Architecture for Talking Buildings

Smart homes are still built up in fairly unstructured ways.   Mine still has pieces of it fail, and can be difficult to repair or add to if the supplier pieces are not well designed.    Several of those things have struck me recently.   Here,  what seems to be  directionally good:

The Era of Talking Buildings Has Arrived 
UNSW Newsroom
By Wilson Da Silva

The University of New South Wales, Sydney (UNSW Sydney) and WBS Technology in Australia partnered on the development of reactive and remotely operated smart building ecosystems. The resulting EMIoT wireless platform utilizes light-emitting diode exit signs to run a low-power meshed network that covers 99.9% of a building; each sign or emergency light is a network node, routing data across the building. Connecting other devices to the network facilitates remote control and monitoring. EMIoT came about from UNSW Sydney researchers' effort to integrate different communications technologies to function seamlessly and support a reliable network across myriad locations. The platform combines wireless sensors for healthcare monitoring, an Internet protocol for small devices, and an experimental network protocol for point-to-point communications; to this was added a gateway bridging the different technologies with cellular telecommunications networks, while Bluetooth provides localized smartphone control.  .... "

Microlearning and the Brain

Interesting idea for thinking about how to classify useful learning.

Microlearning and the Brain
Microlearning is effective for hard skills but detrimental when it comes to people and emotional skills.    by Todd Maddox in Clomedia

Microlearning abounds in the learning and development sector. However, there is confusion around the term’s use, and many incorrectly identify it as simply “short duration training.”

Microlearning is more accurately defined as:

An approach to learning that conveys information about a single, specific idea in a compact and focused manner.

A learning technique that operates within the learner’s working memory capacity and attention span, providing just enough information to allow the learner to achieve a specific, actionable objective.
For example, if a personnel manager was interested in obtaining information about unconscious bias, they might watch a brief piece of video content focused on the definition of unconscious bias and how it can affect leadership behaviors in the workplace. The information would be presented in two to three minutes and would convey a single idea with as few “extras” as possible. The short duration, singular focus and limited extras ensure the learner’s attention span and working memory capacity are not exceeded.

The overwhelming majority of L&D vendors market microlearning as a major component of their offering, as well they should.

Microlearning offers an ideal approach for engaging the cognitive skills learning system in the brain. The cognitive skills learning system is one of at least three learning systems in the brain that includes the emotional learning system and the behavioral skills learning system. A schematic of these three systems, along with the relevant brain structures, is displayed below.

The cognitive skills learning system relies on the prefrontal cortex, is limited by working memory and attentional processes, and is the primary system in the brain for learning hard skills. Combine microlearning with testing and targeted retraining that is spaced over time and you have a tool that speeds the transition from short-term memory in the prefrontal cortex to long-term memory in the hippocampus and fights against the brain’s natural tendency to forget. This allows you to train hard skills for retention. ... " 

Nature of Human Trust in Machines

This topic came up in a recent discussion of AI.  Past evidence had said that in certain contexts people trust AI better than humans,  simplistically because the machines have no ulterior human motives.   But it came up that human goals could also be installed into them by humans.  I like the idea of classifying trust, had not seen that before.   Not also the inclusion of sensors,  how, why and when do we trust sensors?   And how does the inclusion of collaboration change the dynamic of trust?

New Models Sense Human Trust in Smart Machines 
Purdue University News

Purdue University researchers are using new "classification models" to assess the extent of humans' trust in intelligent collaborative machines. Purdue's Neera Jain and Tahira Reid created two types of "classifier-based empirical trust sensor models," which use electroencephalography (EEG) and galvanic skin response to gauge levels of trust. Forty-five research subjects wore wireless EEG headsets and a device on one hand to measure these factors. A "general trust sensor model" used the same set of psychophysiological features for all subjects, while the other model was tailored for each participant; the models had respective mean accuracies of 71.22% and 78.55%. Said Jain, “A first step toward designing intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time.” ... " 

Radical New Neural Network Design

Less definition of the architecture it seems and training adapts to results.

Radical New Neural Network Design Could Overcome Big Challenges in AI   in Technology Review

By Karen Hao     December 12, 2018

Researchers at the University of Toronto and the Vector Institute in Canada have redesigned neural networks without traditional stacked layers of simple computational nodes that work together to find patterns in data; the new design replaces the layers with calculus equations. The researchers, who dubbed this new design an ordinary differential equations (ODE) solver, said it can model continuous change, and changes certain aspects of training for neural networks. In a traditional neural network, the user has to specify the number of layers at the start of the training, then wait until training is done to find out how accurate the model is. The new method lets the user specify their desired accuracy first, and the network then will find the most efficient way to train itself within that margin of error. ... " 

Learn Chinese from a Smart Speaker

Have been experimenting with smart speakers to learn, teach and improve conversational language skills.

Here is an example from Indiegogo.   Note this is still in funding stages, with the usual warnings about it ever being a viable product.  The descriptions and goals are interesting though. 

Lily: The 1st smart speaker that teaches Chinese
Learn Chinese the natural way. No books, no vocabulary lists, no flash cards. Just speak. ... "

Google to Stop Selling Facial Recognition for Now

Hmm, most everyone else will be selling face recognition products.  Can they also be also be 'having no potential for evil'?    Other technical topics of this sort continue to pop up, the ethical conundrums continue to expand.

Google says it won’t sell face recognition for now—but it will be hard to slow its use
Google will stop offering face recognition through its cloud APIs—that is, until it can come up with policies to  prevent misuses of the technology, it said in a blog post today.   ... "  In Technology Review 

Mastering Conversations

In the Google Blog, some useful thoughts on conversations.  We need to think about how we talk to machines, and with people.   

How the Google Assistant masters conversations—and you can, too, this holiday season

Heading home for the holidays? Here’s hoping it’ll be a joyous reunion with friends and family, with plenty of cookies to go around. But if you’ve already been dreading those questions from your great-aunt about your love life, here’s how we can all take a page out of Assistant’s design playbook to help with our communication skills.

I’m on the Assistant’s conversational design team, where we work to make your chats with the Google Assistant as pleasant as possible. I’ve been teaching computers how to talk for nearly 20 years, starting my career working on some of those automated phone systems you’ve probably dealt with when you lost a suitcase at the airport. (In my case on a recent trip to Norway, it took 10 of those phone calls to find that lost bag!)

In my years in the industry, I’ve learned a thing or two about how to make conversations work. And so has the Google Assistant.

Give just the right amount of information.

We’ve all had that one relative who keeps droning on about a boring topic at the dinner table, oblivious to the fact that half the room has dozed off. And sometimes we experience the opposite problem, where we ask someone a question and they don’t provide enough information. Like when I ask my son what time it is, and he responds, “Yes.”  ... " 

Sensing in Complete Darkness: One Photon Per Pixel

Appears to be a very valuable development in a number of sensing domains.

MIT engineers have found a way to use AI to reveal objects in total darkness–even transparent materials like glass and tissue.   In Fastcompany    By Jesus Diazi

Current AI-based “night mode” technology, like the software you’ll find on Google’s Pixel 3, can make nighttime photos remarkably clear by quickly capturing multiple noisy photos and using AI to combine them into a single, noiseless photo. But unlike those techniques that require a lot of light to begin with, MITs method can work in a completely sealed room. In fact, it only requires one photon per pixel.

It’s a breakthrough for imaging, and it could have implications for a broad range of industries. It could reveal invisible details in deep space photography and let doctors see living tissue inside of patients without damaging their cells. As the authors of the research paper, published this week in Physical Review Letters, explain: “When it comes to X-ray imaging, if you expose a patient to X-rays, you increase the danger they may get cancer. What we’re doing here is, you can get the same image quality, but with a lower exposure to the patient.” Coauthor and professor of mechanical engineering George Barbastathis told MIT News, “In biology, you can reduce the damage to biological specimens when you want to sample them.”  ... " 

Technical details in MIT News.

