The Man and the Machine and the impact on the Future of Work

Many people wonder the reasoning of Microsoft’s [1] mission which is: “Empower every person and every organization on the planet to achieve more” . It can sound a cliché, but I could never be more important to reinforce the message, once we are coming out of repetitive work and normalization of processes, which are step by step being totally automated – by the use of technologies like robotic process automation – shifting to the knowledge work space.
I’ve seen based on my experience working with Oil & Gas companies, that the idea of an industry that monolithically crystalized is not a reality anymore. This type of industry sector is being enabled by a set of technologies that took time to mature, combined with challenges related with network infrastructure availability and latency (oil production occur in remote areas of in the middle of the ocean where connectivity is a scarce resource), but today are becoming mainstream. For example using drones to make inspections, mixed reality for executing field inspections, collaboration remotely with engineers in the control center or using cameras for detecting safety (lack of protection equipment) or security (facility intrusion) with image pattern recognition. These new technologies  are also creating a job and career change  widened by the adoption of new technologies and talent scarcity. Two reports from World Economic Forum, named, Towards a Reskilling Revolution and Future of Jobs, highlight:
Companies, “expect to hire wholly new permanent staff already possessing skills relevant to new technologies; seek to automate the work tasks concerned completely; and retrain existing employees. The likelihood of hiring new permanent staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption. However, nearly a quarter of companies are undecided or unlikely to pursue the retraining of existing employees, and two-thirds expect workers to adapt and pick up skills in the course of their changing jobs. Between one-half and two-thirds are likely to turn to external contractors, temporary staff and freelancers to address their skills gaps.”

I was invited as a speaker to the Halliburton Life conference in Abu Dhabi. One of the trends I noticed – as part of the reinforcement on how intrusive artificial intelligence is enabling a new set of possibilities, like for example, improving decision making in terms of financial impact while making seismic data interpretation in terms of addressing exploration and production viability – is how hard is for individuals with strong domain expertise to work. I heard during sessions people expressing frustration on manipulation data sets, being not able to work of the same updated version of a data set, becoming unproductive waiting weeks for results and deal with uncertainty.

I am having a series of meetings with HR function and I’ve been told that the way people work is becoming frustrating and unproductive, despite all ubiquity of cloud, social, bring your own software / device or putting into a different perspective, it is not achieving the expected results. There was definitively productivity improvements, mostly in terms of mobility, meaning individuals can work anywhere at their pace, have access to information they commonly use on every day. However, mirroring self-expression, self-development (be digital ready), achieving a true networked work environment – far from being reached, companies with workforce size higher than 5.000 individuals find how difficult it is to reach out experts, knowing what is happening across domain of expertise which people operate, be part of working groups to construct new identities to replace old ones, avoiding to become redundant as a way of personal fulfilment – and ultimately, how technology ubiquity is bringing lack of integration, distraction shifting our attention on meaningful activities. As an example, people envision a future which individuals participate in a meeting, don’t need to search for meeting content in “teams workspace”, don’t need to connect to a projector, don’t need to write meeting notes, spark and track action items.

Studies carried in an Oil & Gas major using Microsoft’s workplace analytics reveal that in an investment venture appraisal that took 1 year to complete, more than 2.000 individuals were involved with a strong presence of financial departments. As a result, this contributes to:
  • Slow decision making and limited agility;
  • Employees being over-managed;
  • It was possible to gain more than 300 FTEs worth of time by rethinking how work and management is distributed;
  • Cross-functional processes becoming bloated and expensive.
People aspire a future which individuals participate in a meeting, don’t need to search for meeting content in “teams workspace”, don’t need to connect to a projector, don’t need to write meeting notes, spark and track action items. This is not a futurist idea. This a desirable outcome.

