SCS Tech India

Category: Artificial Intelligence & Machine Learning

  • Uprising of the Indian IT sector and it’s future

    Uprising of the Indian IT sector and it’s future

    The 21st century is commonly recognized as the era of technology. This Information Age is marked by newer technologies which are in regard toward problem-solving and critical thinking. As far as our country is concerned, the future of the IT industry in India is quite bright, given the tremendous rate of globalization and industrialization.

    The IT & BPM sector has become one of the most significant growth catalysts for the Indian economy, contributing significantly to the country’s GDP and public welfare. The IT industry accounted for 7.4% of India’s GDP in FY22, and it is expected to contribute 10% to India’s GDP by 2025.

    According to the National Association of Software and Service Companies (Nasscom), the Indian IT industry’s revenue touched US$ 227 billion in FY22, a 15.5% YoY growth. And according to Gartner estimates, IT spending in India is expected to increase to US$ 101.8 billion in 2022 from an estimated US$ 81.89 billion in 2021.

    The Indian software program product industry is predicted to attain US$ 100 billion by 2025. Indian organizations are focused on investing across the world to expand their global footprint and enhance their international delivery centers. The IT enterprise introduced 4.45 lac new employees in FY22, bringing the overall employment in the region to 50 lac personnel.

    Artificial intelligence and machine learning are set to determine the destiny of IT corporations in India. More and more corporations in India are opting for newer technologies to facilitate better functionality.

    Governance around IT

    Some of the major initiatives taken by the government to promote the IT and ITeS sector in India are as follows:

    • In August 2022, the Indian Computer Emergency Response Team (CERT-In), in collaboration with the Cyber Security Agency of Singapore (CSA), successfully planned and carried out the “Synergy” Cyber Security Exercise for 13 countries to build network resilience against ransomware attacks.
    • In June 2022, STPI Director General Mr. Arvind Kumar stated that exports through STPI units have increased from Rs. 17 crores (US$ 2.14 million) in FY92 to Rs. 5.69 lac crore (US$ 71.65 billion) in FY22.
    • In May 2022, it was announced that Indians can now avail of their Digi-locker services through WhatsApp to get easy access to their official documents.
    • In April 2022, the Indian Computer Emergency Response Team (CERT-In) issued Directions to strengthen cybersecurity in the country.
    • In the Union Budget 2022-23, the allocation for the IT and telecom sector stood at Rs. 88,567.57 crores (US$ 11.58 billion).
    • The government introduced the STP Scheme, which is a 100% export-oriented scheme for the development and export of computer software, including the export of professional services using communication links or physical media.

    The Road Forward

    India is the topmost offshoring destination for IT companies across the world. Having proven its capabilities in delivering both on-shore and off-shore services to global clients, emerging technologies now offer an entire new gamut of opportunities for top IT firms in India.

    The Indian IT & business services industry is expected to grow to around INR 1700 crores (US$ 19.93 billion) by 2025. Spending on information technology in India is expected to reach over INR 1200 crores (US$ 144 billion) in 2023. The cloud market in India is expected to grow threefold to over INR 580 crores (US$ 7.1 billion) by 2022 with the help of the growing adoption of big data, analytics, artificial intelligence, and the Internet of Things (IoT).

    As per a survey by Amazon Web Services (2021), India is expected to have nine times more digitally skilled workers by 2025.

    In November 2021, Mr. Piyush Goyal, Minister of Commerce and Industry, Consumer Affairs, Food, and Public Distribution and Textiles, lauded the Indian IT sector for excelling in its competitive strength with zero government interference. He further added that service exports from India have the potential to reach INR 83000 crores (US$ 1 trillion) by 2030.

    All in all, the future of the IT industry in India looks promising. While data security might be a big challenge, it will be exciting to witness the turn of events. Apart from the nation’s perspective, one can make the best of the changing IT industry by upskilling themselves.

  • Fundamentals of Artificial Intelligence

    Fundamentals of Artificial Intelligence

    Artificial intelligence (AI) is a mystery and a wonder. It can help us solve humanity’s most difficult problems. AI is also vastly misunderstood by most people. For some, AI is a magical black box with the intelligence of a PhD. It knows all about everything, you just bring your problems and, voilá, your problems are solved. For others, fears of AI uprisings, loss of human control and Terminator-like scenarios cloud their capability to understand the present utility of cognitive computing.

    Introduction

    When looking for ways to apply this cutting-edge, revolutionary technology, AI can not only dazzle, but can blind us as well. The more buzz and hype, the greater the pressure becomes to rush to create something using whatever new capabilities are presented to us. The benefit of moments like this is the opportunity to discover, experiment, and learn.

    We must focus our design talents upon a new type relationship with machines – machines that can draw from vast stores of human knowledge, hold a conversation, and increase human understanding. What should these relationships look like? Designing for AI requires new considerations and new ways of thinking.

    When we find ourselves in such unfamiliar territory, the best way to begin is by reminding ourselves of the true purpose for any innovation: to improve the quality of human life.

    Consider what we’ve learned living through the past few major technological innovations. The internet brought the world’s information to our beck and call and taught us that whatever we want, whenever we want it is always a finger-length away. Mobile tech enabled humanity to take that information with us at all times. Social computing and messaging changed the foundations of how we communicate with each other and language itself.

    Each innovation provided a new context for communication and expanded (or changed) our understanding of the relationships we have with machines. AI requires us to take notice of new contexts yet again. This time, designers must account for a system that can understand, reason, learn, and interact.

