There are several important ways in which artificial intelligence is aiding e-learning technology to solve some of the most critical challenges faced by education today.
AI helping e-learning revolutionize education
The internet and burgeoning AI technology have made e-learning more accessible than ever before. But AI can also help provide solutions to education’s most pressing challenges in other profound ways, ensuring access to quality teaching for all students. Here’s how:
Meaningful curriculum differentiation
Despite having known for a long time that different pace and style of learning works for different students, limited resources in the traditional classroom model have made it difficult for teachers to differentiate content. E-learning can suggest different paths of learning based on students’ level of mastery, ensuring better success rates.
Encouragement of individual tutoring
E-learning technology equipped with new models of AI can address students’ points of confusion as they arise. Students can form the correct foundational knowledge and build more complex ideas upon that, as against waiting until the end of the class or when the teacher is free in a traditional setup.
Private clarification of confusion points
E-learning courses allow for students to ask questions privately or in a forum of students engaging with the content at a similar pace. This lessens the chance of being burdened by self-consciousness when the need for asking a query arises.
Lower costs for students, a lower burden on teachers
Especially in higher education, the issue of crippling costs associated with a brick-and-mortar institution can be addressed by adopting e-learning technologies. Savings from doing away with faculty benefits, classroom upkeep and administrative burdens can be passed on to students.
Student engagement with relevant course material
While gathering continuous feedback in the traditional model is impractical, e-learning tools and AI can help teachers adjust content in real time to include up-to-date information and innovations.
The future of education
Other challenges, like engagement and course completion, still need to be addressed, which can also be addressed by the interactive environment created by new AI learning models. Professionals in the education space need to work on accountability, more tactile means of engagement, and greater reach. Nevertheless, benefits of e-learning – including customization of content to pique students’ interests and cultivation of more actionable data for teachers – far outweigh these challenges, and possess the potential to drastically improve student outcomes across the world.
7 steps to become an AI-enabled enterprise
While platform companies – firms like Google, Amazon, and Alibaba – continue to collect tons of data and train their systems using information on consumers’ lifestyles, most enterprise leaders seem to underestimate the effect this will have on their businesses. A general AI strategy is needed for old world companies to keep up with this competition.
Challenges for the established economy
Apart from their own inability to change effectively and the intra-industry competition, large companies also face an underestimated challenge in the form of stiff competition from platform companies in three main ways. These are:
The ability to "burn" money
Platform companies have more financial resources they can leverage to invest with no urgency to prioritize budgets. In contrast, companies from the old economy have very little money to play with since they are constantly subject to pressure from the capital market, shareholders, and customers. For example, if Pfizer or Bayer were to invest USD 500 mn in cancer research – which is related to their core business – and come up with no drug, their CEO would be fired immediately. Google or Alibaba, on the other hand, could burn money on the same cancer research – a non-core project – and move on. This is coupled with the mindset companies like Amazon have to take risks and invest massively in R&D, including on untested AI technology, which is lacking in old-world companies even when they do have the cash to spare.
The strategy to hijack direct customer relationships
Today, as customer engagement, advertising and sales have moved on to platforms like Facebook, Google, and Amazon, these companies can easily hijack the direct relationship between established companies and their customers. As an enterprise owner, you might get analytics and some data from platform companies, but they can then use the same data and AI to enhance product recommendations, customize shopping experiences and improve targeting to offer goods and services from a pool of brands that keep customers from switching.
The power to collect massive data for building general AI
The range of products and services offered by platform companies – think Amazon’s marketplace, Kindle, Echo, and more – helps them collect endless amounts of data and thus create their own general AIs. These general AIs can then be deployed in different areas like finance, shipping, entertainment, and healthcare, further cementing the companies’ connection with a customer. This disruptive approach is not possible for enterprises from the old economy..
AI is the strongest tool to overcome the threats
While a strong brand, outstanding services, and innovation can help in survival, turning exponential is the only way to successfully compete against companies that are already exponential and trying to touch billions of customers across industries. AI is possibly the only tool today that can help ward off the competition, making use of the strong side of established companies: their experience. Their own general AIs can help established companies run processes autonomously across their organizations while retaining their knowledge and monetizing their data and experience. But, given the relatively limited breadth of data with these companies, a pooled approach would work better. Here is a step by step strategy to execute this idea:
Established companies collect every piece of data within the company.
Give data to a secure and independent intermediary platform operating a shared data pool for an established economy.
In return, they get access to a shared pool of aggregated and semantically organized data.
They use this data and the necessary technology to build their own corporate general AI.
Outcome-based on corporate general AI - New business models, offerings, services, etc.
Give resulting data from new business models, offerings, services, etc. to the shared data pool.
