Author Archives: Editors

“Post-America World & Post-America, America” By F. Sheikh

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After WWII, two new super powers emerged, United States of America and Soviet Union. America took a central stage in world politics as well as in world economy. Britain and other colonial powers were forced out of their colonial lands. American expanded its influence in many of those countries. America’s Marshal plan for war ridden Europe expanded its influence in Western Europe. Soviet Union took Eastern Europe under its fold and started to compete with America for its own influence in other countries. This gave us cold war, arms race, proxy wars and ideological war around the globe. America supported even dictators and monarch, especially in the Middle East, for its influence and economic interests.

America became a military giant and at the same time an economic powerhouse attracting talent from around the globe. Soviet Union tried to match America’s military might and dominance, but it sorely lacked economic development and disintegrated in 1991, leaving one super power standing-United States of America.

Despite America’s record of supporting favorite dictators, toppling unfriendly regimes and brutal ideological wars, it was still seen in the world as a symbol of human aspirations of liberty, success, and individual prosperity. Overall America projected itself as a force of good, democratic values, and morals; and it considered itself guardian of those values in the world and at home.

All this perception started to change after America’s fierce and revengeful response to brutal 9/11 attack. In anger and short-sightedness, it trampled on the very same values and ideals, both at home and abroad, which it wore as a badge of honor and national identity. It adopted torture forbidden by Geneva Convention, opened Rendition Torture Centers abroad, locked up prisoners, including some innocents, for years at Guantanamo Bay without Habeas corpus, abused prisoners at Abu Gharib and in Afghanistan, and violated civil liberties at home with Patriot Act. Cities were bombed to stone ages with thousands of innocent civilians killed in Afghanistan and Iraq.  America eroded its leadership in the world as guardian and leader of moral, liberal, and democratic values.

President Obama tried to reverse this erosion by half measures, but it has taken its roots. Anti-terrorism evolved into an industry requiring full attention and resources at the expense of other priorities. United States forced other countries to adopt similar anti-terrorist measures and allocate essential resources. Tyrants and oppressive regimes further strengthen their grip on power by utilizing anti-terrorist excuses. Meanwhile globalization created extreme inequality that went unnoticed and unattended by the mainstream political parties. Eroded values and inequality, a fertile ground for populous parties and demagogue leaders, gave us Donald Trump and populous leaders in other countries.

Donald Trump destroyed, within a short time, last vestiges of America’s leadership and moral standing in the world and is isolating itself from rest of the world. We are still superior economic and military power, but we are no longer the leader. It is a post-America world unfolding in front of us. Western Europe is adrift, while China is flexing its newfound economic muscle. New technology, including social media, has provided new tools to adversaries that can sow the seeds of discontent and factionalism without much investment. America lost its leadership at a crucial moment when it was needed the most to create strong global norms and rules to prevent the abuses of new technology which has the potential of great benefits but at the same time potential of unleashing destructive forces. This leadership was critical when the global and domestic infrastructure in every sphere of life depends on new technology.

When you are a world leader, you are under pressure to perform better in every aspect, both abroad and at home. The world holds you at a higher standard. When the leadership standing is eroded, performance deteriorates. It is a vicious cycle. When your own expectations are low, and others also expect low from you, it is hard to improve performance.  In a recent article in Washington Post, Ben Guarino writes that some top researchers from ivy league institutions are moving to China for lack of funding and visa restrictions. China is offering them all the resources needed and better monetary benefits. China is attracting talent from the rest of the world also that was our hallmark and lifeline.

America itself is a post-America country now. American ideals, values and patriotism take a back seat to personal and political party interests. Our political leaders are no different from third world country leaders who do not hesitate to undermine established national institutions of democracy for personal interests. Our business corporations are richer than country, but no longer feel obligation to the country that provided them resources to get rich.

Can it be reversed? Off course, but first we must restore our ideals, morals and stop the slide. It will get harder with every passing day, especially with incompetent and shortsighted leadership.

 

What’s the difference between A.I., machine learning, and robotics?

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Artificial intelligence is everywhere. On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines.

A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything. That’s not exactly the case. A.I., machine learning, deep learning, and robotics are all fascinating and separate topics. They all serve as an integral piece of the greater future of our tech. Many of these categories tend to overlap and complement one another.