 

3 pillars of Human Led Design
To address this, it is necessary to rethink how technology should pursue a human led design strategy:

  • Help people achieve what is important to them. This something that during all the years companies have worked in operational improvement did not fully deliver. For example, can’t we let people organize and define their own User Interface in a way they will focus on what is really most important to them?
  • Building relationships. There is an intense debate on methods to discover how to get to know individuals and understand their real needs and wants based on trust, re-quoting Elizabeth Tunstall that provides an interesting perspective, as our universal individuality is cannot separated by the sacred, profane and spirituality, instead of creating personas framed on a stereotypes which we must be forced to fit.
  • Design technology that seamlessly integrates into the human world. Design for the capabilities and limitations of the human body. Adjust to the human needs dictated by the physical conditions of an environment or device. For example, designing an application to be operated in an explosive and noisy environment is different for designing the same solution to be operated in a control center.
Future of Work scenarios
The future of work is deeply related with employee upskilling and retainment. Being able to make the change in terms of employee experience will contribute to retain talent for next generation of workers.
Below is a spectrum of possible scenarios to explore in terms of what the future of work can be.
Some are more easy to productize, other’s, like augmented knowledge reasoning aren’t, but it is still worth trying – I remember previous engagements related with public policy enactment as an example – i.e. government agency wants to increase taxes and wants to recommend a scenario that is going to maximize tax collection and minimize negative population sentiment, using A.I. for that purpose.
Annotation

Collection of Future of Work Scenarios

In summary, guiding principle is focusing less on jobs descriptions and more on the nature of the work produced and then determining how to help employees accomplish those tasks enabled by scenarios like these:
  • Digital assistants may include a digital shadow that will observe behaviors, build and maintain work preference rules, search information, predict and remind you about important actions to accomplish based on the nature of the work you are working on.
  • Unified interactions allows employees to take actions on tasks, gain access to the data feeds they follow, collaborate with other individuals, all without ever leaving a single customized interface according his needs and wants, instead of jump from application to application.
  • Digital upskilling is about training the workforce, with content hyper-personalization based on a role definition execution requisites and performance review feedback. In a world that is becoming digital, the workforce is must be digital ready instead of digital impaired.
  • Collaboration (at large) as a digital extension of individuals senses, contextual knowledge support it serves as an interface for humans to the physical world. Enhance the user’s surroundings with relevant, and/or actionable information, using as an example, mixed reality to augment our ability to perform.
  • Reasoning leveraged on recommendation agents based on past interactions and other sources of information to jump-start and kick-off knowledge-oriented activities. Incorporate specialized Artificial Intelligence technologies processing functions tuned specifically to answer questions, create models, to support decision making.

You can’t optimize for everyone, but it is worth making a profound change happen in our working habits this will help organizations that they cannot digitally transform unless people do.

[1] I am Microsoft employee when this post was written.

 

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Talent Management Digital Transformation in Oil & Gas Companies

Technology adoption is pushing the workforce to re-skill

Oil & Gas companies like utilities, mining and other natural resources industries, were challenged with barrier of ageing workforce. This actually have an impact. Project overruns, lower asset availability and low production targets. But during this last 5 years, what become some experiments around IoT projects, big analytics about operational performance and most recently, real time collaboration on seismic data studies and reservoir simulation, increased relying on technology – shifting from operational technology to information technology.

Oil & Gas companies need more and more personal with computer science background to keep operations going . Therefore, there is a rise in demand of data scientists, statisticians, individuals that know how to use Artificial Intelligence technologies. This requires a different breed of human talent.

Companies need to deliver to a multi-generational / cultural workforce that demands more flexible work options. And that means it is necessary to be agile in adapting human resource practices to meet the needs of the business and the workforce.  Practically speaking, HR’s role is expanding beyond its traditional focus.

From the organizational side, a new approach to education, and especially up-skilling and re-skilling, is necessary to support digitally enabled workforce and the evolution of new business operations that require with latest technology developments. For example, today it is possible to automate pipeline inspection using drones and combining edge computing solutions [1]. As such, this presents a huge growth area of new jobs opportunities that candidates could apply for or existing workers could take new training go gain role readiness.

