Pomodoro and Productivity Tools – Why not more integration

March 28, 2018 Leave a comment

I have taken a short break from building my AI Bot due to work.  Sometimes priorities take over.  Which led me to wanting to make a quick note.

 I have been using many productivity tools over the years: currently I use OmniFocus more than others.  I have tried Things 3, Todoist and other apps.  All very good and do things somewhat differently.

I am also waiting to see what happens with OmniFocus 3 this year.

I have also tried many Pomodoro technique apps on my computer or iPhone or iPad.  I love the way it helps to focus and chunk your time.

The question I have is why are the two practices not more tightly integrated.  I would hope that if I had a task in OmniFocus I was working on ( whether i allocate time slices or not to it ) that I could initiate a Pomodoro time window to work on the task.

All of the current productivity tools are lacking this integration point.  I do realize many people may not use the Pomodoro technique to do work but I would think that there is a large group of people wanting this functionality.

There have been attempts to integrate through things like Vitamin-R and other apps, but it is awkward and not tightly integrated in a seamless way.

I am still hopeful and holding back purchases until someone steps forward to do this integration of GTD and Pomodoro to allow us to be more productive in an active direct way.

Filed under Personal, Task Optimization, Work Optmization Tagged with , , , , ,

Executive Guide to Cognitive Computing Part 4

January 19, 2017 Leave a comment

Five Dimensions that Cognitive Computing will evolve in Public Sector programs

As Cognitive Systems get deployed into Public Sector programs they will continue to evolve over time due to the nature of algorithmic programming and natural language processing of information.  Cognitive systems are dependent upon a feedback loop and as such these dimensions will have an impact on not only the technology but the programs or projects they support.  This evolution is predicated on a few dimensions that affect perception and adoption.

 Cognitive Evolution

Figure 1: Five Dimensions of how Cognitive Computing will evolve over time

 

Cognitive Computing will evolve over 5 dimensions that span both technology and cultural aspects of a public sector program.

Personalized Interactions

Given the ability for cognitive to interact via natural language and learn from those interactions each business problem may require varying levels of interaction and also the level of personalization.  Therefore, a social service benefit self-serve application will need to have a much more intimate understanding of the citizen.  Each person will need to be better understood on more dimensions of interaction: access rights, location, personality, tone, sentiment, log history etc.  All of these dimensions will provide a more satisfying interaction and outcome regardless of the user type.

Learning

As has been explained cognitive systems relying heavily on machine learning.  Machine learning algorithms can be supervised or unsupervised.  It will come down to what level of complexity and knowledge the algorithms has of a specific domain.  Some applications will constantly need the input of a human subject matter expert in order learn whereas other systems will continue to enhance themselves through automated feedback loops.

Recommendation: Develop a culture where analysis and question asking is supporting in that cognitive systems will aid in decision making outcomes.  Also the need to have SME within a domain or program area to participate in helping the cognitive systems learn.  This will impact workforce dynamics and must be positioned correctly so that users do need feel threatened by the new systems being developed.

Sensing

Since Cognitive system analyze larger data sets and require more dimensions of data one path that cognitive systems will evolve around will be the number of data sets used to answer questions or make decisions upon.  One will see the expansion of various sources of information types to ‘sense’ and decide upon.  The Internet of Things, Dirty Data, Big Data, Open Data, Geo-Spatial and Social Media information sources provide greater contextual understanding for cognitive systems to integrate with and additionally enhance their analysis capability.

Recommendations: Policy changes will be challenged to address the evolution of information access in accordance with public sector regulations and compliance.  Planning and strategy will be required in order for this evolution to occur

Ubiquity

Public Sector workers are younger and are technically ‘rich’ in their personal lives.  The expectation of technology use will increase exponentially as the public sector workforce changes.  The need to embed cognitive systems in how people work and where they work regardless of device or location will need to be planned for.

Recommendations: Since cognitive systems focus on information automation vs. process automation and the information can be presented or integrated in any form the ubiquity of the interplay between systems and users can be supported to meet the demand of the new public sector workforce.

Scalability

The ability to interact with government workers or citizens will continue to enhanced with the continuing development of natural language and conversation algorithms which will ensure that the interaction between user and technology becomes easier over time.

