/* ---- Google Analytics Code Below */

Tuesday, May 22, 2018

Thinking About Virtual Worlds

Even a simple mirror can create a virtual world.  I remember when we experimented with data immersion using VR in virtual worlds, it was remarkable to see how hard navigation was.  To the point that you often had to go back to the expected flat world to make sense of it.   This article hints at why.

The Physics of a Mirror Creates a Virtual World in Wired.
Human eyes are sort of dumb—but you can trick them into being smart ... " 

Laser Power Insect Robotics

Having very small flying robots that can be tasked to jobs, alone or in groups,  will change many things.  We examined how tasks and services might be solved by such methods.  There continue to be updates.

Laser-Powered Robot Insect Achieves Lift Off
Everything is better with lasers, especially tiny robot insects    By Evan Ackerman

For robots of all sizes, power is a fundamental problem. Any robot that moves is constrained in one way or another by power supply, whether it’s relying on carrying around heavy batteries, combustion engines, fuel cells, or anything else. It’s particularly tricky to manage power as your robot gets smaller, since it’s much more straightforward to scale these things up rather than down—and for really tiny robots (with masses in the hundreds of milligrams range), especially those that demand a lot of power, there really isn’t a good solution. In practice, this means that on the scale of small insects robots often depend on tethers for power, which isn’t ideal for making them practical in the long term.

At the IEEE International Conference on Robotics and Automation in Brisbane, Australia, next week, roboticists from the University of Washington, in Seattle, will present RoboFly, a laser-powered insect-sized flapping wing robot that performs the first (very brief) untethered flight of a robot at such a small scale. ...  "

Simplified Machine Learning

Simplified and non-technical view:

Machine learning: A quick and simple definition
Get a basic overview of machine learning and then go deeper with recommended resources.  By James Furbush in O'Reilly

The following overview covers some of the basics of machine learning (ML): what it is, how it works, and what you need to keep in mind before taking advantage of it.

This information is curated from the expert ML material available on O’Reilly’s online learning platform.  ... " 

Microsoft Makes Chat calls in China

More word of  call-making chatbots, akin to recently announced Google Duplex.  Ultimately you will have to have such systems communicating, with people and other systems, but the implications need to be thought through.  Will we always know who is calling?

Microsoft also has an AI bot that makes phone calls to humans
Similar to Google Duplex, but only in China
By Tom Warren  ... in theVerge

 " ... Google demonstrated a jaw-dropping new capability in Google Assistant earlier this month, allowing the Assistant to make calls on your behalf. While Google Duplex generated controversy and discussion around artificial intelligence, Microsoft has been testing similar technology with millions of people in China. At an AI event in London today, Microsoft CEO Satya Nadella showed off the company’s Xiaoice (pronounced “SHAO-ICE”) social chat bot.

Microsoft has been testing Xiaoice in China, and Nadella revealed the bot has 500 million “friends” and more than 16 channels for Chinese users to interact with it through WeChat and other popular messaging services. Microsoft has turned Xiaoice, which is Chinese for “little Bing,” into a friendly bot that has convinced some of its users that the bot is a friend or a human being. “Xiaoice has her own TV show, it writes poetry, and it does many interesting things,” reveals Nadella. “It’s a bit of a celebrity.” ... " 

Geographic Optimization with Bayesian Networks

Had not seen this kind of optimization before with Bayesian Networks.  Webinar leads you through the process, largely non-technical.

 ... By Stefan Conrady
Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning .... 

Geographic Optimization with Bayesian Networks and BayesiaLab
You may not know that you can use BayesiaLab for geographic optimization. Today's webinar explained how you can find an optimal location for a distribution hub that needs to connect thousands of geographically dispersed suppliers and customers. Bayesian networks and BayesiaLab make this type of optimization remarkably quick and easy. ...  "

Pets and Machine Learning Interactions

In Pete Warden's Blog, interesting views.   Have seen some of that in my own menagerie of chatbots and responsive assistants.    But I think ultimately we will want assistant than amusement.  Machine learning is collaborative in the sense that it solves narrow problems.  So does a 'Push Button' model of tech.  So will a 'pet model' be attentive and responsive?  A neighbor has a guide dog, which has been trained to be more attentive and responsive, rather than pet.   Seems more the model we will see.

Why ML interfaces will be more like pets than machines

When I talk to people about what’s happening in deep learning, I often find it hard to get across why I’m so excited. If you look at a lot of the examples in isolation, they just seem like incremental progress over existing features, like better search for photos or smarter email auto-replies. Those are great of course, but what strikes me when I look ahead is how the new capabilities build on each other as they’re combined together. I believe that they will totally change the way we interact with technology, moving from the push-button model we’ve had since the industrial revolution to something that’s more like a collaboration with our tools. It’s not a perfect analogy, but the most useful parallel I can think of is how our relationship with pets differs from our interactions with machines.

To make what I’m saying more concrete, imagine a completely made-up device for helping around the house (I have no idea if anyone’s building something like this, so don’t take it as any kind of prediction, but I’d love one if anybody does get round to it!). It’s a small indoors drone that assists with the housework, with cleaning attachments and a grabbing arm. I’ve used some advanced rendering technology to visualize a mockup below:

On the rise of the Chatbots

HPE provides an a good, non technical view.  Conversational intelligence and its increasing acceptance.  Obstacles will be maintaining the underlying knowledge.

Conversational AI and the rise of the chatbots

It’s important to understand what conversational AI is, why it’s become so popular, the obstacles, and its likely future.

