Artificial Intelligence Updates

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How to build a student-centered Feedback system using AI

With the advent of machine learning and Artificial Intelligence every industry be it Healthcare, Education, or Finance, etc. has been disrupted. Technology can be seen as both, Yin and Yang, it can be beneficent and can be malignant and machine learning gives us enormous power to solve complex problems that cannot be possible with writing rule for every specific case.Machine learning has been disrupting Education Technology and has been prolific across most of the EdTech startup. Be it provision of recommendation to students about the courses or assisting students with feedback or summarizing the content so reader can comprehend the useful part of the content rather than getting into nitty gritty details of every text.We thought of building an application in a similar domain, assisting students with feedback and self-evaluation. In the age of information and technology where data is available in abundance, it’s difficult as well as imperative for a student to learn and understand the concept behind the content; regular assessment and keeping the user progress can be a better way to improvise the learning.Let’s start our Journey – Building a student-centered Feedback systemThere are couple of entities a person needs to keep in mind while building Text processing and Natural language system. A complex Natural language system contains a substantial number of algorithm depending on the use case, be it a key phrase extraction using Tf-idf , Text-rank for Text summarization, Bayes Theorem for Sentiment analysis, POS tagging, NER extraction using Naïve Bayes etc. these are some of the simple algorithms which can be useful while building a simple text processing engine. With the advent of deep learning, it is feasible to replenish the area of Natural language understanding which opens a broader scope of understanding the emotion behind the text the user conveyed, such as sarcasm, humor, disgust, excitement etc.This technology can help us build a complex natural language system and general-purpose AI.The components we need for our purpose are:1. Crawler service that crawls each section of the document, create a token of each term, and create a snippet or tag and save it in the database.2. A Reverse indexer that maps document-id to the term found within the content.3. To measure the similarity of the answer with the actual answer while taking the assessment we’ll be using consine similarity matrix. For complex systems you can use deep-learning RNN to measure the similarity.4. We will be using Watson API to understand the confidence of the students while answering the question.The Query EngineThe user answers to the question asked by the system through Quey API, which will be parsed and decomposed into tokens of N vector. Here we split the answer into token and match each with the actual answer token of M vector and find the cosine similarity between both the answers. Consine similarity doesn’t take into account the magnitude between both the answers but the angle between them.If the given answer is above the given threshold we assume that the answer is correct and try to find the confidence/emotion behind the answer using Watson cognitive API.If the answer is incorrect we will decompose the actual answer which is mapped to the question asked to the student into tokens of N vectors and find the document/ section id of the question from which answers are prepared from. To find the section id we will use Parsed Query and Standard Query where we take the intersection of matched token and use Text rank algorithm to rank the matching document/section.Intricacy of NLP system:There are couple of intricacies while building a Text processing system. Scaling a system, avoiding stops words, navigating to correct section of the document, technique and heuristics of parsed queries to recommend actual section id and finally the confidence of the student on answering the questions. We’ll not build the entire system from scratch but we’ll use some of existing library available.Some of the important libraries and API’s we have used are:1. Tensor flow2. Scikit learn3. NLTK to text processing4. Whoosh / Elastic search for reverse indexer5. Watson cognitive APIThis is a general purpose solution for building a feedback system. It can be used in organization to get feedback from employees and it can also be used to get feedback from the customer and get the emotion out of it and avoid the need of manual evaluation of each feedback response.Here I come to an end of the brief overview of building a student feedback system, we have already gone through few of the algorithm necessary to build an NLP system. I suggest readers to search some of the material related to Natural Language Processing to get a better understanding of the subject.

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4 step approach to Winning in the AI World

