Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
5. How AI Started?• The term artificial intelligence was
first coined by John McCarthy in 1956
when he held the first academic
conference on the subject.
• But the journey to understand if
machines can truly think began much
before that. In Vannevar Bush’s
seminal work As We May Think. he
proposed a system which amplifies
people’s own knowledge and
understanding.
• Five years later Alan Turing wrote a
paper on the notion of machines being
able to simulate human beings and the
ability to do intelligent things, such as
play Chess.
• No one can refute a computer’s ability
to process logic. But to many it is
unknown if a machine can think. The
precise definition of think is important
because there has been some strong
opposition as to whether or not this
notion is even possible.
6. The Chinese paradox
• For example, there is the so-called
‘Chinese room’ argument. Imagine
someone is locked in a room, where
they were passed notes in Chinese.
Using an entire library of rules and
look-up tables they would be able to
produce valid responses in Chinese,
but would they really ‘understand’ the
language? The argument is that since
computers would always be applying
rote fact lookup they could never
‘understand’ a subject. This argument
has been refuted in numerous ways by
researchers, but it does undermine
people’s faith in machines and
so-called expert systems in
life-critical applications.
7. The Invisible Intelligence
• The main advances over the past sixty years have
been advances in search algorithms, machine
learning algorithms, and integrating statistical
analysis into understanding the world at large.
• However most of the breakthroughs in AI aren’t
noticeable to most people. Rather than talking
machines used to pilot space ships to Jupiter, AI is
used in more subtle ways such as examining
purchase histories and influence marketing
decisions.
• In the field of AI expectations seem to always
outpace the reality. After decades of research, no
computer has come close to passing the Turing
Test (a model for measuring ‘intelligence’); Expert
Systems have grown but have not become as
common as human experts; and while we’ve built
software that can beat humans at some games,
open ended games are still far from the mastery of
computers.
9. Imitation Game
• The original game upon which
Turing’s idea was based required a
man, a woman and an interrogator.
The goal was for the interrogator to
identify which of the participants
was a man and which was a woman.
Since the interrogator would be able
to identify the gender of the
respondent by their voice (and
maybe handwriting) the answers to
the interrogator’s questions would
be typewritten or repeated by an
intermediary. For the Turing Test,
one of those two participants would
be replaced by a machine and the
goal of the interrogator would not
be to identify the gender of the
participants, but which is human
and which is a machine.
12. Deep Blue
• The computer
program, Deep Blue,
beat world chess
champion Garry
Kasparov in a
six-game chess match
in 1997. This feat has
not been repeated, and
it does not yet
represent the end of
human supremacy at
this game.
13. •
• What kinds of problems
that humans find difficult
do you think computers
are particularly well suited
to solve?
• Are there any such
problems that you know of
that computers cannot
currently solve but which
you believe computers will
one day be able to solve?
• What advances in
technology or
understanding are
necessary before those
problems can be solved?
14. • One of the most famous fictional
accounts of Artificial Intelligence
comes in the film 2001: A Space
Odyssey, based on the story by
Arthur C. Clarke.
• One of the main characters in the
film is HAL, a Heuristically
programmed Algorithmic
computer.
• In the film, HAL behaves, speaks,
and interacts with humans in much
the same way that a human would
(albeit in a disembodied form). In
fact, this humanity is taken to
extremes by the fact that HAL
eventually goes mad.
• In the film, HAL played chess,
worked out what people were
saying by reading their lips, and
engaged in conversation with other
humans.
• How many of these tasks are
computers capable of today?
HAL—Fantasy
or Reality?
17. Aviation
• The Air Operations Division (AOD) uses AI for the rule based expert systems.
The AOD has use for artificial intelligence for surrogate operators for combat
and training simulators, mission management aids, support systems for tactical
decision making, and post processing of the simulator data into symbolic
summaries.
• In 2003, NASA's Dryden Flight Research Center, and many other companies,
created software that could enable a damaged aircraft to continue flight until a
safe landing zone can be reached.[5]
The software compensates for all the
damaged components by relying on the undamaged components. The neural
network used in the software proved to be effective and marked a triumph for
artificial intelligence.