Amazon and Medical Records

Amazon is Showing Healthcare is the Next Big Thing for Machine Learning

Amazon will save Healthcare industry $Billions via machine learning algorithm that extracts key data from patient records — it’s an EMR revolution by AI.
Michael K. Spencer

Amazon has had a health innovation stealth unit called 1492 for quite some time. We’re slowly starting to understand how sweeping its changes are going to be.

Recently we learned how Amazon will reportedly sell software that reads medical records. With ballooning healthcare costs anticipated in the next two decades globally, AI at the services of healthcare will be extremely important. It appears Amazon’s newest service uses machine learning to extract medical data from patient records. .... "

Blockchain Disruptive force for Car Connects?

Despite all the questioning press, large tech and automotive companies are still studying how to apply Blockchain for key applications, like trusted interaction. ...

IBM Study: Blockchain Brings Trust to How Companies, Consumers and Cars Connect
PR Newswire

IBM Corporation logo. ARMONK, N.Y., Dec. 12, 2018 /PRNewswire/ -- A new IBM (NYSE: IBM) study estimates that 62 percent of automotive executives believe blockchain will be a disruptive force in the auto industry by 2021. However, the research also finds that only a small percentage of OEMs and suppliers are currently ready for blockchain or have a greater perception that blockchain solutions that are ready for commercial use. The new study, "Daring to be first, How auto pioneers are taking the Read the full story  

Tuya Introduces Family level security for Smart Homes

Via RFID Journal, have experimented  with their IOT offerings.

Tuya Smart Intros Smart-Home IOT Security System Using Facial Recognition 

Tuya Smart, which produces the AIoT (AI+IoT) platform, has unveiled a smart-home security system that uses facial-recognition technology, rather than a camera-only system. Leveraging artificial intelligence (AI) and facial recognition, the system can identify each member of a family via a photograph, then integrate security into the home and respond to a variety of scenarios.

For example, if the facial-recognition platform detects a stranger lurking around the home, it can automatically turn on smart lights and music to deter the intruder. Tuya Smart has been working on facial-recognition technology for three years, training its algorithms so they can be put into products made by any smart-home technology manufacturer. .... " 

Alexa Patents a form of Healthcare Assistance?

Only a patent, so its hard to say where this might go.  Look at the link for more details.   Implying a diagnosis and  prescribing a remedy seems a dangerous direction.  But looking at it more broadly and then curating a shopping list for OTC remedies? How different is that from advertisement?  With the potential for having a human health practitioner brought into the loop?

Alexa, Does This Cough Sound Bad?
 Jeff Becker, Senior Analyst  Forrester

Alexa Enters Healthcare

Amazon was recently issued a new patent that will push its Alexa personal assistant into the health care realm. The patent, filed October 9th, describes a relatively straight-forward idea: program Alexa’s speech recognition to identify coughs and sniffles, then use that information to upsell cold medicine and chicken soup. The idea is simple, and its execution should be easy to deploy and scale. Amazon already has an army of devices quietly listening in our homes, and soon to be cars. According to the new patent, the next Alexa update could bring statements like “I hear you have a sore throat, are you interested in buying cough medicine?”   ..... ' 

State of US Autonomous Trucking

From McKinsey.     Have now seen two tests of the concept.  Impressive results. Yet regulation has not caught up. 

Distraction or disruption? Autonomous trucks gain ground in US logistics

As logistics goes digital, profound changes are coming to industry structure, operations, and profits. In the first of a series, we examine the impact of autonomous trucks. ... "

Goal Setting, Nudges and Machine Learning

Interesting study-based results.   Now should the same approach be used for training and goal setting of machine learning?   Not just the ML of current systems, but  machine 'learning' in general.   It would seem that the 'nudge' theory would seem to say its better to set many, easier goals, than big jumps.  Why not here?   Is it because human feedback is different?  A fundamental question, I think.  Especially if the feedback can be more conversational that one-step.

Why You Should Stop Setting Easy Goals
Amitava Chattopadhyay, Antonios Stamatogiannakis, Dipankar Chakravarti in the HBR

When setting team goals, many managers feel that they must maintain a tricky balance between setting targets high enough to achieve impressive results and setting them low enough to keep the troops happy. But the assumption that employees are more likely to welcome lower goals doesn’t stand up to scrutiny. In fact, our research indicates that in some situations people perceive higher goals as easier to attain than lower ones — and even when that’s not the case, they still can find those more challenging goals more appealing.

In a series of studies we describe in our latest paper, we tested how people perceive goals by asking participants on Amazon’s crowdsourcing marketplace, known as Mechanical Turk, to rate the difficulty and appeal of targets set at various levels and across spheres from sports performance and GPA to weight loss and personal savings. We asked about both “status quo” goals, in which the target remained set at a baseline level similar to recent performance, and “improvement goals” in which the target was set higher than the baseline by varying degrees. .... "

Deep Learning vs Reinforcement Learning

Short excerpt, worth reading the whole thing.  But instructive as it is:

Artificial Intelligence: What's The Difference Between Deep Learning And Reinforcement Learning?
 By Bernard Marr in Forbes

" ... What is deep learning?

" Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data. For example, you might train a deep learning algorithm to recognize cats on a photograph. You would do that by feeding it millions of images that either contains cats or not. The program will then establish patterns by classifying and clustering the image data (e.g. edges, shapes, colors, distances between the shapes, etc.). Those patterns will then inform a predictive model that is able to look at a new set of images and predict whether they contain cats or not, based on the model it has created using the training data.

Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. This allows the algorithm to perform various cycles to narrow down patterns and improve the predictions with each cycle.

A great example of deep learning in practice is Apple’s Face ID. When setting up your phone you train the algorithm by scanning your face. Each time you log on using e.g. Face ID, the TrueDepth camera captures thousands of data points which create a depth map of your face and the phone’s inbuilt neural engine will perform the analysis to predict whether it is you or not.

What is reinforcement learning?

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes. This is similar to how we learn things like riding a bike where in the beginning we fall off a lot and make too heavy and often erratic moves, but over time we use the feedback of what worked and what didn’t to fine-tune our actions and learn how to ride a bike. The same is true when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.

A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action.  ... " 

AI Booming, Accelerating

TheVerge reports and comments on the boom.  No indication of a winter here.  Notable also is the broad world-wide boom, in the past expansion it was US, Japan and Europe.  Some issue with the definition, is this automation, machine learning or digitization, and what mixture?  But clearly we have to be ready.

The AI boom is happening all over the world, and it’s accelerating quickly
The second annual AI Index report pulls together data and expert findings on the field’s progress and acceleration  By Nick Statt@nickstatt in the Verge   .... "

Predictions Cheaper, Faster, with Risk

In Strategy+Business.  Provocative thoughts of what AI is.  It is a prediction.  But also the prediction of a number of other (meta) things:  The future, some solution in a particular context, some precision ....   And since you have a forecast at a level of accuracy.  It means you need to carefully consider the accuracy of the risk of getting it wrong.   We are seeing hints of it ... considering Bias, unintended consequences, transparency ...

When Prediction Gets Cheap
In their new book, a trio of Rotman School professors demystify artificial intelligence for business leaders.

By Theodore Kinni 

sb-2015-strategy-business-logo.pngstrategy+business: Corporate Strategies and News Articles on Global Business, Management, Competition and Marketing

Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Harvard Business Review Press, 2018

I don’t usually write mash notes, but I recently sent one to Waze via Twitter. I figured the navigation app had helped me avoid more than 100 hours of traffic jams over a couple of years, and I felt compelled to declare my undying gratitude.

