Progress is always a two
edge sword. The benefits of technology can be used to improve or
decrease its advantages to the environment and society. The increase
in the scope of available soft and hard power impacts on privacy and
destruction. The threat of an AI killer robot is much more unlikely
than the insidious drip drip of central control bleeding into politics
and democracy. We should not create things because we can but because
it has merit.
Learning and other
algorithmic based solutions have received a lot of media attention
which stretches from the helpful to the ridiculous. The development so
far has been two dimensional and uses the speed of the chip and
unlimited resources to back up an apparent front end of encroachment
on human abilities. For those of us who have studied both machine
learning and neuroscience it is apparent that humans and machines are
very different entities. The brain is a collection of highly adapted
cells which are modified to carry out specific tasks and integrated
throughout the brain and body. The synthetic approach so far has been
to use brute force to overcome the weaknesses of artificial design.
The latest developments have sought to use a quasi neuronal type of
inherent weighting to simulate the human brain. The question should be
as always, is what are we trying to do here? If we are trying to out
compete the human brain then we better get ready for a long wait.
One area being
investigated is the use of robot swarms. This envisages small to large
size communities of interlinked robots moving by various means in
co-ordinated manoeuvres to obtain a goal. They may have an autonomous
action controlled by a central or hive type capacity. Some also
suggest they may replace declining bees as pollen spreaders. Although
saving the bees could be a better alternative.
The effect of "AI" in
medicine cannot be overstated. Its use in predictive and analytic
outlooks using the enormous available data is changing the landscape
in genetics and therapy. Robotic surgery whether local or remote
offers a more reliable and detailed ability to improve the outlook for
patients. The ideal scenario would be the increased training and lower
costs for these benefits to become universal.
By Simon Tavasoli
Machine Learning Algorithms
understand the working functionality of this algorithm, imagine how
you would arrange random logs of wood in increasing order of their
weight. There is a catch; however – you cannot weigh each log. You
have to guess its weight just by looking at the height and girth of
the log (visual analysis) and arrange them using a combination of
these visible parameters. This is what linear regression is like.
this process, a relationship is established between independent and
dependent variables by fitting them to a line. This line is known as
the regression line and represented by a linear equation Y= a *X +
– Dependent Variable
– Independent variable
coefficients a & b are derived by minimizing the sum of the
squared difference of distance between data points and the
Regression is used to estimate discrete
values (usually binary values like 0/1) from a set of independent
variables. It helps predict the probability of an event by fitting
data to a logit function. It is also called logit regression.
methods listed below are often used to help improve logistic
a non-linear model
is one of the most popular machine learning algorithms in use today;
this is a supervised learning algorithm that is used for classifying
problems. It works well classifying for both categorical and
continuous dependent variables. In this algorithm, we split the
population into two or more homogeneous sets based on the most
significant attributes/ independent variables.
SVM (Support Vector Machine)
is a method of classification in which you plot raw data as points
in an n-dimensional space (where n is the number of features you
have). The value of each feature is then tied to a particular
coordinate, making it easy to classify the data. Lines called
classifiers can be used to split the data and plot them on a graph.
Naive Bayes classifier assumes that the presence of a particular
feature in a class is unrelated to the presence of any other
if these features are related to each other, a Naive Bayes
classifier would consider all of these properties independently when
calculating the probability of a particular outcome.
Naive Bayesian model is easy to build and useful for massive
datasets. It's simple and is known to outperform even highly
sophisticated classification methods.
KNN (K- Nearest Neighbors)
algorithm can be applied to both classification and regression
problems. Apparently, within the Data Science industry, it's more
widely used to solve classification problems. It’s a simple
algorithm that stores all available cases and classifies any new
cases by taking a majority vote of its k neighbors. The case is then
assigned to the class with which it has the most in common. A
distance function performs this measurement.
be easily understood by comparing it to real life. For example, if
you want information about a person, it makes sense to talk to his
or her friends and colleagues!
to consider before selecting KNN:
is computationally expensive
should be normalized, or else higher range variables can bias the
still needs to be pre-processed.
is an unsupervised algorithm that solves clustering problems. Data
sets are classified into a particular number of clusters (let's call
that number K) in such a way that all the data points within a
cluster are homogenous and heterogeneous from the data in other
K-means forms clusters:
K-means algorithm picks k number of points, called centroids, for
data point forms a cluster with the closest centroids, i.e., K
now creates new centroids based on the existing cluster members.
these new centroids, the closest distance for each data point is
determined. This process is repeated until the centroids do not
collective of decision trees is called a Random Forest. To classify
a new object based on its attributes, each tree is classified, and
the tree “votes” for that class. The forest chooses the
classification having the most votes (over all the trees in the
tree is planted & grown as follows:
the number of cases in the training set is N, then a sample of N
cases is taken at random. This sample will be the training set for
growing the tree.
there are M input variables, a number m<<M is specified such
that at each node, m variables are selected at random out of the
M, and the best split on this m is used to split the node. The
value of m is held constant during this process.
tree is grown to the most substantial extent possible. There is no
Dimensionality Reduction Algorithms
today's world, vast amounts of data are being stored and analyzed by
corporates, government agencies, and research organizations. As a
data scientist, you know that this raw data contains a lot of
information - the challenge is in identifying significant patterns
reduction algorithms like Decision Tree, Factor Analysis, Missing
Value Ratio, and Random Forest can help you find relevant details.
Gradient Boosting & AdaBoost
are boosting algorithms used when massive loads of data have to be
handled to make predictions with high accuracy. Boosting is an
ensemble learning algorithm that combines the predictive power of
several base estimators to improve robustness.
short, it combines multiple weak or average predictors to build a
strong predictor. These boosting algorithms always work well in data
science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These
are the most preferred machine learning algorithms today. Use them,
along with Python and R Codes, to achieve accurate outcomes.
- Convolutional neural network
- Recursive neural network
- Recurrent neural network
short-term memory (LSTM) .
adversarial network (GAN).
- Shallow neural networks.
new ones developing all the time .