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What Business Leaders Need to Know About Machine Learning | @ThingsExpo #AI #ML #IoT #M2M
Many of the algorithms that fall into the Machine Learning category are analytic algorithms that have been around for decades

What Tomorrow's Business Leaders Need to Know About Machine Learning

Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow’s business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business.

By the way, there is an absolute wealth of freely-available material on machine learning, so I’ve included a sources section at the end of this blog for folks who want more details on machine learning.

So strap’em on! Time to dive into the world of machine learning!

Machine Learning Basics
Much of what comprises “Machine Learning” is really not all new. Many of the algorithms that fall into the Machine Learning category are analytic algorithms that have been around for decades such as clustering, association rules and decisions trees. However, the detailed, granularity of the data, the wide variety of data sources and massive increase in computing power has re-invigorated many of these mature algorithms. Today, machine learning is being used for a variety of uses including:

  • Text translation, voice recognition and natural language processing (NLP). Machine Learning is the brains behind the continuously improving “conversations” with Apple Siri, Google Assistant, Microsoft Cortana and Amazon Alexa.

Facial, photo and image recognition. For example, the all-important question of “What is a Chihuahua puppy and what is a blueberry muffin?” can be addressed with a well-trained machine learning algorithm (see Figure 1).

Figure 1: Puppy versus blueberry muffin exercise

More applications of machine learning will be coming soon, including:

  • Cyber security
  • Insider trading
  • Money laundering
  • Personalized medicine
  • Personalized marketing
  • Fraud detection
  • Autonomous vehicles

So exactly what is machine learning?  Let’s start with a definition of machine learning:

Machine learning is a type of applied artificial intelligence (AI) that provides computers with the ability to gain knowledge without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

Fundamentally, there are only two things that Machine Learning does:

  • Quantify existing relationships (quantify relationships from historical data and apply those relationships to new data sets).
  • Discover latent relationships (draw inferences buried in the data).

Machine Learning accomplishes these two tasks using either supervised or unsupervised learning algorithms. What’s the difference? Supervised learning includes the classification or categorization of the outcomes (e.g., fraudulent transaction, customer attrition, part failure, patient illness, purchase transaction, web click) in the observations. Unsupervised learning does not have the outcomes in the observations.

Supervised Learning
Supervised learning
algorithms make predictions based on a set of examples. For example, historical sales can be used to estimate the future prices. With supervised learning, you have an input variable that consists of labeled training data and a desired output variable. You use an algorithm to analyze the training data to learn the function that maps the input to the output. This inferred function maps new, unknown examples by generalizing from the training data to anticipate results in unseen situations.

  • Classification: when the objective field is categorical. For these problems, a Machine Learning algorithm is used to build a model that predicts a category (label or class) for a new example (instance). That is, it “classifies” new instances into a given set of categories (or discrete values). For example, “true or false”, “fraud or not fraud”, “high risk, low risk or medium risk”, etc. There can be hundreds of different categories.
  • Regression: when the objective field is numeric. For these problems, a Machine Learning algorithm is used to build a model that predicts a continuous value. That is, given the fields that define a new instance the model predicts a real number. For example, “the price of a house”, “the number of units sold for a product”, “the potential revenue of a lead”, “the number of hours until next system failure”, etc.

Both classification and regression problems can be solved using supervised Machine Learning techniques. They are called supervised in the sense that the values of the output variable have either been provided by a human expert (e.g., the patient had been diagnosed with diabetes or not) or by a deterministic automated process (e.g., customers who did not pay their fees in the last three months are labeled as “delinquent”). The objective field values along with the input fields need to be collected for each instance in a structured dataset that is used to train the model. The algorithms learn a predictive model that maps your input data to a predicted objective field value.

Unsupervised Learning
When performing unsupervised learning, the machine is presented with totally unlabeled data. It is asked to discover the intrinsic patterns that underlie the data, such as a clustering structure, a low-dimensional manifold, or a sparse tree and graph.

Clustering: Grouping a set of data examples so that examples in one group (or one cluster) are more similar (according to some criteria) than those in other groups. This is often used to segment the whole dataset into several groups. Analysis can be performed in each group to help users to find intrinsic patterns.

Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/modules/clustering.html

  • Association: If-then statements that uncover relationships within the data. An example of an association rule would be “If a customer buys a dozen eggs, he is 80% likely to also purchase milk.”