Cognitive systems continue to enhance themselves through artificial intelligence and machine learning.  The evolution of feedback loops and deep neural networks will ensure that developed algorithms will be enhanced in a more automatic fashion that the system can truly learn how to interact and better respond with increased levels of confidence and information.

Cognitive systems are really a culmination of existing systems and algorithmic programming as well as the need to incorporate more and larger data sets such as geospatial, weather, social media and internet of things data (IoT).

Recommendations:

Public Sector leaders must plan for the fact that in order to scale systems one must be reliant on technology on premise as well as in the Cloud or with a Platform as a Service (PaaS). 

How do I use Cognitive Computing and for what benefit?

To truly understand what and how cognitive systems will benefit your organization it is best to see how other public sector programs are starting to use cognitive machine learning and natural language processing to enhance programs.

Tax

A large taxation department is using advanced methods of natural language processing to analyze structured information (SWIFT Transactions) and unstructured information such as social media and addresses to investigate and analyze off-shore financial transactions.

Health Agency

A national health agency that is tasked with researching and assessing immunization products for the country they serve are looking at the possibility of what natural language processing and text analytics will allow them to do.  Instead of manually reading thousands of medical journals and research documents by hand; which currently takes 10 months they hope to be able to analyze and extract insight within a shorter period of time thereby getting more effective immunization treatments to the populace in a shorter period of time.

Law Enforcement

Investigations into major crimes and drug gang activity is being enhanced by combining many sources of information together and allowing investigators to ask in natural language who someone is or even where they are.   This information is then fed into visualization tools to better see how individuals and organizations are linked.  Advance analysis tools are being used to image analysis and extraction of meaningful evidence that could only have been done by a human before.  This allows a larger body of evidence to be gathered and because it is automated the change of evidence or forensics can be maintained as it is handed over to prosecution.

Filed under Cognitive Computing, Contextual Computing, Government, Task Optimization, Work Optmization

Challenges and Opportunities for Cognitive Computing in Public Sector

January 13, 2017 Leave a comment

Challenges to Cognitive Computing in Public Sector

The challenge in today’s world of improving or innovating government programs is that we have a broad array of information and process automation to co-ordinate.   Another major challenge of being in a data driven world is that information can be wrong, false, incorrect, out of date or inaccessible.  Cognitive Computing with its ability to apply algorithmic programming allows advanced patterns to be identified out of a much larger group data sets; which allows us to reduce the “noise” associated with making decisions and program outcomes.

There is also a need for evidence based decision making; which needs to follow a prescribed methodology.  As well as the need to analyze larger bodies of knowledge and information.  Traditional rules and equation based programming cannot manage or interact with a human in natural language.  Cognitive Computing interacts with a government worker with natural language and with the ability to learn and enhance the algorithms needed to find and test hypothesis or questions.

Government must ensure information is secured and managed effective.  One of the challenges of moving to cloud based computing or sharing data sets of information across government raises the issue of how far can the data or the information move from where is was created.  Information provenance and governance practices must be in place but the need for private cloud or platform as a service cognitive computing service catalogs are needed to ensure the data is kept within the boundaries of how it is to be governed and managed.  Data residency is another issue associated with using cloud services or cloud computing; however most major providers of cloud services have data centres within the country thereby offsetting issues associated with Data Residency.

Recommendations:

Most governments around the world today have a shared services model for core ICT and Enterprise applications support.  We are seeing that government are now looking at cloud brokerage services being managed within the government which deals with Data Gravity issues.  And based on the nature of the API Economy we see that PaaS (Platform as a Service) are now being investigated and tested.   Therefore, we see central shared services agencies being the agent of change and will look to them to deploy Cognitive Computing PaaS as a service catalog that other government agencies and projects can leverage which will then ensure information is secure and protected depending on the type of information.

Opportunity to Innovate in Government Programs

Due to ability of cognitive computing to identify patterns or information at high speed and with large sets of information the opportunities in government are broad.  Since all information sources are able to be analyzed and combined (Databases and text etc.) a more complete picture is provided to an individual to make decisions.

Any area within a government program that houses a large set of information relevant to a specific domain: benefits, policy, regulations etc. Would benefit from cognitive computing since this information can be analyzed as well as added to a corpus of knowledge that the machine learning algorithms can access and analyze across dimensions such as time or relevance etc.

