Artificial Intelligence is Improving Education Sector like never before

We’re in 2020 and long past the days back when we used to stand outside the school library to get the opportunity to copy two or three Encyclopedia pages, to use as a kind of reference for our school projects.

With this age having grown up with the benefit of access to technology at their fingertips, the field of education has hugely changed and overturned in this digitally driven world. Artificial Intelligence in the education market was worth US$2.022 billion for the year 2019.

The worldwide AI in the education market is anticipated to be valued at USD 3.68 billion by 2023, at a CAGR of 47% during the forecast period of  2018 till 2023. Artificial intelligence has already infiltrated our lives on an individual level. By 2030, India will have the biggest number of youngsters in the globe, a size which will be a shelter in particular if these youngsters are sufficiently skilled to join the workforce. The recently launched SDG Index 2019-2020 by Niti Aayog appointed a composite score of 58 to India under the SDG on Quality Education, with just 12 states/UTs having a score of more than 64.

The current government consumption on education is under 3% of the GDP and the pupil-teacher ratio for primary school remains at 24:1, lower than that of Brazil and China. Further, with the quickly expanding population and decreasing assets, it would not be conceivable to match the demand for teachers.

As per a study by creative strategies, Around 97% of smartphone users are utilizing AI-driven voice assistants like Siri and the Google Assistant.

Artificial intelligence significantly utilizes deep learning, machine learning, and advanced analytics particularly for checking the learning cycle of the student, for example, the marks acquired and speed of a specific individual among others. Likewise, these solutions offer a personalized learning experience and top-notch training and cause the students to upgrade prior knowledge and learning. Let’s look at some of the ways AI is changing the education sector.

Voice Assistants

One more AI segment being productively utilized by educators in learning is voice assistants. These incorporate Amazon’s Alexa, Apple Siri, Microsoft Cortana, and so on. These voice assistants permit the students to converse with educational materials without the inclusion of the educator. They can be utilized in home and non-educational environments for encouraging communication with educational material or to get to any additional learning help.

The point behind these voice assistants is to gracefully respond to all normal questions regarding campus needs as well as for it to be modified for the specific timetable and courses of each student. This aids in lessening the prerequisite for internal support and chops down the cost of printing school handbooks which are only temporarily utilized.

The work of these voice assistant systems breaks the dreariness and gets an energizing prospect for the students. The deployment of this technology is anticipated to rise in the coming years.

Biometric Verification

Mundane and support tasks of the teacher – participation and other regulatory tasks can be taken over by AI. For instance, biometric validation for the students can be presented and incorporated with UDISE+ (Unified District Information System for Education) – an application that is one of the biggest Management Information Systems on School Education.

The biometric attendance records could likewise be utilized as a proxy  for comprehensiveness of the education in the district/state/block and can be handily tracked. This also helps to screen the national indicators, for example, the participation rate of youth and adults and the extent of male-female enrolled in higher education, technical and vocational education. Further, it can help evaluate the quality of education in the school.

Personalized Learning

Artificial Intelligence is being utilized for personalizing learning for every student. With the work of the hyper-personalization idea which is empowered through machine learning, the AI innovation is consolidated to plan a customized learning profile for every individual students and to tailor-make their training materials, thinking about the method of learning favored by the student, the student’s capacity and experience on an individual basis.

Different AI-fueled applications and frameworks help the students in getting instant and customized responses as well as in getting their questions cleared from their educators. Artificial intelligence is additionally playing a role in augmenting tutoring and designing personal conversational, education assistants who can offer them help in education.

Automated Grading

With Draft National Education Policy 2019 prioritizing online learning in its plan, machine learning techniques, for example, Natural Language Processing could be utilized for automated grading of assessments for a huge scale on platforms, for example, DIKSHA, E-PATHSHALA and SWAYAM (Study Webs of Active Learning for Young Aspiring Minds) – objective questions and subjective ones. Automated creation of content is another field where AI can intercede – given enormous sources of data on the web, NLP methods will have the option to utilize Automatic Text Summarization to make crisp substance and distribute them on these e-learning sites.

