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Unlocking the Mystery of Machine Learning and Big Data Analytics Robert Barton Jerome Henry Distinguished Architect Principal Engineer @MrRobbarto Office of the CTAO CCIE #6660 @wirelessccie CCDE #2013::6 CCIE #24750 CWNE #45 BRKIOT-2394 Cisco Webex Teams

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BRKIOT-2600 BRKIOT-2213 16:45 Enabling OT-IT collaboration by 17:00 From Zero to IOx Hero transforming traditional industrial TECIOT-2400 networks to modern IoT Architectures IoT Fundamentals 08:45 BRKIOT-1618 Bootcamp 14:45 Industrial IoT Network Management PSOIOT-1156 16:00 using Cisco Industrial Network Director Securing Industrial – A Deep Dive. Networks: Introduction to Cisco Cyber Vision PSOIOT-2155 Enhancing the Commuter 13:30 BRKIOT-1775 Experience - Service Wireless technologies and 14:30 BRKIOT-2698 BRKIOT-1520 Provider WiFi at the Use Cases in Industrial IOT Industrial IoT Routing – Connectivity 12:15 Cisco Remote & Mobile Asset speed of Trains and Beyond Solutions PSOIOT-2197 Cisco Innovates Autonomous 14:00 TECIOT-2000 Vehicles & Roadways w/ IoT BRKIOT-2497 BRKIOT-2900 Understanding Cisco's 14:30 IoT Solutions for Smart Cities and 11:00 Automating the Network of Internet Of Things (IOT) BRKIOT-2108 Communities Industrial Automation Solutions Connected Factory Architecture Theory and 11:00 Practice PSOIOT-2100 BRKIOT-1291 Unlock New Market 16:15 Opening Keynote 09:00 08:30 Opportunities with LoRaWAN for IOT Enterprises Embedded Cisco services Technologies IOT IOT IOT Track #CLEMEA www.ciscolive.com/emea/learn/technology-tracks.html Cisco Live Thursday, Jan. 30th Celebration 18:30 Friday, Jan. 31st

Guest Keynote 17:00

BRKIOT-2548 BRKIOT-2100 Cisco Distributed 08:30 IoT and Intent-Based Networking Automation Solutions Solutions for Smart Cities and Connected Roadways 11:30 BRKIOT-2225 BRKIOT-3511 BRKIOT-2003 A security design for enabling IoT gateway scalable 09:45 Digital Building Theory & Practice deployment with Cisco Industry 4.0 Kinetic Gateway Management Module (GMM)

BRKIOT-2204 BRKIOT-2394 Leveraging industrial BRKIOT-2526 Unlocking the Mystery of Machine 09:00 device visibility and 11:15 Wi-Fi Technology in Learning and Big Data operational intent to 14:45 Industrial IoT inform security policies and controls PSOIOT-2400 Bringing IT and OT together PSOIOT-1151 to drive business benefits Achieving business 13:15 outcomes using IoT 13:30 solutions IOT IOT IOT Track #CLEMEA www.ciscolive.com/emea/learn/technology-tracks.html Agenda

1. An introduction to Data Analytics and AI/ML • Principles

2. Fundamentals of Machine Learning • Principles • Supervised learning • Unsupervised learning • Deep learning (neural networks)

3. Applications of AI/ML at Cisco – IoT and Beyond • Data Analytics for IoT • Data Center • Collaboration • Security

• AI/ML and Big Data are among the most hyped technologies today . . . • This presentation will teach you how AI/ML and Big Data Analytics really work • This presentation will give network engineers a foundation in these technologies to build network architectures for data analysis . . . especially in the world of IoT

• Big data and analytics entails not just a change in the volume of data, but also a change in the way we work with the data. • Traditional data warehouse tools do not uncover connections within those data sets – we need data analytics with Machine Learning!

