Article | December 11, 2020
Intelligence is a much-debated term, with varying connotations to distinct disciplines. Humans have an innate intelligence that is capable of achieving complex, integrative goals through multiple faculties. These faculties involve learning and creativity, deal with ambiguity and uncertainty, critical thinking, strategy and planning, scenario analysis, and more. Humans have an evolutionary mind that is capable of drawing inferences and insights.
Creating machines, bots, or capabilities imbued with human-like intelligence has fascinated humans for a long time and has been the subject of active technical effort since John McCarthy coined the term ‘Artificial Intelligence’ (AI). Interest in AI has waxed and waned, with unrealized hype leading to a long AI winter. However, recent advances, such as Hinton’s backpropagation based deep neural networks for ImageNet that match human accuracy for image recognition, have revived hope and optimism for the advent of ‘Artificial General Intelligence’ (AGI).
AGI is about emulating or even exceeding, human levels of intelligence. At the moment, it is more of a pipe dream in the realm of sci-fi movies like Terminator. Silicon Valley leaders and scientists like Elon Musk, Bill Gates, and Stephen Hawking have predicted a dystopian, even Frankensteinian, world with recursively- improving technological singularity potentially turning against the humans.
Strong Vs. Weak AI
Weak or narrow AI is categorized as mimicking a specific human ability to perform a well-defined task. Humans seem to have become pretty good at aspects of narrow AI lately, such as natural language processing (NLP), image recognition, machine translation, and detecting fraudulent credit card transactions. In the words of Andrew Ng, any task that takes a few minutes of human cognition can be automated with supervised machine learning and the help of labeled data. Recent advances in machine and deep learning have upped the ante on weak AI. For example, DeepMind’s AlphaFold can solve the intractable problem of predicting a protein’s folding structure from its amino acid sequence, thereby circumventing years of laborious work. This goes far beyond narrow AI into the gray zone.
Strong AI, or artificial general intelligence, can solve present-day ‘AI-hard’ problems that require a complex interplay of human cognitive abilities. For example, understanding the nuances of language is hard, but humans are slowly making strides. Some human skills are multifactorial, such as driving that requires image recognition, fine motor skills, or estimation with a high degree of situational awareness. A point has been reached where a self-driving car with level five autonomy can emulate that with simultaneous localization and mapping (SLAM) while being vulnerable to getting tricked at the same time.
Leading voices have articulated several benchmarks for having accomplished AGI, such as:
Turing test: If a human and machine are indistinguishable most of the time while conversing with another human. With OpenAI’s GPT-n series, that is probably not far away.
A bot or computational system successfully passes grad school.
An AGI bot becomes a productive member of society, possibly paying taxes while performing a complex job.
Emulating the Human Brain
Unraveling the human brain is as enigmatic as solving the mysteries of the cosmos. With approximately 100 billion neurons interconnected through a quadrillion synapses, leading to 100 trillion synaptic updates per second (SUPS), the human brain is inordinately complex to simulate. Other than the interconnectedness of the brain, its evolutionary neurophysiology at the molecular and cellular level requires a level of chemical, physical, and biological understanding that leaves one confounded. How the three-pound mass of mostly fat, protein and water, with neurons firing in a chemical soup, allows cognitive abilities is quite hard to fathom.
All the advances in artificial neural networks, IoT sensing, 5G bandwidth, real-time big data, GPUs or TPUs, and storage put together get nowhere close to creating a computational system that has characteristics of sentience, self-awareness, sapience, and consciousness. Some even argue that there can be no human-like intelligence and consciousness without the accompanying embodiment.
Challenging as that may be, the advances in narrow AI are quickly adding up, with a bottom-up approach, to an impressive array of well-defined and compartmentalized human abilities. While AGI is the holy grail, the key point is that such pursuits are enabling scientific and technological advances that are the sweet spot of enabling human-in-the-loop technologies that augment humans instead of replacing them. Progress will likely stay in the augmentation zone for the next couple of decades, as Ray Kurzweil’s prediction of AGI comes true by 2045. Others argue that humans may not accomplish AGI in this century at all. But there is little disagreement over the fact that AI is likely to create US$15 trillion of economic value by 2030, with US$6 trillion being attributed to deeplearning alone. Individuals, societies, and businesses have to brace for that impact.
How Can Businesses Prepare and Respond to General AI
China is leading the AI frontier, as much as due to its lack of regulatory and ethical oversight as to its dogged commitment to winning the AI supremacy race. The US is not far behind whereas other nations occupy different positions on the leaderboard. Expertise in AI is likely to shake up the global economic and geopolitical order in the future world. While individuals grapple with the widespread displacement of world labor markets, enterprises need to sense and respond as well to ensure they thrive in a world replete with AI.
Here are some steps they can take to ensure they are not sidelined in a world of sustained disruption and mere transient advantages:
#1 Create a vision of yourself in the future world of AGI. Make small bets to preserve strategic options in aspects of your business potentially exposed to general AI.