Source: https://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining

  • Neural Networks: Modeled after the human brain, a neural network consists of a large number of processors operating in parallel and arranged in tiers (feedforward). The first tier receives the raw input information and each successive tier receives the output from the preceding tier and performs further analysis. The last tier produces the output of the system. Neural networks are adaptive, which means they modify themselves as they learn from initial training and subsequent runs provide more information about the world.

Source: http://coderoncode.com/machine-learning/2017/03/26/neural-networks-without-a-phd-part2.html

  • Recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network that allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition.

Source: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Figure 2 provides a more detailed inventory of the different types of supervised and unsupervised machine learning algorithms.

Figure 2: Types of Machine Learning Algorithms
Source: http://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/

Putting Machine Learning to Work
In a recent University of San Francisco project that we conducted with a local data science company, I was introduced to a product called BigML (BigML.com).  I was truly blown away by the relative simplicity of the tools (think “Tableau for Machine Learning”). I have no financial interest in BigML and suspect that as soon as this blog gets published, I will hear from other startups that are building something similar. But until I get those calls, I’m going to use BigML to showcase some Machine Learning basics.

BigML is free for the first 16 gigabytes of data and comes with some pre-loaded data sets and an extensive library of documentation, some of which I used for this blog. For this exercise, we’re going to use a data set that comes bundled with the BigML product: Titanic Survivors Data Set (see Figure 3).

Figure 3: Titanic Survivors Data Set

BigML provides a nice feature to allow the data scientist to explore and understand the data sets, and provides some basic statistical information (minimum, median, mean, maximum, standard deviation, kurtosis, skewness) about each of the variables in the data set.

BigML allows you to select from a variety of supervised and unsupervised models. I selected the supervised option (because I knew the classification of the passenger as survived or not survived) and got the decision tree in Figure 4 that predicts the likelihood of a Titanic passenger surviving given a wide variety of different variables (e.g., passenger age, class of travel, fare paid, in what city the passenger boarded).

Figure 4: Titanic Survivors Decision Tree

The resulting Decision Tree provides a series of “If-then” statements; each branch “yields a story” about the chances of survival.

Hint: you want to be young and you want to be rich to improve your odds of surviving the Titanic. That’s something that might be very useful if you ever find yourself on the Titanic.

To learn more about the “Predicting Titanic Survival Outcome” exercise, check out YouTube.

BigML provides a wide variety of machine learning algorithms with which one can play. Plus their documentation on each of the different machine learning algorithms is very impressive. I think these folks would make a fortune if they created an accompanying text book (and I sent them a note telling them such).

Machine Learning Summary
Both Supervised and Unsupervised learning algorithms will find relationships and occurrences in the data that might be relevant. The data scientist and the business stakeholder still must apply common sense to the findings; they must apply domain knowledge to ensure that not only are the uncovered relationships and insights “Strategic, Actionable and Material,” but they simply must apply common sense in order to prevent making statements of fact that just don’t make sense.

No amount of machine learning is going to replace good old common sense.

Appendix: Additional Machine Learning Sources

The post What tomorrow’s business leaders need to know about Machine Learning? appeared first on InFocus Blog | Dell EMC Services.

Read the original blog entry...

About William Schmarzo
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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Sponsorship opportunities are now open for Internet of Things at Cloud Expo 2018 New York, taking place June 5-7, at the Javits Center in New York, NY, and Internet of Things at Cloud Expo 2018 Silicon Valley, November 6-8, at the Santa Clara Convention Center in Santa Clara, CA. For sponsorship, exhibit opportunities and show prospectus, please contact Carmen Gonzalez, carmen (at) sys-con.com.