After reading Prediction Machines, by three Rotman School of Management professors, it turns out I’m not so much enamored with Waze as I am with the technology that powers it: artificial intelligence (AI). It is AI that enables the app to predict the best routes for its users.

According to Ajay Agrawal, Joshua Gans, and Avi Goldfarb, who are also, respectively, founder, chief economist, and chief data scientist of the Creative Destruction Lab, prediction is the essential output of AI. “The current generation of AI provides the tools for prediction and little else,” they write. “Today, AI tools predict the intention of speech (Amazon’s Echo), predict command context (Apple’s Siri), predict what you want to buy (Amazon’s recommendations), predict which links will connect you to the information you want to find (Google search), predict when to apply the brakes to avoid danger (Tesla’s Autopilot), and predict the news you will want to read (Facebook’s newsfeed).”

This is the key insight of Prediction Machines, and it is an extraordinarily useful one for any executive who has been grappling with the implications and ramifications of AI. AI will automate prediction, and as a result, prediction will become cheap. “Therefore, as economics tells us,” explain the authors, “not only are we going to start using a lot more of it, but we are going to see it emerge in surprising new places.” ...  "

Python Tricks

Beginner Python Coding tricks.  Whenever you start a new language, its nice to see lots of relatively simple examples that relate to the kinds of things you might want to do.  This is kind of like that, for beginners, but has a few things that even the expert can appreciate.   Also useful for an overview of the language for those not planning to code.

Newsletters from the HBR, Sloan

I see that HBR has set up some 15 technology and management topic newsletters.  This is similar to what MIT has done with a number of topics.  These newsletters are mores scannable than blogs.  Also can be more up to the minute topical.  A look into the future?  Unclear if this is free, but appears to be.   Interesting to this group, this newsletter:

Managing Data Science
An eight-week newsletter on making analytics and AI work for your organization.  ... 

Changing Nature of Retirement

Correspondent Steve King of Small Business Labs on the Changing Nature of Retirement.   Its a topic their blog has covered many times,  especially with relation to key resource and knowledge retention.   We struggled with it for years, and still do.  AI was seen as a major solution, but ineffective at the time.   Will it be ready?  Some good new points are made.

AI Driven Marketing for CPG

Examples are CPG Food.    We worked on optimization examples of this for years, now the approach is broadening and getting closer to high level decision makers.

Packaged Goods Makers Embrace AI-Driven Marketing
By CGT Staff - 11/28/2018   Source: Path to Purchase Institute

Three leading packaged goods companies announced implementation of an AI-based software solution designed to optimize use of their respective promotions at retail.

Ocean Spray, General Mills and McCormick & Co. are all using the solution, which uses artificial intelligence to sense and monitor market conditions and develop an optimized portfolio that factors in promotional variables along with information inherent to consumer demographics, weather, market dynamics and competitor activity. The AI component lets the tool grow “smarter” as it gathers more information.

The tool, which is provided by trade promotion solution provider Eversight, also tracks promotional performance and compliance against plan.   ... "

Alexa Hunches for Smart Homes

Just started experimenting with Alexa Hunches, a method that claims to makes suggestions for you (Nudges from a Nudge engine?)   Can a skill tune your hunches to a context?  Based on data regarding your behavior ...    Privacy issues?  Will report further, as yet have received no smart home 'hunches'.  Out of toilet paper? 

Alexa Hunches can now suggest actions based on daily behavior
By Shannon Liao@Shannon_Liao in TheVerge

Amazon is now launching Alexa Hunches, where the voice assistant will predict your future needs. It uses deep neural networks to understand behavior and Alexa Hunches is first going to focus on the smart home.

How it works is that Alexa will feel like it’s getting a “hunch” and if it’s confident enough, it will say something like, “Hey, I think you left the porch light on, would you like me to turn it off?” Alexa could also remind you to lock the front door if you haven’t already when you say “Goodnight.”  .... " 

Amazon describes hunches.

Finishing Your Sentences with Common Sense

Yes, its one way to tailor common sense driven sentences.   Here some snippets of work underway in the space, starting with a NYT article, then links to technical details.  The overall challenge for common sense natural language understanding research at this level is well described, but the solutions are technical:

Finally, a Machine That Can Finish Your Sentence

Completing someone else’s thought is not an easy trick for A.I. But new systems are starting to crack the code of natural language.    By Cade Metz  in the WSJ.   ... "

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.  ..."

And of course,   a data challenge for this problem,with early success results:

A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Rowan Zellers. Yonatan Bisk, Roy Schwartz, Yejin Choi
Paul G. Allen School of Computer Science & Engineering, University of Washington
Allen Institute for Artificial Intelligence

Further description of the data challenge in Swag:

Given a partial description like “she opened the hood of the car,” humans can reason about the situation and anticipate what might come, next (“then, she examined the engine”). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning.

We present Swag, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the, recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-theart language models to massively oversample a diverse set of potential counterfactuals.

Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.  ... "

Optimizing Infrastructure Resources

Optimizing Infrastructure Through AI

Makes sense that Cisco would be into this, nicely explained below.   I like the visualization of the process.  Optimization of all sorts of static and dynamic resources to achieve a goal is common.  Might be more broadly applied through a combination of parts of process flow and optimization.  For example the prediction of customers might imply changed use of resources.   Or the operation traffic might be bracketed between max and mins to understand alternative needs, and varying costs vs budgets.

AI Ops includes the ability to dynamically optimize infrastructure resources through a holistic approach. Cisco Workload Optimization Manager is an important component in our strategy of delivering enhanced customer benefits through AI Ops.

Guest Blogger: Vish Jakka, Product Manager, UCS Solutions

Our Strategy for Delivering the Benefits of AI Ops
Cisco is executing a strategy to consistently enhance the customer benefits we deliver through AI-driven Operations (AI Ops). This blog is the latest in a series that describes our strategy, our open architecture, and how we are implementing each of the benefits. In the first blog in this series we defined four categories of benefits from AI Ops:  ... " 

Improved user experience
Proactive support and maintenance
Self-optimization of resources
Predictive operational analytics  .... "

Tracking Location and Activity

Further, related to my current data and activity investigation, its also very useful to see how Google uses Location History Tracking to infer aspects of behavior and activity, implications are that it works very well.   You can easily explore it from the Google Maps App.  It integrates with other behavior like nearness to stores, picture taking, and likely other key entries.  You can then see this on a map, or in a significant event trace.

In fact, it works too well.  And though Google painstakingly provides a means of stopping or selectively deleting all or any part of it, they are now accused of still using the underlying data to infer commercially behavior, even if you turn it off.     They are being sued regarding that:

See;  https://mashable.com/article/google-location-history-tracking-lawsuit.amp

Whats Blockchain Good For?

Nothing?  Well at least not what they expected, or were willing to reveal.  Still, negative examples are useful.

Blockchain: What’s it good for? Absolutely nothing, report finds

In a joint report for the Monitoring, Evaluation, Research and Learning (MERL) Technology conference this fall, researchers who studied 43 blockchain use cases came to the conclusion that all underdelivered on claims.

And, when they reached out to several blockchain providers about project results, the silence was deafening. "Not one was willing to share data," the researchers said in their blog post.

Asset Management

Management in context and some forecast of future context is powerful.

" ... Achieving digital alpha in asset management

The ability to generate value through digitization will increasingly separate leaders and followers in North American asset management.

Sent from McKinsey Insights .... ' 

Asking Questions to Gather Knowledge

The Game of Questions, once again looking at a means of questioning to gather knowledge ... we explored this idea from a game perspective.

