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Cognitive Computing | A Primer
WHITEPAPER
2018
AI & Cognitive Computing are some of the most popular business an technical words out there. It is critical to get the basic understanding of Cognitive Computing, which helps us appreciate the technical possibilities and business benefits of the technology.
Cognitive computing re-defines BI and Information Technology. It is a combination of simplified analytical algorithms, natural language processing, machine learning, and massive computer processing power resulting in increased predictive analysis and pattern discovery.
Use of cognitive systems in organizations processing large volumes and a wide range of data enhances the system’s predictive analysis and results in actionable business insights.
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Cognitive Computing | A Primer
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Cognitive Computing
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1. Getting the definition right
At present, there is no single, agreed-upon definition for cognitive computing. One of the best definitions I have come across is that of Bernard Marr’s. He defines Cognitive Computing “as the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works.”
2. Technologies that fuel Cognitive ComputingOne of the most common misconceptions among the general public is that Cognitive Computing is a standalone technology. But Cognitive Computing is a concept that is a combination of multiple technologies that helps mimic the human thought process. Some of the key technologies that enable Cognitive Computing are provided in the following pages.
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Cognitive Computing
Machine Reasoning (MR) systems generate conclusions from available knowledge by using logical techniques like deduction and induction. Machine Reasoning acts as the brain or decision engine within a Cognitive System. Machine Reasoning systems are mainly employed to reason / validate the outcomes of other modules like ML, Statistical Analysis, NLP, etc. Apart from validating the outcomes of other modules, they can also function as a standalone module by individually solving a problem. Some of the most common types of reasoning systems include rules engine, case-based reasoning, procedural reasoning systems, deductive classifiers, and machine learning systems. For further reading on Machine Reasoning, I would recommend you to go through the paper titled, “From Machine Learning to Machine Reasoning” by Leon Bottou.
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Machine Learning
Machine Learning (ML) is a discipline where a program or system can learn from existing data and dynamically alter its behaviour based on the ever-changing data. Therefore, the system has the ability to learn without being explicitly programmed. Machine Learning algorithms can be broadly categorized as classification, clustering, regression, dimensionality reduction, anomaly detection, etc. The Machine Learning module acts as the core computing engine, which using algorithms and techniques, helps Cognitive Systems identify patterns, and perform complex tasks like prediction, estimation, forecasting, and anomaly detection.
Machine Reasoning
Natural Language Processing
Wikipedia defines Natural Language Processing (NLP) as a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are two of the most prominent sub-fields within NLP. NLP helps cognitive systems comprehend natural language data sources, as well as present insights in the form of Natural Language. NLP is critical for applications like Search, Text Mining, Sentiment Analytics, Large Scale Content Analysis, Text Summarization, Narrative / Dialog Generation, Chatbots, and Virtual Assistants.
Speech Recognition
TechTarget defines Speech Recognition as the ability of a machine or program to identify words and phrases in spoken language, and convert them to a machine-readable form. Speech Recognition is also commonly known as speech-to-text, automatic speech recognition, or computer speech recognition. Common applications of speech recognitions include voice search, Home Automation (like Amazon Echo, Google Home), Virtual Assistants, Speech Analytics, Interactive Voice Response, Contact Centre Analytics, etc.
Computer Vision
The British Machine Vision Association and Society for Pattern Recognition (BMVA) defines Computer Vision as a field concerned with the automatic extraction, analysis, and understanding of useful information from a single image, or a sequence of images. Computer Vision deals with the creations of theoretical and algorithmic foundations to achieve automatic visual understanding. Some key applications of computer vision include facial recognition, medical image analysis, self-driving vehicles, asset management, industrial quality management, content-based image retrieval, etc.
Cognitive Computing
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Human Computer Interaction
Interaction Design Foundation defines Human-Computer Interaction (HCI) as “a field of study focusing on the design of computer technology and, in particular, the interaction between humans (the users) and computers.” It encompasses multiple disciplines, such as computer science, cognitive science, and human-factors engineering. The goal of HCI is to ensure that human–computer interaction is very similar to that of human–human interaction. Some popular examples of modern HCI include voice-based systems, gesture controls, facial recognition systems, and Natural Language Question Answering (NLQA).
Adaptive
The systems must have the capability to learn as information changes, and as goals and requirements evolve. The system must have the capability to overcome ambiguity and tolerate unpredictability. Also, the systems should have the capability to process and analyze real-time/near real-time data.
Interactive
The systems should enable users to interact with them as close to a human–human interaction by employing gestures, touch, voice, and natural language. They might also need to seamlessly interact with other systems like processors, devices, and Cloud services, as well as with people.
Iterative and Stateful
If the requirement is not clear, the systems should help define a problem statement by asking questions or asking for more information. They must remember inputs, results from previous iterations, and should be able to choose the right action applicable for a particular scenario.
3. Key Attributes of a Cognitive Computing System
The Cognitive Computing Consortium mentions that for any system to be qualified as a cognitive system, it should meet the criteria provided below.
Cognitive Computing
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Contextual
Systems should be able to identify and extract relevant context required, such as users’ details, location, time, syntax, etc. The system should be able to work with both structured and unstructured data sources in addition to sensory inputs (speech, visual, gesture, and sensor data).
Big Data & Cloud Computing
Some Cognitive Computing applications like computer vision or speech recognition require good storage and computing infrastructure. Enterprises can now elastically scale their storage and processing infrastructure with Big Data Platforms like Hadoop, and Cloud Computing Platforms like Azure, AWS, and Google Cloud.