You can hardly turn on the television news, pull a magazine off a rack in a doctor’s office, or check out your social media without being confronted by a discussion about artificial intelligence. Whether the writer or talking head is decrying the imminent robot apocalypse or celebrating our deep-learning-based salvation, most of the coverage has one thing in common: an imprecise definition of AI. AI is, at its base, nothing more than software that simulates intelligence.

One specific type of AI is cropping up all around the Internet: conversational AI, mostly in the form of chatbots. The most recent and high-profile news about AI was Google’s announcement that its AI, called Google Assistant, beat the Turing test—150 times. The Turing test evaluates a machine’s ability to successfully mimic human intelligence by presenting as indistinguishable from human communication. .... " 

Synthetic Data

Companies may often have mixes of real and synthetic data,  early on we used simulations to create streams of data that were realistic for particular context. Synthetic data can also be assembled from snippets of data from other sources. Behavioral data is a good example. Good to think of a plan to make this available.

Deep learning with synthetic data will democratize the tech industry
From Evan Nisselson in TechCrunch.

" .... Synthetic data is computer-generated data that mimics real data; in other words, data that is created by a computer, not a human. Software algorithms can be designed to create realistic simulated, or “synthetic,” data.

This synthetic data then assists in teaching a computer how to react to certain situations or criteria, replacing real-world-captured training data. One of the most important aspects of real or synthetic data is to have accurate labels so computers can translate visual data to have meaning.

Since 2012, we at LDV Capital have been investing in deep technical teams that leverage computer vision, machine learning and artificial intelligence to analyze visual data across any business sector, such as healthcare, robotics, logistics, mapping, transportation, manufacturing and much more. Many startups we encounter have the “cold start” problem of not having enough quality labelled data to train their computer algorithms. A system cannot draw any inferences for users or items about which it hasn’t yet gathered sufficient information.

Startups can gather their own contextually relevant data or partner with others to gather relevant data, such as retailers for data of human shopping behaviors or hospitals for medical data. Many early-stage startups are solving their cold start problem by creating data simulators to generate contextually relevant data with quality labels in order to train their algorithms.  ... "

P&G Uses SmartLabel Platform

P&G is leader in using means to get to details about thousands of their products.

P&G using technology to peel back curtain on thousands of products on the shelf   By Andy Brownfield  – Reporter, Cincinnati Business Courier

Cincinnati-based consumer goods giant Procter & Gamble Co. is giving consumers an easier way to get insight into thousands of its products using new technology.

Procter & Gamble (NYSE: PG) announced Monday that more than 3,500 of its products are using SmartLabel, a platform that gives consumers on their smartphones or computers detailed information on products, such as ingredients, use instructions, certifications and endorsements. According to a news release, P&G now has more items across more categories on the SmartLabel platform than any other consumer product goods company.

The SmartLabel platform works like this:

Monday, May 21, 2018

Data Science and Machine Learning for Healthcare

To be updated ...

Cognitive Systems Institute Talk   Join us.

24 May 2018: 10:30 AM, ET   (Access Instructions below)

Talk by: Farah Shamout, Oxford University

Title: “Data Science and Machine Learning for Healthcare   

Abstract:     (Will be placed here)
Slides and Recording will be placed here.

Join the meetings by pointing your web browser to:  https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.

Slides and Recording will be placed here: http://cognitive-science.info/community/weekly-update/

 Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Use twitter: #CSIGNews & #OpenTechAI

Replays before Dec 2015:  Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number.   The Replay ID is listed in the Recording column of each date.

Hacking Back: An Active Defense

Interesting thought, but am not sure I would want to get into the battle with the hackers.  Still may be a place someone will have to go to provide an active defense.  Intriguing thoughts that include both hacking and business process.

Active Defense and 'Hacking Back', A Primer
 By Scott Berinato  in the HBR

In the lead piece in this package, Idaho National Lab’s Andy Bochman puts forth a provocative idea: that no amount of spending on technology defenses can secure your critical systems or help you keep pace with hackers. To protect your most valuable information, he argues, you need to move beyond so-called cyber hygiene, the necessary but insufficient deployment of security software and network-monitoring processes. ... " 

Sharepoint Virtual Reality

We experimented with similar ideas.  How do you immerse yourself in messy data?   In information architecture.   We never thought of Sharepoint as a place to start, though we were an MS shop with lots of data of many kinds there.   But will the employee be willing to pick up the headgear, and will that add enough of an engagement to make it worth it?    Maybe if it were complex data we need to navigate?  There will be a gallery of templates to start with, lets see where that takes us.

Microsoft turns SharePoint into the simplest VR creation tool yet
SharePoint spaces is like the PowerPoint of Mixed Reality.

By Devindra Hardawar, @devindra in Engadget

Microsoft is sticking with its pragmatic approach to VR with SharePoint spaces, a new addition to its collaboration platform that lets you quickly build and view Mixed Reality experiences. It's a lot like how PowerPoint made it easy for anyone to create business presentations. Sharepoint spaces features templates for things like a gallery of 3D models or 360-degree videos, all of which are viewable in Mixed Reality headsets (or any browser that supports WebVR). While they're certainly not complex virtual environments, they're still immersive enough to be used for employee training, or as a quick virtual catalog for your customers.  .... " 

Acer Ships with Alexa

Seems to hurt Cortana, but Cortana will also be installed on these same machines along with Windows 10.  But as mentioned, Cortana has been poorly marketed, especially as to its value to support particular consumer needs.  Paul Thurott says it well:

Acer announced this morning that it is the first to ship notebook PCs preinstalled with Amazon Alexa. It won’t be the last.

