What is involved in Cognitive Computing

Find out what the related areas are that Cognitive Computing connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Cognitive Computing thinking-frame.

How far is your company on its Cognitive Computing journey?

Take this short survey to gauge your organization’s progress toward Cognitive Computing leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Cognitive Computing related domains to cover and 95 essential critical questions to check off in that domain.

The following domains are covered:

Cognitive Computing, Adaptive system, Adaptive user interface, Affective computing, Artificial intelligence, Artificial neural network, Automated reasoning, Cognitive computer, Cognitive reasoning, Computer vision, Computing platform, Context awareness, Data analysis, Dialog system, Enterprise cognitive system, Face detection, Fraud detection, Human brain, Human–computer interaction, Machine learning, Risk assessment, Sentiment analysis, Signal processing, Social neuroscience, Speech recognition, Synthetic intelligence, Unstructured data, Unstructured information:

Cognitive Computing Critical Criteria:

Deliberate Cognitive Computing governance and devote time assessing Cognitive Computing and its risk.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Cognitive Computing models, tools and techniques are necessary?

– How do mission and objectives affect the Cognitive Computing processes of our organization?

– How can you measure Cognitive Computing in a systematic way?

Adaptive system Critical Criteria:

Guard Adaptive system visions and frame using storytelling to create more compelling Adaptive system projects.

– Who will be responsible for deciding whether Cognitive Computing goes ahead or not after the initial investigations?

– Are accountability and ownership for Cognitive Computing clearly defined?

– What are the short and long-term Cognitive Computing goals?

– Is There a Role for Complex Adaptive Systems Theory?

Adaptive user interface Critical Criteria:

Talk about Adaptive user interface adoptions and catalog Adaptive user interface activities.

– What are our best practices for minimizing Cognitive Computing project risk, while demonstrating incremental value and quick wins throughout the Cognitive Computing project lifecycle?

– How do we measure improved Cognitive Computing service perception, and satisfaction?

– What are specific Cognitive Computing Rules to follow?

Affective computing Critical Criteria:

Have a meeting on Affective computing projects and summarize a clear Affective computing focus.

– What role does communication play in the success or failure of a Cognitive Computing project?

– What are the barriers to increased Cognitive Computing production?

– What are the usability implications of Cognitive Computing actions?

Artificial intelligence Critical Criteria:

Reorganize Artificial intelligence quality and be persistent.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Cognitive Computing?

– When a Cognitive Computing manager recognizes a problem, what options are available?

– How do we know that any Cognitive Computing analysis is complete and comprehensive?

Artificial neural network Critical Criteria:

Distinguish Artificial neural network projects and frame using storytelling to create more compelling Artificial neural network projects.

– What management system can we use to leverage the Cognitive Computing experience, ideas, and concerns of the people closest to the work to be done?

– Does Cognitive Computing create potential expectations in other areas that need to be recognized and considered?

– How can the value of Cognitive Computing be defined?

Automated reasoning Critical Criteria:

Grade Automated reasoning goals and look at it backwards.

– In what ways are Cognitive Computing vendors and us interacting to ensure safe and effective use?

– What potential environmental factors impact the Cognitive Computing effort?

– What are our Cognitive Computing Processes?

Cognitive computer Critical Criteria:

Bootstrap Cognitive computer strategies and triple focus on important concepts of Cognitive computer relationship management.

– What are the long-term Cognitive Computing goals?

– How much does Cognitive Computing help?

Cognitive reasoning Critical Criteria:

Jump start Cognitive reasoning risks and inform on and uncover unspoken needs and breakthrough Cognitive reasoning results.

– What are the success criteria that will indicate that Cognitive Computing objectives have been met and the benefits delivered?

– Who will be responsible for making the decisions to include or exclude requested changes once Cognitive Computing is underway?

Computer vision Critical Criteria:

Group Computer vision results and improve Computer vision service perception.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Cognitive Computing process?

– Is Cognitive Computing Required?

Computing platform Critical Criteria:

Be responsible for Computing platform tasks and learn.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Cognitive Computing process. ask yourself: are the records needed as inputs to the Cognitive Computing process available?

– What is the purpose of Cognitive Computing in relation to the mission?

Context awareness Critical Criteria:

Face Context awareness visions and pay attention to the small things.

– Information/context awareness: how can a developer/participant restore awareness in project activity after having been offline for a few hours, days, or weeks?

– Is Cognitive Computing dependent on the successful delivery of a current project?

