The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

May 3, 2018

Facial biometrics going mainstream...

Recognizing someone by sight has been the building block of human interaction and more importantly has helped conduct commerce through the course of known history. It has helped build trust over time and eased many interactions and transactions. Of course, humans carry their very own powerful computer that instantly helps them recognize, recollect, build context and communicate effectively. In the recent times however, interactions with machines have increased substantially bringing in the need for many artificial means to establish identity - mechanisms such as cards, passwords, finger prints etc. While these have helped to an extent, humans have had to learn new ways to interact with systems while also opening up potential loop holes for exploitation.

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Quantum Computing- The next computing revolution

In a conference hosted by MIT's Laboratory for Computer Science in 1981, Richard Feynman proposed the concept of computers which would harness the strange characteristics of matter at the atomic level to perform calculations. Last year, IBM open-sourced its quantum computing network called the IBM Q- Experience to encourage researchers and enterprises to explore various possibilities of quantum computing. Other companies like Google, Microsoft and Intel are also in the race to build their own quantum computer to leverage its exceptional computing capabilities.

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March 29, 2018

Cognitive System-Mimicking Human Understanding

With advancements in artificial intelligence algorithms, it's possible for machines to mimic human understanding. They are able to analyze and interpret information, make deductions and identify patterns from the information sets analogous to human brain. These new generation of machines are categorized as cognitive systems. These systems aggregate machine intelligence, predictive analytics, machines learning, natural language engines and image/video/text analytics to enhance human-machine interaction.

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March 12, 2018

Trends and Innovation in HR

"Human resources isn't just a thing we do, it's THE thing that runs our business"
- Steve Wynn, Entrepreneur

The importance of the HR department has, till recent times, been overlooked. The HR department was initially handling record keeping, compliance to laws and regulations and compensation & benefits for employees. Over the past decade, with an onset and adoption of technologies and automation, the HR department has evolved remarkably. In addition to payroll process automation and streamlined on-boarding, new platforms and technology has enhanced the talent management systems, allowing more focus on personalized employee engagement.

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January 31, 2018

Emerging Tech in Airports

Emerging Tech in Airports

Air travel has become the most favored and convenient mode of long distance travel with the 20 busiest airports in the world moving more than 700 million travelers last year.

Meanwhile airports are becoming more than a gateway for people to travel through on their way to their destinations. Today, airports offer hospitality services, duty free shopping and dining experiences to billions of travelers who walk through their doors. Airports are leveraging technology to engage with travelers passing through their facilities to provide customized and seamless services.


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January 30, 2018

Computer Vision enabling a Retail Utopia

Computer vision (CV) is the technology that enables a machine to 'see' and 'understand' its surroundings, just like or even better than humans. As per the British Machine Vision Association, "computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images (video). It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding." It plays a vital role in providing innovative, immersive, futuristic solutions and applications across industries, including traffic management, surveillance, medical image analysis, payments, autonomous vehicles, quality control and many more.

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December 16, 2013

Data Virus Guard

Clients are, or soon will be, ingesting all sorts of data thanks to information brokerages and the Internet of Things (IoT) and processing that data in novel ways thanks to the Big Data movement and Advanced Analytics.  Decisions made through business intelligence systems require that the data being used is trusted and of good quality.  How will companies ensure that the data being ingested and acted upon is untainted?  This has been an interest of mine as I work to protect the integrity of my clients' decision making processes and systems.

Last year I shared a forward looking concern about the concept of a data virus: data that has been purposefully manipulated to render operations on an entire data set flawed, and it perpetuates its induced error. As noted in the original What will you do about a Data Virus? blog, a tricky situation arises when data fed into the enterprise is determined to be corrupted.  How do you unroll all the down stream systems that have made decisions based on the bad data?  Maintaining this data contamination is tricky.  Many legacy enterprise systems simply don't have the ability to "roll back" or "undo" decisions and/or persisted synthetic information.  So, the first and obvious line of defense is blocking, or sequestering, suspect data before it enters the enterprise.  Much as a network Firewall blocks suspect requests to ports or machines in your network, a similar concept can be employed..... a Data Virus Guard if you will .... in many situations as a first line of defense.

Please keep in mind that my focus has been on streaming sources of data, which are typically sensor based (maybe a velocity reading, or temperature, or humidity, or ambient light, ...) and associated with a thing (a car, train, or airplane for example) and comes in for processing in a streaming manner.  What I'm sharing in this blog could be applied to other kinds of "streaming" things such as feeds from Social Web systems, for example.

What is a Data Virus Guard? 
A Data Virus Guard is a logical unit that has the responsibility of identifying, annotating, and dealing with suspicious data. 