@ThingsExpo Silicon Valley All-Star Speakers Include

MATTHIEU
Octoblu

MAHADEV
Cisco

MCCARTHY
Bsquare

FELICIANO
AMDG

PAUL
VenueNext

SMITH
Eviot

BEAMER
goTraverse

GETTENS
goTraverse

CHAMBLISS
ReadyTalk

HERBERTS
Cityzen Data

REITBAUER
Dynatrace

WILLIAM-
SON

Cloud
Computing

SCHMARZO
EMC

WOOD
VeloCloud

WALLGREN
Electric Cloud

VARAN-
NATH

GE

SRIDHARA-
BALAN

Pulzze

METRIC
Linux

MONTES
Iced

ARIOLA
Parasoft

HOLT
Daitan

CUNNING-
HAM

ReadyTalk

BEDRO-
SIAN

Cypress

NAMIE
Cisco

NAKA-
GAWA

Transparent
Cloud

SHIBATA
Transparent
Cloud

BOYD
Neo4j

VANDER-
MINDEN

Flatiron
Strategies

JAME-
NSKY

Embotics

KOCHER
Grey Heron

SPROULE
Metavine

BLACK
SQLstream

WARD
DWE

MILLER
Covisint

EVAVOLD
Covisint

MEINER
Oracle

MEEHAN
Esri

WITECK
Citrix

LIANG
Rancher Labs

BUTLER
Tego

ROWE
IBM Cloud

SKILLERN
Intel

SMITH
Numerex
@ThingsExpo New York All-Star Speakers Include

CLELAND
HGST

VASILIOU
Catchpoint

WALLGREN
Electric Cloud

HINCH-
CLIFFE

7Summits

DE SOUZA
Cisco

RANDALL
Gartner

ARMSTRONG
AppNeta

SMALLTREE
Cazena

MCCARTHY
Bsquare

DELOACH
Infobright

QUINT
Ontegrity

MALAUCHLAN
Buddy Platform

PALIOTTA
Vector

MITRA
Cognizant

KOCHER
Grey Heron

PAPDO
POULOS

Cloud9

HARLAN
Two Bulls

GOLO
SHUBIN

Bit6

PROIETTI
Location
Smart

MARTIN
nfrastructure

MOULINE
Everbridge

MARSH
Blue Pillar

PARKS
SecureRF

PEROTTI
Plantronics

HOFFMAN
EastBanc

WATSON
Trendalyze

BENSONOFF
Unigma

SHAN
CTS

MATTELA
Redpine

GILLEN
Spark
Coginition

SOLT
Netvibes

BERNARDO
GE Digital

ROMANSKY
TrustPoint

BEAMER
GoTransverse

LESTER
LogMeIn

PONO
-MAREVA

Google

SINGH
Sencha

CALKINS
Amadeus

KLEIN
Rachio

HOASIN
Aeris

SARKARIA
PHEMI

SPROULE
Metavine

SNELL
Intel

LEVINE
CytexOne

ALLEN
Freewave

MCCALLUM
Falconstor

HYEDT
Seamless

@ThingsExpo Silicon Valley All-Star Speakers Include

SCHULZ
Luxoft

TAMBURINI
Autodesk

MCCARTHY
Bsquare

THURAI
SaneIoT

TURNER
Cloudian

ENDO
Intrepid

NAKAGAWA
Transparent

SHIBATA
Transparent

LEVANT-LEVI
testRTC

VARAN NATH
GE

COOPER
M2Mi

SENAY
Teletax

SKEEN
Vitria

KOCHER
Grey Heron

GREENE
PubNub

MAGUIRE
HP

MATTHIEU
Octoblu

STEINER-JOVIC
AweSense

LYNN
AgilData

HEDGES
Cloudata

DUFOUR
Webroot

ROBERTS
Platform

JONES
Deep

PFEIFFER
NICTA

NIELSEN
Redis

PAOLALANTORIO
DataArchon

KAHN
Solgenia

LOPEZ
Kurento

KIM
MapR

BROMHEAD
Instaclustr

LEVINE
CytexOne

BONIFAZI
Solgenia

GORBACHEV
Intelligent
Systems

THYKATTIL
Navisite

TRELOAR
Bebaio

SIVARAMA-
KRISHNAN

Red Hat
Cloud Expo New York All-Star Speakers Included

DE SOUZA
Cisco

POTTER
SafeLogic

ROBINSON
CompTIA

WARUSA
-WITHANA

WSO2 Inc

MEINER
Oracle

CHOU
Microsoft

HARRISON
Tufin

BRUNOZZI
VMware

KIM
MapR

KANE
Dyn

SICULAR
Basho

TURNER
Cloudian

KUMAR
Liaison

ADAMIAK
Liaison

KHAN
Solgenia

BONIFAZI
Solgenia

SUSSMAN
Coalfire

ISAACSON
RMS

LYNN
CodeFutures

HEABERLIN