Automation for Faster Reveal Make Data Focus on Results Hidden Patterns Driven Decisions Important Things

Machine Learning Definitions Traditional, Rules-based

Arthur Samuel (1959) Live data Field of study that gives computers the Output ability to learn without being explicitly Program programmed

Tom Mitchell (1997) Machine Learning / AI

A computer program is said to learn if Training data Program its performance at a task T, as (Model) measured by a performance P, Output improves with experience E

Big Data is the term for a collection of data sets so large and complex that it becomes difficult to process using traditional data processing tools

OLTP

Business OLTP ETL EDW Intelligence Extract, OLTP Transform, Enterprise Load Data Online Transactional Warehouse Processing Systems

Customer Name ID Phone Email Irene 14213 408-923- irene@gmail.com 1242 Walter 62342 514-231- [email protected] 2315 Miyuki 12344 416-231- [email protected] 2341

Bank Account Account Balance Transactions Number Credit Debit 234123 $1,232 $123 423142 $5,231 $611 521231 $50 $512

• Unstructured data is a poor fit for traditional relational databases

10,000

•More than 90% is unstructured data UNSTRUCTURED DATA •Quantity doubles every 2 years •Most unstructured data is neither

stored nor analyzed!

(IN (IN BILLIONS) GB of GB Data

STRUCTURED DATA

Big Data refers to distributed computing architectures specifically aimed at the “3 V’s” of data: Volume, Velocity, and Variety

VOLUME VELOCITY VARIETY

NoSQL (Not Only SQL) Massive Parallel Fast key-value Processing (MPP) store/retrieve in real time Shared-nothing Structured and Unstructured Structured Data (SQL)

Hadoop Distributed batch, query, and processing Unstructured Data

Top-Level Interfaces ETLs Viz Tools BI Tools

Mid-Level ZOO Many PIG HIVE Abstractions KEEPER more . . . (Projects)

Distributed Data MapReduce / YARN Processing

The foundational Hadoop File System (HDFS) file system

Master Node

Name Node Scalability of data management and processing Data is linear with the addition of new slave nodes

Data

Slave Nodes . . . Data Node Data Node Data Node

Master Node

Name Node

Data

. . . Slave Nodes Data Node Data Node Data Node

Master Node Batch processing of Name Node Java API Application the application Job job (the job sits Job Tracker in the queue)

Task Tracker Task Tracker Task Tracker Slave Nodes . . . Data Node Data Node Data Node

Master Node Batch processing of Name Node Java API Application the application Job job (the job sits Job Tracker in the queue)

Task Tracker Task Tracker Task Tracker Slave Nodes . . . Data Node Data Node Data Node

Master Node

Name Node Application Job Job Tracker Output

Cisco Cloudera CVD: https://www.cisco.com/c/en/us/td/docs/u nified_computing/ucs/UCS_CVDs/Cisco _UCS_Integrated_Infrastructure_for_Big _Data_with_Cloudera_28node.html

Task Tracker Task Tracker Task Tracker . . . Data Node Data Node Data Node

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 22 Making Sense of Data – Fundamental Concepts of Machine Learning Machine learning will decide which IoT ‘things’ survive - Jaques Touillon, Airboxlab

Artificial Intelligence

Any technology that allows a computing system to mimic human thinking functions Example 1 Example 2

If XYZ: do blah else: do blih

Artificial Intelligence Machine Learning

An AI technology where all the rules are not set in the program, but are learned Example while the program is used

Lip Reading Deep Blue Watson Deep Mind Playing Music WaveNet (TTS)

Counting people Recognition NLP Translation Self-driving Computer Networks

And Many more: CRM, Healthcare, Personal Assistants, ….

Artificial Intelligence

Binary threshold neuron

Von Neumann used this logic when designing the “universal computer”

If sum > threshold, output 1 Send weighted inputs Output 0 otherwise

• 1962, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Frank Rosenblatt

When I show these shapes to the camera

This IBM 704 computaah can say “it’s a triangle”

(you will see why this works soon)

• Minsky and Papert did not write that Perceptrons could not learn at all • But the promises of Rosenblatt’s work brought many researchers to use perceptrons for everything and anything • Many claimed that they “could learn anything”, like a human brain • “Perceptrons” proved that the model had mathematical limitations • And that therefore many claims were simply false • Big embarrassments for many researchers and their sources of fund…

Your career in Machine Learning in 1975

Computer Sciences and GPUs appear Statistics merge Graphical Processing Units are ML is seen as a statistical tool, not very good at processing as a way to emulate the human images, which is what brain “neurons” look like

Big Data Changed Everything

ML is more and more often seen as an efficient way to process large data sets

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 36 Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. ... Let’s look deeper.

