According to IDC, spending on cognitive and AI systems will reach $77.6 billion in 2022, more than three times the $24.0B forecast for 2018. Banking and retail will be the two industries making the largest investments in cognitive/AI systems. (IDC, 2019) Cognitive automation mimics human behaviour and is applied on task which normally requires human intelligence like interpretation of unstructured data, understand patterns or make judgement calls.
Key areas in cognitive automation
1. Speech Recognition & Natural Language Processing (NLP)
Amazon Alexa, Google Assistant and Apple’s Siri are all application of NLP where computer understands human language as it is spoken. NLP can be used to identify and analyse free text, a format used to store a tremendous amount of information like patient medical records, conversational, articles, documentaries, novels etc
2. Machine Learning (ML)
Relates to computers learning on its own from a large amount of data without the need to be specifically programmed. Prediction for doctors, fraud detection in banks, sentiment analysis like favourite movie recommendation on Netflix, surge pricing on Uber are all real-world machine learning application. Deep learning a subset of ML teaches computers to learn by example. This technology is behind driver-less cars to identify a stop signal, facial recognition in today’s mobile phones.
3. Visual / Text Recognition
Image recognition refers to technologies that identify places, logos, people, objects, buildings, and several other variables in images. Facial recognition is used by security forces to counter crime and terrorism. Text recognition (OCR) transforms characters from printed /written or scanned documents into an electronic form to be further processed by computers or other software programs. Job application tracking system uses OCR to search through resumes for key words.