18. Computer science
• AI can be used to create other AI. For example, around November 2017,
Google's AutoML project to evolve new neural net topologies
created NASNet, a system optimized for ImageNet and COCO. According to
Google, NASNet's performance exceeded all previously published ImageNet
performance.
19. Education
• There are a number of companies that create robots to teach subjects to
children ranging from biology to computer science, though such tools have not
become widespread yet. There have also been a rise of intelligent tutoring
systems, or ITS, in higher education.
• Ex:- an ITS called SHERLOCK teaches Air Force technicians to diagnose
electrical systems problems in aircraft. Another example is DARPA, Defense
Advanced Research Projects Agency, which used AI to develop a digital tutor
to train its Navy recruits in technical skills in a shorter amount of time.
• This led to an explosion in popularity of MOOCs, or Massive Open Online
Courses, which allows students from around the world to take classes online.
Data sets collected from these large scale online learning systems have also
enabled learning analytics, which will be used to improve the quality of
learning at scale.
20. Finance
• Algorithmic trading:-Automated trading systems are typically used by large
institutional investors.
• Market analysis and data mining:-BlackRock’s AI engine, Aladdin, is used
both within the company and to clients to help with investment decisions.
Goldman Sachs uses Kensho, a market analytics platform that combines
statistical computing with big data and natural language processing.
21. Personal finance
• Several products are emerging that utilize AI to assist people with their
personal finances.
• For example, Digit is an app powered by artificial intelligence that
automatically helps consumers optimize their spending and savings based on
their own personal habits and goals
• The app can analyze factors such as monthly income, current balance, and
spending habits, then make its own decisions and transfer money to the savings
account.
• Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze
data that a consumer would leave behind, from Smartphone check-ins to
tweets, to inform the consumer about their spending behavior.
22. Portfolio management
• Robo-advisors are becoming more widely used in the investment management
industry.
• Robo-advisors provide financial advice and portfolio management with
minimal human intervention.
• This class of financial advisers work based on algorithms built to automatically
develop a financial portfolio according to the investment goals and risk
tolerance of the clients.
• It can adjust to real-time changes in the market and accordingly calibrate the
portfolio.
23. Underwriting
• An online lender, Upstart, analyze vast amounts of consumer data and utilizes
machine learning algorithms to develop credit risk models that predict a
consumer’s likelihood of default.
• Their technology will be licensed to banks for them to leverage for their
underwriting processes as well.
• ZestFinance developed their Zest Automated Machine Learning (ZAML)
Platform specifically for credit underwriting as well.
• This platform utilizes machine learning to analyze tens of thousands
traditional and nontraditional variables (from purchase transactions to how a
customer fills out a form) used in the credit industry to score borrowers.
• The platform is particularly useful to assign credit scores to those with limited
credit histories, such as millennials.
24. Job Search
• The job market has seen a notable change due to Artificial intelligence
implementation.
• According to Raj Mukherjee from Indeed.com, 65% of people launch a job
search again within 91 days of being hired.
• AI-powered engine streamlines the complexity of job hunting by operating
information on job skills, salaries, and user tendencies, matching people to the
most relevant positions.
• Artificial Intelligence impact on jobs research suggests that by 2030 intelligent
agents and robots can eliminate 30% of the world’s human labor.
25. Heavy industry
• Robots have become common in many industries and are often given jobs that
are considered dangerous to humans.
• Robots have proven effective in jobs that are very repetitive which may lead to
mistakes or accidents due to a lapse in concentration and other jobs which
humans may find degrading.
• In 2014, China, Japan, the United States, the Republic of
Korea and Germany together amounted to 70% of the total sales volume of
robots.
• In the automotive industry, a sector with particularly high degree of
automation, Japan had the highest density of industrial robots in the world:
1,414 per 10,000 employees
26. Hospitals and medicine
• Artificial neural networks are used as clinical decision support
systems for medical diagnosis, such as in Concept Processing technology
in EMR software.