An AI technology where all the rules are not set in the Artificial Intelligence program, but are learned while the program is used

Machine Learning

Deep Learning A form of ML that uses Neural Learning Nets

Supervised Naïve Nearest Classification SVM Learning Bayes Neighbor Develop predictive Linear Neural Decision models Regression Machine Regression Networks Trees Learning Deep ANNs CNNs Learning

Unsupervised Gaussian Hidden Clustering K-Means Learning Matrix Markov

Discover patterns Neural and anomalies Networks based on input data

The decrease in storage cost has led Training ML takes a lot of compute to the emergence of Big Data power, which has become much cheaper through cloud computing and GPUs Other key factors include: Mathematical advances for training of NNs A thriving public-private ecosystem keeps on driving rapid progress in the field

Sources: Domingos, Ibid.; Mary Meeker, “Internet Trends 2014”, Kleiner Perkins Caufield Byers, 28May2014 BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 40 Supervised Learning Supervised Learning: (When You Know the Answer)

• You know the right answer, but there is too much data for you to produce the right answer for each case input into the system • Example: Oil flowing in a pipe • Comes with all sorts of viscosity / density levels • You can pump it at different pressure levels • You want to know what will be the output (cuft per seconds) • How do you do that?

• You collect data about oil viscosity and pressure, and you plot these points

• Then you let the machine find the relationship between measure viscosity and output volume

• Next, when you’ll measure viscosity, you should be able to predict output

Let’s do some math The red line is an equation y = ax + b

(in ML, we say 휃0 + 휃1x)

Take two random 휃0 and 휃1 Correct y

Then, for every blue dot (휃0 + 휃1x)- y you have (xn, yn)

휃0 + 휃1x

Do 휃0 + 휃1x (that’s your hypothesis function) and check how far you are from yn

Take this x

Repeat for each point for which you have (xn, yn) Add all these mini- differences (휃0 + 휃1x)- y The total is “how far is your theoretical line from the best line for these points”

Another way to say it:

1 퐽 (휃 , 휃 ) = 푚 σ푚 ( 휃 + 휃 푥 − 푦 )2 0 1 2 푖=1 0 1 푖 푖

Okay, it’s the same thing, just said in math compact form

This is called the cost function:

1 퐽 (휃 , 휃 ) = 푚 σ푚 ( 휃 + 휃 푥 − 푦 )2 0 1 2 푖=1 0 1 푖 푖

How far am I from the real y, if I use my random 휃0 and 휃1 and do 휃0 + 휃1x

Then?

Take two new random 휃0 and 휃1 And repeat

Then ask yourself:

Am I closer to the real y with the new 휃0 and 휃1 ?

This changing process is called gradient descent

You can call it brute-force

trying all 휃0 and 휃1 until you find the right values

Math can help find the best next 휃0 and 휃1 We can use Calculus and derivatives

“When slope is 0… you can’t get much lower”

푦 = 3.1−14푥6 - 6.4−11푥5 + 5−8푥4 - 1.8−5푥3 + 2.6−3푥2 -0.15x+95

The equation of the line you try to find can be complicated

And instead of 2 dimensions (x,y), You maybe have 1 million dimensions

But the idea is the same

• This was (linear) regression. You try to find a number (y). • There is also classification, where your line separates two groups

• In all cases, it is “supervised”, because you know the right answer from a bunch of samples.

• Use large number of calculations to find the ‘right’ line equation.

• The main challenge in Supervised Learning is to find the right equation… and figure out if the samples represent the full population

• Vending machine empties based on: Sensor data is • Product type, day of week, aggregated (sent to weather, location, local event, cloud for analysis) advertising campaigns… • Sensors report daily product quantity left • 6-dimension linear regression re-computes every day the “down to empty” curve to predict when to send the truck • Apply the same logic to ATMs, parking meters (with paper or coins) etc.

• Model is trained in the cloud (infinite Cisco Kinetic Training Data is compute resources and lots of training GMM data passed to cloud

• Deploy AI/ML workload to IOx on IoT router (e.g. TensorFlow)

• New data is input to TensorFlow on edge router

Fully Trained Model is Passed to IOx

• You do not know the right answer, and there is too much data for you to guess

• Example: you manufacture small engines

• Some of them will fail

• You want to spot the failures before they get installed on mowers, chainsaws, etc.

