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Ethics and Responsibility in Artificial Intelligence: A Guide for Business Leaders

This guide outlines key principles for transparent, fair, accountable, and socially responsible AI implementation. Learn how to ensure ethical use of AI and improve your business operations while acting in an ethical and responsible manner.A few facts to know before we move forward. Read onArtificial Intelligence (AI) has become an essential part of many businesses today. As per MarketsnadMarkets, the artificial intelligence market is projected to be valued at $86.9 billion by 2022 in terms of revenue, with a forecasted compound annual growth rate (CAGR) of 36.2% from 2022 to 2027.As AI continues to advance and become more widespread, it is important for business leaders to understand the ethical considerations and responsibilities that come with its use.In just two months following its launch, ChatGPT achieved a remarkable milestone of reaching an estimated 100 million monthly active users in January 2023, making it the fastest-growing consumer application ever.In this article, we will explore the ethics and responsibilities of AI and provide a guide for business leaders to ensure their AI systems are used ethically and responsibly.TransparencyOne of the primary ethical considerations of AI is transparency. AI systems can be complex, and it can be challenging to understand how they make decisions. However, it is essential that businesses ensure that their AI systems are transparent in their decision-making processes.Transparency means that the rationale behind the system’s decisions should be clear and understandable to humans. This is particularly important if the decisions made by the AI system could impact individuals or communities. For example, if an AI system is used to determine creditworthiness, the criteria used to make that determination must be clear and explainable.FairnessAnother critical ethical consideration of AI is fairness. AI systems should not discriminate against any particular group of people. Business leaders must ensure that their AI systems are trained on unbiased data and that they are regularly audited to detect and address any bias in their algorithms.Bias can be introduced into AI systems in many ways. For example, if an AI system is trained on historical data that reflects societal biases, the system may perpetuate those biases. It is crucial that businesses take steps to mitigate bias in their AI systems to ensure that they are fair to all individuals.PrivacyAI systems often require large amounts of data to function effectively. However, this data must be collected and used responsibly. Business leaders must protect the privacy of their users’ data. They should only collect data that is necessary for the functioning of their AI systems, and they must have strict security measures in place to prevent unauthorized access to this data.The collection and use of personal data is governed by laws and regulations in many jurisdictions. Business leaders must ensure that they are complying with all applicable laws and regulations, including the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States.AccountabilityAnother critical ethical consideration of AI is accountability. Business leaders must take responsibility for the actions of their AI systems. This means that they should have mechanisms in place to detect and correct any errors or harm caused by the AI systems. They should also be prepared to compensate individuals or communities that have been adversely affected by their AI systems.AI systems can make mistakes, just like humans. However, when an AI system makes a mistake, the consequences can be far-reaching and potentially devastating. It is crucial that businesses have processes in place to ensure that they are accountable for the actions of their AI systems.Human OversightAI systems should not be used to replace human decision-making entirely. Business leaders must ensure that there is always human oversight of AI systems and that humans have the ability to intervene if necessary.Humans can provide critical oversight to AI systems. They can identify errors or biases that may be present in the system and intervene to correct them. Additionally, humans can bring a level of empathy and understanding to decision-making that AI systems may not be capable of.Social ResponsibilityAI systems can have far-reaching social implications. Business leaders must consider the impact that their systems may have on society and take steps to mitigate any negative effects.For example, an AI system may be used to automate a particular task, which could result in job losses for individuals who previously performed that task. It is crucial that businesses consider the broader social implications of their AI systems and take steps to mitigate any negative effects.Continual ImprovementFinally, AI systems should be continually monitored and improved upon. Business leaders must have processes in place to regularly evaluate their AI systems and ensure that they are performing as intended. This includes monitoring the data used to train the system and ensuring that it remains relevant and unbiased.As technology continues to evolve, AI systems will also need to be updated and improved. Business leaders must have a plan in place to ensure that their AI systems are continually updated and improved to meet the changing needs of their business and society as a whole.Final ThoughtsThe ethical considerations and responsibilities of AI are crucial for business leaders to understand. AI systems have the potential to impact individuals and communities in significant ways, and it is essential that they are used ethically and responsibly.Business leaders must ensure that their AI systems are transparent, fair, protect privacy, accountable, have human oversight, consider social responsibility, and are continually improving. By following these ethical principles, businesses can use AI systems to improve their operations while also ensuring that they are acting in a socially responsible and ethical manner.Let Your Business Take a Leap Forward with Aziro (formerly MSys Technologies)Looking to take your business to the next level? Look no further than Aziro (formerly MSys Technologies). Our digital services can help you deliver new and exciting experiences to your clients, thanks to our bespoke touchpoints and modernized end-user experiences.Our expert architects can help you create innovative, user-friendly software that will keep your customers coming back for more. We make your business agile using microservices and machine learning-powered processes, creating multichannel experiences that work across platforms.We’ll also help you up your data skills, with robust data governance capabilities, information silo unification, and flexible data architecture. Our digital services cover everything from mobility to analytics to IoT, AI, and big data, creating scalable, intelligent products and custom solutions.Ready to accelerate your business? Contact Aziro (formerly MSys Technologies) today at marketing@aziro.com and let’s get started.

