The difference between machine learning and Artificial intelligence
Machine learning and Artificial Intelligence are some fancy words in the industry of technology, speaking of which, are playing a key role in the growth of tech-driven industries (almost all). Because of this, every industry entrant today strives to incorporate AI and machine learning in their business in some way expecting immediate and higher tangible returns. Many of them might not even understand the difference between the two or how it works.
If needed to ‘define’
Artificial Intelligence is a technological advancement which holds potential to perform human tasks such as visual perception, decision making, speech recognition etc.
Machine Learning is a branch of Artificial Intelligence that uses computer science techniques on trained data to make decisions.
To explain it further:
You must have come across the automatic recommendations while buying a product. Machine Learning algorithms are being employed over a range and extensively neural networks, on the basis of which the Machine Learning is formulated.
These Neural networks are built for training and learning wherein the programmer adjusts the factors of importance in the result. This is adjusted until the network reaches the net and final result from the data and details it holds. Machine learning perfects the understanding and adjusting part of the neural networks and makes it easy to obtain the final outcome searched from millions of database extracted.
Machine Learning is an extremely complex, however, an astute and smart technique which possess artificial intelligence. Speaking of AI, when a machine learning reaches a position where it can cast back and communicate with humans in a manner that it can make decisions by itself, Artificial Intelligence is there.
When a machine is capable of taking decisions and does not rely on human intelligence for most of its tasks, it’s the artificial intelligence that is being incorporated and played. The concept is beyond mere machine learning and can self-do the operational tasks.
There are some startups which are striving to identify the various patterns of machine learning and incorporating them in different fields.
Gamalon is a startup that streamlines the identification of ideas from large amounts of data. The newly started venture has been funded with $20 million which is led by Intel Capital. The startup is taking a Bayesian approach to notion comprehension. It proclaims BPS i.e. Bayesian Program Synthesis running on a tablet demonstrates potential to identify the objects which are otherwise identified through complex deep learning models and a huge amount of data from large server farms. BPS identifies it using a small dataset.
This concept can help firms optimize the huge amounts of unstructured data such as email exchanges, feedback questionnaire, cell transcripts, surveys and product reviews. Something that today’s deep learning models cannot process in a speedy and coherent manner.
If you compare the decision trees running in a database by Gamalon, these are much lightweight than the massive GPU farms which generate deep learning workloads. The idea is to accelerate human understanding by integrating machine learning and human.
Another startup, LightTag has taken up a bit different strategy to take the big data summons. Companies usually face a major challenge of time taken for the labeling process which creates a hurdle in using the machine learning. This venture has created a text footnote platform that it asserts and can make the labeling process faster and quick. Again, this system is built in a lightweight environment for operators to tag textual data.
The system automatically apportions the annotation work in the team of operators and tracks the markup. The technique encourages the level of accuracy in the labeling actions with only a few samples.
GitHub is also a data science startup that can be used with various machine learning environments and frameworks. Keras, Scikit learn, TensorFlow and Pytorch to name a few. It provides a tracking code to their users for their projects which keep a track of aspects of their ML projects.
Only a few lines of coding into the machine learning and data scientists are availed with the status and run activities of the project.
This has enabled efficacy, transparency, and reproducibility to its users by allowing them to get a follow up on model change, data sets and experimentation history. With its unusual techniques that update users with their machine learning projects, it is gaining quite a good popularity and the startup has been acquired by Microsoft to streamline the work with disperse data science groups.
Machine learning has improved the user experience to perfection and further has got into every facet of everyday human life making it hassle free and effortless. It is offering a whole gamut of opportunities for startups and tech giants to collaborate and work on a global level.