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

Thursday, May 31, 2018

Adversarial AI Toolbox

Todays  Cognitive Systems Institute Talk:

IBM: Research Scientist: Irina-Maria Nicolae  on “Adversarial AI & Adversarial AI Toolbox IBM

Talk Description: Following the recent adoption of deep neural networks (DNN) in a wide range of application fields, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of producing a specific response from the system. Multiple attacks and defenses have been proposed in the literature, but the lack of better understanding of sensitivity of DNNs justifies adversarial samples still being an open question. In this talk, we will introduce the Adversarial Robustness Toolbox (ART), a newly open-sourced IBM Research library against adversarial attacks for machine learning models https://github.com/IBM/adversarial-robustness-toolbox

Bio: Irina Nicolae, PhD, is currently a research scientist in the AI & Machine Learning team of IBM Research Ireland. Her main interests include learning representations for complex data and security for deployed models. She has received her PhD from University of Saint-Etienne, France, for a research project on similarity learning with theoretical guarantees for numerical and temporal data. Previously, she has graduated from Politehnica University of Bucharest in Computer Science in 2011, and from ENSIMAG in Information Systems in 2013 .... "

Slides and Talk recording.

Amazon Announces Graph Database

Amazon continues to bring out advanced tools for AWS, with useful examples of their practical use, at low trial and initial testing cost.  I particularly like the examples given here about how and why you would use graph databases.  Note the implications for embedding 'intelligence' in a database.

AWS Announces General Availability of Amazon Neptune
Amazon Neptune, a fast and reliable graph database, makes it easy for customers to build applications on highly connected datasets

Thousands of customers, including Samsung Electronics, Pearson, Intuit, Siemens, AstraZeneca, FINRA, LifeOmic, Blackfynn, and Amazon Alexa, participated in the preview, building new graph applications and battle-testing their production workloads

May 30, 2018 04:46 PM Eastern Daylight Time
SEATTLE--(BUSINESS WIRE)--Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced general availability of Amazon Neptune, a fast, reliable, and fully managed graph database service. Amazon Neptune efficiently stores and navigates highly connected data, allowing developers to create sophisticated, interactive graph applications that can query billions of relationships with millisecond latency. In the preview, customers used Neptune to build social networks, recommendation engines, fraud detection, knowledge graphs, drug discovery applications, and more. 

With Amazon Neptune there are no upfront costs, licenses, or commitments required; customers pay only for the Neptune resources they use. To get started with Amazon Neptune, visit https://aws.amazon.com/neptune

Also in SiliconAngle.

Simple Sales Conversations with Considerable Profit

If we consider a conversation to be of multiple interactions,  and using multiple channels, the object of the seller is to maximize your returns, over this and often future interactions.   Conversations might include marketing of many kinds,  human tastes, advertising signage and the complete context of how and when a sale is made.  Now with the placenent of assistants in the home and elsewhere, we have the opportunity to manage conversations in completely new ways.

This Inc article shows a specific example, of how the US fast food company 'Burger King' uses this to maximize profit and loyalty.    The article is entitled " How does Burger King make a 240 percent  profit when you say Yes to this quick question".    So here is at least one question in a multi-turn conversation.  Not criticizing Burger King here,  but noticing elements of conversation that influence.

Burger King Makes a 240% Profit When You Say "Yes" to this Quick Question
This brilliant pricing scheme extorts huge margins while simultaneously increasing customer loyalty. ....  In Inc by Geoffrey James, Contributing editor, Inc.com@Sales_Source  .... " 

Wired Predicts

Wired Predictions for Bots, Blockchains, CrispR and more ..

SOMETIMES THE FUTURE shows up so fast it hits us in the face, like a brick wall in a VR headset. Other times the miraculous promises of technology—the rearrange­ment of our very DNA, the blockchain-­enabled toppling of Facebook—are frustratingly slow to arrive. But either way, the future is coming, and we should be ready. In the following pages we lay out a series of predictions, starting with some changes that are immediately upon us. Then, looking down the road, we get ever-bolder in our prognostications, year by not-so-far-off year. —The Editors

Blockchain for Advertising?

Let's Try to See Clearly on Blockchain for Advertising  

Blockchain is simply too slow to work for the real-time aspects of our programmatic trading world, Jaisimha Muthegere writes.

By Jaisimha Muthegere in Adage

The release of the Interactive Advertising Bureau's Blockchain for Video Advertising white paper has the digital ad industry abuzz. The emerging technology, best known for powering cryptocurrencies, is apparently coming to save us! Forget that much of the conversation last year revolved around AI as the solution to our transparency and fraud woes. Now, we see a raft of claims that it's blockchain, with decentralized power and public ledgers, that will reinvent financial systems and disrupt every industry, including advertising.

Much like the talk of AI, today's conversation around blockchain includes a good amount of hyperbole. Still, today's wishful thinking is driving tomorrow's solutions, so it's worth toning down the enthusiasm to see where the real potential of blockchain for advertising lies. .... " 

Wednesday, May 30, 2018

Microsoft now More Valuable than Google

Surprising perhaps, but the value of basic business efficiency is driving.

Microsoft is now more valuable than Google
Third most valuable company behind Apple and Amazon ..... "
By Tom Warren  @tomwarren   in TheVerge

Individually Zapping Weeds

Its even suggested that these AI driven,  weed recognizing machines are being scaled down for home use.   The snide comment about feeling for the pesticide company (really more about herbicides) should also be copied to starving people, who should also benefit.

Mind and Machine
Weed-wacking robots are a threat to plants and the pesticide industry    By Melissa Locker in Fast Company

It’s pretty hard to feel bad for the $100 billion pesticide industry, but according to Reuters, it’s feeling scared. The monster under its bed? Weed-wacking robots that use artificial intelligence to individually decimate wayward plants.

This plant-by-plant weed-killing approach is a threat to the business models of companies like Monsanto, which has invested a great deal in developing genetically modified (GM) crops. Those crops are resistant to Monsanto’s weedkiller Roundup, which can then be sprayed over entire fields, laying to waste everything but the GM plants.