From the labor market side, there is the ambition of have much more flexibility in the terms work is performed, mimic the work pattern habits, like in personal life – utilization of social media style tools for internal communications and bring your own device. And a part of that, new generational workforce like the Millennials believe oil & gas companies is lacking innovation, agility and creativity, as well as, opportunities to engage in meaningful work. Strategically “employee value proposition” and pull-factors will attract the most talented. Improving brand awareness captivating candidates to apply for a job opening. Attracting the next wave of the workforce can also include using new approaches like “Recruitainment” – gamification in talent management, that includes among others, recreating a real work environment and understand how well the candidate fits into role execution [2]; [3].

Creating the workforce of the future involves to consider the combination of the following dimensions:

  • Modern workplace – which the workforce can collaborate across multidimension kind of data, using the device and the platform of choice. Contrary to other industry sectors, oil & gas companies are data intensive and data tasks oriented that use neural deep learning network algorithms, fuzzy logic, and others to improve data analysis.
  • HR departments, must rethink how core processes are designed. Artificial Intelligence, when combined with other available technologies can free up considerably of a recruiter’s job and can then use time more efficiently attract the best candidates. Video interview is on the loom, as well as, application and job description matching, eliminating unconscious bias. Plan and predict workforce capacity, upskilling needs and employee disengagement can also be supported by AI technologies.
  • Workforce safety will see a significant leap forward due to digitalization. With digital oilfields and the automation of dangerous tasks, fewer workers will be placed at risk. In addition, Artificial Intelligence will reduce the possibility of human error, contributing further to workforce safety. An example of using AI in field inspections is Equinor (formerly Statoil) – Statoil digital field worker a company that is pushing boundaries in terms of digital transformation.

Contrary to the power shift that occurred towards the customer related with consumer industries, in labor market – unless there is talent shortage – the bargaining power is still much on the recruiter side. However, HR workforce, is aware that it is needed to be investing more in recruiting and retaining the best talent, due to the fact that oil & gas companies are becoming IT companies with a necessity of becoming, fast, a data driven company. For example, in scenarios related with  optimizing drilling, by using historical drilling data, it is feasible to quantitatively identify best and worst practices that impact the target. Advanced analytical methodologies would ultimately develop a model that would provide early warning of deviations from best practices, lessons learned or other events that will adversely impact drilling time or costs. Hence, on one hand there is technological dimension related with what IT solutions are best fit to build the models and make the analysis.

New set of technologies for the evolution of Talent Management

On evolving HR function from hire to retire, increasing hiring quality, contributing to knowledge diffusion and retain talent, HR professionals should consider to invest in these new set of technologies taking in to consideration their priorities.

