We live in an age where logical reasoning matters the most, whether we use apps on smartphones for weather forecast, or software for improving driving. Ever since the invention of computers, we have grown to expect larger-than-life outputs from them. Computers must handle our basic day-to-day chores as well as solve complex problems. The race to make computers smarter has never ceased. But what more we can expect from computers?
Cognitive Computing by Definition
No matter what, we have partially succeeded in making computers smarter. After all, Netflix recommendations and Amazon click-streams are evident examples of this success. The race comes down to cognitive computing – aiming to make computers agile and intelligent – and that looks quite feasible because of artificial intelligence (AI). Often AI and cognitive computing are used interchangeably. However, they differ in purpose. Cognitive computing augments human capabilities and AI aims to automate many routine and complex tasks. Therefore, the common fuel that drives both relative technologies is data. When you feed data to machines, they perform.
Cognitive computing makes use of big data, machine learning algorithms, and AI collectively to make computers understand data types and generate data-sets that can produce values. Additionally, they can learn from their past as well as continuous experience. Cognitive computing brings forth the artificial intelligence of computers while they are in operations.
Cognitive computing is an emerging market. According to Allied Market Research, global cognitive computing market is expected to generate revenue of $13.8 billion by 2020, registering a CAGR of 33.1% during the forecast period of 2015 – 2020.
The global cognitive market is segmented into Natural Language Processing (NLP), Machine Learning (ML), and Info Retrieval. Cognitive computing is a highly potential and influencing technology. It is likely that seven out of ten companies will embrace this technology along with other trending technologies in the near future. Influential SaaS models, sensor generated data, cognitive visualizations, and contextual analytics are the current trends in cognitive computing.
The Underlying Support for Cognitive Computing
To utilize the full potential of cognitive computing at any level and scale, you need to have the following set of technologies.
Big Data Analysis
Since cognitive computing consumes huge sets of data, can it also analyze it properly before throwing out the output? Huge amounts of data pop up online every minute. The foremost task of cognitive computing is to produce value from data sets. Based on the value obtained from data, business intelligence is prepared, which subsequently defines the goals of businesses. Big data is generated every second from various sources like logs, web servers, social media, streaming platforms, and so on, in the form of structured, unstructured, and semi-structured data. People store their data at data clusters through data frames like Hadoop and Apache HBase. Data frames help us to fetch and retrieve data from clusters. However it is the algorithms that derive analysis off of it. Big data analysis is an important part of the cognitive computing process.
Machine Learning (ML)
Machine learning is as good as self-learning. ML makes predictions and recommendations – it learns from past experiences and moves on data. By making use of data feeds and algorithms like Decision Tree, Linear Regression, Logistic Regression, Random Forest, and K-means, we can have accurate predictions and build models accordingly. Since cognitive computing is about coded algorithms, it takes help from supervised machine learning. Additionally, it learns enough through training and testing to work unsupervised. The best example of ML is e-commerce website browsing.
Have you ever noticed that it keeps recommending you products based on your previous or current browsing history in the search engines? From your browsing experience, machines learn what to suggest and what to avoid. The machine is acting smart because of data. Amazon, YouTube, and Netflix are some popular examples – in fact ML gave them the much-needed advantage over their immediate rivals. It is ML which has nearly made these platforms exclusive in their fields.
Imagine you have one Quintilian bytes of data and want to use that data for Cloud Computing. Your data spread across data centers all over the world. Therefore, you need to fetch it to analyze and build predictive models to augment the overall human capacity. To access and analyze such huge sets of data in real time, you require reliable computing power with nearly perfect data frameworks. Keeping so much data handy at one place or and keeping it private is impossible. Additionally, sudden surge in data queries and demand may hinder the capabilities of cognitive computing. Therefore, it is feasible to keep data stored in data center so that you can manage, access, and use it remotely. Processing data takes place in two ways – batch processing and parallel processing. You can use Hadoop for batch processing and Apache Spark for parallel processing.
Applications of Cognitive Computing
Cognitive computing comes with many applications that are effective in various fields.
Facial detection or recognition is a biometrics technology that analyzes some aspects of the holder’s face and then matches it with the given data. This kind of physical facial detection using camera or lenses has made the airport and transport terminals smarter, as it allows people to travel on e-passports. This was carried on 2D image but now it works on 3D as well. Facial recognition has opened new vistas in the field of data privacy and digitization of important details.
Cognitive computing can help brands, government, and service providers pick out the relevant message and communication which can help them to increase and improve their business landscape. Online people leave comments, tweets, images, and much more either to express or form their opinions about the experience they got based on a service or product. With the help of data-fed algorithms, it is possible to exact sentiments for a given product. You can fetch data for sentiment analysis. However, its tone and motif may require huge data feeding and some really some good algorithms, as machines cannot understand the feelings the way humans do.
Chatbots work on contextual analytics. At this point, chatbots can understand the human conversation to some extent. The advanced version of chatbots is natural language processing (NLP). Picking out the message and context from the voice message, chatbots form the conclusion and reply accordingly and logically. Using cognitive computing, chatbots can engage into basic conversation with a few layers of artificial intelligence, which would have been otherwise noted by a customer care executive.