The Importance of Cybersecurity in the Age of Digital Transformation

Cybersecurity has become a critical issue in today’s digital world. With the increasing reliance on technology and the Internet, protecting sensitive information and critical systems from cyber threats has become essential. As we enter the age of digital transformation, cybersecurity is more important than ever before. 

Digital transformation refers to the integration of technology into all areas of business, leading to fundamental changes to how companies operate and deliver value to customers. This transformation has given rise to a new generation of digital businesses and services and has increased the amount of sensitive information stored online. 

As a result of this increased reliance on technology, cyber threats have become more sophisticated and widespread. Hackers, cyber criminals, and nation-state actors are constantly searching for vulnerabilities to exploit for their own gain. This can lead to data breaches, theft of sensitive information, and disruptions to critical systems, with devastating consequences for businesses and individuals. 

Cybersecurity is the practice of protecting computer systems, networks, and programs from digital attacks, theft, and damage. It involves implementing a range of security measures to prevent unauthorized access, protect sensitive information, and ensure the continuity of critical systems. This includes everything from firewalls, antivirus software, and intrusion detection systems, to employee training, risk assessments, and incident response plans. 

One of the key challenges in cybersecurity is keeping up with the constantly evolving threat landscape. As technology continues to advance, so too do the methods used by cyber criminals to attack it. This means that businesses must constantly adapt and improve their security measures to stay ahead of these threats. 

In addition to protecting sensitive information, businesses also have a responsibility to their customers to protect their personal data. In the age of digital transformation, customers are entrusting companies with increasing amounts of personal information, such as their name, address, and financial details. If this information is compromised, it can have profound consequences for both the customer and the business.  

Cybersecurity is not just an IT issue; it is a business issue. It affects all aspects of an organization, from its reputation and financial stability to its ability to deliver products and services to customers. As such, it is essential that businesses take a comprehensive and proactive approach to cybersecurity. 

In conclusion, in the age of digital transformation, cybersecurity is more important than ever before. With the increasing reliance on technology and the growing threat of cyber-attacks, it is essential that businesses implement strong security measures to protect sensitive information and critical systems. Cybersecurity is not just an IT issue; it is a business issue that affects all aspects of an organization. By taking a proactive approach to cybersecurity, businesses can ensure their continued success and protect their customers in the digital age. 

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How will AI (Artificial Intelligence) shape the Metaverse?

 

What is a Metaverse?

A metaverse is a digital universe created by the convergence of enhanced physical reality and persistent virtual reality. It encompasses all virtual worlds, augmented reality, and the internet. The concept of a metaverse is often associated with science fiction and virtual reality, but it is also being developed as a potential future reality. To create and support a metaverse, various technologies such as virtual and augmented reality, 3D modelling and animation software, game engines, cloud, and edge computing, blockchain, 5G networks, and artificial intelligence and machine learning are utilized. These technologies will continue to develop and improve as the metaverse progresses.

Let us dive deep into one such technology that is important in shaping and developing the metaverse: Artificial Intelligence.

 

How does Artificial Intelligence contribute to the metaverse?

Artificial Intelligence is instrumental in the development and operation of the metaverse. It allows for the creation of highly realistic and engaging virtual worlds, ensuring optimal performance, enhancing security measures, and providing users with personalized experiences. As the metaverse continues to expand and progress, the importance of AI in its growth will become increasingly evident.

 

Why is Artificial Intelligence important to the metaverse?

Artificial Intelligence can enhance the metaverse by allowing for the creation of realistic and interactive virtual worlds. The technology can improve the immersion of the metaverse by providing users with the ability to interact in a more natural way with the virtual environment. AI can be used to create virtual characters that can interact with users in a lifelike manner, and to develop virtual economies, tracking user behavior and analyzing data to create virtual marketplaces. Additionally, AI can be used to tailor virtual worlds to each user’s interests and preferences, providing a more personalized and engaging experience.

 

How will Artificial Intelligence shape the metaverse?

Artificial Intelligence creates sophisticated, responsive virtual characters and environments which is used to provide personalized experiences to users, based on their interactions and preferences. This helps to create a more engaging and immersive experience within the metaverse.

AI can manage and optimize the performance of the metaverse. It can be used to monitor and analyze data to identify and resolve bottlenecks, ensuring smooth and seamless experiences for users. This helps to ensure that the metaverse is always running at peak performance.

AI enhance security within the metaverse. It can be used to identify and prevent potential security threats, such as hacking or fraud. This can help to ensure that the metaverse remains a safe and secure place for users.

In conclusion, AI is a key technology that can help shape and enhance the metaverse. It can be used to create realistic and immersive virtual worlds, provide personalized experiences to users, manage, and optimize performance, and enhance security. As the metaverse continues to evolve and grow, AI will play an increasingly key role in its development and operation.