Talent

Identifying digital enablers for improved talent management

  • Social Sourcing. Storing a static talent pool is not sufficing. Managing a dynamic talent pool that continuously is aware of the activity the candidate is broadcasting in terms of career achievements, new roles or professional activity is becoming ultimately important to spot the changes and trends of the prospective labor market. Social Sourcing is about make it visible a pipeline of sourcing with internal / external candidates integrated with social workforce analytics and from employee service providers (e.g.: LinkedIn). It also includes identify automatically candidate profiles that match your job description requirements.
  • Intelligent Application Screening. Despite of the evolution related with managing job applications in digital mode become a standard, processing an immense volume of applications is not practical and is time consuming. Robotic Process Automation can help with text extraction, candidate profiling and role matching and workflow automation.
  • Automated Candidate Interviewing. This is one of the trends that is moving very fast, in some cases, is becoming fully automated that with the combination of cognitive services (e.g.: image pattern recognition and natural language processing), it is possible to analyze emotions and enhance candidate profiling, makes a pre-screening of the interviews before they are short-listed to the recruiter.
  • Neuroscience Assessment in combination with advanced analytics. Supports decisions about candidate’s degree of fit, matching candidates to positions and at the same time removing recruiter / hiring manager unconscious bias.
  • Mixed Reality. Is able to replicate in the digital world a natural performance environment. It can be used in “Recruitainment”, as well as in training (e.g. safety, operations performance and optimization, geomodeling).
  • Social Business. Provides a platform for finding relevant information, collaborate, ideate, innovate and reach out to colleagues and SME’s. Knowledge diffusion is improved combined with integrated collaboration and productivity tools. – Social Business is a platform that allows to :
    • Know about the Human: Who the worker is, how the worker identify himself and what you pretend to be, the person’s social graph.
    • Human Interactions: What the worker does. Whim whom the worker engages with. How he react to participation in company forums . What kind of work activities are pursued.
    • Search and Analytics: Search for knowledge, gather feedback, get trends, spot patterns, sentiments, learn.
  • e-Learning.  Enable organizations to train and upskill the workforce at scale, with content personalization, and curation, based on particular role execution requisites and performance review feedback.
  • Social performance analysis. Enables workforce analysis in order to improve performance measurement. It also includes, alignment between the nature of work performed and the type of social network configuration the employee is fitted.

The set of technologies uniquely referred above are supported enhanced by a foundational layer composed of:

  • Data Analysis Services. Supports all the moments which decision making is executed, by combining a multitude of data sources. Enables the exploration of data that go beyond those available in spreadsheets. Visualization is possible by direct connection to data sources.
  • Machine Learning. Like in operational improvement scenarios, Machine Learning can be used from predict employee disengagement and the propensity of leaving the organization, to recommend personalized training, and benefits packages;
  • Cognitive Services. Multiple set of Artificial Intelligence technologies supporting workforce planning, automated conversational applications, emotional analysis, recommendation engines and intelligence search:
    • Role description matching and skills / candidate experience.
    • Recommendation engines, used to propose personalized training program, career path, perform workforce impact analysis in terms of future role needs and new skills to be acquired and ultimately;
    • Business Process automation, by the use of chatbots, for candidate or HR staff self-servicing.

Evolving the HR mission and processes by the adoption of Artificial Intelligence, Big data, analytics, and gamification in the hiring process is picking traction, nevertheless, HR professionals also consider the employee experience if talent must be retained and keep low the propensity of leaving the organization.

 

References:

[1] – Gartner defines edge computing solutions as: “facilitate data processing at or near the source of data generation and serve as a decentralized extension of cloud, data center or campus networks. Typical sources of data generation in the context of industrial Internet of Things (IoT) include sensors and control devices such as programmable logic controllers (PLCs) and distributed control systems (DCSs)” – Market Guide for Edge Computing Solutions for Industrial IoT – Gartner 2018. Microsoft’s Azure – IoT Edge is such kind of solution.

[2] – This is not a new trend only in Oil & Gas companies. Marriott hotels, who created a virtual reality Sims-like game in which players have to juggle all the responsibilities of a hotel kitchen manager, was one of the first to try a gamification approach.

[3] – Building the Next Generation of Petrotechnical Professionals through Gamification – Paul Ugoji (Total Automation Concepts Ltd) | Akii Ibhadode (Federal University of Petroleum Resources Effurun, Delta State) | Anslem Amadi (Federal University of Petroleum Resources Effurun, Delta State).

 

 

Retail Digital Transformation

In the last 7 to 5 years an unprecedent shift occurred in the way consumers interact with retail companies. Consumers complete transactions either online or in-store. Consumers still buy goods at physical stores and switch from physical to digital and back to physical. Contrary of what we may think – and this is also confirmed by retail customers with international presence I am working with – stores continue to contribute almost half of all retail growth; brick-and-mortar sales are not shrinking, but actually growing [1].

