THE MEANING OF AI

AI, ETHIC, GOVERNMENT ROLE AND SOCIETY 

How do we describe our future work? Is it something obsolete that will be replaced by machines? And how do we continuously drive the economy into the digitalization process? Which steps are effective and how can we use human power and capital in a way that manners for a more sophisticated job landscape? 

Looking from the perspective of the 1970’s we saw and recognized an ongoing polarization of good jobs and bad jobs and the people working in them. Overall, we have seen this not so far in Europe, but it started slowly within the 2000s. More jobs have been uplifted by computation and easier “work to be done” routines. On the other hand, in many fields and segments, this does only increases the paperwork to be done. 

We didn’t solve the challenges of a world economy because the dispute about the qualification of jobs and the payroll to them have become controversial and the debt economy creates simultaneously serious problems regarding the value of work and the possibility to pay for. 

We created platforms where people become paid by short term tasks and working flow models as incentives for reaching yearly milestones or on commission base as part of the overall income. We need a predictable future of income models to ensure a reasonable quality of life. This leads to a question of ethical dimension what the true definition for quality of life is. 

Blockchain and AI of course are two ways to boost efficiency. But efficiency is not always the best answer to the problems of a growing society with a lack of resources. The higher the efficiency the faster resources move away. 

Could AI solve this? Yes and No. On the one hand, we ease the way we come to conclusions and it will help seeing key opinions in an unstructured world of random data collected by IoT and other information technologies. The understanding of this big data let us fasten up the speed we change things, create things, and adapt things. We got a powerful recognition of how inventions will impact markets, material, and price influence, and how the price influence affecting developing countries and their living resources.

The big change is not only making them autonomous from money depending systems but more to overcome educational difficulties and knowledge by how to do. Augmented Reality, for example, can connect those people with instructors from all over the world and the instructor can “teleport” to your place via AR. Good examples you can see from the HoloLens project. 

Technology in the form of machines, robots or digital assistants competes with humans for tasks. Machines in factories and computers in workplaces have taken on the repetitive, but cognitively demanding work of, for instance, office clerks (automatic teller machines).

Fewer workers with intermediate skills are needed to execute tasks of intermediate complexity, and these workers then compete with both low skilled and high-skilled workers for low- and high-complexity tasks. Intermediate-level jobs will fare less well, with lower employment and lower wages

The ongoing automation and the evolving technology of DLT and smart contracts lead to a fatal situation by creating a computerized economy where the “normal worker” is obsolete because the data from Big data collection and personalized by AI will be subsequent transmitted to the robots doing the work in a better and faster task than any human ever will do because of his physical and psychological limits. 

This will change every business in accounting, production, and tracing of goods in a way industrial 4.0 looks like a hitchhiker on a fast speed highway left far behind no one regards. 

We can count endless examples and at the end of the day, everyone will agree we entered the age of technology aristocracy and job replacement. 

Some tasks appear difficult to automate, especially if they involve social skills (negotiating, coordinating, teaching, or caregiving) or creative skills (inventing new products and services, creating art and culture). Therefore, automation does not seem to threaten the bulk of employment in areas such as management, engineering, science, education, medicine, culture, or the police. New product innovation will create new tasks, and new tasks are typically given to workers who explore and develop them before they are encoded and entrusted to a machine.

But if we think the road to an end: We do not need any more workers to explore and develop because once an idea brought out to the expression from the managers to be built, the machines will know everything about human life, genomics, genetic and more. Therefore over thousands of tasks will be tested in a human life and environment simulation and the chance that machine learning will produce reliable results are near 100% because we will finally get into a digital economy without the privacy of data only between user groups but not the government. 

Hence our argument would be that there is no shortage of work to be done in contemporary economies. Rather, there is a lack of financial means to pay for all the work that would be socially desirable. It suffices to think of the development and maintenance of public infrastructure (public transport), healthcare (care of the elderly), or education (affordable quality pre-schools). 

Rather, than a jobless economy, the two great challenges in the labor market may then be massive dislocation on the one hand, and the distribution of productivity gains on the other. While the technological change will not lead to the end of work, it will certainly displace people from occupations and sectors. 

In this context, broad access to initial and further education will become increasingly important for people’s life chances. Likewise, popular support for technological progress may grow weak and weaker if the resulting productivity gains continue to be pocketed by a small elite of winners – rather than be shared widely across the workforce as was the case during the post-war decades. 

Western societies benefited in the post-war decades from an institutional framework that responded well to the technological challenge created by Fordist mass production: the Keynesian class compromised with full-employment policies, strong unions, and the development of the welfare state. The democratic challenge of the next decades will be to develop a new institutional framework that allows modern societies to fully harness –and broadly share

This is maybe the most valuable scenario where Artificial Intelligence will have a crucial impact

With machine learning, we can determine the degree of co-working between machines and humans. We can surpass the problem of trust without killing the responsibility. This means the future of accuracy in data is the trusted source. The guy who labels the parcel before it is tracked by a blockchain. The people who access the results or delivery. Means machine learning will guide this process to make sure the uncertainty of the origin of data collection and transforming in the digital process will be led by AI which is observing the process and 

Comparing with thousands of data showing a successful and efficient way of delivering the supply. In an automated environment, the machine judges over the quality of the data entry point from humans to machines and not humans become the failure in a trustless system. 

