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Tuesday, January 17, 2017

3D Data Optimization by Simplygon

Interesting play,  taking a close look.    Initially used for game applications, and I expect further used for dealing with multiple dimensional data in interactive 3D displays and virtual reality.  Design applications are likely early applicatinos.   Good examples at the Simplygon site.  In ZdNet: 

Microsoft acquires 3D data-optimization vendor Simplygon
Microsoft's latest acquisition, 3D data-optimization vendor Simplygon, is part of Redmond's strategy to make 3D part of future users' experiences.    By Mary Jo Foley

" .... Simplygon will help Microsoft simplify the process of capturing, creating, and sharing information in 3D, Tsunoda said. The Simplygon technology will complement the new Paint 3D application (codenamed "Beihai") and new online creator community, Remix3D.com.

Simplygon was developed by Donya Labs AB in Sweden, a company developing "automatic 3D data-optimization solutions." .... ' 

IEEE on the Kuri Robot

Previously mentioned.  The Kuri Robot, by Bosch and Mayfield,  scheduled to be out later this year.  ... Mayfield Robotics Announces Kuri, a $700 Home Robot   By Evan Ackerman.   Appears to be a solution that attempts to create emotional interaction with the household.   That part remains to be seen.  The concrete information supplied makes it appear more like a mobile security camera.

Predicting Machine Problems with Deep Learning

Maintenance prediction is something we worked on.  Here a related application that has many possibilities.   How about very rare breakdown occurences?  In IEEE Spectrum: 

Deep Learning AI Listens to Machines For Signs of Trouble    By Jeremy Hsu

" .... The service of 3DSignals, a startup based in Kefar Sava, Israel, relies on the artificial intelligence technique known as deep learning to understand the noise patterns of troubled machines and predict problems in advance. 3DSignals has already begun talking with leading European automakers about possibly using the deep learning service to detect possible trouble both in auto factory machinery and in the cars themselves. The startup has even chatted with companies about using their service to automatically detect problems in future taxi fleets of driverless cars. .... " 

Leadership in a Cognitive era

Discussion starting in the Cognitive Systems Linkedin Group.   Join us in the conversation.

Points to a Forbes article.
 How will leadership change in the Cognitive era?   Companies like IBM, Facebook, Microsoft and Amazon are bringing cognitive computing to the masses ....     By Jacek Frankowski

I comment:  Some thought provoking ideas in the article. But insufficiently covered. Will this lead to more following and less leading? Being led by intelligent algorithms? I don't think so. It will mean that we need to better understand the implications of more data driven and quicker decisions. Cognitive (Voice, reasoning, collaboration, assistance .... ) means that we will have the opportunity for closer connections to what is happening. Need to string it all together by better understanding the process. ..... " 

Raspberry Pi Upgrade

Raspberry Pi upgrades Compute Module with 10 times the CPU performance ... Compute Module for embedded computing gets first big upgrade since 2014.   by Jon Brodkin

 Am a big fan of this, its cheap.  Easy to get started. Would suggest that any child with a hint of interest in computing, electronics or the Internet of Things should get access to one of these.  Admit its a little obscure, and many will need some initial hand holding, but there is no better way to get a start.  Lots of resources on the internet and opportunities to collaborate.  In my era, we had breadboard systems where you could easily set up circuits and test them.  That gave me a lifelong appreciation of the details.   This is a similar way to gen up interest in useful skills.  See more in my tags below on Raspberry Pi.

Forbes on Uses of AI today

In Forbes:  Mostly well known, and can be said that most of these are very focused applications.  The assistant applications like Siri and Alexa, can be said to be appear general, but they are still not providing general intelligence, but more efficient usage of better sources of plentiful data and some kinds of service specific skills, like natural language understanding and pattern recognition.

Monday, January 16, 2017

Trends in Digital Transformation


Four Trends in Digital Transformation

In  CustomerThink by Jacob Morgan

If one of the main goals of the future of work is to increase productivity and collaboration, there’s a great place to start: digital transformation. Many companies are embarking on a digital transformation in an effort to connect employees and customers around the world digitally. The future of work and digital transformation are both vital to each other’s success, and they can work together to help organizations prepare for a new wave in the workforce. A digital transformation can provide employees the tools they need to create the best work environment for the future.

