A Great CTO Talk about Technology at Walmart

OrangeScape has this new initiative called CTO Talks. I think it is a brilliant idea. “While there are a lot of conversations taking place at the software development level, there are none at the CTO level”, says Suresh. I agree. We need different levels of conversations on technology.

I enjoyed  The talk on Technology at Walmart – a few Glimpses. I hope to see other more comprehensive blog posts. I was looking for the use of Machine Learning at Walmart, and I was not disappointed.

Here is a list of uses of Machine Learning (ML) at Walmart.

    • Competitive Intelligence and Analytics
    • Crawl frequency prediction (how frequently you can crawl certain sites for price information – too many crawls, and you will be blocked. Too few and you an miss useful information. Different sites update information at different intervals)
    • Natural Language Processing (NLP) of product catalogs
    • Bossa Nova robots roaming the aisles at Walmart locations checking out of stock items mislabeled shelf tags, and incorrect prices.
    • IOT  at Walmart – Monitoring temperatures of Refrigerators in real time
    • Visual inspection and Spoilage predictions
    • Predictive analytics of future failure of equipment
    • Predicting attrition (they have 2.3 million associates in over 11,000 locations)
    • Predicting absenteeism based on weather patterns an HR application (and an important contributor to maintaining service levels in their stores)
    • Hari briefly touched upon blockchain. They are looking at using it for tracing grocery items from source to customer.

Walmart is one of the leading indicators of technology adoption in retail. Hari mentioned that they were the first to introduce Satellites dishes in their store locations, barcode scanning, use of RFID and providing a direct view of store items to their suppliers.

It was a great talk. It was no wonder that we had an amazing turnout (more than 250 registrations). Hari answered all the questions patiently and in depth.

Discovering Information with a Little Help from Tweet Assistant

If you noticed a spike in my Twitter posts today, there is a reason.  I have been searching #machinelearning using Tweet Assistant (more on Twitter Assistant in a future post)

These tweets, hashtags, mentions and popular posts provide a gold mine of information.  However, there is more to analyzing tweets than getting news and opinions. Within minutes, I was following some cool people and started discovering awesome tweets and visualizations.

Machine Learning – Top 10 Mentions

 

Machine Learning – Top 5 Hashtags

I started retweeting some of the posts I found but felt that there are far too many posts and a discussion of these may fill a couple of blog posts. So that is what I am going to do.

 

 

Applications of ML – Improving Depth detection, Night Sight, Video Play and Notifications

Here are a few fascinating applications of ML. I mostly track business applications of ML so I was pleasantly surprised to see how Unsupervised Learning and Reinforcement Learning (two ML techniques that do not get much coverage) was being used by two of the biggies in the AI and ML space.

The first two – Depth detection and Nightsight are posts from Google AI Blog.  The following concepts were covered in these posts.

  • PSL Positive shutter lag
  • Motion metering
  • Exposure stacking
  • Astrophotography
  • Super-resolution
  • Auto white balancing and Learning based AWB algorithm
  • Ultra long shot photography
  • Optical Image stabilization

    Facebook Horizon article covered how they were improving user experience with Reinforcement Learning.

  • Improving video playing using Reinforcement Learning (RL)
  • Improving user notifications using Reinforcement Learning

Links to articles:

  1. Under the hood of the Pixel 2: How AI is supercharging hardware – Google AI 
  2. Google AI Blog: Night Sight: Seeing in the Dark on Pixel Phones
  3. Horizon: An open-source reinforcement learning platform – Facebook Code 

Little Bits of History of my Programming Journey

1972 – I wrote my first program in PDP-8 assembly language

1973-74 – Diagnostics for a clone of PDP-11 called TDC-16, early device drivers (they were called IOCS – input/output control systems). Early programs were written in Machine Language (coded in octal) since no assembler was available, keyed programs into the console using toggle switches (as binary code) and debugged

1974 – Had paper tape – ASR-35 later high-speed reader and mylar tapes

1974 – Debugged device drivers for magnetic tapes and discs, wrote memory diagnostics that detected noise in core memory (and required shielding)

1975 – My First commercial program in assembly language for Bombay stock exchange for matching buys and sells of stocks. The records were punched in cards and fed to the computer, stored in magnetic tapes and matches performed. The memory configuration was a whopping 16KB.

1976 – Taught, RSX-11M (a real-time operating system in PDP-11) at Tata Electric. Wrote first set of PDP-11 program in RSX-11M an operating system for PDP-11

1978 – Learned  operating systems (RT-11, 11M, IAS, RSTS/E) all PDP-11

1978-79 Built the first soap survey program on RSTS/E in Basic Plus (for IMRB)

1979 – Wrote first commercial applications in Cobol (mostly for training others) and several small Basic-Plus utilties. Worked on performance tuning of RSTS/E operating system.