– Does the Cognitive Computing task fit the clients priorities?

Data analysis Critical Criteria:

Group Data analysis governance and get out your magnifying glass.

– What are the top 3 things at the forefront of our Cognitive Computing agendas for the next 3 years?

– What knowledge, skills and characteristics mark a good Cognitive Computing project manager?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– Meeting the challenge: are missed Cognitive Computing opportunities costing us money?

– What are some real time data analysis frameworks?

Dialog system Critical Criteria:

Investigate Dialog system tasks and pay attention to the small things.

– How will you know that the Cognitive Computing project has been successful?

Enterprise cognitive system Critical Criteria:

Categorize Enterprise cognitive system quality and oversee Enterprise cognitive system management by competencies.

– Do Cognitive Computing rules make a reasonable demand on a users capabilities?

– Is the scope of Cognitive Computing defined?

Face detection Critical Criteria:

Accommodate Face detection leadership and change contexts.

– Will Cognitive Computing have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Have the types of risks that may impact Cognitive Computing been identified and analyzed?

Fraud detection Critical Criteria:

Give examples of Fraud detection engagements and test out new things.

– Can we add value to the current Cognitive Computing decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– Who will be responsible for documenting the Cognitive Computing requirements in detail?

Human brain Critical Criteria:

Experiment with Human brain projects and frame using storytelling to create more compelling Human brain projects.

– How do we make it meaningful in connecting Cognitive Computing with what users do day-to-day?

– What are current Cognitive Computing Paradigms?

Human–computer interaction Critical Criteria:

Collaborate on Human–computer interaction outcomes and innovate what needs to be done with Human–computer interaction.

– Are we Assessing Cognitive Computing and Risk?

Machine learning Critical Criteria:

X-ray Machine learning decisions and do something to it.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– Who is the main stakeholder, with ultimate responsibility for driving Cognitive Computing forward?

– Do you monitor the effectiveness of your Cognitive Computing activities?

Risk assessment Critical Criteria:

Have a session on Risk assessment issues and define Risk assessment competency-based leadership.

– Do we have a a cyber Risk Management tool for all levels of an organization in assessing risk and show how Cybersecurity factors into risk assessments?

– Are interdependent service providers (for example, fuel suppliers, telecommunications providers, meter data processors) included in risk assessments?

– Is the risk assessment approach defined and suited to the ISMS, identified business information security, legal and regulatory requirements?

– Are standards for risk assessment methodology established, so risk information can be compared across entities?

– What prevents me from making the changes I know will make me a more effective Cognitive Computing leader?

– Are standards for risk assessment methodology established, so risk information can be compared across entities?

– How frequently, if at all, do we conduct a business impact analysis (bia) and risk assessment (ra)?

– Does the process include a BIA, risk assessments, Risk Management, and risk monitoring and testing?

– How does your company report on its information and technology risk assessment?

– How often are information and technology risk assessments performed?

– Do you use any homegrown IT system for ERM or risk assessments?

– How are risk assessment and audit results communicated to executives?

– Are regular risk assessments executed across all entities?

– Do you use any homegrown IT system for ERM or risk assessments?

– What drives the timing of your risk assessments?

– Who performs your companys IT risk assessments?

– Are risk assessments at planned intervals reviewed?

– What triggers a risk assessment?

Sentiment analysis Critical Criteria:

Have a meeting on Sentiment analysis adoptions and oversee implementation of Sentiment analysis.

– How representative is twitter sentiment analysis relative to our customer base?

– How can we improve Cognitive Computing?

– Are there Cognitive Computing Models?

Signal processing Critical Criteria:

Cut a stake in Signal processing leadership and pioneer acquisition of Signal processing systems.

– what is the best design framework for Cognitive Computing organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– What business benefits will Cognitive Computing goals deliver if achieved?

– What is our Cognitive Computing Strategy?

Social neuroscience Critical Criteria:

Grade Social neuroscience failures and oversee implementation of Social neuroscience.

– Is there a Cognitive Computing Communication plan covering who needs to get what information when?

– Are assumptions made in Cognitive Computing stated explicitly?

Speech recognition Critical Criteria:

Differentiate Speech recognition projects and probe using an integrated framework to make sure Speech recognition is getting what it needs.

– Is the Cognitive Computing organization completing tasks effectively and efficiently?

– How do we Identify specific Cognitive Computing investment and emerging trends?

