The document discusses the advancements in soft computing and its transformative role in technology and society, covering topics such as intelligent systems, artificial neural networks, fuzzy logic, and evolutionary algorithms. It highlights the importance of soft computing in modeling real-world problems where traditional methods fail, and outlines its applications across various industries including healthcare, finance, and education. The future impact of AI is explored, along with the challenges, risks, and ethical considerations associated with its integration into daily life.

1. Advances in
Soft
Computing
Transforming
Technology and
Society
Dr. K.Kavitha
Software Engineer
Callaway Digitech
Hyderabad.

2. Structure of My Talk
 Intelligent Systems
 Soft Computing
 Importance of Soft Computing and how it is achieved
 Artificial Neural Networks
 Fuzzy Logic
 Evolutionary Algorithms
 Applications of Soft Computing
 Artificial Intelligence
 Breakthroughs in AI
 How will AI impact the Future?
 What Industries Will AI Impact the Most?
 Challenges and Limitations

3. Intelligent Systems (ISs)
 Intelligence: System must perform meaningful operations.
In essence be a replacement for human beings in difficult situations.
Everyday routine tasks of human beings:
 Vision, Language Processing, Common Sense, Reasoning,
Learning, Robotics.
Interpret information, Comprehend the relations between phenomena or
objects, Apply the acquired information to new conditions.
 Artificial routine tasks identified and developed by human beings: Games,
Mathematics, Logic, Programming.
 Expert tasks developed by human beings: Doctors, Physicists, Mechanical
Engineers, accountants, other specializations.

4. Hard Computing
 Requires Programs to be written.
 It uses two-valued Logic.
 It is Deterministic.
 Requires exact input data.
 It is strictly sequential.
 It produces precise Answers.
Soft Computing
 Can evolve its own programs.
 It uses multi-valued or fuzzy Logic.
 It incorporates Stochasticity.
 It can deal with ambiguous and noisy
data.
 It allows parallel Computations.
 It can yield approximate answers.

5. Soft Computing
“Soft Computing is an emerging approach to computing which
parallel the remarkable ability of the human mind to reason and
learn in an environment of uncertainty and imprecision”.
Soft Computing is the fusion of methodologies designed to model
and enable solutions to real world problems, which are not modeled
or too difficult to model mathematically.
The aim of Soft Computing is to exploit the tolerance for
imprecision, uncertainty, approximate reasoning, and partial
truth in order to achieve close resemblance with human like
decision making.

6. Why Soft Computing approach ?
 Mathematical model & analysis can be done for relatively simple systems.
 More complex systems arising in biology, medicine & management
systems remain intractable to conventional mathematical & analytical
methods.
 Typically, human can:
1. Take decisions
2. Derive inference from previous situations experienced.
3. Expertise in an area.
4. Adapt to changing environment
5. Learn to do better
6. Social behavior of collective intelligence.
1 & 2 : Fuzzy Logic 3 & 4 : Neural Network 5 & 6 : Evolutionary
Algorithms

7. Unique Property of Soft computing
 Learning from experimental data
 Soft computing techniques derive their power of generalization from approximating or
interpolating to produce outputs from previously unseen inputs by using outputs from
previous learned inputs.
 Generalization is usually done in a high dimensional space.
 Advantages of Soft Computing
 Models based on human reasoning.
 Closer to human thinking and biologically inspired.
 Models can be
 Linguistic.
 Fast when computing.
 Effective in practice.

8. Soft-Computing (SC) is a key part of Artificial Intelligence (AI) focused
on the design of robust intelligent systems.
• Fuzzy systems => imprecision
• Neural networks => learning
• Probabilistic reasoning => uncertainty
• Evolutionary computing => optimization

9. How Soft Computing is Achieved?
 How a student learns from his teacher?
 Teacher asks questions and tells the
answers.
 Teacher puts questions and hints answers
then asks whether the answers are correct
or not?
 Student thus learn a topic and store in his
memory.
 Based on the knowledge he solves new
problems.
 This is how human brain works.
 Based on this concept Artificial Neural
Network is used to solve problems.

