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AIDC India - AI Vision Slides

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Slides from AIDC Summit Series - India 2019

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AIDC India - AI Vision Slides

  1. 1. AI Vision A hybrid approach to delivering accurate and robust AI.
  2. 2. It is hard to know what it cannot do! Promise of AI It is hard to know what it can do. Performance of AI The Gap AI in Vision • AI in data science applications has been embraced by many enterprises. • Typical tools range from classical statistical techniques to machine learning, deep nets and occasionally reinforcement learning. • There has been a lot of noise of AI in IOT - where sensor data has been treated with data science techniques. • However, the real challenge comes in the Vision world. Where data is analog, amorphous, arbitrary and awfully heavy. • With the advent of sight, there was an explosion of species. • Eyes, became a weapon in the war of survival. • Given vision’s importance, human brain uses almost 35% of its capacity to process visual inputs. • Essentially there are two pathways - the ventral stream that identifies the “what” and the dorsal stream that analyzes “where, when” • With so much power, humans are able to effortlessly classify, count, recognize etc. Vision is Messy
  3. 3. AI Vision - hybrid approach • Given the complexity of vision processing, it becomes important to analyze images both at the • Edge, and at the • Server. • Edge analytics is equivalent to the ventral stream, ideal for with scalable deployments • Server analytics, is like the dorsal stream - gives better correlation and inferential remediation • Together the Hybrid approach solves real world problems - just as it does in biological life. • Intel provides an excellent balance between Sever and Edge Centric computing. • While edge CPUs support inbuilt GPU and new era Neural Accelerators, the Servers support host of own and third party discrete GPUS as needed. • The high speed and high capacity memory - for CPU and GPU further accelerate performance. • Finally a host of optimized frameworks - Openvino, MediaSDK, IMKL etc speed up mathematical and matrix operations. Hybrid - GPU + CPU, Edge + Server
  4. 4. Real-world AI in Vision • Define the business problem • Assess the vision related inputs available • Design Capture and Illumination system • Rolling/ global shutter • Visible, SWIR, MWIR LWIR • FPS, FOV • Collect data and curate the same • Architect and deploy Classical and Neural Net Algos for vision processing • Deploy, test and improve accuracy • Optimize for performance and security.
  5. 5. Curating the data • Data being critical, proper experiment design to collect data becomes critical • Mixing anomalous data with regular data is easier said than done. • Even creating synthetic data, needs simulation of reality. • Finally, based on the exact nature of the problem data sets need to be collected. • A sample tree structure for vision problems is as shown. “Data is the source code”
  6. 6. Bringing things together • A trivial approach to AI Vision problem solving today is - to keep adding layers to the Net, try different loss functions and train with greater amounts of data. • Capture system, Algorithms and the Compute system together deliver results in the real world. • We have developed multi-spectral camera systems, worked with visible, IR, Thermal illumination, and handcrafted algorithms to run on Intelx86, Movidius and Openvino platform to deliver results to clients.
  7. 7. Vision Pipeline • Real world applications require robust pipeline between sensor input and algorithms • Video frames need to be buffered and fed to multiple processes • Right video frames need to be selected for analysis • Drumbeat across incoming video, algos, analyzed output needs to be maintained • This is as important as the algos • Standard datasets - ImageNet, • Architectures - ResNet, VGG, Inception, • CNN, RCNN, LSTM, GANs • Frameworks - TensorFlow, MXNet, Caffe, Keras • Deep vs Speed, Sparse vs Dense AI at Work
  8. 8. Few Case Studies • A client wanted to study audience in a high footfall event - 200,000 people were expected in few days. • A client wanted to read number on his warehousing trucks, in very poor lighting condition. • Another client wanted to create night vision camera systems for driver assistance in inclement weather. • EPC company wanted to monitor progress of a project across sites with inexpensive cameras, instead of 3D Laser Lidars. • Intel is an established platform • Linux support is simply awesome • Body of knowledge is very high - both at the edge - embedded areas; as well as at the server Linux OS. • Host of optimized tools squeeze the performance out of systems • There are solutions for every size and scale, unlike options in the market. • Blend of CPU and GPU allows easy introduction of new models, till the implementations come on the NN platforms. Intel Difference
  9. 9. Outdu - Open AI Vision Platform • Outdu has built, tested, deployed and enterprise grade solutions across industries • Its multi-spectral camera systems and host of optimized AI algorithms in People, Vehicles and Scenes deliver high accuracy results • Its Robust X-OPS platform allows Configuration, Visualization and Orchestration of 1000s of Edge AI devices. • Its Open X-OPS platform allows third party software developers and Domain experts to deliver AI algorithms on target devices easily. • There are brilliant companies, individuals and experts who create algorithms that can solve problems. • Enterprise ready and friendly Intel technology becomes the fastest way to develop AI solutions. • Our open architecture platform allows these algorithms to reach the edge devices, and harness the - camera, mic, imu, gps, and other sensors. • Enterprises can effortlessly deploy large scale AI solutions with Outdu. Working Together It is hard to know what it cannot do! Promise of AI It is possible to do. Performance of AI Outdu - bridging the Gap.

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