AI Rule-Based vs Machine Learning Approach for Development

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AI Rule-Based vs Machine Learning Approach for
Development AI and machine learning are two of
the most commonly misunderstood terms in business
today. They both have a lot to offer, but not
all job functions are well suited for either AI
or machine learning development. Some jobs can
be improved with rule-based AI while others work
better with machine learning algorithms. Deciding
to choose either machine learning or AI for your
business can be a difficult one. Rule-based AI
is often used for smaller tasks while machine
learning evolves as it does more tasks. It's
important to remember that rule-based AI and
machine learning are not mutually exclusive
rather they have different strengths and
weaknesses in their applicability to various
types of applications. In this blog post, we will
compare these two approaches so you can make an
informed decision about which type of artificial
intelligence software would work best for your
company!
What is the Rule-based AI approach? Rule-based
AI is a computer science approach to developing
intelligent systems that can be divided into two
types of subcategories symbolic and
connectionist. Symbolic AI uses rules based on
logic, while connectionist approaches use neural
networks or other models that are loosely
inspired by biological processes. Rule-based AIs
have been around since the 1960 s, and
throughout the decades they have been used for a
variety of tasks. To make sense of large amounts
of data, organizations often employ rule-based AI
that helps them find patterns and trends from
larger sets of information. One example is an
antivirus program that scans for known malicious
code or files before they can affect your
computer. What is Machine learning? Machine
learning is a type of Artificial Intelligence
that includes algorithms and processes to
automatically learn from data without any human
input. It constantly learns as it accesses more
data over time, meaning the system can adapt to
changing environments - like web pages or images
- and improve its performance in areas such as
classification accuracy on unseen materials,
natural language processing for forms of
communication with users, and even customer
service interactions. Machine learning approaches
rely heavily on pattern recognition techniques
including artificial intelligence methods such as
deep neural networks (DNN) and support vector
machines (SVM). These technologies can be
particularly useful when there isnt an
abundance of information about how something will
work or the results. One example would be
Googles DeepMind which was created to play Atari
games at an expert level after being trained
only using random inputs.
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Key Differentiators between Rule-based AI and
Machine Learning models Machine learning has
many advantages over rule-based algorithms when
dealing with more complex data sets however,
both types have their own individual strengths
that may make them suitable depending on the
situation 1. Probabilistic and Deterministic
Models Rule-based AI models provide a
deterministic output for every input, while
machine learning provides probabilistic outputs.
In many cases, this may not be an issue however,
when working with data that has characteristics
such as multicollinearity and nonlinear
relationships it is best to use machine learning
algorithms in order to apply more complex
solutions. Rule-based AI makes the assumption of
linearity which does not account for these
complexities. 2. Feedback Control Machine
Learning uses statistical analysis and estimation
techniques to make predictions by creating
correlations between variables (i.e., inputs) and
outcomes (i.e., target). Machine Learning can
also incorporate some level of feedback control
from observed results which improves its
predictive ability over time through the use of a
hypothesis test. Rule-based AI does not have
this ability to feedback control because its goal
is to identify the best rule for input and apply
it in order to achieve specific outputs. 3.
Project Scale Rule-based AI is best suited for
smaller projects and problems where the number of
possible solutions is limited. Machine Learning
has a higher ceiling because it can be applied to
any size data set or problem space but requires
more resources than rule-based AI (i.e., time,
money). 4. Data requirements Rule-based AI does
not need a large data set and can operate with
only a few examples. Machine Learning requires
more evidence to make accurate predictions
because it is based on statistical probabilities
of events, so the larger the data set or
database, the more accurate its testing results
will be. 5. Functional Programming
Language Rule-based AI is created using a
functional programming language such as Lisp or
Prolog, while machine learning uses a procedural
programming language. Though the syntax of these
languages is different, they use similar logic to
solve problems and create predictions because
both rely on rules that dictate what will happen
next in response to input data.
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6. Processing Time Machine Learning has an
advantage over rule-based AI when it comes to
processing time. Algorithms can be developed
more efficiently if there's room for error due to
large amounts of training data (i.e., noise). A
small setup error could cause major consequences
with Rule-Based Algorithms but not Machine
Learning. 7. Mutable and Immutable Data Machine
Learning algorithms are more efficient at using
mutable data sets, while Rule-Based Algorithms
excel with immutable data. This means that
Machine Learning is better suited for real-time
learning and can be applied to a wider range of
applications in the Internet of Things
realm. Conclusion As you can see, both Machine
Learning and Rule-Based Algorithms have
advantages in different fields. The key to
finding the right solution is understanding what
your business requirements are. Machine Learning
and Rule-Based Algorithms are not
competitorsthey both have strengths in
different fields. The best solution is one that
fits your companys needs.