 Patched RSTS/E corrupted disk writing programs in Basic-Plus

1980-81 Wrote commercial programs in Cobol for consulting at Ashok Leyland

1983 – Developed benchmarks in Cobol for Wipro in Cobol

1984 – First C program (a database schema analyzer in Decus C)

1984 – My first Comdex in Las Vegas

1985 – First relational database metadata design as part of Integra SQL development and wrote small C programs mostly for testing the database

1986 – Integra SQL Version -1 with no nested selects, designed and built entirely by reading C.J.Date’s book on Relational Database Systems

1986 – Licensed Integra SQL to SCO (Santa Cruz Operations)

1987 – 1990: C-Trieve (an ISAM file management system), Objectrieve – C-Trieve extended to support Blobs, Licensing of C-Trieve to the White Water Group (they called it WinTrieve)

1991 – Objectrieve/VB was born and exhibited at Comdex May 1991

1992 – DbControls a set of custom controls for building database applications

1993 – Integra VDB – The first relational database set of components. Got covered in the BYTE magazine

1994-1996 – Layered SQL on top of dBase, Paradox, Btrieve (the last one was a project for Varian systems). Most of the coding was writing small examples in C, VB.

1996-2008 Coding winter

2009-Now – Dabbling in Python, little bits of ML, Chatbots

Tim O’Reilly: It is up to us

Listening to this conversation with Tim O’Reilly, was one of the most rewarding experiences. In this conversation, Tim and Byron discuss several topics that make you think. I listed a few here.

  1. Fitness functions (see the quote below)
  2. On Amazon’s use of robots for business
  3. About Cognitively Augmented workers
  4. The law of conservation of attractive profits
  5. On Intelligence (human and artificial)
  6. How to pair humans with machines
  7. Step changes and their impact
  8. On anticipating  and countering the worst fears
  9. The Robustness Principle
  10. Agreement Protocols
  11. On Platforms and Eco-systems
  12. On doing meaningful work

I will pick a few of the topics (1-5) and take the liberty of quoting from the transcript. My goal is to kindle your interest enough to read the article and then the book.

On Fitness functions and how they focus companies on delivering value with technology (including AI).

If you look at Google; their fitness function on both the search and the advertising side is relevance. You look at Facebook; loosely it could be described as engagement.

On Amazon’s use of robots in their business:

an analysis of Amazon. In the same 3 years which they added 45,000 robots to their factories, they’ve added hundreds of thousands of human workers.

About Cognitively Augmented workers.

Then, Sidecar and Lyft figured out the other piece of the equation, because Uber was just black cars. They figured out that in order to have enough drivers to really fill out the marketplace, other than a small segment of well-off people, you’d get ordinary people to supply their cars. And you could do that because those drivers are cognitively augmented. It used to be that you had to be a professional driver, because [when] somebody says, “I want to go to such and such an address,” you’d need to really know the city. You [would] need to have a lot of experience to know the best routes. Well, guess what, with these apps [like] Google Maps and Waze, anybody can do it. So I started looking at that and [saw that] we have a marketplace of small businesses managed by algorithms to help them match up with customers.

Law of conservation of attractive profits:

Clay Christensen, back in 2004  talked about  “the law of conservation of attractive profits,” and that’s what helped me get from open source to web 2.0—[is] when one thing becomes a commodity, something else becomes valuable. So if self-driving cars commoditize driving, you have to ask yourself, what becomes valuable. And I think it’s going to be new kinds of augmentation for humans, new kinds of services that you’ll put on top of driving.

On Intelligence (artificial):

And so what would be something that we have today that would qualify or come close to qualifying as that in your mind?

You mean, in terms of machines?

Yes.

Nothing.

And why do you think that?

I’m with Gary Marcus on this, you know. He kind of talked about how the frontier of AI right now is deep learning, and it’s great, but you still have to train it by showing it a gazillion examples of something, and after you show it a gazillion examples, it can figure stuff out. That’s great, but it can’t figure that out without being exposed to those examples. So, we’re a long way from kind of just flicking the switch, having a machine take in its experience of the world, and basically come to conclusions about it.

Tim ends with this appeal and an inspiring message.

“Hey, we have a lot of things to worry about, we have enormous new powers, let’s put them to work, in the right, way, tackling the hard problems.”

I have been a big fan of Tim’s “Work on stuff that matters” and quoted him. It framed some of my decisions on what to spend time on.

Meta:

I have been following Tim for a couple of decades. More about that in a later post. I recently started listening to Byron’s One Minute AI podcast and the GigaOm AI podcast.