Windstream

RAMA
MURTHY

Virtusa

BOSTOCK
IndependenceIT

DE MENO
CommVault

GRILLI
Adobe

WILLIAMS
Rancher Labs

CRISWELL
Alert Logic

COTY
Alert Logic

JACOBS
SingleHop

MARAVEI
Cisco

JACKSON
Softlayer

SINGH
IBM

HAZARD
Softlayer

GALLO
Softlayer

TAMASKAR
GENBAND

SUBRA
-MANIAN

Emcien

LEVESQUE
Windstream

IVANOV
StorPool

BLOOMBERG
Intellyx

BUDHANI
Soha

HATHAWAY
IBM Watson

TOLL
ProfitBricks

LANDRY
Microsoft

BEARFIELD
Blue Box

HERITAGE
Akana

PILUSO
SIASMSP

HOLT
IBM Cloudant

SHAN
CTS

PICCININNI
EMC

BRON-
GERSMA

Modulus

PAIGE
CenturyLink

SABHIKHI
Cognitive Scale

MILLS
Green House Data

KATZEN
CenturyLink

SLOPER
CenturyLink

SRINIVAS
EMC

TALREJA
Cisco

GORBACHEV
Systems Services Inc.

COLLISON
Apcera

PRABHU
OpenCrowd

LYNN
CodeFutures

SWARTZ
Ericsson

MOSHENKO
CoreOS

BERMINGHAM
SIOS

WILLIS
Stateless Networks

MURPHY
Gridstore

KHABE
Vicom

NIKOLOV
GetClouder

DIETZE
Windstream

DALRYMPLE
EnterpriseDB

MAZZUCCO
TierPoint

RIVERA
WHOA.com

HERITAGE
Akana

SEYMOUR
6fusion

GIANNETTO
Author

CARTER
IBM

ROGERS
Virtustream
Cloud Expo Silicon Valley All-Star Speakers

TESAR
Microsoft

MICKOS
HP

BHARGAVA
Intel

RILEY
Riverbed

DEVINE
IBM

ISAACSON
CodeFutures

LYNN
HP

HINKLE
Citrix

KHAN
Solgenia

SINGH
Bigdata

BEACH
SendGrid

BOSTOCK
IndependenceIT

DE SOUZA
Cisco

PATTATHIL
Harbinger

O'BRIEN
Aria Systems

BONIFAZI
Solgenia

BIANCO
Solgenia

PROCTOR
NuoDB

DUGGAL
EnterpriseWeb

TEGETHOFF
Appcore

BRUNOZZI
VMware

HICKENS
Parasoft

KLEBANOV
Cisco

PETERS
Esri

GOLDBERG
Vormetric

CUMBER-
LAND

Dimension

ROSENDAHL
Quantum

LOOMIS
Cloudant

BRUNO
StackIQ

HANNON
SoftLayer

JACKSON
SoftLayer

HOCH
Virtustream

KAPADIA
Seagate

PAQUIN
OnLive

TSAI
Innodisk

BARRALL
Connected Data

SHIAH
AgilePoint

SEGIL
Verizon

PODURI
Citrix

COWIE
Dyn

RITTEN-
HOUSE

Cisco

FALLOWS
Kaazing

THYKATTIL
TimeWarner

LEIDUCK
SAP

LYNN
HP

WAGSTAFF
BSQUARE

POLLACK
AOL

KAMARAJU
Vormetric

BARRY
Catbird

MENDEN-
HALL

SUPERNAP

SHAN
KEANE

PLESE
Verizon

BARNUM
Voxox

TURNER
Cloudian

CALDERON
Advanced Systems

AGARWAL
SOA Software

LEE
Quantum

OBEROI
Concurrent, Inc.

HATEM
Verizon

GALEY
Autodesk

CAUTHRON
NIMBOXX

BARSOUM
IBM

GORDON
1Plug

LEWIS
Verizon

YEO
OrionVM

NAKAGAWA
Transparent Cloud Computing

SHIBATA
Transparent Cloud Computing

NATH
GE

GOKCEN
GE

STOICA
Databricks

TANKEL
Pivotal Software


Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
General Manager of Window Azure
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
Yasser Khan
CEO of @Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Peak_Ten


Who Should Attend?
Senior Technologists including CIOs, CTOs & Vps of Technology, Chief Systems Engineers, IT Directors and Managers, Network and Storage Managers, Enterprise Architects, Communications and Networking Specialists, Directors of Infrastructure.