Hence, the big question is, where it relies the difference on how we interact and sell, if everyone is doing the same and copy the approaches? Where it lies the competitive advance of doing it differently?
Make a difference means mastering the art of:

  • In-store experience: is related with virtual shopping assistants, with sensing emotions by analyzing expressions related with prices, promotions and the products offered, mixed reality, which the customer can experience the product virtually or get more features by connecting to his personal profile and discover if there is a match. By recognizing and interpreting facial, biometric and audio expressions, new artificial intelligence technologies can identity customers emotions, reactions or mood and deliver contextualized products, recommendations or support – by the use of bots, that use natural language processing to help customers effortlessly navigate questions (“is this size or color available?”; “Can I ship back home the products while I am going to watch a movie?” — improving the customer experience.
  • e-commerce: providing a mobile app is not enough, actually, there is being sensed that there is a point of saturation about using apps because all of them do exactly the same which most of the time is a simplified version of the web site with a different form format. The next wave of innovation is related with prescription and recommendation engines. The customer will see the products and the offers that really matter to him based on the customer profile and combining other sources of external data which he expresses his needs and wants. Recognizing customers and customizing the e-retail experience to reflect their current context, previous purchases and shopping behavior. As customers look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on customers needs, preferences and lifestyle.

 

 

 

Capture II

The data focused organization

 

The art of mastering data in the retail sector will be dependent on the collection of in-store data , product data , and customer data new data sources coming from experiences expanded by the art of the possible using artificial intelligence.
Other key challenge is how to create and expand an ecosystem, a business ecosystem [2], like its biological counterpart, gradually moves from a random collection of elements to a more structured community, adding more partners, more products, more offers and enlarging the customer base. In a business ecosystem, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations [2]. Of one key success of Amazon is how they grow in terms of product offerings. They started as book store. Today, they sell virtually anything.
In business ecosystems, a smart company manages information and its flows. In terms of data, this is not about our view of customer, that can be expanded how each partner that belongs to ecosystem increases that view of the customer and how it detects new opportunities by having access to a much more bigger customer and customer profile, alone or even partnering with other companies together. Retailers by being the anchor of such immense data lake, will monetize from each transaction occurred as all the other connected partners, the more the customer buys, the ecosystem grows as the business opportunities. On the other hand this will allow to evolve business capabilities. If a highly efficient logistics company joins the ecosystem, all the merchants can if they want benefit from the new innovative added capability, if a Retailer attract a fintech company to support new ways of doing payments, they will drive new interactions with millennials or digital savvy customers.
References:

[1] – The great retail bifurcation – Deloitte Consulting 2018

[2] – Predators and Prey A New Ecology of Competition – James F. Moore – Harvard Business Review

The man and the machine manifesto – Part II

 

In the man and the machine original ramblings, I alluded to the to the discerning challenge between humans and powered Artificial Intelligence machines will work together, against the “tangled recursion” [14] as an example of machine intelligence. This log entry expands deeper the doubts and wonders about man and machine co-existence.

Cybernetics the need of adopting other forms of intelligence

Cybernetics become popular when the works of the first generation of cyberneticians like Norbert Wiener and Ross Ashby grow reputation from the lectures of Stafford Beer [10] and the works of Gordon Pask on training and teaching machines [11]. Applying the laws and principles of cybernetics, especially the law of requisite variety – control can be obtained only if the variety of the controller is at least as great as the variety of the situation to be controlled [8] – to the design of effective organizations, Stafford Beer formulated the Viable System Model (VSM) [9] as a method for designing organizations that are able to survive and thrive in a changing environment [9]. The VSM was then a vision of the organization in the image of the human species. Functions of management and control were envisioned on the lines of the human brain and nervous system. The brain and nervous system, were simulated by a combination of information technologies and human interaction. The VSM is probably one of the most elaborated representations of the fusion of the man and the machine [12] and the primacy of the role played by information in control systems and decision making. The fusion or combination of man-machine can be considered in form as a cyborg – that has certain physiological and intellectual processes aided or controlled by mechanical, electronic, or computational devices [13].

