#ExtremeEvents and short term reversals in #RiskAversion
Posted on April 17th, 2017
04/17/2017 @ 101 NJ Hall, 75 Hamilton Street, New Brunswick, NJ
Kim Oosterlinck @FreeUniversityOfBrussels presented work done by Matthieu Gilson, Kim Oosterlinck, Andrey Ukhov. Kim started by reviewing the literature that shows no consensus on whether risk aversion increases or decreases in following extreme events such as war. In addition, these studies often have only two points on which to make this evaluation.
He presented a method for tracking overall risk aversion within a population on a daily basis for several years. His analysis values the lottery part of Belgian bonds which consisted of a fixed coupon bond with the opportunity to win a cash prize every month. These bonds were sold to retail customers and made up 11% of Belgian bond market in 1938. By discounting the cash flows based on the yields for other, fixed coupon Belgian bonds, one can compare the risk neutral price (RNP) relative to the market price (MP).
When MP/RNP > 1 this indicates the average holder is risk loving.
There are three periods in their observations from 1938 to 1948.
- Risk neutral to risk averse from 1938 to 1940, when German invaded and occupied Belgian
- Risk aversion to risk seeking from 1940 to 1945 during the German occupation
- Risk seeking to risk neutral from 1945 to 1948.
Lots of competing theories on when people become more or less risk averse
These data give the strongest support is habituation to background risk as the best explanation of the increase in risk aversion. Prospect theory also does well as an explanation.
[The findings of increased risk seeking form 1940 to 1945 could also be consistent with a flat yield curve at 3% from 1month to 3 years in 1940 to a steep yield curve in 1945 going from 0% at 1 month to 3% at 3 years. ]
From #pixels to objects: how the #brain builds rich representation of the natural world
Posted on April 15th, 2017
04/06/2017 @RutgersUniversity, Easton Hub Auditorum, Fiber Optics Building, Busch Campus
Jack Galliant @UCBerkeley presented a survey of current research on mapping the neurophysiology of the visual system in the brain. He first talked about the overall view of visual processing since the Felleman and Van Essen article in Cerebral Cortex in 1992. Their work on macaque monkey showed that any brain area has a 50% chance of being connected to any other part of the brain. Visual processing can be split into 3 areas
1.Early visual area – 2.intermediate visual areas – 3.high level visual areas
With pooling nonlinear transformations between areas (the inspiration for the non-linear mappings in convolutional neural nets (CNN)). The visual areas were identified by retinotopic maps – about 60 areas in humans with macaques having 10 to 15 areas in the V1 area.
Another important contribution was by David J. Field who argued that the mammalian visual system can only be understood relative to the images it is exposed to. In addition, natural images have a very specific structure – 1/f noise in the power spectrum – due to the occlusion of images which can be viewed from any angle (see Olshausen & Field, American scientist, 2000)
This lead to research resolving natural images by characterizing them by the correlation of pairs of points. Beyond pairs of points that approach becomes too computational intensive. In summary, natural images are only a small part of the universe of images (most of which humans classify as white noise)
Until 2012, researchers needed to specify the characteristics to identify items in images, but LeCun, Bengio & Hinton, Nature, 2015 showed that Alexnet could resolve many images using multiple layer models, faster computation, and lots of data. These deep neural nets work well, but the reasons for their success have yet to be worked out (He estimates it will take 5 to 10 years for the math to catch up).
One interesting exercise is running a CNN and then looking for activation in a structure in the brain: mapping the convolutional layers and feature layers to the correspondence on layers in the visual cortex. This reveals that V1 has bi-or tri-phasic functions – Gabor functions in different orientations. This is highly efficient as a sparse code needs to activate as few neurons as possible.
Next they used motion-energy models to see how mammals detect motion in the brain Voxels in V1 (Shinji Nishimoto). They determined that monitoring takes 10 to 20ms using Utah arrays to monitor single neurons. They have animal watch movies and analyze the input images using combination of complex and simple cell models (use Keras) to model neurons in V1 and V2 using a 16ms time scale.
High level visual areas
Jack then talked about research identify neurons in high level visual areas that respond to specific stimuli. Starting with fMRI his groups (Huth, Nishimoto, Vu & Gallant, Neuron, 2012) has identified many categories: face areas vs. objects; place minus face. By presented images and mapping which voxels in the brain are activated one can see how the 2000 categories are mapped in the brain using wordmap as the labels. Similar concepts are mapped to similar locations in the brain, but specific items in the semantic visual system interact with the semantic language areas – so a ‘dog’ can active many areas so it can be used in different ways and can be unified as needed. Each person will have a different mapping depending on their previous good and bad experiences with dogs.