Adapting this approach can allow companies to keep their intellectual property and create value from their experience.
AI-enabled enterprise: anything that is a process can be and will be run by AI
Any process that can be automated today can be run by AI, leading to massive savings in money and manpower. This also spares resources to innovate at a faster pace. The following seven steps can help your company become an “AI-enabled enterprise”:
Create a semantic map of your data - accept continuous data flow as a foundation for future strategy.
Automate your IT operations - automate IT operations to receive immediate value brought by AI and to collect data; also make them autonomous.
Rethink your strategy - think about a new (exponential) business model.
Retrain your entire organization top-down - prepare and train your organization for an AI-enabled enterprise and for accepting a new business model.
Expand autonomous operations to other business processes - use company knowledge gathered through IT automation to make more processes autonomous.
Embrace predictive analytics - use data from the semantic map to expedite, improve business processes and future business events.
Consider data-driven processes - use data and AI to generate outcome-based processes.
Strengthen your core, widen your horizons
All data collected in the entire company eventually ends up in the IT environment: stored in applications, databases or storage systems. Thus, it is recommended to start setting up an AI in the IT environment – the core of your enterprise. Most importantly, come out of your comfort zone – your existing business and the industry you operate in right now. It doesn’t help you to simply develop something for the media industry when you are a media company. You have to think across industries and develop something that adds an additional value for your customers and thus opens a new industry for you.
A new Accenture report, which looked at investments, revenue growth and acquisitions in the AI space, predicts that the healthcare AI market will reach $6.6 billion by 2021 from just $600 million in 2014.
· The rapid growth is driven by factors like move to population health, and greater acceptance of machines in healthcare delivery by consumers, driven by experiences in other services.
· Accenture’s survey of over 3000 consumers shows that one in five US patients have already used AI-powered healthcare services, like robots, virtual clinicians and home-based diagnostics.
· In keeping with the move in the wider healthcare industry from volume of care to value-based models, business and venture funds are funding development of products and systems based on AI.
Examples: Use of AI by Anthem and Cigna to curtail opioid addiction; funding by Optum Ventures of the startup Buoy Health that has developed an AI-powered digital health assistant that helps patients better understand symptoms and advises on next steps.
· Regulators are stepping up approvals of AI systems and related products.
Example: Approval of three of the seven robotics products developed by Bionik Laboratories, a venture whose solutions have been used for treating neurological disorders in over 200 hospitals in 20 countries.
The future of AI in healthcare
According to Bionik’s CEO, Eric Dusseux, there will be a steady evolution of AI both in the medical industry and beyond. Humans have long relied on technology to improve efficiency, productivity and process quality. Innovations like AI, machine learning and brain-computer interfaces will encourage continued usage of technology in the medical space to further optimize patient treatment and care.
AI: The theory and practice of building machines capable of performing tasks that require intelligence, using technologies like machine learning, artificial neural networks and deep learning.
Blockchain: A new filing system for digital information, which stores data in an encrypted, distributed ledger format. Encryption and distribution of data across many different computers enables the creation of tamper-proof, highly robust databases that can be read and updated only by those with permission.
The theoretical benefits of combining these two technologies haven’t translated as yet into real world applications, but this could change soon. Three ways why this should happen are:
1. AI and encryption work well together
The cost of securing the vast amounts of sensitive, personal data (think healthcare systems, banks or even Netflix) handled by AI systems is immense. Blockchain databases hold information in an encrypted state, requiring only the safety of the private keys – a few kilobytes of data – lowering the costs of handling AI data tremendously.
Conversely, an emerging field of AI is concerned with building algorithms that can process data while still encrypted. This complements the safety of blockchains, by eliminating the risk of exposure of unencrypted data.
2. Blockchain can help us track, understand and explain AI decisions
AI systems make complex decisions by assessing a large number of variables. In areas like banking or the retail industry, these still need to be audited for accuracy by humans, which can be very difficult. If decisions are recorded, on a datapoint-by-datapoint basis, on a blockchain, it makes it far simpler for them to be audited, with the confidence that the record has not been tampered with between the recording of the information and the start of the audit process. This is a step towards achieving the level of transparency and insight into robot minds that will be needed to gain public trust.
3. AI can manage blockchains more efficiently than human or conventional computers
Conventional ‘stupid’ computers apply brute force when working with encrypted blockchain data, requiring large amounts of computer processing power. AI can perform such tasks – like verification of Bitcoin transactions – more intelligently by becoming adept at cracking codes almost instantaneously if fed the right training data.
Clearly, the two ground-breaking technologies have the potential to become even more revolutionary together. They can enhance each other’s capabilities, while offering better oversight and accountability.