Tech addiction is the new frontier of human dependency , so is it beneficial or detrimental?

Today  60 % of work is manually  done by human beings , but  in upcoming time 90% of the work will be taken over by AI i.e- artificial  intelligence . 

Before moving forward let is have a look on what exactly AI is 
AI-   In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans



There are both ETHICALand UNETHICAL impacts / effect of AI 
in this blog we will discuss few-

ARTIFICIAL STUPIDITY !:HOW CAN WE GUARD AGAINST MISTAKES?

   Artificially intelligent bots are becoming better and better at modelling human conversation and relationships.In 2015,a bot named EUGENE GOOSTMAN won the TURING CHALLENGE for the first time. In this challenge,human raters used text inputs to chat with an unknown entity, then guessed whether they had been chatting with a human or a machine. EUGENE GOOSTMAN fooled more than half of the human raters into thinking they had been talking to a human being.

This milestone is only the start of an age where we will frequently interact with machines as if they are humans;whether in customer services or sales. While humans are limited in attention and kindness that they are humans;whether is customer service or sales.While humans are limited in attention and kindness that they can expend on another person, artificial bots can channel virtually unlimited resources into building relationships.

Even thoughts not many of us are aware of this,  we are already witness to how machines can  trigger the reward centers in the human brain. Just look at click-bait headlines and video games. These headlines are often optimized with A/B testing, a rudimentary form of algorithmic optimization for content to capture our attention. This and other methods are used to make numerous video and mobile games become addictive. TECH ADDICTION IS THE NEW FRONTIER OF HUMAN DEPENDENCY .

On the other hand, maybe we can think of a different use of software, which has already become effective at directing human attention and triggering certain actions. when used right,this could evolve  into an opportunity to nudge society towards more beneficial behavior. However,in the wrong hands it could prove detrimental

 Artificial  intelligence will save us not destroy us GOOGLE'S CEO, SUNDAR PICHAI,said at DAVOS.

"AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire," he said ."Any time you work with technology, you  need to learn to harness the benefits while minimizing the downsides."
while some thinkers-notably professor Stephen  hawking -have warned that AI could wipe out mankind , Pichai was optimistic. he said that the technology could eliminated many of the constraints we now face , helping us for example to make "clean cheap,reliable energy " a reality.

Pichai,who grew up in India ,spoke of the trans formative power of technology.

"Growing up,i didn't have a phone for a while , i waited five years.We got telephone,it fundamentally changed our lives...i remember the joys of technology and i think that will be true for AI. It's important for us to explain that and bring the world along us."
He conceded that the risks were "important",and called for international  cooperation on the scale of the Paris climate agreement to manage them.
"countries need to demilitarize Al, that's a common goal countries should work towards,"he said.
GOOGLE has recently announced it will open AL research centers in china and in France.

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impact of AI technologies on society. In this paper we discuss one such potential impact : the problem of accidents in machine learning systems, defines as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We represent a list of five practical research problems related to accident risk,categorized according to whether the problem originates from having the wrong objective function ("Avoiding side effects" and "Avoiding reward hacking"),an objective function that is too expensive to evaluate frequently ("scalable supervision"), or undesirable behavior during the learning process ("safe exploration"and "distributional shift"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems . For instance , facial recognition algorithms made by MICROSOFT, IBM and FACE++ all had biases when it came to detecting people's gender. These AI system were able to detect gender of white men more accurately than gender of darker skin men. Similarly, Amazon's.com inc's termination of AI cannot be fair. The algorithm preferred more male candidates than female .This was because Amazon's system was trained with data collected over 10-year period that came mostly from male candidates.Finally the high-level question of how to think most productively about the safety of forward-looking application of AI.

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