Synthetic intelligence Critical Criteria:

Devise Synthetic intelligence failures and explain and analyze the challenges of Synthetic intelligence.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Cognitive Computing services/products?

– What are the Essentials of Internal Cognitive Computing Management?

Unstructured data Critical Criteria:

Demonstrate Unstructured data tactics and know what your objective is.

– How do we ensure that implementations of Cognitive Computing products are done in a way that ensures safety?

– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?

– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?

– Do several people in different organizational units assist with the Cognitive Computing process?

Unstructured information Critical Criteria:

Own Unstructured information leadership and ask what if.

– Is the solution going to generate structured, semistructured, unstructured information for its own use or for use by entities either internal or external to the enterprise?

– Where do ideas that reach policy makers and planners as proposals for Cognitive Computing strengthening and reform actually originate?

– Who sets the Cognitive Computing standards?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Cognitive Computing Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Cognitive Computing External links:

“Cognitive Computing” by Haluk Demirkan, Seth Earley et al.

Adaptive user interface External links:


Affective computing External links:

Affective Computing: The Power of Emotion Analytics – …

Technology Trends: Affective Computing & Robotics

Artificial intelligence External links:

Security analytics and artificial intelligence as a service

Home | Neura Artificial Intelligence | AI As A Service

Artificial Intelligence for Sales & Marketing | Fiind Inc.

Artificial neural network External links:

What is bias in artificial neural network? – Quora

[PDF]Artificial Neural Network Travel Time Prediction …

Best Artificial Neural Network Software in 2017 | G2 Crowd

Automated reasoning External links:

ARCOE – Workshop on Automated Reasoning about …

Cognitive computer External links:

restb AI – Cognitive Computer Vision | Crunchbase

Cognitive reasoning External links:

Cognitive Reasoning – vapevibe.store

Cognitive Reasoning – seoclerk.store

Computer vision External links:

Augmented Reality & Computer Vision Solutions – Blippar

Yandong Guo, researcher, computer vision – microsoft.com

Deep Learning for Computer Vision with TensorFlow

Computing platform External links:

Private Social and Computing Platform | Appiyo

Cloud Foundry Training – Cloud Computing Platform | …

MCP50 | Mobile Computing Platform | USA Fleet Solutions

Context awareness External links:

Chameleon: Context Awareness inside DBMSs

Data analysis External links:

Regional Data Warehouse/Data Analysis Site

AnswerMiner – Data analysis made easy

Methods | Data Analysis

Dialog system External links:

Ply — Amazing layer/modal/dialog system. Wow!

Dialog system – Revolvy
www.revolvy.com/topic/Dialog system

Dialog system – Object Technology Licensing Corporation

Enterprise cognitive system External links:

Enterprise cognitive system – WOW.com

Face detection External links:

Face Detection Concepts Overview | Mobile Vision | …

Face Detection – YouTube

Face Detection & Recognition Homepage – Official Site

Fraud detection External links:

Title IV fraud detection | University Business Magazine

Human brain External links:

Inside the Bronx’s human brain bank – NY Daily News

5 Stages of Human Brain Development | Nancy Guberti, …

Human Brain Cloud – Official Site

Machine learning External links:

DataRobot – Automated Machine Learning for Predictive …

Microsoft Azure Machine Learning Studio

Risk assessment External links:

Hazard Identification and Risk Assessment | FEMA.gov

HIPAAwise – Home – HIPAA Security and Risk Assessment

Healthy Life HRA | Health Risk Assessment

Sentiment analysis External links:

YUKKA Lab – Sentiment Analysis

Baahubali 2 sentiment analysis | skytv – YouTube

Signal processing External links:

CASPER – Collaboration for Astronomy Signal Processing …

Embedded Signal Processing Laboratory

Roozbeh Jafari – Embedded Signal Processing Laboratory

Social neuroscience External links:

UCLA Social Neuroscience Lab

| Computational Social Neuroscience Lab

Home | Developmental Social Neuroscience Laboratory

Speech recognition External links:

Use speech recognition

Certified eSupport: Dictation & Speech Recognition …

How to use Speech Recognition – Windows Help

Synthetic intelligence External links:

Rights for Synthetic Intelligence – Home | Facebook

Synthetic Intelligence Network – Home | Facebook

Synthetic Intelligence Network · GitHub

Unstructured data External links:

Isilon Scale-Out NAS Storage-Unstructured Data | Dell …

Unstructured information External links:

MedEx-Unstructured Information Management …