Back in 2013, posted about this:

https://eponymouspickle.blogspot.com/2013/09/asking-questions-to-tell-story.html

https://althouse.blogspot.com/2013/09/questions-is-game-that-is-played-by.html

Nielsen and Microsoft Strategic Data Alliance

Making data more intelligently available to analytics.   Integrating data assets.

Nielsen, Microsoft Unveil Strategic Data Alliance

Nielsen and Microsoft have jointly developed an enterprise solution that brings the former’s consumer data set to life through the latter’s intelligent cloud platform.

The strategic alliance seeks to “democratize” the vast Nielsen Connect data set through the global-scale Microsoft Azure platform. The goal is to help consumer packaged goods manufacturers and retail companies find growth and accelerate innovation within an open data environment. 

Already, Nielsen Connect is inspiring companies to glean more value from their data and sparking a movement for the industry to reimagine its approach to data strategy. Through advanced analytics and artificial intelligence services built on Azure, Nielsen Connect is helping companies integrate data assets to more easily spot emerging trends, diagnose performance gaps, and act faster on opportunities to grow. Most notably, this platform enables clients to use their data as an enterprise asset across all parts of their organization.  ...  "

AINow Publishes Recommendations

Followup with AINow,  promoting regulation and transparency in the AI development space.

After a Year of Tech Scandals, Our 10 Recommendations for AI  (Outline Overview) 

Let’s begin with better regulation, protecting workers, and applying “truth in advertising” rules to AI
Today the AI Now Institute publishes our third annual report on the state of AI in 2018, including 10 recommendations for governments, researchers, and industry practitioners.

It has been a dramatic year in AI. From Facebook potentially inciting ethnic cleansing in Myanmar, to Cambridge Analytica seeking to manipulate elections, to Google building a secret censored search engine for the Chinese, to anger over Microsoft contracts with ICE, to multiple worker uprisings over conditions in Amazon’s algorithmically managed warehouses — the headlines haven’t stopped. And these are just a few examples among hundreds.

At the core of these cascading AI scandals are questions of accountability: who is responsible when AI systems harm us? How do we understand these harms, and how do we remedy them? Where are the points of intervention, and what additional research and regulation is needed to ensure those interventions are effective? Currently there are few answers to these questions, and existing regulatory frameworks fall well short of what’s needed. As the pervasiveness, complexity, and scale of these systems grow, this lack of meaningful accountability and oversight — including basic safeguards of responsibility, liability, and due process — is an increasingly urgent concern.  ... " 

Full report from AINOW.

Opsgenie for Incidents in Process Modeling

In the process of looking at modeling processes that link to and handle incident driven operations, I was pointed to:

Atlassian: Opsgenie

Plan and prepare for incidents
Determine who should respond
Use templates to prepare messaging and communication channels to responders and stakeholders
Predefine collaboration methods including video conferences, and chat channels
Create status pages to communicate proactively to all stakeholders  ... "

Whats Best?

I like the pieces from Think with Google, good to follow.

We often addressed the problem when dealing with the term 'Optimal', which often followed with the question:  In what context?  under what Constraints?    When we use 'best' there are often many implied constraints in our search or request.  Its also common to include in conversation.  Search, Google's language of interaction, is a conversation, and includes common sense interpretations of 'Best'.

Ask a researcher: What does ‘best’ really mean?
Ken Wheaton August 2018 Mobile, Search, Consumer Insights

It seems fairly straightforward. When people set out to shop for an item or service, they hope to end up with the best possible outcome. But it turns out that “the best” isn’t an objective absolute. In fact, finding “the best” isn’t necessarily about finding the best thing that exists, it’s about finding the best thing for your needs.

It was pretty clear to us from consumer search data that people’s quest for the best is still on the rise. Mobile searches for “best” have grown over 80% over the past two years.1 And they’re searching for “best” for even the smallest stuff: We’ve seen strong growth in things like “best toothbrush” over the past couple years. ....  "

Robot Scientist Creates New Materials

Machines doing design, and the creative process.  How do they interact with people?

A Robot Scientist Will Dream Up New Materials 
Technology Review   By Will Knight

Cambridge, MA-based startup Kebotix has created machine learning software that learns material chemistry from three-dimensional models of molecules with known properties in order to design novel compounds. Kebotix feeds the molecular models to a neural network that learns a statistical representation of their properties, which can devise new examples aligned with existing models; a second network screens out undesirable designs, then a robotic system tests the chemical structures of the remaining models. The outcomes are input back into the machine learning channel so it can yield results closer to target properties. MIT's Klavs Jensen said the use of such automation in chemistry "won't replace the expert, but you'll be able to do things a lot faster."  ...

R&D in the Age of Agile

Pharma R&D in the ‘age of agile’  From McKinsey

As innovation reshapes the pharma landscape, pharma companies will need to revisit their R&D operating models to thrive. ... 

Sent from McKinsey Insights, available in the App Store and Play Store.

Report from the Stanford AI100 Study

Initial report from this work:

Stanford:    One Hundred Year Study on Artificial Intelligence (AI100)

Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.  .... 

-----------

Barbara J. Grosz and Peter Stone. A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society. December 2018. Communications of the ACM (CACM).Doc: groszstone_cacm2018.pdf

Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016.   

Wal-Mart Floor Cleaning and Complex Environments

Note the claim of being able to operate ' ... to effectively and safely function in complex, crowded environments, ensuring increased productivity and efficiency across applications .... '  That is,  in the same environments as people.   This will grow as a challenge to get robots cooperating with people, passively or actively ...

Walmart Leads the Way...in Floor Scrubbing Robots? 
in ZDNet    by Greg Nichols, with Video

By the end of next month, Walmart will deploy floor-scrubbing robots programmed to map out pathways via demonstration through the BrainOS operating system (OS) from Brain Corp. Store associates will walk the stores’ floors, guiding the robots along a quick demonstration route. Afterwards, BrainOS will take over to scan the area to be cleaned, while monitoring for people or new obstacles using onboard sensors. Said Brain Corp.'s Eugene Izhikevich, "BrainOS technology allows robots to effectively and safely function in complex, crowded environments, ensuring increased productivity and efficiency across applications." The scrubbing robots are the latest artificial intelligence solution explored by Walmart, as the retailer also uses robots from the Bossa Nova hardware company to scan shelves and aggregate customer activity in select retail outlets. ... "

New IFTTT Functions for Assistants and Home

A number of new and interesting IFTTT  (If This Then That) functionalities for the smart home.  I always explore these offerings.   They are free.   Some new interactions that link things like doorbell pushes and multiple step 'scenes'.   Also many examples of  logging information.     A pointer to a future where there will be many selectable skills aka actions that improve the smart home by using its data.  Everyone who has assistants should explore these possibilities.

 For a number of Smart Home assistants.

 For the Ring Doorbell and connected smarthome systems.

And many more.

Amazon Go Cashierless at Airports?

Overall an excellent, hands-on demonstration of the advance of fast reliable sensors.   Will further open people to the idea of the convenience of 'AI' type solutions.  They have already done it with Alexa.   These developments will make people willing to engage and trust new interfaces, and will lead to yet newer ideas.   An opening to driverless cars, sooner?

Amazon's cashierless Go stores may come to an airport near you
It has reportedly requested meetings from several US airport operators.
By Steve Dent, @stevetdentin  in Engadget

Airport shopping is mostly about perfumes, booze and overpriced electronics, but that could soon change. Amazon has reportedly inquired about installing its cashierless Go stores at several US airports, according to Reuters. Emails from a public records request revealed that Amazon asked for meetings with managers at San Jose and Los Angeles international airports and received a positive response. "I am looking forward to moving forward with the Amazon Go technology at the airport," wrote one airport IT manager.  ... " 

Robotic Houseplant Moves to Light

Kind of obvious, but made me think more generally about the robotics of the problem.   And adding other dimensions like nutrients and water.    Plus the sensors needed to do that effectively, and the dependence on the architecture of the 'field'.  Reminds me too of robot weed pulling and zapping solutions.