The broader AI field of study is an extensive place where you have a lot to study and choose from. Understanding the difference between these four areas are foundational to getting a grasp and seeing the whole picture of the field.

Artificial intelligence

At the root of AI technology is the ability for machines to be able to perform tasks characteristic of human intelligence. These types of things include planning, pattern recognizing, understanding natural language, learning and solving problems.

There are two main types of AI: general and narrow. Our current technological capabilities fall under the latter. Narrow AI exhibits a sliver of some kind of intelligence – be it reminiscent of an animal or a human. This machine’s expertise is as the name would suggest, is narrow in scope. Usually, this type of AI will only be able to do one thing extremely well, like recognize images or search through databases at lightning speed.

General intelligence would be able to perform everything equally or better than humans can. This is the goal of many AI researchers, but it is a ways down the road.

Current AI technology is responsible for a lot of amazing things. These algorithms help Amazon give you personalized recommendations and makes sure your Google searches are relevant to what you’re looking for. Mostly any technologically literate person uses this type of tech every day.

One of the main differentiators between AI and conventional programming is the fact that non-AI programs are carried out by a set of defined instructions. AI on the other hand learns without being explicitly programmed.

Here is when the confusion starts to take place. Often times – but not all the time – AI utilizes machine learning, which is a subset of the AI field. If we go a little deeper, we get deep learning, which is a way to implement machine learning from scratch.

Furthermore, when we think about robotics we tend to think that robots and AI are interchangeable terms. AI algorithms are usually only one part of a larger technological matrix of hardware, electronics and non-AI code inside of a robot.

Robot… or artificially intelligent robot?

Robotics is a branch of technology that concerns itself strictly with robots. A robot is a programmable machine that carries out a set of tasks autonomously in some way. They’re not computers nor are they strictly artificially intelligent.

Many experts cannot agree on what exactly constitutes a robot. But for our purposes, we’ll consider that it has a physical presence, is programmable and has some level of autonomy. Here are a few different examples of some robots we have today:

  • Roomba (Vacuum Cleaning Robot)

  • Automobile Assembly Line Arm

  • Surgery Robots

  • Atlas (Humanoid Robot)

Some of these robots, for example, the assembly line robot or surgery bot are explicitly programmed to do a job. They do not learn. Therefore we could not consider them artificially intelligent.

These are robots that are controlled by inbuilt AI programs. This is a recent development, as most industrial robots were only programmed to carry out repetitive tasks without thinking.  Self-learning bots with machine learning logic inside of them would be considered AI. They need this in order to perform increasingly more complex tasks.

“I’m sorry, Dave…” — Hal 9000 from Stanley Kubrick’s 2001: A Space Odyssey

What’s the difference between Artificial Intelligence and Machine Learning?

At its foundation, machine learning is a subset and way of achieving true AI. It was a term coined by Arthur Samuel in 1959, where he stated: “The ability to learn without being explicitly programmed.”

The idea is to get the algorithm to learn or be trained to do something without being specifically hardcoded with a set of particular directions. It is the machine learning that paves way for artificial intelligence.

Arthur Samuel wanted to create a computer program that could enable his computer to beat him in checkers. Rather than create a detailed and long-winding program that could do it, he thought of a different idea. The algorithm that he created gave his computer the ability to learn as it played thousands of games against itself. This has been the crux of the idea ever since. By the early 1960s, this program was able to beat champions in the game.

Over the years, machine learning developed into a number of different methods. Those being:

  1. Supervised

  2. Semi-supervised

  3. Unsupervised

  4. Reinforcement

In a supervised setting, a computer program would be given labeled data and then be asked to assign a sorting parameter to them. This could be pictures of different animals and then it would guess and learn accordingly while it trained. Semi-supervised would only label a few of the images. After that, the computer program would have to use its algorithm to figure out the unlabeled images by using its past data.

Unsupervised machine learning doesn’t involve any preliminary labeled data. It would be thrown into the database and have to sort for itself different classes of animals. It could do this based on grouping similar objects together due to how they look and then creating rules on the similarities it finds along the way.

