Although organic and
synthetic entities are governed by their physical attributes they
still have the same problems and targets to achieve. Both exist in the
same world and both need methods to further their aims and survival.
The ultimate shared final objectives are to carry out a required
action, whether it is planned or homeostasis. Existing long enough can
be a prerequisite in its own right. The high level heuristics depend
on the receipt of data from external and internal sensors which
stimulate some reaction from effectors that can be excitatory or
inhibitory, these balance each other to form a timed neutrality. The
two states of conscious and unconsciousness are different in their
approach to actions, in AI this would be foreground and background
processing but still similar in concept. The aware thoughts are likely
to originate in the fore brain outer cortex while the default brain is
located in the inner medial area. In both organic and complex
synthetic entities, there is no single ‘idea’ but many competing
possible alternatives. Intelligent agents can operate independently
and combine to form a more complex procedure. This allows parallel
actions to speed up the final answer. In humans the hardly understood
conscious decision making is made at the highest level whilst the
unconscious decisions are made at a lower level, often locally.
Synthetic entities also have to distinguish between house keeping and
making a decision that affects the whole outlook.What both have in
common is that they are virtual systems and rely on sensors to access
the external world. These views allow scenarios to be created to
compute possible outlooks and actions. The ability for synthetic
systems to beat humans in certain games and other closed rule based
endeavours has fanned publicity that they will take over the world.
History has shown that isruption works for awhile and then its
advantages become the norm and things settle down. One of the more
interesting events has been when humans use fairly modest computers to
outperform sophisticated machines. What we offer is direction and
novel ideas that the synthetics lack. Of course machines might break
away from their rule based straight jacket but this could be a long
way off and even then we still have millions of years of evolutionary
design behind us. Whether we become augmented cyborgs or human/machine
teams this seems to be the most advantageous way to use this brave new
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.
Although DNA has been
lauded as the basis of life, it is the proteins that do all the work
and often more complex than RNA strands. Like genes, proteins need to
unfold into a natural structure that exposes their active amino acids
for production of chemical actions. This structure is very important
and bad unfolding also has bad results. Alphafold predicts folding and
is a very important part of research into illnesses, both for
understanding and possible cures. The Deepmind team used an AI system
they have designated as GDT-net, a neural network architecture that
may be fully released.
This is a major step in
medical advancement and the Google/Deepmind team deserves all the
plaudits it will have showered on it.
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.
By Simon Tavasoli
- 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 .