MIT researchers create a robot houseplant that moves on its own

By Rachel England, @rachel_england in Engadget

Google Labs Announces Image Compression Demos

Had a need for comparing alternate methods of this recently. Technical:

Google Labs Announces Squoosh: Image Compression PWALike  | by Dylan Schiemann 

At the 2018 Google Chrome Developer Summit, Google announced Squoosh, an open source image compression Progressive Web App (PWA) that doubles as a practical demonstration of modern web technologies.

Squoosh provides a quick and easy mechanism for leveraging many image compression formats. Users may browse Squoosh.app, drag and drop an image into a browser tab, and experiment with many image optimization and conversion settings. The app displays before and after views side by side for the selected image compression settings.

Squoosh in its current form is likely not meant as a competitor to the numerous image compression apps, ranging from traditional image editing tools as Photoshop and Sketch.app to web-based services like TinyPNG, ImageResize.org, and Compressor.io, to various desktop apps.

Google Labs' primary objective with Squoosh is to demonstrate how advanced web apps can leverage modern technologies to deliver a high-performance experience in today's web browsers.  ... "

Mars and Marketing

In Gartner:
Mars and Marketing – What We Can Learn From NASA    By Christopher Ross

Google Wants to be Your AI Edited News Radio

So Google will control editing and presentation of news.  Placement of Ads.  Assembled with AI analysis.  Already being delivered to some assistants.  Right now assistants stream selected radio stations.  More control from mega computing?  What are the implications?

Google wants to replace your radio as an audio news source

Google is getting into AI-curated news playlists
By Shannon Liao@Shannon_Liao  in theVerge

You’ll soon be able to listen to an audio news playlist curated by Google Assistant to inform you on the topics you’re interested in. Google’s latest Assistant feature uses artificial intelligence to help make these custom news bundles, and it’s available today for a limited number of users.  ... "

Alexa Conversationally Constructs Playlists

This is something of interest, playlist conversational construction.  If you think of a playlist as a goal, could this be applied to business efforts?   Was thinking something related for business application, is this far away?  More than just tweaks will be needed, but have already noticed some subtlety in recommendation.    Alexa:  Find a better pricing strategy using the latest data.   A Playlist for Business as a set of choices for a process?

Alexa will pepper you with questions to build better playlists   in Engadget, By Mallory Locklear, @mallorylocklear

Amazon is rolling out a few tweaks to Alexa that will make it easier to find the music you want to hear. By telling Alexa what you like and don't like and by conversing with Amazon's assistant about what you enjoy listening to, Alexa will be able to create more personalized suggestions and playback even when you just say, "Alexa, play music."

If you're looking for Alexa to offer some suggestions for playlists that are tailored to you and what you want, the virtual assistant will now be able to talk to you about what you're looking for. When you say something like, "Alexa, help me find a holiday playlist," or "Alexa, help me find dinner music," Alexa will respond with a few questions to help get a better grasp on what genre, tempo or mood you're hoping to incorporate. Alexa will then customize playback based on how you responded and your past listening history.  ... "

China Takes on Cashierless Tech

Alternative tech idea:

Why China's take on cashierless tech might be the right one in Axios

China's retail giants might be ahead of US innovators, such as Amazon, in creating no-checkout retail locations, Erica Pandey writes. While US companies are focused on a hands-free payment experience, Chinese retailers are letting consumers use QR codes to pay quickly and easily, she writes. ... " 

Assessing Progress in Automation Technologies

Useful coverage of automation.  I like that this is defined broadly.

Assessing progress in automation technologies  In O'Reilly by Ben Lorica.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.

In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future. .... " 

A ChatBot Asset Exchange

Brought to my attention by the talk this morning on Bots integrated with AR/VR . Slides here.  The slides include instructive videos.  IBM Sponsored.  I will extract some additional examples.

Some interesting examples of Bot use, coding and design, and a means to exchange examples:

Bot Asset Exchange
Your community-driven chatbot development hub.  (via Anamita Guha 
 @anamitag of IBM)

Discover, configure, deploy, and be rewarded for bots built with Watson Assistant. Join the revolution--build a chatbot today.   ... "

Echo Devices now Support Skype Calling

In further indication of a link between Microsoft and Amazon,  Voice and Video Skype calls can now be made-hands free on Echo devices.  I frequently use Skype for international communications.  Signed up for this and it worked well.  Another move towards Echo for business scenarios.   Text messaging is not yet available, but is coming.

You can now make Skype calls on Amazon Echo devices   By Bruce Brown in DigitalTrends

Just in time for the holiday season, Microsoft and Amazon announced that you can now ask Alexa to make Skype voice and video calls on Echo devices.

In all, 34 countries will soon be Alexa and Skype-enabled. Skype support for the Alexa platform is available now in the U.S., U.K., Ireland, Canada, India, Australia, and New Zealand. Support for other countries will be coming soon, according to Microsoft.

You can use Alexa-enabled devices such as the Echo Plus or Dot to make hands-free Skype voice calls.  ... "

Talk: How Bots Fit into the AR/VR Space

Join us Dec 6,  at 10:30am US Eastern - hear @anamitag at @IBMDeveloper

Talk Title: How Bots Fit in the AR/VR Space 

Speaker: Anamita Guha 

Abstract: This talk will describe the use cases of chatbots in AR/VR that are in existence — and Al's potential within those —  in addition to ways you can develop bots within AR/VR applications and make them intelligent. Anamita will also discuss  current and subsequent trends and why AR/VR accompanied with Al is going to have more of an emerging impact in the enterprise space. 

Bio: Anamita Guha is currently the Lead Product Manger for IBM Watson building developer offerings. Her focus has been  on conversational interfaces like chatbots, voicebots, IOT, and AR/VR, in addition to promoting girls in STEM. She recently  launched the Bot Asset Exchange, a tool of bot developers to use when building their bots. She also helped champion  Chatbots for Good, a free cloud-based learning experience where anyone — even those with no prior bot development  experience — can use Watson Assistant and Tone Analyzer services to design, test, and build a chatbot. She holds a  degree in Cognitive Science from UC Berkeley, and has spent most of her career prior to IBM at early-mid stage startups. 

Zoom meeting Link: https://zoom.us/j/7371462221; Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 
Zoom International Numbers: https://zoom.us/zoomconference 
Check http://cognitive-science.info/community/weekly-update/for recordings & slides, and for any date & time changes 
Join Linkedin Group: https://www.linkedin.com/groups/6729452/ (Cognitive Systems Institute) to receive notifications 
Thu, Dec 6, 10:30am US Eastern https://zoom.us/j/7371462221 
More Details Here : http://cognitive-science.info/community/weekly-update/  (Slides and talk recording will be posted after talk) 

@sumalaika  
#CSIGnews #opentechai #issip   @KarolynSchalk @mattganis @ibmcodait #chatbots @rama_akkiraju @yassimoghaddam @hyurko @oniak3

P&G Responds to Changing Customer Preferences

Intriguing directions, especially regarding customization.  How does a big company react to customer preferences,  when it can no longer direct them?

Q&A | How P&G Responds to Changing Customer Preferences   in SupplychainBrain

Phil Ruotolo, associate director of merchandising solutions customization with Procter & Gamble, details the consumer-products giant's strategy for adjusting to ever-shifting consumer preferences.

Q: How is P&G responding to changing customer preferences?