Digital transformation builds on the current state of global connectivity and can include a wide variety of features. However, according to Adam Warby, CEO of global technology solutions firm Avanade, there are four main trends being seen in digital transformations across industries:  .... " 

Course about AI in the Enterprise

Ajit Jaokar of Futuretext has an upcoming enterprise AI course.
I like the idea of aiming this at the enterprise, which is rarely done,  but needs to be done more.   Its not just about solving the problems, it is about making it easier to do in the future.  The details in the link below, still some room as I understand it.  He writes:

Hello all

We had a great response to the Enterprise AI course and about to close it now
If you are interested in signing up, please let me know

Course outline 
https://drive.google.com/file/d/0BzipSlf0e7yCUllpZjk5ZGwxbjQ/view

Syllabus
http://www.kdnuggets.com/2016/11/futuretext-implementing-enterprise-ai-course.html

kind rgds
Ajit

Simulation Against Real World Data for Learning

Points to how games can act as simulations of the world, and emulate their operation against real world data.    Experimented with this in the supply chain space.  In TechnologyReview:

Robot Cars Can Learn to Drive without Leaving the Garage
Playing video games and surfing Google Street View can teach software a lot about driving. by Will Knight  .... 

" ... Researchers at Princeton University recently developed a computer vision and mapping system that gathered useful information about the physical properties of roads by studying Google Street View and comparing the scenes to the information provided in open-source mapping data. This allowed it to, for example, learn where the edges of an intersection should be based on images captured by Google’s mapping cars. .... " 

Sunday, January 15, 2017

Service Science Readings

Service Science Innovation:  Wanted to remind people of this resource, it has recently been updated.   I have contributed with some of the work we did in enterprise.  Feel free to dive in and contribute if you like.  This is particularly relevant to the use of AI  and expertise based systems to augment service systems.  And one common kind of such a system has an assistant model.   Location and recent updates.    @The_ISSIP  #ISSIP   Via Jim Spohrer.

Complex Systems Similarity through Fractal Folding


By folding fractals into 3-D objects, a mathematical duo hopes to gain new insight into simple equations. .... " 

The only direct application I ever saw of fractals in industry was for an unusual packaging application.    This article suggests that they can be used to suggest some similarity between otherwise different equations.  And maybe then used to solve them in new, perhaps faster ways?   But no examples are given, so am hard pressed to see it.  But good luck, and it is always astounding to see beauty arise from such simple statements of mathematics.  Why?  - FAD


There are lots of Apps and packages online for experimenting with fractals, if you have a child that is even a little math inclined, easy to demonstrate the ideas. -   FAD 

Benefits of RFID Data in Retail

Recently received, quite interesting. 75 pages based on Macy's data.
From the Platt Research Institute.

The new PRI Working Paper, Quantifiable Benefits and Analytical Applications of RFID Data, analyzes historical data provided by Macy's related to its RFID program. RFID technology provides unprecedented visibility into the location of retail merchandise and, therefore, provides benefits that may include reduced inventory requirements, enhanced omni-channel fulfillment, influence sales, improved customer satisfaction, reduced markdowns and labor costs, as well as improved supply chain coordination. ... " 

The research detailed in this 75-page Working Paper is focused on four Use Cases:

Display Audit.
Inventory Accuracy.
Single Unit Fulfillment.
Back to Front.

The use of RFID technology in Macy's Stores has resulted in improved display compliance, inventory accuracy, and making single merchandise units visible and available for sale.

"To our knowledge, this is the most extensive set of data made available on the quantifiable attributes associated with RFID within a retail store," said Steven Keith Platt, Director and Research Fellow, Platt Retail Institute, and Research Director, Retail Analytics Council, Northwestern University. "We would like to thank Macy's for its ground-breaking work with item level RFID and its participation in this study."   .....' 

Humans are Still at Work

In the HBR.    For now the humans are clearly still in the loop. we can learn from some areas, like autopilots or process control here.    Helping them effectively be in the loop is part of our near term task.    It means integrating a conversation, leading from problem to solution, that may include a number of machines and humans.  We do this already, when an accountant uses a spreadsheet.  Now how will each human or machine most efficiently and credibly contribute in a problem solving process?

The Humans Working Behind the AI Curtain
Mary L. Gray,  Siddharth Suri

There is a human factor at work in tasks promoted as artificial intelligence (AI)-driven, in the form of people paid to respond to queries and requests sent to them via application programming interfaces of crowdwork systems, write Microsoft Research scientists Mary L. Gray and Siddharth Suri. "The creation of human tasks in the wake of technological advancement has been a part of automation's history since the invention of the machine lathe," they note. "We call this ever-moving frontier of AI's development the paradox of automation's last mile: as AI makes progress, it also results in the rapid creation and destruction of temporary labor markets for new types of humans-in-the-loop tasks." 