• How do you do that?

• You collect data about engine sound frequency, temperature, rotating speed, gas pressure, etc. • Then you plot (e.g. 2 parameters) • Then you ask the system: can you tell me what are the patterns? Which engine is “out of range”?

Your engine will group engines that have similar 4 groups = 4 engine categories characteristics. • In math, this is simply grouping points that are close to one another

And will spot the outliers • Those are the engines likely to fail (different from the others)

Outlier

The math can take may forms, but a common form is K-means Want 3 groups? Take 3 random points (3 ‘centroids’, 휇푐 푖 ) Then take a known point, calculate which centroid is closest

K-means Want 3 groups?

Repeat for all points. That’s our usual 퐾 푖 2 distance equation: min σ푖=1 | 푥 − 휇푐 푖 | 푐(푖)

Then you move your ‘centroids’, 휇푐 푖 to the center (mean x,y) of each group you formed

And you repeat. You are done when no point jumps to another group anymore

You manage water in a city, you want it: • clean (no chemical contamination or poisoning), • “Working” (no breakage) • Is water flowing too much? Not enough? Is there a Superbowl commercial event? • “Safe” (no attack against sensors, valves, pumps • Yes water is an attack vector that can take control over a critical city resource… before sticking with full paralysis… can you detect the attack as it brews?

• What is “normal” water consumption? Hard problem Linear Clustering shows outliers Multi-dimensional measurements when accidents or attacks are simulated

• Utility Management

• You can use supervised learning to predict power consumption (weather, ToD, etc.)

• The model goes wrong when chance hits

• Will this storm affect the grid as projected?

• Can you predict where issues will popup first?

• Can you organize shoppers in groups, even when new fashion pop up?

Just like a neuron gets input (electricity) from one or more dendrites, and fires output (electricity in axon) if the input gets beyond a threshold

A neural network unit gets inputs, and outputs 1 if the combined input is beyond a given threshold

Then you put tons of units, possibly in multiple layers (in this case, it is called deeeeep learning) Also called Artificial Neural Networks (ANNs)

• The way you connect the units can vary immensely • And this is what makes this family very rich • Tons of possible applications depending on what data you are looking at, and what you try to find

© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 79 Supervised or Unsupervised? It depends if you know the answer, or if you try to find patterns you do not see yet! SUPERVISED LEARNING

image by Jeff Dean

• Cost depends on the algorithm, and the amount of data • You can run some on an IR829 router, or you may need a larger compute platform!

• Common IoT use cases: • Image recognition (faces, handwriting, but also vehicles and objects) • Any complex combination of patterns that influence one another • e.g. multi RF parameters combined to produce connection efficiency • Deploy Kinetic EFM with TensorFlow on trucks

• Machines can see, hear, talk, and … they can learn! • Machines can produce outcomes they have not been explicitly programmed for (a huge paradigm shift from traditional Computer Programming) • But . . . they do not have common sense, no true thinking (this is still the realm of science fiction) • Really good a things that take humans 1 second to think about

Understanding what ML algorithms can and cannot do is one of the key to success in data analytics

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 83 What Can Humans do in Less than 1s? • Machines can: • Translate from one language to another • Predict if it is going to rain or be sunny • Position targeted ads to a person visiting a web site • Identify objects in an image • Identify fraudulent credit card transactions • Actions in driving a car • Machines Can’t: • Typically anything that takes deeper reasoning, creativity, intuition, or a small amount of data

Reimagine Secure Transform Empower your applications your data your infrastructure your teams

DNA-Center AMP Hyperflex AppDynamics Accompany

ETA Cloudlock Intersight IoT Edge Cloudcherry Intelligence DNA Analytics CTR UCS ML480 Computer Vision Tetration SD-WAN NGFW MindMeld

Stealthwatch Speaker Track

Talos “Okay Webex”

• Imagine a mega-field of oil with no way to extract it from the ground

• Data needs to be extracted before it can be refined, and then used

App App App

App App App App

of data will be created and Smart Critical processed outside a Insights biz decision 45%75% traditional centralized data center or cloud by 2025*