“We’re delighted to work with Acer to bring Alexa to customers in new ways,” Amazon Alexa vice president Steve Rabuchin says. “We believe customers should be able to interact with Alexa wherever they might need her, including from their PCs, in order to take advantage of the simplicity of voice control.”

That says a lot, I think, about one of Microsoft’s most recent failures. After all, Windows 10 PCs already ship with voice control in the form of Cortana. But that is, perhaps, something that many consumers would never even notice: Cortana usage and capabilities lack far behind those of the digital personal assistant market leaders, Amazon Alexa and Google Assistant. And a PC will work as a secondary device, when it comes to voice control, behind smartphones and even smart speakers. ...." 

AI for Smart Houses

The AI we are using today is simplistic, where will it grow?

Deep Learning, Artificial Intelligence Leading the Way to Smart  Houses
In the Baylor Lariat (TX)   By Samantha Amaro

Baylor University researchers are studying deep learning, with a focus on improving medical imaging and advancing the future of truly smart houses that will perform all manual labor for occupants. The research is divided into two categories: distributed deep learning and energy-efficient deep learning. Distributed deep learning involves investigating how to use several local machines to compute different parts of the main neural network, while energy-efficient deep learning focuses on the problem of being able to provide a constant source of energy for continuous projects. The researchers are using deep learning to analyze medical images, including positron emission tomography (PET) scans and computed tomography (CT) scans. The team is also leading a smart home project to determine whether a house can measure a person's overall health; sensors throughout the house would read a person’s biorhythms and send alerts to the home's occupants if needed.  ... "

Microsoft Buys Semantic Machines

Towards more conversational machines.  We spent many years trying to figure out how analytics, systems and machines could better 'understand' the meaning of data.  Now this will be essential to lead to better conversational interaction.  Note the term 'multiturn' exchanges.  Ultimately its all about the intelligent conversation.

Microsoft snaps up Semantic Machines to build out its conversational AI technology  By Duncan Riley in SiliconAngle
Microsoft Corp. Sunday said it has acquired Semantic Machines Inc., a Berkeley, California-based company that has built a conversational artificial intelligence platform that competes with the likes of Google Inc., for an undisclosed sum.

Founded in 2014, Semantic Machines has designed a new, language-independent technology platform that claims to go beyond understanding commands to understanding conversations. Compared with a neurolinguistic programming approach, the company said, it offers a new technology that extracts semantics across “multiturn” natural language exchanges to maintain contextual understanding over time, enabling computers to communicate, collaborate, understand goals and accomplish tasks.

The acquisition for Microsoft is aimed at boosting its existing conversational AI efforts in services such as Microsoft Cognitive, Cortana and the Azure Bot. The technology and the company itself will be used by Microsoft to establish a conversational AI center of excellence in Berkeley “to push forward the boundaries of what is possible in language interfaces.”  ... " 

Replacing Powerpoint with Narratives

Stories are good, when constructed well.   What if you just want the essential and concise points to carry away.  Narrative also has the stronger possibility of Confirmation Bias.  Its a good story, so its real, true?  And the more you construct it with glossy or animated colorful visuals, the more its correct?  Not saying we to not tell a storywell, but bullet points are useful too.  Brilliant?  No, incomplete.

Jeff Bezos Banned PowerPoint in Meetings.  .....
Narrative memos have replaced PowerPoint presentations at Amazon. Here are three reasons why.
By Carmine Gallo ... 

Sunday, May 20, 2018

Smart Diapers Design with Sensors

Used to work at a company that competed in this space.  In Design, manufacturing and marketing.    Will this compete?

Alphabet’s Verily has a “smart diaper“ design that distinguishes pee from poo   Beyond simple moisture detectors, this techy nappy will analyze the latest download.  By Beth Mole in ArsTechnica

Tech companies are always hoping to clear out the competition with their latest wearable. But Alphabet's life sciences division, Verily, is likely expecting a blow-out with this one.

The company, formerly known as Google Life Sciences, has a patent-pending plan for a wirelessly connected “smart diaper” that would not only alert a caregiver when there’s a new “event” but also analyze and identify the fresh download—i.e., is it a number one or number two? The connected, absorbent gadget will sound the alarm via a connected device and potentially an app, which can catalogue and keep a record of events.

Verily is not the first to try to plumb the potential of derrière devices for babies. Many companies have come before with simple to high-tech moisture sensors—from color-changing strips to wireless alarms. But, Verily argues in its patent application, the market is lacking a convenient, affordable, all-in-one design that can differentiate between a wee squirt and a code brown. While both require attention and a change, a festering or explosive diaper bomb often requires more urgency, particularly if a baby is dealing with diaper rash.  ... " 

Crime Matching with GEDMatch

Interesting this has just become apparent, genetic matching starts to work against increasing stored data and matching.  Shows the power of cowdsourced databases.  Other examples?    Technology Review Shows why and how:

Another arrest shows why no one can hide from the genetic detectives
For the second time this year, investigators used a public DNA database to solve a cold case and find a murderer.

The bust: A 55-year-old truck driver, William Talbott, was arrested today in Washington State after being fingered in a 30-year-old double murder.