Gray and Suri predict the enhancement of human services by AI will augment daily productivity, but present new social challenges. "The AI of today can't function without humans in the loop, whether it's delivering the news or a complicated pizza order," the researchers note. Technology and media companies therefore employ people to perform content moderation and curation, while many jobs are outsourced overseas and paid a low, flat rate. "This workforce deserves training, support, and compensation for being at-the-ready and willing to do an important job that many might find tedious or too demanding," according to Gray and Suri.  .... " 

Another Face and Voice for the Alexa Engine

A new kind of speaker, Jam Voice,  it's based on the Alexa engine, but it won't listen.    Another cheaper solution in the space.   How different than that, don't know.  In theVerge.

Saturday, January 14, 2017

EU to Give Robots Personhood?

Considerably premature.

Give Robots 'Personhood' Status, EU Committee Argues   
by The Guardian  in the CACM.

The European parliament has urged the drafting of a set of regulations to govern the use and creation of robots and artificial intelligence, including a form of "electronic personhood" to ensure rights and responsibilities for the most capable AI.  .... " 

McKinsey on Automation

The broader issues of how we automate is important.  As methods like AI start to be inserted into the business process, clear choices will have to be explored, made and continually monitored and improved.  A good article in McKinsey: 

" ... Automation is happening, and it will bring substantial benefits to businesses and economies worldwide, but it won’t arrive overnight. A new McKinsey Global Institute report finds realizing automation’s full potential requires people and technology to work hand in hand. .... " 

Forty Techniques for Data Science Analytics

An excellent list and pointers to introductory articles on over forty data science techniques.  Have mentioned these before, worth the repeat.  By Vincent Granville.  The practitioner should know the basics of many of these.  All very well done.  From DSC.   Nicely done.   Join DSC and get their newsletters,  which are useful.

Friday, January 13, 2017

Alexa Becoming the Voice of the IOT?

 Maybe it won't be the 'brain', but will it be the voice of the IOT,  and through the IOT to many kinds of goods and services?   I would still bet on Google or others sculpting the brain,  but Amazon is leading the pack on voice services and existing infrastructure.

" ... Amazon has sold “millions” of its Echo Bluetooth speakers with Alexa inside. Now that hundreds more Alexa-enabled devices are coming to market, the benefits to Amazon will grow, and quickly.

Amazon smartly made Alexa an easy to work with, flexible program, much like Apple did with the iPhone to spur app development. It is essentially creating an operating system for the home, one that will connect the myriad of smart devices in the Internet of Things to each other and to Amazon. ... " 

Self Organizing Robotics

Had not realized that Harvard was working on this.  Potential medical applications at least.   In the CACM:   " ... Harvard University's Self-Organizing Systems Research group has developed a "large-scale robot collective" that can self-assemble into different shapes. ....  The system is based on a subtractive approach, instead of the additive approaches to autonomous self-assembly robotics. ... " 

Electric Cars Going Mainstream

A question many of us have been thinking about.    In Knowledge@Wharton, a good economic oriented view of the direction.    Innovation: When Will Electric Cars Go Mainstream?  
Costs, Range and Infrastructure Still Impose Limits ... " 

January-February 2017 Analytics Magazine



Informs January-February 2017 Analytics Magazine
An issue with emphasis on the IoT and its evolution.  Such as on related business opportunities.

Sensors in the Internet of Things

Good early introductory and pictorial piece on the interaction of sensors and sensing for the Internet of things.  Pointers to more resources.

Thursday, January 12, 2017

Pinterist uses Deep Learning

Nice example of a use of deep learning.

 Pinterest uses deep learning AI for better pin recommendations 
By Eric David

Pinterest Inc. revealed today that its Related Pins feature will now be powered by deep learning neural networks, making it even easier for users to find all of the dessert recipes and pictures of things in mason jars they could possibly want ... "

Autonomous Collaborating Micro-Drone Swarms

The autonomy of such drones and drone swarms indicates an unprecedented depth of decision making driven by AI.  Implications are very broad.

Pentagon successfully tests micro-drone swarm  In mPhys: 
The Pentagon may soon be unleashing a 21st-century version of locusts on its adversaries after officials on Monday said it had successfully tested a swarm of 103 micro-drones. The important step in the development of new autonomous weapon systems was made possible by improvements in artificial intelligence, holding open the possibility that groups of small robots could act together under human direction.

Military strategists have high hopes for such drone swarms that would be cheap to produce and able to overwhelm opponents' defenses with their great numbers.