 

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How AI Can Improve Pricing and Discount Management?

Many companies and businesses struggle with customer engagement, especially when it is related to promotion and pricing strategies. Deciding the prices of products or services is not an easy task. It requires a lot of research and hard work to come up with the right price. This is where Artificial Intelligence (AI) comes in.

AI technology has penetrated almost all the major industries in the world. Recently, it is being introduced in retail, and eCommerce businesses to promote more effective pricing and discount management. For example, both businesses and individuals use Ximple and similar platforms to compare the prices of similar products. It helps you to browse through different price rates of a particular product offered by different brands. That being said, let’s find out how AI improves pricing for businesses.

AI and Pricing

Brands can’t keep up with the fast-changing trends and conduct surveys to look for opportunities. Providing regular rotation of discounts to customers is more challenging than it sounds. It is because of this reason that retailers are utilizing the power of AI and machine learning to roll out promotions and discounts. AI is highly efficient when it comes to setting optimal prices. It analyzes both competitive and historical data, customer behaviour, and seasonal trends to come up with the right pricing.

Intelligent AI can help retailers with dynamic pricing based on customer engagement reports. To set optimal prices for thousands of products weekly requires your manager to be a superhuman. It is nearly impossible to handle so many things for a human. This is where the need for AI comes in as it can help with fast decision making.

Retailers opting for AI-led price optimization have to go through several stages. They need to learn to set optimal prices by stock-keeping unit, channel, product portfolio, point of sale, and also by customer. This helps in improving profitability. In other words, it can help boost sales and revenue by up to 9% or more. (Read – Diving Head First into Deductions- and Why It’s Costing You)

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Why is AI so effective for pricing and discount management?

AI makes use of self-learning algorithms to scan huge amounts of data and check through endless pricing scenarios to come up with the most relevant price for products and services. This is why more brands are implementing AI in the retail pricing landscape.

Algorithm-based models consider several unrevealed relationships between the products in the portfolio to suggest individual prices to boost sales and revenue for brands. AI can be used to automate all the labor and time-consuming tasks and thus, allow your team to make faster customer-centric decisions.

AI & Discount

Knowing what discount to offer is always a tricky situation. You want to get more customers, but at the same time, you don’t want to lose out more on profit. Luckily, you have AI to help you decide the right discounts for your products and services.

An efficient AI system can suggest the ideal discount rate and ensure that you are making more profit from it. It looks into specific features of each past deal that was lost or won. This is what makes AI so useful and efficient in discount management.

Getting Ready For AI-Driven Pricing

Price optimization led by AI requires retailers to follow five steps.

This include:

  • Prepare the required data
  • Hire a technological provider or look for an in-house solution
  • Select products for AI to suggest prices
  • Educate and train managers
  • Keep the system up and running

AI is a very powerful and beneficial technology when used correctly. Retailers can gain a lot from implementing the power of AI in price and discount management.

This article is written by Purtika Choudhury Arc Digitech (arcdigitech.com)

Countries leading the way in AI

Merely one mention of artificial intelligence invokes the pictures of robots, chatbots, or self-driving cars. Artificial intelligence, a branch of computer science deals with devising technology and machines that are capable of performing tasks that require human intelligence. AI is everywhere and it has not stepped down since its invasion. The new decade will witness massive investments by global technology giants into AI technologies.

In the race to acquire and develop the most efficient and ingenious technologies, countries worldwide have been setting guidelines and investing immensely in the field of AI. Following are the statistics of the global AI readiness Index conducted by Oxford Insights and the International Development Research Centre (IDRC) in 2019. The index covers 194 countries and territories and ranks them according to their preparedness to implement AI in the delivery of public services and internal operations. Each country is allocated a score that considers 11 input metrics under 4 clusters: governance, infrastructure and data, skills and education, and government and public services. The report suggests that the rankings are dominated by countries that have robust economies, an abundance of data and information, and a government with a strong vision.

  • Singapore comes first in the list of artificial intelligence readiness with a score of 9.186.
  • The top 20 positions are acquired by Canada, New Zealand, Australia, and some European countries, and Asian countries. China, currently 21st in the global rankings will improve in the upcoming years because of the government’s inclination to optimize data and artificial intelligence.
  • Most of the countries in North America were awarded higher ranks in the list while the African and Asia-Pacific regions were the worst performers.

