Home > Uncategorized > Weekly QuEST Discussion Topics and news, 5 Feb

Weekly QuEST Discussion Topics and news, 5 Feb

QuEST Feb 5 2016

So much to do and so little time J.  It has been a very eventful two weeks since we met. Some of the QuEST team went to the deep learning summit in San Francisco last week.  We want to communicate to the rest of you the excitement for the major advances in deep learning and how it is the tool of choice for virtually all machine learning in the commercial world (Twitter, Facebook, Google, eBay, … and too many startups to count).  There was the news while we were on the road of a deep learning solution defeating a profession Go player – a feat that was not expected to be solved for another decade (we also had the Deep Mind previous work we were going to review in more detail also that was q-learning based).  We still have the compositional captioning work to cover and a discussion on hypnosis and a set of generative model papers also.  Let’s start with a review of the Deep Learning Summit and go from there:

 

Summary notes deep learning summit Jan 2016 – slides / videos available:

 

Artificial intelligence in general and Deep Learning specifically is fundamentally changing how humans interact with technology.  The range of commercial companies and enormous investment demonstrated by the examples below exemplify a major shift in emphasis – one that the DOD has to leverage for a successful 3rd Offset strategy.

 

Several AFRL subject matter experts attended the deep learning summit in San Francisco.  Below are the summary notes.  Additional information is available – any questions contact ‘Cap’ –

 

Companies/Schools presenting DL impact on their business:

a.)  Google – several Google papers, one on new approaches to learning algorithms versus learning,  a presentation on Google brain – how to improve generalization – when the training data distribution doesn’t match the test distribution (acknowledged current sensitivity to hyperparameters – art of deep learning, also Ian good fellow – has deep learning book – tutorial on optimization of deep learning, also from Google brain- vinyals – sequence to sequence learning – now moved to Google deep mind – in London source of Go success some discussion on that – but details are in article

b.)  Enlitic – didn’t spend much time talking about their application – most of discussion was on limitation of current tools and infrastructure reduce flexibility in experimentation

c.)   Baidu Step functions in breakthroughs will not seem like breakthroughs by those in the field – speaks to why we need to stay engaged with silicon valley and other innovation centers – Road construction makes its difficult to navigate autonomously – changing features – need government cooperation / help – Just as Apple change the way we interact with technology (iPhone), AI will transform the way we interact with technology

d.)  Stanford – Cap talked to prof manning after talk – long discussion on a common framework for humans/machines for the third offset strategy – also on the definition of meaning – I owe him our definition of meaning – also phd student Karpathy from Stanford – rnn work – sequence applications – api for rnn – vanilla rnn shown – at character level from precious characters predict next one – went through blog on rnn example – example feed in Shakespeare and one character at a time gets more Shakespeare like – takes about 100 lines of Python code

e.)  MITPredicting human memory – mit – pdd student khosla- deep learning for human cognition – Predict human behavior from visual media – what will people like  – what will they remember – gaze following without all that expensive equipment – from the camera on your phone via deep learning

a.)  Twitter – impacting all that company is doing – timeline ranking personalization content discovery and suggestion periscope ranking personalization search indexing retrieval ad targeting click and rate prediction. Spam

b.)  Flickr yahoo – photo search and discovery Importance of data yahoo100 m dataset  – magic view about 2 k tags – photo aesthetics model – Flickr 4.0 new magic view – photos organization around 70 Categories

c.)   Clarifi – 2 yrs old 30 people – understanding images and video is vision – api made for end users – multi language support – 20 languages – 11k concepts via low latency api – Forevery in  App Store

d.)  BayLabsCap had discussion with them after – they have interest in additional discussions – both on the hurtles we faced in my breast cancer efforts using neural networks for image processing for detection of medical issues – they had lots of business model questions and regulatory questions – they also expressed interest in i2i

e.)  Pinterest – 50 billion pins over a billion boards – image of living room – want information about the objects – like the chandelier – users care about objects in image – find similar objects – keva like – in 250 msec – visually similar available search has been launched – also a web version – use deep learning for image representation and for retrieval system

f.)    Panel discussion from principles from Nervana system, SAP and AirBnb on economic impact of deep learning

g.)  intelligent voice -Lateral spiking networks – Glackin – deep laterally recurrent networks for fast transcription of speech – noisy environments – speech enhancement – noise reduction is only partially successful

 

Summary day 2:  Companies and organizations presenting:

a.)  Fashwell – converting fashion images into online shopping assets

b.)  Descartes Labs – processing satellite images – spun out of LANL – Cap spoke with the CEO / founder – will follow up to consider as an alternative to the Orbital Insight collaboration

c.)   Deep instinct – Game changer for cyber security –deep learning with folded in the idea of permuting learned signatures to recognize variations of known signatures used as APTs or Zero Day attacks

d.)  Sightline innovation – idea is to use DL for process management like in construction and quality control of manufacturing – generating revenue now

e.)  Metamind  – most impressive presentation / results seen – From classification to Multimodal question answering for language and vision –

f.)    Maluuba – Building language understanding and conversational systems

g.)   H2o – Basically a sales pitch for their environment – scalable data science and DL although they can’t do much of CNN/DL yet

h.)   Sentient Technologies – Visual intent new way to understand consumers –Duffy – how deep learning will transform online shopping experience – both characterizing the users request and finding similar online products

i.)     eBay – impressive scale issues – 800 million items in 190 countries –  using deep learning in ebay for doing machine translation – now expanded to cognitive computing versus just machine translation – they’ve expanded dramatically

j.)    bio beats – quantified self + behavior intelligence = actionable insight – 3 years old – collected in the wild – cardiovascular focus

k.)  AiCure Jaimes – AI in improving healthcare outcomes and derisking clinical trials  was at yahoo – to visually confirm medication ingestion

l.)     Eyeris – emotion recognition through embedded vision using DL – didn’t make it so will have to look up later

m.)                       Uc Berkeley –deep reinforcement learning for robotics – object – detection – 2012 – DL in 2012 if enough data – same in speech – much faster progress – how about robotics – standard robot percepts – estimates state of robot via a kalman filter … .- motor command – replace with deep nn – output is motor commands – something fundamentally different – vision and speech is supervised learning – robotics you have feedback loop – action changes the world and deal with consequences of actions – typically get a reward function – maybe sparse – marketing and advertising similar – dialogue also interactive – not supervised – need stability – deploy – takes exploration credit assignment and stability issues – approach here isnot q learning –guided policy search – policy optimization – right now train by task – will head towards transfer learning where one net for many tasks – or multiple robots – seems to be now used by Deep Mind also

n.)  Orbital insight – we visited during trip –

o.)  Panasonic SV lab – autonomous action – human ai interaction – HAI – focus on human AI interoperability, autonomous systems

p.)  UC Santa Cruz – scalable collective reasoning in graph data –have structure in the data – want collective scalable reasoning – lot of data not flat – often multimodeal – spatio temporal multimedia …

news summary (41)

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