He talked about other topics including the challenges of determining how things are stored in places: Fourier power, object categories, subjective distance. In order to activate any of these areas in isolation, one needs enough stimulus to activate the earlier layers. They have progress by building a decoder from the knowledge of the voxel which run from the brain area backwards to create stimulus. A blood flow model are used with a 2 second minimum sampling period. But there is lots of continuity so they can reconstruct a series of images.
Intermediate visual area
Intermediate visual areas between the lower and higher levels of processing are hard to understand – looks at V4. They respond to shapes of intermediate complexity, but not much else like a curvature detector. Using fMRI they know what image features correlate with specific areas, but there is no strong indication differentiating one layer from another. Using the Utah array, they need to do a log-polar transform to improve prediction in V4. Using a receptor field model, they can create a predictor frame and match brain activity to images that gave the largest response.
To improve prediction on V4, Utah arrays need to do a log-polar transform. However, the images are messy and predicting V4 is not the same as understanding V4.
Finally, he talked about attenuation and tuning effects on single neurons. In an experiment in subjects watched a movie and were asked to search for either humans or vehicles, there were changes in the semantic map based on the search criterion. These tuning shift effects are a function of distance to visual periphery: Attentional effects are small in V1 and get larger in the ensuing layers.
In the Q&A, he made the following points:
- The visual word form area in the brain becomes active as you learn to read. This change does not occur for people who are illiterate.
- One of the experimental assumptions is that the system is stationary, so there is not adaptation. If adaptation does occur, then they cannot compute a noise ceiling for the signals.
[Neural nets take inspiration from the neurobiology, especially the creation of convolutional neural nets, but there is now feedback with neurobiology using the tools created in machine learning to explore possible models of brain mapping. Does the pervasive existence of Gabor filters lead to an argument that their presence indicates that natural images are closely allied with fractal patterns?]
How to build a #MixedReality experience for #Hololens
Posted on April 14th, 2017
4/14/2017 @MicrosoftReactorAtGrandCentral, 335 Madison Ave, NY, 4th floor
Mike Pell and John gave a roadmap for generating #MixedReality content. They started with general rules for generating content and how these rules apply to building MR content.
- Know your audience –
- Role of emotion in design – we want to believe in what is shown in a hologram.
- Think situation – where am I? at home you are comfortable doing certain things, but there are different needs and different things you are comfortable in public
- Think spatially – different if you can walk around the object
- Think inclusive – widen your audience
- Know Your medium
- For now you look ridiculous when wearing a VR headset– but maybe this eventually becomes like a welder shield which you wear when you are doing something specialized
- Breakthrough experience – stagecraft – so one can see what the hololens user is seeing
- Know Your palette
Interactive Story Design – a fast way to generate MR content
- Who is your “spect-actor” (normally someone who observers – have a sense of who the individual is for this moment – avoid blind spot, so pick a specific person. )
- Who are your “interactors” – will change as a result of the interaction – can be objects, text, people
- This creates a story
- Location – design depends on where this occurs
- Journey – how does participant change
How to bring the idea to life: how to develop the script for the MR experience
3-step micro sprints – 3 to 6 minute segments – so you don’t get attached to something that doesn’t work. Set 1 to 2 minute time limit for each step
- Parameters – limited resources help creative development
- Personify everything including text has a POV, feelings, etc.
- 3 emotional responses – what is the emotional response of a chair when you sit in it?
- 3 conduits
- Facial expression – everything has a face including interfaces and objects
- Body language
- Playtest – do something with it
- 3 perspectives
- Interacters – changes in personality over time
- 3 perspectives
- Audience – who is watching
- PMI – evaluative process – write on index cards (not as a feedback session) so everyone shares their perspective. Next loop back to the parameters (step 1)
- Plus – this is interesting
- Minus – this weak
- Interesting – neither of the above “this is interesting”
How to envision and go fast:
- Filming on location – randomly take pictures – look for things that speak to you as creating an interesting experience.
- Understand the experience – look at the people (i.e. people viewing art)
- Visualize it – put people into the scene (vector silhouette in different poses) put artwork into scene along with viewers.