10. Artificial Neural network
 ANN is a parallel distributed information processing structure
consisting of a number of nonlinear processing units called Neurons.
 The neuron operates as a mathematical processor performing specific
mathematical operations on its inputs to generate an output.
 It can be trained to recognize patterns and to identify incomplete
patterns by resembling the human-brain processes of recognizing
information, burying noise literally and retrieving information correctly.
 ANN are strongly interconnected systems of neurons which have simple
behavior, but when connected they can solve complex problems.
Changes may be made further to enhance its performance

11. Learning
• A process by which a Neural Network adapts itself to a stimulus by
making proper parameter adjustments, resulting in the production of
desired response.
• The process of modifying the weights in the connections between
network layers with the objective of achieving the expected output
is called training a network.
• This is achieved through
– Supervised learning: Child learns from a teacher
– Unsupervised learning: How a fish or tadpole learns
– Reinforcement learning: The trial-and-error learning process
that humans use to achieve their goals.

12. • Neurons are connected with others by connection link.
• Each link is associated with weights which contain information about the input signal.
• Each neuron has an internal state of its own which is a function of the inputs that neuron receives-
Activation level.
• Scalar product of weight and input vector.
• Neuron as a processing node performs the operation of summation of its weighted input.

13. Network of neurons
13
Network of neurons
Neural Network: An inspiration from Human Brain

14. How a doctor treats his patients?
 Doctor asks the patient about suffering.
 Doctor finds the Symptoms of diseases.
 Doctor prescribes tests and medicines.
 This is exactly the way fuzzy logic works.
 Symptoms are correlated to diseases with uncertainty.
 Doctor prescribes tests and medicines fuzzily.
 FUZZY LOGIC: A form of multi-valued logic that deals with reasoning that is
approximate rather than fixed and exact.

15. Example : Fuzzy logic vs. Crisp logic
Are you coming to the party ?
Reality is fuzzy
Language isn’t usually precise…
Reality isn’t actually that precise…

16. Traditional Representation Of Logic
Slow Fast
Speed = 0 Speed = 1
bool speed;
get the speed
if ( speed == 0) {
// speed is slow
}
else {
// speed is fast
}

17. Fuzzy Logic Representation
 For every problem
must represent in
terms of fuzzy sets.
 What are fuzzy
sets?
Slowest
Fastest
Slow
Fast
[ 0.0 – 0.25 ]
[ 0.25 – 0.50 ]
[ 0.50 – 0.75 ]
[ 0.75 – 1.00 ]

18. Fuzzy Logic Representation
Slowest Fastest
float speed;
get the speed
if ((speed >= 0.0)&&(speed < 0.25)) {
// speed is slowest
}
else if ((speed >= 0.25)&&(speed < 0.5))
{
// speed is slow
}
else if ((speed >= 0.5)&&(speed < 0.75))
{
// speed is fast
}
else // speed >= 0.75 && speed < 1.0
{
// speed is fastest
}
Slow Fast

19. Temperature Controller
 The problem
Change the speed of a heater fan, based off the room
temperature and humidity.
 A temperature control system has four settings
Cold, Cool, Warm, and Hot
 Humidity can be defined by:
Low, Medium, and High
 Using this we can define the fuzzy set.

20. Benefits Of Using Fuzzy Logic

21. Genetic algorithms
 Evolutionary algorithms (EA) were invented to mimic some of the processes
observed in natural evolution. Evolution occurs on chromosomes - organic
devices for encoding the structure of living beings.
 Processes of natural selection then drive those chromosomes that encode
successful structures to reproduce more frequent than those that encode failed
structures. In other words, the chromosomes with the best evaluations tend to
reproduce more often than those with bad evaluations.
 By using simple encodings and reproduction mechanisms, the algorithms can
then display complicated behavior and turn out to solve some extremely difficult
problems.
 Based on the principles of natural evolution, GAs are robust and adaptive
methods to solve search and optimization problems.
 In addition, by simulating some features of biological evolution, GA can solve
problems where traditional search and optimization methods are less effective.