The test of the world's largest micro-drone swarm in California in October included 103 Perdix micro-drones measuring around six inches (16 centimeters) launched from three F/A-18 Super Hornet fighter jets, the Pentagon said in a statement.

"The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying and self-healing," it said. ...." 

And in Ars Technica.

Jill Watson at Ga Tech

Have worked with many TA's myself, so this is interesting.  It also relates to chatbots too, lessons to are to be learned here.   As in all tests of this type I wonder about the difference between providing value to someone versus fooling them.  Not the same thing. The goal can sculpt the results.    Read the full piece.

Jill Watson, Round Three.
The Georgia Institute of Technology (Georgia Tech) is beginning its third semester using a virtual teaching assistant (TA) system, called Jill Watson, in an online course about artificial intelligence (AI).

Jill, which is implemented on IBM's Watson platform, was first used last spring to successfully answer particular types of frequently asked questions without the help of humans.

Georgia Tech professor Ashok Goel told the students at the beginning of the semester some of their TAs may or may not be computers. "Then I watched the chat rooms for months as they tried to differentiate between human and artificial intelligence," Goel says.   ... " 

Full list of advisory systems being followed.

ACM Statement on Algorithmic Transparency and Accountability

The well known ACM professional society issued a statement about algorithmic transparency and accountability today.  I have been a member and participant for many years.   Algorithms have been part of computing forever, but only recently have they been closely examined regarding their implications, especially as they interact with the public.  And we interact with them every day.

 Algorithms are models, and inherently have bias.  They make and position decisions for and with us. This will further expand with the use of AI.   The ACM statement, the first few paragraphs below, the complete document is at the link, does an excellent job of laying out the problem, and their professional position.  Well stated:

Statement on Algorithmic Transparency and Accountability
Computer algorithms are widely employed throughout our economy and society to make decisions that have far-reaching impacts, including their applications for education, access to credit, healthcare, and employment.    The ubiquity of algorithms in our everyday lives is an important reason to focus on addressing challenges associated with the design and technical aspects of algorithms and preventing bias from the onset.

An algorithm is a self-contained step-by-step set of operations that computers and other 'smart' devices carry out to perform calculation, data processing, and automated reasoning tasks. 

Increasingly, algorithms implement institutional decision-making based on analytics, which involves the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

There is also growing evidence that some algorithms and analytics can be opaque, making it impossible to determine when their outputs may be biased or erroneous. Computational models can be distorted as a result of biases contained   ..... " 

Chatbot Retention Problem

The open question is, how well will people accept non human online interaction?    Bots can be novel, but that is not enough.  Bottom line, they need to provide a faster and more accurate interaction than talking to a human or reading a list of solutions.  They also need to be able to utilize the context of a problem as much as possible. I want a system to detect a problem and solve it without interaction if at all possible.  Bring on intelligent autonomy.   Below some good general guidelines and bot examples.

3 tips on improving chatbot retention   by Stefan Kojouharov  in Venturebeat: 

Chatbot retention has been a real problem. It’s so poor that most people don’t even get past the first two messages. According to İlker Köksal, the CEO of BotAnalytics, the initial drop-off is huge: “About 40 percent of users never get past the first text, and another 25 percent drop off after the second message. Daily retention rate is at a paltry 1–2 percent, and the monthly retention rate for bots isn’t much better, sitting at about 7 percent.” Fortunately, after hacking for the better part of 6 months, a few bots — such as the weather bot Poncho — have found the light and are seeing awesome retention and engagement rates.

There is such a wide variety of chatbot use cases that it does not make sense to compare against the average. You might have a use case that solves a one-time problem, in which event you hope the user never has to come back (such as the DoNotPay lawyer bot). The best way to benchmark is by comparing your bot to mobile apps in your category. At the bare minimum, your goals should be to surpass them.  ... " 

Industrial Lab for IoT

National Instruments industrial IoT lab unites rivals

Cisco, HPE, Intel and others help sponsor the lab to show enterprises that IoT systems can work    By Stephen Lawson  

There are many companies vying to build the industrial internet of things, but the systems involved are so complex that those vendors also need to cooperate. A new lab at National Instruments, in Austin, Texas, is bringing some competitors together.

The NI Industrial IoT Lab opened on Wednesday and will house testbeds for applications like predictive maintenance, time-synchronized industrial networking and “microgrids” for renewable energy. It will also be a place where companies can show off joint solutions to customers. ... " 

Wednesday, January 11, 2017

Digimarc at the NRF

I had mentioned Digimarc and their advanced product marking technologies, recently in a post.   They will be present at the upcoming 2017 NRF show on January 15-17 in NYC.   Here is an invite and more information about their company.  See the tag below for more up to date information about them in this blog.