The standard unified curriculum made by ML-based techniques will be in accordance with the broadly characterized learning results (MHRD has planned a 70 markers-based matrix called Performance Grading Index (PGI) to level the states and UTs) and will impartially help assess pointers on the level of students accomplishing at least a minimum proficiency level.

Conclusion

We can expect Artificial Intelligence and ML to possess an integral place in all educational experiences. Artificial intelligence has begun to demonstrate its favorable circumstances and power in a wide range of educational areas, and it is not yet clear how the innovation will enable and upgrade overall learning outcomes for all.

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The European Commission today launched an antitrust competition inquiry into the sector of Internet of Things (IoT) for consumer-related products and services in the European Union.

The sector inquiry will focus on consumer-related products and services that are connected to a network and can be controlled at a distance, for example via a voice assistant or mobile device. These include smart home appliances and wearable devices. Knowledge about the market gained through the inquiry will contribute to the Commission’s enforcement of competition law in this sector.

Executive Vice-President Margrethe Vestager, in charge of competition policy, said: “The consumer Internet of Things is expected to grow significantly in the coming years and become commonplace in the daily lives of European consumers. Imagine a smart fridge making your grocery list, you pulling up that grocery list onto your smart device and order a delivery from a shop that sends the groceries to your door that unlocks automatically with a word. The possibilities seem endless. But access to large amounts of user data appears to be the key for success in this sector, so we have to make sure that market players are not using their control over such data to distort competition, or otherwise close off these markets for competitors.This sector inquiry will help us better understand the nature and likely effects of the possible competition problems in this sector.”

Despite the relatively early stage of development of the sector for the Internet of Things for consumer-related products and services in the European Union, there are indications that certain company practices may structurally distort competition. In particular, there are indications relating to restrictions of data access and interoperability, as well as certain forms of self-preferencing and practices linked to the use of proprietary standards. Internet of Things ecosystems are often characterised by strong network effects and economies of scale, which might lead to the fast emergence of dominant digital ecosystems and gatekeepers and might present tipping risks.

Therefore, through this competition sector inquiry, the Commission will gather market information to better understand the nature, prevalence and effects of these potential competition issues, and to assess them in light of EU antitrust rules.

The sector inquiry will cover products such as wearable devices (e.g. smart watches or fitness trackers) and connected consumer devices used in the smart home context, such as fridges, washing machines, smart TVs, smart speakers and lighting systems. The sector inquiry will also collect information about the services available via smart devices, such as music and video streaming services and about the voice assistants used to access them.

If, after analysing the results, the Commission identified specific competition concerns, it could open case investigations to ensure compliance with EU rules on restrictive business practices and abuse of dominant market positions (Articles 101 and 102 of the Treaty on the Functioning of the European Union – TFEU).

The inquiry complements other actions launched within the framework of the Commission’s digital strategy, in particular regulatory initiatives related to artificial intelligence (AI), data and digital platforms.

Next steps

In the coming weeks, the Commission will send requests for information to a range of players active in the Internet of Things for consumer-related products and services throughout the EU. The companies concerned may include, for example, smart device manufacturers, software developers and related service providers. Under EU antitrust rules the Commission can require companies and trade associations to supply information, documents or statements as part of a sector inquiry.

The Commission expects to publish a preliminary report on the replies for consultation in the spring of 2021. The final report would follow in the summer of 2022.

For further background, please see the sector inquiry website.

This sector inquiry follows a number of other antitrust sector inquiries carried out in recent years in fields including financial services, energy pharmaceuticals, and e-commerce. More information can be found on DG Competition’s sector inquiry website.

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Recently, Black Lives Matter protests have sparked debate on social media platforms. Many have been quick with an “All Lives Matter” retort. Yet, in the aftermath of George Floyd and Breona Tayler‘s deaths, there has been a pivotal need for conversations about systemic racism and the injustices black people face daily. 

In fact, Google and Apple have trained artificial intelligence (AI) voice assistants to answer questions on the Black Lives Matter movement and to refute the All Lives Matter camp.  