The limitations or the utopia of singularity

 

 

The achievement of singularity is perceived or motivated by advocators that show fear of death or want to reach a stage of immortality and therefore are irrational, combined with the fact of the looming of the ubiquitous computing, which, computing is made to appear everywhere and anywhere [6] with possible endless workloads combinations, omnipresent, making computing an embedded, invisible part of today’s life. Singularity is perpetually 15 to 25 years in the near future and that future keep being delayed [5]. Singularity supporters, tend to forget about the computing limitations on some of the use cases that are just about to become a reality, like for example, brain simulation. To simulate 10 seconds of real brain time would require about one year of computer simulation [7] in the current most powerful existing supercomputer.

Do we want machines to feel or do we foster a future which humans will augment their natural born capabilities?

 

 

Feelings can only be won by creatures who already have a mind and you can only have a mind if you have a nervous system [1], which in the case of machines, is absent. In the past decade, there an intense debate about if machines can or will feel. Emotions are considered as a non-detachable part of intelligence. Emotions be catalogued as rage, fear, panic, love, happiness and it can drive to take actions. Humans classify emotions and assign them some other emotional value [2]. Such value changes taking into consideration the societal environment humans live or are surrounded by. What can be condemned in one society can be accepted in other. The meaning of the the emotional values is the basis for conscious experience. Therefore, understanding the cartography of the interaction the human and the environment is critical for understanding the nature of consciousness [2]. Our universal individuality is cannot separated by the sacred, profane and spirituality [3]. The flow of other people, nature, environment and ancestor influence that provided us the life principle guidelines we adopt or tend to ignore that is a consequence of what surround us and make us unique individuals. Because machines do not and most likely will not possess consciousness, they are incapable of having free will and intentionality, something that is an essential criteria for moral agency [4].

 

References:

[1] António Damásio – In The Strange Order of Things: Life, Feeling, and the Making of Cultures – ISBN – 9780307908759

[2] Mark Solms – The Brain and the Inner World: An Introduction to the Neuroscience of the Subjective Experience – ISBN – 9781590510179

[3] Elizabeth Tunstall – Decolonizing Design Innovation: Design Anthropology, Critical Anthropology and Indigenous Knowledge in Design Anthropology Theory and Practice – ISBN – 9780857853691

[4] Kenneth Einar Himma – Artificial agency, consciousness, and the criteria for moral agency: What properties must an artificial agent have to be a moral agent?. Ethics and Information Technology, 11(1), 19–29 – ISSN: 13881957

[5] Stuart Armstrong, Kaj Sotala – How We’re Predicting AI or Failing To – In Beyond AI: Artificial Dreams, edited by Jan Romportl, PavelIrcing, Eva Zackova, Michal Polak, and Radek Schuster, 52–75. Pilsen: University of West Bohemia.

[6] Dietmar Möller – Guide to Computing Fundamentals in Cyber-Physical Systems Concepts, Design Methods, and Applications – ISBN – 9783319251769

[7] Arlindo Oliveira – The Digital Mind: How Science is Redefining Humanity – ISBN – 9780262036030

[8] W. R. Ashby, An introduction to cybernetics – ISBN – 9781614277651

[9] Stafford Beer, Brain of the Firm – The Managerial Cybernetics of Organization – ISBN – 9780471948391

[10] Roger Harnden and Allenna Leonard – How Many Grapes Went into the Wine: Stafford Beer on the Art and Science of Holistic Management – ISBN – 978-0471942962

[11] Andrew Pickering – The Cybernetic Brain: Sketches of Another Future – ISBN – 9780226667904

[12] Stafford Beer – Diagnosing the systems for organizations – ISBN – 9780471951360

[13] Woodrow Barfield – Cyber-Humans – Our Future with Machines – ISBN – 9783319250489

[14] Gödel, Escher, Bach: An Eternal Golden Braid – ISBN – 9780465026562

 

BPM Blogs worth reading 2017

Here it is the list of BPM blogs which I normally consume on a random basis for research purposes.