Solar-powered, AI-driven robots like the one created by EcoRobotix, which Reuters describes as looking like “a table on wheels,” can roll through fields and blast individual plants, reducing the need for both GM crops and herbicides. Per Reuters, EcoRobotix claims its weed-wacking robots will decrease total herbicide use by a factor of 20. ... "

DeepLens Camera for Video Learning with AWS


"... AWS DeepLens is a new way to learn machine learning

AWS DeepLens is the world’s first deep learning enabled video camera. Designed from the ground-up for developers, it provides tangible, hands-on learning with tutorials, sample projects, and integration with familiar AWS services, to support learning and experimentation.

Whatever your skill level, you can get started with deep learning in less than 10 minutes.

Start your machine learning journey today by ordering AWS DeepLens on Amazon.com.

What will you build?  .... " 

More on the Camera.

Upcoming Webinar:

Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.
May 24, 2018 | 9:00 AM - 9:45 AM PT
Register Now

Tuesday, May 29, 2018

Ford and Smart Digital Mobility

Last week heard Ford give an excellent talk on their advances.    Especially about data gathered in cars. Autonomous cars will ultimately gather up to 4 Terabytes per hour. How will this be used, protected?

How Ford Is Thinking About the Future    By Mark W. Johnson in HBR

Everyone’s talking about a future in which vehicles are shared rather than owned, autonomous rather than driven, and where car companies make large shares of their profits on digital “mobility services.” But if you are the Ford Motor Company and face the prospect of investing billions in new technology while your century-old business model is overturned, you might first have a few questions. How are consumers going to react to all of this? What do they really want? How can you tell which opportunities are real and which are science fiction?

To help test drive the future, in 2016 Ford paid about $50 million to acquire Chariot, a startup mobility service. Incubated at Y Combinator, the venture was aimed squarely at the most important, most reliable, most consistent mobility need that consumers have every day: getting to and from work. While this seemed like a small bet for a $165 billion company built on the mass production of vehicles, the deal was scouted, in part, by Jim Hackett, then head of Ford Smart Mobility who has since been elevated to CEO. ....  " 

Why AI Will Create Jobs

Continued look at jobs vs more autonomous technologies, including AI.

Why AI Will Create Jobs  by Peter Schwartz in Strategy-Business

Even in a world of robots, continuous education can keep people employed.  

A growing number of people are worried that robots — and other machines with artificial intelligence — will imminently steal so many jobs that it will lead to a future of pervasive unemployment. But even a cursory reading of history will show that we’ve been here before.

Consider a series of headlines pulled from just one newspaper, the New York Times, as an illustration: In 1928, the Times ran an article titled “March of the Machine Makes Idle Hands.” In 1956, it announced “Workers See ‘Robot Revolution’ Depriving Them of Jobs” (for an article about labor unrest in London). In 1980, the newspaper declared “A Robot Is After Your Job.” And in December 2017, a Times op-ed headline asked, “Will Robots Take Our Children’s Jobs?” with the answer being an anxious “probably.”

Yet, at the same time, the U.S. is nearing full employment. Once again, there is a raft of warnings about lost jobs, and once again, there is reason to doubt the warnings. The real challenge engendered by AI may be a persistent shortage of skilled labor. ... " 

ACM Article Examines Bias on the Web

An interesting and considerable look at many kinds of bias and how they are ultimately combined in algorithmic bias used in 'AI'  in subtle ways.   Good starting point.   Excerpt:

Bias on the Web  By Ricardo Baeza-Yates 
Communications of the ACM, Vol. 61 No. 6, Pages 54-61

Our inherent human tendency of favoring one thing or opinion over another is reflected in every aspect of our lives, creating both latent and overt biases toward everything we see, hear, and do. Any remedy for bias must start with awareness that bias exists; for example, most mature societies raise awareness of social bias through affirmative-action programs, and, while awareness alone does not completely alleviate the problem, it helps guide us toward a solution. Bias on the Web reflects both societal and internal biases within ourselves, emerging in subtler ways. This article aims to increase awareness of the potential effects imposed on us all through bias present in Web use and content. We must thus consider and account for it in the design of Web systems that truly address people's needs. ....

The problem of bias is much more complex than I have outlined here, where I have covered only part of the problem. Indeed, the foundation involves all of our personal biases. On the contrary, many of the biases described here manifest beyond the Web ecosystem (such as in mobile devices and the Internet of Things). The table here aims to classify all the main biases against the three types of bias I mentioned earlier. We can group them in three clusters: The top one involves just algorithms; the bottom one—activity, user interaction, and self-selection—involves those that come just from people; and the middle one—data and second-order—includes those involving both. The question marks in the first line indicate that each program probably encodes the cultural and cognitive biases of their creators. One antecedent to support this claim is an interesting data-analysis experiment where 29 teams in a worldwide crowd-sourcing challenge performed a statistical analysis for a problem involving racial discrimination.3

In early 2017, US-ACM published the seven properties algorithms must fulfill to achieve transparency and accountability:1 awareness, access and redress, accountability, explanation, data provenance, auditability, and validation and testing. This article is most closely aligned with awareness. In addition, the IEEE Computer Society also in 2017 began a project to define standards in this area, and at least two new conferences on the topic were held in February 2018. My colleagues and I are also working on a website with resources on "fairness measures" related to algorithms (http://fairness-measures.org/), and there are surely other such initiatives. All of them should help us define the ethics of algorithms, particularly with respect to machine learning.

As any attempt to be unbiased might already be biased through our own cultural and cognitive biases, the first step is thus to be aware of bias. Only if Web designers and developers know its existence can they address, and if possible, correct them. Otherwise, our future could be a fictitious world based on biased perceptions from which not even diversity, novelty, or serendipity would be able to rescue us. .... " 

Monday, May 28, 2018

'Albert' Digital Autonomous Marketing

Brought to my attention, we did minor bits of this for years,  this takes it much further.  Especially with regard to being autonomous and value driven.  Formerly called Adgorithms and mentioned here.  See also the linked to HBR article in my previous post,  which also provided more background.  Considerable detail at the link:

Albert, created by Albert Technologies, LTD., is the world’s first and only fully autonomous digital marketer. The enterprise-level artificial intelligence platform drives digital marketing campaigns from start to finish for some of the world’s leading brands. Albert liberates businesses from the data and technology complexities of digital marketing—not just by replicating their existing efforts, but by executing them at a pace and scale not possible by human teams. “He” accomplishes this by wading through mass amounts of data, converting this data into insights, and autonomously acting on these insights, across channels, devices and formats, in real time. Brands such as Harley-Davidson, Gallery Furniture, Natori, and Dole Asia credit Albert with significantly increased sales, an accelerated path to revenue, the ability to make more informed investment decisions, and reduced operational costs. .... "

AI Aiding Retail

Its all about finding and leveraging the patterns from the data.