Multi-Cloud Top Drivers

Cost, Efficiency, Regulatory IoT Edge & Data

Complexity

Instrumenting / Sensors / Measuring stands in the way - bringing HW/SW components together

* Gartner

• Predictive Analytics

• Proactive Part Replacement

CELL 07

REPLACE BEARINGS

Cisco Confidential Intelligence at the Edge is Needed Cisco IOx

• Run distributed compute at the edge

• Leverage secure connectivity of Cisco IOS software

• Manageable with on-premises or cloud- based interface

• Runs on wide variety of IoT platforms

• Builds on existing developer tools and trainings on DevNet

IOx

Cisco Cisco Application IOS / IOx Services Local Application Hosting Manager IOS-XE Framework Software (CAF) Application Management

Linux

Edge

Network #> export DOCKER_HOST=tcp://***.**.***.***:****

#> exportdocker DOCKER_TLS=*run -–network=container:*****

#> export--volume=/software/ DOCKER_API_VERSION=*.*caf/work/repo-docker/*** Enable Docker Access And Create App Profile On Edge --memory= 64m docker_image_name Setup Remote Access Environment On Dev Machine

Transfer Docker Image To Edge

Run & Test Container With App Profile < / >

Developer

IoT Edge

Compute Network

CGR 1120, 1240 IC3000 IR1101 IR829 IR809 IE3000 with Compute Module

Management App & Analytics App & Analytics EI Manager

Cloud On-Prem App Management Provider DC GW Management

Cyber Vision Edge Intelligence ISV Micro-svc

IOx - Edge Compute Infrastructure

IoT GW - Ready IoT networking/compute portfolio

Edge to multi-cloud Out of the box experience with Allows Cloud-Based data delivery centralized deployment for scale Data Analytics and AI/ML

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 96 Customer / Partner apps Customer / Partner User • Technician apps Persona • SI/OT Developer • SI/OT Soln Architect Azure/AWS IoT Hub Azure IoT Hub EI Manager for OT UI & Work Flow Azure/ AWS Infra Azure Stack/ DC Infra

Control Path Data Path Edge Intelligence Edge

GovernanceEngine NB Connector 1 NB Connector 2 NB Connector 3 NB Connector 4 NB Connector 5

Azure IoT MQTT Client AWS IoT …. …. EngineScripting

Broker

SB Connector 1 SB Connector 2 SB Connector 3 SB Connector 4 SB Connector 5 OPC ModBus EIP/CIP MQTT Server …..

IOx IC, IR, IE

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 97 Smart & Centralized to deploy across hundreds of Gateways -- Templates automatically push data policy to all locations

Streaming DB

Local Hadoop

Machine Vendor

Google AI

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 98 Building Better Engines in the Data Center for Big Data and AI/ML CPUs vs. GPUs ▪ GPUs are highly-specialized ▪ CPUs are capable of almost any processors used to solve task – but at a price complex math problems

CPUs are like a swiss army knife GPUs are like specialized surgical instruments

• Graphical Processing Units are specialized types of electronic circuitry designed to rapidly manipulate memory for graphics • GPUs support parallel processing, accelerating their ability to execute algorithms that require parallel processes • GPUs are at the heart of deep learning and neural networks

Raw data Low-level features Mid-level features High-level features

Neural GPU Networks

Inherently ✓ ✓ Parallel

Matrix ✓ ✓ Operations

Bandwidth ✓ ✓

“NOW YOU CAN BUILD GOOGLE'S $1M ARTIFICIAL BRAIN ON THE CHEAP” – WIRED MAGAZINE

GOOGLE DATA CENTER STANFORD AI LAB

3-GPU Accelerated 1000 Servers / Servers / 18,432 16,000 cores cores $1,000,000 $20,000 600 KWatts 4KWatts

GPUs 8 X V100 32GB:1st GPU to break 100 teraflops NVLink interconnect: >300GB/s bandwidth RAID controller

Storage Network Up to 24 SAS/SATA SSD/HDDs Choice of 10/25 Up to 6 NVMe drives or 40/100G M.2 SATA Four PCIe slots Two 10G Base-T shared LOMs on I/O module

