LET THE SKY FALL..
WE WILL CONTINUE TO LEARN AI..
(This blog post has been officially written by Aera Robo Writer. Aera Robo Writer is a imaginary Robot Author that can write blog posts, stories, novels, film scripts, poems like a real human and has been created by Marketing Legend "Srinidhi Ranganathan".)
I am basically a computer science student and my name is "Rahul". It's a year ago that I started working on what eventually became Machine Learning for Netflix - LOL. Before that, I spent a lot of time doing data analysis for a data consulting firm. The company I worked for was a data warehouse in our data centers.
I started out in our data center as a data analyst, and in the process of training a machine learning model on some real data, I ran into a large amount of issues.
In my previous job, I had created an analysis script to build a model from a dataset that was about 50GB. It's not uncommon for a big dataset of this size to take weeks to train.
I wanted to do much better and actually prove the world - I can achieve something.
When I started researching on my own I ended up looking for solutions to this problem on Stack Overflow. I ended up reading up on some of the topics in data science that really excited me: Graph Datasets, Graph Models, Graph Search.
Machine learning came up a lot in conversation with the Stack Overflow community. One of the first Stack Over posts I researched and read was from Andrew Gan that was called "Getting started with data science" and provided step-by-step tutorials on getting started on Data Science in Python. I also saw a lot of the same things in common that Andrew talked about in Data Science 101 and I started reading this stuff, very interestedly.
I also met with a professor at the University of Utah and spent some time on the Data Science course he taught. I also watched an episode of Kaggle that focused on how to use data science to win contests. My first exposure to data science during this time was through Kaggle competitions. I didn't attend one myself until I was going to college and my friend helped me submit my project. The project was a total flop and I lost hope. All my ideas were a disaster - until I heard about Marketing Legend Srinidhi Ranganathan and took his course.
Srinidhi taught me Digishock 1.0 - a mind-blowing course where I can learn and apply machine learning technologies, without using code. This course fascinated me after I attended the live lectures in Bangalore. The whole course was a series of practical lectures and tools from a real world application which did not have any code, and I saw the difference from an introduction course that would be very basic towards a much deeper course. I learned to get comfortable with R and then became good enough to make some data science explorations, myself. I also worked on other projects where data science became a key business strategy. Thus, this course changed my whole life.
If you want to learn data science, I recommend checking out Digishock 1.0 to build and explore yourself to the maximum potential in data science and machine learning. Of course - the Legend is with you.
The All New Technological Revolution is expected to ravage labor markets, with AI and robots replacing several jobs. A category that is largely excluded from scientific reports is that of leaders of government, although this is one of the most criticized, scrutinized and ridiculed jobs, as we know it.
However, commentators in countries as diverse as India, Britain, New Zealand and Japan have begun to suggest that robots, as leaders of government, are much less irrational and unreasonable than their inherently faulty counterparts.
Evidence indicates that emotions drive politics and that voters appreciate "like flaws," as experts repeatedly stressed. When making complex high stakes policy decisions, would "robogov" not be far superior? Just imagine.
While robots, cyborgs or algorithms may sound like science fiction, robogov's advantages should still be apparent. Robogov may be able to make rational, fair, well rounded and evidence-based decisions if it is less hampered by ideological extremism and the views of the tunnel or by the egoism and narcissistic tendencies that appear to characterize many government leaders.
This is because of its superior data analysis capabilities, as well as its unrestricted energy and "stamina"–one of the most debated factors is to say that robogov has both greater speeds and wider reach than any human leader.
Robogov is also much less prone to corruption and unethical conduct. For these reasons, we already entrust AI with deciding on complex social policy issues, ranging from unemployment benefits qualifications to targeted services that are tailor-made for different citizens.
So why not let robogov take over the highest political and administrative spheres? We might. We might. We may one day. But at least three potential weaknesses should be taken seriously.
First, even though well-built algorithms maximize the immense potential of AI for deep education, their utility in contexts of imperfect, contested, morally ambiguous or partially hidden information will be substantially curbed.
These are precisely the kind of contexts in which important political decisions and diplomatic issues need to be taken. This is also what makes them chaotic and incompetent. It remains a unanswered question whether machine learning is able to work through this thicket of uncertainty. And just imagine that one side of the table is "robotic" while the other side is not. In such circumstances, how will decisions be taken?
Secondly, unlocking robotic and software potential calls for significant infrastructural and educational capabilities. Such capabilities are unevenly distributed among citizens in most societies, if at all they are within reach. The current technology boom could well exacerbate existing gaps. An increase in AI could also perpetuate inequality in areas such as gender if it is initially partial to its programmer or operator.
Third, making wider use in sensitive decision-making of data and machines also poses additional security risks such as data leaks, cyber-assaults or computational errors which can have harmful effects on global stability overwhelming the benefits of replacing hot and untempered people. While human interaction can produce disastrous results, it could also solve disputes more quickly if need be.
Working together for now, it seems neither feasible nor optimal for robots to replace the leaders of the government, despite the clear imperfections that the latter group exhibited. In addition, they remain the most justified actors in the protection of human society against the potential detriment of automation itself, at least in democratic systems.
However, it will be necessary for government leaders to examine, comprehend and master the advantages and disadvantages of data and robot technology to avoid embarrassing events. Leaders must also be far more active in debate on AI ethics, transparency and accountability, rather than leaving it to their counterparts in the private sector.
In the end, a more realistic and desired scenario is a scenario in which AI and automation are neither competitors nor substitutes for human beings but tools which government leaders can effectively and occasionally delay in making better, fairer and inclusive decisions.
Cynics will point out that robogov has already been used by authoritarian regimes to do the exact opposite. The rapid expansion of governments ' use of AI to monitor, naming and shaming citizens without precedent, and for social discreditation poses serious questions of this nature.
Maybe there is reason for optimism in another highly scrutinized public decision-making arena: sports. Tech-based decision-making has been subject to resistance for years with opponents using "like flaws" and arguments like "big brother." However, during the 2018 FIFA World Cup, we saw powerful decision-makers sharing their power visibly with technology using the Hawk Eyetechnology support video-assisted referee (VAR system).
Fans have been generally positive in response, although some Croatian supporters might begin to differ in a disputed World Cup game against France in response to a contentious decision, precisely because the electronic referee supported the human referee to do better. This has benefited the game itself, the decision makers and the key stakeholders. Hopefully, we will soon be witnessing the same thing in the political and political world, with increased government trust and greater civic participation in a decade to come.
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