Artificial Intelligence: Evolution, Challenges, and Future

This study material provides a comprehensive overview of the evolution, challenges, and future of Artificial Intelligence (AI). It explores the factors that hindered the realization of AI expectations in the past and the potential of AI in the future. The content also provides insights into the impact of AI on various sectors such as business, health, and transportation.

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Case 1
The study of artificial intelligence is gaining more and more attention every day. It
started in the 20th century and had vigorously evolved over the years, but there were no tangible
results. The author gave scenarios of what he expected by the year 2000. Cases such as the use of
smart refrigerators to determine what type of food it has and their nutritional content as ‘Jason’
was used in the context. Voice recognition to open doors like for the case of ‘Kenny’ when he
entered the house. Use of robotics to do some house chores like cleaning, opening and closing
the door was also some of the expectations. The author also gave cases where one could use
virtual reality helmets to experience a pace virtually. Other artificial intelligence such as smart
washers, smart blankets, medical expert system, intelligent vehicle highway system, among
others was also anticipated by the year 2000 (Flasiński, 2016). AI does not only require massive
algorithms, access to a massive volume of data but also there are other crucial aspects that are
needed to successfully achieve AI goal. However, authors expectation did not come to pass by
the year 2000 because of the following factors:
There was insufficient capital for the implementation of artificial intelligence (AI) during
the 20th century. Investors, governments, and companies were not willing to fund AI projects
deeming them to be impossible and not achievable. This fact led to slow growth of the Ai
technology. It is a new field, and its importance had not been fully understood. The fear that AI
would take over the business entities drove away sponsors fearing that its introduction will lead
to a closure of their businesses concerning substitution by robotics among other AI products.
By the year 2000 essential algorithms such as hierarchical pattern recognition and deep
learning had not been developed. Algorithms are the driving force in AI evolution. Thus,
meeting the author’s expectations in the year 2000 was impossible. Datasets available during that
time were insufficient for machines to learn algorithms by refining hypothesis repeatedly. AI
technology needs a lot of information that has been tested on a computer to use it to determine
and simulate a behavior.
Talent and skills had not advanced to integrate and interpret AI technology into practice.
Humans are very essential to artificial intelligence equations to be able to interpret them and
convert them to machine-readable form. Virtual assistant applications that would be used to
augment Ai were non-existent. Such applications aids in developing an enhancing AI experience.
People feared to take the responsibility of AI implementation. AI comes with agreements that
often deduce to trust (Madi, Al Issa, Trad & Smadi, 2015). In the business environment, this
advocates that human-plus-AI processes are the ultimate winning formula. Math talent on
algorithms during the time of the author was still inadequate to create new algorithms, discover

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patterns, and the capacity to convert human intuition into machine language. This greatly
required advanced math knowledge and experience.
Computer processing power was still low. AI application requires massive processing
power and speed which by the year 2000, it had not met the AI threshold. AI largely depends on
computers, and thus Ai will not evolve if computer technology is not evolving to meet such
changes. Controlling a robot to open a door, or programming a fridge to be able to identify the
food inside it requires immense speed (Tegmark, 2017). In the early years, processing speed had
not evolved to support AI applications and systems.
By the year 2000, natural user interface and experiences were not imitating human
behaviors. The author expected that by that time user interfaces should have been mimicking
human interaction like visualization, sensory capabilities, voice recognition, and gestures.
However, more precise and accurate algorithms were needed to achieve this which had not been
created by 2000.
Recommendations engines to accelerate decision making and provide filters to deliver
situational awareness had not been developed. During the years of the author, there were a lot of
speculations that AI emergence could replace a lot of work being done by humans, and thus was
faced with rejection and reluctance to embrace it. It brought about an ethical debate on policies
and privacy (Yonck, 2017). Due to this, governments and organizations were not buying the idea
of AI and thus no support to the research and development team. People were still weighing their
judgments on the impact of AI.