How AI Helped One Retailer Reach New Customers  By Dave Sutton in HBR

When Naomi Simson founded RedBalloon, an online gift retailer that sells personal experiences, she was pioneering the category in Australia. With a $25,000 personal investment and a small office in her home, she began aggregating sales leads and aggressively acquiring customers through very traditional marketing means — like yellow page advertisements. It was 2001, and  online advertising was at its nascent stage. Internet Explorer was the leading Internet browser and Google AdWords had only just recently launched. With a cost of customer acquisition of just 5 cents, Simson’s traditional approach to advertising was generating an impressive return on investment. RedBalloon was setting the pace for gifting experiences like outdoor adventures, wine tastings, concert tickets, and spa treatments. ... 

 .... Enter “Albert”, a digital marketing platform powered by artificial intelligence (AI). Working across Facebook, Google, YouTube and other paid and earned media channels, Albert autonomously targets audiences, mixes and matches creative assets, buys media, runs campaigns, measures performance, applies insights from one channel to another, and then makes adjustments based on what “he” learns to optimize the return on marketing investment. I met the team at Albert (formerly known as Adgorithms) while I was doing research into artificial intelligence, machine learning, and data-driven marketing technologies for my book, Marketing, Interrupted. As part of my research, I interviewed several of Albert’s customers, including Simson. .... "

The Sleeve Communicates Emotions through Touch

Sounds remarkable,  good article.  Had a long time look at how people reacted emotionally to product.

Gadget Communicates Human Emotions through Touch , by Elizabeth Stinson on Wired.

ASK ENGINEERS WHAT the future of communication looks like and they’ll show you a fiber-optic cable. Ask artists and they’ll conjure something like the Sleeve. For the past year, engineers at Nokia Bell Labs, the famed New Jersey research facility that birthed the transistor, have been developing this wearable armband with input from artistic collaborators. “We’re reductionist in our thinking; artists are divergent,” says Domhnaill Hernon. He's the head of a ­program called Experiments in Art and Technology, founded in the ’60s and newly resurrected in partnership with the design incubator New Inc. In this right-brains-meet-left coalition, engineers and artists team up to explore big questions: Can humans communicate through touch? Is it possible to transfer empathy? What’s the successor to smartphones? The Sleeve tries to answer them. This early model gathers information about the user’s physical and emotional state through gyroscopes, accelerometers, and optical sensors, then communicates that intel via haptic pulses and screen-displayed messages. The collaborators aim to inspire more engineers to consider the emotional plane. Soon you’ll be able to express your heart through your sleeve.  .... "

Are Robots Better at Jobs?

BBC Looks at

 Are intelligent robots better at our jobs?
With advances in technology robots are increasingly being used to do human jobs.

But are they actually better at doing them?

Will Gompertz goes to the exhibition exploring just that.  ..."

How Can Agile Teams Work Together

How to Make Sure Agile Teams Can Work Together   By Alia Crocker, Rob Cross, Heidi K. Gardner in the HBR

Increasing volatility, uncertainty, growing complexity, and ambiguous information (VUCA) has created a business environment in which agile collaboration is more critical than ever. Organizations need to be continually on the lookout for new market developments and competitive threats, identifying essential experts and nimbly forming and disbanding teams to help tackle those issues quickly. However, these cross-functional groups often bump up against misaligned incentives, hierarchical decision-making, and cultural rigidities, causing progress to stall or action to not be taken at all.

Consider the case of an organization in our consortium, the Connected Commons, that uncovered a ground-breaking audio/visual technology which would differentiate the organization in existing channels but also had the potential to open up entirely new markets. The CEO heralded it as a pivot point in growth and formed a cross-functional initiative of 100+ top employees to bring it to new commercial channels. Yet, unfortunately progress did not match expectations. Employees assigned to the effort struggled to make time for the work. They often did not understand the expertise or values of different functions, and advocated too aggressively for their own solutions. The group was surprised several times by the demands of external stakeholders. 

Despite this project’s visibility, critical mandate, and groundbreaking technology, the organization was ultimately hindered when it came to agile collaboration. This story is not unique..... " 

Sunday, May 27, 2018

AI Agriculture Weed Management

Been involved in agriculture and horticulture operations.  And if this works commercially,  it will be a huge breakthrough.   The AI part, distinguishing between planted crop and weeds, should be 'easy', the mechanical and navigational part, somewhat less so to deliver. Weeds by using a micro-dose of herbicide.  Why not a laser blast, perhaps not as easily controlled?  They have even suggested it can be scaled to home systems.  Consider too how it changes the $26 Billion herbicide market.

A Swiss weedkiller robot could curb our dependence on herbicides
By Dyllan Furness in DigitalTrend (includes a video of it in operation)

It takes a lot of work to keep produce aisles stocked in grocery stores — there’s the planting, tending, picking, and shipping. And even though most of these tasks rely on human labor, farms are becoming increasingly automated.

Researchers at the University of Illinois have developed a Roomba-like robot that can tend to crops autonomously. At Carnegie Mellon, they’re building a suite of A.I. and drones to take on some of agriculture’s most demanding tasks. And just last year, a team of automated machines farmed an acre and a half of barley, from planting to harvesting, without a single human setting foot on the field. ... " 

Google Working on BlockChain Related Technology

With some cautions on its use.