How they found him: According to Buzzfeed, investigators located Talbott’s family members after uploading old crime scene DNA to GEDMatch, a crowdsourced database that genealogists use to compare DNA and build family trees.  ...  "

It further comes to mind that this is akin to:

 ' ...   "The Selfish Ledger,” was shared internally within Google. The video examines the possibility of a dystopian world where our use of devices such as smartphones creates a sort of digital DNA, which, like physical DNA, could exist within the context of future generations. ..."

More on that and links to the video on my post here.   Will the crimes of the past always match the crimes considered in the future?

Tesla Releases some of its Code

Intriguing, but as you might expect, the autopilot is very technical.   Tesla is not known for releasing its source code.   Hardly directly understandable, but gives you an impression of the complexity involved.  I am still of the school that says you may not want to release all your code secrets.

More overview:
Tesla releases source code for some of its in-car tech ....It's not everything, but it's finally here. ... " 

By Jon Fingas, @jonfingas  in Engadget

Potential of Data Science

Towards a better definition of data science.

Realizing the Potential of Data Science
By Francine Berman, Rob Rutenbar, Brent Hailpern, Henrik Christensen, Susan Davidson, Deborah Estrin, Michael Franklin, Margaret Martonosi, Padma Raghavan, Victoria Stodden, Alexander S. Szalay

Communications of the ACM, Vol. 61 No. 4, Pages 67-72   10.1145/3188721

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field—data science—that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy.

The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise? Convened by the Computer and Information Science and Engineering (CISE) Directorate of the U.S. National Science Foundation as a Working Group on the Emergence of Data Science (https://www.nsf.gov/dir/index.jsp?org=CISE), we present a perspective on these questions with a particular focus on the challenges and opportunities for R&D agencies to support and nurture the growth and impact of data science. For the full report on which this article is based, see Berman et al.2

The importance and opportunities inherent in data science are clear (see http://cra.org/data-science/). If the National Science Foundation, working with other agencies, foundations, and industry can help foster the evolution and development of data science and data scientists over the next decade, our research community will be better able to meet the potential of data science to drive new discovery and innovation and help transform the information age into the knowledge age. We hope this article serves as a basis for dialogue within the academic community, the industrial research community, and ACM and relevant ACM special interest groups (such as SIGKDD and SIGHPC).  ... '

Gartner on the Value of AI: $1.2 Trillion

Such valuations are always difficult.  But it can be expected to be high if it truly augments human effort.

Artificial intelligence will be worth $1.2 trillion to the enterprise in 2018
Gartner says that AI-based customer experience technologies are boosting market value.  By Charlie Osborne for Between the Lines

The artificial intelligence (AI) industry will be worth $1.2 trillion in 2018, with customer experience solutions creating the most business value.

On Wednesday, Gartner released estimates on the projected value of AI over the course of this year. According to the research firm, the global enterprise value derived from AI will total $1.2 trillion this year, a 70 percent increase from 2017.

AI-derived business value is projected to reach up to $3.9 trillion by 2022.

"AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner. "One of the biggest aggregate sources for AI-enhanced products and services acquired by organizations between 2017 and 2022 will be niche solutions that address one need very well."

These sort of needs may include methods to improve customer experiences, ways to drive new revenue streams, and means to reduce costs, whether operational or in serving existing products. .... "

Blockchain Definitions

A thoughtful set of definitions:

What is a blockchain?
Unpacking the complexity of blockchain, term by term.

By Mike Loukides May 17, 2018

Read "What are Enterprise Blockchains?" and learn how organizations are applying blockchain technology.

So, what is a blockchain? It's a complicated question because the inventor of Bitcoin, the pseudonymous Satoshi Nakamoto, didn't use the term in the original Bitcoin paper. For many, “the blockchain” is nothing more than a shorthand for "how Bitcoin works." But more usefully, the blockchain is a distributed ledger, shared by untrusted participants, with strong guarantees about accuracy and consistency. What does that mean? Let's unpack it term by term: .... " 

Judea Pearl Criticizes Machine Learning

Quite interesting view.  Pearl's view is interesting.  Bayesian networks in particular has shown a more broadly insightful and transparent view to modeling than machine learning.    But machine learning deep learning can target narrower problems more specifically.

How a Pioneer of Machine Learning Became One of Its Sharpest Critics
Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can't compute cause and effect.

 By Kevin Hartnett in The Atlantic

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his latest book, The Book of Why: The New Science of Cause and Effect, he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is.

Three decades ago, a prime challenge in artificial-intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria. In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work.

But as Pearl sees it, the field of AI got mired in probabilistic associations. These days, headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can master ancient games and drive cars. Pearl is underwhelmed. As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.  .... " 

AT&T Builds a Dash-Like Button

Always thought there was a place for simple buttons to link to IOT networks, to make requests of many kinds, beyond just ordering something.   Why doesn't IFTTT have something like this?  This seems to be that,    The way I read it,  although provided through AWS,  it is not an Amazon Echo infrastructure thing.  Will be interesting if that  changes.

AT&T's Dash-like smart button doesn't need WiFi
It's also not pre-programmed like Amazon's one-click device.
By Mariella Moon, @mariella_moon in Engadget

AT&T has launched a new product called LTE-M button, which allows users to place an order online in one click. Yes, it sounds just like Amazon Dash -- in fact, it's powered by Amazon Web Services -- but since it's connected to AT&T's LTE-M network, it doesn't need a WiFi connection to work. AT&T's button was also designed more for businesses than homes and individuals. It's not pre-programmed like Amazon's Dash buttons are, and companies can program it to accomplish tasks that fit their needs. .... "

More details from AT&T

Saturday, May 19, 2018

Business of Artificial Intelligence

 Good statement of expectations, what it is doing, what has been promised, delivered and not,  and where it needs to go.  As a practitioner, I have seen it all to date. ...  It needs care,  caution and closer links to business needs to make it feasible.

