I am sure that the term ‘Big Data’ must be crossing your path for some time now. But there is still a colossal amount of confusion revolving around it, and deep learning technology. To be honest, the entire concept is constantly changing its form and evolving to create something revolutionary.
Also, it is being reconsidered at every stop of technical development, as it is the fuel that drives the ongoing wave of the digital transformation of the world. It is offering a huge set of advancements especially in the field of artificial intelligence, Internet of things and Data Science.
But now the question arises that what exactly are Big Data and Deep learning technologies? And how they are going to change the complete world?
So if you are keen on knowing more about it, just keep on reading further.
I cannot help but notice the gigantic push AI’s big data problem has received in recent months. And because of this, everyone can witness some amazing breakthroughs. Innovations and technical advancement have begun to make artificial intelligence accessible to a huge number of organizations and businesses.
What is a big data problem?
It is a challenge of getting a considerable amount of data to get infused with deep learning technology.
What is deep learning technology?
It is an extremely propitious and popular technique, that is based on artificial intelligence. It allows machines to discover patterns and relationships present in the data, all by itself.
Are you still confused?
Think of it in this way, after being fed a number of pictures of mountains or any other creature or object, a deep learning program can create its own understanding of what classifies as a mountain. It then uses that knowledge to further identify an image as either ‘mountain’ or ‘not mountain’, even if you change the images of mountains to human beings. And, this is the main reason why many tech giants are eager to make further advancements in this technology.
But if you are thinking that the complete process is as simple as it sounds, then you are sadly mistaken! Deep learning algorithms often require a huge number of training examples in order to perfectly perform their operations. And many organizations or companies don’t have any access to a large cache of whooping data, in order to train their models.
Now imagine how hard it is to get a hold of millions of images of mountains, so fetching such huge amount of pictures of customers’ profiles, or considering applications from an educational institute, are next to impossible.
And on top of that, in many fields, data is present in lots of small fragments and is actually scattered. It requires tremendous hard work and resources in order to clean and consolidate for AI training. In some domains, any kind of data and information is subject to a few regulations and privacy laws. This situation weakens the reach of an AI engineer over a glut of data.
Are you now understanding the pressure faced by AI researchers? They are keen on finding out ways that are going to successfully work for huge data requirements for deep learning. The growing curiosity is the reason why a lot of effective solutions have emerged in recent months. Some of them are going to require less amount of training data, and some of them are going to allow companies to create their own training examples.
Have a look at two emerging solutions for deep learning technology…
Hybrid AI models
A huge chunk of AI history is marked by a rivalry between the two types of AI engineers i.e. symbolists and connectionist. On one hand symbolists believe AI must be based on clear-cut rules coded by programmers, and on the other hand, connectionists believe that AI must learn with the help of experiences, which is the approach that is being used in deep learning.
But very recently researchers have found that by merging both the symbolist and connectionist models, it is possible to create something revolutionary. Inventors can create AI systems that need much less training data. Researchers from MIT and IBM introduced the ‘Neuro-Symbolic Concept Learner’.
What is NSCL?
It is simply an AI model that joins rule-based AI with neural networks. It uses neural networks to fetch features from pictures and hence composes a structure table of information. NSCL then uses a prevalent rule-based program to provide solutions to the questions and hence solve problems with the help of provided features.
By merging the learning capabilities of reasoning power of rule-based AI and neural networks, NSCL can improve its adaptability to new problems and settings, that too with much fewer data. Researchers tested this AI model CLEVR. It is a dataset that is used for solving the visual question and answering(VQA). In this, an AI must answer questions based on elements and objects that are based on a given picture.
When an AI model is entirely based on neural networks, it needs a lot of training in order to solve VQA problems with decent accuracy. But NSCL was able to conquer CLEVR with a small amount of the data.
Last but not least…
Few-Shot Learning And One-Shot Learning
The use of transfer learning is the traditional approach to cut down training data. Well, it is a process of tacking a pre-trained neural network and fine-tuning it for a new task. Transfer learning decreases the amount of training data required to develop an AI model. However, it might still require a lot of examples, and the process also requires a lot of trial and error. Recently, AI researchers have developed a technique that can train for new operations with extremely fewer examples.
Many think that with the sudden rise of deep learning technology, many companies and organizations are going to have a pool of data to dominate. Even though its hard to predict how long it is going to take less-data-intensive AI models to be easily available for all the businesses to use, many top mobile app development companies are making remarkable developments in the field of AI and IoT with the help of deep learning technology. One such company is Techugo.
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