Monthly Archives: March 2019

A History of Chile









Ever since graduate school, I have been tracking Chile’s rise. It has emerged today as one of the most economically and socially stable countries in the world today. 

Its history is the stuff of a great novel: high drama, because of its wide swings between right-wing dictatorship and left wing socialism; blood and guts, because of a trail of murders and revenge; triumph and tragedy, because of its not-so-steady course toward economic prosperity; intrigue, because of its history of political polarization; and strife, as its varied regions and cultures struggled to capture the heart of such a diverse country. 

A great novel deserves a great landscape, and Chile meets the test.  Chile is beautiful. It is a magnificent aggregation of landscapes, climates  and cultures. The country’s landscapes range from mountains tp deserts, and from forests to coastlines. 

Most of the Earth’s climates can be found in Chile. Over 10 have been identified in Chile. This wide range of climates can be divided into three general zones: the desert provinces of the north, central Chile (with a Mediterranean climate), and the humid regions of the south. Each of the three zones have different ecosystems, topography, and vegetation.

Chile is a long (2,670 miles long) and narrow (217 miles wide at its widest) country at the southwest corner of South America. It is among the longest countries in the world. 

The country has 18 million people, a GDP of $451 billion, with a 2.2% compounded 5-year growth. Importantly, it enjoys a per capita income of $24,537 – which classifies it globally as “upper middle-income”. Chile enjoys almost universal literacy with 95.7% of the population 15 years or older.

Chile’s historical investments in sanitation, nutrition, potable water, and basic education dating back to the 1920s have resulted in significant reductions in communicable diseases (WHO, 2006).

It’s prosperity is hailed by conservative commentators. The Heritage Foundation says: “Chile’s economic freedom score is 75.4, making its economy the 18th freest in the 2019 Index. Its overall score has increased by 0.2 point, with increases in labor freedom, business freedom, and monetary freedom offsetting a steep decline in judicial effectiveness. Chile is ranked 3rd among 32 countries in the Americas region, and its overall score is above the regional and world averages.”

Chile has struggled to maintain a kinder and gentler face to the world. With Pinochet, the country endured 16 long years of brutal dictatorship. This was followed by 20 years of center-left leadership. Then, the country elected a far right candidate, followed by a return of the center-left. All the while, Chile was growing economically, thanks to the implementation of economic theories attributed to Milton Friedman and economics form the University of Chicago. 

For all of its brutality, Pinochet is credited with instituting the economic reforms that set Chile on the course it enjoys to this day. 


This history starts from the beginning. However, this section – “overview” – intentionally starts from the end, today, and goes backward, from present-day Chile to the original inhabitants of Chile. 

Sebastian Pinera won the presidential election in December 2017, having served as president for four years until 2014.

He is a billionaire conservative. The conservative movement in the country holds power for now, but the country has experienced wild swings in power from right to left over the years. 

Pinera presides today over a growing economy and a country which has proudly reached all of the UN’s “Millenium Development Goals.”

Chile today is the beneficiary of economic reforms instituted after 1973 – over 45 years ago. Economists trace the fits and starts that followed economic reform: a sharp recession followed the reforms; investment averaged only 16% in the ten year period 1974 – 1984; a turnaround followed, and investment grew strongly – averaging 25% of GDP in a decade-long boom from 1987 – 1998. 

For 20 years, the country was governed by a center-left coalition, which came to and end in 2000.

From 1973 – 1990 Pinochet ruled as a dictator of the country. Much has been written about this traumatic period in the Country’s history. 

American government worried for years about the ascendancy of communism in Chile. This worry reached a zenith when Salvatore Allende was elected as a socialist in 1970.

Chile was a seesaw politically for years. During World War II, the government veered left, attempting to emulate the social policies of FDR in America.

The Spanish arrived in the early 16th century, and never left. Chile declared its independence from Spain in 1810, but never achieved that independence until 1818. 

Prior to the arrival of the Spanish in the 16th century, the Inca ruled northern Chile for nearly a century while an indigenous people, the Mapuche, inhabited central and southern Chile.


Key Periods

Early 1500’s – Spanish adventurers arrive in Chile and fight with indigenous peoples, especially the Incas and Araucanians. 

1600’s – 1800’s – Spanish domination

Early 1800’s – Locals try to capitalize on Napoleon’s takeover of Spain by declaring independence, are crushed by Napoleonic forces, and are ultimately victorious, thanks to the “Army of the Andes”.  Military leaders Jose de San Martin and Bernardo O’Higgins are heroes of the independence movement. Bernardo O’HIggins becomes first leader of country in 1818.

1823 – 1830 – Civil war, a fight between “federalists” and “centralists”. The conservative centralists win. 

1851-61 – New constitution. President Manuel Montt liberalises constitution and reduces privileges of landowners and church.