Business of AI

By Erik Brynjolfsson and Andrew Mcafee in the HBR

Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT he teaches courses on the economics of information and the Analytics Lab.

The Business of Artificial Intelligence

What it can — and cannot — do for your organization

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination. ....

Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.  ...." 

How to be a Systems Thinker

Podcast video interview.   Its a good idea to create deep understanding.  But shallower understanding sometimes leads to things that are useful,  there has been quite a long history of engineering to show that.  Thoughtful piece,

How To Be a Systems Thinker
A Conversation With Mary Catherine Bateson [4.17.18]

Until fairly recently, artificial intelligence didn’t learn. To create a machine that learns to think more efficiently was a big challenge. In the same sense, one of the things that I wonder about is how we'll be able to teach a machine to know what it doesn’t know that it might need to know in order to address a particular issue productively and insightfully. This is a huge problem for human beings. It takes a while for us to learn to solve problems, and then it takes even longer for us to realize what we don’t know that we would need to know to solve a particular problem.

The tragedy of the cybernetic revolution, which had two phases, the computer science side and the systems theory side, has been the neglect of the systems theory side of it. We chose marketable gadgets in preference to a deeper understanding of the world we live in.

MARY CATHERINE BATESON is a writer and cultural anthropologist. In 2004 she retired from her position as Clarence J. Robinson Professor in Anthropology and English at George Mason University, and is now Professor Emerita. Mary Catherine Bateson's Edge Bio  ... "

OpenAI Lets Robots Learn from Hindsight

In IEEE Spectrum.

OpenAI Releases Algorithm That Helps Robots Learn from Hindsight

It's not a failure if you just pretend that you meant to do it all along  By Evan Ackerman

Being able to learn from mistakes is a powerful ability that humans (being mistake-prone) take advantage of all the time. Even if we screw something up that we’re trying to do, we probably got parts of it at least a little bit correct, and we can build off of the things that we did not to do better next time. Eventually, we succeed.

Robots can use similar trial-and-error techniques to learn new tasks. With reinforcement learning, a robot tries different ways of doing a thing, and gets rewarded whenever an attempt helps it to get closer to the goal. Based on the reinforcement provided by that reward, the robot tries more of those same sorts of things until it succeeds. ... " 

Friday, May 18, 2018

Smart Speakers Show us What AI is

I do like the way that Amazon is promoting citizen construction of skills.  Will this beat out the professional delivery of assistant intelligence?  Probably the first time this kind of competition has ever been tried.  The experiment will be interesting.

Amazon Echo smart speaker feels squeeze as Google and Apple make gains by Trevor Mogg in Digital Trends

Smart speakers are all the rage just now, with the market becoming more competitive with every passing month.

Amazon started the craze with the launch of its first Alexa-powered Echo speaker back in 2014, but more recently other big hitters such as Google and Apple have entered the market with their own offerings, putting pressure on sales of Amazon’s growing range of smart speakers.

Global smart speaker shipments reached 9.2 million units in the first quarter of 2018, the latest data from research firm Strategy Analytics suggests.  ... " 

Google's Selfish Ledger

Remember this from then, but at the time not too much in the way of reaction.  Now there seems to be more caution regarding what large companies can think about leveraging.

Google's Selfish Ledger is an unsettling vision of Silicon Valley Social Engineering

This internal video from 2016 shows a Google concept for how total data collection could reshape society   By Vlad Savov   @vladsavov     in theVerge

Google has built a multibillion-dollar business out of knowing everything about its users. Now, a video produced within Google and obtained by The Verge offers a stunningly ambitious and unsettling look at how some at the company envision using that information in the future.

The video was made in late 2016 by Nick Foster, the head of design at X (formerly Google X) and a co-founder of the Near Future Laboratory. The video, shared internally within Google, imagines a future of total data collection, where Google helps nudge users into alignment with their goals, custom-prints personalized devices to collect more data, and even guides the behavior of entire populations to solve global problems like poverty and disease.  ... " 

Introducing Vulnerability Management

The specific term is new to me, but have worked the risk management direction for a long time.  At the link much more including the references mentioned.

We Scan and We Patch, but We Don’t Do Vulnerability Management  by Anton Chuvakin  in Gartner

Lately, we’ve been flooded with calls about vulnerability management (VM). Many of the calls seem to be from organizations of medium to low security operations maturity, that are just starting with vulnerability management [and that’s OK – a wise mentor once told me ‘always remember that ‘90% of people are not in the top 10 percentile!’” :-)]

Many of them say something similar to “we scan and we patch, but we don’t do vulnerability management.” Essentially, they are coming to a realization that I often like to summarize as “VA is easy, but VM is hard.”

Of course, we have a lot of excellent research written on this topic:

“A Guidance Framework for Developing and Implementing Vulnerability Management” (39 pages of juicy VM stuff!)
“How to Implement Enterprise Vulnerability Assessment”
“A Comparison of Vulnerability and Security Configuration Assessment Solutions”  ... "

Cryptocurrency and IOT

This company hopes its cryptocurrency can help the internet of things reach its true potential  Helium, a startup focused on connecting low-power devices, thinks a blockchain can seed the spread of cheap, ubiquitous connectivity.

by Mike Orcutt in Technology Review

Anyone who hears that an internet-of-things startup is getting into blockchain technology would be forgiven for laughing it off as another hollow scheme. But Amir Haleem, cofounder of Helium, says he has no interest in making a quick buck off the irrational exuberance that permeates the cryptocurrency world. In fact, just the opposite—he’s interested in cryptocurrency only as a way to build a true internet of things, once and for all. And he knows his idea is so far-out it could take a while before people catch on.