Of course, this combines the necessity of work and income with the end of any privacy inside a company structure no matter which role is your job. Everyone will be measured on the consensus between humans AND machines. Regardless to say machines will judge your efficiency and humans your personality. 

The development of society to smart cities will use AI to find the leaks in the information chain. It will help to structure our daily business to an autonomy workflow where the people will be more focused on education and their working skills rather than doing unnecessary work. This work will be taken away from you by computers and robots. 

Understanding AI in an Abstract way!

Deep-learning-based programs can already decipher human speech, translate documents, recognize images, predict consumer behavior, identify fraud, and help robots “see.” Most computer experts would agree that the most direct application of this sort of machine intelligence is in areas like insurance and consumer lending, where relevant data about borrowers – credit score, income, credit card history – is abundant, and goals such as minimizing default rates can be narrowly defined. This explains why, today, no human eyes are needed to process any credit request below USD 50,000.

For these businesses, the question of where and how to deploy AI is easy to answer: find out where a lot of routine decisions are made, and substitute algorithms for humans.

But data can be expensive to acquire, and investment conventionally involves a trade-off between the benefit of more data and the cost of acquiring it. For many traditional banking incumbents, the path to AI is anything but straightforward. Managers are often tasked with considering how many different types of data are needed. How many different sensors are required to collect data for training? 

How frequently does the data need to be collected? More types, more sensors, and more frequent collection mean higher costs along with the potentially higher benefit. In thinking through his decision, managers are asked to carefully determine what they want to predict, guided by the belief that this particular prediction exercise will tell them what they need to know. This thinking process is similar to the “re-engineering” movement of the 1990s, in which managers were told to step back from their processes and outline the objective they wanted to achieve before re-engineering began.

It is a logical process, but it is the wrong one.

Consider the process of shopping at Amazon. Amazon’s AI is already predicting your next purchase under “Inspired by your browsing history.” Experts estimate the AI’s success rate at about 5%, which is no small feat considering the millions of items on offer. Now imagine if the accuracy of Amazon’s AI were to improve in the coming years. At some point, the prediction would be enough to justify Amazon pre-shipping items to your home, and you would simply return the things you did not want. 

That is, Amazon would move from a shopping-then-shipping model to shipping then shopping, sending items to customers in anticipation of their wants.

The complication lies in when Amazon should introduce this AI-driven fulfillment service. With the underlying technology improving, Amazon might choose to launch such a service just a year ahead of the competition, when the AI prediction is not yet perfect, and suffer a hit on returns and a dip in profitability. Why? Because launching the service slightly sooner will give Amazon’s AI more data sooner than the competition, which will mean its performance will improve slightly faster than that of others. 

Those slightly better predictions will in turn attract more shoppers, and more shoppers will generate more data to train the AI faster still, leading to a sort of virtuous cycle.

Do we support full-time jobs in the future? No, we call the “gig” economy!

Giggers are freelancers who are only hired for the time it takes to perform specific tasks. Platform-related services that connect buyer and seller need only a part-time work to do finishing the real-world task as for example uber needs a driver.

Everything we need and jobs offered will be connected on a platform. You need a specialist for your specified problem. Find the right experienced guy whose skills have endorsed and performed on a platform where the AI can manage if this guy suites best for your job offering. Think about construction workers, coders of physical engines for computer games, and so on. Wherever we need only a task completed not the whole project by one person, we can use for each task the best available human resource. Machines support the work, and you will become an image of why Iron Man has been showing working on his suites assisted by robots. The combination of the idea, the beauty of the platform structured access to data which will be transformed to create the perfect product out of the idea. 

 It’s the infinity solution in our not defined demand market and the unnecessary production of thousands of similar items to keep the price on a low level. If AI takes over the control of a human idea -to perfect the process - the maximum of efficacy has reached.

Ethical principal and necessaries as the key challenge in the new economy

What if the computer evaluation of employees (e-rating or e-profiling) were to automatically lead to the exclusion or dismissal of employees without the employers’ involvement? i.e., leaving machines to perform unpleasant tasks in contractual relationships? These are just some examples of how important it is to consider measures protecting people against potentially life-changing automated decisions and their consequences.

Human dignity is a philosophical concept that is now anchored in many international treaties and national constitutions. In the workplace, dignity requires human intervention. Putting a person’s decision or a “wall” between the machine and the data subject becomes all the more important as computers might develop concepts they are not directly programmed for.

How do we define the roles and what are the promises? If the ministry of defense is questionable in ethic and they do research in AI logistic, it makes sense to ask whether AI has to control ethical rules or otherwise ethical rules created to prevent AI from unethical ruling. 