    As a result, this changes the nature of our design considerations, some of which we take for granted based on the last 40+ years of software design. If we’ve moved past typing into a text field and pressing “submit,” what does it mean to design for human/machine relationships?

    AI Design Foundations

    As experts says, design is the rendering of intent. This intent drives our outcomes. It’s the “why” behind what we do and the cause for us to affect change. Intentions require us to identify purposes and values for our efforts. Outcomes require going beyond ideas and create meaningful, trusted solutions for the people we serve.

    Design is more than a job or role—it’s an essential part of what makes us human. The value of design is to improve lives and leave the world better than we found it. When designing for AI, our core intents are always rendered through the following lenses:

    Purpose

    The reason for the user to engage with the system. This will evolve as the user and system grow with each other.

    Value

    The augmented capabilities provided by the system that tangibly improves a user’s life.

    Trust

    The willingness of a user to invest in an emotional bond with the system. This trust is predicated on security of the system’s data, the feeling of human control, and the quality of the results the system provides.

    Characteristics of AI

    AI is the simulation of human thought processes in a computerized model. AI involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.

    These systems learn at scale, reason with purpose and interact with humans naturally. What search is to information retrieval, AI is to better, more-informed decision-making. In short, they help human experts make better decisions. We characterize AI as having four main qualities:

    Understands

    AI deeply understands its domain. It does this primarily through data – structured and unstructured, text-based or sensory – in context and meaning, at speed and volume.

    Reasons

    AI reasons towards specific goals. It has the ability to form hypotheses by making considered arguments and prioritized recommendations to help humans make better decisions.

    Learns

    AI learns continuously through experience. In ingests and accumulates data and insight from every interaction at all times. It is trained, not programmed, by experts who enhance, scale and accelerate their expertise. Therefore, these systems get better over time.

    Interacts

    AI interacts naturally with people and systems. The interaction model of an AI is expected to flow unobtrusively while continuously building a sustainable relationship between itself and its users.

    Design factors for AI

    When it comes to designing for AI, our focus must be unwavering in providing experiences that put the user above all else.

    AI isn’t about data, it’s about insights. Data is the fuel for the car, insights are the destinations. This is what people care about.

    AI is capable of delivering more human-like interactions with people — based on the mode, form, and quality each person prefers. It reasons through the sum total of structured and unstructured data to find what really matters but this isn’t the full picture. Our solutions must primarily address user needs instead of being force-fit to accommodate technical capabilities or requirements.

    Philosophically and psychologically, we find purpose, value and trust in others when we can perceive intended meaning, consider this meaning from differing points of view, and acquire knowledge so that we can act to have an effect on one another. If a system has the capability to understand, reason, learn, and interact then it has the basis to form a relationship with a human.

    For AI to truly amplify humanity we must have and maintain meaningful relationships between humans and machines.

    To design authentic AI-based relationships requires us, as designers, to consciously understand ourselves before anything else. The more we understand of ourselves, the better we will be able to teach machines how to help our users be better.

    Relationship development

    At this point, you might be asking yourself, “how do I design a relationship?” You wouldn’t be alone in this. For most people, forming and maintaining relationships is an autonomic process. We hardly give it a second thought. If you take a deeper look at the psychology behind relationships, you’ll probably find Knapp’s Relational Development Model.

    Mark Knapp is a teaching professor at the University of Texas and is known for his works in nonverbal communication research. His relationship model explains how relationships grow and last and also how they end. This model is categorized into ten different stages which come under two interrelating stages “coming together” and “coming apart.” This helps to understand how a relationship progresses and deteriorates. For the purposes of our work with AI, we will focus on the “coming together” stages exclusively.

    Initiating

    A collection of first impressions and snap judgements are made. Even if these are inaccurate, they significantly influence if each party wants to continue to the next stage.

    Experimenting

    If there’s a degree of mutual interest, the parties start exploring, looking for commonalities of interests, acquaintances and value.

    Intensifying

    With enough in common, we look for reciprocal sharing by the other person that signals their interest in deepening the relationship.

    Integrating

    AI interacts naturally with people and systems. The AI interaction model is expected to flow unobtrusively while continuously building a sustainable relationship between itself and its users.

    Bonding

    Both parties are fully partnered through trust and mutual appreciation. The relationship is indefinite and only to be broken through a formal notice.

    A symbiotic relationship

    So what do we need to consider when one person in a relationship is replaced by a machine? By establishing tone and personality first, the system will have the means to endear itself to the user. As these systems are like “digital toddlers,” the means to make an emotional connection with its users will be essential.

    AI ecosystem

    Artificial intelligences are probabilistic systems. This means that they are taught instead of being programmed. Since they are being taught, they must have context to be able to utilize their learnings. An AI needs this context to understand its place and provide value.

    Human

    The person whose needs are ultimately being served

    Machine

    The system and its network of embodiments & connections

    Context

    The holistic view of a complete human experience. Includes emotional, physical, system, and domain knowledge.

    Business

    The business needs and market goals to be served by the system

    World

    The external factors that will both inform and educate the system

    AI/human context model

    Intent

    The goals, wants, needs, and values of users and businesses. Intent provides your solution’s purpose.

    Data and policy

    All of the significant raw data a machine can collect from the user and the world and the policies that protect it.

    Understanding

    The process of putting incoming structured and unstructured data in context of your domain. Also known as machine learning.

    Reasoning

    The system’s application of logic to decide on the best course(s) of action.