Where should a Data Virus Guard be deployed?
A Data Virus Guard should be deployed at the initial ingestion edge of your data processing system, within the data capture construct.  The data capture sub-system normally has the responsibility of filtering for missing data, tagging, and/or annotating anyway so it is the perfect location to deploy the Data Virus Guard capability.  If you identify and contain data at the "edge", then you run less risk of it containing your enterprise.

How do you Identify a Data Virus?
This area of the Data Virus Guard is what drew my research interest .... how do you go about discerning between normal data and data that has been manipulated in some way?  The approach that I've been taking is focusing on steady state data flows because I'm interested in a generalized solution, one that can work in most cases.  If one can discern what constitutes steady state, then deviations to steady state can be used as a trigger for action.   More elaborate, and case specific, identification approaches can be created and placed easily with the framework I'm proposing.

What kind of Annotation do you do?
As data enters into an enterprise, ideally there is meta-data that helps with maintaining data lineage.  That is, what was the source system that produced the data, what is the "quality" of the data, when was the data generated, when did the data enter the enterprise, is it synthetic (computed versus a sensor reading), etc. etc.  Added to this could be an annotation that indicates which Data Virus Guard algorithm was applied (model, version), and the resulting score of likely suspicion. 

How would the Data Virus Guard deal with suspect data?
Based on the rules of your data policies, the data judged as suspect may be set free to flow into your enterprise, discarded as if it never existed, or kept in containment ponds for further inspection and handling.  In the former case, if you let it in the enterprise and it was annotated as suspect, when data scientists work with the data, they will see that it is suspect.  If you have automated algorithms that make decisions, they could use the suspect score to bias the thresholds of making a choice. 

What are characteristics of a Data Virus Guard?
In the search for "the best ways" to guard against a data virus, a few criteria have popped out to make the system practical.  Firstly, it has to work on all common types of data.  To be truly useful in an enterprise setting, the Data Virus Guard can't work with only strings or only integers, it must work on all common types to provide true utility.  Secondly, its determination of suspicious or not data must be very fast.  How fast?  As fast as practically possible as the half-life of data value is short. This is a classic "risk vs reward" scenario, however, and can be done on a scenario by scenario basis.  Thirdly, it must have the ability to learn and adjust on its own of what constitutes normal, or not-suspicious, data.  Without this last capability, I suspect enterprises would start strong with a Data Virus Guard, but then it would find itself out of date as other pressing matters would trump updating the Data Virus Guard with the latest Data Virus identification models.  In summary, it must work with all types of data, it must be fast, and it must learn on its own.

How would you implement a Data Virus Guard?
Putting together a Data Virus Guard can be a straight forward endeavor.  By blending a stream processing framework with a self-tuning "normal" state algorithm, it would be possible to identify, and annotate, data flows that deviate from some norm (be it values, range of values, patterns of values, times of arrival, etc.).  One could envision a solution coming to life by using, for example, Storm, the open source streaming technology that powers Twitter, and a frequency histogram implemented as a Storm "bolt" (the processing unit of a Storm network) to discern out of norm conditions.

Admittedly, the usage of a frequency histogram would create a weak Data Virus Guard, but it would get the Data Virus Guard framework off the ground and be easy to put in place.  However, by using Storm as the underlying stream processing framework, swapping in a more powerful "out of norm" algorithm would be relatively easy.  Do you go with a Markov chain, a Boltzmann machine, or even the very interesting Hierarchical Temporal Memory approach of Numenta? This would all depend upon your system, the characteristics of the data you're ingesting and the amount of false-positives (and false-negatives) your enterprise can withstand.  Of course, you even go further and apply all three of the approaches and come up with some weighted average for discerning if some piece of data is suspicious. 

Summary
This is a forward looking post about what we can expect to be issues in Enterprises as all companies embrace the concepts of Big Data, Advanced Analytics, the Internet of Things, and true Business Intelligence: a Data Virus, and what we can do about it: a Data Virus Guard.  My work in this area is still evolving, and is intended to keep our clients a few steps ahead of what's coming.  Bad data plagues all enterprises.  It can be incomplete, malformed, incorrect, unknown, or all of these.  Unfortunately, we now also have to watch for malicious data.  Putting in safeguards for this condition now before the malicious data issue becomes rampant is a much cheaper proposition than re-hydrating your enterprise data stores once a contamination occurs. If nothing else, if you don't implement a Data Virus Guard, be sure you have your data policies in place for addressing this coming issue.

October 11, 2013

Can formal requirement methods work for agile?

By Shobha Rangasamy Somasundaram & Amol Sharma

Formal methods adapted and applied to agile, provide clear and complete requirement, which is fundamental to the successful build of any product. The product might be developed by following Methodology-A or Methodology-B, which changes very few things as far as knowing what to build goes. So we could safely state that the development methodology used by the project team could be any, but good requirements are absolutely necessary. The manner in which we go about eliciting and gathering requirements would differ, and needless to say, this holds true for agile development too.