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5 Top AI Challenges in Cybersecurity You shouldn’t Overlook

Advancement in technologies has created umpteen opportunities for cybercriminals to steal data. The rise in the use of cloud technology has accelerated the process of sharing of data online – information is now available irrespective of place and time. The odds are far more favorable than before for cybercriminals to get into your system. Organizations are firefighting cyber threats at two fronts – from amateur script artists who consider hacking more as awarding than rewarding, and attacks backed by organized crime syndicate with intentions to de-stabilize operations and damage the economy. Per a report by Security Intelligence, the average cost of a data breach is $3.92 million as of 2019. Cybersecurity Ventures predicts that the damage to the world due to cybercrime will reach $6 trillion annually by 2021. This represents the greatest transfer of economic wealth in history, risks the incentives for innovation and investment, and will be more profitable than the global trade of all major illegal drugs combined. This amount will only climb up until we do away with the firefighting approach and think more proactively, It takes a thief to catch a thief To beat some in their own game, you must think like them. If they are fast, you must be fast; if they are cutting-edge, you must be cutting edge. To counter the threats posed by cybercriminals, organizations ought to be faster. It requires to do away with traditional security measures and embrace new age, automation-driven practices that could put us ahead of any hacker. The regular practice includes securing only mission-critical parts within an infrastructure. This leaves room open for hackers to target non-critical components. Therefore, organizations must implement comprehensive and robust cybersecurity procedures that cover every component within an infrastructure. Further, organizations should align themselves with the practice of leveraging automated scripts to facilitate continuous monitoring and reporting in real-time. Ushering an era of proactive cybersecurity via Machine Learning Artificial intelligence (AI) and machine learning (ML) gives an edge to modern software that are primarily created to protect from unethical cyber practices. With AI and ML, the cybersecurity software products get an extra sense to underline concurrent behavioral patterns of the workflows, assess its threat level and, based on it, alert the concerned team. The key reason why AI/Ml can perform such activity is its ability to gauge data, compare it with past actions, and derive an inference. This inference provides the security team an insight into future events that could lead to a possible cyber-attack. However, AI application is still in the nascent stages. Per IDC, one in four AI project usually ends up failing. This means there are challenges we must counter in order to make AI a success. These challenges become significant when the matter is about the organization’s data security. Let us now analyze 5 top challenges that prevent the successful implementation of AI/Ml for cybersecurity. 1. Non-aligned internal processes Most companies have optimized their infrastructure, especially its security components, by investing in tools and platforms. Yet, we see that they face security hurdles and fail to safeguard themselves against an external attack. This is a result of a lack of internal process improvements and cultural change that prevents capitalizing the investments in security operation centers. Further, the lack of automation and fragmented processes creates a less robust playground to defense against cybercriminals. 2. Decoupling of storage systems Most organizations do not leverage data broker tools like RabbitQ and Kafka to initiate analytics of the data outside the system. They do not decouple storage systems and compute layers, which doesn’t allow AI scripts to execute effectively. Further, a lack of decoupling of storage systems increases the possibilities of vendor lock-ins in case of a change in the product or platform. 3. The issue of malware signature Signatures are like fingerprints of malicious code that assist security teams in finding the malware and raising an alert. The signatures do not match the growing number of malware every year. The concern is that any change in the script of the virus makes the signature invalid. In short, signatures will only help debug malware if the code is pre-established by security teams. 4. The increasing complexity of data encryption The rise in the use of sophisticated and advanced data encryption strategies are making it difficult to isolate an underlying threat. The most common way to monitor external traffic is via deep packet inspection (DPI) that helps filter external packets. However, these packets consist of a predefined code characteristic that can be weaponized to infiltrate in the system by the hackers. Further, the complex nature of DPI puts pressure on the firewall, slowing down the infrastructure speed. 5. Choosing the right AI use cases More than 50 percent of the AI implementation project fails in the first go. This is because organizations try to adopt AI on a company-wide level. They often neglect the importance of baby steps – narrowing down on AI-based use cases. Thus, they miss out on initial learning curves and fail to absorb critical hiccups that often jeopardize the AI projects. AI/ML isn’t a magic bullet rather AI/Ml isn’t a cure-all to the activities of cybercriminals. Rather, a fierce defense that is rooted in intelligence and intuition. AI/ML will help create intelligent systems that work as a potent defensive force against activities. They could detect and alter, but they can’t reason why and how these activities were triggered. It is the security teams that need to carry out root-cause analysis of the incident/s and then remediate it. Take Away Mature processes, cultural alignment, and skillful teams and choosing the right AI use cases in cybersecurity are the key to the success. For this, security teams must carry out an internal audit and tick mark areas in infrastructure that are the most vulnerable. Ideally, they can start with data filtering to segregate unauthenticated sources. This isn’t the thumb rules, though. The bottom line is taking mindful steps towards adopting AI for cybersecurity.

