiment would be to present the computer model with the picture of a blue lion and see how it is classified. Unlike computers, humans encode information beyond their list of items they have learned and this encoding includes extraneous information such as color or location. Maybe the difference is that humans incorporate a semantic layer that considers not only the category of the items, but other characteristics that define ‘lion-ness’.  Color may be more central to human image processing as it has been conjectured that we have color vision so we can distinguish between ripe and rotten fruits. Our vision also taps into our expectation to see certain objects within the world and we are primed to see those objects in specific contexts, so we have contextual information beyond what is available to the computer when classifying images.]

To improve the generated pictures of ‘lions’, he next used a generator to create pictures and change them until they get a picture which has high confidence of being a ‘lion’. The generator is designed to create identifiable images. The generator can even produce pictures on objects that it has not been trained to paint. (Need to apply regularization to get better pictures for the target.)

Slides at http://s.yosinski.com/nyai.pdf

In the second talk, Ken Sanford @Ekenomics and H20.AI talked about the H2O open source project. H2O is a machine learning engine that can run in R, Python,Java, etc.

Ken emphasized how H2O (a multilayer feed forward neural network) provides a platform that uses the Java Score Code engine. This easies the transition from the model developed in training and the model used to score inputs in a production environment.

He also talked about the Deep Water project which aims to allow other open source tools, such as MXNET, Caffe, Tensorflow,… (CNN, RNN, … models) to run in the H2O environment.

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Automatically scalable #Python & #Neuroscience as it relates to #MachineLearning

Posted on June 28th, 2016

#NYAI: New York Artificial Intelligence

06/28/2016 @Rise, 43 West 23rd Street, NY, 2nd floor

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Braxton McKee (@braxtonmckee ) @Ufora first spoke about the challenges of creating a version of Python (#Pyfora) that naturally scales to take advantage of the hardware to handle parallelism as the problem grows.

Braxton presented an example in which we compute the minimum distance from target points a larger universe of points base on their Cartesian coordinates. This is easily written for small problems, but the computation needs to be optimized when computing this value across many cpu’s.

However, the allocation across cpu’s depends on the number of targets relative to the size of the point universe. Instead of trying to solve this analytically, they use a #Dynamicrebalancing strategy that splits the task and adds resources to the subtasks creating bottlenecks.

This approach solves many resource allocation problems, but still faces challenges

  1. nested parallelism. They look for parallelism within the code and look for bottlenecks at the top level of parallelism and split the task into subtasks at that level, …
  2. the data do not fit in memory. They break tasks into smaller tasks. They also have each task know which other caches hold data, so they can be accessed directly without going to slower main memory
  3. different types of architectures (such as gpu’s) require different types of optimization
  4. the optimizer cannot look inside python packages, so cannot optimize a bottleneck within a package.

Pyfora

  1. is a just-in-time compiler that moves stack frames from machine-to-machine and senses how to take advantage of parallelism
  2. tracks what data a thread is using
  3. dynamically schedules threads and data
  4. takes advantage of mutability which allows the compiler to assume that functions do no change over time so the compiler can look inside the function when optimizing execution
  5. is written on top of another language which allows for the possibility of porting the method to other languages

In the second presentation, Jeremy Freeman @Janelia.org spoke about the relationship between neuroscience research and machine learning models. He first talking about the early works on understanding the function of the visual cortex.

Findings by Hubel & Wiesel in1959 have set the foundation for visual processing models for the past 40 years. They found that Individual neurons in the V1 area of the visual cortex responded to the orientation of lines in the visual field. These inputs fed neurons that detect more complex features, such as edges, moving lines, etc.

Others also considered systems which have higher level recognition and how to train a system. These include

Perceptrons by Rosenblatt, 1957

Neocognitrons by Fukushima, 1980

Hierarchical learning machines, Lecun, 1985

Back propagation by Rumelhart, 1986

His doctoral research looked at the activity of neurons in V2 area. They found they could generate high order patterns that some neurons discriminate among.

But in 2012, there was a jump in performance of neural nets – U. of Toronto

By 2014, some of the neural network algos perform better than humans and primates, especially in the area of image processing. This has lead to many advances such as Google deepdream which combines images and texture to create an artistic hybrid image.