For AI the winter is over and the spring has just arrived. At Aziro (formerly MSys Technologies) we believe this is the right ripe time to invest in AI and stay ahead of the competition. The AI practice team at Aziro (formerly MSys Technologies) has developed a futuristic sustainable model to help its customers win in these challenging yet exciting times. This would involve a deep collaboration between machines and human. Machines will take up most of the mundane jobs and humans will do what they are best at– ‘noble decision making’. The model includes four key blocks:- 1. INNOVATE: A lot of our current work will be done by machines and this will create the occasion for human race to evolve and discover new opportunities future. In 1973 when Motorola researcher Martin Cooper invented the handheld mobile phone little did he know that in 2017 Stanford Graduate Andre Esteva and his team would add a new dimension to this device by making it a handheld dermatologist detecting skin cancer. 2. AUTOMATE: Achieving all the three- cheaper, faster and better quality together was impossible until AI became mainstream . Now by deploying AI driven Intelligent automation we can completely alter the cost structure, create better quality in operations and significantly decrease our timelines to achieve ROI. 3. ELEVATE: Smart machines are not our competitors but our companions that will increase our productivity and give customers satisfaction. Think of how location enabled mobile maps have elevated our driving experience; AI is something similar. In future, every vertical, from Finance to Healthcare, Manufacturing to Education, all will be enhanced by these new machines. Most of us will choose to work with these elevated humans – one who is equipped with sophisticated tools powered by AI. 4. INUNDATE: The new machines will soon help us experience a new wave of increase in productivity not only limited to Finance, Healthcare, Manufacturing, Education or Storage. These new machines will drive the price points, convert luxuries to commodities and bump up sales to unimagined levels. Will your organization seize this as an opportunity or fall victim to it? Whether you’re a large enterprise or just starting up, let us collaborate, partner and innovate. This new mix of AI, models, bots and data — will be the biggest determinant of your future success. This is similar to the previous industrial revolutions, except that this one will be both harsh and massive; one that will steamroll those who wait and watch; and unleash enormous prospects and prosperity for those who adapt, adopt and harness the new machine.

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AI the Agile Way

Most of the future facing large companies are aligning themselves as AI companies. This is like a natural progression from apps to chatbots and Big Data to Machine Learning. 62% of organizations will be using Artificial Intelligence (AI) Technologies by 2018, says a recent survey done by narrative science. This is also the reason why we see so many companies feel a pressing need to invest in AI. With passing time, the competition space is heating up and there is a steep task of fully understanding what to achieve using AI. Coupled with this comes the biggest challenge how to achieve it via the traditional engineering delivery teams. This is where partnerships play a vital role. Pivot on Idea Idea should be the pivot not AI. AI is only a great enhancer; it can create a self learning system, reduce human curation cost, or build a human like natural language interface. End product idea should be thought of first; as in is there a market and need for the end product?. AI should not be considered as the selling point. This can even start with a non- AI product to test if there is market fitment for the end product. Begin Small Fast iterations and Lean Startup principles of beginning with an MVP still hold good. Start with leveraging some tested and already validated techniques that can help increase the performance. Few of the validated techniques include reduced human efforts, improved user experience by replacing human intervention with machine driven intelligence, better recommendations etc. to list a few. From this small beginning you can showcase the value that can be added while getting the AI infra tested and proven. Research and Develop in Sprints Cycles Iteration and collaboration between research and engineering holds the key. Both sides should work in similar sprint cycles. This will allow both the teams to understand how the overall work is progressing. The input from engineering, that is the issues and changes are very valuable for the direction of research and vice-versa. Research takes time, having sprint cycle check helps to keep things in control. Ideas can be discussed and demoed; this helps in complete progression.