Reinforcement learning is a little bit different than all of these subsets of machine learning. A great example would be the game of Chess. It knows a set amount of rules and bases its progress on the end result of either winning or losing.

A.I., 2001, Stephen Speilberg

Deep learning

For an even deeper subset of machine learning comes deep learning. It’s tasked with far greater types of problems than just rudimentary sorting. It works in the realm of vasts amounts of data and comes to its conclusion with absolutely no previous knowledge.

If it was to differentiate between two different animals, it would distinguish them in a different way compared to regular machine learning. First, all pictures of the animals would be scanned, pixel by pixel. Once that was completed, it would then parse through the different edges and shapes, ranking them in a differential order to determine the difference.

Deep learning tends to require much more hardware power. These machines that run this are usually housed away in large data centers. Programs that use deep learning are essentially starting from scratch.

Of all the AI disciplines, deep learning is the most promising for one day creating a generalized artificial intelligence. Some current applications that deep learning has spurned have been the many chatbots we see today. Alexa, Siri and Microsoft’s Cortana can thank their brains because of this nifty tech.

A new cohesive approach

There have been many seismic shifts in the tech world this past century. From the computing age to the internet and to the world of mobile devices. These different categories of tech will pave the way for a new future. Or as Google CEO Sundar Pichai put it quite nicely:

“Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an A.I. first world.”

Artificial intelligence in all of its many forms combined together will take us on our next technological leap forward. Full Artcle

posted by f.sheikh

Human Passion to Techno-Power Submitted by Mirza Ashraf

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Introduction

HUMAN PASSION TO TECHNO-POWER

Nature of Power and its Appearance 

In the Hyperconnected World

It has been asserted that man alone is capable of progressive improvement; 

that he alone makes use of tools or fire . . . mainly due to his power 

of speaking and handling down his acquired knowledge.

(Charles Darwin, The Descent of Man)

Whereas we find man having fatherly power naturally at the birth of physical life, we also find appearance of power spontaneously at the birth of social life. Arising from its root force, power started revealing its creative aspect when the Homo erectus, using his freed hands and energetic imagination, shaped the dimension of his passion to power as “techno-power” to give meaning to life and his society. It began to expand when the descendants of an ancestral line of apelike creatures first picked up stones as tools and laid the foundation of the power of science and technology. Power’s appearance as a brutish, cruel, and savage force, determined its application as unjustified behavior. But its creative aspect viewed as noble and altruistic, to have helped man and society grow into a large group and a humanity, the passion to power is a justified and noble motive to have worked in mankind’s evolution, growth, and progression. However, changing its many faces, emerging from the hands of a family head, passing on to the control of a tribal chief, monopolized by nobility, exploited by religious and political leaders, dominated by the wealthy, instructed by the sages, directed by ideologies, principled by knowledge, systemized by science and technology, and managed by the elected authority, power before today’s hyperconnected world has been inaccessible to those it oppressed and subjected to obey. 

Whereas in the past, power has remained in the hands of centrally operating authority just like a state-controlled currency, today in the contemporary era of network, in which everyone is connected with everyone in every corner of the globe, power is like a current accessible freely to everyone. The revolution of modern technology is fast diffusing power from the possession of a centralized authority and is empowering individuals, even those who in the past have been victims of rich and powerful lords. Modern technology is a function of how smart we are, not how rich or powerful we could be. Cyber-revolution, heading rapidly towards enlightening a cyber-renaissance, is developing amazingly a borderless, nonracial, non-preferential, physically alienated, yet intellectually close-connected humanity, where highest value would be the power of smart brain. Thus, the culminating enlightenment of the cyber-renaissance would be, that the spectrum of power freeing from the hold of rich and elite, is rapidly passing on into the hands of those who were powerless in the past. With everyday technological progress it is slipping out of the hands of rich and powerful and is shining in the hands of intellectually smart ones. 

MIRZA ASHRAF_______________________________________________________________

To read full article, please visit https://independent.academia.edu/MirzaAshraf

Don’t be shocked if Imran Khan does not win next election!

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Worth reading analysis in Dawn. Next election is a fateful election for Pakistan. If Imran Khan loses and PML-N wins, it will bring the confrontation to forehead between Nawaz Party and Army plus Judiciary. f.sheikh

AND they are off! The front runner has been turbo-charged by the racing club management that has hobbled the next horse. Bringing up the rear is a thoroughbred that has seen better times, but is now way past its peak.

