Machine Learning  and Intelligence

Machine Learning and Intelligence


Machine learning can simply be described as a type of artificial intelligence (AI) that has the ability for computers to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-learning.

Technical Approach

Back in the days, programmers, without the help of Oracle, MySQL, or MongoDB, had to find a way to store, sort, and join tables full of data. These programmers had to write codes without depending on any database system.

Developers are however upgrading the sophistication of the big, number-crunching algorithms that plow through our log files and clickstreams (i.e. big data). This upgrade makes it easy and possible to store and analyze big data with the help of a database system.

We have softwares and applications that helps to run the gamut from machine learning frameworks to cognitive computing, thereby helping us solve our problems by just a single command sent to them as seen in the case of IBM’s Watson.

Machine learning algorithms can be divided into 3 broad categories:
supervised learning,
unsupervised learning, and
reinforcement learning.
Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances.

Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned).

Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message.

Examples of Machine Learning Applications

An example of machine learning, Facebook’s News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling in order to read or “like” a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly.

Some other examples are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.

Machine Learning Risks and their Controls


Machine learning like every other end-product of technological advancements has its own risks. These risks include large false positives due to bad learning algorithms that hackers can exploit , however having no false positives doesn’t mean there aren’t any risks. Another risk to a machine learning model is a contaminated or compromised data from a recently hacked host.

Also, hackers can exploit loopholes in the system running the machine learning applications platform. The hacker can use fake biometric fingerprints and iris and facial characteristics to impersonate a legitimate user.

Another risk is that the hacker can deceive a machine learning model into classifying malicious training samples as legitimate at test or execution time. This can cause the model to behave significantly and widely different than the expected outputs.


Risks in machine learning applications/models can be avoided and controlled by :
Performing ethical hacking
Cleaning out training data
Security logs encryption
Implementation of an adequate security policy
Introduce the DevOps technology to check for the lifecycle

Future Scope

There are still wide beliefs that Machine learning algorithms are not still standard enough for proper analysis of big data so it is expected that programmers and data scientists need to write much of their own code to perform complex analysis. Soon, languages like R and some of the cleverest business intelligence tools will stop being special and start being a regular feature in most software stacks. They’ll go from being four or five special slides in the PowerPoint sales deck to a little rectangle in the architecture drawing that’s taken for granted.

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