Haleem started Helium in 2013, along with Napster creator Shawn Fanning. The company’s first product, which is now on the market, is a hardware system that uses a homegrown wireless standard to provide long-range, low-power wireless coverage for devices like sensors that track and monitor medicine or food supply chains. Software routes the data to internet-based applications hosted by whoever owns the sensors. .... "  

Social Networks and Innovations

An interesting view of how social networks influence elements of opinion.   In particular addressing the kinds of innovations.

How Social Networks Contribute to the Spread of Unproven Innovations

Wharton's Valentina Assenova discusses her research on social networks and the adoption of complex innovations.

There are some new products and services that are very obviously good  — a cure for a deadly disease, for example, or some other type of medical innovation. But other innovations have value that is more uncertain, such as an unproven technology. In her latest research paper, Wharton management professor Valentina Assenova examines the role of social networks, both online and offline, in the spread of these complex innovations. Her paper is titled, “Modeling the Diffusion of Complex Innovations as a Process of Opinion Formation Through Social Networks.” She joined Knowledge@Wharton to discuss her findings about which kinds of innovations spread more quickly than others in different networks, the role of influencers, and what that means for entrepreneurs.

An edited transcript of the conversation follows.

Knowledge@Wharton: What was the inspiration for this research?

Valentina Assenova: The inspiration for this research was looking at the spread of microfinance. Microfinance is one of those innovations that is not obviously good or bad, and there is a lot of mixed evidence around whether or not it is actually beneficial for women, whether it improves welfare and so forth. But it was something that really got me intrigued about the role of public opinions and of social networks — in the sense of people who you talk to for advice, for help in making a decision — and how some of these complex innovations spread.    ... " 

AI and the Future of Work

In the SAS Blog,  some notes about AI, Jobs and an inevitable future to consider.

Optimistic about AI and the future of work  
by  Randy Guard.  in the SAS Blog

I have good news to share about the future. Despite what you may have heard elsewhere, the future of work in a world with artificial intelligence (AI) is not all doom and gloom. And thanks to a research-backed book from Malcolm Frank, What to Do When Machines Do Everything, we have data to prove it. Also, thanks to new educational approaches, we are better equipped to prepare students and misplaced workers for a future with AI.

All of these topics were covered at Cornell’s Digital Transformation Summit, where my colleague Radhika Kulkarni and I spoke alongside Frank and some of our country’s top educational leaders.

Frank, Executive VP of Strategy and Marketing at Cognizant, says we’re experiencing the fourth industrial revolution. He anticipates that the percentage of job loss from AI will correspond with job loss rates during other periods of automation throughout history, including automation through looms, steam engines and assembly lines. Fundamentally, workforce changes from AI will be like those during the industrial revolution and the introduction of the assembly line. About 12 percent of jobs will be lost. Around 75 percent of jobs will be augmented. And there will be new jobs created.

Read the article, “Why Artificial Intelligence will Create More Jobs Than it Destroys,” for more predictions on this topic, including a few from SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer Oliver Schabenberger.  ... "

Thursday, May 17, 2018

Separating Better Data from Big Data

Interesting Podcast and transcript that leads towards operational considerations for analytics:

Separating Better Data from Big Data: Where Analytics Is Headed

Wharton's Eric Bradlow, Peter Fader and Raghuram Iyengar discuss what's next for customer analytics.

Ten years ago, the most forward-thinking companies were just starting to dive into the potential of data and analytics. Since then, brands have moved from using analytics to answer what customers are doing to exploring the how and why, and also to figure out what they will do in the future.

The Wharton Customer Analytics Initiative (WCAI) is celebrating its 10th anniversary this year and has seen every step of that evolution. Knowledge@Wharton recently sat down with Wharton marketing professors Eric Bradlow, Peter Fader and Raghuram Iyengar to discuss how the field has developed over time, and what they expect to be the key trends over the next decade. Bradlow and Fader are the founding directors of WCAI, and Bradlow and Iyengar are the current co-directors.

An edited transcript of the conversation follows. ... " 

Kroger Links to Ocado

In FMI Daily Lead:

Meet Ocado, Kroger's Newest Weapon in Its Grocery Delivery War with Amazon and Walmart ... Kroger to become exclusive US licensee of Ocado technology ... 

In Fortune.
Kroger has forged a deal with British online grocer Ocado, which has been on the forefront of warehouse and delivery automation for online grocery orders. Kroger will be the exclusive US licensee of Ocado's technology and now owns a 5% stake in the UK firm. ... " 

Amazon SageMaker

Brought to my attention.  Scale being the most interesting claim.

Amazon SageMaker
Build, train, and deploy machine learning models at scale
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. All of this takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time to experiment and optimize every part of the process. In the end, it's not a surprise that the whole thing feels out of reach for most developers.

Amazon SageMaker removes the complexity that holds back developer success with each of these steps. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.  ... " 

Also,Infoworld Review.

Convergence of Analytics Products, Services

Below contains a link to a complimentary report. Inescapable direction.  Few would buy a single capability.

