Article | October 21, 2020
Consciousness—it’s one of the biggest questions out there.
One thing that people today have in common with those from the earliest ages is questioning consciousness and our own existence. It’s taken different forms through the years, but the questions are largely similar at their core and the answers are still at large after all this time.
The good news is that while we don’t have the answers yet, or even a timetable for when we might get those answers, we know more now than we have at any other point in human history.
It’s easier to share information than it ever was in the past, and in this era, even an average person can study the big questions about life without the requirement of a formal education or access to a university.
But in this age where it’s easier to ask questions, what kind of answers are we actually being led towards?
We could be in a simulation—but not in the way you think
One of the more modern theories on consciousness proposes that we might be living in a simulation. And modern really is the right word to describe this one, because it would have been an unthinkable idea even 20 years ago.
However, as computing has grown stronger and stronger over the years, a key question was raised by these advancements: Is it possible that somewhere, computers are already powerful enough to run an entire universe? And if that’s the case, are we living in one of these simulations?
While this sounds outlandish, it’s certainly a theory that has at least some support. That includes support from Elon Musk, who says we probably are living in a simulation.
Don’t think, however, that we’re living in some version of the Sims catered to an alien audience. Games might be the first thing that comes to mind for us when simulations are brought up, but a more serious answer is quite a bit different from that idea.
Rather than a game, such a simulation may be for, to put it simply, historical purposes. That is to say, instead of some advanced alien civilization running their own simulated universe, it may be advanced humans from the future simulating the lives of their ancestors.
But this simulation of the past would be real enough that for the simulated person on the other side, everything feels real and there’s no way to tell that it is a simulation.
This isn’t just an idea from science fiction, as much as it might sound like one. It was proposed by Nick Bostrom, an Oxford professor. There’s a lot of possible reasons why a future society might want to run a simulation in this way, ranging from studying history to preserving the records of the past.
If you don’t think this would be possible from a technical perspective, just consider the jump in quality between early computers and the computers of today. Computing has already improved exponentially within our lifetime. In the very far future, this growth may have continued to heights that would have been unimaginable previously, just like computers today would have been unimaginable to someone used to the first computers.
Quantum mechanics could be part of the explanation
We don’t know much about how the brain works. While there’s been a lot of scientific progress since the questions around the nature of consciousness were first raised, there’s still a long way to go in figuring out just what makes the brain tick so to speak.
Quantum mechanics, however, is good at explaining these kinds of things that don’t operate along the regular laws of physics. It’s hard to explain exactly how quantum mechanics work also, but we do know a bit more about them than we know about the brain.
Essentially, if you break things down to a small enough level, they begin to respond differently. Some of the laws and theories that would have dictated their behavior previously begin to behave more loosely.
Take a toothpick for example. You can move it around or drop it or throw it and it follows the same laws of physics. And if you snapped it in half, those halves would also follow the same rules. However, if you kept doing this until you reached a certain tiny, microscopic level, things would get weird.
But just saying that the brain might work on quantum mechanics doesn’t actually explain much. After all, that statement says nothing about what these mechanics may actually do, and more importantly, what that means for us.
Fortunately, though, more detailed theories on the subject do exist. It’s been said that quantum mechanics could explain these different quantum laws working with our brain to create consciousness from a “fourth dimension” around us.
Quantum laws may also dictate that particles behave differently depending on if they’re being observed or not. Of course, the definitions are complex. Observation is a general term that doesn’t literally mean looking at something in the context that the word would come up in a regular conversation.
But at least in theory, it’s possible that much of how we experience the world has to do with our brain observing and interacting with particles around us at the quantum level. These observations may be a basic building block behind everything—a source code, so to speak, for the universe at large.
Universal consciousness remains a theory
Universal consciousness might be the oldest theory on this list. It predates the more modern ideas mentioned with quantum mechanics and the simulation theory, but there’s enough anecdotal evidence surrounding the subject to at least consider it.
It’s not complicated such as quantum mechanics.
On the other hand, it’s probably the easiest theory to understand between the three. It’s the idea that essentially we come from the same place, or that consciousness itself is an extension of the universe.
This belief has been seen in religions from differing times and places, with Buddhism notably claiming that consciousness is around us everywhere. It’s not just Buddhism that has reflected these ideas, however.
There’s many anecdotal stories over the years of people who have been close to death or have medically died and believed that during these experiences, they’ve become one with the universe or something else along those lines.