22. The Genetic Algorithm
 Directed search algorithms based on the mechanics of biological evolution
 Developed by John Holland, University of Michigan (1970’s)
 To understand the adaptive processes of natural systems
 To design artificial systems software that retains the robustness of natural systems
 Provide efficient, effective techniques for optimization and machine learning
applications
 How world selects the best?
 It starts with a population random.
 Reproduces another population (next generation)
 Ranks the population and selects the next generation individuals.
 Genetic Algorithm is based on this natural phenomena.
 population is synonymous to solutions.
 Selection of superior solution is synonymous to exploring the optimal solution.

23. Some GAApplication Types
Domain Application Types
Control gas pipeline, pole balancing, missile evasion, pursuit
Design semiconductor layout, aircraft design, keyboard
configuration, communication networks
Scheduling manufacturing, facility scheduling, resource allocation
Robotics trajectory planning
Machine Learning designing neural networks, improving classification
algorithms, classifier systems
Signal Processing filter design
Game Playing poker, checkers, prisoner’s dilemma
Combinatorial
Optimization
set covering, travelling salesman, routing, bin packing,
graph colouring and partitioning

24. Applications of Soft Computing
 Healthcare:
 Predictive analytics for patient diagnosis and treatment planning.
 AI algorithms analyze patient data to predict the risk of developing specific
conditions, allowing early intervention and preventive measures.
 Robotics in Surgery: AI powered robots assist surgeons in complex procedures
enhancing precision, reducing invasiveness and shortening recovery times.
 Remote Patient Monitoring: Through wearable devices AI analyzes real-time
data and provides insights for proactive health care management.
 Finance: Risk assessment and stock market predictions.
 Manufacturing: Quality control and process optimization.

25. Advancements/ Breakthroughs in Artificial Intelligence (AI)
 AI refers to the simulation of human intelligence in machines.
 AI has come a long way since 1951, when the
first documented success of an AI computer program was
written by Christopher Strachey, whose checkers program
completed a whole game on the Ferranti Mark I computer.
 IBM’s Deep Blue defeated chess grandmaster Garry Kasparov
in 1997, and the company’s IBM Watson won Jeopardy! in
2011.
 Generative AI has spearheaded the latest chapter in AI’s
evolution, with OpenAI releasing its first GPT models in 2018.
This has culminated in OpenAI developing its GPT-4 model
and ChatGPT, leading to a proliferation of AI generators that
can process queries to produce relevant text, audio, images and
other types of content.

26. How will AI Impact the Future
 Improved Business Automation
 With the rise of chatbots and digital assistants, companies can rely on AI to handle
simple conversations with customers and answer basic queries from employees.
 AI’s ability to analyze massive amounts of data and convert its findings into
convenient visual formats can also accelerate the decision-making process.
 Job Disruption
 Business automation has naturally led to fears over job losses. In fact, employees
believe almost one-third of their tasks could be performed by AI. Although AI has
made gains in the workplace, it’s had an unequal impact on different industries and
professions.
 Data Privacy Issues
 Companies require large volumes of data to train the models that power generative
AI tools, and this process has come under intense scrutiny. Concerns over
companies collecting consumers’ personal data have led the
FTC to open an investigation

27. What Industries Will AI Impact the Most?
 AI in Manufacturing
 Manufacturing has been benefiting from AI for years. With AI-enabled robotic arms and
other manufacturing bots dating back to the 1960s and 1970s, the industry has adapted
well to the powers of AI. These industrial robots typically work alongside humans to
perform a limited range of tasks like assembly and stacking, and predictive analysis
sensors keep equipment running smoothly.
 AI in Healthcare
 It may seem unlikely, but AI healthcare is already changing the way humans interact
with medical providers. Thanks to its big data analysis capabilities, AI helps identify
diseases more quickly and accurately, speed up and streamline drug discovery and even
monitor patients through virtual nursing assistants.
 AI in Finance
 Banks, insurers and financial institutions leverage AI for a range of applications like
detecting fraud, conducting audits and evaluating customers for loans. Traders have also
used machine learning’s ability to assess millions of data points at once, so they can
quickly gauge risk and make smart investing decisions.