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Case 2
The future of AI revolution is already here technically speaking. What has been achieved
in the recent years has dramatically transformed our lives. For instance, today’s thumb drive
storage technology has led to a growth of big data which commits to enhance each standard of
business operations. With the introduction of several forms of communication gadgets, people
are now interconnected through mobile phones, satellites, televisions, internet, and radios. We
are starting to look like a huge computer spectrum across the globe, linked together via these
emerging communication channels. The language we speak, styles, morals, morals, and music is
changing every year due to AI evolution (Tegmark, 2017).
The future is anticipated to develop of a supercomputer that works exactly like the human
brain, but it is important to note that the human mind is mysterious and works differently and it
will be technically impossible to develop a computer that has cognitive abilities precisely that of
a human being. Several people are considering the MapReduce versus spark arguments in
enhancing architecture for data processing. Latest news around the world is actively exhibiting
that self-driven cars are on the rise and soon it will be common. The technology is predicted that
by 2019 the cars will be dominating our roads yet its almost 2019 and little efforts are being done
to ensure the expectations are met. Our mundane lives can be enhanced though internet of things
by using computers to embed every aspect of our environment.
As much as efforts are being put to address this anticipation, less and inadequate results
have been seen towards it. Great strides are being made to increase computer power to equal that
of the human brainpower, but this will not achieve the singularity objective. Currently, we are
not even half of what is expected of AI. A lot of inventions and innovations are yet to come. By
2029 advancements in technology would be rapid and explosive that people would not be
capable of doing anything without symbiotically merging with machines (Tegmark, 2017).
Future people are imagined to be a hybrid of no-biological and biological intelligence that will
be ruled by non-biological elements.
A lot of problems are expected to be solved by machines exhibiting artificial intelligence,
and this will require extensive and collaborated knowledge of the world. Many talents, skills, and
knowledge are required to develop a correctly functioning AI system. However, it is important
to realize that science has evolved and has ventured into several sectors, and artificial
intelligence is an area that has a lot of potential that could bring about a lot of scientific
inventions in future (Yonck, 2017).
Human-like artificial intelligence won’t be achieved anytime soon. In this era, a lot of
research and steps are being taken to achieve this objective, but it is nearly impossible to build
something like true AI that is able to abstract, be flexible and carry out activities exactly the way
a person does. We are still miles away from seeing this come to pass. In the coming years, a lot
of programming ca be done on a system to do somethings that people do, but it is important that

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to note that people poses some complex patterns that are impossible to translate it for machines
to learn and adapt to them. For example, smart cars are currently on the rise and the worry is, if a
little kid emerges from nowhere riding a bicycle, will the car be able to identify and prevent an
accident form happening? These are some of the issues that make it difficult to achieve true AI in
future.
Today, we have several computer games such as alpha Go or chess that can beat many
world best players. We cannot use games only to measure human intelligence, take the case of
you identifying your grandfather in a crowd, for a human being this can be done easily, but it
would be difficult for a computer program (Yonck, 2017). In future, it is critical that rules to
govern such intelligence should be considered.
Companies are shifting to big data that forms a goldmine for them. However, big data has
been the driving force behind AI, technologies of machine-learning can gather and organize a
huge amount of data to make projections and give predictions that cannot be achieved through
manual processing. Organizational efficiency will be increased, and possibilities of making
critical errors reduced greatly (Flasiński, 2016). AI will be able to detect irregular activities, for
example, payment frauds and spam filtering and notify the management in real time on the
suspicious patterns. Also, the future will facilitate the business to train their system to handle
customer requests and calls, and this will reduce costs. AI system can also be used to scan the
web database to identify customers’ buying patterns with the existing clients.
AI has so much potential it will be nearly impossible to imagine the future without it.
Advancements have already brought about increased productivity in the workplace, and this is
expected to grow in future. AI will be considered a commonplace by the end of the decade. More
precise and accurate weather predictions, self-driven cars, or space exploration (Flasiński, 2016) .
Machines with the capability with the ability to prevent cyberterrorism will be created. AI will
facilitate advancement in the health sector because of the capability to evaluate the huge volume
of genomic data and giving more precise prescription and treatments.

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References
Flasiński, M. (2016). Introduction to artificial intelligence. Switzerland : Springer
Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. New York :
Alfred A. Knopf
Yonck, R. (2017). Heart of the machine: Our future in a world of artificial emotional
intelligence. New York : Arcade Publishing
Madi, T. , Al Issa, H. , Trad, E. And Smadi, K. (2015) Artificial Intelligence for Speech
Recognition Based on Neural Networks. Journal of Signal and Information
Processing, 6, 66-72. doi: 10.4236/jsip.2015.62006.