Intro to Neural Networks and Meta-Frameworks for Deep Learning with TensorFlow

This starts with the basics of neural networks, which I find very useful.  We worked with Neural networks for learning as early as the 80s.   Good straightforward overview of the topic.   Also addresses the kinds of learning and memory involved.  This will be crucial in the longer term to understand how we converse with AI enabled assistants.

Introduction to Neural Networks and Meta-Frameworks for Deep Learning with TensorFlow
Posted by Roger Strukhoff, Director of Research, in Machine Learning
Tags: Machine Learning, TensorFlow
long-short-term-memory-networks-and-tensorflow
With sample code and demos, this blog post highlights major topics covered at a recent TensorFlow webinar: what it takes to train a recurrent / convolutional neural network, four unique object types, meta-frameworks, etc.  .... " 

Why: A Guide to Finding and Using Causes

I brought this book up some time ago.    And as I read it am struck by how essential is is to understand cause and effect when working with data, analytics, machine learning and assistant services.  This is MORE important than understanding code or data structure.

The book does an excellent job, starting non-technically, and extending to explain recent research. It is worth at least a thoughtful scan.  If you don't understand this, your modeling can be dangerously invalid.   The link below goes to a free 23 page sample pdf of the book. After that, buy it for reference.

Why: A Guide to Finding and Using Causes
by Samantha Kleinberg

"Kleinberg expertly guides readers on a tour of the key concepts and methods for identifying causal relationships, with a clear and practical approach that makes Why unlike any other book on the subject. Accessible yet comprehensive, Why is essential reading for scientific novices, seasoned experts, and anyone else looking to learn more from data."  .... '

Samantha Kleinberg's Site.  and book ordering information.

Developing Services for the Google Home

Want to know what it takes to develop new services for systems like the assistants Amazon Echo and Google Home? Came upon this largely non technical and simplified set of posts that address the approach for the just released Google Home that I am testing for assistant use.   Worth a look to start to understand the process.  Starting to review myself.   I will report back as I can.

Transfer Learning for AI Projects

Had always thought that intelligence was about learning, so this concept struck me.   Note mention of improbable events and model correctness maintenance,  always of concern in such studies.  Technical.

'Transfer learning' jump-starts new AI projects
Machine learning, once implemented, tends to be specific to the data and requirements of the task at hand. Transfer learning is the act of abstracting and reusing those smarts

'Transfer Learning' Jump-Starts New AI Projects  in InfoWorld by James Kobielus

Abstracting and reusing knowledge gleaned from a machine-learning application in other, newer apps--or "transfer learning"--is supplementing other learning methods that constitute the backbone of most data science practices. Among the technique's practical uses is productivity acceleration modeling, which is viable when prior work can be reused without extensive revision in order to speed up time to insight. Another transfer-learning application involves the method helping scientists produce machine-learning models that exploit relevant training data from prior modeling projects.

This technique is particularly appropriate for addressing projects in which prior training data can easily become obsolete, which is a problem that frequently occurs in dynamic problem domains. A third area of data science in which transfer learning could yield benefits is risk mitigation. In this situation, transfer learning can help scientists leverage subsets of training data and feature models from related domains when the underlying conditions of the modeled phenomenon have radically changed. 

This can help researchers ameliorate the risk of machine-learning-driven predictions in any problem domain vulnerable to extremely improbable events. Transfer learning also is critical to data scientists' efforts to create "master learning algorithms" that automatically obtain and apply fresh contextual knowledge via deep neural networks and other forms of artificial intelligence. ... " 

Uncertainty in US Economy

In Knowledge@Wharton,  on uncertainty in the US economy

" ...  “The biggest risk we face is uncertainty. If you ask every business leader, their biggest concern is: ‘Whatever changes occur, just do them gradually. Let us adapt.’ They have been in a world of change for a long time. That’s not going to go away; we can’t take away change…. Their biggest concern is if something hits them, and they simply can’t or don’t have the time and the resources to adjust.”

Harker and Wharton finance professor Jeremy Siegel discussed the outlook for the U.S. economy in 2017 on the “Behind the Markets” show on Wharton Business Radio on SiriusXM channel 111. Siegel hosts the show with Jeremy Schwartz, director of research at WisdomTree. (Listen to the podcast at the top of this page.) ... "