On BPM at large:

  • Bp-3 – Scott Francis’s blog brings a balanced viewpoint on BPM trends and challenges and entrepreneurship.
  • Cape Gemini blog – A good source to look for trends on digital transformation.
  • Column 2 – Independent source on BPM systems review by Sandy Kemsley.
  • IEEE Spectrum – Future technologies brought by IEEE.
  • Improving Enterprise BPMS – A great blog to look for frameworks by Alexander Samarin.
  • Jim Sinur – Good insights about BPM future trends.
  • PeterVan – A 1.0 version type of blog that curates interesting articles from Peter Vander Auwera.
  • On Web Strategy – Dion Hinchcliffe writes about social business.

On Analytics:

On Complexity:

On Enterprise Architecture:

  • Found In Design –Nigel Green writes about advanced Enterprise Architecture.

 

If you are interested in 2016 list click here.

From Know Your Customer to Know Your Tax Payer

I have been working with a tax authority on developing some scenarios that were translated into a transformation roadmap with the objective of increasing tax and non-tax revenue.

One of the challenges is to predict active debt management before arrears occur. The idea is using advanced analytics by modelling the risk that company will fail to pay their taxes, by creating clusters of potentially high-risk debtors, based on available information regarding the annual, quarterly and monthly returns filling. One of challenges that exists in this approach, despite being proven that is more effective that discover “after the fact” the existence of a tax debt, is by the constant change in business models, companies makes new investments or shift to new business models that drags quickly the company to a position it cannot comply with its tax payment obligations.

Know Your Customer (KYC), is an approach that is being used, mostly in financial institutions, in terms of opening a bank account, require trading operations or process payments. Some banks are enhancing the concept of KYC to a point that they rely on information that is not related with transactions between the customer and the bank. As I pointed in this previous post, about creating a Banking Platform as a Service, that goes beyond on implementing a new IT capability that can spark new business opportunities and expand the traditional bank value chain, banks today can have a very precise view of a profile of an individual or enterprise. In some KYC scenarios and because of the magnifying effect of such profile. As an example, if you are an individual, instead of asking what was the brand of your first car, they will start asking questions on which part of the globe you were 2 months ago, where you stayed, what brands did you spend money with and what kind of affiliation you have with a 3rd party loyalty program or even if you have contracted loans with other institutions and which under what circumstances such loans were requested.

From a government perspective, using a shared and expanded Know Your Tax Payer (KYTP) is a valuable approach not only to prevent tax arrears or unrecoverable tax debt, as well as uncover misreporting and non-compliance related with the structure of income flows or unreported income or even claiming subsidies or tax deductions regardless of the source. Despite there is a convergence of government data interchange effort, as well as, trying to overcome some legal constraints in terms of sharing tax information across multiple government agencies that own the responsibility of collecting different non-tax revenue sources, it is still clear that there are missed opportunities in terms of securing tax collection.

One of the events that is being used to detect failure on future tax payment, is when is flagged by bank that a customer is missing payments regarding a loan or its ratting was put with a negative outlook. That event can be used by the government agency to start working with the individual or enterprise in order that will not exist missed tax revenue. The Know Your Tax Payer (KYTP), can be also used by all the entities that feed knowledge base, e.g. other banks that belong to the system (Telecom’s, Utilities, Insurance Companies) will also be alerted by a missed loan payment contributing to decrease their risk exposure, when debt carousel schemes are implemented. There are other scenarios that such an approach can also be beneficial, not only related with tax compliance. In terms of investment programs, related with shared responsibilities on entitlement of government funds, which access is granted by a government subsidy, as well as, by banks, even under a syndicated loan approach, using a KYTP will contribute access to grants or subsidies to entities that deserves such access and in better terms and change tax payer behavior.