Six forces that will impact the future evolution of cognitive computing in Public Sector. 

Each facet has its own issues and challenges for this technology to be adopted.

Society

  • Tremendous demand for more intelligent machines and access through mobile devices can facilitate familiarity and comfort with technology
  • Fears of privacy breaches and machines taking human jobs could be a deterrent

Perception

  • Perceptions and expectations must be well managed
  • Unrealistic perceptions of risk and expectations could lead to a third “Artificial Intelligence AI Winter”

Policy

  • Wider adoption will require the modifying policies (e.g., data sharing) and creating new policies (e.g., decision traceability)
  • Fear, uncertainty and doubt may be addressed by new policies (e.g., data security & privacy)

Technology

  • Advanced, intelligent devices will enable a greater understanding of entity context and contribute to the robustness of available information corpora
  • Greater scalability needs drive new architectures and paradigms

Information

  • Variety and scalability capabilities of future systems will advance rapidly to cope with information exhaust
  • Information explosion could advance evolution and adoption rates

Skills

  • Cognitive computing demands unique skills such as natural language processing, machine learning
  • Greater availability of key skills will be key in the evolution and adoption of the capability

Recommendations: One must co-ordinate a strategy that revolves around the areas discussed above.  The fundamental challenges similar to cloud based computing in pubic sector will be policy and cultural change that needs to be managed in order for the information and technology to develop.

Filed under Cognitive Computing, Context 3.0, Contextual Computing, Government, Task Optimization, Work Optmization Tagged with ,

What is Cognitive Computing and Why Should Program Executive Care ?

January 3, 2017 Leave a comment

What makes up Cognitive Computing

Cognitive Computing is comprised of three main functional areas.  Which are: natural language processing, machine learning and hypotheses testing.  All three functions of cognitive computing combine to provide greater flexibility.  This helps address a broader array of business problems in public sector. Business problems that could not have been solved earlier.  Natural Language Processing enables machine learning and discovery algorithms to interact with the end user in a more meaningful way.

Natural Language Processing (NLP)

            NLP describes a set of linguistic, statistical, and machine learning techniques that allow text to be analyzed. Which allows key information extraction for business value.  Natural Language analysis uses a pipeline processing approach.  Wherein the question or text is broken apart by algorithms.  So that the structure, intent, tone etc. is understood.  Specific domains of knowledge; such as legal, finance or social services, require targetted “dictionaries” or filters.  This helps to further improve the ability of the technology to understand what is being asked of it.

Some of the key benefits of NLP is it improves the interaction between human and systems.  Some additional benefits from NLP are as follows:

A questions contextual understanding can be derived from NLP. IT organizations develop a meta-data (information about information) strategy.  This gives more context to data and information sources.  The more meta-data and the more context added to a system; the better the understanding. This allows the improvement of finding information and then providing an answer back in natural language.  Instead of a page ranking result one would get from a typical search engine the response is in a form the user will understand.

The intent of a question is then better understood. Which means the cognitive system can better respond with a more meaningful response.  As well as return various responses with an associated confidence level.  This then gives the end user a more meaningful response with which to make a decision upon.

Natural Language Processing has taken interaction and access to information to a whole new level.  Which will in turn provide increased productivity and satisfaction to the end user.

Machine Learning

Machine Learning is all about using algorithms to help streamline organization, prediction and pattern recognition.   Big Data by itself can be daunting and only data scientists can build and interpret analysis by incorporating machine learning and natural language processing Big Data can only benefit from being easier to interpret by a broader user group.  Part of Machine Learnings “secret sauce” is deep neural networks to do information pattern analysis.

Deep neural networks, due to their multi-dimensional view can learn from regularities across layers of information which allows the machine learning algorithm to self-enhance its model and analysis parameters. This capability takes the onus away from the end user or data scientist to mine information from multiple sources.

The benefit to public sector programs is that deep domain knowledge will not be needed by the end user or the citizenry but have the machine learning algorithms to the heavy lifting and analysis for them.   