Google Is Working on Its Own Blockchain-Related Technology
Bloomberg    By Olga Kharif; Mark Bergen; Joe Mayes In Bloomberg

Google researchers are developing blockchain-related technology to support its cloud business and ward off competition from emerging startups. Google insiders recently noted the cloud business is a natural place for blockchain-related services. To construct its ledger, Google has explored technology from the Hyperledger consortium, but it could choose another type that may be less difficult to scale to run millions of transactions. Google's Sridhar Ramaswamy says his division has a "small team" investigating blockchain, but the current core technology cannot accommodate many transactions quickly. One possible application of blockchain technology by Google could entail reassuring its customers that their information is shielded when stored on the giant network of computer servers that power Google's cloud services. "Like many new technologies, we have individuals in various teams exploring potential uses of blockchain but it's way too early for us to speculate about any possible uses or plans," says a company spokesperson.   ...

Further Deception Detection

See other models, for example the U of MD, which also been working on this.  Of course lie detection using biometics has been around for a long time, but is still not generally accepted in US courts of law.  Will this be treated similarly?  But then if this detection is integrated with other facial recognition data and rolled into an algorithm?  Implications unclear.

Using Data Science to Tell Which of These People Is Lying    By University of Rochester

University of Rochester researchers are applying data science and an online crowdsourcing framework to read facial and verbal cues for signs of deception.

The Automated Dyadic Data Recorder framework was used to generate the largest publicly available deception dataset currently in existence. Participants sign up on Amazon Mechanical Turk to be assigned the roles of describer or interrogator. The former is displayed as an image they must memorize thoroughly, and the computer instructs them to either lie or truthfully relate the image details. The interrogator then asks the describer a set of irrelevant baseline queries to record individual behavioral differences that are fed to a "personalized model."

The researchers have culled 1.3 million frames of facial expressions from 151 pairs of individuals conducting this experiment, analyzing the information with data science. Among their findings is the detection of five types of smile-related expressions people make in response to questions, including one most frequently associated with lying.    .... "

From University of Rochester

Soft Robotics

Had looked at some examples for manufacturing line handling applications, has advanced considerably.

Soft Machines   By University of California, Santa Barbara 

University of California, Santa Barbara researchers have combined the electromagnetic drives used in most conventional robotic systems with soft materials, a development that yields robotics with both speed and softness.

The researchers built an actuator that could realize speeds greater than what have typically been possible with soft robotic actuators, which often rely on slow processes such as air flow or thermal effects. The researchers used unique liquid-metal alloy conductors encased in hollow polymer fibers and magnetized polymer composites to create patterned, three-dimensional components to form the basis of soft analogs of standard electrical motors. "We realized components that are each soft and stretchable, and combined them to create these motor-like structures that can move things," says UCSB's Yon Visell.

The researchers employed their discovery to create a tiny, millimeters-wide gripper that can close in milliseconds, and a soft tactile stimulator that can operate at frequencies of hundreds of cycles each second. .... "

From University of California, Santa Barbara

Macy's and Storytelling

Had previously mentioned how Jungle Jims did this.   Also saw at the recent UC Business Summit saw presentations about their approaches.  More to follow on that.

Macy’s latest acquisition is all about STORYtelling   by George Anderson in Retailwire

Macy’s, Inc. announced that it has acquired STORY, an experiential concept store in New York. STORY’s founder and CEO, Rachel Shechtman, will join Macy’s as the company’s brand experience officer focused on the retailer’s in-store experience.

STORY completely changes its store layout and merchandise every four to eight weeks, focusing on a new theme with each changeover. The concept, which was founded in 2011 by Ms. Shechtman, a former brand consultant for Kraft and TOMS shoes, emphasizes brand collaboration as it builds unique retail experiences for consumers. .... " 

“It’s exciting to have a national stage to leverage STORY’s learnings and relationships to create impact at scale,” said Ms. Shechtman. “I’m energized by the opportunity to further build new customer experiences across the Macy’s portfolio, while also continuing to pursue new business models and brand partnerships.” ...  '

Human Touch for Future Auto Plants

Will be looking forward to see how collaborative works in the near future and beyond. Is there any human initiative, or is it fully automated with human and machine assistance?

The auto plants of the future may have a surprisingly human touch
By Norihiko Shirouzu, Edward Taylor, Nick Carey

(Reuters) - Carmakers have big plans for their next generation of factories: smarter designs, artificial intelligence and collaborative robots building a wide range of vehicles on the same line. .... "

Saturday, May 26, 2018

Serving the B2B Buyer

Have not seen too much advice about the details of selling B2B.  Here from ThinkwithGoogle

3 insights that will help you serve today’s B2B buyer    By Sarah Travis  

The way B2B buyers research and shop has changed dramatically in the last five years. The days when buyers would simply visit their local B2B store, talk with their sales rep, or order through a catalog are disappearing. With digital at the helm, most buyers are starting their purchase journey without setting foot in a store — even if they ultimately purchase offline.

In fact, recent research shows that on average, 67% of purchases for multiple industrial manufacturing and pack-and-ship industries were influenced by digital.1 As a result, we’re seeing a new type of B2B shopping behavior emerge — buyers are sitting at the intersection of online and offline.

As marketers, we need to better understand this new behavior so we can meet people in the moments they need us most — whether that is online or offline — and, ultimately, drive customer lifetime value. Here are three insights to help you better serve today’s B2B buyer: .... " 

Data Science Views for IOT

Data Science for Internet of Things - The Big Picture in DSC  See the diagram at the link.   Posted by ajit jaokar  

This big picture view lays the foundation of our book Data Science for the Internet of Things. (Co-authored by Ajit Jaokar, Jean Jacques Bernard and Sukanya Mandal)

We address the question: at what points can we add analytics to the data after it leaves the sensor and what are the implications of doing so at various stages.

In this diagram, we present the big picture through two process flows:

Technology flow: Edge to Stream to Store
Deployment flow: Model build, deploy and refresh in production including at the edge

Data Science for IoT implementation differs from traditional Data Science in four key aspects

Edge Computing
Feature Engineering for IoT
Complex event processing
Embedded AI  ... "

Addressing Causation vs Curve Fitting

The below comes from Inference.vc   It addresses some of the comments by Judea Pearl's recent post commented on here.  Beyond the first few paragraphs it is thoughtful but very technical.

ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus 

You might have come across Judea Pearl's new book, and a related interview which was widely shared in my social bubble. In the interview, Pearl dismisses most of what we do in ML as curve fitting. While I believe that's an overstatement (conveniently ignores RL for example), it's a nice reminder that most productive debates are often triggered by controversial or outright arrogant comments. Calling machine learning alchemy was a great recent example. After reading the article, I decided to look into his famous do-calculus and the topic causal inference once again.

Again, because this happened to me semi-periodically. I first learned do-calculus in a (very unpopular but advanced) undergraduate course Bayesian networks. Since then, I have re-encountered it every 2-3 years in various contexts, but somehow it never really struck a chord. I always just thought "this stuff is difficult and/or impractical" and eventually forgot about it and moved on. I never realized how fundamental this stuff was, until now.

This time around, I think I fully grasped the significance of causal reasoning and I turned into a full-on believer. I know I'm late to the game but I almost think it's basic hygiene for people working with data and conditional probabilities to understand the basics of this toolkit, and I feel embarrassed for completely ignoring this throughout my career.

In this post I'll try to explain the basics, and convince you why you should think about this, too. If you work on deep learning, that's an even better reason to understand this. Pearl's comments may be unhelpful if interpreted as contrasting deep learning with causal inference. Rather, you should interpret it as highlighting causal inference as a huge, relatively underexplored, application of deep learning. Don't get discouraged by causal diagrams looking a lot like Bayesian networks (not a coincidence seeing they were both pioneered by Pearl) they don't compete with, they complement deep learning. .... " 


In Knowledge @ Wharton:

How Companies Can Instill Mindfulness

Mindfulness and meditation have made deep inroads into the corporate world. The benefits are proving out, notes this opinion piece by Christian Greiser and Jan-Philipp Martini of the Boston Consulting Group. Greiser is a senior partner, managing director and the global leader of the firm’s operations practice who works with senior leaders around the globe. Martini is an associate who supports clients around the world on enterprise-wide agile transformations.

Volatile markets, challenging consumer demands, and the technological disruptions resulting from digitization and Industry 4.0 are producing unprecedented rates of change. In response, companies have worked to increase organizational agility, hoping to foster innovation and shorten go-to-market cycles. Yet organizational experiences and sociological conditioning often impede true agility. As a result, many of these efforts fall short of their objective to manage the uncertainty generated by change. But another movement — mindfulness — will help companies overcome these challenges.

Mindfulness is a centuries-old idea that has been reinvented to address the challenges of our digital age. In essence, mindfulness describes a state of being present in the moment and leaving behind one’s tendency to judge. It allows one to pause amid the constant inflow of stimuli and consciously decide how to act, rather than react reflexively with ingrained behavior patterns. Mindfulness, therefore, is perfectly suited to counterbalance the digital-age challenges of information overload and constant distraction. ... " 

Gigaom Continues Voices in AI

Voices in AI – Episode 44: A Conversation with Gaurav Kataria
Byron Reese in GIgaOM

In this episode, Byron and Gaurav discuss machine learning, jobs, and security.

Byron Reese: This is Voices in AI brought to you by GigaOm. I am Byron Reese. Today our guest is Gaurav Kataria. He is the VP of Product over at Entelo. He is also a guest lecturer at Stanford. Up until last month, he was the head of data science and growth at Google Cloud. He holds a Ph.D. in computer security risk management from Carnegie Mellon University. Welcome to the show Gaurav!

Gaurav Kataria: Hi Byron, thank you for inviting me. This is wonderful. I really appreciate being on your show and having this opportunity to talk to your listeners.

So let’s start with definitions. What is artificial intelligence?

Artificial intelligence, as the word suggests, starts with artificial and at this stage, we are in this mode of creating an impression of intelligence, and that’s why we call it artificial. What artificial intelligence does is it learns from the past patterns. So, you keep showing the patterns to the machine, to a computer, and then it will start to understand those patterns, and it can say every time this happens I need to switch off the light, every time this happens I need to open the door, and things of this nature. So you can train the machine to spark these patterns and then take action based on those patterns. A lot of it is right now being talked about in the context of self-driving cars. When you’re developing an artificial intelligence technology, you need a lot of training towards that technology so that it can learn the patterns in a very diverse and broad set of circumstances to create a more complete picture of what to expect in the future and then whenever it sees that same pattern in the future, it knows from its past what to do, and it will do that in the future. .... " 

Friday, May 25, 2018

Learn Like a Child

Learn the way a child does?   Tabula Rasa?  A very old idea that has not seen much success over decades.   Is this a new hybrid neural approach?

How Researchers Are Teaching AI to Learn Like a Child    In Science Mag

Researchers in machine learning argue that computers trained on mountains of data can learn just about anything—including common sense—with few, if any, programmed rules. These experts "have a blind spot, in my opinion," says Gary Marcus, a developmental cognitive scientist at New York University. He says computer scientists are ignoring decades of work in the cognitive sciences and developmental psychology showing that humans have innate abilities—programmed instincts that appear at birth or in early childhood—that help us think abstractly and flexibly. He believes AI researchers ought to include such instincts in their programs.

Yet many computer scientists, riding high on the successes of machine learning, are eagerly exploring the limits of what a naïve AI can do. "Most machine learning people, I think, have a methodological bias against putting in large amounts of background knowledge because in some sense we view that as a failure," says Thomas Dietterich, a computer scientist at Oregon State University in Corvallis. He adds that computer scientists also appreciate simplicity and have an aversion to debugging complex code.

In the longer term, computer scientists expect AIs to take on tougher tasks that require flexibility and common sense. Some computer scientists are already trying.  .... " 

From Science with very good embedded Video.

Convincing Customers to Share Data After GDPR

Good look at the general idea of sharing under GDPR

How to Convince Customers to Share Data After GDPR  By Einat Weiss in the HBR

In recent years, marketers have lived through the Era of Big Data, and the Era of Personalization, and now we are living through the “Era of Consent.” With the General Data Protection Regulation (GDPR) going into effect on May 25th, businesses will be required to protect the personal data and privacy of EU citizens. For marketers, this means updating your privacy policies, but more importantly, it means finding innovative new ways to connect with customers and gather consent to use their data in order to continue your “marketing relationship” with them.