Business Executives including CEOs, CMOs, & CIOs , Presidents & SVPs, Directors of Business Development , Directors of IT Operations, Product and Purchasing Managers, IT Managers.

Download Cloud Expo Show Guide
Cloud Expo Show Guide
Download PDF

Join Us as a Media Partner - Together We Can Rock the IT World!
SYS-CON Media has a flourishing Media Partner program in which mutually beneficial promotion and benefits are arranged between our own leading Enterprise IT portals and events and those of our partners.

If you would like to participate, please provide us with details of your website/s and event/s or your organization and please include basic audience demographics as well as relevant metrics such as ave. page views per month.

To get involved, email Lissette Mercado at lissette@sys-con.com.

@ThingsExpo Blogs
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be looking at some significant engineering investment. On-demand, serverless computing enables deve...
In his session at 21st Cloud Expo, Carl J. Levine, Senior Technical Evangelist for NS1, will objectively discuss how DNS is used to solve Digital Transformation challenges in large SaaS applications, CDNs, AdTech platforms, and other demanding use cases. Carl J. Levine is the Senior Technical Evangelist for NS1. A veteran of the Internet Infrastructure space, he has over a decade of experience with startups, networking protocols and Internet infrastructure, combined with the unique ability to iterate use cases, bring understanding to those seeking to explore complicated technical concepts and ...
"IBM is really all in on blockchain. We take a look at sort of the history of blockchain ledger technologies. It started out with bitcoin, Ethereum, and IBM evaluated these particular blockchain technologies and found they were anonymous and permissionless and that many companies were looking for permissioned blockchain," stated René Bostic, Technical VP of the IBM Cloud Unit in North America, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
There is a war a-brewin’, but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will be the ruler of the world,” the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet. Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the b...
“Why incur the expense of generating and collecting all of this IoT data if you’re not going to monetize it?” Organizations are racing to embrace the Internet of Things (IoT) as the pundits create “visions of sugar-plums dancing in their heads.” McKinsey Global Institute released their study “The Internet of Things: Mapping the Value beyond the Hype” in June 2015 that highlighted the staggering financial value that IoT could create! (See Figure 1.)
Recently I read somewhere this statement – As we end 2017 and look ahead to 2018, topics that are top of mind for data professionals are the growing range of data management mandates, including the EU’s new General Data Protection Regulation that is directed at personal data and privacy, the growing role of artificial intelligence (AI) and machine learning in enterprise applications, the need for better security in light of the onslaught of hacking cases, and the ability to leverage the expanding Internet of Things.
So data warehousing may not be cool anymore, you say? It’s yesterday’s technology (or 1990’s technology if you’re as old as me) that served yesterday’s business needs. And while it’s true that recent big data and data science technologies, architectures and methodologies seems to have rendered data warehousing to the back burner, it is entirely false that there is not a critical role for the data warehouse and Business Intelligence in digitally transformed organizations.
Will I ever understand the nuances of the advanced analytics landscape? Well, maybe the better question is will the advanced analytics landscape ever stop changing? The advanced analytics landscape, into which I include Deep Learning (DL), Machine Learning (ML), Reinforcement Learning (RL) and Artificial Intelligence (AI), seems to be in a constant state of evolution. New advanced analytic algorithms and tool sets seem to be coming out of every university, every startup, every digital media company and every technology company. And many of these new advanced analytic algorithms and tool sets a...
Coca-Cola’s Google powered digital signage system lays the groundwork for a more valuable connection between Coke and its customers. Digital signs pair software with high-resolution displays so that a message can be changed instantly based on what the operator wants to communicate or sell. In their Day 3 Keynote at 21st Cloud Expo, Greg Chambers, Global Group Director, Digital Innovation, Coca-Cola, and Vidya Nagarajan, a Senior Product Manager at Google, discussed how from store operations and optimization to employee training and insights, all ultimately create the best customer experience b...
Well, my developer friends, 2018 is your year. Businesses in practically every industry have a fever. And the only prescription? You. I know, I know. The demand for developers is nothing new. But 2018 is different. 2018 is the year where we see some of this decade's most exciting technologies become commercially viable, and others finally go mainstream. We'll see new forms of immersive entertainment, and inanimate objects will come alive in ways that really feel real. Trying to undermine this progress are intelligent and dangerous threats that must be outsmarted and outworked. There's endles...
