Article | September 22, 2021
Humanity is ready for the next technology step – an interactive virtual world, where everyone can hang out with games, adventures, shopping, and the best new virtual identities. A parallel universe, if you may!
Take a look at the first promotional video of the first virtual world and avatars. Circa 1986.
Enter – The Metaverse – the tech world’s latest obsession that traces its origins to a dystopic sci-fi novel. Sounds like science fiction? It is not. Through the years, the journey from Habitat to a fully functioning metaverse has come together piece by piece, supported by gaming, social media, and a vibrant digital economy.
We are in the Metaverse now!
The Metaverse is a shared, virtual space akin to a digital mirror of the natural world, without any constraints. In a recent earnings call, Mark Zuckerberg shared his vision to turn Facebook into a metaverse company, describing it as “an embodied internet, where instead of just viewing content — you are in it.”
The Metaverse literally means ‘beyond universe’ since it combines a physical reality with a virtual space only limited by our imagination. If you google the term, you’ll find numerous definitions. It has been called the Mirror World, the AR Cloud, the Magic Verse, the Spatial Internet, or Live Maps. But, one thing is sure – it’s coming and it is a big deal.
Like “cyberspace,” a term coined by fiction writer William Gibson, “metaverse” also has literary origins. The term first appeared in the novel “Snow Crash,” written by Neal Stephenson in 1992. It spoke of a new space where humans, as avatars interact with each other and software-generated entities, in a three-dimensional space that uses the natural world metaphor
Twenty-six years later, “Ready Player One” provided a glimpse of our possible future in the year 2045, where the world is faced with multiple crises. To escape this reality, people go to an alternate virtual universe called the OASIS. It functions as a virtual society, with its currency, own set of rules, and all. The movie could be a prototype of a future Metaverse.
The video game Second Life, released in 2003 by Linden Lab, created a virtual world where users could wander, building their structures; people bought land there for either U.S. dollars or Linden Dollars’ in-game currency.
With some cooperation between technology companies and futurists, the Metaverse could be just around the corner. Companies like Facebook, NVIDIA, Huawei, Microsoft, and others seem very clear of their intent to create a metaverse (their Metaverse?). The target? Compounding the size of the addressable market manifold by fusing the digital economies of the physical world with the virtual. FB is investing billions of dollars in creating devices that would make this a reality, starting with a smart wristband and VR goggles that project the wearer’s eyes. FB’s acquisition of Oculus in 2014 seems to be a step towards “getting ready for the platforms of tomorrow.”
Microsoft is equipped with enough AI and mixed reality tools in their Metaverse tech stack to be a worthy contender. Satya Nadella has already declared their intention to build an ‘enterprise metaverse” using digital twins, mixed reality, and what they call Metaverse apps. FYI – Microsoft already owns Minecraft, which has its own virtual ecosystem.
Gaming companies like Epic Games have already released simulation software and VR services. Epic Games (Fortnite) has raised USD 1 billion for its metaverse plans; Roblox and computing giant, Nvidia, is also working in this direction.
The idea isn’t new. People have toyed with the idea of living in a virtual world for ages. A lot of money has been spent (and lost) in virtual economies like Second Life and EVE Online.
Brands and retailers are already trying to create new customer engagement channels in this new world. FinTechs are attempting to seize the opportunity to capitalize on unique financial needs in the virtual world, while a battery of startups is creating new virtual products entirely, from avatars to crypto-collectibles.
But the question stays, is a metaverse possible? Is it real? What does it mean for the general population – both as individuals and businesses? Will there be one Metaverse to rule them all?
No single company can or should own or run the Metaverse. However, it requires cooperation to create consistency. For example – assets that one acquires in the Metaverse will need to be portable, with digital rights preferably moving between platforms owned by different corporations.
Silicon Valley is a great proponent of this new world. However, critics are more guarded – they believe the Metaverse could easily become a catch phrase – like “artificial intelligence” and “blockchain” – an attempt by start-ups to woo venture dollars.
Whatever form the Metaverse ultimately takes, its wider adoption will require a revolution across technologies across infra, consumer-facing devices, platforms, content, and more.
Till then, we have the Marvel parallel universes to revel in – the Multiverse where the God of Mischief reigns, lives, and perhaps dies!Enable GingerCannot connect to Ginger Check your internet connection
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Article | September 22, 2021
Researchers have used Artificial Intelligence (AI) to train algorithms and predict tumour sensitivity in three advanced non-small cell lung cancer therapies which can help predict more accurate treatment efficacy at an early stage of the disease. The researchers at Columbia University's Irving Medical Center analysed CT images from 92 patients receiving drug agent nivolumab in two trials; 50 patients receiving docetaxel in one trial, and 46 patients receiving gefitinib in one trial. To develop the model, the researchers used the CT images taken at baseline and on first-treatment assessment.
Article | September 22, 2021
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.
"name": "How has AI changed LMS?",
"text": "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."
"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."
Article | September 22, 2021
Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind.
In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before.
AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”
“To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner.
However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines.
“To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization.
Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability.
Manifesting new market opportunities
Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus.
Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians.
Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof.
Reconcile emotions with actualities
Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages.
“Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.”
Scale statistical models for actionable models
We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike.
Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board.
Future-proof, anti-fragile data supply chains
From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights.
It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered.
“Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.”
Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization.
Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind.
Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson.
In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate.
“All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.”
As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia.
“New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.”
For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.”
“Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.”
Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.