Marketers across the European Union (EU) have been preparing for this new regulation for months. Yet the regulation impacts all companies globally, including those in the United States, that collect and manage data on citizens in the EU. Many global marketers are still struggling to understand what steps they need to take to prepare for this regulation and how it will impact their marketing strategy moving forward.

Regardless of whether you are collecting and managing data on EU citizens now or plan to in the future, here are some tips for surviving this new Era of Consent: ...... "

GDPR is Here

Been following for some time,  made adjustments to my blog.  Blog visits are up rather than down so far.  Thought more from Asia.  Here is a quick overview from Technology Review.   Click through to get more information.  What will the ultimate consequences be?

 GDPR is finally here and it’s already chaos  in Technology Review

The General Data Protection Regulation, or GDPR, goes into effect today, threatening huge fines for businesses that abuse Europeans’ data.

The dos: From now on, companies everywhere must:
get EU citizens’ consent to collect their personal data and explain what it will be used for, let them see, correct, and delete it upon request,  make it easy for users to shift their data to other firms

The don’ts: Companies must not ignore regulators’ requests to fix GDPR failings, nor take more than 72 hours to report a security breach involving personal data. Many still aren’t fully ready for the new regime. .... "

Alexa adds Better Smart Meetings

Probably essential if they hope to have a credible  Alexa for business.  Will be testing. Most of my issues were regarding the voice to text, rather than the appointment entering.

Alexa gets smart meeting scheduling, a boost to its workplace presence
Amazon says the new Smart Scheduling Assistant feature allows the popular virtual assistant to cut the time needed to manage personal schedules in the office. .... " 
By Matthew Finnegan in Computerworld

via Walter Riker.

Saving Lives with Swarms of Drones

Self assembling and autonomous drones for safety and rescue platforms.

Saving Lives with Aerial Drones in CACM

Researchers at the University of Pennsylvania General Robotics, Automation, Sensing & Perception Laboratory have created a system of aerial drones that can connect to form rigid structures for emergency rescues. .... " 

Chatbots for Setting up and Maintaining Meetings

For some time have been using assistants on Siri, Alexa and Google to manage meetings.   And several times its been suggested that this kind of very common activity should be done by a meeting chatbot.  I agreed, but quickly found it was far less than trivial.  Sounds simple, but the complexity assistant logic among multiple human and machine agents is hard.    Which further suggests that further extending other multi agent intelligent conversations are not easy either.

Read this article for a good intro:

However trivial it may sound, creating an AI program to successfully schedule meetings is a monstrously difficult challenge. But the employees of X.ai are some of the most dedicated nerds you’ll ever meet. 

Author:  John H. Richardson in Wired. 
AI Chatbots try to schedule Meetings without Enraging us.

McKinsey AI Frontier: 400 Use Cases

Mostly non-technical:

McKinsey: Notes from the AI frontier: Applications and value of deep learning

An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques. ... " 

Thursday, May 24, 2018

Blockchains Talking to Each Other

For backups, and for other housekeeping and updating functions?  For assembling contracts among component pieces?  Smart Contracts are mentioned as a form of Blockchain architecture being enabled.

How to get blockchains to talk to each other
If blockchains are really going to give us the internet of money, they’ll need to work together.
by Mike Orcutt in Technology Review ... 

".... Hardjono and two colleagues at MIT argue in a new paper (PDF) that today’s blockchain developers should borrow a concept from the internet protocol suite called the datagram, which is a common unit of information that can move across different networks. “Every network that sees it knows how to parse it and knows how to forward it,” Hardjono says. “What is the datagram equivalent for blockchain systems?” .... " 

When to Hold Em, When to Compete

Nash Equilibrium's use in Competitive Situations is re-examined, with hope for its use in competitive behavior situations.  We looked at it for that and found no golden egg, but it doesn't mean others couldn't find it.   A reexamination.   Complexity technical.  Just because optimum solutions are known to be probably impossibly hard, very good solutions are probably better than what we are doing today.

When to Hold 'Em    By CACM Staff 
Communications of the ACM, Vol. 61 No. 6, Pages 6-7

Neil Savage deserves praise for his informative overview of recent computational results related to Nash equilibrium in his news story "Always Out of Balance" (Apr. 2018). I fully agree that the notion of Nash equilibrium does not always reflect how competitors behave in competitive situations, and that the fact that Nash equilibrium is provably computationally intractable makes it less useful than John Nash himself might have envisioned when he developed it. However, Savage also overstated (somewhat) the effect of intractability by claiming the intractability of computing Nash equilibrium necessitates researchers abandon this notion in favor of other competition-related ideas.

While looking for Nash equilibrium yields additional computational complexity, the decision-making problem is, in general, already computationally intractable (NP-hard) for non-competitive situations (such as when a company makes internal planning decisions). In doing so, a company would be looking for an optimal solution (such as one that would aim to help produce maximum profit), but computational optimization is, in general, NP-hard. Such computational intractability does not mean researchers have to abandon the idea of optimization and look for other ideas. Many real-life problems are NP-hard (such as robotic movement) and what makes working on them such an intellectual and computational challenge.

Indeed, there is no general feasible algorithm (unless P = NP), so computer scientists need to be creative when designing algorithms for specific practical problems.  .... " 

Vladik Kreinovich, EL Paso, TX, USA

Dysfunctional Relationship with AI

Nice thoughts on  our relationship with Analytics and ultimately AI.  But its not new, its been changing since the industrial revolution.  The details are just getting more and more buried into the complexity.

 It’s Me, Not You – Our Dysfunctional Artificial Intelligence Relationship   By Michele Goetz, Principal Analyst  Forrester

We have a tendency to blame technology when things go wrong. I’m the first to admit that after years of working in the technology industry I’ve become more and more annoyed with the technology I use. As artificial intelligence (AI) capabilities have emerged in my smart phone keeping me on schedule, telling me how to get somewhere, or generally keeping me in line, I’ve gotten conditioned to technology just working.  Except when it doesn’t. That’s when I want to throw that phone, espresso machine, laptop or home security pad into a blender. (Yes, it was a rough morning.)