#2 Make big, bold moves on narrow AI for quick wins. This will instill confidence and purpose to respond to general AI as it comes of age. Embrace AI augmentation as opposed to resisting it.
#3 Put your digital maturity on the front burner and prioritize digital transformation initiatives. Be a digital leader, not a laggard.
#4 Data maturity is a precursor to digital maturity. Invest in advantaged data with internal data or external data from partnerships, acquisitions, or ecosystem orchestration. AI is contingent upon data and algorithmic advances.
#5 Democratize technology by expanding it beyond the traditional IT organization of the company.
#6 Embrace a digital culture with rapid test-and-learn abilities. Don’t ostracize failure as long as you pivot fast and fail cheaply.
#7 Institutionalize innovation incubation. Also, explore open innovation models by partnering with other businesses and institutions.
#8 Orchestrate between exploitation and exploration strategies – the former for the here and now and the latter for the future.
#9 Deploy a forward-thinking governance framework that can orchestrate across near, mid-, and long-term growth.
#10 Deploy your workforce in fluid, agile, self-organizing teams that can ‘flow to the work’.
Article | April 22, 2020
Ease in doing business.” That is what every C-level execs strive to achieve in their business process and it is no secret that they’ve increasingly turned to Robotic Process Automation (RPA) to streamline enterprise operations.
The first digital computers were invented mostly to calculate tasks but as the technology progressed, we learnt to program hand-code automation through bespoke applications. What brought the RPA into existence was the slow and laborious hand-code automation.
But, as we no longer need to keep our fingers glued to systems to enter data fields and value, there exists some brittleness to the robotic process automation.
Table of Contents:
- What is Robotic Process Automation?
- What ails Robotic Process Automation?
- What is Low-Code Development?
- Why to program in a Low-Code Development environment?
- How does Low-code development help in mitigating RPA implementation?
- Concluding Thoughts
What is Robotic Process Automation?
Deloitte defines RPA as software that “automates repetitive, rules-based processes usually performed by people sitting in front of computers.” Picture your mouse automatically scanning your email for 70 new unread invoices, adding the data to a spreadsheet, and inputting information into your CRM, while sending two outliers to an employee for manual review – all within a fraction of the time it would take a person to do the same tasks and with far fewer errors. RPA workflows are established on logic-based inputs and tasks across applications for the bot to efficiently carry out manual, repetitive tasks with greater accuracy.
Additionally, by separating the uniquely human skills like critical thinking, empathy, and decision making from the manual, repetitive tasks, corporations can provide a more fulfilling and rewarding career for their employees.
Sounds great, right? Of course, it does.
That’s why it’s the fastest-growing market in enterprise software, with 48% of companies saying they are planning to invest in RPA and is projected to be worth nearly USD 4B by 2025. Corporations across industries are buying in to streamline a wide variety of operational tasks, connect legacy systems, and drastically remove errors introduced by humans.
Operations that can benefit from RPA technology include:
Generic office tasks – gathering quarterly cross-department data into an excel sheet, automating CRM inputs, and inventory management.
Back office processes – instead of five people checking for new orders and applying discounts, the tasks are reorganized so the employee is providing a human-level of validation to the order.
Manufacturing – order fulfillment, purchase order processing, and transportation and inventory management.
Retail – product categorization, automated checkout, and delivery tracking.
Customer service – credit checks, account number assignment, and activation tasks can be allocated to bots and employees can speak to a customer and apply empathy and discernment to the situation at hand.
What ails Robotic Process Automation?
The raised fostering of RPA highlights the advantages of the modern technology, however the trip of automation is not without some bumps in advance.
Presently, a bulk of RPA options deal with a typical weak point– a small adjustment to information layouts, service procedures, or application user interface can lead the whole software program to damage down.
By style, RPA is durable software program however that likewise shows its frailty in adjustments.
If anything changes, that can break the automation.
- Jason Bloomberg, Leading IT market expert
For instance, Bloomberg discussed, if an interface component like a switch relocates or transforms dimension, the automation may damage. Or probably the information style adjustments due to the fact that a person included a brand new area. “In other cases, the business requirement for the process logic changes, requiring a rework of the bot.”
RPA functions best with older and also tradition applications powered by regular procedures that go through little adjustment and also secure information layouts. For companies looking to take advantage of the modern technology, the brittleness of RPA might lead to tightening alternatives and also applications in companies.
Financial establishments, as an example, are typically wed to tradition systems and also applications, for which RPA is well matched to aid take care of. However, in a vibrant electronic age– which calls for service dexterity– RPA’s absence of versatility when incorporated can be restricting.
What is Low-Code Development?
Low-code software development could be compared to a car manufacturing assembly line. Both processes automate difficult and time-consuming tasks, in order to increase delivery speed and free up people to focus on high-level tasks.