- Build a prototype using Unity. Put on the Hololens and see how it feels
They then went through a example session in which a child is inside looking at a T-Rex in the MOMA outdoor patio. The first building block was getting three emotional responses for the T-Rex:
- Positive – joy looking at a potential meal: the child
- Negative – too bad the glass barrier is here
- Neutral – let me look around to see what is around me
To see where we should be going, look at what children want to do with the technology
Beyond Big: Merging Streaming & #Database Ops into a Next-Gen #BigData Platform
Posted on April 13th, 2017
04/13/2017 @Thoughtworks, 99 Madison Ave, New York, 15th floor
Amir Halfon, VP of Strategic Solutions, @iguazio talked about methods for speeding up a analytics linked to a large database. He started by saying that a traditional software stack accessing a db was designed to minimize the time taken to access slow disk storage. This is resulted in layers of software. Amir said that with modern data access and db architecture, processing is accelerated by a unified data engine that eliminate many of the layers. This also allows for the creation of a generic access of data stored in many different formats and a record-by-record security protocol.
To simplify development they only use AWS and only interface with Kafka, Hadoop, Spark. They are not virtualization (eventually reaches a speed limit), they do the actual store.
Another important method is to use “Predicate pushdown” =’ select … where … <predicate>’; usually all data are retrieved and then culled; instead if the predicate is pushed down, only the relevant data is retrieved. A.k.a. as an “offload-engine”.
MapR is a competitor using the HDFS database, as opposed to rebuilding the system from scratch.
#Driverless #Trucks will come before driverless #cars
Posted on April 13th, 2017
04/12/2017 @MetroTech 6, NYU, Brooklyn, NY
Seth Clevenger – technology editor, Transport Topics News, @sethclevenger, talked about the rollout of driverless trucks. His main message was that there are many intermediate stages from adaptive cruise control (already exists in some cars) to fully autonomous operation.
Truck manufacturers are concentrating on systems that assist rather than replace drivers. These include
- Truck platooning – could roll out by year-end. – synchronize breaking; trucks can draft off each other for a 10% increase in efficiency. Brakes are linked, but still need drivers.( Peloton Technology plans to begin fleet trials)
- Connected vehicles – just starting to be regulated. (V2V, V2I). For instance, safety messages sent by each vehicle.
- auto docking at loading docks
- traffic jam assist – move forward slowly without driver assistance
Startups include: Uber/Otto, Embark, Starsky Robotics, Driver.ai
[One of my major concerns is the integrity of the software controlling the vehicle. A failure in software could cause accidents, however, my main concern is the potential insertion of a malicious virus as a sleeper cell within the millions of lines of code. In this case, the results could be catastrophic as all breaking and acceleration systems could be programmed to fail on a specific date in the future. At that moment, all vehicles on the road would be out of control potentially resulting in millions of accidents and thousands of deaths and injuries. Preventing such an event will require coordinating amongst suppliers and enforcement of strict software standards. The large number of suppliers makes this job especially complicated. This sleeper cell could lie dormant for years before it is activated.]
#Self-learned relevancy with Apache Solr
Posted on March 31st, 2017
03/30/2017 @ Architizer , 1 Whitehall Street, New York, NT, 10th Floor
Trey Grainger @ Lucidworks covered a wide range of topics involving search.
He first reviewed the concept of an inverted index in which terms are extracted from documents and placed in an index which points back to the documents. This allows for fast searches of single terms or combinations of terms.
Next Trey covered classic relevancy scores emphasizing
tf-idf = how well a term described the document * how important is the term overall
He noted, however, the tf-idf’s values may be limited since it does not make use of domain-specific knowledge.
Trey then talked about reflected intelligence = self–learning search which uses
- Collaboration – how have others interacted with the system
- Context – information about the user
He said this method increases relevance by boosting items that are highly requested by others. Since the items boosted are those currently relevant to others, this allows the method to adapt quickly without need for manual curation of items.
Next he talked about semantic search which using its understanding of terms in the domain.
(Solr can connect to an RDF database to leverage an ontology). For instance, one can run word2vec to extract terms and phrases for a query and them determine a set of keywords/phrases to best match the query to the contents of the db.
Also, querying a semantic knowledge graph can expand the search by traversing to other relevant terms in the db
Applications of #DeepLearning in #Healthcare
Posted on March 28th, 2017
03/28/2017 @NYU Courant Institute (251 Mercer St, New York, NY)
Sumit Chopra, the head of A.I. Research @Imagen Technologies, introduced the topic by saying that the two areas in our lives that will be most affect by AI are healthcare and driverless cars.
Healthcare data can be divided into
- Other – cell phones, etc.