AI pioneers have provided us with a glimpse of and conditioned us to ambient AI making it hard to break up with each other. They have also set a very high bar on our expectations of what AI should do for our businesses. But, let’s understand, Google was able to do this after two decades of research, curating collections and observing our every move. Apple too has tracked our app usage, music preferences, and daily lives through its iCloud. And Facebook sees our public and private conversations, what we share, and our personal opinions. Creepy, yes, but that is another conversation.

The point is that enterprises embarking on AI need to radically shift their approach to technology adoption and analytics. This is not a plug and play and bolt on strategy. It takes work to go from POC to a capability that comes close to our expectations of AI based on our consumer experience.  .... "

Contract Guardian Acquired

A company to look at to understand the value basis of simple agreements, and where this can be taken beyond for much more value.   Note the inclusion of risk assessments, a good start beyond simple contract expirations and renewals.  Check them out.

UCG Technologies Launches into Healthcare with Acquisition of Contract Management SaaS Provider Contract Guardian, Inc.

INDEPENDENCE, Ohio, May 24, 2018 /PRNewswire/ -- UCG Technologies (UCG), a global information technology services firm, has acquired Cincinnati-based Contract Guardian, Inc. a leader in automated contract management.

Contract Guardian provides a software as a service solution that enables healthcare and cross-industry organizations to manage large numbers of contracts. The system enables clients to easily store/retrieve contracts and verify that they meet regulatory guidelines, manage signatures, workflows, automatically track expirations, review contracts, and conduct risk assessments.

The regulatory requirements healthcare technology solutions must satisfy are some of the most demanding when compared with other industries. UCG brings a level of expertise in endpoint security, backup/disaster recovery, and cloud infrastructure that will serve as a point of differentiation from other contract management solution providers. Jim Kandrac, President of UCG, comments, "The foundation of our business is built on data protection. We recognize the importance of this in healthcare and are looking forward to leveraging our experience for healthcare and cross-industry clients."

The acquisition also provides significant expansion opportunity for UCG into the healthcare market, as healthcare comprises the majority of Contract Guardian's client base.  UCG Regional Director Matt Paterini, PharmD brings his healthcare experience to the company and comments, "After working in health system administration, I have seen many contract management challenges firsthand. It is a unique advantage to be able to provide an offering that will solve regulatory challenges and provide cost efficiencies for healthcare organizations."  ... "

Automation Requires Lifelong Learning

Agreed, most important will the kind of learning and training that will be required, which will also be a dynamically changing target.    Our own look at tasks that comprised jobs, and the continual redefinition of jobs, made that clear.

Automation Will Make Lifelong Learning a Necessary Part of Work
By Jacques Bughin, Susan Lund, Eric Hazan  in HBR ... ' 

Business Intelligence vs Operations Intelligence

Had heard the Operational Intelligence term, but was not often mentioned in the enterprise.   This piece defines both methods and how they interact.   OI could be a place to start when you are trying to define and model specific business process.    But more detail of the kind in BPM can be very useful, and get you to more detail you can improve.

In iiAnalytics Blog:

BI versus OI, A Distinction with very big difference    By Geoffrey Moore

As a reader of this blog, you are likely quite familiar with BI (Business Intelligence). It has been a foundational element of enterprise computing for over thirty years, the mainstay of iconic companies like SAS, Cognos (now IBM), and BusinessObjects (now SAP). And I expect you may also have heard of OI (Operational Intelligence), but I am willing to bet you do not have a clear sense of what precisely that latter term refers to.

Looking up Operational Intelligence in Wikipedia does not help much. The definition there blurs the distinction between BI and OI by combining attributes from each. I have reprinted it below with what I consider to be the BI attributes in blue bold and all the OI ones in red italics:

It is not that this definition is wrong. It is just that it suppresses the differences between OI and BI, differences that are key for enterprise executives to understand. I think we would all be better served, therefore, if we first began by defining Operational Intelligence in direct contrast to Business intelligence, along the following lines:

Consider the Bento Browser

Makes much sense if you do organize the information well.

Bento Browser Makes It Easier To Search On Mobile Devices
By Carnegie Mellon University, original article.

Carnegie Mellon University (CMU) researchers have developed a new Web browser that brings order to complex searches in a way not possible with conventional tabbed browsing.

The Bento browser stores each search session as a project workspace that monitors the most interest or relevant parts of visited web pages, allowing users to move from site to site without having to keep every tab open for fear of losing information. With the Bento browser, these projects are stored for later use, can be handed off to others, or can be moved to different devices, according to CMU professor Aniket Kittur.

In user studies that compared Bento with the Safari browser, users said Bento kept their search better organized.

The researchers reported on the new browser at CHI 2018, the Conference on Human Factors in Computing Systems, last week in Montreal, Canada.   .... "

Summary of AI in Retail

Been asked to look at this, here is a start.  And the continued moves in the smarter home, and as assistants.

Artificial Intelligence (AI) in Retail Market to hit $8bn by 2024    Posted by Sagar  in DSC.

Artificial Intelligence (AI) in Retail Market size is set to exceed USD 8 billion by 2024; according to a new research report by Global Market Insights, Inc. 

The AI in retail market is driven by the increasing investments in it across the globe. The growing investment in the technology is attributed to the wide applications of the AI technology along with advanced analytics, machine learning. AI is set to unleash the next phase of the digital disruption and the market participants are preparing themselves for it. The investment in the technology is growing rapidly, dominated by the tech giants such as Google, Microsoft, IBM, AWS, and Baidu. In 2016, approximately USD 30 billion investment in the technology has been witnessed, with more than 90% on the R&D activities and remaining 10% on the merger & acquisition activities. Furthermore, private equity financing, seed investment, and venture capital investment also grew significantly amounting to a cumulative total of over USD 6 billion.  .... " 

Wednesday, May 23, 2018

Samsung Wants AI Features in all of its Devices

I was was recently shopping for new home appliances, and paid attention to what the major players had in terms of integrated 'smart' capabilities.   I noted that Samsung was mentioning their Bixby assistant language in their sales material.    I asked the sales people, but they knew nothing about it, only that it would come sometime.  Were also unclear if what I would buy could be upgraded later.  Still Samsung is making some strong statements on their future.  Further what AI will mean then is still unclear.

