We are living in an age where we simply need to speak to the VA (voice assistant) and command to get things done for us. This is where NLP or Natural language processing with AI comes into the picture. As the subset of machine learning and an AI component, “NLP was first implemented in around 1952 as per the Hodgkin-Hexley model”. While, it was Alan Turing in 1950, who first recognized that a ‘thinking machine’ should be able to interpret and understand conversations in the language spoken by humans.
As a means to form a bridge between communication for machines…
Machine learning (ML) and complexity are not synonymous. In fact, machine learning is a way to wade through complexities posed by the data. But, what if we try to examine the level of algorithmic complexity before applying the ML algorithms?
Most of the ML algorithms are definite answers to solve complexities and arrive at an optimal solution. Yet, some algorithms have complexities that can make us contemplate the level of expertise to examine and use them; and to apply them for getting the best results.
In general, algorithms are a number of algebraic instructions to find a solution or a…
Transcending human perception, today autonomous vehicles or AVs have become a reality. Auto giants such as Tesla, BMW, Google, Volkswagen and Volvo have been the front runners in introducing autonomous transportation to ease the pressure off. Meanwhile, the market growth for AV or autonomous vehicles is estimated to touch 40% of CAGR, annually in the next six years.
Before going further, it would be worth knowing how vehicles can function autonomously. An AV or autonomous vehicle is equipped with a vision system, LIDAR or radar based sensing and multiple neural networks trained with data and machine learning algorithms. The multi-layer…
Among other tasks made possible through machine learning algorithms, face detection and recognition is a crucial computer vision task. To begin with, both face detection and recognition are co-related yet colloquially different. Face detection is a wider aspect than face recognition and is applied with the help of machine learning. Whether it is about face detection in surveillance, mapping images for medical diagnosis, or deep analysis of human faces in videos for intelligence purposes, ML models apply for both detection and recognition purposes.
Evolution: Machine learning approaches for face detection & recognition
Face detection is considered a part of the computer vision domain with relation to AI or Artificial…
The performance of an AI system undeniably depends on the training data rather than the programming. Data is necessary for machine learning models to work. Even the most performant algorithms can be rendered worthless without a foundation of high-quality training data. Indeed, when substandard or irrelevant data is provided to machine learning models in the early stages, then it will hamper the overall result and the whole thing might also cripple. Therefore, quality data is necessary for successful machine learning projects.
According to research firm Cognilytica, around 80% of AI project effort is spent in acquiring, organizing, and categorizing the…
Many firms want to automate processes using technology to reap benefits, reach business goals faster, or to simply get on with the digital transformation wave for heightening efficiency. At the back of every AI program is specialized training data that enable machine learning algorithms and AI programs to work self-reliantly. And thus, data annotation is central to machine learning models.
Normally, data with which machines can be trained is available in an unstructured format, often accompanied by discrepancies inside. The unstructured data sets from multiple sources are required to be regularized for the ML algorithm to learn and predict. …
We’re opening up our blog to talented writers who want to share their projects and thoughts on data science, machine learning and AI — is this you?
Our parent website Cogito, has been listed amongst the Top 100 Artificial Intelligence Blogs and Websites for second year in a row. We want to continue this streak by expanding our network of data science lovers and writers. We are looking for experienced and buddying writers, data scientists, machine learning engineers & students to curate content for our community and audiences.
What in it for you?
Your content would reach our medium subscribers, websites subscribers, shared on our social media handles and a our wide list of clientele from AI & Data Science community.
We encourage you to create a Medium account as this ensures…
As artificial intelligence apes the human speech, vision, and mind patterns, the domain of NLP is buzzing with some key developments in place.
NLP is one of the most crucial components for structuring a language-focused AI program, for example, the chatbots which readily assist visitors on the websites and AI-based voice assistants or VAs. NLP as the subset of AI enables machines to understand the language text and interpret the intent behind it by various means. A hoard of other tasks is being added via NLP like sentiment analysis, text classification, text extraction, text summarization, speech recognition, and auto-correction, etc.
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AI programs are well learned, developed to perform complex computation and provide dependable results. The efficiency of an AI program depends upon the data it feeds on.
When conversation on machine learning and AI (Artificial Intelligence) programs and machine learning (ML) started a decade ago, the relevancy of data utilization came to the fore. Going a level higher than software programming, artificial intelligence with the help of machine learning, artificial neural networks, and computer vision has changed the way the world is solving complex problems and finding their solutions. …
Machine learning is influencing an extensive range of applications and sectors. But in order to produce a flawless machine learning model, data collection is crucial. It is a critical component of any modern invention; timely data collection from credible sources is important to maintain the invention’s essence and usability, but nowadays data collection is becoming one of the main bottlenecks in machine learning.
Data preparation, which typically includes data collection, analysis, visualization, and cleaning, consumes most of the time when building a machine learning model from start to finish. …
Roger Brown
Cogito shoulders AI enterprises by deploying a proficient workforce for data annotation, content moderation and Training Data services. www.cogitotech.com