    Knowledge

    This is what the system knows. This is all past data, insights, and learned attributes measured up against the overall intent of the system.

    Expression

    How the system delivers its response based on the content of the message and its understanding of the user.

    User reaction

    This is the user’s genuine reaction to the system’s expression. Based on the quality of the system’s response.

    Learning

    The user is continually teaching the system to improve through direct and indirect responses.

    Outcome

    The consequences of an actualized system used in the real world to solve real users’ real problems.

  • The Role of Artificial Intelligence in the Future of Education

    The Role of Artificial Intelligence in the Future of Education

    “Artificial Intelligence is no longer a distant utopia. Many things have happened since John McCarthy coined the term at the 1956 Dartmouth Conference”.

    What was once just a dream is now a reality – smart virtual assistants, chatbots, smart home devices, self-driving cars, drones, and other intelligent systems have become commonplace.

    AI technologies are now all around us, shaping every aspect of our lives and changing the world in the process. It’s a booming domain that brings us one step closer to the world of tomorrow.

    It’s obvious that AI has had a tremendous impact on all industries in recent years. Manufacturing, healthcare, transportation, finance and e-commerce are just some of the industries taking advantage of AI systems.

    But there’s one field that even though is not normally linked to AI, is taking big leaps towards embracing its applications. We’re talking about education.

    Education systems all over the world are regarded as being somewhat rigid and reluctant to change.

    However, educational institutions realized the great potential AI technology has and how it can empower both teachers and students, so they are now trying to catch up with the trends and join the AI movement.

    Professionals in the domain agree that AI is essential for the future of learning and it will reshape the way we approach education. So let’s take a look at how AI will influence education in the years to come.

     

    Learning as a personalized experience

    We all know that people learn differently. Every person has personal preferences as well as strengths and weaknesses when it comes to learning. Traditional teaching methods make no difference between students, having a one-size-fits-all approach.

    As a result, some students fail to attain their full potential, while others struggle to keep up with the curriculum. AI helps correct these shortcomings and transform learning into a personalized experience.

    Adaptive learning platforms are able to create learning profiles for students, based on their abilities, preferences, learning styles and the challenges they face. The curriculum and the teaching methods are adapted to students’ needs.

    Teachers can give personalized counsel and adjust class-assignments so that each student can work on what he needs to improve.

    Additionally, AI can help students receive extra support. Educational assistants enhance the learning process by answering questions from students, providing the information they need and helping with assignments, without the teacher’s intervention, so students can set their own pace and learn whenever it’s more convenient for them.

     

    Helping teachers with administrative tasks

    Besides teaching, tutors and educators have other tasks they need to fulfill that require just as much effort and commitment. They also have organizational and administrative responsibilities that can take up a lot of their time.

    Teachers have to handle tasks such as organizing educational material, evaluating assignments, grading exams, managing paperwork, communicating with the parents etc.

    Fortunately, AI can make their job easier and help with many of the above-mentioned activities. Let’s take grading for example. Automated grading is a life savior for teachers when they have to go through thousands of tests.

    AI can take care of other tasks such as logistics issues, keeping the paperwork up to date, and providing feedback for students or serving as a communication channel for teacher-parent interactions.

    Furthermore, education administrators can really benefit from integrating AI solutions into their activity. AI can help with a series of administrative duties from budget management to processing student application forms, procurement of materials or HR management.

    The result will be higher administrative efficiency, lower costs and a clear overall view of the institution.

     

    Education without boundaries

    A traditional education system comes with many limitations. AI can help get rid of boundaries and make learning easily accessible for everyone.

    There are students who can’t attend school in person or who, for various reasons, skipped school years. There’s also an increased interest in high-quality online courses for students all ages.

    AI gives people the possibility to pursue their educational objectives, no matter where they are in the world. Those who want to further their education can forget about time and space restrictions, thanks to AI systems. They can learn anytime, anywhere and adjust the learning process to their needs and environment.

     

    Customized textbooks

    AI is slowly changing the way we use textbooks. Soon enough, thick old-school textbooks, packed with unnecessary information will be a thing of the past. New technologies are making way for customized study guides that cater to students’ needs.

    Teachers will no longer waste their time going through manuals to extract the necessary information and put it in a form that makes it easier for students to understand and assimilate.

    Digital textbooks created by AI systems will become commonplace in the educational environment. They put the spotlight on smart content, helping students learn in a more efficient manner.

     

    Course creation

    AI can do a lot more than providing support for courses. It can also be used as a tool for creating courses and bringing real-time improvements in the educational process. AI-powered courses come with many advantages.

    For example, when attending an online class, students will receive suggestions and assistance as they progress, according to their personal evolution and they will receive support each time they struggle to complete a task.

    Teachers will also be helped by receiving notifications when complicated issues arise, so they can offer further details and focus more on important aspects.

     

    What AI means for teachers

    Since intelligent systems will handle so many tasks that were once performed by teachers, some people got the idea that the rise of AI technologies in education might render teachers obsolete. But in reality, this is highly unlikely to happen.

    A more realistic scenario would be a future in which AI becomes a reliable assistant for teachers, helping them fulfill their responsibilities with higher efficiency.

    There’s a good reason why things will go in this direction. People will always require and respond better to human interaction.

    While AI can be of great help, students still need a teacher to connect with, someone that can guide and inspire them in a way that no machine ever will.