Of course, these stories won’t hold up in the opinion of the scientific community and it’s obviously hard to study this kind of phenomenon in a meaningful way.
But to consider a subject like consciousness, something that we don’t understand, entirely using the same scientific methods used for other things may be a mistake.
After all, the concept of the universal mind has been around since at least 480 B.C., when it was introduced by Anaxagoras, a philosopher from before the time of Socrates. While this much time passed doesn’t necessarily mean the theory is true, a lot of people have put their belief behind it between that time period and now.
Optimism about the future
Earlier in this article, we mentioned Elon Musk’s belief that humanity is living in a simulation. It’s not the only time Musk has spoken about things that would be considered outlandish by a lot of people.
He’s spoken of other things that might as well sound like something out of a science fiction novel, such as the threat of artificial intelligence.
When Musk did speak about AI, however, he had a notable quote that didn’t have to do directly with that specific subject matter at all. Rather, it was a general outlook on philosophy and life.
“You kind of have to be optimistic about the future. There’s no point in being pessimistic,” Musk said. “I’d rather be optimistic and wrong than pessimistic and right.”
It’s philosophical advice worth keeping in mind.
The fact of the matter is, we don’t have the answers. There’s various places to draw the answers from, whether it’s conventional theories or these newer modern ones about simulations and quantum physics, or even religions which have been around for hundreds or thousands of years.
Whatever you do believe about the mind, or even if you don’t believe anything at all and you’re just waiting to see what answers scientists come up with in the future, keep your head up.
When the answers aren’t around yet and all of them could be wrong, you can only keep a positive outlook on things and hold a hope that your preferred theory is one with truth behind it.
Article | October 21, 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 | October 21, 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 | October 21, 2020
With the introduction of Siri and Alexa, AI has transformed our lives with speech-to-text implementations. Scientists are working their brains to make machines as intelligent as they can be. The introduction of AI in the fields of healthcare, security, entertainment is already at its peak. Thus, parallelly AI is being introduced to make work easy in the field of education, transportation, and everything else.
The implementation of artificial intelligence in LMS- learning management systems is proved to be a breakthrough in education. The company with AI implementation is paid 40% more than the one without AI.
In the current scenario, the world had adapted to the e-Learning platform and its techniques tremendously. Standard LMS is already being utilized widely. However, although it has proved to be a great success in revolutionizing the education sector, it still has a long way to go. And this long way can be easily covered with its opportunity with AI.
Before we learn the advantages of artificial intelligence in education, let us educate you about the limitations of standard LMS.
Limitations of standard LMS
The age-old teaching methods were scientifically and innovatively transformed into a standard LMS. The learning management systems have all the content managed in one place. Evaluators can track progress, give ratings and recommend courses on one platform. The digital learning platform offers a plethora of learning, teaching, and evaluating techniques, but it still has its limitations.
• The standard LMS requires a lot of manual labor.
• They require human effort to keep the content up-to-date.
• They are trainer-focused rather than learner-focused.
• It requires tedious effort to introduce different learning styles.
The application of AI-based LMS removes all the above limitations and much more. It will streamline the learning process in a better way and provide endless learning opportunities.
How will artificial intelligence in LMS prove to be valuable?
The standard LMS has standard content data fed into it. Therefore, all the employees, students, or trainees follow the same procedure of the standard learning module. This is where the application of artificial intelligence in LMS transforms the system.
AI enables the LMS to design the learning process according to the individuals opting for the course. For example, if a student/employee chooses a particular course, he can choose whether to read the lessons, watch videos, or study it in any other way. Thus, when a learner learns a course in his kind of way, he grasps and understands well.
AI also understands the weaknesses and strengths of the learner and recommends future courses accordingly. Even the quizzes and tasks are determined based on the learner’s knowledge level. So, every learner has a personalized learning course, and a content module in AI-based LMS curated exclusively for him.
Automated evaluations and suggestions
This is the best advantage when you are transforming AI with LMS. The evaluation process and the suggestions for the future (based on those evaluations) will be immaculately automated. Also, the employers will benefit as they will not have to keep track of employers progress, learning, or training schedules. The support of artificial intelligence in LMS will make this process seamless for employers.