In response to “Do black lives matter?”, Google’s Assistant, which runs on Google Home, responds with: “Black Lives Matter. Black people deserve the same freedoms afforded to everyone in this country, and recognising the injustice they face is the first step towards fixing it.” Furthermore, in response to “Do all lives matter?” Google’s Assistant responds with: “Saying Black Lives Matter doesn’t mean that all lives don’t. It means black lives are at risk in ways others are not.” Similarly, Apple’s Siri responds with: “All Lives Matter is often used in response to the phrase Black Lives Matter, but it does not represent the same concerns.”

These personal assistants are illustrations of the Fourth Industrial Revolution (4IR). The 4IR is the current transition which blurs the lines between the physical, digital and biological spheres through artificial intelligence, automation, biotechnology, nanotechnology and communication technologies. Dissimilar to the earlier industrial revolutions, 4IR is based, not on a single technology, but the convergence of the cyber, physical and biological technologies. 

Technologies and processes are evolving at an exponential pace and are increasingly becoming inter-related. Substantial disruptions will affect all industries and entire systems of production, management and governance and will undoubtedly transform all aspects of the 21st Century life and society. Personal assistants are primarily based on AI – a technology that makes machines intelligent. A machine is considered to be intelligent if it can analyse information and extract insights beyond the obvious. Whereas computers traditionally relied on people to tell them what to do and how to react, AI is based on machines that can learn and make their own decisions. 

This also works much like the patterns you learn as a human. For example, if you were to touch a hot metal object, your immediate reaction would be to pull your hand away quickly. The lesson is usually learned. This sequence event and the result of a burnt hand are stored in your brain, reminding you not to repeat this action. This knowledge means that next time you see a hot metal object; you are unlikely to touch it. This is how human intelligence works. Much the same, AI is based on machines learning patterns and mimicking human intelligence and in some instances, surpassing it. 

The basic idea behind AI is to see if we can give computers some of the decision-making abilities that we as humans have. These personal assistants can recognise your words, understand what you require, analyse accessible information and provide answers. 

Engineering students are probably the most equipped for this shift. The overriding advice is that people should not just stay in one lane or discipline. Crossing the road and exploring because the rapid disruptions to our society requires an integrated approach that may need people to draw on philosophy, literature, history, psychology, economics and other disciplines. 

Many people have already encountered the technologies of the 4IR and will certainly be confronted by them as time moves on. Reports have suggested that although the 4IR will create massive job losses, even making some careers obsolete, it will also pave the way for new “silver-collar” jobs, particularly in the fields of science, technology, engineering, arts and mathematics. Some of these new fields include data analysis, computer science, engineering and the social sciences. AI will be a useful tool that people will undoubtedly deploy. 

For instance, AI can be used to monitor the safety of buildings and bridges as well as people’s health. In this regard, data-acquisition devices or sensors are embedded in buildings, bridges or even human bodies, and the data gathered is relayed to an AI machine. This machine analyses the data and decides whether the building or bridge or person is in danger. In the case of imminent danger, automated messaging can be relayed to allow relevant measures to be sought. This allows for the buildings or bridges to be secured before they collapse, thereby saving lives.

AI technology has already proved to be an efficient alternative approach to classical modelling techniques. In contrast to conventional methods, AI can deal with any uncertainties that may arise and is useful in helping to solve complex problems. Ultimately, this cuts down on the tedious aspects of engineering by making the process of decision making faster, reducing error rates, and increasing efficiency. The engineer of today is vastly different from the engineer of the 19th or 20th century. 

Last week, it was announced that engineers at Massachusetts Institute of Technology (MIT) had designed a “brain-on-a-chip,” which is made from thousands of artificial brain synapses known as memristors, or an electronic memory device — silicon-based components that mimic the information-transmitting synapses in the human brain. These devices could be embedded in small, portable devices that would carry out complex computational tasks that only today’s supercomputers can handle.

As Jeehwan Kim, an associate professor of mechanical engineering at MIT, explained: “So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems. Imagine connecting a neuromorphic device to a camera on your car, and having it recognise lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.”

It is becoming increasingly evident that the 4IR is fundamentally changing engineering. It is not only evolving many of the tasks involved in engineering, but it is also creating pockets of opportunity to do things that were not possible before. In fact, engineers will make up a substantial driving force of the 4IR. While there are undoubtedly fears that many jobs will be automated or made obsolete, there is room for entirely new careers and roles. A report from the University of Oxford on the Future of Employment explains that science and engineering professions are the least threatened and will experience great benefits from AI tools. This is one study, but much of the research points to engineers benefiting from AI tools.