  • AI in Retail: The Ideal Tool to Deliver Seamless Customer Experience and Drive Profitable Growth

    AI in Retail: The Ideal Tool to Deliver Seamless Customer Experience and Drive Profitable Growth

    Traditional analytics methods have worked perfectly fine for the data-driven retail industry for decades. However, Artificial Intelligence (AI) and Machine Learning (ML) have established an entirely new level of data processing which leads to richer business insights and customer relations. Data scientists could open a new world of opportunities to business owners to extract correlations and variance from thousands of AI /ML models. The importance of AI in the retail industry is finally being recognized. According to surveys, the retail business currently spends roughly $49 million. By 2023, when AI is factored in, it is predicted to reach $12 billion. In terms of spending, AI in the retail business is expected to grow by more than 200 percent by 2023. The growth in the global artificial intelligence in the retail market is driven by factors such as eternally growing smart device and internet users, increasing awareness about AI and big data and analytics, government initiatives on digitalization, and supply chain optimization. Furthermore, enterprises’ demands for restructuring business processes, adoption of the multichannel or omnichannel retailing strategy, mainlining inventory accuracy, and growing need to enhance end-user experience are fuelling the growth of this market.

    The use cases of AI in the retail sector are quite compelling. AI empowers both backends and frontend of e-commerce business. It can help retail businesses to estimate demand forecasts to drive higher margins, can increase the efficiency of the supply chain, give valuable insights for making smart business decisions. On the customer side, AI can personalize customers’ shopping experience by providing personal recommendations, 24×7 chatbots, etc.

    Because of the rapid rise in online retail businesses, the retail industry was less affected by COVID-19 than other industries. Before the COVID-19 outbreak, over 80% of purchasers were accustomed to purchasing things in physical storefronts. However, due to quarantine orders, customers purchased their products more online.

    AI solutions, whether for online or offline commerce, have a lot of room to grow in the future. For the time being, we can show you several real-world AI applications that have shown business value.

    Why Does Your Retail Business Need AI Optimization? 

    Today customer experience has surpassed product and pricing as the one major brand differentiator. Brands that fail to deliver the best possible customer experience will likely deliver less than optimal returns regardless of having a world-class product. To seamlessly create a more engaged business-to-consumer interaction and a great experience for customers, retailers need more tools at their disposal, with AI as their first choice.

    Here are the few benefits businesses can gain from boosting customer engagement, experience, and success by utilizing AI:

    Everyday operations become efficient

    Creating a seamless customer experience and profitable business starts behind the scenes. Retailers must find efficient and profitable ways to deal with these mundane and routine tasks like:

    AI in the retail supply chain can be used for quick product delivery and improved inventory control like restocking — forecasting the demand for a particular product by considering a sales history, weather, location, trends, promotions, and other parameters.

    Picking and packing delivered by the robots is relatively fast ROI when compared to the manual processes. Moreover, robots can collaborate and are designed to open, fill, seal, label, and pack orders together.

    Machine Learning models help in recognizing and classifying millions of items from various sellers and categorizing them according to customer requirements.

    The number of products returned can be reduced due to the early analysis of future purchase trends. Algorithms analyze previous searches, purchases, transactions, and even seasonal trends.

    Personalized customer experience

    With an increasingly digitized world, today, brands are realizing that AI is the real gamechanger in personalizing the customer experience. As customers’ expectations are changing dramatically, retailers are mounting to the challenges with the help of AI, Machine Learning, and Big Data.

    To gauge customer interests and preferences, retailers use advanced Machine Learning algorithms to analyze browser history, past purchases, page views and clicks, social interactions (impressions, likes, shares, and comments), the duration of a page viewed, location, etc. In-store, AI can further enrich the shopping experience with:

    Navigation: AI-enabled kiosks or robots can improve the in-store experience of customers. In-store kiosks and robots use natural language voice commands or touch screen interfaces to provide useful information to customers regarding product location.

    Smart checkouts: Shopping carts with ARM-based cameras can automatically recognize the customer’s purchases, prepare orders, and enable payments through a mobile device.

    AI-driven AR: Many retail stores offer virtual trial rooms, make-up, or product apps, which use AI-driven Augmented Reality interfaces to let customers test products without physically trying them on.

    Visual search and recommendations: Image recognition technology allows people to upload images of the item they are interested in and get products of similar patterns, colors, and shapes. AI recommends products related to the one reviewed by the user and based on the previous purchases.

    Better pricing strategy

    Retail pricing is a core aspect of any business, several studies show that the price that customers pay for an item is almost always among their top concerns. Also, most of the customers wait for the time for a price drop, and they wait for arriving of the product at an ideal cost. Retailers attract customers by forecasting the price of a product based on demand, seasonal trends, competitors’ price, various product characteristics, the release date of new models of the same item in the market, etc.

    AI helps businesses alter the prices of their products, by envisioning the likely outcomes of various pricing strategies. To be able to execute this, systems collect information about other products, promotional activities, sales figures, customer preferences, product locations, and additional data. Businesses can exhibit the best offers to increase customers’ footfall and boost companies’ bottom line.

    Data security efforts have become accurate

    Retailers are combating data leaks, cyber threats, malicious activities, and the prevention of shoplifting by adopting various advanced security measures such as IoT, AI, and Machine Learning. Also, the amount of information that the retailers collect, and process are immense. While it’s not possible to eliminate retail shrinkage, AI excludes time-consuming research tasks and provides curated analysis of risks, reducing the amount of time security analysts take to make the critical decision. Retailers, considering the vast amount of consumer and employee data they possess, should pull out all stops to ensure a robust data security platform to avoid the devastating effects of cybercrime. For this reason, most retailers use AI abilities to provide solid data security.