28.  AI in Education
 AI in education will change the way humans of all ages learn. AI’s use of machine
learning, natural language processing and facial recognition help digitize
textbooks, detect plagiarism and gauge the emotions of students to help determine
who’s struggling or bored. Both presently and in the future, AI tailors the
experience of learning to student’s individual needs.
 AI in Media
 Journalism is harnessing AI too and will continue to benefit from it. One example
can be seen in The Associated Press’ use of Automated Insights, which produces
thousands of earning reports stories per year. But as generative AI writing tools,
such as ChatGPT, enter the market, questions about their use in journalism
abound.
 AI in Customer Service
 Most people dread getting a robocall, but AI in customer service can provide the
industry with data-driven tools that bring meaningful insights to both the customer
and the provider. AI tools powering the customer service industry come in the
form of chatbots and virtual assistants.

29.  Risks and Dangers of AI
 Despite reshaping numerous industries in positive ways, AI still has flaws that leave
room for concern. Here are a few potential risks of artificial intelligence.
 Job Losses
 Between 2023 and 2028, 44 percent of workers’ skills will be disrupted. Not all workers
will be affected equally — women are more likely than men to be exposed to AI in their
jobs. Combine this with the fact that there is a gaping AI skills gap between men and
women, and women seem much more susceptible to losing their jobs. If companies don’t
have steps in place to upskill their workforces, the proliferation of AI could result in
higher unemployment and decreased opportunities for those of marginalized
backgrounds to break into tech.
 Human Biases
 The reputation of AI has been tainted with a habit of reflecting the biases of the people
who train the algorithmic models. For example, facial recognition technology has been
known to favor lighter-skinned individuals, discriminating against people of color with
darker complexions. If researchers aren’t careful in rooting out these biases early on, AI
tools could reinforce these biases in the minds of users and perpetuate social inequalities.

30.  Data Privacy
 Training AI models on public data increases the chances of data security breaches that
could expose consumers’ personal information. Companies contribute to these risks by
adding their own data as well. A 2024 Cisco survey found that 48 percent of businesses
have entered non-public company information into generative AI tools and 69 percent
are worried these tools could damage their intellectual property and legal rights. A single
breach could expose the information of millions of consumers and leave organizations
vulnerable as a result.
 Automated Weapons
 The use of AI in automated weapons poses a major threat to countries and their general
populations. While automated weapons systems are already deadly, they also fail to
discriminate between soldiers and civilians. Letting artificial intelligence fall into the
wrong hands could lead to irresponsible use and the deployment of weapons that put
larger groups of people at risk.


31. Challenges and Limitations
 Scalability: Difficulty in handling large datasets.
 Interpretability: Complex models can be hard to interpret.
 Integration: Combining different soft computing techniques effectively.
 Future Directions
 AI has the potential to revolutionize society but requires careful
implementation and regulation.
Explainable AI: Improving the interpretability of soft computing models.
Interdisciplinary Research: Collaborations between soft computing and
fields like bioinformatics and robotics.
Real-time Processing: Enhancements for applications requiring
immediate feedback.

32. References
 Kumar, Manish, et al. "State of art soft computing based simulation models for bearing capacity of pile
foundation: a comparative study of hybrid ANNs and conventional models." Modeling Earth Systems
and Environment 9.2 (2023): 2533-2551.
 https://devabit.com/blog/top-11-new-technologies-in-ai-exploring-the-latest-trends/
 https://www.koombea.com/blog/7-recent-ai-developments/
 https://online-engineering.case.edu/blog/advancements-in-artificial-intelligence-and-machine-learning
 https://www.sciencedaily.com/news/computers_math/artificial_intelligence/
 https://in.element14.com/latest-trends-in-artificial-intelligence?srsltid=AfmBOooAeJ2ZsQOJmw3Nv-
reyu3xvyN2-sKeeQVN7JxsDc7gOsEWXam7
 https://ai100.stanford.edu/gathering-strength-gathering-storms-one-hundred-year-study-artificial-
intelligence-ai100-2021-1/sq2
 https://builtin.com/artificial-intelligence/artificial-intelligence-future
 https://www.youtube.com/watch?v=289mVc7PDsU
 https://www.youtube.com/watch?v=Df56mSGf5tA

33. Questions?