Top 113 Cognitive Computing Things You Should Know

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 113 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, Use case:

Cognitive Computing Critical Criteria:

Debate over Cognitive Computing tasks and gather practices for scaling Cognitive Computing.

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

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

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

Adaptive system Critical Criteria:

Detail Adaptive system adoptions and plan concise Adaptive system education.

– Does Cognitive Computing include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?

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

– Can Management personnel recognize the monetary benefit of Cognitive Computing?

– Is There a Role for Complex Adaptive Systems Theory?

Adaptive user interface Critical Criteria:

Be responsible for Adaptive user interface quality and define what do we need to start doing with Adaptive user interface.

– What other jobs or tasks affect the performance of the steps in the Cognitive Computing process?

– How is the value delivered by Cognitive Computing being measured?

– What are specific Cognitive Computing Rules to follow?

Affective computing Critical Criteria:

Focus on Affective computing planning and oversee Affective computing requirements.

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

– How would one define Cognitive Computing leadership?

Artificial intelligence Critical Criteria:

Interpolate Artificial intelligence tactics and look in other fields.

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

– Why is it important to have senior management support for a Cognitive Computing project?

– How can we improve Cognitive Computing?

Artificial neural network Critical Criteria:

Distinguish Artificial neural network quality and oversee implementation of Artificial neural network.

– How do senior leaders actions reflect a commitment to the organizations Cognitive Computing values?

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

– What are the Key enablers to make this Cognitive Computing move?

Automated reasoning Critical Criteria:

Model after Automated reasoning leadership and finalize the present value of growth of Automated reasoning.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Cognitive Computing in a volatile global economy?

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Cognitive Computing processes?

– What are our needs in relation to Cognitive Computing skills, labor, equipment, and markets?

Cognitive computer Critical Criteria:

Derive from Cognitive computer outcomes and devote time assessing Cognitive computer and its risk.

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

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

Cognitive reasoning Critical Criteria:

Analyze Cognitive reasoning planning and attract Cognitive reasoning skills.

– How can we incorporate support to ensure safe and effective use of Cognitive Computing into the services that we provide?

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

– Does the Cognitive Computing task fit the clients priorities?

Computer vision Critical Criteria:

Chart Computer vision planning and define Computer vision competency-based leadership.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Cognitive Computing processes?

Computing platform Critical Criteria:

Chart Computing platform quality and don’t overlook the obvious.

– Why should we adopt a Cognitive Computing framework?

– How do we maintain Cognitive Computings Integrity?

– How do we go about Securing Cognitive Computing?

Context awareness Critical Criteria:

Track Context awareness management and clarify ways to gain access to competitive Context awareness services.

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

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

– Which individuals, teams or departments will be involved in Cognitive Computing?

Data analysis Critical Criteria:

Trace Data analysis failures and work towards be a leading Data analysis expert.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Cognitive Computing. How do we gain traction?

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

– Risk factors: what are the characteristics of Cognitive Computing that make it risky?

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

– What are some real time data analysis frameworks?

Dialog system Critical Criteria:

Brainstorm over Dialog system visions and look at it backwards.

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

– Have you identified your Cognitive Computing key performance indicators?

– How do we manage Cognitive Computing Knowledge Management (KM)?

Enterprise cognitive system Critical Criteria:

Coach on Enterprise cognitive system management and spearhead techniques for implementing Enterprise cognitive system.

– What are the business goals Cognitive Computing is aiming to achieve?

– Have all basic functions of Cognitive Computing been defined?

Face detection Critical Criteria:

Contribute to Face detection results and find answers.

– For your Cognitive Computing project, identify and describe the business environment. is there more than one layer to the business environment?

– How to deal with Cognitive Computing Changes?

Fraud detection Critical Criteria:

Weigh in on Fraud detection quality and drive action.

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

– How does the organization define, manage, and improve its Cognitive Computing processes?

Human brain Critical Criteria:

Depict Human brain quality and budget the knowledge transfer for any interested in Human brain.

– What are the Essentials of Internal Cognitive Computing Management?

Human–computer interaction Critical Criteria:

Pay attention to Human–computer interaction goals and transcribe Human–computer interaction as tomorrows backbone for success.

– What are all of our Cognitive Computing domains and what do they do?

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

Machine learning Critical Criteria:

Have a session on Machine learning failures and reinforce and communicate particularly sensitive Machine learning decisions.

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

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

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

Risk assessment Critical Criteria:

Pilot Risk assessment decisions and differentiate in coordinating Risk assessment.

– Have the it security cost for the any investment/project been integrated in to the overall cost including (c&a/re-accreditation, system security plan, risk assessment, privacy impact assessment, configuration/patch management, security control testing and evaluation, and contingency planning/testing)?

– 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 are your results for key measures or indicators of the accomplishment of your Cognitive Computing strategy and action plans, including building and strengthening core competencies?

– 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?

– Does the risk assessment approach helps to develop the criteria for accepting risks and identify the acceptable level risk?

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

– 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?

– What operating practices represent major roadblocks to success or require careful risk assessment?

– Is the priority of the preventive action determined based on the results of the risk assessment?

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

– Who performs your companys information and technology 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 risk assessments?

– Are risk assessments at planned intervals reviewed?

– What triggers a risk assessment?

Sentiment analysis Critical Criteria:

Transcribe Sentiment analysis failures and probe using an integrated framework to make sure Sentiment analysis is getting what it needs.

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

– Which Cognitive Computing goals are the most important?

Signal processing Critical Criteria:

Gauge Signal processing tasks and create a map for yourself.

– 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?

– Does Cognitive Computing analysis show the relationships among important Cognitive Computing factors?

– How do we Lead with Cognitive Computing in Mind?

