Latest Techno Trends

While reading about artificial intelligence, you might have heard terminologies such as machine learning or data science or neural networks or deep learning. What do these terms mean?    Let's say you have a housing dataset with the size of the house, number of bedrooms, number of bathrooms, whether the house is newly renovated as well as the price it is listed at. If you want to build a mobile app to help people price houses, so these parameters would be the input A, and price would be the output B. Then, this would be a machine-learning system, and in particular would be one of those machine learning systems that learns inputs to outputs, or A to B mappings.    Machine learning often results in running an AI system. It is a piece of software that you can input A, these properties of house anytime and it will output B, the price automatically. So, if you have an AI system running, serving dozens or hundreds of thousands of millions of users, that's usually a machine-le

 

In the Part 1 of Big Data in Artificial Intelligence blog, we saw how organizations are using Big Data Analytics to formulate corporate strategies and achieve bigger goals. Today, we will look at how to better use Big Data to make analytics derive the right results from the huge data available with the corporations.   There are two types of data that is available for the organizations to use for building their AI systems: Unstructured Data -  These are the types of data that humans find it very easy to interpret but the systems need to be trained really hard in order to comprehend this type of data. For example, images, audio, videos and text. There's a certain types of AI techniques that could work with images to recognize cats or audios to recognize speech or texts or understand that email is spam. Structured Data -    These are types of data that is uniformly formatted and easy to interpret by the systems. Examples could be a feed from radar or the data that lives in a gi

Data is the backbone for building AI systems. We live in a digital era, so our world is built on information and data. We see data of all kinds being collected by the organizations from everywhere these days. While data was at one time structured and fairly easy to process, the sheer volume of information now available has resulted in data that is raw, unstructured, and complex.   The data flows constantly from social media interactions, smart technologies, sensors, gauges, mobile devices, videos, texts, and countless other sources. Since much of this data is generated through personal habits, behaviours, and activities, it contains valuable and actionable insights that can influence decisions and help companies formulate more targeted and personalized business strategies. For the organizations to make use of this huge data, we use Big Data as a tool for collecting, managing, and analysing massive amounts of unstructured data and that is where big data analytics platforms do be

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt USD763416190 ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.   

The rise of AI has been largely driven by one tool in AI called Machine Learning (ML). So what is Machine Learning? As per Wikipedia, Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly  programmed to do so.   The most commonly used type of machine learning is a type of AI that learns A to B,  or input to output mappings. This is called supervised learning.    Let's see some examples. If the input A is an email and the output B is whether email is spam or not (0 or 1), then this is the core piece of AI used to build a spam filter. Another example is if you want to input English and have it output a different language, Chinese, Spanish, or something else, then this is machine translation. The most luc

AI is changing the way we work and live and we see a lot of examples of how AI is affecting the society and impacting our lifestyle. But what really is AI? Let us understand that first before we delve into how Machine Learning (ML) is used in AI.   You've probably seen news articles about how much value AI is creating. According to a study by McKinsey Global Institute, AI is estimated to create an additional 13 trillion US dollars of economic output annually by the year 2030 , boosting global GDP by about 1.2 percent a year . Even though AI is already creating tremendous amounts of value into software industry, a lot of the value to be created in the future lies outside the software industry. In sectors such as retail, travel, transportation, automotive, materials, manufacturing and so on. I should have a hard time thinking of an industry that I don't think AI will have a huge impact on in the next several years.    As per Wikipedia, Artificial Intelligence (AI), som