• Computer-aided interpretation of medical images. Such systems help scan
digital images, e.g. from computed tomography, for typical appearances and to
highlight conspicuous sections, such as possible diseases.
• Heart sound analysis.
• Companion robots for the care of the elderly.
• Mining medical records to provide more useful information.
• IDx's first solution, IDx-DR, is the first autonomous AI-based diagnostic
system authorized for commercialization by the FDA.
27. Music
• Artificial neural networks are used as clinical decision support
systems for medical diagnosis, such as in Concept Processing technology
in EMR software.
• Computer-aided interpretation of medical images. Such systems help scan
digital images, e.g. from computed tomography, for typical appearances and to
highlight conspicuous sections, such as possible diseases.
• Heart sound analysis.
• Companion robots for the care of the elderly.
• Mining medical records to provide more useful information.
• IDx's first solution, IDx-DR, is the first autonomous AI-based diagnostic
system authorized for commercialization by the FDA.
28. Media
• While the evolution of music has always been affected by technology, artificial
intelligence has enabled, through scientific advances, to emulate, at some extent,
human-like composition.
• Among notable early efforts, David Cope created an AI called Emily Howell that
managed to become well known in the field of Algorithmic Computer Music.[31]
The
algorithm behind Emily Howell is registered as a US patent.
• Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on
composing symphonic music, mainly classical music for film scores.
• It achieved a world first by becoming the first virtual composer to be recognized by
a musical professional association.
• Moreover, initiatives such as Google Magenta, conducted by the Google Brain team,
want to find out if an artificial intelligence can be capable of creating compelling art.
• At Sony CSL Research Laboratory, their Flow Machines software has created pop
songs by learning music styles from a huge database of songs. By analyzing unique
combinations of styles and optimizing techniques, it can compose in any style.
29. Telecommunications
maintenance
• Many telecommunications companies make use of heuristic search in the
management of their workforces, for example
• BT Group has deployed heuristic search in a scheduling application that provides the
work schedules of 20,000 engineers.
30. Toys and games
• The 1990s saw some of the first attempts to mass-produce domestically aimed types
of basic Artificial Intelligence for education, or leisure.
• This prospered greatly with the Digital Revolution, and helped introduce people,
especially children, to a life of dealing with various types of Artificial Intelligence,
specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and
the first widely released robot, Furby. A mere year later an improved type
of domestic robot was released in the form of Aibo, a robotic dog with intelligent
features and autonomy.
• Companies like Mattel have been creating an assortment of AI-enabled toys for kids
as young as age three. Using proprietary AI engines and speech recognition tools,
they are able to understand conversations, give intelligent responses and learn
quickly.
• AI has also been applied to video games, for example video game bots, which are
designed to stand in as opponents where humans aren't available or desired
• Alpha GO.
31. Transportation
• Fuzzy logic controllers have been developed for automatic gearboxes in
automobiles. For example, the 2006 Audi TT, VW Touareg and VW
Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of
Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.
• Today's cars now have AI-based driver assist features such as self-parking and
advanced cruise controls. AI has been used to optimize traffic management
applications, which in turn reduces wait times, energy use, and emissions by as
much as 25 percent.
• In the future, fully autonomous cars will be developed. AI in transportation is
expected to provide safe, efficient, and reliable transportation while minimizing the
impact on the environment and communities.
• The major challenge to developing this AI is the fact that transportation systems are
inherently complex systems involving a very large number of components and
different parties, each having different and often conflicting objectives.
32. AI geni for you
• IRCTC confirm ticket
prediction
• Confirm tkt
• Streak algo – zerodha
• Google chatBot
• Degit
• Alexa
33. AI Hierarchy
• Stages of Artificial Intelligence
• Stage 1 – Machine Learning – It is a set of algorithms used by intelligent
systems to learn from experience.
• Stage 2 – Machine Intelligence – These are the advanced set of algorithms
used by machines to learn from experience. Eg – Deep Neural Networks.
• Artificial Intelligence technology is currently at this stage.
• Stage 3 – Machine Consciousness – It is self-learning from experience
without the need of external data.