Social neuroscience Critical Criteria:

Think carefully about Social neuroscience strategies and frame using storytelling to create more compelling Social neuroscience projects.

– Is Cognitive Computing Realistic, or are you setting yourself up for failure?

– What will drive Cognitive Computing change?

Speech recognition Critical Criteria:

Reorganize Speech recognition strategies and finalize the present value of growth of Speech recognition.

– Think about the kind of project structure that would be appropriate for your Cognitive Computing project. should it be formal and complex, or can it be less formal and relatively simple?

– Does Cognitive Computing appropriately measure and monitor risk?

Synthetic intelligence Critical Criteria:

Reason over Synthetic intelligence planning and maintain Synthetic intelligence for success.

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

– Is a Cognitive Computing Team Work effort in place?

Unstructured data Critical Criteria:

Confer over Unstructured data projects and correct better engagement with Unstructured data results.

– How do your measurements capture actionable Cognitive Computing information for use in exceeding your customers expectations and securing your customers engagement?

– What is the total cost related to deploying Cognitive Computing, including any consulting or professional services?

– 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)?

– What potential environmental factors impact the Cognitive Computing effort?

Unstructured information Critical Criteria:

Grasp Unstructured information projects and forecast involvement of future Unstructured information projects in development.

– 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?

– In a project to restructure Cognitive Computing outcomes, which stakeholders would you involve?

– Think of your Cognitive Computing project. what are the main functions?

Use case Critical Criteria:

Contribute to Use case goals and balance specific methods for improving Use case results.

– While a move from Oracles MySQL may be necessary because of its inability to handle key big data use cases, why should that move involve a switch to Apache Cassandra and DataStax Enterprise?

– Are responsibilities clear across monitoring market developments, engagement, internal communication, driving use cases?

– What tools specific functionality do clients use the most often in data management to what degree and for what use case?

– Are efforts focused on identifying use cases from real pain points (and not finding a problem for blockchain to solve)?

– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?

– Are there select partners in your peer group that may allow you to share thinking and build use cases together?

– What are the use cases that your org is targeting currently for its CMDB/CMS?

– Are there potential use cases that your organization wants to drive?

– What are the use cases that your org is targeting currently for its CMDB/CMS?

– How can the best Big Data solution be chosen based on use case requirements?

– What are the best use cases for Mobile Business Intelligence?

– Are any competitors experimenting with use cases?

– What use cases are affected by GDPR and how?

– Are there recognized Cognitive Computing problems?

– What are ideal use cases for the cloud?

– Do we have Things use cases?


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 |

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:

IBM Watson Cognitive Computing – Become A Cognitive Business
http://Ad ·

IBM Watson Cognitive Computing – Become A Cognitive Business
http://Ad ·

Adaptive user interface External links:


US8656305B2 – Adaptive user interface elements – …

What is Adaptive User Interface | IGI Global

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

Talla – Your Knowledge, Powered by Artificial Intelligence

RPA and Artificial Intelligence Summit 2017

Artificial neural network External links:

Artificial neural network – ScienceDaily

[PDF]Artificial Neural Network Travel Time Prediction …


Automated reasoning External links:

ARCOE – Workshop on Automated Reasoning about …

Cognitive computer External links:

restb AI – Cognitive Computer Vision | Crunchbase

IBM’s Watson cognitive computer has whipped up a cookbook

IBM demos cognitive computer chips | EE Times

Cognitive reasoning External links:

Cognitive Reasoning – Parrot Software

Cognitive Reasoning –

Computer vision External links:

Sighthound – Industry Leading Computer Vision

Deep Learning for Computer Vision with TensorFlow

Computer Vision – Symptoms of Eye Strain – Verywell

Computing platform External links:

Private Social and Computing Platform | Appiyo

In-Memory Computing Platform | GigaSpaces

MCP50 | Mobile Computing Platform | USA Fleet Solutions

Context awareness External links:

Chameleon: Context Awareness inside DBMSs


Semusi – Context Awareness Made Easy

Data analysis External links:

AnswerMiner – Data analysis made easy

Seven Bridges Genomics – The biomedical data analysis …

Methods | Data Analysis

Dialog system External links:

Dialog system – Object Technology Licensing Corporation

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

Enterprise cognitive system External links:

Enterprise cognitive system –

Face detection External links:

Face Detection Homepage: Facial recognition and finding

CV Dazzle: Camouflage from Face Detection

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

10 Fun Facts About the Human Brain – Take the Quiz!

Making Connections: Teaching and the Human Brain. – …

Risk assessment External links:

Healthy Life HRA | Health Risk Assessment

Ground Risk Assessment Tool – United States Army …

Sentiment analysis External links:

YUKKA Lab – Sentiment Analysis

Sentiment Analysis – Thomson Reuters Solution
http://Ad ·

Sentiment Analysis – Thomson Reuters Solution
http://Ad ·

Signal processing External links:

Embedded Signal Processing Laboratory

CASPER – Collaboration for Astronomy Signal Processing …

Social neuroscience External links:

Social Neuroscience – Michigan State University

Summer School in Social Neuroscience & Neuroeconomics

Home | Developmental Social Neuroscience Laboratory

Speech recognition External links:

eCareNotes – eCareNotes – Speech Recognition Software

How to use Speech Recognition – Windows Help

VoiceBase – APIs for Speech Recognition & Speech …

Synthetic intelligence External links:

Synthetic Intelligence Network – Home | Facebook

Unstructured data External links:

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

Data Governance of Unstructured Data and Active …

Unstructured Data Management in the Cloud | Panzura

Unstructured information External links:

MedEx-Unstructured Information Management …

Use case External links:

TeamViewer Use Case – Remote Access

TeamViewer Use Case – Remote Support

Identify use case scenarios | Microsoft Docs