It gets more manageable for the managers to track employee’s growth and training. The AI process guarantees faster, convenient, and reliable results. Based on the performance and the results generated, the employer can grant rewards, growth, recognition, and appraisals to the employees. In addition, advanced analytics allows employers to obtain detailed information about the learner’s progress and understanding.
With such a positive outlook towards growth, learning, and training, companies' employee retention rate and ROI with artificial intelligence in LMS are high.
Personalized Learning Module
The learning module of artificial intelligence in LMS is designed to increase the effectiveness of any learning course. The system design courses for learning in minimal time. As a result, the functioning of the LMS module in AI is made engaging and intuitive.
Why is it that a learner does not complete his training or does not find it engaging enough? 70% of learners have blamed the content or activity procedure for being boring. This is where the AI-induced LMS proves its worth.
This system curates content based on the learner’s interests and then designs the training module accordingly. Thus, the exclusive learning experience is what makes your employer feel connected to the organization.
Chatbots and virtual assistants
When an employer seeks training, he/she has several questions. These questions need to be answered by the trainer as per the standard LMS. However, some employees will refrain from asking questions due to many possible reasons. This is where chatbots play an essential role.
Chatbots are an essential part of artificial intelligence in LMS. Chatbots can answer the queries number of times till the concept is clear. Also, chatbots can predict the questions in a learner’s mind by analyzing their course and study behavior. Chatbots and virtual assistants provide all types of help needed by the trainees. They are a ‘learners guide’ for students.
Training beyond territories
Organizations have multiple offices across different geographical locations. Therefore, to have a training program schedule for the same department at the same time is difficult. Also, setting up the same programs for different time zones is a tedious task. This is where artificial intelligence in LMS comes to the rescue.
This enables offices to train their employees according to the convenience of the employees. In addition, the AI with LMS infrastructure allows the employer to complete the training according to their time zones with no loss of the trainer’s time and organization’s resources.
Benefits of introducing LMS with AI in organizations
If an organization introduces artificial intelligence in LMS for its organizations, it can work wonders for them. These are some of the benefits of introducing LMS with AI in your organization.
• Increase employee retention and engagement by 60%.
• Shows the employee that you care about their growth.
• Save time and money.
• Upskill your current employees efficiently.
• Generate personalized content to meet learner’s proficiencies.
To sum it all up,
By 2025, the investment of companies in AI technology is set to reach $190.61 billion. Thus, this technology assures that employees are well trained, upskilled, and perform complex tasks with minimal issues.
Duolingo is one of the best examples of using artificial intelligence in LMS. It uses natural language processing to train its chatbots and provide a personalized experience to the user. It studies the user intricately and then trains them.
Artificial intelligence in LMS can be made a part of the current HRIS/HRA system of the organization. This helps the organization achieve growth and development with minimal efforts and shows employees that they matter.
There are many factors by which AI with LMS proves better than the standard LMS - personalization being the best one. The streamlining of the training process and providing opportunities to learn in one’s way, time, and language is a win-win situation for both employer and employee.
AI may take some time to be utilized entirely as the future of learning management. But when it is executed, it will do wonders for organizations in various sectors. Of course, it will never replace humans in the training programs but will be their biggest support system.
Frequently Asked Questions
How has AI changed LMS?
Artificial Intelligence has played a significant role in changing the standard LMS. AI offers personalized learning, easy evaluation, and automation of all the tasks required in a training program. As a result, it has reduced manual efforts while enhancing the learner’s experience.
How is AI used in LMS?
AI uses the data fed into it to make decisions. It also determines the activity of the learner and provides him with personalized suggestions. It is used in a chatbot to provide answers and guidance to learners anywhere and anytime.
What is the future of AI in LMS?
The future of AI in LMS is as bright as it can. AI will suggest, evaluate and automate pieces of training for the learner. It will answer questions and let the learner study according to his convenience and interests. Employers will have a reduced cost of training and a high retention rate once they implement AI in LMS.
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"name": "How is AI used in LMS?",
"text": "AI uses the data fed into it to make decisions. It also determines the activity of the learner and provides him with personalized suggestions. It is used in a chatbot to provide answers and guidance to learners anywhere and anytime."
"name": "What is the future of AI in LMS?",
"text": "The future of AI in LMS is as bright as it can. AI will suggest, evaluate and automate pieces of training for the learner. It will answer questions and let the learner study according to his convenience and interests. Employers will have a reduced cost of training and a high retention rate once they implement AI in LMS."