Using AI technology to analyze the trusted data of a blockchain from big data collection who are under ethical control and other rules need a strong focus on the benefit to a society and the workers in them. AI should not become the subject of workers' failure. The subject should be AI assists in a way the worker can improve himself. 

And we should always remember: There is no AI only machine learning!

Creative is also a logical boundary to the human recognizing that a failure is a nature of a creative process. This is something a machine can’t experience and therefore does not create out of that. 

Because everyone is regarding new technologies from the business and the society all over the world the departure for defense for instant must be research the possibilities long before an incident happens. 

The announcement of China becoming a global leader in AI infrastructure and usage by 2030 is the best example how the world is changing and more about how serious concerns taken AI could become an important role to destabilize a defense system. 

The overwhelming reality for the future is clear to Lieutenant General Jack General Jack Shanahan, Director of the Joint AI Center (JAIC): “What I don’t want to see is a future where our potential adversaries have a fully AI-enabled force and we do not…. I don’t have the time luxury of hours or days to make decisions. It maybe seconds and microseconds where A.I. can be used”

From a strategy point A.I. must have principles in the military because the misuse of case sensitives information may be a strong decision that could lead into a world war. 

The United States of course not the only one to conquer uncertain waters in A. I. and the department of defense is not the first institution to discover artificial intelligence. Also, the US is faced by authoritarian powers that are pursuing AI application in ways inconsistent with the legal, ethical, and moral norms. Like Covid19 shows us even a democratic structure turns suddenly into an authoritarian role model. A.I. may be prevented by delivering results on simplified data not used emotionally but logically. 

However, we acknowledge that AI’s unique characteristics and fragilities require new ways to address its potential unintended negative consequences. In the national security arena, analysis of unanticipated behavior is key when considering whether to field emerging technology. The uncertainty around unintended consequences is not unique to AI; it is and has always been relevant to all technical engineering fields. 

For example, humans-built bridges and buildings and manipulated energy and physical materials before the respective fields of civil and chemical engineering crystallized as formal disciplines, leading to many unforeseen accidents. Today, despite the lack of agreed-upon ways to use AI that maximize societal benefit and curtail unintended consequences, “humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans and the environment.

Can we provide ethical basics over cultural behaves through A. I. or do we have to stop A. I. becoming a universal tool providing a unique solution to ethics without care of social and cultural differences?

Where does harming people, animals, and nature by cultural rituals a questionary AI scenery, and does our technology allows taking away this ritual? 

AI is neither inherently positive nor negative. It is an enabling capability, akin to electricity, the internal combustion engine, or computers, and as such, it is the decisions of human beings that will determine whether AI will advance or undermine our efforts to make the world safer and more prosperous.

There are key factors defining A. I. Ethical rules and the output whether a suggestion or cognition is an ethical decision or not should not obtain a machine only because over the decades the point of view about ethic has changed a lot, as we know from different countries ethic is primary a definition of the government. This ethical definition not always build on logical rather than emotional points. The Nazis had its own declaration of ethics and also Sars-Cov2 leads the ethical commissions in countries to doubtful decisions and accepting disregarding of human natures rights of a free mind. 

Therefore we have a part of governmental rules for AI 

1)    Responsible 

Human beings should exercise appropriate levels of judgment and remain responsible for the development, deployment, use, and outcomes of AI systems.

2)    Equitable

Take deliberate steps to avoid unintended bias in the development and deployment of combat or non-combat AI systems that would inadvertently cause harm to persons.

3)    Traceable

AI engineering discipline should be sufficiently advanced such that technical experts possess an appropriate understanding of the technology, development processes, and operational methods of its AI systems, including transparent and auditable methodologies, data sources, and design procedure and documentation.

4)    Reliable

AI systems should have an explicit, well-defined domain of use, and the safety, security, and robustness of such systems should be tested and assured across their entire life cycle within that domain of use. 

5)    Governable 

AI systems should be designed and engineered to fulfill their intended function while possessing the ability to detect and avoid unintended harm or disruption, and for human or automated disengagement or deactivation of deployed systems that demonstrate unintended escalators or other behavior.

Conclusion

Blockchain and AI will definitely kill role models in jobs but become an opportunity to create thousands of share jobs where humans only working on tasks completed by machines. The polarization of jobs will come sooner or later as industry, politics, and regulator's incentives work with the financial world. The challenge will be governments have to protect the logical outsourcing of humans by replacing and killing the highest level of creative processes and as a conclusion killing themselves solving problems that have been uncovered. Machine learning is the necessary process shifting our society into a reliable decision managed government and not the belief that something could be. Decisions affect everyone and quality is the key. Processes have to be optimized, products to be perfected and humans life quality to be increased with incentives like basic machine income. This kills uncertainty and increases productivity by regarding to stop higher resource waste. The standards of ethics are the key to turn the mistakes of the past into gold.