Machine Learning: A Report on the Development and Impact

This report provides an overview of machine learning, its development, and impact in various fields. It discusses the limitations of machine learning and its influence on content consumption and social networks.

Running head: MACHINE LEARNING 1
Machine learning
Institution
Student name
Date
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Executive summary
The dynamics of the world, especially on matter technology, has challenged every field of the
world; as the man struggles to make the world a better place and ease the way of doing things
from the traditional methods of using hand tools and equipment’s to use sophisticated machines.
The man has also created an artificial brain with the application of artificial intelligence
knowledge. This article is a report on machine learning.

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Table of Contents
Executive summary.....................................................................................................................................2
Introduction.................................................................................................................................................4
What can machine learning not do?.............................................................................................................4
Machine learning and robotics.....................................................................................................................5
Machine learning influence over the content we consume in the web.........................................................5
Influence of machine learning based bots in the social networks................................................................5
The role of science fiction in reporting in machine learning........................................................................6
Conclusion...................................................................................................................................................6
References...................................................................................................................................................7

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Introduction
Machine learning can be defined as the branch of artificial intelligence; it is the development of
computer systems using algorithms and mathematical knowledge and models such that the
machines can perform a specified task that requires human expertise effectively. The
performance is not explicitly programmed, and the system should be able to predict and make
decisions in a reflex manner and gain knowledge to cope with such related scenario or a similar
one in the future. Machine learning technology has been extensively administered in; medical
diagnosis, vision, and images recognition systems, extraction, prediction, regression,
classification and speech recognition system among other uses (Mohri, Rostamizadeh &
Talwalkar, 2018).
Machine learning has advanced the level of technology to greater heights. It is now secure in the
field of medicine; the patients are being diagnosed using the technology of algorithms, treated
and given prescription efficiently without a human medic’s expert. In the same note, image
recognition is computerized in the current date (Xiao, Rasul, & Vollgraf, 2017).
What can machine learning not do?
However, machine learning has brought a positive impact in various fields of the world; it has
been impossible for it to work in some environment. In the first place, it is essential to appreciate
the fact machine learning is not readily available anywhere. The systems are complex and
require in-depth knowledge to develop and implement (Kourou, Exarchos, Exarchos,
Karamouzis, & Fotiadis, 2015).
Machine learning technology cannot work in the fields calling for creativity. The design in which
these systems are developed is in the form of dealing with a specified feature or event in the

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environment and solving it out based on the experience the machine has either through learning
or encountering several similar cases in the environment. Prediction of what will happen or how
the action is taken will affect the environment is also base on the experience the machine has
(Raschka, 2015).
Machines learning in speech recognition technology have a critical limitation. The machine will
hear the words clearly but never understand the meaning of the words, for instance, on saying
‘time flies like an arrow’ the machine will recognize the words time, flies, like, and arrow but
connecting the two and deduce the meaning of the phrase from the relationship of the words in
the phrase is nearly impossible (Papernot, McDaniel, Goodfellow, Jha, Celik, & Swami, 2017).
Research has been done extensively on how a child acquires the first language and be able to
communicate in it at the tender age of two years. Of worrying is that no machine has been
identified to be capable of sitting in a room with a given family for say two years or even more
and be able to communicate in the language of those people in two years or even more. These are
some of the things machine learning technology has not been able to do however significant and
rampant it is in the industry.
Machine learning and robotics
Robots are machine developed and designed to perform repetitive tasks that originally were
performed by human experts (Feurer, Klein, Eggensperger, Springenberg, Blum, & Hutter,
2015). Robots are designed in a manner that resembles human resource in the sense that they
have vision systems in the form of cameras, infrared and range finders. The brain in the human is
resembled or substituted by a computerized processing unit with a given program for the
performance of the task intended. The robots react to the environment using actuators. The