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AI artificial intelligence cyber security

4 AI and Analytics trends to watch for in 2020-2021

Never did we imagine the fictional robotic characters in novellas to become a reality. However, we wished, didn’t we? The theory of ‘Bots equal to Brains’ is now becoming a possibility. The mesmerizing and reverence Artificial Intelligence (AI) that we as children saw in the famous TV show- The Richie Rich has now become a plausible reality. Maybe, we are not fully prepared to leverage AI/Robotics as part of our daily lives; however, it has already created a buzz, profoundly among the technology companies. AI has found a strong foothold in the realms of data analytics and data insights. Companies have started to leverage advanced algorithms garnering actionable insights form a vast set of data for smart customer interactions, better engagement rates, and newer revenue streams. Today, Intelligence-driven Machine Learning intrigues most companies in different industries globally; however, not all exploit its true potentials. Combining AI with Analytics can help us drive intelligent automation delivering enriched customer experiences. Defining AI in Data Analytics This can be broad. However, to summarize, it means using AI in gathering, sorting, analyzing a large chunk of unstructured data, and generating valuable and actionable insights driving quality leads. Big players triggering the storm around AI AI may sound scary or fascinating in the popular imagination; however, some of the global companies have understood its path-breaking impact and invested in it to deliver smart outputs. Many big guns like IBM, Google, and Facebook are at the forefront, driving the AI bandwagon for better human and machine co-ordination. Facebook, for instance, implements advanced algorithms triggering automatic photo tagging options and relevant story suggestions (based on user search, likes, comments, etc.). However, with big players triggering the storm around AI, marketers are slowly realizing the importance of humongous data available online for brand building and acquiring new customers. Hence, we can expect a profound shift towards AI application in Data Analytics in the future. What’s in store for Independent Software Vendors (ISVs) and Enterprise teams With the use of machine learning algorithms, Independent Software Vendors and Enterprise teams can personalize the product offerings using sentimental analysis, voice recognition, or engagement patterns. The application of AI can automate the tasks while giving a fair idea of their expectations and needs. This could help product teams in bringing out innovative ideas. Product specialists can also differentiate between bots and people, prioritize responses based on customers, and identify competitor strategies concerning customer engagements. One of the key elements that AI will gain weight among product marketers will be its advantage in real-time response. The changing business dynamics and customer preferences make it crucial to draft responses in real-time and consolidate customer trust. Leveraging AI will ensure that you, as a brand, are ready to meet customer needs without wasting any time. Let us understand a classic example of how real-time intelligent social media analytics can create new opportunities. Lets read about 4 AI and Analytics trends to watch for in 2020-2021 1. Conversational UI Conversational UI is a step ahead from pre-fed and templated chatbots. Here, you actually make a UI that talks to users with human language. It allows users to tell a computer what it needs. Within conversational UI, there is written communication where you would type in a chatbox and voice assistant that facilitate oral communication. We could see more focus on voice assistants in the future. For example, we are already experiencing a significant improvement in the “social” skills of Crotona, Siri, and OK Google. 2. 3D Intelligent Graphs With the help of data visualization, insights are presented interactively to the users. It helps create logical graphs consisting of key data points. It provides an easy to use dashboard where data can be viewed to reach to the conclusion. It helps quickly grasp the overall pattern, understand the trend, and strike out elements that require attention. Such interactive, 3D graphs are increasingly used by online learning institutes to make learning interactive and fun. You will also see 3D graphs used by data scientists to formulate advanced algorithms. 3. Text Mining It is a form of Natural Language Processing that used AI to study phrases or text and detect underlying value. It helps organizations to segregate information from emails, social media posts, product feedbacks, and others. Businesses can leverage text mining to extract keywords, important topic names, or highlight the sentiment – positive, neutral, or negative. 4. Video and Audio Analytics This will become a new normal in the coming few years. Video Analytics is computer-supported facial recognition, gesture recognition used to get relevant and sensitive information from video and audios to reduce human efforts and enhance security. You can use it in parking assistance, traffic management, access authorization, among others. Can AI get CREEPY? There is a growing concern over breach of privacy by the unethical use of AI. Are the concerns far-fetched? Guess not! It is a known fact that some companies use advanced algorithms to track your details such as phone numbers, anniversaries, addresses, etc. However, some do not limit to the aforementioned data, foraying into our web-history, traveling details, shopping patterns, etc. Imagine your recent picture on Twitter or Facebook, which has a privacy setting activated used by a company to create your bio. This is undoubtedly creepy! Data teams should chalk down key parameters to acquire data and share information with the customers. Even if you have access to individual customer information like their current whereabouts, a favorite restaurant, or favorite team, one should refrain from using it while interacting with customers. It is your wisdom to diligently using customer data without intruding on their privacy. Inference Clearly, the importance of analytics and the use of AI for adding value to the process of data analysis is going up through 2020. With data operating in silos, most organizations are finding it difficult to manage, govern, and extract value out of their unstructured data. This will make them lose on a competitive edge. Therefore, we would experience a rise of data as a service that will instigate the onboarding of specialized data-oriented skills, finely grained business processes, and data-critical functions.

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AI artificial intelligence Big Data Analytics

