Artificial Intelligence(Current state and future of A.I) by Mudasir Khushik student of University Of Sindh Campus at Thatta. This Presentation will be really helpful for those students who are researching on Artificial Intelligence, these slides will tell you all about Artificial Intelligence its current state as well as its future.

1. Current State And
Future of A.I
By: Mudasir Khushik
Batch: 2k16
Department: Computer Science
University of Sindh Campus at
Thatta

2. The field has been growing at a rapid rate over
the past couple of years, and it often is
somewhat hard to keep up with the subject,
especially if you are not directly involved.
Before proceeding it is important to mention
that “real” AI does not exist at the present
moment, otherwise known as Artificial General
Intelligence (AGI). These would be AI systems
that can perform all human-level tasks with
equal proficiency. Currently, the only systems
that exist are known as “narrow” AI.

4. Alan Turing
I propose to consider the
Question, “Can Machines
think.”
To get its answer Alan
Turing test that can
machine make fool any
human kind by means of
conversation or any other
thing but…

5. Generic A.I or Strong A.I The Artificial
intelligence that can do everything that
human can do and more and better.
That kind of A.I is no where in the
world.
However one of the technology which
leaps it, is called Deep learning.
Whenever you listen any news regarding
A.I most of the time you are listening
about Deep learning.

7. Machine Learning:
Machine learning systems can learn how
to represent the data’s statistical
structure via a training process. So what
does a machine learning algorithm
require to learn? Data! The more data
(Big Data), the more the algorithm can
learn, no data, nothing to learn.

11. Deep Learning:
Another Algorithm approach from the early machine
learning crowd aritificial neural networks, came and mostly
went over the decades.
Neural networks are inspired by our understanding of
biology of our brains.
They are commonly called Neural Networks. They draw
inspiration from the human brain but should not be
confused with how the brain works on any level, in this
respect, how the brain functions is still a bit of a mystery.
Deep learning has enabled many practical applications of
machine learning and by extension the overall field of AI.
Deep learning breaks down tasks in ways that makes all
kinds of machine assists seem possible, even
likely. Driverless cars, better preventive healthcare, even
better movie recommendations.

13. Google DeepMind’s AlphaGo program defeated
South Korean Master Lee Se-dol in the board
game Go

15. So what has been achieved with this in
technology/science?
1:Human level image classification
2:Near human level autonomous driving
3:Near human level text translation
4:Superhuman Go playing
5:Digital assistant from Google and Amazon
6:Near human level speech recognition

16. Zachary Lipton
MACHINE LEARNING
RESEARCHER
Current applications of AI
revolve entirely around
machine learning. But
researcher Zachary Lipton
says that's not the
message you get from
most media coverage of
AI.

17. The general public is poorly informed about
the current state of AI technologies, and
researchers and the journalists who cover
their work are doing a poor job of
explaining to people what recent
advancements in AI are really about.
That's the view of Zachary Lipton, a professor
and researcher at Carnegie Mellon University.
And given some of the recent coverage of AI
in the popular press.

18. Current state of AI has little to do with
Hollywood's ideas:
These topics are a far cry from what today's AI is really all
about, which is machine learning. The most advanced
applications, from natural language generation to facial
recognition, are driven purely by pattern matching.
Developers have indeed found astoundingly clever ways to
apply pattern matching to a wide array of tasks Yet, these
are the sorts of things most people think about when they
think about AI.
In his view, Lipton said researchers need to do a better job
of clearly describing what their technologies are doing.
Rather than just slapping the term AI.

19. Lipton talks about how people
with expert-level development
skills need to be the drivers of AI
discussions. It implies that, to
have a respectable opinion
about AI, one needs to have a
Ph.D. from a top-flight research
university.

20. Limitations of AI:
Ruh says In his experience, a general- purpose AI specialist
tends to lack the domain expertise to solve an application-
specific problem. “When I go to conferences and the
presenter asks, ‘how many people have AI projects?’, all
the hands go up,” he says. “When the presenter asks ‘how
many people are doing pilots?’ maybe a little more than
half the audience put their hands up. But almost zero are
actually in production.”
The reason businesses are not yet seeing value in AI, says
Ruh, is because the people building the AI systems are not
sophisticated enough to engineer in domain expertise. “It
is not true that with machine learning you just pump in
some data and it works,” he says. “A lot more work is
required. The more domain knowledge built into the AI,
the more valuable it becomes.”

21. The AI will eventually become very good at spotting a
pattern and can tell you about it,” he says. “Now that may
be good at picking salespeople, but in an oil or gas
pipeline, while the AI is used to identify that corrosion has
occurred, the question of what to do next is complex.
“If an expert sees corrosion, they can start to do analysis,
understand the physics of the corrosion to decide whether
it needs to be repaired now or can wait another year. A
wrong decision is very costly, but a correct decision is
highly valuable.”
AI will never be able to give the user the correct answer to
the question of what to do next, says Ruh. “The only way to
do it is through modeling and simulation, looking at every
instance of corrosion to understand the physics of what is
happening to the pipeline. AI doesn’t understand this
physics; it understands patterns.”

23. Future of A.I:
1:A.I itself
2: Automated Transportation
3:Cyborg Technology
4:Taking over dangerous jobs
5:Solving climate change
6:Robots as a friend
7:Improved elder care

24. Thanks