Wednesday, August 2, 2017

Neuroscience inspired Computer Vision

Source: https://www.pinterest.com/explore/visual-cortex/

Having read the profound master piece “When breath becomes air”, by Neuroscientist – surgeon Paul Kalanithi, I was curious about how neuroscience could contribute to AI (Computer vision in particular). 

Then, I found an comprehensive article in Neuron Review journal (written by Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick) titled “Neuroscience inspired Artificial Intelligence”.  Here goes a brief excerpt of concepts I found inspiring in that article, related to computer vision.


Past
CNNS
  • How visual input is filtered and pooled into simple and complex areas of cells in area V1in visual cortex
  • Hierarchical organization of mammalian cortical systems 
Object recognition 
  • Transforming raw visual input into increasingly complex set of features - To achieve invariance towards pose, illumination and scale
Present
Attention 
  • Visual attention shifts strategically among different objects (no equal priority for all objects) - To ignore irrelevant objects in a given scene in the presence of a clutter, multi object recognition, image to caption generation, generative models to synthasize images 
Future 
Intuitive understanding of physical world 
  • Interpret and reason about scenes by decomposing them into individual objects and their relations 
  • Redundency reduction (encourages the emergence of disentangled representations of independent factors such as shape and position) - To learn objectness, construct rich object models from raw inputs using deep generative models, E.g., Variational auto encoder 
Efficient Learning 
  • Rapidly learn new concepts from only a handful of examples (Related with Animal learning, developmental psychology) 
  • Characters challenge - distinguish novel instances of an unfamiliar hand written character from another - "Learn to learn”  networks
Transfer Learning
  • Generalizing or transferring generalized knowledge gained in one context to novel previously unseen domains (E.g., Human who can drive a car drives an unfamiliar vehicle) - Progressive networks 
  • Neural coding using Grid codes in Mammalian entorhinal cortex - To formulate conceptual representations that code abstract, relational information among patterns of inputs (not just invariant features) 
Virtual brain analytics 
  • Increase the interpretability of AI computations, Determine response properties of units in a neural networks 
  • Activity maximization - To generate synthetic images by maximizing the activity of certain classes of unit 
From AI to neuroscience
  • Enhancing performances of CNNs has also yielded new insights into the nature of neural representations in high-level visual areas. E.g., 30 network architectures from AI to explain the structure of the neural representations observed in the ventral visual stream of humans and monkeys 


Friday, May 12, 2017

Process of innovation through “The five rivers of creativity”


Some insightful concepts I learnt related to the roots of innovation during our visit to Miraikan - The future museum in Odaiba (The best science museum I've ever visited so far and I'm so glad it's just there in our neighborhood. One day is definitely not enough to completely explore this place.)


  • Association - Associating novel concept from one field for the advancement of another field,  Conventional computer vs Quantum computer (Associating the properties of quantum mechanics with computer science) 

Examples:
  • Quantum dot marking (Associating placing an marker on an object to detect target substances on the smallest scale) 
  • Intra body communications (Associating conductivity with telecommunications with communicating via the human body 




  • Integration - Combining and integrating things with different properties for a single purpose gives us the ability to generate new things (The idea of lab on a chip) 


Examples:
  • Bio machine hybrid system (Insect controlled robot to investigate the ability of an insect to adopt to perturbations 
  • Mechano bionic machine (Integrating living muscles as a power source to machines) - Power source coming from the heart of an insect 
  • Metal plated fibres (Make fabric conductive by plating the surface of a synthetic fibre with a metal - lightness, strength, flexibility along with conductivity)  - Applications in Electronics products 




  • Serendipity - Unexpected developments give us the ability to make fortunate discoveries - The idea of conductive polymer (conductive plastics) by Dr. Shirakawa 

Examples: 
  • Post-it notes (Easily attachable and detachable memo slips as a solution for falling book marks using low tack adhesive) 
  • Hook and loop fasteners - idea inspired by the pet dog afflicted with burrs 
  • Large scale synthesis of carbon nano tubes



  • Mimic - Taking hints from the existing functions and forms gives us the ability to create things that formally didn’t exist or achieve things that couldn’t be done - Artificial Photo synthesis (Bio inspired) 

Examples: 
  • Learning super water repellency from lotus leaves 
  • Morphotex - development of fibre that generates beautiful colors without dying inspired by the wings of morpho butterfly 



  • Alternative- New ideas unconstrained by traditional values give us the ability to create new things (Color filter for a LCD) 
Examples: 
  • Making artificial skin using the hair thrown away during a hair cut (self recycling) 
  • Retinal imaging display (project a video directly into retina in the eye) same as pouring music using ear phones)
  • Power generating floor (using the force applied to the floor while walking)