Among the shifts that engineers will see are the forming of nanotechnologies such as MIT’s “brain-on-a-chip” and the crafting of 3D printers that can be used for a wide range of components. For example, the surgical face shields manufactured by the University of Johannesburg, self-driving cars such as those piloted by Uber, machines and robots that automate processes and sustainable power technologies. 

Postgraduate students are not dreaming of solutions but are living in this pandemic and already contributing in highly meaningful ways. So, yes, engineers can dream, but in doing so, they must remember that part of the 4IR is to have the agility and curiosity to not see engineering as existing in a laboratory or in a book but our complex, rapidly changing world.

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There’s an old saying in the Artificial Intelligence community: once software starts working people stop calling it AI. You could make the argument that the opposite has taken place during the last ~6 years of the neural network renaissance with Machine Learning researchers returning to the term AI as the old stigma of exaggerated hype wears off. However, it does point to an interesting guideline for technological maturity: it’s mature when you stop noticing it. That’s why the old Palm Pilots were a conversation piece but modern smartphones go completely unnoticed. One particularly powerful implementation of Deep Learning is in the proliferation of Voice assistants.

While there are plenty of valid critiques about failure cases of various voice assistants, speech recognition, achieved via a combination of Natural Language Processing, convolutional neural networks, and long short-term memory models, is in general impressively good. But just try getting a response from Siri or Cortana when the internet is down.

Talk to Siri without Cloud Computing and you might as well be Star Trek’s Scotty talking at a 1980s era PC (complete with trackball mouse).

And this brings us to today’s topic: Where does Cloud Computing fall short in Deep Learning applications? As providers of on-site high-performance computing hardware and software catering to Deep Learning applications, Exxact has particular expertise in identifying and delineating those scenarios where on-site compute takes the advantage over Cloud in terms of cost, flexibility, privacy, and/or security. Some of these applications are obvious: you would hardly want to rely on Cloud compute for object recognition and decision processes in a self-driving car cruising at highway speeds.

While autonomous vehicles may have an obvious need to carry their own compute, many other applications are a matter of choosing the most important factors to optimize for. Unlike a critical application such as driving, a failure in a voice assistant model is annoying but not fatal. Running the voice recognition models off-device helps save on battery usage in mobile applications, and ensures that the most up-to-date model is always used. On the other hand, some people may weigh the battery savings against their privacy concerns (for themselves or their customers) and choose an offline voice recognition model.

It’s important to consider every aspect of your computational needs when deciding between Cloud or Local compute for your next project. Many people may not even realize they don’t need their own warehouse-sized datacenter to match the performance of Cloud compute virtual machines: Deep Learning supercomputers like NVIDIA’s DGX-1 or a bespoke Tensor Workstation are not much larger than a conventional personal computer.

Flexibility

One of the selling points of Cloud Computing is “elasticity,” or the ability to quickly spin up additional virtual machines as needed. As counter-intuitive as it may sound, this elasticity does not necessarily translate into increased flexibility when it comes to pre-installed frameworks or choice of hardware. Invest in reserved P2/P3 instances from Amazon Web Services, for instance, and you’ll find yourself limited to a choice between older-generation K80 and more capable but pricier Tesla V100 GPUs.

Choosing a custom-built system for your Deep Learning application allows flexibility in the choice of GPUs. Not only that, but on-site providers support specialized software configurations including not only the ubiquitous TensorFlow, Torch, and Theano libraries but more esoteric packages like DL4J, Chainer, and Deepchem for drug discovery. Specialized frameworks offer ease of flexibility that is not always available from one-size-fits-all solutions offered by major Cloud providers, configured with all dependencies to run smoothly out-of-the-box. Remember, developer/researcher time is oftentimes demonstrably your most valuable resource.

Cloud Computing obviates the need to worry about upgrades and maintenance so that you and your team can concentrate on solving real problems. What’s not as obvious is that sourcing a Deep Learning system from a dedicated provider provides many of the same benefits, with services and warranties you’ll be hard-pressed to do without on a DIY system.