    Retail requires these data-centric solutions, that result in highly personalized experiences and product recommendations, accurate forecasts, inventory efficiencies, and overall smarter business. Our AI, ML, Data, and Analytics Engineering Services power their Digital Next transformation actions by providing transparency, agility, and flexibility with retail-driven advanced analytics through flexible and scalable solutions.

    We being an AI platform-led digital transformation, product engineering, and solutions company, conversational AI is an area of focus for us. Our solutions enable enterprises to engage customers and ensure speedy resolution of issues with the use of natural language processing, AI, and analytics to intelligently recognize and respond to customer text and voice queries.

  • 4 ways AI can help us enter a new age of Cybersecurity

    4 ways AI can help us enter a new age of Cybersecurity

    • AI is helping companies recover from the pandemic more efficiently.
    • The growing uptake of AI is causing concern for data security in a time of escalating cyberthreats.
    • Well-deployed AI can be used to counter these cybersecurity threats.

    Global catastrophes have historically brought moments of truth for all fields of business. In such times, their inner workings, strengths and weaknesses are laid bare for the whole world to see, as organizations rapidly alter their processes to come to terms with the new reality.

    Businesses that can make bold moves during such challenging times can quickly turn the misfortune into a benefit. So early indications are that businesses that value information as a currency, and have been quick to adapt machine learning and advanced data analytics, have emerged better from the economic aftermath of the pandemic.

    AI and business optimization

    The coronavirus pandemic that continues to ravage the world has forced small businesses into building online ventures. It has also compelled them to adopt AI-enabled platforms that offer consumer insights and help enterprises to deliver “hyper-personalized” products to online buyers.

    Additionally, AI has so far helped companies that are struggling to create safe and contagion-free work environments by setting up on-demand online labour forces.

    In a business context, AI has the potential to perform automated, repetitive tasks that would otherwise have been left for humans. It helps improve efficiency, reduces cost and saves time that could be invested in other business functions.

    However, AI requires massive amounts of data to learn about consumer trends, predict consumer behaviour and find the “next best action” to enhance customer satisfaction and boost sales. But this predictive intelligence can also foresee demand and supply behaviours and help in quality control processes in manufacturing facilities

    The trouble is that gathering and storing this data securely while safeguarding the interests of your stakeholders is no easy task. In the age of digital transformation, where everything is interconnected and shared online, internet of things (IoT) security poses a significant risk for users.

    In recent years, there has been a dramatic increase – 15% to 21%, according to various estimates ­– in security breaches. Leading platforms such as Facebook, Twitter, and Yahoo have become victims, compromising millions of dollars’ worth of users’ data.

    This does not only mean people must set better passwords, but also mandates that these platforms have higher standards for cybersecurity. Since data science and AI will be shaping the next stages of IoT development, data-rich companies must create efficient and trustworthy approaches to turn data into useful and actionable insights.

    Cybersecurity in the age of AI

    Data collection and AI algorithms are becoming the cornerstone of the cybersecurity industry. Automated decision-making and evaluation processes provide a wider range of protection from malicious activity than legacy solutions. For instance, AI can be proactive and monitor devices for suspicious activity, instead of relying on slowly updated malware databases.

    AI developments in cybersecurity should emphasize on making systems safer and more secure for consumers to use. Let’s take a look at how to do this right:

    1. Identify threats early

    Combine conventional threat intelligence (a list of all known cyberthreats to date) and use machine learning to understand risks. This should result in a better, more efficient system of threat detection and prevention.

    This can also help to identify any loophole or threat present in the data.

    In fact, machine learning can also be used to spot any abnormality or potential vulnerability in the midst of “normal” activity and warn users of a threat before it could compromise essential data. With the right systems in place, your hackers won’t even realize that you know of their presence, so you can take immediate measures to ensure the safety of your digital infrastructure.

    1. Prevent credit-card fraud

    Unusual activity, such as purchases made from a different device or unusual transactions, can be instantly detected using AI-powered services that help verify the credit-card holder.

    Machine learning can also help users choose passwords by warning them if a password is not safe enough.

    1. Build on the blockchain

    In recent years, cryptocurrencies like Bitcoin and Ethereum have been rising in popularity. These cryptocurrencies are built upon blockchain, an innovative technical solution to store a secure, decentralized record of transactions.

    Blockchain can be used to enable medical records and help in security management by identifying criminal identity loopholes in the system.

    With blockchain technology, verification keys wouldn’t be required anymore. If someone tries to hack the data, the system analyzes the whole mass of data chains. Even if one data node is left uninterrupted by the hacker, the entire system can be restored successfully.

    This makes the entire system far more secure, ensuring that there is no discrete way of tampering with blocks in the chain, and the stored data can remain safe.

    1. Go deep into the data

    One of the main areas where AI can help cybersecurity is by responding to threats almost immediately. For example, in 2016 Google listed around 20,000 sites for having malware within their system. While humans can’t scrutinize millions of websites, machine learning can. It is possible to use relevant AI solutions to analyze every visit to the site, categorize visitors based on their threat level, and deal with them accordingly.