CPUs 2 * Intel® Xeon® Scalable processors (up to 28 cores per socket) 24 DDR4 DIMMs — up to 3 TB memory

Redundant fans

Testing and development, Deep learning/training Inferencing and model training Cisco UCS C240 Cisco HyperFlex 240 Cisco UCS C480 Cisco UCS C480 ML C/HX 220 C/HX 240

2 x P100/ V100 2 x P100/V100 per node

Option of GPU-only nodes

2 x P4 6 x PCIe P100/ V100 8x V100 with NVLink 6 x P4 Available today CY Q3’ 18 Available today CY Q4’ 18 Available today

Unified management

Cisco IMC XML API

Simplified management, customer choice, Cisco Validated Design

BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 105 Tying it Together: Big Data with Machine Learning Cisco Validated Hadoop Design with Cloudera on GPU-Powered AI/ML Workloads

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ENV 2 x UCS 6332 41 LS 41 L1 L2 CISCO UCS-FI-6332

40 40

Fabric Interconnect 39 39

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25 25 24 24 Datanodes 23 23

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16 x UCS C240 M5 16 16

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datanode 14 14 13 13

12 12

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10 10 Nvidia CUDA containers

09 09 08 08 orchestrated with YARN for Deep 07 07

06 06

05 05 Learning

S 04 04 X

03 03 X

X X X X X X X X X X X X X X X X

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M U L T I ReW ritabl e DVD+ReWritable RECORDER 800 GB 800 GB 2 TB 2 TB 2 TB 2 TB 800 GB 800 GB 800 GB 800 GB 2 TB 2 TB 2 TB 2 TB 800 GB 800 GB NVMEHWH800 NVMEHWH800 HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN NVMEHWH800 NVMEHWH800 NVMEHWH800 NVMEHWH800 HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN NVMEHWH800 NVMEHWH800 UCS 01 01 NVME SSD NVME SSD SATA HDD SATA HDD SATA HDD SATA HDD NVME SSD NVME SSD NVME SSD NVME SSD SATA HDD SATA HDD SATA HDD SATA HDD NVME SSD NVME SSD C480 M5 Cisco Cloudera CVD: https://www.cisco.com/c/en/us/td/docs/unified_com puting/ucs/UCS_CVDs/Cisco_UCS_Integrated_Infr astructure_for_Big_Data_with_Cloudera_28node.ht ml BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 106 Applications of AI/ML to Collaboration AI-Powered Computer Vision

Spark Assistant (April) Face recognition (roadmap) In-room analytics

Webex Board 70 Webex Board 55 Room 70D Room 70S Room 55 Room Kit Plus Room Kit

NVIDIA Jetson Platform - The same electronics engine powering self-driving cars

• Voices…but also: • Table tapping • Keyboard • Siren • Dog barking • And more…

Voice Dog Siren Tapping

WAV Spectrogram

Voices and “noise” have a distinct “image” that can be detected and filtered. Deep Learning at Work in Cisco Collaboration Systems

Brenda Song Brandon Burke Emma Totti

Simon Jones Barbara German AI Development in Computer Vision

If We Want Machines to Think, We Need to Teach Them to See. - Fei-Fei Li, Stanford AI Lab Director

Malware Reputation Protection Feeds

IPS Rules Vulnerability Database Sandboxing Updates Machine Learning Big Data Infrastructure

Millions of file Umbrella AMP Community FTD / IPS samples daily

SPARK Program Advanced Industry 100,000 True Open Source Threat Grid Disclosures Positive Communities Community Events/Day

Approach Linear Decision Boundary • Construct nominal model (aka baseline) Clustering • Assume that data which are far from the baseline - K-means are generated by a different process (anomalies) - K-NN - …

UADP 2.0 ASIC Machine learning Classify NetFlow (Classification and “known with Outliers) malware” and % enhanced “known 99 telemetry at benign” Threat Detection 1110110110000 line rate Accuracy* 0100011110011 Stealthwatch 1101001000100 001 0.01% Catalyst 9K False Positives* Switch

*Source : Identifying Encrypted Malware Traffic with Contextual Flow Data, Oct 2016 Cognitive Analytics

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