Continued Convergence Of Analytics Products And Services

By Boris Evelson,  Vice President, Principal Analyst
I’ve been watching this trend with great curiosity (first wrote about it in 2007).

On the one hand, software product vendors are slowly but surely migrating from just selling products to selling solutions. And solutions always require professional services. IBM led the trend when it acquired PwC in 2002. For the last few years GoodData has also been concentrating on building embedded BI solutions with a strong emphasis on professional services. Most recently MicroStrategy (and I hope other BI/analytics/big data vendors will follow soon) made a significant investment into its management consulting capabilities hiring hundreds of consultants and coming out with a “BI/analytics maturity assessment” service offering – formerly solely the realm of management consultants.  80% of BI projects success depends on people/process part of the equation and that’s why strong management consulting capabilities are key. .... " 

Coresight and Retail Robotics and AI Conference

Via Pratt Retail Institute

The Coresight Research team recently attended the Retail Robotics & AI Conference on April 26 in San Francisco. Here are our top takeaways from the event:

As consumers increasingly rely on technology when they shop, the retail industry is nearing a state of “retail singularity” in which bots, natural language voice interfaces and broadly integrated data react to and predict the what, when and why of shopper preferences and behavior.

Store-based retailers need to integrate the best of the online and offline shopping experiences in order to create synergies that help unlock new potential, said Jeff Donaldson, CEO of Intriosity and former SVP of GameStop.

Consumers are increasingly opting to engage in experiences that create the illusion of participating versus spectating. The rise of Amazon has coincided with this cultural shift.

Coresight Research CEO and Founder Deborah Weinswig commented that China is a leader in artificial intelligence (AI) and robotics in the New Retail environment, where the integration of online and offline retail, logistics and data across a single value chain is essential. AI and automation are the keys to making everything work, she noted.

Mark Mathews, of the National Retail Federation (NRF), said that the retail technologies that are resonating most with consumers are those that enable them to buy online and pick up in-store, navigate in-store and pay by mobile device.

The Coresight Research team attended the inaugural Retail Robotics & AI Conference in San Francisco on April 26. The conference was hosted by the Retail Analytics Council (RAC), an initiative between Northwestern University and the Platt Retail Institute that focuses on studying consumer shopping behavior across retail platforms and the impact of technology on retail.

At the event, discussion topics included emerging retail robotics applications, building a 360-degree view of the consumer, the use of AI in retail, advanced analytics and industry trends. Attendees ranged from C-level executives to academics to retail and technology industry leaders. .... " 

AI Based Recommendation Engines

Makes much sense to broaden the approach.  Link to PDF.

Recommendation Engine Based on AI: High Revenue Margin by 2022
 By Akshita Banker in Linkedin

 With the help of Recommendation engine more than 100 business logic and personalization configurations make best offers to their customers. Recommendation engine are common among e-commerce, social media and content-based websites. It Analyses customer behavior, order history, and like-minded shopper intent. This information enables e-retailers to offer the right products to the right customers and at the appropriate time. ... " 

Graphene Feeling Sensor

Another example of connecting a new kind of sensor to gather new data, which can then be connected to machine learning methods.    The value of a sensor is then if it can produce data that is useful for training.

Graphene-Based Sensor Learns to Feel Like a Human 
Chemistry World  By Hannah Kerr

Researchers at Hanyang University in South Korea have integrated an electric sensor with a machine learning program, creating a device that can differentiate between surface textures, with potential applications in virtual reality, robotics, and medical prosthetics. The sensor is fabricated from a graphene-flake film deposited onto a polyethylene naphthalate substrate. The device registers changes in electrical conductance and resistance via the film when strain causes deformation, boosting the physical contact between individual flakes in the film. The machine-learning program applies the sensor's conductance and resistance data to define specific features connected with different surface texture types. The researchers have applied the graphene film to an artificial fingerprint structure so it reacts to tiny vibrations caused by the ridges on the fingerprint rubbing against a textured surface; the sensor analyzes these signals to identify the "feel" of differently textured fabrics. In a blind test of 50 people, the sensor outperformed humans in classifying 12 new fabrics. ... "

Storytelling: The Last Mile of Analytics

In Today's Summit Meeting, this presentation ultimately poses the challenge:  How do we effectively convey something that is inherently usually very boring?    Make it into a story that conveys some result, and makes you remember it.

Stuart Stockton of Macy's presentation:

You goal as an analyst is simple:  Use the tools, data and skills at your disposal to extract insights and prescribe action.   But how do we ensure that those who will implement those findings will take that action?  Telling a compelling story and engaging the emotions of our audience is critical.  In this session we will explore why stories work, and how to construct and tell a story that will increase your chances of successfully influencing your organization. .... 

He recommended the podcast  'The Moth',  readily available, as an example of many, many well told stories.

Amazon Food Safety Safety Strategy

Surprising perhaps, but good direction given all the systems in place.

Amazon plans to become the fresh food safety leader   by Ron Margulis in Retailwire.

When it comes to food safety innovation, not many industry experts would consider Amazon.com a leader. The online retail giant is working diligently to change that opinion as it strives to do in fresh foods what it’s done in books and dozens of other categories.

