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Leveraging Open Source Browsers to Optimize Apps and UI Performance on Set-Top Boxes Albert Dahan, Co-Founder and CTO

Thank you!

Streaming OTT Video Apps Making it Simpler

Transformation of TV

• There is no limit to the content found on the internet. The content is either free or fee based.

• OTT services are accessed via 3rd party platforms, for example, Apple TV, Roku, Fire TV and the list goes on.

• The OTT apps provide services to watch movies or TV shows or even Live TV. • Traditional TV industry is changing and the cable TV is no longer limited to one

screen. • Cable companies have their own TV Apps for live TV and On Demand content. This

approach gives their customers Live TV experience on multiple screens. • MSO’s are trying IP only Video services using 3rd party platforms which could

benefit from developing a common framework to integrate the OTT Apps with Live TV MSO App.

Challenges

• With so many media OTT Apps available, the consumer is challenged on which Media App should he watch and why should he invest in that App.

• Different login credentials for different apps

Challenges (Cont.)

• Not flexible as changing TV channels.

• Incompatible ecosystems on different platforms.

Solution Ideas

• MSO can be an App aggregator. • Content from various Apps made available regardless of origin. • Treat each App as an individual channel and aggregate the content to offer

multiple channels. • MSO TV app can be a Master App. Apart form the regular subscribed channels it

should also display all the free channels available on the internet. • Create a common frame work for authentication, create a profile on the box or

network. • Provide an App framework to integrate to all the channels in multiple app

environment to one common App to provide better remote experience.

OTT Video App Common Framework

• Common framework can be achieved by creating a controlled environment, using a specific operating system and a platform where Video Apps can be developed in one unified way.

• Common framework can be integrated in the cloud which could be used across different STB and browser based platforms.

• Apps need to be developed once, across different platforms. • The common framework could support over the air channels, Live TV and On

Demand content. • Leveraging the customers antenna for over the air channels could allow the

operators to avoid retransmission fees for local stations.

New Features

• Voice search on Remote which could be enhanced to work across all Apps on common framework.

• Common framework will also allow the cable operators to integrate social media and live video experience with family and friends.

• Pairing of Mobile and other devices to common framework could simplify content sharing.

• Digital tracking of content could be leveraged across devices.

Conclusion

• Common framework will provide the MSOs a mechanism to integrate live TV, VOD and App content seamlessly. It could work on legacy STB as well as newer IP-only STB. This framework will also allow MSOs to deploy a branded App store and Apps. It would also provide an easier path to integrate new features and improve the user experience

Thank You

How Automatic Content Analysis enables the Entertainment Experiences of the Future

Jan Neumann Senior Manager, Technical R&D

Comcast Labs DC May 17, 2016

Most metadata is at the asset level

• Genres • Credits • Synopsis • Keywords

Much more data exists within the asset

• Chapters • Moments • Annotations

Movie Frame Shot

Scene Chapter

Why is this useful?

Who is in this scene?

What are the best moments on TV?

In-game highlight

navigation

Also good for better search and recommendations

How does Automatic Content Analysis work?

Computer Vision

Audio Analysis

Natural Language Processing

Machine Learning

Chaptering

Scene-level Annotations

Video

Frame-level Annotations

Example - Annotation

Example - Chaptering

Normal program

Normal program

Normal Program with Station logo

Spot Spot

Black frame Structure of TV

program

Why is it possible now?

Large-scale Image recognition performance

Big Data

Better Algorithms

(Deep learning)

Cloud/GPU Computing

• TV content contains a lot of untapped information • Need it to enable next-gen TV experiences • Automatic content analytics can unlock it

Summary

Applications of Machine Learning in Cable Access Networks

Karthik Sundaresan, Nicolas Metts, Greg White (CableLabs)

Albert Cabellos-Aparicio (UPC BarcelonaTech)

Tons of Unused Network data

Lots of Hidden Knowledge in data

3

Hidden patterns

Predictions vs. static

instructions

Dynamically adapt, learn

from new data

Learn from historical data

Family of ML algorithms

Machine learning

Supervised (Task Driven)

Support Vector

Machine

K Nearest Neighbors

Neural Networks

Unsupervised (Data Driven)

Clustering

K-Means, Fuzzy,

Hierarchical

Principal Component

analysis

Apriori Algorithm

Machine Learning in Cable

PNM : Upstream

equalization PNM :