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September 30, 2013

Ten Challenges of a Chief Innovation Officer

It is very easy to allocate funds and develop a strategy for innovation within an organization, however it is very difficult to make it successful and keep the interest of innovators. If innovation program is not managed well then it loses its importance in very short period of time. While implementing innovation program, there are many challenges such as operational, financial and resources, etc. However, ten main challenges for sustainable innovation management are discussed as following.    

1. Innovation Governance is the key for major decision making, such as acceptance of an idea for incubation, funding of an idea and marketing of finished product or service. Innovation governance should have decision makers from various functions of an organization as well as from outside of the organization. It should also have subject matter experts. This varied group helps to select the best ideas for incubation. They also decide the funding required and helps to calculate the risk associated with the innovation idea. The challenge for innovation officer is to define most effective governance structure and proactive members for various roles of governance.

2. Innovation Team is essential to execute the innovation program. This team must have entrepreneurial mindset. Thus, they have to be psychologically assessed on their entrepreneurial skills before they are selected for various roles of innovation office. These team members should have varied experience and skills. Identifying such team is a serious challenge for chief innovation office as entrepreneurial quality people are few.

3. Innovation Process has to be very simple and transparent. There should not be delay/gap between two connecting processes. For example, after submission of an idea by innovator, its result has to be declared in very short time. Otherwise, innovator usually loses interest and rarely pursues idea to the next level. Also, chances of innovator to participate again are rare. Thus, innovation processes should speed up the procedure and not to hurdle the innovation program. Also, numbers of processes have to be minimal. Innovation office has to work like a startup. Thus, has to be more informal in processes and quick in action. Therefore, innovation officer has to manage the balance between organizational formal set of processes and informal set up of innovation office. Automated innovation program management portal is very useful for managing innovation processes.

4. Innovation Tools are essential for training of innovators. Very first time, innovators mostly fail to realize the concept of innovative idea. Thus, their ideas are unrealistic or less valuable to the organization. Many participants are enthusiastic to contribute in innovation program but may not be able to understand how to participate because they face challenges in generating ideas. Thus, innovation tools come handy for innovators. These tools help them to make their idea more powerful and useful to the organization. There are many tools available. But, the real challenge for innovation officer is how to train innovators on these tools. It is very difficult to train every interested person in class room. Therefore, best way is to use online learning.   

5. Idea Selection for incubation is the process after idea submission by innovators. In this step, every idea is thoroughly investigated for its possible business potential and investment required. As investment is required in incubation and commercialization of an idea, every idea has to be analyzed on various risky parameters. Usually idea selection model is used to quantify the potential of an idea. Thus, innovation officer has to see that idea selection model is well defined and customized according to the need of domain and organizational business needs.

6. Innovation Portfolio has to be well balanced with combination of ideas having low (L) risk, medium (M) risk and high (H) risk. More the risk better is the rewards. However, more risk also leads to chances of investment loss. Thus, innovation officer has to define initial portfolio with lower risk and has to transform it to moderate risk portfolio over a period. Thus, first innovation portfolio should look like 10-20-70; it mean that 10% resources allocation for high risk projects, 20% for medium risk and 70% on low risk projects. After transformation, new portfolio should have more projects from both medium risk and high risk. Thus, new innovation portfolio should be 15-35-50 or 15-45-40 on H-M-L risk. The challenge for innovation office is to select the best proportion of projects of various risks, which suits the organization.

7. Rapid Prototype is the key in assessing the idea quickly and also reduces time required to take the idea to market. Agile methodology has to be adopted to develop the quick prototype of an idea. It reduces the investment required and gives better picture of an idea in a short period of time. It also reduces the risk because the idea can be judged before going into full-fledged production. Survey can be conducted on prototype to understand an acceptance of idea by customers. If the idea is well accepted by customers then prototype is transferred for production otherwise idea can be scrapped, thus it reduces further investment. The challenge for innovation officer is to select tool(s) and methodology for quick development of prototype with less investment.

8. Rewards and Recognitions is the integral part of innovation program. It attracts innovators for participation as well as it is the way to recognize their contribution. Though, the overall structure of rewards and recognition has to be in parallel with an organization policy, but considering the importance of innovation and psyche of participants, it has to be tweaked to match their interest. It is essential to give rewards or recognitions at each stage of innovation process. This keeps participants involvement very high. The challenge for innovation officer is to decide the rewards and the recognition given which will appeal the innovators. Officer also has to balance the organization's rewards and recognition policy and rewards given to innovators.