Ruotolo: Several years ago, we embarked on an innovative supply-chain solution. We created what we call mixing centers. It was the first time we had brought a portfolio of different P&G products into one location. The next question was, how do we think about merchandising in a more unique way? So we went to a 14-day order lead time. Previously, there was typically a four- to six-week window, and most of it was on a forecast. What inevitably happens is that you're building inventory of something that a customer might want to order.

In this new model, we differentiate the finished product in our customization locations, and then do all of the forecasting for our materials. Now we're able to react very quickly to what a customer might want, and can fulfill that within 14 days.

Q: So this is execution, not planning?

Ruotolo: Correct. It's taking all your favorite P&G products, and bringing them all together into one facing. As a consumer, you’re drawn to that display. We want to drive trials with consumers as they come across our products. Hopefully, they’ll make multiple purchases.   ..... " 

Better Facial Recognition, Says NIST

Interesting stats.   Our own experience says it depends strongly on the contextual aspects of the image acquisition.  And often out of complete control.    Introduce the metadata of context?  But the improvement continues.

Facial Recognition Algorithms Are Getting a Lot Better, NIST Study Finds   in FedScoop   By Tajha Chappellet-Lanier

The U.S. National Institute of Standards and Technology (NIST) determined facial recognition software has made huge gains in accuracy over the past five years. NIST said the technology has undergone an "industrial revolution," making certain algorithms about 20 times better at searching databases and finding matches. NIST researchers tested 127 algorithms developed by 45 vendors, using a primary database of 26.6 million reasonably well-controlled portrait photos of 12.3 million individuals; when provided with good quality photos, the most accurate algorithm could identify matches with only a 0.2% error rate. The same test found at least a 4% failure rate in 2014, and a 5% failure rate in 2010. NIST said this improvement can be attributed to the widespread adoption of convolutional neural networks, which were not being used in 2014.  ... " 

Protein Folding with Alphabet Deep Mind

A big, big deal we took a look at for industry, even suggested a neural net possibility, but methods were still too primitive at the time. This still not a compete solution,  but looks to be a step forward.

Alphabet's DeepMind AI Algorithm Wins Protein-Folding Contest 
V3.co.uk   By Dev Kundaliya

DeepMind's latest artificial intelligence (AI) software won the Protein Structure Prediction Center's Critical Assessment of Structure Prediction contest by accurately predicting the three-dimensional structures into which proteins can be folded. The AlphaFold algorithm predicted the configurations of 25 out of 43 proteins, making it far more accurate than any other software. AlphaFold was designed and taught to model target shapes from scratch, without using previously solved proteins as templates. The DeepMind team used two distinct neural networks to predict the proteins' structures. DeepMind's Demis Hassabis said, "We've not solved the protein folding problem, this is just a first step. It's a hugely challenging problem, but we have a good system and we have a ton of ideas we haven't implemented yet."    ... " 

AI Will Make You Smarter Through Augmentation

Course that is what we are aiming at,  it augments us.   Conversation is a great start.

Artificial intelligence will make you smarter
People plus machines will surpass the capabilities of either element alone. 

By Terrence Sejnowski

Francis Crick Professor and Director of the Computational Neurobiology Laboratory at Salk Institute for Biological Studies, and Distinguished Professor of Neurobiology, University of California San Diego

MIT Press provides funding as a member of The Conversation US.

University of California provides funding as a founding partner of The Conversation US.
Under Creative Commons license.

The future won’t be made by either humans or machines alone – but by both, working together. Technologies modeled on how human brains work are already augmenting people’s abilities, and will only get more influential as society gets used to these increasingly capable machines.

Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life’s drudgery and administrivia, benefiting everyone. Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives. And fearmongers worry that AI might eventually make human beings obsolete.

However, people are not very good at imagining the future. Neither utopia nor doomsday is likely. In my new book, “The Deep Learning Revolution,” my goal was to explain the past, present and future of this rapidly growing area of science and technology. My conclusion is that AI will make you smarter, but in ways that will surprise you.

Recognizing patterns

Deep learning is the part of AI that has made the most progress in solving complex problems like identifying objects in images, recognizing speech from multiple speakers and processing text the way people speak or write it. Deep learning has also proven useful for identifying patterns in the increasingly large data sets that are being generated from sensors, medical devices and scientific instruments.

The goal of this approach is to find ways a computer can represent the complexity of the world and generalize from previous experience – even if what’s happening next isn’t exactly the same as what happened before. Just as a person can identify that a specific animal she has never seen before is in fact a cat, deep learning algorithms can identify aspects of what might be called “cat-ness” and extract those attributes from new images of cats. .... " 

Alibaba has a Better Intelligent Assistant than Google's

In particular the claim that it is more conversationally powerful is interesting.  Such advances could lead to deeper interaction with humans and further engagement.

Alibaba already has a voice assistant way better than Google’s
It navigates interruptions and other tricky features of human conversation to field millions of requests a day   by Karen Hao  in Technology Review excerpt: 

In May, Google made quite the splash when it unveiled Duplex, its eerily humanlike voice assistant capable of making restaurant reservations and salon appointments. It seemed to mark a new milestone in speech generation and natural-language understanding, and it pulled back the curtain on what the future of human-AI interaction might look like.  Still only in Chinese. 

But while Google slowly rolls out the feature in a limited public launch, Alibaba’s own voice assistant has already been clocking overtime. On December 2 at the 2018 Neural Information Processing Systems conference, one of the largest annual gatherings for AI research, Alibaba demoed the AI customer service agent for its logistics company Cainiao. Jin Rong, the dean of Alibaba’s Machine Intelligence and Technology Lab, said the agent was already servicing millions of customer requests a day.

The demo call involved the agent asking a customer where he wanted his package delivered. In the back-and-forth exchange, the agent successfully navigated several conversational elements that demonstrated the breadth of its natural-language capabilities.

Take this exchange at the beginning of the call, translated from Mandarin: ... "

Wal-Mart Mobile Out of Stock App

We developed a similar mobile App on a Blackberry , and tested it in multiple retail settings, but before home ordering/shipping was common.   Consider how this could be integrated with predictive sales and pricing analysis. The idea has been frequently covered in this blog.   How similar?

 Walmart gives associates a tool to deal with out-of-stocks
by George Anderson in Retailwire

Walmart is not known for having large numbers of associates on its sales floors, but it may need to add more if a new tech tool for workers becomes popular with customers during the holiday season. A new mobile app feature enables Walmart associates to search walmart.com for items not sold at their location, order the item for customers and have it shipped free to a customer’s home or to the store for free pickup.

The retailer claims that one of the best features of the mobile app tool is that it provides a variety of payment options. Once an item is ordered, customers are given a receipt. They then can go to any checkout in the store and pay with cash, check, credit or debit card as well as Walmart Pay.  ... "

Operations 4.0: Pilots

From McKinsey.  Thoughtful examination of essence and value of pilots

The Operations 4.0 podcast: Productivity and ‘pilot purgatory’
The value from Operations 4.0 comes from how it unleashes productivity gains across a wide range of measurements. But to achieve those results, businesses must do more than launch pilot after pilot. ... "

An Architecture for Intelligence?

Is there an underlying model for intelligence?    Is it a structurally simple enough one that we could readily convert into code, we could create something that thinks like the brain?    Still unknown.  Also looking for that secret part we still don't know.