Recent scientific research allows one to look at thousands of neurons simultaneously. He also talked about some of his current research which uses “tactile virtual reality” to examine the neural activity as a mouse explores a maze (the mouse walks on a ball that senses it’s steps as it learns the maze).

Jeremy also spoke about Model-free episodic control for complex sequential tasks requiring memory and learning. ML research has created models such as LSTM and Neural Turing Nets which retain state representations. Graham Taylor has looked at neural feedback modulation using gates.

He also notes that there are similar functionalities between the V1 area in the visual cortex, the A1 auditory area, and the S1, tactile area.

To find out more, he suggested visiting his github site: Freeman-lab and looking the web site neurofinder.codeneuro.org.

posted in:  AI, data analysis, Data science, NewYorkAI, Open source, Python    / leave comments:   No comments yet

DataDrivenNYC: bringing the power of #DataAnalysis to ordinary users, #marketers, #analysts.

Posted on June 18th, 2016

#DataDrivenNYC

06/13/2016 @AXA Equitable Center (787 7th Avenue, New York, NY 10019)

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The four speakers were

Adam @NarrativeScience talked about how people with different personalities and jobs may require/prefer different takes on the same data. His firm ingests data and has systems to generate natural language reports customized to the subject area and the reader’s needs.

They current develop stories with the guidance of experts, but eventually will more to machine learning to automate new subject areas.

Next, Neha @Confluent talked about how they created Apache Kafka: a streaming platform which collects data and allows access to these data in real time.

Read more…

posted in:  data, data analysis, Data Driven NYC, Data science, databases, Open source    / leave comments:   No comments yet

Advanced #DeepLearning #NeuralNets: #TimeSeries

Posted on June 16th, 2016

#DataScience+FintechJC

06/15/2016 @Qplum, 185 Hudson Street, Jersey City, NJ, suite 1620

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Sumit Chopra@facebook first gave a general review of neural nets and then talked about how neural nets can be adapted to text analysis and time series data.

For a review of neural net learning models, see my previous post from this series and other posts on machine learning.

Sumit then broke the learning process into two steps: feature extraction and classification. Starting with raw data, the feature extractor is the deep learning model that prepares the data for the classifier which may be a simple linear model or random forest. In supervised training, errors in the prediction output by the classifier are feed back into the system using back propagation to tune the parameters of the feature extractor and the classifier.

In the remainder of the talk Sumit concentrated on how to improve the performance of the feature extractor.

In the general text classification (unlike image or speech recognition) the length of the input can be very long (and variable in length). In addition, analysis of text by general deep learning models

  1. does not capture order of words or predictions in time series
  2. can handle only small sized windows or the number of parameters explodes
  3. cannot capture long term dependencies

So, the feature extractor is cast as a time delay neural networks (#TDNN). In TDNN, the words are text is viewed as a string of words. Kernel matrices (usually of from 3 to 5 unit long) are defined which compute a dot products of the weights of the words in a contiguous block of text. The kernel matrix is shifted one word and the process is repeated until all words are processed. A second kernel matrix creates another set of features and so forth for a 3rd kernel, etc.

These features are then pooled using the mean or max of the features. This process is repeated to get additional features. Finally a point-wise non-linear transformation is applied to get the final set of features.

Unlike traditional neural network structures, these methods are new, so no one has done a study of what is revealed in the first layer, second layer, etc. Also theoretical work is lacking on the optimal number of layers for a text sample of a given size.

Historically, #TDNN has struggled with a series of problem including convergence issues, so recurrent neural networks (#RNN) were developed in which the encoder looks at the latest data point along with its own previous output. One example is the Elman Network, which each feature is the weighted sum of the kernel function (one encoder is used for all points on the time series) output with the previously computed feature value. Training is conducted as in a standard #NN using back propagation through time with the gradient accumulated over time before the encoder is re-parameterized, but RNN has a lot issues
1, exploding or vanishing gradients – depending on the largest eigenvalue
2. cannot capture long-term dependencies
3. training is somewhat brittle