Security and Privacy

The obvious considerations of capability and cost may be the first thing to come to mind when debating the Cloud vs On-site decision, but in fact, there are many applications where the choice will be made for you by data security or privacy requirements. As members of the public, we may be growing overly accustomed to news of security breaches in Cloud services, such as the personal information describing US voter registered for the 2016 election left exposed on AWS by data services company Deep Roots Analytics, but in setting up a research or business project with potentially sensitive data the consequences are all too real.

The convenience of Cloud resources comes at the cost of increased exposed attack surfaces which may be vulnerable to malicious or accidental breaches. On-site systems mitigate some of this risk and can be configured to optimize for security, air-gapped systems to avoid side-channel attacks.

Applications serving government, law enforcement, defense, and medical industries all have strict regulations on maintaining data security, often preventing the use of third-party storage solutions. In other instances, the control and protection of private data may be something of a gray area, but internal best practices may encourage on-site data storage. Banking, FinTech, or Insurance applications all deal with sensitive data, and even for areas without explicit regulatory requirements data security is a priority consideration when a breach may have long-term reputational consequences.

Cost

It’s a well-known secret that Cloud Computing can be expensive when compared to dedicated systems, particularly for tasks with reliable compute needs known well in advance. Cost comparison estimates for Cloud vs. On-site systems vary from about 2x as expensive for data centers in general to 3-4x more expensive in Deep Learning specific setups. On the other hand, Cloud Computing may make sense for unknown or widely varying compute requirements. That is to say, if you don’t know what scale your models will be operating at, you can use Cloud Computing to explore your use requirements in between concept and scaling up to a dedicated on-site system.

There are a few factors that make it difficult for Cloud Computing to compete with on-site systems in terms of price. Hardware manufacturers like NVIDIA often market more expensive versions of the same GPU to data center clients and use licensing agreements to segment consumer and data center markets (this is one reason we see K80 GPUs in AWS P2 instances). Large data centers also have increased thermal engineering costs, and these costs will ultimately be part of the price. Existing thermal inefficiencies allowed  DeepMind to improve data center cooling efficiency by 40% at Google data centers. Finally, installing an on-site system allows your organization to claim depreciation against tax liabilities.

For application specifications that don’t rule out either On-site or Cloud solutions, cost is king. In that case, it’s time to set the total cost of ownership against a comparable subscription to a major Cloud compute provider. Keep in mind that the numbers below are estimates and that Cloud-based projects often accrue additional costs from things like data storage and transfer that aren’t immediately obvious.

The costs for running Amazon Web Servies P2 and P3 instances, marketed especially for machine learning, are shown below with and without a 3-year subscription (the 3-year commitment entails partial payment in advance). For the latest pricing, check the AWS EC pricing pages for P2 and P3 instances. Custom on-site systems are more sensitive to price fluctuations in the underlying hardware, allowing providers to pass on price drops associated with rollouts of new GPU architectures.

Therefore the cost of ownership for on-site systems reported below is represented as a relatively conservative approximation and assuming total depreciation over 3 years. Other cost comparisons estimate maintenance and running costs at 50% of the original purchase cost per year, but it probably makes more sense to consider only the cost of electricity (estimated as ~$0.20 per kW*hr in the cost estimates), especially if additional maintenance costs are covered by warranty. It’s worth noting that even with an estimate of 50% maintenance costs per year, on-site systems at 100% utilization would still be significantly cheaper than slower Cloud counterparts. While the “lower-end” P100 on-site configuration costs about 50% less per hour than a reserved p2.xlarge AWS instance, P100 GPUs perform about 4x faster than the older K80 GPUs on Tensorflow benchmarks.

Cloud Computing makes a lot of sense for small, unknown, or variable compute requirements, but for Deep Learning at scale, there are numerous advantages to considering a dedicated on-site system. For continuous, large scale and anticipated Deep Learning compute requirements the cost savings of using dedicated on-site systems are significant. Computational needs in between a DIY system and a full-scale setup for smaller or more experimental workload can be met by a cost-effective yet capable Deep Learning workstations.

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