This scenario is what we see from the echo chamber that much of our media has become. But the other day as I was flipping channels, I came across an interesting two-part interview with Dr Ijaz Gilani, founder and chairman of Gallup Pakistan.

I was so fascinated by the facts and figures he discussed that I watched the programme online the next day so I could take notes. Relying on data from all the elections held in Pakistan from 1970 onwards, he made the point that while there had been rigging against the PPP in the 1988 and 1993 polls, it was no longer possible to doctor the results on election day.

Another point Dr Gilani made was that while only 15 per cent of voters were influenced by the personal appeal of leaders, 85pc of them voted for parties. This would seem to minimise the impact of Imran Khan’s undeniable charisma. In terms of personal popularity, Gallup Pakistan’s findings are that Nawaz Sharif leads Imran Khan by 50pc to 45pc.

The figures reveal that we are talking about two Pakistans.

Overall, the PTI’s support has increased from 17pc in the 2013 elections to 25pc in the most recent Gallup poll. In the same period, the PML-N has gone up from 33pc to 38pc. The PPP remains stagnant at around 15pc.

But it is in Punjab that the gap between the two top parties is widest: Gallup Pakistan puts the PML-N far ahead with a 20pc lead over PTI. This difference, if translated into assembly seats, would almost certainly make PML-N the biggest parliamentary party, and therefore in the best position to form the next government. In KP, the PTI’s appeal has risen, while PPP continues to dominate in rural Sindh. Karachi has become extremely volatile, with voter intentions changing from day to day.

When Gallup Pakistan published its forecast before the 2013 elections, placing the PTI far behind PML-N, Imran Khan dismissed the polling organisation as politically motivated. But Dr Gilani’s outfit has been remarkably accurate in predicting the results in 2008 and 2013.

Political forecasters have earned a bad name following Donald Trump’s surprise win in 2016, and the shock victory of the Leave campaign in the Brexit referendum. But in terms of the popular vote, pollsters had Hilary Clinton ahead by a large margin; in the event, she received nearly three million more votes than Trump, only to be defeated in the electoral college.

If Dr Gilani’s numbers turn out to be accurate in the coming elections, what will it mean for Imran Khan? Will we have to endure another five years of destabilising rigging charges, appeals, court cases, long marches and sit-ins? Should Imran Khan fail to achieve his dream of becoming prime minister again, despite all the help he has allegedly received from the establishment, his frustration will know no bounds.

Just as relevant is the question of what a PML-N victory will mean for an establishment that has tried every trick in the book to prevent the party’s victory by a concerted campaign to blacken Nawaz Sharif’s name. But even after he was unseated as prime minister on flimsy grounds, his party remains popular in Punjab. And as we have noted earlier, 85pc of voters cast their ballots for a party, and not a leader.

These polling figures also underline what some of us have said before: allegations of corruption do not greatly influence voters, most of whom look at the performance of local politicians as well as their parties. And here, the PML-N is widely perceived to have delivered in Punjab.

So why this huge gap between the perceptions of actual voters and the chattering classes who inhabit the bubble created by TV chat shows? Dr Gilani has an explanation: according to his data, 70pc of those polled indicate that they are satisfied that Pakistan is headed in the right direction. This is in stark contrast to the media babble about doom and gloom, and how we are in freefall.

Clearly, then, we are talking about two Pakistans here: one comprising around 10pc of the population that consists of the educated middle class with high aspirations, and the vast majority of people who are relatively poor, but who want a better life for their children. Unsurprisingly, it is this second group that ultimately decides the outcome of elections. For them, elections are the only means for their voices to be heard, and they vote in large numbers.

All this is undoubtedly bad news for our political engineers who need to internalise the lessons hard numbers teach us. Wishful thinking, not backed by data and rigorous analysis, is not enough to base strategy on. Luckily, Dr Gilani has provided us with both.

Memo to PTI trolls: Please don’t shoot the messenger.

https://www.dawn.com/news/1410116/the-numbers-game

irfan.husain@gmail.com