    We are entering an era of hypercomplexity, where all of our information is interconnected

  • The Role of Artificial Intelligence in the Future of Education

    The Role of Artificial Intelligence in the Future of Education

    Gone are the days of visiting the library to photocopy a few pages from an encyclopedia for a school project. As generations of children grow up with technology at their fingertips, we live in a world where the internet is their primary source of information, education, and entertainment. A recent survey found that children in the U.S. aged between eight and 12 spend almost five hours a day looking at screens, while teenagers are clocking nearly seven hours a day of screen time – and that’s not counting the time they spend doing schoolwork. Hours spent learning from chalkboards in physical classrooms has also reduced significantly since the start of the COVID-19 pandemic, and the ensuing social restrictions and lock-downs. As technology and society continue to evolve and develop, the way we learn will also continue to change, for children and adults alike.

    The rapid advancement of technologies such as artificial intelligence (AI), machine learning (ML), and robotics impacts all industries, including education. If the education sector hopes to utilize AI’s full potential for everyone, the focus should be to continue exposing the next generation to AI early on and utilizing the technology in the classroom. Teachers are already finding that many students use AI through social media and are, therefore, open to its educational applications.

    There’s also a great professional need for these abilities. “The U.S. Bureau of Labor Statistics sees strong growth for data science jobs skills in its prediction that the data science field will grow about 28 percent through 2026,” says Bernard Schroeder, senior contributor for Forbes. With increased technology comes increased data operations and analysis sophistication, as well as more AI. These changes will ultimately increase the demand for data scientists and other AI specialists

    The role of artificial intelligence in education

    Global Market Insights Inc. predicts that the AI education market could have a market value of $20 billion by 2027. The industry growth is good news, as AI can ultimately reduce the burden on teachers across the globe.

    However, some educators fear that in the future, AI technology might replace the role of the teacher altogether. Fortunately, it doesn’t look like teachers are at risk of being replaced by robots anytime soon. While AI programs can teach students literacy or math, the more complex impartation of social and emotional skills will remain in the domain of humans.

    How artificial intelligence is currently used in education

    How technology is used in classrooms has changed significantly in response to COVID-19. Rather than teaching in front of a classroom full of students, lock-downs forced many educators across the globe to teach remotely, from their homes. Edtech company Promethean surveyed teachers and learned that 86 percent thought AI should be an important part of education.

    Using AI in education holds many benefits for both students and teachers:

    • Learning resources can be accessed from anywhere, at any time
    • Time-consuming, tedious tasks such as record keeping or grading multiple-choice tests can be completed through AI automation
    • Frequently asked questions can be answered through chatbots
    • AI tutors and chatbots can be available to answer questions at any time
    • Learning can be tailored and adapted to each student’s goals and abilities through personalized programs

    How AI is set to change the education market

    The World Economic Forum estimates that, by 2025, a large proportion of companies will have adopted technologies such as ML. They strongly encourage governments and educational institutions to focus on rapidly increasing related education and skills, focusing on both STEM and non-cognitive soft skills to meet the impending need. Advances in technology will cause major disruptions in the workforce, as automation could replace up to 50 percent of existing jobs in the U.S. alone, Microsoft reported. The Microsoft report continues, suggesting students will need to have mastered two facets of this new world by the time they graduate.

    They need to:

    • Know how to utilize ever-changing technology, such as AI, to their advantage
    • Understand how to work with other people in a team to problem-solve effectively

    Preparing students to work alongside AI in the future can start early. As many children are already comfortable with digital technology before entering school, it’s essential to teach them the skills to thrive in a digital workplace. The workforce of the future has its foundation in the now.

  • The 6 Most Important Technologies in Machine Learning

    The 6 Most Important Technologies in Machine Learning

    With the sudden technological boom within the IT and development organizations a couple of years ago, both Artificial Intelligence (AI) and Machine Learning have now become the trending careers for a lot of people to follow. With so many businesses coming up and clamouring for the best new talent, today, the industry is experiencing a shortage of skilled and qualified professionals. However, a plethora of tech professionals have rushed to fill in this gap by learning all of the technologies associated with machines learning and AI and adding them to their skillset.

    Since this is mainly limited to key learning languages and does not break new ground, most people in these industries are now beginning to realise the importance of looking beyond the learning languages as these will decide the future. There is no simple solution as to which technology to watch out for as things are in a constant state of flux and all the new and old frameworks are constantly evolving.

    However, since it has been established that AI is rapidly transforming every sphere of our life (think Siri and the like), there are certain key AI technologies to focus on so that one can take machine learning projects to the next level. Here is an informative list of the six best technologies one can use:-

    • Keras: This is an open source software library that focuses on simplifying the creation of deep learning models. Written in Python, it can also be deployed on top of many other AI technologies such as Theano and TensorFlow. It runs optimally on both CPUs and GPUs, plus it is known for its user-friendliness as well as fast prototyping.

     

    • Torch: One of the oldest such technologies released all the way back in 2002, it is a machine learning library that has a variety of algorithms to offer for deep learning. With an open source framework, you will be provided with the best speed and flexibility without having to worry about any complexities getting in the way.

     

    • Caffe: Being one of the more recent options, the best part about Caffe is that it inspires a degree of innovation with an expressive architecture along with the provision of a vibrant community. This machine learning framework primarily focuses on speed, expressiveness and modularity.

     

    • TensorFlow: With the initial release of this open source machine learning framework being 2015, it has been deployed across many different platforms and is easy to use. Created by Google at first, now all the top tech giants such as eBay, Dropbox, Intel and Uber use it extensively. With the help of flowgraphs, one can develop neural networks.