Cheaper Processing Technology
Exponential decrease in processing cost is also one of the key factors enabling cognitive computing adoption. Higher processing costs in the 1970s were one of the major inhibitors that prevented further research and adoption of AI. Nick Ingelbrecht from Gartner, in a Financial Review article, explains that in the past eight years, there has been a 10,000-fold increase in processing speeds.
Access to Machine Learning & Deep Learning
Open-source Machine Learning libraries like Mahout and Spark ML made Machine Learning algorithms accessible to a wider audience. Google, Microsoft, Intel, and IBM played a key role in making deep learning capabilities accessible to the developer community through their Cognitive Services and APIs, which could easily be embedded into other applications.
4. Key Enablers of Cognitive Computing
The following factors play a significant role in helping cognitive computing become mainstream, and move away from the confines of academic research.
Cognitive Computing
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Innovative Start Ups
As per Bloomberg’s estimate, there are around 2,600+ startups in the AI and Cognitive Computing space alone, and in the last year, around 200 startups raised around $1.5 billion in equity funding. Gartner predicts that these startups will be giving large players like IBM, Google, Microsoft tough competition due to their niche focus and rapid pace of innovation.
Data Availability
IDC predicts that there is around 160 ZB of data in the present digital universe. This data is available across multiple formats like machine logs, text, voice, and video, waiting for enterprises to exploit their potential. Data Availability is also a key factor for enterprises wishing to embrace cognitive computing.
Increased Customer Experience
In a survey conducted by IBM, 49% of respondents mentioned that Cognitive Computing helps in improving customer engagement and service. Cognitive Computing can help enterprises enhance customer experience by enabling them with cognitive applications like cognitive assistants, personalized recommendations, social intelligence, and behavioural predictions.
Enhanced Productivity
Since the focus of Cognitive Computing is to mimic human capabilities and tasks, this type of computing helps enhance employee productivity and the quality of outcomes. In an article by Josh Bersin, he claims that using Cognitive Computing to interpret commercial loans, JPMorgan Chase & Co was able to reduce 360,000 hours of lawyer time each year. Similarly, other applications that help enterprises enhance employee productivity include cognitive assistants for doctors, robo advisors for wealth management, automated data scientists, and more.
5. Major Benefits of Cognitive Computing
Cognitive Computing has interesting use cases catering to multiple industries and functions. Listed below are some of the major business benefits of cognitive computing.
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Business Growth
Based on a study by IDC, 1.7 MB of data is generated per second for each person on the planet. On the other hand, 99.5% of the world’s data is not analyzed. Cognitive Computing can help enterprises unlock business opportunities and revenues from these untapped data assets. Analyzing this dark data can help enterprises identify the right markets for expansion, new customer segments to target, and new products to launch.
Increased Operational Efficiency
Nanette Byrnes, in an MIT Technology Review article, mentions that General Electric is using AI and Cognitive Computing technologies, like computer vision, to improve service on its highly-engineered jet engines. Post adoption of these technologies, GE was able to effectively detect cracks and other problems in airplane engine blades. Enterprises can enhance operational efficiency by implementing cognitive applications like predictive asset maintenance, contact center bots, automated replenishment systems, and more.
Cognitive Computing
mAdvisor is a patent pending AI & Cognitive Computing platform, which helps enterprises translate data into meaningful insights and narratives without any manual intervention. Using mAdvisor, enterprises can now reduce analytics timelines from weeks to mere minutes.
mAdvisor employs cognitive technologies like machine learning, machine reasoning, deep learning, natural language generation, natural language processing, and expert rules systems. It is designed to consume a wide range of enterprise data and result in greater predictive and preventive analysis, reduce customer churn, increase customer satisfaction, and improve revenue streams.
• Automated Pattern Discovery - Helps enterprises analyze big data without any manual intervention, thereby reducing time and cost to insights.
• AI-based Narratives - Identifies key insights and composes them in a natural language for easy interpretation and ready-to-consume reports and decks.
• Automated Machine Learning - Accelerates the development of advanced analytics solutions with a comprehensive machine learning framework that spans across industries.
• Cognitive & Predictive Apps - Pointed predictive analytics apps that solve specific business use cases and are designed for scale and accuracy.
• Scalable Platform - Platform designed to linearly scale based on data volume, variety, and veracity. Available with both cloud and on-premise deployment options.
• Real-Time Analysis Capabilities - Ability to store, process, and analyze data in real-time from sensor logs, social media streams, etc.
• Connectors - Pre-built connectors available for data sources like SQL Server, Salesforce, HANA, Oracle, MySQL, Postgres, and Hive
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About mAdvisor
Key Features
Cognitive Computing
As an experienced Data Science practitioner, Senthil Nathan R has executed several BI and Data Science projects across the globe. Heading the product management function at data analytics firms, he has led large teams of big data and data science professionals.
Machine learning is an area of special interest to Senthil. He was instrumental in conceptualizing and launching “Smart Machine Insights” - an automated machine learning platform similar to IBM’s Watson.
Another solution that Senthil created was a big data-based mobile social network analysis solution that won numerous accolades and was featured in the NASSCOM productexcellence matrix for Analytics.
Customer experience management and analytics is another area where Senthil hasconsulted with several clients.
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Author
Senthil Nathan R Practice Head, BI, Data Science, & Big Data at Marlabs
Cognitive Computing
Marlabs Inc.(Global Headquaters)One Corporate Place South, 3rd FloorPiscataway, NJ - 08854-6116
Tel: +1 (732) 694 1000 Fax: +1 (732) 465 0100
Email: [email protected]