Could this then lead use for AGI      Artificial General Intelligence, AKA "Strong AI"  or   "the intelligence of a machine that could successfully perform any intellectual task that a human being can"?  We don't know that either,  but we know the brain thinks, and its made of things we can dissect piece by piece  (Technical)

The Genius Neuroscientist who might hold the key to True AI.  By Shaun Raviv in Wired

See also:  Karl Friston   https://en.wikipedia.org/wiki/Karl_J._Friston

And further: https://en.wikipedia.org/wiki/Free_energy_principle

The free energy principle tries to explain how (biological) systems maintain their order (non-equilibrium steady-state) by restricting themselves to a limited number of states.[1] It says that biological systems minimise a free energy functional of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of variational free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception in neuroscience,[2] where it is also known as active inference.

Markov Blanket   https://en.wikipedia.org/wiki/Markov_blanket

Prove Your Algorithms are Fair

See some previous work on this,.   Proofs in specific goals and context.

To Build Trust In Artificial Intelligence, IBM Wants Developers To Prove Their Algorithms Are Fair
by Dan Robitzski in Futurism.com

We trust artificial intelligence algorithms with a lot of really important tasks. But they betray us all the time. Algorithmic bias can lead to over-policing in predominately black areas; the automated filters on social media flag activists while allowing hate groups to keep posting unchecked.

As the problems caused by algorithmic bias have bubbled to the surface, experts have proposed all sorts of solutions on how to make artificial intelligence more fair and transparent so that it works for everyone.

These range from subjecting AI developers to third party audits, in which an expert would evaluate their code and source data to make sure the resulting system doesn’t perpetuate society’s biases and prejudices, to developing tests to make sure that an AI algorithm doesn’t treat people differently based on things like race, gender, or socioeconomic class. ... "

Game Theory and Society

A favorite topic is how game theory can be made practical   We tried that and got little out of it beyond descriptive rather than prescriptive models.   Is this new approach useful beyond that?  Note especially regarding networks and social dynamics.

What game theory tells us about politics and society
Economist Alexander Wolitzky uses game theory to model institutions, networks, and social dynamics.   By Peter Dizikes | MIT News Office

Foundational Barriers for AI

Agree, and real feedback from surveys is interesting ...

 AI adoption advances, but foundational barriers remain, results from surveys.

Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count. Yet few companies have in place the foundational building blocks that enable AI to generate value at scale.

The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 and nearly half of respondents say their organizations have embedded at least one into their standard business processes, while another 30 percent report piloting the use of AI. Yet overall, the business world is just beginning to harness these technologies and their benefits. Most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions. Indeed, many organizations still lack the foundational practices to create value from AI at scale—for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires.

One critical factor of using AI effectively, the results confirm, is an organization’s progress on transforming the core parts of its business through digitization. At the most digitized firms,3 respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI. Another foundational challenge with AI is finding skilled people to implement it effectively. Many respondents say their organizations are addressing the issue by taking a diversified approach to sourcing talent. On the whole, despite reasonable concerns about AI being used to automate existing work, respondents tend to believe that AI will have only a minor effect on overall company head count in the coming years ... "

Brand Loyalty Changing for Natives

Never liked the term Digital Native.    We are all more like digital tourists now, using the convenience and intelligence of it more or less.   .........

Marketing to Digital Natives: How Brand Loyalty Is Changing

Wharton's Americus Reed and Erik Gordon of the University of Michigan discuss reviving old brands for millennials and Gen Z. ... 

Marketing Content

Brand loyalty used to be something companies could rely on to grow and retain their customer base. It was driven in part by cool commercials on network TV and catchy jingles that consumers couldn’t get out of their heads. But younger people, specifically millennials and the Gen Z cohort, aren’t looking at the same media or ads that their parents did. Companies that were popular in past generations are quickly discovering that they need new strategies if they want their brand to appeal to the next generation of shoppers who can easily click and choose from millions of products from around the globe.

The Knowledge@Wharton radio show on SiriusXM invited two professors to talk about marketing to digital natives and what it means for companies. Americus Reed is a marketing professor at Wharton, and Erik Gordon is a professor at the University of Michigan’s Ross School of Business. The following are five key points from their conversation. (Listen to the full podcast at the top of this page.)   ... "

AI Is Watching Employee Expenses

A classic approach to look anomalies in streams of data.

AI Is Watching Employee Expenses   in Bloomberg  By Olivia Carville

AppZen has developed an artificial intelligence program that can identify dubious work expense claims and educate employees about travel and expense policies. The company, which touts Amazon, IBM, Salesforce.com, and Comcast as users, estimated that it has saved its clients $40 million in fraudulent expenses. AppZen can audit 100% of claims in real time by running receipts through an algorithm that looks for duplication, discrepancies, or inflated expenses. The program reimburses legitimate employee expenses on the same day and kicks back any suspicious claims to human auditors for further investigation. In addition, the algorithm can compare the average cost of a flight from New York to Chicago against the amount expensed, and flag it if the price seems out of line for other similar flights that day.  ... " 

Can Amazon Scale up GO?

Scale up is very likely, what are the implications for in store marketing?

Has Amazon figured out how to scale its Go cashier-free tech to bigger stores?
by George Anderson in Retailwire, with expert comment.  Refers to WS Journal article.

Amazon is known for doing things in a big way. So, it should come as no surprise that the e-tailing giant is reported to be working on a way to put deploy the technology behind its Amazon Go convenience stores in much larger store environments.

The Wall Street Journal reports that Amazon is working at a location in Seattle to test how it needs to adjust the technology based on a bigger footprint. Higher ceilings and more items to track are two of the challenges Amazon is looking to address in the test, which is set up to look like a big box store.

In the current seven Amazon Go stores, customers with the Go app are tracked by a variety of technologies including “computer vision, deep learning algorithms and sensor fusion” as they enter and move around the location. Items are automatically added to a virtual shopping cart when a customer takes them off the shelf. When all done, customers simply walk out with their products and Amazon bills their accounts.  ... " 

Difference Between Business Intelligence and Data Science

Nicely done piece.  Good charts at the links below.   Though in some ways I have to ask if there should be a difference in practice?   Depending on Goals and potential value?   I always say you should start any 'advanced'  analytics/science with a descriptive examination.    Or you should have the experts of that description closely accessible to your team.  Attitude should be the same:  Achieve the business goal.

Updated: Difference Between Business Intelligence and Data Science

Posted by Bill Schmarzo in DSC.

I'm reposting this blog (with updated graphics) because I still get many questions about the difference between Business Intelligence and Data Science. Hope this blog helps.

I recently had a client ask me to explain to his management team the difference between a Business Intelligence (BI) Analyst and a Data Scientist.  I frequently hear this question, and typically resort to showing Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each)...  " 

Morse Code for Acccesibility

Most interesting.  Clever idea that has been developed further. Nice idea Google with GBoard!

Google’s Morse code-powered games aim to serve kids with limited mobility In Digitaltrends

Google is now offering access to five games controlled entirely by Morse code, thanks to a 48 hour “hackathon,” and a partnership with Adaptive Design Association. The games use the Morse code functionality introduced into Gboard in May 2018, and are intended for people with limited mobility who cannot use other control methods, as well as for people who are interested in learning Morse code.

The games were created over a period of two days by five teams of game designers and developers, each working with a child with limited mobility. Each of these children worked as the game’s creative director, and their specific vision helped to shape the games, making them uniquely molded around each child’s interests. For instance, Olivia’s “Alphabet’s Got Talent” is modeled after the talent shows she loves, while Hannah’s game uses Morse code to play musical notes. Players will be able to shoot soccer balls at targets in Matthew’s game, and Ben’s passion for trains is clear in his game that shows YouTube videos on a train once the correct letters are typed. Emmett — whose learning of Morse code through a similar Google-built game inspired this challenge — created a maze solved by typing different letters.  .... " 

See more on morse code assistant technology.  In experiments with Google.

The Farm is the Grid

Intriguing partnership for data.  Need more of this.   Metadata and data.   Data has value.