     

    • Theano: This is basically an open source Python library that you can use to fashion various machine learning models. Being one of the oldest libraries, it is regarded as an Industry standard. It simplifies the process of optimizing, defining and assessing mathematical expressions.

     

    • Microsoft Cognitive Toolkit: Initially released about three years back, this is an AI solution that you can use to take your machine learning projects to the next level in every way. Certain studies have revealed that the open source framework can train certain algorithms to function like the human brain.

     

    One has to take note of the fact that building a machine learning application is one thing, but selecting the ideal technology from the many options out there is another ball game altogether. It is anything but a simple task to achieve and evaluating many different options before selecting the final one is a must.

    Furthermore, learning how the various machine learning technologies work separately and with each other will be a key component of your decision-making process in totality. Most importantly, it will also play a decisive role in ensuring that you stay ahead of the pack with regard to your contemporaries.

  • Current trends in Artificial Intelligence (AI) Application to Oil and Gas Industry

    Current trends in Artificial Intelligence (AI) Application to Oil and Gas Industry

    In recent years, artificial intelligence (AI), in its many integrated flavors from neural networks to
    genetic optimization to fuzzy logic, has made solid steps toward becoming more accepted in the mainstream of the oil and gas industry.On the basis of recent developments in the field of Oil & Gas upstream, it is becoming clear that petroleum industry has realized the immense potential offered by intelligent systems. Moreover, with the advent of new sensors that are permanently placed in the wellbore, very large amounts of data that carry important and vital information are now available.

    To make the most of these innovative hardware tools, an operator intervention is required to handle the software to process the data in real time. Intelligent systems are the only viable techniques capable of bringing real-time analysis and decision-making power to the new hardware.

    An integrated, intelligent software tool must have several important attributes, such as the ability to integrate hard (statistical) and soft (intelligent) computing and to integrate several AI
    techniques. The most used techniques in the Oil and Gas sector are:

    • Genetic Algorithm (GA), inspired by the biological evolution of species in natural
    environment, consists of a stochastic algorithm in which three key parameters must be
    defined:
    1. Chromosomes, or better, vectors constituted by a fixed number of parameters
    (genes).
    2. A collection of chromosomes called genotype, which represents the individuals of
    a population.
    3. The operations of selection, mutation, and crossover to produce a population from
    one generation (parents) to the next (offspring).

    • Fuzzy Logic (FL) is a mathematical tool able to covert crisp (discrete) information as
    input and to predict the correspondent crisp outlet by means of a knowledge base
    (database) and a specific reasoning mechanism. To achieve such goal, the crisp
    information is firstly converted into a continuous (fuzzy) form, secondly processed by an
    inference engine and at least re-converted to a crisp form.

    • Artificial neural network (ANN) is constituted by a large number simple processing
    units, characterized by a state of activation, which communicate between them by sending
    signals of different weight. The overall interaction of the units produces, together with an external input, a processed output. The latter is also responsible of changing the state of
    activation of the units themselves.

    AI applications in Oil and Gas industry

    Exploration & Production (E&P) sector

    Most of the resources in the Oil and gas field is centered in drilling operation in which artificial
    intelligence finds natural application. Drilling success and safety are related to an accurate
    prediction of the likely performance of different factors such as:
    • Pre-drilling settings (rig, logistics and associated drilling risks)
    • Drilling equipment (casing and tubing pipes, drilling mud)
    • Downhole machinery behavior (vibrations, torque limits)

    The development of models, implemented by AI systems, permits to avoid the necessity of
    disposing of real-time data and to produce smart outcomes in order to quickly re-establish optimum operating conditions.

    Relatively to the selection of Drill bits, trained artificial neural networks (ANNs) have been used:
    they are able to suggest the best drill bit to select (roller cone, diamond insert or a hybrid) analyzing a user defined database. The latter should include information relative to the IADC bit codes correlated with specific geological data.

    Neural and network system (commonly GRNNs) gave accurate results in the prediction of mud
    the fracture gradient. As input parameters to the model, the depth of the well, the overburden
    gradient and the Poisson ratio must be provided. It is important to keep in mind that the results will strictly depend upon the range of the data set, and that extrapolations may loss in accuracy.

    In the planning stages of a well, drilling engineers are responsible for the establishment of the
    different depths at which the well must be cased to ensure an overall desired perforation depth. To avoid casing collapse, a neural network approach adopting a BPNN based spreadsheet program can be used. Back-propagating neural networks (BPNN) are constituted by a defined number of “layers”. Each layer is interconnected with the other: in particular, the input layer is connected with hidden layers which are in turn connected to the output layer. This neural net, provided of an historical well archive, is fed (input layer) with specific data of the well under consideration (i.e. location, depth, casing strength). Furthermore, the BPNN is able to estimate an “experienced”casing case probability.

    Another example of AI application is given by the real time drilling optimization in which
    artificial intelligence system are adopted to improved monitoring of downhole parameters
    optimizing the drilling operation.

    A crucial real-time operation is the Estimation of hole cleaning efficiency in terms of cutting
    concentration. During the drilling process, the wellbore is filled with many rock fragments
    (cuttings) generated by the mechanical action of the drill bit. In order to remove those cuttings
    from the well, a drilling fluid, or drilling mud, is pumped from the drill bit and exits from the
    wellhead: the cuttings are lifted and carried on the top of the well. According to this, the cutting concentration (expressed as a %) is the residual amount of rock fragments into the well after the cleaning action of the mud (Figure 5 gives a visual idea of the situation described). Inefficient removal of the drilled cuttings may lead, in some severe cases, to the loss of the well due to stuck pipe.

