Even though industrial robots steadily have
improved their capacity and cost efficiency for many years, implementing them
in actual production still requires substantial resources for programming,
safety measures and handling of inflexibility. Robots also still have problems
handling complex and soft components. These problems hinder robot
implementation for many applications. However, advancements in several fields
such as programming, robot sensor and control technology, force sensing,
environment recognition, human–machine-interfaces and safety system technology
are about to change this. These advancements could e.g. make it possible for
operators to guide or collaborate with robots that assist operators at close
range, without compromising safety, often referred to as cobot installations.
This have shown that this mode of robot operation promises several potential
benefits as it takes full advantage of robot as well as human strengths, while
at the same time avoiding automation drawbacks. The technological developments
and this mode of operation could make robot installations competitive for many
more applications than today. If cobot installations turn out to be as cost
efficient as the Stanley Automation study indicates, this will make this type
of robot installations very competitive and the range of operations that can be
automated will increase significantly. Implementing this type of robot could
hence add substantial value for producing companies and more detailed
information on possible economic benefit could be useful. The set of problems
warranting a collaborative technique is equivalent to the set problems where
there is an opportunity to effectively leverage affordances on both sides of
the partnership in pursuit of the solution.


Need of the study


The study of Man-Machine collaboration may
reveal various dimensions implementation and effectiveness of this new age
approach. Some have voiced fears that artificial intelligence could replace
humans altogether. But that isn’t likely. A more valuable approach may be to
view machine and human intelligence as complementary, with each bringing its
own strengths to the table. Because of the implementation of many software,
automation, new technology reduces human effort. All of this has created
considerable uncertainty about our future relationship with machines, the
prospect of technological unemployment, and even the very fate of humanity. Working from these factors, we can begin to come to
consensus regarding what constitutes human computation. First, the problem must
involve some form of information
processing. This may occur as part of an algorithmic process, or may
emerge through the observation and analysis of technology mediated human
behaviour. Second, human participation must be integral to the computational system or process. In this work, we do not consider systems with only
superficial human involvement to fall under the umbrella of Human Computation.
On the other extreme, algorithms for unsupervised learning functions with near
autonomy from the human collaborator. Here, the human’s role is to set the
parameters of the algorithms and to verify the results. While less common,
there are an increasing number of algorithmic approaches that attempt to
maximize the contributions from both collaborators. Without question, the term human computation spans a wide
range of possible applications and computational distributions. Among all
these, many of the most interesting and successful human computation systems
not only balance the contribution of human and machine, but also leverage the
complementary computational strengths of both parties. In the following
sections, we will explore some of these strengths and how they can impact the
distribution of labour in a human computation system.
The automation and other advance technologies minimize the human errors and
provides greater accuracy. Human is an inevitable part of the automation since
it should be operated by him. This study provides the knowledge and serves as
literature for Man-Machine collaboration and depicts the advantages and
importance of ‘Silver Handshake’ in
an Organisation.

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Statement of the Problem

Man-Machine collaboration is one of the current
HR trend which has various dimensions and perspectives. Man-Machine
collaboration initially emphasizes automation in processes on both IT and Manufacturing
sector. Generally, Man-Machine collaboration is viewed as a threat to the work
of humans in an organization. There is a fear prevails that the automation may
replace human work force and everything will be automated. On the other hand,
the work without automation or computerized process will be less effective. The
problem arises when the balance is not in a proper manner. The success of
human-computer collaborative systems hinges on leveraging the skills of both
the human and the computer. To comment
man-machine interaction it is required to define what is named “machine” and
how to classify them. Numerous possibilities exist. Here, due to the subject,
it is simple to consider a classification based upon the machine situation
relative to the environment. So, there are, Machines that act; they are used
either as transportation tool or they physically modify their environment such
as manufacturing tools, Machines that perceive; they gather information from
their environment to transform it according to treatments or shaping, Machines
that reason; they have to understand information or to shape it in order to be
treated. Numerous machines at present time own the three previous features or
at least two because the presence of computer and sensors is now generalised.


This is one of the major problem in
Man-Machine Collaboration. The threatening situation is the replacement of the
jobs by the machines. The Grand Thornton 2015 gives the list of the percentage
of automation to replace the workforce. According to that, the percentage of
business which expects automation to replace the workforce is Manufacturing
(45%), Cleantech (39%), Technology (35%), Food and Beverages (35%),
Agriculture, Hunting, Forestry and Fishing (30%), Other business services
(30%), Transport (26%), Financial Services (24%), Energy and natural Resources
(23%), Professional Services (23%), Retail (19%), Construction and real estate
(19%), Education and health care (9%), Hospitality (9%) of the can be replaced
by Automation or of Artificial Intelligence.

The natural trend of engineer or researcher is
to go toward fully automatic machines with autonomous decision making. That is
possible only accepting strong limitations in the tasks.


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