Payer data – from insurance provider
Clinical data – incomplete since hospitals don’t share their datasets; digital form with privacy concerns
Payer data more complete unless the patient switches the payer, less detail.
He focuses on medical imaging – mainly diagnostic radiology – 600mm studies in the U.S., but shortage of skilled radiologists. Prevalence of errors. The images are very large size, high resolution, low contrast, highly subtle cues => radiology is hard to do well
Possible solution: pre-train a standard model: Alexnet/VGG/… on a small number of images, but this might not work since the signal is subtle.
Also radiology reports, which could be used for supervised training, are unstructured and it’s hard to tell what the report tells you. => weak labels at best
Much work has been done on this problem, usually using deep convolutional neural nets.
First step: image registration = rotate & crop.
Train a deep convolutional network (registration network) , the send to a detection network for binary segmentation.
Could use generative models for images to train doctors
Leverage different modalities of data
Sumit has round that a random search of hyperparameter space works better than either grid search or optimizer search.
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.
- Capture assumptions with new distributions – is the new thing different?
- Embedded into more complex model structures
Next he talked about moving beyond the “flat” LDA model in which
- No structural dependency among the topics – e.g. not a tree model
- 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
- Batch inference – ingest all documents at once, so its very slow, but eventually optimal
- optimize the probability estimates for the local variables across documents (could be very large)
- optimize the probability estimates for the global variables.
- Stochastic inference – ingest small subsets of the documents
- optimize the probability estimates for the local variables across documents (could be very large)
- take a step toward to improve the probability estimates for the global variables.
- Repeat using the next subset of the documents
- 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.
Building an #AI #AutonomousAgent using #SupervisedLearning with @DennisMortensen
Posted on March 23rd, 2017
03/21/2017 @ Rise, 43 West 23rd Street, NY, 2nd floor
In mid 2013, firstname.lastname@example.org 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
- Define intents then hire AI trainers. Need to get the intents right since it’s expensive to change to a different scheme
- 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
- 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.
- 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
- Some of the things humans want to do are very complicated. Especially common sense
- don’t do meeting after 6:00, but if there is one at 6:15, there is a reason for this happening.
- a 6:30 PM call to Singapore might be a good idea.
- When to have a meeting and when to have a phone call
Natural Language Generation (NLG) challenges
- They have 2 interaction designers
- 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
- customer-centric metrics, such as the number of schedule changes
- is the customer happy?
Critical Approaches to #DataScience & #MachineLearning
Posted on March 18th, 2017
3/17/2017 @Hunter College, 68th & Lexington Ave, New York, Lang Theater
Geetu Ambwani @HuffingtonPost @geetuji spoke about how the Huffington Post is looking at data as a way around the filter bubble in which separates individuals from views that are contrary to their previously help beliefs. Filter bubbles are believed to be a major reason for the current levels of polarization in society.
The talked about ways that the media can respond to this confirmation bias
- Show opposing point of view
- Show people their bias
- Show source crediability
For instance, Chrome and Buzzfeed have tools that will insert opposing points of view in your news feed. Flipfeed enables you to easily load another feed. AlephPost clusters articles and color codes them indicating the source’s vantage view. However, showing people opposing views can backfire.
Second, Readacross the spectrum will show you your biases. Politico will show you how blue or red you by indicating the color of your information sources.
Third, one can show source credibility and where it lies on the political spectrum
However, there is still a large gap between what is produced by the media and what consumers want. Also this does not remove the problem that ad dollars are given for “engagement” which means that portals are incented to continue delivering what the reader wants.
Next, Justin Hendrix @NYC Media Lab (consortium of universities started by the city of NY) talked about emerging media technologies. Examples were
- Vidrovr – teach computers how to watch video – produce searchable tags.
- Data selfi project – from the new school. See the data which Facebook has on us. A chrome extension. 100k downloads in the first week.
- Braiq – connect the mind with the on-board self-driving software on cars. Build software which is more reactive to the needs and wants of the passenger. Technology in the headrest and other inputs that will talk to the self-driving AI.
The follow up discussion covered a wide range of topics including
- The adtech fraud is known, but no one has the incentive to address. Fake audience – bots clicking sites
- Data sources are readily available lead by the Twitter or Facebook APIs. Get on github for open source code on downloading data
- Was the 20th century an aberration as to how information was disseminated? We might just be going back to a world with pools of information.
- What are the limits on what points of view any media company is willing to explore?
- What is the future of work and the social contract as jobs disappear?