The man and the machine manifesto

“Self-organised systems, lie all around us. There are quagmires, the fish in the sea, or intractable systems like clouds. Surely, we can make these work things out for us, act as our control mechanisms, perhaps most important of all, we can couple these seemingly uncontrollable entities together so that they can control each other. Why not, for example, couple the traffic chaos in Chicago to the traffic chaos in New York in order to obtain an acceptably self-organising whole? Why not associate brains to achieve a group intelligence?”

Gordon Pask, The natural history of networks

 

Today there is a shadow or sense of doubt if we as humans want machines to think or to do – some people already argue they do think, detached from the consciousness bond – and it would replace humanity soon, as many others advocate singularity is near with systems that can adapt themselves, command, control other systems, something as Douglas Hofstadter referred to “tangled recursion” as an example of machine intelligence.
I ultimately believe that humans and powered AI machines will work together, not compete against each other. Humans will be much more empowered by the symbiotic combination of machine work, meaning that we will need to continuous to adapt to rather than become indispensables, we increase our own capabilities.
However, there are many untamed challenges in terms of such man and machine symbiosis, as we the human species, have the responsibility to define by which rules we want to live, as machines progress in new areas, changing the foundations on our society is organized, as per bellow.

Governance
Much has been discussed how to define machine design rules and relevant regulation. Some experts believe that is the hands of humans to define such rules and while machines are tightly controlled by humans we can define how machines should be engineered. However, some examples related with war machines, demonstrate that Isaac Asimov’s laws do not apply anymore, once the potential for harm is increasing rapidly.
Hence the challenge stands. Consider the scenario of an intelligent medical system that provides counselling and advisory, induces a medical doctor with error. Who is responsible? The Doctor? The system? The entity that conceived the system? The trainer that trained the system to make decisions, based on a knowledge base? How do we deal with human life loss? Regulatory bodies for the engineering profession or other domain expertise, have clearly defined rules for design or professional act decisions, made by humans, however, in terms of machines endowed with any kind of intelligence, governance appears missing and there is no common broad agreement.

Societal impact, innovation and economy growth
There is no doubt that technology was always the common denominator that sparked economic growth, the press, steam engines and lastly the internet, created in three different moments in time tectonic shifts. However, prosperity also contributes to unemployment. Technology tends to automate at scale and replace repetitive tasks, but the last wave of technological developments is already targeting knowledge workers as well. Hence, the challenge is not related with low income workers only. The balancing act should be how the use of technology can contribute to higher living standards, diminish inequality and drive inclusion.

Human and Machine Interface
Mixed reality is becoming a popular interface in human-computer interaction for combining virtual and real-world environments, and it has recently been a common technique for human-robot interaction, it price is however a barrier for adoption and creates digital imparity. Natural language processing is becoming another de-facto interface, applied for example on business to consumer interactions, but some questions are still not addressed in terms of humans that speak a language blending influence of their own culture that a machine is not aware of. Despite the advance interacting with devices like smartphones, as technology progresses, it is relevant do involve interface designers to make a reflection how machines affect human to human interactions and human to machine interactions.

Ethics
Tackling the trade-off between privacy and security is today already a challenge, related to the fundamental but complex separation between what constitutes the private and the public space of an individual. The definition of a concept, a domain, is a consequence of the surroundings, of the environment we live and the multitude of human principles and beliefs. What in a society can be accepted as a practice, in another can be condemned. The concept of privacy is constantly being redefined to a point that can be transform into a matter of transparency, for example, sharing publicly your taxes declarations if you are a politician. How we deal with ethics in terms of a machine that have access and share our medical records that will make decisions in terms of triage or sense of urgency related with medical treatment? It’s in ethical that a machine can make judgment about predicting future crimes or provide a credit risk score based on data that is related with our profiles?