Since releasing the University of San Francisco research paper on “How to Determine the Economic Value of Your Data” (EvD), I have had numerous conversations with senior executives about the business and technology ramifications of EvD. Now with the release of Doug Laney’s “Infonomics” book that builds upon Doug’s EvD work at Gartner, I expect these conversations to intensify. In fact, I just traveled to Switzerland to discuss the potential business and technology ramifications of EvD with the management team of a leading European Telecommunications company.
Special thanks to Brandon Kaier (@bkaier) for his research and thoughts on the Digital Twins concept. Unilever, one of the Consumer Package Goods (CPG) industry’s titans with over 400 brands and annual sales greater than $60B, recently bought Dollar Shave Club for $1B. Now normally I would not think twice about such an acquisition, peanuts in the world of mergers and acquisitions. However, this one feels different. Two billion people use Unilever products every day according to Unilever’s 2015 annual report. Dollar Shave Club only has around two million members; the vast majority of w...
From government to retail to oil and gas, it seems like everyone is exploring how to use AI in their industry or business. It’s time for you to do the same. There’s no question that Artificial Intelligence (AI) is on a lot of people’s minds these days, and is beginning to grow rapidly in adoption. Quoting Accenture, Forbes reports AI-driven productivity gains of perhaps 40% by 2035, and publications like the New York Times are noting the buzz, even as they ask, why now? Even if you don’t think you’ll be adopting AI for yourself just yet, you need to at least consider the broader impact the te...
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, which can process our conversational commands and orchestrate the outcomes we request across our persona...
SYS-CON Events announced today that Evatronix will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Evatronix SA offers comprehensive solutions in the design and implementation of electronic systems, in CAD / CAM deployment, and also is a designer and manufacturer of advanced 3D scanners for professional applications.
This month, an AI (artificial intelligence) system passed a medical exam in China for the first time. I wonder how its bedside manner will be? In addition, Saudi Arabia granted citizenship to a robot named Sophia. With all these rapid advancements, I think it is time we explore the spiritual life of robots. Up till recently, programmers coded and configured algorithms, AI, automation and machine learning system and took personal responsibility for all the code. Today, however, AI has escaped the confines of human oversight and has been empowered and employed to self-program, self-optimize, ...
The Federal Communications Commission announced that it will vote on December 14 to enact the exceptionally misleadingly titled “Restoring Internet Freedom” order. If passed, it will do the opposite of restoring anything resembling freedom — it will repeal the current net neutrality rules which were enacted to ensure that Americans would have equal access to the Internet. If you’re reading this, chances are you’re already interested in the topic. Still, some quick background: Renamed “Open Internet” a while back, net neutrality provided a regulatory framework that specifically prohibited:...
Digital technologies have altered how people and businesses interact. The potential for dislocation from ongoing digital transformation has created unprecedented levels of C-suite discussion. The decisive market leaders have heeded the warnings and taken bold actions. That said, if you’re one of those Chief Technology Officers (CTO) that previously responded to this scenario by making small incremental adjustments to your IT agenda, then you’re potentially at risk. Any relief from those prior tweaks tend to be short lived. The same issues will likely resurface.
Over the last few years, the Internet of things (IoT) has become a trending phrase for consumers and a top priority for businesses embarking on their digital transformation. Even with the growth and interest in IoT however, the meaning can still confuse people. So, what is IoT? IoT is a network of things connected to the internet and is uniquely identifiable through its embedded computing system. These “things” may include a variety of devices like home appliances, commercial vending machines, fitness trackers, industrial gateways, connected cars, and smart factories.
I love it when I get feedback from a blog that I’ve written. I appreciate the different perspectives and insights that others bring to a topic of interest. And no blog that I’ve written has drawn more comments than my blog, “Isaac Asimov: The 4th Law of Robotics.” The section of the blog that fueled the most comments stem from a scene in the movie I, Robot where Detective Spooner (played by Will Smith) is explaining to Doctor Calvin (who is responsible for giving robots human-like behaviors) why he distrusts and hates robots. He is describing an incident where his police car crashed into anot...