Airbus, John Deere connect tractors, satellite data
By Lawrence Specker | lspecker@al.com

Flying tractors may not be in the cards just yet, but the seemingly odd-couple pairing of Airbus and John Deere has jointly won a major European agriculture innovation award.

In conjunction with the upcoming 2019 SIMA agriculture industry show in Paris, the two companies have received a silver medal for their Live NBalance program, which Airbus describes as “a service merging satellite and tractor data to monitor intra-field nitrogen balance even more precisely during the growing season.”

Tractor data? So much for the dream of going back to the farm to get off the grid. Apparently now the farm is the grid.   ... "

Immortalizing Ourselves as a Chatbot?

We are still far afield from this.  Will we be able someday to chat with long gone people?   Made me think of TheBrain, this blog, Brand equities like Mr Clean?  None close.   I returned to a long ago employer and found my technology explanatory entries in a wiki we created ....  but they had removed all the bylines! ... So shall it be unless you are name-famous enough to draw a crowd.   Anyway, they are researching the idea below.

Soon you can immortalize yourself as an A.I. chatbot. But should you?   In Digital Trends by @lukedormehl

Until technology allows us to upload our consciousness to a computer when our physical bodies start irreparably failing, death is going to remain a real thing. But what if you could continue communicating with loved ones — or, at least, a reasonable facsimile of them — long after they’ve shuffled off this mortal coil? It might sound like an episode of Black Mirror (it is!), but it’s also the basis for a recently announced research project being carried out at India’s Shree Devi Institute of Technology.  ... " 

Power and Digital Surveillance

In HBR Big Idea: private data and targeted advertisement.

How to Exercise the Power you Didn't ask for    By Jonathan Zittrain

I used to be largely indifferent to claims about the use of private data for targeted advertising, even as I worried about privacy more generally. How much of an intrusion was it, really, for a merchant to hit me with a banner ad for dog food instead of cat food, since it had reason to believe I owned a dog? And any users who were sensitive about their personal information could just click on a menu and simply opt out of that kind of tracking.

But times have changed.

The digital surveillance economy has ballooned in size and sophistication, while keeping most of its day-to-day tracking apparatus out of view. Public reaction has ranged from muted to deeply concerned, with a good portion of those in the concerned camp feeling so overwhelmed by the pervasiveness of their privacy loss that they’re more or less reconciled to it. It’s long past time not only to worry but to act.

Advertising dog food to dog owners remains innocuous, but pushing payday loans to people identified as being emotionally and financially vulnerable is not. Neither is targeted advertising that is used to exclude people. Julia Angwin, Ariana Tobin, and Madeleine Varner found that on Facebook targeting could be used to show housing ads only to white consumers. Narrow targeting can also render long-standing mechanisms for detecting market failure and abuse ineffective: State attorneys general or consumer advocates can’t respond to a deceitful ad campaign, for instance, when they don’t see it themselves. Uber took this predicament to cartoon villain extremes when, to avoid sting operations by local regulators, it used data collected from the Uber app to figure out who the officials were and then sent fake information about cars in service to their phones.  ... "

Data Non Normal

A challenge we often encountered, good examples of how to address it in this article   What if your Data is not Normal? in Towards Data Science  

I Add:    A lot of hand things we usually assume will not work when our data is not normally distributed.   So its important to know. This piece does a good job of surveying the assumptions and alternatives.  I like in particular the list of methods you can no longer be sure of.  The list contains many approaches that are 'understood'  by management and decision makers.

But I will add something that was not covered. If you can't use the common assumption normality , it will typically be harder to convince decision makers that your methods are correct.   So preparation  for that will also be needed.   Depending how the management has been trained, also the measures and risk involved with the decision being made. This may also point to formal tests for normality to be included in analytical process.  "

People's Attitude Towards Algorithms

Though humans have broad concerns of the use of computer algorithms, it is inevitable they will be used ...

Public Attitudes Toward Computer Algorithms  By   Aaron Smith in Pew
Americans express broad concerns over the fairness and effectiveness of computer programs making important decisions in people’s lives

Real-world examples of the scenarios in this survey
All four of the concepts discussed in the survey are based on real-life applications of algorithmic decision-making and artificial intelligence (AI):

Numerous firms now offer nontraditional credit scores that build their ratings using thousands of data points about customers’ activities and behaviors, under the premise that “all data is credit data.”
States across the country use criminal risk assessments to estimate the likelihood that someone convicted of a crime will reoffend in the future.

Several multinational companies are currently using AI-based systems during job interviews to evaluate the honesty, emotional state and overall personality of applicants.
Computerized resume screening is a longstanding and common HR practice for eliminating candidates who do not meet the requirements for a job posting.

Algorithms are all around us, utilizing massive stores of data and complex analytics to make decisions with often significant impacts on humans. They recommend books and movies for us to read and watch, surface news stories they think we might find relevant, estimate the likelihood that a tumor is cancerous and predict whether someone might be a criminal or a worthwhile credit risk. But despite the growing presence of algorithms in many aspects of daily life, a Pew Research Center survey of U.S. adults finds that the public is frequently skeptical of these tools when used in various real-life situations. .... "

Delta Biometric Atlanta Terminal

Thought this was only in China, but its here now.    A needed use for security.

Here’s a look at Delta’s all-seeing, face-scanning, biometric airline terminal    By Melissa Locker in Fast Company

Delta Air Lines promised it would open the country’s first all-biometric terminal before the end of the year, and it has delivered. Starting December 1, customers flying Delta through the Atlanta airport’s Terminal F will be able to use facial recognition technology “from curb to gate” as a way to make it easier to fly through the airport.

For the biometric terminal, Delta worked with the Customs and Border Protection and the Transportation Security Administration to let travelers check in, drop bags, pass through TSA checkpoints, and board their flights–all using facial recognition systems powered by in-terminal cameras to verify their identity. The airline already lets some customers use their fingerprint as a boarding pass. ....  "

E-Ink New Digital Paper

We worked with e-ink for a time in the lab.

E Ink’s new digital paper lets you draw with almost no lag in Thenextweb.
Almost like the real thing.

The hypothetical pinnacle of digital paper is when it becomes indistinguishable from the real article, both in terms of reading and writing. Today, at the Connected Ink conference in Tokyo, E Ink Holdings took us a little bit closer with its new JustWrite technology.

JustWrite is designed to feel as close as possible to writing on a sheet of standard A4, without the inclusion of a bulky TFT backplane. It requires very little electricity to run, and boasts very low latency, in order to offer a natural-feeling writing experience.

So, how does this work in practice? You can see an artist demonstrate the technology in this video. As you’ll see, pencil strokes appear virtually instantaneously.

According to E Ink Holdings, JustWrite only requires a writing stylus and simple electronics to work, and works with a variety of compatible writing implements, including pens, brushes, markets, and stamps. Thanks to the simple construction of the JustWrite film, the e-ink displays are lightweight, bendable and highly flexible.  ...." 

E-Skin Functions as Bionic Compass

New idea, with details at the link:

E-Skin Functions as a Bionic Compass in IdeaConnection

An electronic skin able to detect motion relative to the Earth’s magnetic field could have applications in humans and robotics.

The e-skin was created by a team from Helmholtz-Zentrum Dresden-Rossendorf (HZDR) using a thin sheet of polymer foil equipped with layers of magnetic sensors able to detect geomagnetic fields. The resistance of the layers will alter based on their orientation to the Earth’s magnetic field, resulting in a bionic compass that can be affixed to a surface (including human skin). When worn on the tip of a finger, the e-skin was able to detect the direction the wearer was walking—displaying the information on a compass—and also transmit that information to a VR character on nearby screen. ... "