    For the estimation of the hole cleaning efficiency, artificial feed-forward neural network with
    back-propagation (BPNN) can be used. As input to the model all the parameters which affect the cutting concentration must be given. The latter are divided in specific parameters of the drilling (rate of penetration, inclination angle of the wellbore) or in parameters regarding the rheology conditions of the mud (viscosity, density).

    Future-proof your operations, onshore or offshore

    Whether you focus on exploration, extraction, transportation, storage, or production, at SCS Tech, we cater to all sectors of the oil and gas industry. From crude oil to natural gas and natural gas liquids, from refineries to gas treatment and petrochemical production, from pipelines to storage facilities, our service solutions and expertise give you the competitive edge. We ensure and boost the performance of the turbomachinery that lies at the heart of your value-adding process.

     

     

  • Why Conversational AI and Chatbots are Perfect for Digital Health

    Why Conversational AI and Chatbots are Perfect for Digital Health

    Over the last decade, the continued evolution of digital healthcare and the impact that next-generation technology will have on patient wellness and diagnostic outcomes has been a recurring conversation. Providers have been looking to make the most of the tech-centric tools that are increasingly available, while those in need of care or the right advice at the right time now expect the medical sector to be investing in digital solutions to physical problems.

    The integration of tech into public health and wellness is nothing new, but the demands of the connected society have only raised awareness of what is possible but also flagged up the opportunities for engagement in the digital space.

    Healthcare has never shied away from technology, albeit some recent improvements can be hit-and-miss – electronic health records are only as useful as the data that has been input, for example. However, there is a consensus among providers and medical professionals that the industry is willing to accept some disruption if it has a positive impact on the overall patient experience.

    In terms of technology with the power to both disrupt and improve the way that people engage with digital health, there is an argument to be made that conversational AI (which includes Machine Learning) and Natural Language Processing (NLP) can be the missing link. After all, if Alexa et al can answer questions about the weather, start your car in the morning and tell jokes, then there is no reason why virtual assistants can’t provide you with the medical advice that you need, when you need it.

    The Chatbot will See You Now

    According to the World Economic Forum, virtual assistants – and, by association, chatbots – are not only being used in a diverse set of industries (healthcare, education, retail, tourism, and more), but also offer opportunities for companies to integrate NLP into routine or mundane activities. For instance, Amazon’s Alexa is the chatbot that the average person associates with the tech, but the key element in every interaction is the ability of NLP to understand what it is being asked and respond with the appropriate information.

    The problem, the WEF said in a November 2021 blog post, is that building the chatbot is the easy part. Often, it is the conversational aspect that throws a digital spanner into the works. When you are talking to a finance bot or trying to return a pair of shoes, the stilted nature of these exchanges is rarely an issue. In a sector such as healthcare, it is the conversation and the information that is being dispensed that is a key part of how comfortable an end-user is with knowing that they are not engaged with a human being.

    In rural parts of the world, the use of so-called health bots has already helped alleviate some of the pressure on localized health providers.

    This is not limited to first-world or so-called developed countries, but their integration takes on a particular resonance in regions where access to tech is not easily available. In Rwanda, for example, there is often only one doctor and six healthcare workers per 10,000 people, with the WEF noting that these tools are allowing patients to have access to doctors, nurses, and relevant healthcare information.

    The caveat is that there is still some concern that a well-documented stigma surrounding chatbots in general could prevent people from interacting with them in the first place – for the record, this is also not unique to developing countries, there seems to be a global mistrust of chatbots as an accurate or effective engagement tool!

    Conversational AI and chatbots are not new per se, but they can suffer from falling under a broad definition of what they actually are. Simply put, a “chatbot” is AI software that simulates a human conversation with end-users – this can be text or voice – with the aim being to leverage machine learning algorithms and NLP to deliver required outcomes.

    The healthcare sector has been experimenting with these solutions for some time, with Mobile Health News reporting in May 2020 that a leading healthcare provider was integrating bots into its Emergency Department EHR system. More recently, the World Health Organization launched a women’s health chatbot dedicated to breast cancer messaging – for context, this new digital experience follows hot on the heels of bots that delivered information on COVID-19, mental health and smoking cessation.

    So, the bots are already here, but the question that we need to address is how we can use conversational AI and/or chatbots in addressing not only patient concerns but also alleviate the worry or concerns that a person has. Additionally, we should consider both the challenges to overcome and the advantages of using virtual assistants to deliver care and wellness.

    The Future is Already Here

    We all interact with digital experiences every day of our lives, so it makes perfect sense that the technologies we often use without thinking should be mirrored in the healthcare sector. And while the industry still relies far too much on legacy technology (disparate IT systems, pagers instead of wearables), the integration of digital health solutions is picking up pace.

    Conversational AI has been part of the digital landscape for at least five years, although the average person is likely to only know these virtual assistants as Alexa, Siri and Google Assistant (useful tool, unimaginative name). Chatbots fit into the same category, but their integration into the connected society is less heralded and, arguably, overlooked in terms of the access to knowledge and the assistance they can provide.

    However, these tools are here to stay and there is a defined need for them to become not only part of the digital health landscape but also a familiar resource for people to turn to. The effective use of chatbots to provide the right medical or clinical information at the right time is not a futuristic concept, these solutions are here now. What matters is how healthcare and life sciences companies choose to use them.

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