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actuators resemble the arms and the legs in human capacity; these are in the form of joint limbs
for the movement or even wings where flying is the most effective movement form of motion in
the robot (Meng, Bradley, Yavuz, Sparks, Venkataraman, Liu, & Xin, 2016)..
The joint between machine learning and a robot is the use of artificial intelli0gence in the robot
to enable it to make suitable decisions and appropriately react to the environment. Robots are
more effective in performance in cases involving repetitive task that bring boredom monotony to
a human resource like in the motors assembly firms among other manufacturing and processing
firm. Also, robots are most suitable for carrying out duties in hazardous environments like
mining of ores (Burrell, 2016). Further, a robot can perform duties faster than human expertise
bearing on the fact that they are not subject to fatigue and emotions; hence they operate all round
the clock. Machine learning and robotics are all built at the center of artificial intelligence.
Machine learning influence over the content we consume in the web
Machine learning has contributed a significant impact on the content on the internet and has
hence made it more efficient. First, machines learning are very fast, and hence predictions are
made to the client in a real-time manner. The ages when you had to search for long sentences
from the web are gone, and today you only have to click a few words, and the rest is predicted
instantly, and the relevant information from the web is retrieved (Deo, 2015). The machine
learning has made it easy to and faster to retrieve a piece of information from an extensive
database on the internet, further, the technology has an autocorrecting feature for the spelling
mistakes and also suggest to the person searching for a content using a wrong domain name.

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Influence of machine learning based bots in the social networks
The number of social media users in the globe is around 3billion people, and the number is
growing exponentially every day. It is therefore predicted that social media platforms will at one
time have to rely on the use of bots and the machine learning technology to monitor the posts and
comments of individuals in the social networks (Tramèr, Zhang, Juels, Reiter, & Ristenpart,
2016). Therefore it will be easy to identify and monitor the crisis. The bots will also help the
internet providers to provide information that is relevant to the activities and things the people
are actively engaging in at a particular moment. Online marketers will also be at a far point by
the use of the social media incorporating the machine learning technology to plot the
demography on the various places and hence identify the viability of businesses at those places
(Abadi, Barham, Chen, Chen, Davis, Dean, & Kudlur, 2016). The image recognition technology
also has helped the social network used to identify the behavior and reaction of people to a
particular phenomenon based to the fact that people have a more attraction to study of images
than reading through a text.
The role of science fiction in reporting in machine learning
There are sources of information from every corner claiming that the machine learning
technology will eventually take away jobs from the people; the information is incorrect because
machine learning cannot work without human resource monitor. Furthermore, the knowledge in
these machines is derived from human expertise and therefore human presence in the monitoring
of the automated tasks is a must (Hengl & et al, 2017). The technology is not a substitute for the
human resource; it is a complementary meant for automation and increasing the speed of
carrying out the tasks. It is crucial to appreciate the sense that machine learning lacks the

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capability of dealing with events that have not occurred or encountered in the environment, it
does not have the capacity of creativity, and therefore it will remain ineffective when subjected
to new scenarios (Witten, Frank, Hall, & Pal, 2016).
Conclusion
Machine learning is changing the world day by day; it has advanced the performance and the
way of doing things in terms of speed and efficiency by working round the clock without fatigue
and emotions. It has also made it possible to carry out processes in a hazardous environment
where human power is not suitable for operation on life security basis.

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References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Kudlur, M. (2016).
Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium
on Operating Systems Design and Implementation ({OSDI} 16) (pp. 265-283).
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning
algorithms. Big Data & Society, 3(1), 2053951715622512.
Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930.
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., & Hutter, F. (2015).
Efficient and robust automated machine learning. In Advances in neural information
processing systems (pp. 2962-2970).
Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotić, A., ... &
Guevara, M. A. (2017). SoilGrids250m: Global gridded soil information based on
machine learning. PLoS one, 12(2), e0169748.
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015).
Machine learning applications in cancer prognosis and prediction. Computational and
structural biotechnology journal, 13, 8-17.
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., ... & Xin, D. (2016).
Mllib: Machine learning in apache spark. The Journal of Machine Learning
Research, 17(1), 1235-1241.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT
press.

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Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017, April).
Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM
on Asia conference on computer and communications security(pp. 506-519). ACM.
Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016). Stealing machine
learning models via prediction apis. In 25th {USENIX} Security Symposium ({USENIX}
Security 16) (pp. 601-618).
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for
benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.