How to build an AI app using Tensorflow and Android

Abstract:This article describes a case study on building a mobile app that recognizes objects using machine learning. We have used Tensorflow Lite. Tensorflow Lite machine learning (ML) is an open source library provided by Google. This article mentions a brief on Tensorflow Lite.Object Identification in Mobile app (Creative visualization)Tensorflow Lite:Using Tensorflow, Implement the Machine Learning (ML) or Artificial Intelligence(AI)-powered applications running on mobile phones. ML adds great power to our mobile application. TensorFlow Lite is a lightweight ML library for mobile and embedded devices. TensorFlow works well on large devices and TensorFlow Lite works really well on small devices, as that it’s easier, faster and smaller to work on mobile devices.Machine Learning:Artificial Intelligence is the science for making smart things like building an autonomous driving car or having a computer drawing conclusions based on historical data. It is important to understand that the vision of AI is in ML. ML is a technology where computer can train itself.Neural Network:Neural network is one of the algorithms in Machine learning. One of the use cases of neural networks is, if we have a bunch of images, we can train the neural network to classify which one is the image of a cat or the image of a dog. There are many possible use cases for the combination between ML and mobile applications, starting from image recognition.Machine Learning Model Inside our Mobile Applications:Instead of sending all raw images to the server, we can extract the meaning from the raw data, then send it to the server, so we can get much faster responses from cloud services.This ML model runs inside our mobile application so that mobile application can recognize what kind of object is in each image. So that we can just send the label, such as a cat, dog or human face, to the server. That can reduce the traffic to server. We are going to use Tensorflow Lite in mobile app.TensorFlow Lite Architecture:DEMO: Build an application that is powered by machine learning — Tensorflow Lite in Androidhttps://www.youtube.com/watch?v=olQNKvMbpRgGithub link:Pull below source code, import into Android Studio.https://github.com/Chokkar-G/machinelearningapp.git(Screencast)Tensorflow Lite object detectionThis post contains an example application using TensorFlow Lite for Android App. The app is a simple camera app that classifies images continuously using a pretrained quantized MobileNets model.Workflow :Step 1: Add TensorFlow Lite Android AAR:Android apps need to be written in Java, and core TensorFlow is in C++, a JNI library is provided to interface between the twoThe following lines in the app’s build.gradle file, includes the newest version of the AARbuild.gradle:repositories { maven {url ‘https://google.bintray.com/tensorflow'} } dependencies { // …compile ‘org.tensorflow:tensorflow-lite:+’ }Android Asset Packaging Tool should not compress .lite or .tflite in asset folder, so add following block.android { aaptOptions {noCompress “tflite”noCompress “lite”} }Step 2: Add pretrained model files to the projecta. Download the pretrained quantized Mobilenet TensorFlow Lite model from herehttps://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zipb. unzip and copy mobilenet_quant_v1_224.tflite and label.txt to the assets directory: src/main/assets(Screencast) Placing model file in assets folderStep 3: Load TensorFlow Lite Model:The Interpreter.java class drives model inference with