Carletta Ooton, Amazon’s vice president, health, safety, sustainability, security and compliance (wow, that’s a mouthful), outlined the company’s approach to food safety at the 2018 Food Safety Summit, held last week in Chicago. Ms. Ooton described Amazon’s innovation as a mission critical element for the retailer, one that has the support of every level of the organization, up to and including CEO Jeff Bezos. .... " 

Wednesday, May 16, 2018

Talk: Democratizing AI Through Open Data

Cognitive Systems Institute Talks:

17 May 2018: 10:30 AM, ET  Access Instructions below.

Talk by: Michael Henretty

Title: “Finding our Common Voice: Democratizing AI through Open Data”      Mozilla


  Michael Henretty is an engineer and open innovation strategist working for Mozilla from Berlin. He is currently leading the Common Voice project, Mozilla's initiative to crowdsource a public dataset of human voices to be used in open speech technology. In a former life, Michael was a video game developer and designer. 

The future of the Web is built on voice: voice recognition tools, speech directed commands, as well as civic movements. If we want to work towards a more inclusive, people enabled, and empowered future for the Internet as a global public resource, we need to act now. The challenge is complex, it starts with the training data used for speech algorithms, is connected to technical challenges for compatible code and systems, and finds its expression in continuously changing political contexts.... 

Slides now in place.


Join the meetings by pointing your web browser to:  https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.

Slides and Recording will be placed here: http://cognitive-science.info/community/weekly-update/

 Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Use twitter: #CSIGNews & #OpenTechAI

Replays before Dec 2015:  Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number.   The Replay ID is listed in the Recording column of each date.


International Institute of Analytics

Brought back to my attention, the IIA, International Institute of Analytics

See also:

Bill Franks
Analytics & big data focused speaker, blogger, consultant, and author.   Chief Analytics Officer for The International Institute For Analytics (IIA), where he provides perspective on trends in the analytics and big data space and helps clients understand how IIA can support their efforts to improve analytic performance. In his role, he helps guide IIA’s global community of analytics practitioners in determining the best strategy and path forward for their particular analytics journey.  .... 

IIA is the authority on analytics maturity and best practices.

Founded in 2010 by Jack Phillips and Thomas H. Davenport, the International Institute for Analytics is an independent research firm that works with organizations to build strong and competitive analytics programs.

IIA offers unbiased advice in an industry dominated by hardware and software vendors, consultants and system integrators. With a vast network of analytics experts, academics and leaders at successful companies, we guide our clients as they build and grow successful analytics programs.

Since its inception, IIA has worked with more than 200 organizations, sharing the keys to analytics maturity so that our clients gain an edge in an economy increasingly driven by data. Through our in-depth research library, moderated phone calls, webinars and events, our clients get the guidance and expertise needed to compete on analytics and win. ... " 

Augmented Intelligence in Healthcare

Expert discusses Augmented Intelligence in Healthcare

 in Public Library of Science

The potential of using machine learning techniques in medicine is immense. As electronic health records have become widely available, there is hope that machine learning will improve diagnosis and care. However, integrating these new methodologies into medical practice is challenging. New methods need to meet healthcare standards, for example around doctor accountability and patient privacy, and must be smoothly integrated into clinical decision-making practices.

We had the pleasure of speaking to Arthur Papier who has been working on problems like these for decades. A dermatologist by training, he started working with electronic health records in the 1980s and launched a clinical decision support tool called VisualDX at the turn of the millennium. VisualDX aids physicians in exploring all diagnostic possibilities through visual clues. The tool combines a search through a database of which symptoms and findings convey which diagnoses with images of how the disease in question looks on skin, eyes, mouth and in radiography. .... " 

How Will GDPR Impact Machine Learning

Well done piece on the issue.  Still unclear how this effect experimentation and delivery. 

How will the GDPR impact machine learning?
Answers to the three most commonly asked questions about maintaining GDPR-compliant machine learning programs.

By Andrew Burt in O'Reilly

Much has been made about the potential impact of the EU’s General Data Protection Regulation (GDPR) on data science programs. But there’s perhaps no more important—or uncertain—question than how the regulation will impact machine learning (ML), in particular. Given the recent advancements in ML, and given increasing investments in the field by global organizations, ML is fast becoming the future of enterprise data science.

This article aims to demystify this intersection between ML and the GDPR, focusing on the three biggest questions I’ve received at Immuta about maintaining GDPR-compliant data science and R&D programs. Granted, with an enforcement data of May 25, the GDPR has yet to come into full effect, and a good deal of what we do know about how it will be enforced is either vague or evolving (or both!). But key questions and key challenges have already started to emerge. .... "

Virtual Doctor Comes via 5G

An interesting preview.  Will 5G be fast enough everywhere to provide services like imaging?  I note also the term IOMT Internet of Medical Things.

Previewing 5G’s effect on the health care industry  via Qualcomm
Your (virtual) doctor will see you now

Faster connection speeds are transforming the doctor/patient relationship, integrating electronic communications into medical care. From the comfort of their homes, patients wear remote medical sensors, transmitting their vital signs to health care providers. This data allows doctors and caregivers to monitor an array of vitals, dynamically manage treatment plans, and conduct a consult or intervention over webcam. The arrival of 5G networks will take this recent medical trend to the next level and provide a significant economic boost to the medical community. According to IHS Markit, 5G will enable more than $1 trillion dollars in products and services for the global health care sector.

How will this affect you? 5G represents a whole new way you’ll accomplish digital networking and is likely to upgrade your health care experiences. It’ll help you maintain your wellness through three primary areas of capabilities: Massive Internet of Medical Things (IoMT), Enhanced Mobile Broadband (eMBB), and mission critical services. All three will come together, delivering a holistic, personalized view of the patient anytime and anywhere.  .... "