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Applied AI

Digital data around us is growing exponentially, this has powered the phenomenon of Artificial Intelligence. This phenomenon will augment human capabilities making us more productive, and positively impact our lives. The AI Ecosystem A Smart device and all its underlying components, be it the software or the hardware, need multiple specialized players to come together, contribute, and build it. The AI world is similar, which has varied dimensions of human like intelligence such as social, creative, emotional and judgmental intelligence embedded within it. At Aziro (formerly MSys Technologies), our applied AI approach brings all these dimensions closer and knit them logically together to define cognitive intelligence. We believe we are part of this ecosystem of AI solutions where we augment our partners by bringing these dimensions of human like intelligence; collaborating using system of intelligence. Applied Artificial Intelligence Machines will exhibit intelligence by perceiving and behaving in the human way. They will also provide scale, iterative learning, ingestion of information from vast, varied and variable data troves. The opportunity is to introduce humanized AI that can simplify business processes, complement human resources and supplement decision making with all possibilities of insights from information. We thus benefit from endless possibilities of building systems that are able to think, act, learn, and perform from every possible interaction. Identifying Opportunities Opportunities are endless in AI; this makes decision making a tough job. A well thought mechanism coupled with some well thought gears are important to derive a right action list. We believe in looking through:- Value :- The trending individual technologies that support AI like IBM Watson or Amazon AI or Microsoft Cognitive or Google Deep mind’s Alpha Go made great headlines. Can those be applied to your business to serve a broader goal that matches with your company strategy, driving profits? Business should always ask:- How can AI improve product outcomes? Can service quality be made better with AI? Whether AI can help create new user experience and improve the existing setup, Can AI bring down cost and uncertainty for critical projects? Will it be possible to Apply, Scale, Preserve and Enhance human learning and experiences with AI? Applying Applied AI Taking AI out of research laboratory and making it part of daily use is all about applying AI. Think big, start small and use agile. Experience the example below:- Rule based Digital Assistant You have 2 meetings tomorrow 9:00 – 11:00 16:00 – 17:00 Digital Assistant – Powered by AI Today is Thursday, You have a travel planned tonight to New York. You are low on your BP medicines, I have placed an order which will be made available to you at your hotel in NY. Tomorrow your first client meeting is at 9:00 am but your report is not ready yet as inputs are awaited from research team; I have already sent them a reminder. Your next client meeting is at 16:00 hrs. Do you want me to research and prepare on the latest findings in cancer medication before you meet your client? This example helps us to look at AI as a companion rather than a competitor. It will enrich families and businesses by simplifying how human and machines work with each other, collaborating among themselves. We strongly believe applied AI will enhance, evolve its own components and devices to work in harmony. This will create a real-world impact at enormous scale.

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What are the Advantages of Predictive Storage Analytics?

Predictive Analytics can play a big role in taking your storage stack to the next level. Machine learning algorithms can pierce through the data and uncover critical insights. By analyzing the data patterns, you can improve capacity planning of your storage infrastructure and accelerate its efficiency as well as performance. Aziro (formerly MSys Technologies) as a revered big data analytics solutions providers and expertise in predictive storage analytics, present you the following infographic:Are you looking to adopt Predictive Analytics for your storage company? Our team can provide expert consulting and support services. Contact us today!

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Artificial Intelligence – the fuel for digital growth