TensorFlow Lite.tflite = new Interpreter(loadModelFile(activity));Step 4: Run the app in device.Conclusion:Detection of objects like a human eye has not been achieved with high accuracy using cameras, i.e., cameras cannot replace a human eye. Detection refers to identification of an object or a person by training a model by itself. However, we do have great potential in MI. This was just a simple demonstration for MI. We could create a robot that changes its behavior and its way of talking according to who’s in front of it (a child/ an adult). We can use deep learning algorithm to identify skin cancer, or detect defective pieces and automatically stop a production line as soon as possible.References:https://www.tensorflow.org/mobile/tflite/https://www.tensorflow.org/mobile/

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artificial intelligence blog

Artificial Intelligence Taking over Wall Street trading

One of the biggest reason trading decisions are affected is because of human emotions. Machines and algorithms can make complicated decisions and execute trades at a rate which no human can match and is not influenced by emotions. The parameters these algorithms take into consideration are price variations, macroeconomic data volume changes similar to accounting information of different corporate companies and news articles from various times topredict the nature of a particular stock.Stock prediction can be done using the company's historical data. This historical data can be used to perform either Linear regression or Support Vector Regression depending on the complexity of the system, to discover trends in the stock market. The algorithm can access various real time news papers and journals to retrieve the latest news and information regarding a specific company. This data is then processed and analysed along with the historical data and data derived from the quarterly results and press releases of that company. This helps in predicting a stock price of a specific company.If we need to analyze the whole market, consisting of more than 6000 companies listed in the New York Stock Exchange, we can do that too in the similar manner by navigating through the regulatory filings, social media posts, real time news feed and other finance related metrics also involving elements such as correlations and valuations in order to predict investments which are considered undervalued.AI is already in use by institutional traders and are incorporated in tools used for stock trading. Some of which are completely automated and are used by Hedge Funds. Most of these systems can detect minute changes caused by a number of factors and historical data. As a result thousands of trades are performed on single day.An interesting example:It was noticed that, everytime Anne Hathaway was mentioned in the news, the share price of Berkshire Hathaway increased. This was probably because, there was some algorithm from a trading firm running automatic trades whenever it came across “Hathaway” in the news.This particular example is a false positive and the fact that this system can run automatic trades based on real time news feed is pretty interesting. This technique requires data ingestion, sentiment analysis and entity detection.If the system or algorithm can detect and react to positive news feed faster than anybody else in the market, then one can make the profit that is the leap(or decrease) in price.Citation: http://www.eurekahedge.com/Research/News/1614/Artificial-Intelligence- AI-Hedge-Fund-Index- Strategy-Profile