The fix is called Long short-term memory. #LSTM, has additional memory “cells” to store short-term activations. It also has additional gates to alleviate the vanishing gradient problem.
(see Hochreiter et al . 1997). Now each encoder is made up of several parts as shown in his slides. It can also have a forget gate that turns off all the inputs and can peep back at the previous values of the memory cell. At Facebook, NLP and speech and vision recognition are all users of LSTM models

LSTM models, however still don’t have a long term memory. Sumit talked about how creating memory networks which will take a store and store the key features in a memory cell. A query runs against the memory cell and then concatenates the output vector with the text. A second query will retrieve the memory.
He also talked about using a dropout method to fight overfitting. Here, there are cells that randomly determine whether a signal is transmitted to the next layer

Autocoders can be used to pretrain the weights within the NN to avoid problems of creating solution that are only locally optimal instead of globally optimal.

[Many of these methods are similar in spirit to existing methods. For instance, kernel functions in RNN are very similar to moving average models in technical trading. The different features correspond to averages over different time periods and higher level features correspond to crossovers of the moving averages.

The dropoff method is similar to the techniques used in random forest to avoid overfitting.]

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#Visualization Metaphors: unraveling the big picture

Posted on May 19th, 2016

05/18/2016 @TheGraduateCenter CUNY, 365 5th Ave, NY

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Manuel Lima ( @mslima  ) @Parsons gave examples of #data representations. He first looked back 800 years and talked about Ars Memorativa, the art of memory , a set of mnemonic principals to organize information: e.g. spatial orientation, order of things on paper, chunking, association (to reinforce relations), affect, repetition. (These are also foundation principals of #Gestalt psychology).

Of the many metaphors, trees are most used: e.g. tree of life and the tree of good and evil. geneology, evolution, laws, …

Manuel then talked about how #trees work well for hierarchical systems, but we are looking more frequently at more complex systems. In science, for instance:

17-19th century – single variable relationships

20th century – systems of relationships (trees)

21st century – organized complexity (networks)

Even the tree of life can be seen as a network once bacteria’s interaction with organisms is overlaid on the tree.

He then showed various  15 distinct typologies for mapping networks and showed works of art inspired by networks (the new networkism) : 2-d: Emma McNally, 3-d: Tomas Saraceno and Chiharu Shiota.

The following authors were suggested as references on network visualization: Edward Tufte, Jacques Bertin (French philosopher), and Pat Hanrahan (a computer science prof at Stanford extended his work, also one of the founders of Tableau)

posted in:  Art, Big data, data, data analysis, Data science, UX    / leave comments:   No comments yet

#DeepLearning and #NeuralNets

Posted on May 16th, 2016

#DataScience+FinTechJC

05/16/2016 @Qplum, 185 Hudson Street, Suite 1620  Plaza 5, Jersey City, NJ

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#Raghavendra Boomaraju @ Columbia described the math behind neural nets and how back propagation is used to fit models.

Observations on deep learning include:

  1. Universal approximation theory says you can fit any model with one hidden layer, provided the layer has a sufficient number of levels. But multiple hidden layers work better. The more layers, the fewer levels you need in each layer to fit the data.
  2. To optimize the weights, back-propagate the loss function. But one does not need to optimize the g() function since g()’s are designed to have a very general shape (such as the logistic)
  3. Traditionally, fitting has been done by changing all inputs simultaneously (deterministic) or changing one input at a time during optimization (stochastic inputs) . More recently, researchers are changing subsets of the inputs (minibatches).
  4. Convolution operators are used to standardize inputs by size and orientation by rotating and scaling.
  5. There is a way to Visualize within a neural network – see http://colah.github.io/
  6. The gradient method used to optimize weights needs to be tuned so it is neither too aggressive nor too slow. Adaptive learning (Adam algorithm) is used to determine the optimal step size.
  7. Deep learning libraries include Theano, café, Google tensor flow, torch.
  8. To do unsupervised deep learning – take inputs through a series of layers that at some point have fewer levels than the number of inputs. The ensuing layers expand so the number of points on the output layer matches that of the input layer. Optimize the net so that the inputs match the outputs. The layer with the smallest number of point s describes the features in the data set.

 

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