Driving Digital Transformation with AI Artificial Intelligence has become the fuel of digital disruption. The real-life benefits for a few initial adopters have already started yielding results. For others it has become more important to begin their digital transformation without further delay. AI technology systems like computer vision, robotics and autonomous vehicles, natural language understanding, virtual advisors, and self learning machines that use deep learning and support many recent advances in AI, have become mainstream. As industries and businesses struggle to yield the benefits of AI, they are realizing that it is easier said than done. A good company that can render profound Artificial Intelligence Services is what most businesses need, so that they can continue to focus on the development and marketing of their products. The Roller Coaster Ride The idea of Artificial Intelligence started gaining impetus post the development of computing. It has also experienced its wave of glory and dismay. One thing AI was yet to experience was the large scale commercial deployment, but that is slowly changing too. Machines powered by Deep Learning, a subset of AI, can perform multiple activities that require human cognition. This includes understanding complex patterns, curating information, reaching conclusions and even giving out predictions with suggested prescriptions. The capabilities of AI have significantly broadened, so has its usefulness in many fields. Although one key thing we should not forget is that machines do have some limitations. To take a relevant example, machines are always susceptible to bias as they depend on training data and are trained on specific data sets. Comprehensive dataset is still a relative term. It is both driven by available data and the modellers understanding of use case. Although, irrespective of all these limitations we are experiencing commendable progress. Driving out of the dreaded ‘AI Winter’ of 1980’s, AI powered by machine learning has scaled up since 2000 and has driven deep learning algorithms. The key things that have facilitated these advances are Availability of huge and varied datasets that are comprehensive in nature Improved models and modelling techniques that can self learn using reinforcement Increase in R&D funding Powerful computing hardware and processing units such as GPU, NPU etc. that are 80 – 90 times faster than normal Integrated Circuits The Promise – Boosting Profit and Driving Transformation Adoption of AI still remains in its very initial days. Thus it still remains a big challenge to assess the real potential impact of AI on various sectors. Early evidence suggests that if AI is implemented at scale it does deliver good returns. AI can even transform business activities. It can reshape functions across the value chain and the cases can have major implications for many stakeholders, ranging from MNC, SMB, Government, and even social organizations. “Extensive financial growth will be seen by those organizations, which will combine a proactive AI strategy with its strong digital capability.” Some of the digital native companies have made early investments in AI and they have even yielded a potential return on investment. A case in point can be Netflix that uses algorithms to personalize recommendations to its worldwide subscribers. Customers tend to have a patience span of only 90 seconds and give up if they are not able to find their desirable content within this time. Netflix satisfies this discovery through better search results. This has helped it to avoid cancelled subscriptions that otherwise would have reduced its revenue annually by $1 billion. The expectation that has been set on AI will need it to deliver economic applications that can significantly reduce costs, enhance utilization of assets and increase the revenue. AI can help create value in following avenues: Enable organizations to better budget and forecast demands, Optimize research and better sourcing; Enhance ability to produce goods and deliver services at lesser cost but higher quality; Help tag the right price to offering, with an appropriate message, and targeted to the right customers; Provide personalized and convenient user experience The listed points are not exhaustive but are based on the current knowledge of applied AI. AI will also have unique degrees of relevance for each industry, the prospect and application levers are particularly rich with troves of opportunities. Machine Learning powered by deep learning can bring deeper and long term value to all sectors, few technologies are exceptionally suited for business applicability. Some specific use cases are cognitive robots for retail and manufacturing, deep machine vision for health care, and natural language understanding and content generation for education. Industries disrupted by AI Financial Services AI has significantly helped disrupt this industry in multiple avenues. It has enhanced security to better safeguard assets by analyzing large volumes of security data to identify fraudulent behavior, suspicious transactions and potential future attacks. Document processing is a key activity in financial services. It involves time, is prone to human error and vulnerable to duplications. AI speeds up the processing time and reduces the errors significantly. However, the most valuable benefit is ‘data’. The future of financial services is mostly reliant on acquiring data to stay ahead of competition, here AI plays a significant role. Powered by AI, organizations can process massive volume of data, this will offer them game-changing insights that in turn will provide better experience for its customers. Healthcare In healthcare, AI will help identify high risk patient groups, and launch preventive medication for them. Hospitals typically can use AI to both automate and optimize operations. Diagnosis which used to get delayed due to multiple opinions can now become faster and accurate. Healthcare expense can now be accurately estimated with focus on healing. In this journey of healthcare, specialists can now formulate better drugs and dosage, and virtual agents can help deliver a great healing experience. Education In education, AI can connect need with content. It can help identify key drivers of performance for students to highlight and build their strengths. It can personalize learning and shift from break test model to continuous feedback based learning empowered by virtual tutors. It can also automate human tutors’ mundane tasks, detect early disengagement signs in students, and help form groups on focussed learning objectives. Storage Enterprises are rapidly shifting towards cloud storage. Lesser dedicated storage arrays driven by dynamic storage software will now be run by deep learning brains. This will help companies add or remove storage capacity in real time, thus reducing 70 percent in cost. Next generation scale-out computing environments will have a few thousand cores (neurons) and they will be connected at tremendously high speed and at exceptional low latencies. Servers that are part of these neural-class networks are instrumented for the telemetry that is needed to build and automate self-driving data centers. They are instrumented to process packets that are needed for real-time analytics. The key trends that have led to the emergence of “Neural-Class Networks” are the computing environments which are used for AI that uses the distributed scale-out architecture, and data of massive size. They can be found in the data centers service providers in public cloud, exchanges, retailers, financial organizations and large carriers, to handpick a few. The digital enterprises that are successfully flourishing today depend a lot on algorithms, automation, and analytics driven by AI. These emerging technologies which were previously available only to large enterprises have now become accessible and affordable, thanks to democratization of AI. Today even SMBs have the required AI tools, access to skilled AI partners, and the right people to financially back the disruptive ideas that can effectively help them compete with larger players. The exciting times have just begun.

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Artificial Intelligence Updates

How to build a student-centered Feedback system using AI

4 step approach to Winning in the AI World

AI the Agile Way

Applied AI

What are the Advantages of Predictive Storage Analytics?

Artificial Intelligence &#8211; the fuel for digital growth

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Artificial Intelligence – the fuel for digital growth

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