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artificial intelligence blog

Using Predictive Artificial Intelligence for the future of Healthcare

Artificial Intelligence (A.I) is used in all the sectors for improvement and better outcome. For example NASA used Google AI to find new planets in the galaxy. This has become a news for the world. Would like to give you a brief introduction on AI on healthcare. Using AI in healthcare will reduce the complications which come into existence when a person goes through an surgery and also can give him best possible information depending or diet to not have a surgery. We could balance the ethics and efficiency of the healthcare industry. By using AI, points which are overlooked or missed in urgency can be taken care at the primary level than at a critical level. Since I came across an health emergency in my family, so thought how I can use AI to contribute in healthcare. I determined the best possible match of Kidney Transplantation, started reading the information present around me to find out the information related to these organs. In my findings I found out that each have many complications can also lead to death of an individual. Transplant option would be thought of when the kidneys stop functioning entirely. This condition is called end-stage renal disease (ESRD) or end-stage kidney disease (ESKD). If you reach this point, your doctor is likely to recommend dialysis. In addition to the dialysis, your doctor will inform you whether you are a good candidate for kidney transplant. You will need to be healthy enough to have major surgery and go through a strict, lifelong medication after surgery to be a good candidate for a transplant. You must also be willing and able to follow all instructions from your doctor and take your medications regularly. If your doctor thinks you’re good candidate for a transplant, and you’re interested in the procedure, you’ll need to be evaluated at a transplant center. This examination usually involves several visits to the hospital to assess your physical, psychological and familial condition. The doctors will run tests on your blood and urine and give you a complete information to ensure you’re healthy enough for surgery. The Matching Process At the time of evaluation for transplant, blood tests would be conducted to determine your blood type (A, B, AB, or O) and your human leukocyte antigen (HLA). HLA is a group of antigens which is located on the surface of your white blood cells, they are responsible for your body’s immune response. If donor and your HLA type matches, then your body will not reject the kidney. Each person has six antigens, three from each biological parent. The more antigens matches you have with the donor, the greater the chance of a successful transplant. Once a best possible donor has been identified, another test has to be take up to ensure sure that your antibodies won’t attack the donor’s organ. This is done by mixing a small amount of your blood with the donor’s blood. The transplant can’t be taken up if your blood forms any antibodies in response to the donor’s blood. If the blood shows no antibody reaction which is called “negative crossmatch”, i.e. transplant can proceed. Algorithm Process During this process, we take the old data related to this and train our engine. So how does the engine work? Engine works but connecting all the hospital present around that town/city/country, it will recognise the best possible chances of the success. We get to know this from the data present with us from the old cases. Our engine reads the information and gets best possible procedure to go ahead. For a simple example, a person in a certain city will be in need to transplantation and he does get suitable match. There is one more person in another city with the same problem. The donor who is ready for that person matches with this person1 and donor of person1 matches with person2. We can plan the best possible match and provide the information to concerned person such that a best possible help will be provided to patients who are in the hospital. The algo helps doctors to get to know the complication which many come to a person while undergoing the procedure such that a solution can be ready while the process is going on. This will reduced the risk of the patients who undergo major operations. So we are looking forward to work on this and make it a best scenario for showing the capabilities of the AI engine which will increase the life expectancy of a human being.

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artificial intelligence blog

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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artificial intelligence blog

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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artificial intelligence thought leadership articles

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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artificial intelligence thought leadership articles

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