1879-84 – “War of the Pacific”, which Chile wins. Chile increases its territory by one third after it defeats Peru and Bolivia in War of the Pacific.

Late 19th century – Pacification of Araucanians paves way for European immigration; large-scale mining of nitrate and copper begins.

1891 – Civil war over constitutional dispute between president and congress ends in congressional victory, with president reduced to figurehead.

1925 – New constitution increases presidential powers and separates church and state.

1927 – General Carlos Ibanez del Campo seizes power and establishes dictatorship.

1938-46 – Communists, Socialists and Radicals form Popular Front coalition and introduce economic policies based on US New Deal.

1948-58 – Communist Party banned.

1952 – Gen Carlos Ibanez elected president with promise to strengthen law and order.

1964 – Eduardo Frei Montalva, Christian Democrat, elected president and introduces cautious social reforms, but fails to curb inflation.

1970 – Salvador Allende becomes world’s first democratically elected Marxist president and embarks on an extensive programme of nationalisation and radical social reform.

1973 – 1990 – Chief of Staff General Augusto Pinochet ousts Allende in coup and proceeds to establish a brutal dictatorship.

1990 – Christian Democrat Patricio Aylwin wins presidential election; Gen Pinochet steps down in 1990 as head of state but remains commander-in-chief of the army.

1994-95 – Eduardo Frei succeeds Aylwin as president and begins to reduce the military’s influence in government.

1998 – Gen Pinochet retires from the army and is made senator for life. He is arrested in Europe, but returns.

2000 – 2004 – Socialist Ricardo Lagos is elected president.

2002 Gen Pinochet resigns from his post as a lifelong senator.

2005  Revised constitution, (revisingPinochet-era constitution), including one which restores the president’s right to dismiss military commanders.

2006  Michelle Bachelet wins the second round of presidential elections to become Chile’s first woman president and the fourth consecutive head of state from the centre-left Concertacion coalition.

2006 August – Chile and China sign a free-trade deal, Beijing’s first in South America.

2006 December – Pinochet dies.

2008 Peru files a lawsuit at the International Court of Justice in a bid to settle a long-standing dispute over maritime territory with neighbouring Chile.

2008 May – Unexpected eruption of Chaiten volcano which has been dormant for 9,000 years. Authorities order complete evacuation of two towns in Patagonian region.

2009 February – President Bachelet makes the first visit to Cuba by a Chilean leader in almost four decades.

2009 October – Relations with Peru are strained further after Chile stages a military exercise in the north, close to the disputed border.

2010 Right-wing candidate Sebastian Pinera defeats former President Eduardo Frei in presidential election, ending 20 years of rule by the left-wing Concentracion coalition.

2010 February – Earthquake: Hundreds die and widespread damage in central Chile. 

2011 Protest throughout the country

2013 April – Bolivia files a lawsuit against Chile at the International Court of Justice in The Hague to reclaim access to the Pacific Ocean. Bolivia lost access to the coastline in a 19th century war with Chile, leaving it landlocked ever since.

2013 May – Chile, Colombia, Mexico and Peru agree to scrap most of the tariffs on trade between their countries, hailing the move as an historic step towards regional integration.

2014 Left-wing candidate Michelle Bachelet returns to power.


Using 1990 as baseline, Chile has accomplished the World Health Organization (WHO)’s Millennium Development Goals for developing nations

Deep Learning

Singularity, Deep Learning, and AI

DRAFT: January, 2019

CREDIT: The Deep Learning Revolution, by Terrence J. Sejnowski



Sadly, I was giving up on speech recognition just as it was emerging. I gave up, after 30 years of waiting, around 1995. Bad idea.

Speech recognition stopped being just cute, and exploded onto the world scene during the late 1990’s. It has taken almost two decades to commercialize, but the technologies birthed in the late 1990’s have now yielded commercial grade results. 

Why then? Why the late 1990’s?  

In reading “The Deep Learning Revolution”, by Terrence J. Senjnowski, I learned why: an underlying technology called “deep learning” had come of age. 

“Deep Learning” was birthed in the late 1990’s, but the research leading up to the term goes back to the 1980”s (and the foundations of all this goes back to 1965).

Many trace the current revolution in deep learning to October 2012. Researchers proved successful in a large-scale ImageNet competition. Their approach won the ICPR contest on analysis of large medical images for cancer detection.

In 2013 and 2014, the error rate on the ImageNet task using deep learning was further reduced, following a similar trend in large-scale speech recognition. The Wolfram Image Identification project publicized these improvements.

Image classification was then extended to the more challenging task of generating descriptions (captions) for images, often as a combination of CNNs and LSTMs.

IN 2015, spectacular practical application began to burst on the scene in 2015. Speech recognition was one. Facial recognition was a second. Pattern recognition can identify cats, dogs and dog breeds, and applications that allow medical diagnosticians to improve their diagnoses. 