In technical terms, low-code is a set of tools that developers can use to build applications inside a drag-and-drop visual interface – including complete UI, integrations, data management, and logic.
READ MORE: DISPELLING FIVE MYTHS OF LOW-CODE APP DEVELOPMENT
Why to program in a Low-Code Development environment?
In a quote to address the brittleness of RPA, the arising idea of low-code reveals appealing possibility. Its ability to faster way and also separate software program parts streamlines the design procedure.
For RPA software program that calls for an upgrade, low-code offers the all set-to- code design to convenience the restructuring of systems.
Low-code simplifies the work of developers, whether they be building applications or constructing bots. But even more importantly, low-code empowers developers and business stakeholders to work together more effectively to manage change in the behavior of the software.
- Jason Bloomberg, Leading IT market expert
In significance, low-code opens brand-new opportunities for designers to focus on establishing special software program systems that are matched for particular companies.
READ MORE: BENEFITS OF LOW CODE DEVELOPMENT WITH REUSABLE COMPONENTS
How does Low-code development help in mitigating RPA implementation?
Here’s where low-code development can save the day.
Low-code platforms enable cross-functional teams of professional developers, citizen developers, and functional staff to easily collaborate and connect multiple applications for end-to-end solutions. Because the platforms are built on open standards and are cloud-native, they can easily connect internal legacy and third-party applications in a bot-friendly interface and quickly establish bot workflows that model the real business processes. Enterprise RPA initiatives can get off the ground in a fraction of the time without bringing on additional staff and infrastructure.
What does low-code and RPA implementation success look like in real life? Just ask Avertra and 2 Sisters Food Group.
Avertra provides technology and consulting solutions for telecom and utility companies, including a modular digital customer experience framework built with the Mendix ecosystem and integrated via API with enterprise solutions like ERP systems, work management applications, and external data sources. Alongside their clients, Avertra establishes which processes to automate, builds user stories, and deploys bots which then follow workflows, transfer data between systems, select appropriate resolution paths, and follow through with documentation and compliance – all within a fraction of the time it takes an individual agent.
Meanwhile, UK poultry supplier 2 Sisters used low-code to implement RPA across 11 accounting transactional processes, moving from 100% manual work to 97% automated within six weeks. They used Mendix to build a data-structuring application that extracts, parses and cleans the data. 2 Sisters was able to reduce their customer invoice verification process from 65% of invoices needing manual data verification to only 8%. Manual data entry was nearly eliminated, save for a few outliers identified by the bots, and employees have more time to analyze the data and costs.
Low-code enables both technical and non-technical users to play an active role in implementing and maintaining RPA initiatives, taking the burden off of the IT team, operating securely within their infrastructure and parameters, and reducing the need for additional developers. Avertra empowered their client’s citizen developers to make workflow iterations in the Mendix platform based on data results and their internal business knowledge. With the assistance of Mendix partner AuraQ, 2 Sisters built 300 unique customer remittance templates in 3 months and over 3,000 have been created to date (and they’re still going).
The beauty of low-code platforms is that applications can be easily adjusted as the business evolves, RPA technology improves, and new automation opportunities are identified, enabling companies to be more agile and competitive. Avertra’s clients have used data insights to produce new and revised resolution paths addressing outlying issues not caught by the RPA framework and 2 Sisters is now analyzing their data to identify their next digital transformation target. Their investments in RPA implementation and low-code development have quickly paid off and will continue to return dividends in the months and years to come.
Low-Code Development is the simpler way to adjust and improve RPA as per the business demands. With the entry of IoT powered by high-speed 5G, low-code programing is touted to be the tool to speedy up RPA innovations. AI has become the most important trend in the low-code RPA market thus making implementation of RPA with low-code quick and agile.
READ MORE: THREE SMART WAYS TO USE LOW-CODE DEVELOPMENT PLATFORMS
Article | March 11, 2020
After realizing the potential to affect change while studying systems engineering at the University of Virginia, Brigitte Hoyer Gosselink began her journey to discover how technology might have a scalable impact on the world. Gosselink worked within international development and later did strategy consulting for nonprofits before joining Google.org, where she is focused on increasing social impact and environmental sustainability work at innovative nonprofits. We talked to her about her efforts as head of product impact to bring emerging technology to organizations that serve humanity and the environment.
Article | August 17, 2020
As a result of the coronavirus pandemic, all contact centres have rushed to ensure the safety of their employees and find ways to move their agents to work from home. However, according to a recent Contact Babel Report, before the pandemic struck only 4% of UK contact centres were working remotely on a permanent basis. The ‘new normal’ still feels far from normal and contact centres now need to look at building a solid foundation for a work-from-home model, one which addresses technology needs and environment specifics like security and reliability. Small businesses are now looking to best practice guides to have agents up and running in a work at home environment, as well as build agility and scale.