Downstream spectral analysis

Profile Management

Predictive Video

Channel Lineups/ DVR

Recording

Network Health

Patterns Internet Traffic

Classification

Network Traffic

Engineering

Customer Churn

Prediction

SDN Routing

Allocating VNFs to

Appropriate VMs

Problem Space

5

Applications of interest

PNM

Full Band Capture

Predictive Video Lineups

Multicast channels

Network Health

CM/STB Patterns

Applications of interest (2)

SDN Routing

Network Optimization

Customer Churn Prediction

Usage Tracking

BW prediction

Fiber Node Splits

Best Practices, Challenges Clear Problem

Definition Objective functions

Proper Features

Suitable Methods and

Algorithms

Adequate Representative

Data Training data

Testing and Cross

Validation

Metrics / Evaluation

Criteria

Evaluating a Hypothesis

Narayan Srinivasa INTX, Boston May 17, 2016

Machine Learning – Past, Present and Future

Intel Confidential — Do Not Forward 2

Past: Artificial Neural Networks (ANNs)

Synapses Dendrite

Dendritic Trunk

Cell Axon

• Biological analogs for various elements of an artificial neuron • Networks of these neurons represents an artificial neural network • Conventional machine learning was developed based on this layered architecture • Abstracted details away – simplified


Past: Shallow Learners

• Goal: Learn an input to output transform/mapping

• Supervisor provides ground truth

• Feedforward fully connected architecture

• Gradient descent using output error to assign credit or blame to neurons

• Local minima issues that gets worse with depth of network

Shallow learners that required hand crafted feature data for learning (1000s of parameters) but also susceptible to irrelevant features in data


Present - Deep Learning

• Multiple modules with feedforward network to compute a nonlinear input-output mapping

• Increases both selectivity and invariance of representation with depth

Idea # 1: Contrastive Divergence for Feature Detection (Hinton 2002)

Visible

Hidden

Data Point

Idea #2: Pooling of semantically similar units after filtering – (Fukushima 1986, Poggio 1998)

Deep learners trainable with large depths (millions of parameters) using raw data – sensitive to features of object and not background

1 4 2 6

9 6 10 5

4 2 0 3

3 2 4 5

9 10

4 5

C1 Feature MapsS1 Pooled Maps

(ReLU Units)


Convergence leads to Deep Learning Everywhere

Fast and Affordable Compute &

Memory

Large Labelled Data sets for

Training & Benchmarking

RBM and ConvNets

enable deep learners

Global Supply Management 6

Future: Neuromorphic Computing

Original term “neuromorphic electronics” coined by Carver Mead (Caltech) in the late 80’s to describe electronic analog circuits that mimic neurobiological circuits and architectures in the nervous system

Lately the term “neuromorphic engineering/computing” was introduced to expand the scope to include analog, digital, mixed-mode analog/digital VLSI and software systems


Phenomenological Models

Synapses Dendrite

DendriticTrunk

CellAxon

Morphological features, large connectivity, laminar structure and recurrence

Time is implicit in its dynamics – neurons and synapses are dynamic elements


Asynchronous Processing with Spikes is both Efficient & Scalable

• The brain is asynchronous – no global clock • Single wire to encode analog information (Lazar

2002) – area efficiency & scalability • Dissipate power only during spike events –

energy efficiency • Robust transmission over long distances–

reliability & scalability


Learning via plasticity • Reweighting – refers to change in synaptic

strength (most models today focus on this)

• Rewiring – refers to the processes that create/prune new axonal arbors

• Reconnection – refers to the processes that form/prune new synapses

• Regeneration – refers to the processes that create/remove neurons


Memory and Processing are Strongly Co-located

CPU MEM Weakly Co-located

Neural Dynamics

Synaptic/Structural Dynamics

Strongly Co-located


Neuromorphic vs. Von Neumann Computers

Operates at low speeds (< 10-100 Hz) Asynchronous (no global clock) – clock free Composed of noisy and imprecise parts and yet can produce reliable behaviors Spike encoding enables energy efficient computations Very large connectivity (1:10000) between processing elements Memory and computation are integrated Sparse and distributed processing Can learn on chip in an unsupervised, supervised or with reinforcements received from its interaction with its environment

Operates at very high speeds (> GHz) Synchronous (global clock) – clock aligned Composed of precise and noise free parts and fails even for a single bit error Digital Encoding produces energy hungry computations This is not present or even if present (as in GPUs and FPGA) is very limited – 1:100 Memory and computation are separated Dense and modular processing Needs human in loop to acquire any form of knowledge

Neuromorphic Computer Von Neumann Computer

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