Historically organizations had to depend on limited ways to analyze and report on information which to a certain degree limited decision making and program outcomes.  Now with machine learning a key benefit is to access these systems with natural language or extremely flexible visualization tools which make decision making easier and more productive.  Since Cognitive systems are about knowledge automation vs. process automation.

Hypotheses Testing

A hypothesis is a proposed answer to pre-existing or understood responses.  From there a cognitive application will use the information that resides within a certain corpus or domain of knowledge to test the hypothesis.  Unlike humans who typically test hypothesis in a serial fashion one of the key benefits of a cognitive system is that it can test hundreds of hypothesis in parallel.  We see this occurring in areas such as health care or intelligence where various proposed outcomes are tested against a domain of knowledge.  The domain of knowledge can be comprised of many different sources and types of information. Given that cognitive systems have the ability to test large volumes of hypothesis at a high volume.  Programs and applications can benefit from the ability to provide improved means to make decisions with confidence and also to remove the “noise” surrounding what is trying to be resolved.  Some benefits from hypothesis testing are:

Causal Induction provides a key benefit to the user since it is based on statistical models deriving insight from a corpus or domain of knowledge.  As these models become more refined the ability to derive insightful responses provide more meaningful interactions with the end user or citizen.

Probabilistic Reasoning can generate multiple responses to a question which provides the user to see all aspects of an outcome versus generating a specific bias to the problem at hand.  This predicated on the system having enough context and also arriving at a specific level of confidence to provide an answer to the question.  As systems learn through interaction and feedback they will be able to identify if information is missing in order to provide an answer which again enhances the decision making process of a project or program

In summary; Cognitive Systems combine natural language processing with advanced algorithms and modelling tools to aid workers to make decisions in a shorter period of time and/or to provide more meaningful insight to a larger domain/corpus of information which the end user would never have been able to access or analyze prior to Cognitive Computing technologies

Filed under Cognitive Computing, Context 3.0, Contextual Computing, Government, Task Optimization, Work Optmization

Contextual Computing

October 30, 2014 Leave a comment

Contextual Computing unlocking the power of enterprise data Infographic

Filed under Context 3.0, Contextual Computing, Government, Information Lifecycle Governance, Open Data, Task Optimization, Work Optmization Tagged with , ,

The demise of Water Cooler Decision Making

July 31, 2013 Leave a comment

 

On a recent meeting I had with a government department wrestling with providing better service; and to improve effectiveness and efficiency to it’s workers:  I started to discuss how government departments are attempting to eliminate water cooler decision making within in it’s programs.

They looked at me with a strange look. I sometimes get a tilted head as people try to understand what I mean.  Fundamentally it comes down to using technology to capture the decision making process.

If you have workers standing around a water cooler and making decisions on work or a case file; you are not capturing the knowledge or the information associated with the discussion or the decision.  If you have workers making decision around the “water cooler” it also means that the technical infrastructure is not providing a robust enough work environment that the workers feel comfortable enough in order to make a decision.

If workers are making decisions at the water cooler you have lost the ability to go back and understand who made the decision, why did they make that decision and how did they make the decision.  You want the technology to capture as much as that as possible to ensure compliance to policy and legislation.

 

IMG 1022

So we start to have a discussion about Advanced Case Management (ACM).  ACM is recognized by the majority of the analyst firms globally.  ACM is a strategy not a product.  The strategy typically involves planning around ECM, Task Automation and Metadata then leveraging technology that puts more of the information, knowledge and collaboration in the hands of the worker.  

So as a worker is dealing with a case or work order etc. they can; within the technology or application, access email and IM or other collaborative tools to be able to reach others to ask and share knowledge to help them make a decision on the task or case file at hand.  ACM technologies need to be able to provide a rich UI and work experience so that government workers trust the application or technology.

In a recent business value assessment I was involved in we found finance clerks “PRINTING” screen shots of their work in an ERP system !  So they were using technology but did not trust it in case they would be audited.  They then kept those printed screen shots and not only filed them but copied them for their manager because the manager did not trust the system of engagement or system or record !!!  This blows my mind.

So technology and strategy around case file management in government departments has to be well thought out from best practices and the benefits of an integrated ECM, Task and Metadata strategy.

More on this topic soon.

Filed under Advanced Case Management, Case Management, ECM, Government, Task Optimization Tagged with , , , ,

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