Samsung wants AI features in all its devices by 2020
Let's just hope Bixby is working well by then.
Jon Fingas, @jonfingas in Engadget ... "

(Update) Data Science and Machine Learning for Healthcare

Cognitive Systems Institute Talk  

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:     This talk will highlight the opportunities of AI in healthcare and how current advancements in the field are improving patient outcomes. By focusing on the use of electronic health records (EHRs), I will provide practical data preparation tips and summarize important considerations to keep in mind when using EHRs. Next, I will compare machine learning methods to Early Warning Scores, or the traditional predictors of acutely ill patients that are currently being used in hospitals. Finally, the talk will conclude with the limitations of translating research into clinical settings. 

Bio: Farah Shamout is a PhD student in Engineering Science in the Computational Health Informatics Laboratory at the University of Oxford. Shamout gained her BSc in Computer Engineering at New York University Abu Dhabi, before coming to Oxford as a Rhodes Scholar in 2016. Her DPhil focuses on machine learning systems developed using the HAVEN project, and aims to produce a hospital-wide alerting system to assess patients continuously based on machine learning methods and large-scale data acquisition from the Oxford University Hospitals NHS Foundation Trust and Portsmouth NHS Foundation Trust. Her research touches on both Bayesian nonparametrics and deep learning methods. 
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.

Benchmark Suite for Assessing Machine Learning

Ultimately key to making this work.    Measuring the results, starting with benchmarks.

How to Evaluate Machine Learning?  U of Toronto Research Supports Latest Benchmark Initiative 
U of Toronto News   By Nina Haikara

An industrial-academic consortium that includes Google, the University of Toronto (U of T) in Canada, and Harvard and Stanford universities is developing a new benchmark suite for assessing machine learning (ML) performance. U of T's Gennady Pekhimenko says the MLPerf consortium is investigating two benchmarking areas--an "open" category in which any model can be applied to a fixed dataset, and a "closed" category in which both model and datasets are fixed, making execution time, power requirements, and design-cost evaluations helpful. Pekhimenko notes his laboratory has developed an open source benchmark suite called TBD (To Be Determined) as a training benchmark for deep neural networks. "We're interested in understanding how well available hardware and software perform, but we also look at both hardware and software efficiency," he says. "We then provide hints to the ML developers, so they can make their networks more efficient, and hence develop new algorithms and insights faster." .... ' 

Unilever and WPP Collaborate for Disruptive Tech

Unilever and WPP Launch Collaboration in Singapore

SINGAPORE – Unilever and WPP have established an in-house collaboration with Unilever Foundry and its start-up community.

The partnership, which involves a flexible new Team Unilever model along with specialist input from WPP, is designed to meet the FMCG giant’s marketing needs in a period of technological change and drive innovation. ...

Peter Dart, WPP’s global team leader for Unilever, added: “We wanted to co-create a structure that allows us to work more closely with Unilever and focus on all the various services along the consumer journey that can help Unilever and its brands grow. The key is agility and the enhancing ability to respond to disruption in the consumer products market as well as in marketing services offerings.” ... " 

Authentic Emotional Intelligence

Can they be separated?  Can deep learning be yet more precise in making a distinction?   In some early work with MIT Media lab we tried to detect emotion and engagement when people interacted with product.   Can artificial neurons now do it the same way?

Is Your Emotional Intelligence Authentic, or Self-Serving?  By Ron Carucci in the HBR

It’s possible to fake emotional intelligence. Similar to knockoffs of luxury watches or handbags, there are emotions and actions that look like the real thing but really aren’t. With the best of intentions, I’ve seen smart leaders charge into sensitive interactions armed with what they believed was a combination of deep empathy, attuned listening, and self-awareness but was, in fact, a way to serve their own emotional needs. It’s important to learn to spot these forgeries, especially if you’re the forger. ..... "

Ahold Delhaize Focusing Digital Efforts

Have been impressed wth AHold efforts over the years, now with a lab for Digital:

Ahold Delhaize focuses its digital efforts
By Rachel England, @rachel_england   in Progressive Grocer

Ahold Delhaize is launching a new department in the US called Peapod Digital Labs to drive its e-commerce efforts, ramp up digital sales and hone in on its personalization efforts. The company named JJ Fleeman president and chief e-commerce officer of the new arm.  ... "

Counterfeit Goods Detector

We worked on the detection of counterfeit goods for some time, this would have been very useful. Will take a deeper look.

IBM built a handheld counterfeit goods detector
The AI tricorder knows those aren't official Yeezys.

By Rachel England, @rachel_england in Engadget

Just a month after IBM announced it's leveraging the blockchain to guarantee the provenance of diamonds, the company has revealed new AI-based technology that aims to tackle the issue of counterfeiting -- a problem that costs $1.2 trillion globally. IBM Crypto Anchor Verifier brings together AI and optical imaging to help prove the identity and authenticity of frequently forged goods such as fine wine, diamonds and medicine, as well as analyze water quality and detect bacteria, such as E.coli. And the technology is small enough to use with a cell phone camera. ... " 

Walmart Pauses on Scan & Go

Been following for a while.   Tested many checkout options.  Seems to work better at Sam than Wal-Mart.

Walmart drops Scan & Go tech – again      by George Anderson in Retailwire

Walmart’s self-checkout Scan & Go technology has been a hit with the company’s Sam’s Club members. The same cannot be said for customers at the retailer’s namesake stores. Walmart announced it has ended a test of the mobile technology in its stores.

“We’re testing things all across the country at different stores and it’s about what works best for the customer,” Ragan Dickens, a Walmart spokesperson, told the Northwest Arkansas Democrat Gazette. “We want the whole checkout process to be that seamless process. So, if there’s points in the process that are not quite there yet on the seamless front, we take those learnings and we’re plugging them into other areas of the store.” ... "

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

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).  ... '