Today, applications address computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

It turns out that new approaches to Deep Learning have broad applicability. But one of those applications that has broken into mass commercialization is …. speech recognition. These breakthroughs trace back to breakthroughs in “speaker recognition” – results that were achieved at SRI.

To understand the massive improvements, consider this: In 2015, Google Voice Search experienced a dramatic performance jump of 49%.

Or consider this: All major commercial speech recognition systems (e.g., Microsoft Cortana, Xbox, Skype Translator, Amazon Alexa, Google Now, Apple Siri, Baidu and iFlyTek voice search, and a range of Nuance speech products, etc.) are based on deep learning.

More on speaker recognition: The recent history traces back to breakthroughs at SRI in the late 1990’s. The research arms of NSA and DARPA needed answers. To get the answers, they turned to SRI international. SRI made the biggest breakthroughs. They cracked “speaker recognition” at that time. They failed, however, to crack “speech recognition”. That came later, around 2003.

Specifically, important papers were published in the late 1990’s describing how deep learning could solve the nagging issues of speaker and speech recognition. 

The deep learning method used was called long short-term memory (LSTM). (Hochreiter and Schmidhuber, 1997.)

Deep learning for speech recognition came later, in the early 21st century. In 2003, LSTM started to become competitive with traditional speech recognizers on certain tasks.. Later it was combined with connectionist temporal classification (CTC) in stacks of LSTM RNNs.

Google Voice Search drew upon “CTC-trained LSTM” – in other words, the LSTM technologies birthed in the late 1990’s had by 2015 yielded commercial-grade results.

Today, lay people understand the power of speech recognition by using “Siri” – or by using the voice transcription technologies on their iPhones. Everyone has noted the vast improvements in the last several years. All of these improvements are due to Deep Learning. 

Let me step back at this point and trace the breakthroughs by researchers. I begin with a glossary:

AI – Artificial Intelligence

ANN – Artificial Neural Networks

DNN – Deep Learning Networks – a variant of artificial intelligence in which software “learns to recognize patterns in distinct layers

RNN – Recurrent Neural Networks

“Deep” – The “deep” in “deep learning” refers to the number of layers through which the data is transformed.

“Layers” – each layer represents a level of abstraction that allows the machine to group like data from unlike data (the machine classifies). Each successive layer uses the output from the previous layer as input. The “deep” in “deep learning” refers to the number of layers through which the data is transformed. Deep learning helps to disentangle these abstractions and pick out which features improve performance.[1]

Pattern Recognition

Image Recognition (In 2011, deep learning-based image recognition has become “superhuman”, producing more accurate results than human contestants.)

Speech Recognition (and ASR – Automatic Speech recognition)

Speaker Recognition (In 1998, deep learning-based speaker recognition was proven to be effective)

Visual Recognition – recognizing object, faces handwritten zip codes etc

Facial Recognition – for example, Facebook’s AI lab performs tasks such as automatically tagging uploaded pictures with the names of the people in them.

Object recognition – (in 1992, a method of extracting 3D objects from a cluttered scene

Medical Imaging – where each neural-network layer operates both independently and in concert, separating aspects such as color, size and shape before integrating the outcomes” of medical imaging

Deep Learning Techniques

Supervised – uses classifications

Unsupervised.  – uses pattern recognition (without human assistance)

Backpropogation (Backprop) – passing information in the reverse direction and adjusting the network to reflect that information.

LSTM – long short-term memory 

CTC – connectionist temporal classification

CAP – the chain of transformations from input to output. CAPs describe potentially causal connections between input and output.

More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output.


TAMER – in 2008, proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.[

TAMER (Deep TAMER) – in 2018, is a new algorithm using deep learning to provide a robot the ability to learn new tasks through observation. (robots learn a task with a human trainer, watching video streams or observing a human perform a task in-person). The robot practices the task with the help of some coaching from the trainer, who provided feedback such as “good job” and “bad job.”

CRESCEPTRON, in 1991, a method for performing 3-D object recognition in cluttered scenes. 


GPU – in 2009, Nvidia graphics processing units (GPUs) were used by Google Brain to create capable DNNs. This increased the speed of deep-learning systems by about 100 times.

Training Sets

TIMIT (Automatic speech recognition trainer)

MNIST (image classification trainer)

The MNIST database is composed of handwritten digits. it includes 60,000 training examples and 10,000 test examples. As with TIMIT, its small size lets users test multiple configurations.

With this glossary, a few simple statements can pinpoint why the current revolution is exploding:

Hardware has advanced, thanks to GPU commercialized in 2009.

Software has advanced, thanks to GPU-based successes in cancer image identification in 2012. 

Pattern recognition has advanced, with speech recognition leading the way. The TIMIT training set has allowed exponential progress, especially in 2015, leading the way. 

Robotics have advanced, thanks to deep TAMER breakthroughs in 2018.