If You Know How to Program…

Recently I have come across a slightly different view of programming. In this view, programming is used as a way of learning Mathematics and other topics.

The premise of books in the Think X series is that if you know how to program, you can use that skill to learn other topics.

from Think Bayes by Allen B. Downey.

I see programming as a way of learning Mathematics.

Recently, several countries have included basic programming in the national curriculum. In some of these countries (such as Estonia and France) programming is placed in direct curricular connection to mathematics, whereas in others (England, and Sweden) programming is related more to a design and engineering agenda. However, in all cases the focus is not on developing general “humanistic” skills with technology, rather it is on thinking in algorithms, writing programs, and developing technology. In other countries such curricular changes are being discussed and tested on a small scale. Hence, it makes sense to take a closer look at the arguments that have previously been proposed for utilising programming in mathematics education.

from Learning Mathematics through Programming

This is a fascinating concept. If we believe in it (after looking at various case studies), teaching kids programming may be a good move. I always thought of programming as a way of thinking and solving problems.

Meta:

There was a course on Coursera called “Coding the Matrix: Linear Algebra through Computer Science Applications”. But I am not able to locate it now.

 

When you Look at Life as a Series of Small Experiments

I think of life as a series of small experiments. When you frame it that way, you are no devastated by failures. Some experiments succeed and others fail. You don’t have to stress too much about the outcome. Whether they fail or succeed, you always gain a bit, because you learn something.

I think I have had more failures in my life than successes. But a few successes more than wiped out most failures. The rest were swallowed by time.

It is not a happy ending, though (as happens in movies). I am still living with the effects of some of those failures.

 

Ideas From KCG Innovation Challenge

We started with 40 ideas ( 5 each from 8 departments). After initial screening, we selected 16 ( 2 from each department) and had them present to an external jury. Here are a list of these ideas. we will award three top ideas. We will support many of these ideas turn to prototypes.

  1. Robotic sewage Cleaners
  2. Detecting Landmines Using QuadCopter
  3. Partial Replacement of Natural Course Aggregate with Plastic Aggregate
  4. BIM Modelling using alternate Realities
  5. Voice ATM
  6. IOT based fire alert system
  7. Detecting and helping Dyslexia in Children
  8. Detecting early signs of foot problems for Diabetic Patients
  9. Automating powering up and down classrooms in a college
  10. Water Management system using IOT
  11. Automatic segregation of  recyclable material
  12. Hybrid Solar Panel
  13. MTC Bus Tracking
  14. IOT based Smart glasses
  15. Flexible and Compact couch
  16. Temperature control Jacket

What is Build to Learn?

Build to Learn is an initiative by a group of volunteers to help people learn programming by building useful micro-products. Our motto is – Build to Learn and Learn to Build.

Anyone who wants to learn or build or do both can participate. We plan to meet a few times a week in 3-4 hour coding sessions and build useful products.

The setting is informal. You can start with a simple one paragraph definition of a product and recruit volunteers to work with you on the idea. We do not have any rigid processes. The team can decide how to interact.

We had the first session on the 3rd of February and 10 of us were present. We started 4 projects. We hope you can all join and either learn or help others learn.

Who can participate? Anyone who wants to help  define  a product, code, design, and  test.

 

Technology in Farming – Robots, Mixed Reality, Machine Learning

Is manual farming sustainable as the need for agricultural products grow in demand? Can technology help? How does it impact lives of farmers? Is it the right thing to do? Like any other applications of technology, there are more questions than answers. The following links are just a set of leading indicators of trends.

Agricultural vehicles known as “cucumber flyers” enable as many as 50 seasonal workers to harvest crops.
Experts from Fraunhofer IPK in Berlin, along with other German and Spanish researchers, are studying the potential for automating cucumber harvests in the scope of the EU project CATCH, which stands for “Cucumber Gathering – Green Field Experiments.” Project partners are the Leibniz Institute for Agricultural Engineering and Bioeconomy in Germany and the CSIC-UPM Centre for Automation and Robotics (CAR) in Spain.
During the Hands Free Hectare project, no human set foot on the field between planting and harvest—everything was done by robots. This includes:
  • Drilling channels in the dirt for barley seeds to be planted at specific depths and intervals with an autonomous tractor;
  • Spraying a series of fungicides, herbicides, and fertilizers when and where necessary;
  • Harvesting the barley with an autonomous combine.

How mixed reality and machine learning are driving innovation in farming

The Economist, in its Q2 Technology Quarterly issue, proclaims agriculture will soon need to become more manufacturing-like in order to feed the world’s growing population. Scientific American reports crops will soon need to become more drought resistant in order to effectively grow in uncertain climates. Farms, The New York Times writes, will soon need to learn how to harvest more with less water.