9. Innovator Engagement tactics keep innovator's participation high in the innovation program. Better engagement is possible only by transparent and automated processes. Innovators also have to be supported for idea elaboration and should be allowed to lead their ideas if selected for incubation. The innovation officer has to identify and implement all possible means to keep innovators engaged. Rewards and recognition also helps keep them engaged.

10. Success Measurement of innovation program is necessary as organizations are investing into program.  Hence, they are interested to know how innovation program is effective within the organization. Challenge for innovation officer is to define the best measurement method. While defining measurement method, innovation officer has to decide the combination of non-financial and financial parameters for measurement. The non-financial parameters are very important during the early stages of innovation program. Also, it is essential to note that success measurement has to be aligned with the purpose of innovation program. Linking success only with the return on investment is most likely to showcase as failure of innovation program, which is not always true because many in-tangible benefits can be realized through innovation program.

How should an Innovation Portfolio be?

Deciding an innovation portfolio is one of the big challenges for chief innovation office. It involves deciding about which innovation ideas have to be incubated, considering the investment required and risk involved in every idea.

Every potential idea may look like a gold mine which may change the fate of the company. However, it may not be always true because there is a risk of failure in every idea. Thus, before investing into an idea, it is required to assess an idea to manage the investment risk. Adoption of portfolio approach for incubating ideas is a better way to manage the investment risk.

Innovation portfolio should typically look like a combination of multiple ideas of low, medium and high risk. We can use the analogy of A-B-C rule where, A stands for bucket of projects of high risk, B stands for bucket of projects of medium risk and C stands bucket of projects of low risk.

Organizations should have their initial innovation portfolio based on A-B-C- or H-M-L Rule. Thus, initial innovation portfolio should look like:

  1. A or H Bucket of Projects: This is a bucket of high risk innovation projects. These projects are usually radical/transformational and mostly new to industry. These projects are focused on developing new generation products or services, and usually use emerging or very new technologies. Being high risky projects, the numbers of projects are limited to few so that sufficient attention can be provided to control the risk and maximize return. Return on investment on this bucket of projects can give up to 60%, while which resources allocation can be up to 10% of total resources/efforts used in innovation. 
  2. B or M Bucket of Projects: This is a bucket of medium risk projects. These innovation projects are mostly new to organization and focuses on development of products or services those are new to the organization but known to industry. These projects usually leverage existing technologies. Considerable return can be expected from this bucket which may rise up to 25% and resources allocation can be up to 20% of total resources/efforts used in innovation. 
  3. C or L Bucket of Projects: This is a bucket of low risk projects. Resources allocation can be 70% of total resources/efforts used in innovation. These projects are usually incremental changes in existing products or services of the organization. Thus, risk is very low; hence, return on investment is also not much and may be up to 15%. Many times benefits may be non-financial or in-tangible. The use of technologies is restricted to mature and well accepted technologies.

How to allocate the project into L, M or H bucket is a critical task. Organization can have their own model for allocation of the project into their respective buckets. While developing the model, following parameters and questions can be used:

  • Novelty: What is novelty of an idea?
  • Market: What is market size of an idea?
  • Customer: What is the benefit to a customer?
  • Growth: How is it supporting the growth of the organization?
  • Allocation of resources: What are the resources required?
  • Investment: How much is the investment required?
  • Return on investment: How much is expected return on investment? Is it tangible or intangible?
  • Competition pressure: Is there any pressure because the competitor has adopted it?
    Risk: How much risk is involved?
  • Technologies Used: What kind of technologies are required to develop an idea and, are those mature?

Aggregated reply to the above questions on certain measurement scale will help to allocate project to any one of the buckets H,M, or L.

During initial phase of innovation, typically innovation portfolio may have maximum number of projects of incremental changes or low risk. These types of projects will never generate expected growth from the innovation portfolio, thus portfolio has to be transformed over a period from low risk to medium risk.

As organization matures on innovation, it should systematically analyze innovation portfolios and manage risk to increase the proportion of innovation projects having more risk and more rewards. Thus, H-M-L portfolio should not look like 10-20-70 or low risk portfolio. The new portfolio should have more projects from medium risk and from high risk. Thus, new innovation portfolio should be 15-35-50 or 15-45-40 on H-M-L risk.

Periodically or every year organization should evaluate their innovation portfolio to see the ratio of H-M-L. It should neither be in low risk zone nor be in very high risk zone. Having low risk or very high risk in innovation portfolio may always lead to failure of innovation program.

References:

  • Day, G. S., (2007). Is It Real? Can We Win? Is It Worth Doing? Managing Risk and Reward in an Innovation Portfolio. Harvard Business Review, December, pp. 110-120.
  • Nagji B., & Tuff G., (2012).Managing Your Innovation Portfolio. Harvard Business Review, May, pp. 68-74.