Machine learning: Thou aimest high.

Was reading the book Why Nation fails by by Daron Acemoglu and found this anecdote. This reminded me of similar thoughts, many have advocated about artificial intelligence and machine learning.

In 1583 William Lee returned from his studies at the University of Cambridge to become the local priest in Calverton England. Elizabeth I (1558–1603) had recently issued a ruling that her people should always wear a knitted cap. Lee recorded that “knitters were the only means of producing such garments but it took so long to finish the article. I began to think. I watched my mother and my sisters sitting in the evening twilight plying their needles. If garments were made by two needles and one line of thread, why not several needles to take up the thread.”

This momentous thought was the beginning of the mechanization of textile production. Lee became obsessed with making a machine that would free people from endless hand-knitting. He recalled, “My duties to Church and family I began to neglect. The idea of my machine and the creating of it ate into my heart and brain.” Finally, in 1589, his “stocking frame” knitting machine was ready. He travelled to London with excitement to seek an interview with Elizabeth I to show her how useful the machine would be and to ask her for a patent that would stop other people from copying the design.

He rented a building to set the machine up and, with the help of his local member of Parliament Richard Parkyns, met Henry Carey, Lord Hundson, a member of the Queen’s Privy Council. Carey arranged for Queen Elizabeth to come see the machine, but her reaction was devastating. She refused to grant Lee a patent, instead observing, “Thou aimest high, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars.”

Crushed, Lee moved to France to try his luck there; when he failed there, too, he returned to England, where he asked James I (1603–1625), Elizabeth’s successor, for a patent. James I also refused, on the same grounds as Elizabeth. Both feared that the mechanization of stocking production would be politically destabilizing. It would throw people out of work, create unemployment and political instability, and threaten royal power. The stocking frame was an innovation that promised huge productivity increases, but it also promised creative destruction.

I am not smart enough to know if the apprehensions are right or wrong, but as a machine learning enthusiast, I am fascinated with the field. 

Deep learning, AI, ML are tools like knife and hammer that we are now beginning to understand better and put them to practical use.
Exciting times.

The Best Asset

At one of his Berkshire Hathaway annual meetings, Warren Buffett said:
“The most important investment you can make is in yourself. Very few people get anything like their potential horsepower translated into the actual horsepower of their output in life. Potential exceeds realization for many people…The best asset is your own self. You can become to an enormous degree the person you want to be.”

Switch from Discrete Mathematics to Probability, Statistics

From the book Foundations of Data Science by John Hopcroft and Ravindran Kannan

Computer science as an academic discipline began in the 60’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata,regular expressions, context free languages, and computability.

In the 70’s, algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications.

There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks, which are by far the largest such structures, presents both opportunities and challenges for theory

All this entails, there is the switch from discrete mathematics to more emphasis on probability and statistics.

Joint plot with Matplotlib

Today I am releasing a simple module to create joint plot with Matplotlib on github. Joint plot is available in the excellent seaborn library but unfortunately it’s not always available on many systems. Recently I needed this functionality, so wrote this simple module with matplotlib.

The functionality is almost similar to seaborn but with limited feature. This has helped me in my work, releasing it in the hope that others might find it useful.

Sample usage:

Import Jointplot
tips=pd.read_csv(r'tests/tips_p.csv')
data=np.c_[tips['total_bill'].values,tips['tip'].values]
jointPlot(data,kde=True)

Find the code at this github repository.

The origins of Internet of things

Anywhere you look in the engineering domain, people are talking about IoT (Internet of Things). All OEM’s and service providers to OEM and many start-ups are looking at this new thing.

But is it really new?

It turns out no. Its origin goes back to 1999. Today the focus is new, mostly because of the possibility of using advancing machine learning on the data generated with the sensors.

So if you are interested in IoT, the following short article by Professor Duncan McFarlane should be an interesting read.

Most of you have probably heard the Internet of Things, or the IoT, mentioned but have you ever wondered what it means and where it all began?

Well here’s my version of it:

In 1999, the Auto-ID Centre was founded, which subsequently formed a unique partnership between around 100 international companies and 7 of the world’s leading research Universities, including the MIT and University of Cambridge. Kevin Ashton, Professor Sanjay Sarma and David Brock were the early co-founders and I became involved as European Research Director a year later setting up the European side of things and pushing the industrial research.

The Auto-ID Centre’s aim was to investigate and understand what came next after the barcode – and particularly what an electronic barcode would look like. Sanjay came to see me in Cambridge in March, 2000.

We discussed barcodes and RFID as an electronic replacement and I think my initial comment was that it all seemed a reasonably dull research activity! I was of course later forced to eat my words as the project expanded but also in our research we realised that RFID was actually a solution to a manufacturing control problem we had been trying to resolve – how to establish an Internet connection for parts and products while they were being made.

The focus of the Centre from the beginning was to research ways in which an electronic tag could be put on every single object in the world, allowing each to be uniquely tracked and potentially controlled – and to do so in the most cost effective way. We realised that to make RFID cheap we needed the smallest chip possible – Silicon was/is expensive – and thus we needed to put all stored data in memory elsewhere. The Internet was the obvious place to start, hence the phrase “Internet of Objects” or “Internet of Things” became a clear reference point and the origin of the internet of things that we refer to today. Believe the term “Internet of Things” was in fact coined by Kevin Ashton in 1999 during a presentation he made at P&G.

Read the full article