Country

 Rank Score
Singapore 1 9.186
United Kingdom 2 9.069
Germany 3 8.810
United States of America 4 8.804
Finland 5 8.772
Sweden 6 8.674
Canada 6 8.674
France 8 8.608
Denmark 9 8.601
Japan 10

8.582

 

  • India stands third in the Asia Pacific region after Singapore and Hong Kong and 19th in the global index overall. According to surveys, it is one of the most prepared economies with regard to government readiness to artificial intelligence. But despite this availability, it lacks creativity, innovation, and awareness which are the reasons behind Indian start-ups lagging behind those of the American and other developed countries. 76% of start-ups in India believe that there is a dearth of skilled professionals in the field of AI and hence hesitate from using these technologies in their business. But the established companies like Amazon, Google, and Apple are employing AI technologies like deep learning, language processing, and machine learning and are acquiring start-ups to provide new and improved experiences across their services.
Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

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Singapore’s artificial intelligence industry will be booming in the upcoming decade. Singapore is one of the first countries to announce a national AI strategy in 2017. One of the most effective ideas that were put into action by the government of Singapore to achieve this was the establishment of the AI Ethics Advisory Council. This council has been established to assist the government to develop standards and guidelines and issue practical guidance for the voluntary adoption of AI in businesses and organizations. (Everything you need to know about Singapore’s AI strategy)

Singapore is a trailblazer in the Asia-Pacific region when it comes to accepting new technologies. Singapore has chosen five big AI-driven development projects that address national challenges keeping into consideration the impact on society and economy with an aim to become the global hub of AI technologies. Also, educational institutions there have increased emphasis on fields such as mathematics, statistics, computer science, and information technology to allow students to understand the significance and trend of data science, machine learning, and artificial intelligence. Business and management organizations too are focusing on data trends as well apart from their regular curriculum (Analyze data and build stunning reports using our services). UAE and Singapore were trendsetters in bringing AI decision-making at the cabinet-level. They have justified the strengths and strategies that have the potential to make these countries the leading producers of AI technologies.

Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

Latest Applications of Computer Vision

How fascinating would it be to extract hidden values from images and understand them even better! Computer vision is a subpart of artificial intelligence that provides AI and computer science enthusiasts the ability to modify and transforming images according to their imagination and creativity using algorithms.

Computer vision (CV) is defined as an interdisciplinary scientific field that deals with how computers can gain a high-level understanding from digital images or videos. It helps computers to understand the content and characteristics like shape, color, texture, and size of digital images and videos.

Following are some of the latest applications in computer vision.

General use cases-

  • Creating a 3D model using 2D images – Using computer vision to make 3D models from 2D images is called photogrammetry. Several images taken at 360 degrees around the object are supplied to an algorithm that returns the 3D model. Photogrammetry is used in fields such as topographic mapping, architecture, engineering, manufacturing and geology.

 

  • Human pose estimation – Pose estimation is the technique of understanding the pose of a person or an object from a digital photo or video. It is also sometimes referred to as the localization of human joints as it predicts the positions of a person’s joints in an image or video Pose estimation can be done in 2D as well as in 3D. Human pose estimation has many applications such as those in the field of augmented reality and motion capture. It is also used for training robots where instead of manually programming robots to follow trajectories, they are made to follow the trajectories of human pose skeletons.

 

  • Digital Image Processing – Many real-world problems in computer vision require integrating a large number of images to create computer vision systems. When approaching such computer vision problems, processing one image in the context of some other might be required. GAN (Generative Adversarial Networks) is a technique that comes under the machine learning domain and uses training set data to generate new data. There are many applications of GAN in computer vision with some of them being generating realistic photographs, generating cartoon characters and emojis, and editing photographs.

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Industry-Specific Use Cases

  • Computer vision in healthcare and medicine- Computer vision has a wide range of applications in the medical sector.
    • Computer vision techniques are used in the healthcare sector for diagnoses like CT scans, X-rays, and MRIs. These technologies are used to convert images obtained on scans to 3D models for better understanding and detect anomalies like tumors and neurological illnesses. Images are extracted from scans and used to train models that detect anomalies.
    • Gaze tracking and eye-area analysis are also widely used applications of computer vision to detect cognitive impairments.

 

  • Automotive industry– Tesla is one of the few automotive companies which uses Autopilot features in its cars. These self-driving vehicles have cameras attached to them that can record live footage and allow computer vision to create 3D visualizations. Using these 3D maps and visualizations, accidents can be prevented.

 

  • Agriculture– There can be numerous ways to use computer vision techniques in agriculture for purposes like crop monitoring, livestock management, and forestry management. Cameras installed in drones click pictures of agricultural fields which can be used later to train computer vision models to enable them to do work like spraying adequate amounts of pesticides and hence monitor crop growth. Also, a well-trained drone can identify livestock, count, and monitor them. They can also be used to check the growth and health of trees in forests and take into account disruptive activities if any like deforestation.
Computer Vision in COVID times

  • Development of social-distancing tools- One of the precautionary measures to prevent infection from coronavirus is social distancing. In order to ensure whether people in an area are following the social distancing norms, computer vision techniques are being used. One of these techniques is using object detection and tracking. Each person appearing in the video being recorded in real-time is detected using a bounding box. Further, the movement of the boxes is tracked and distances between two adjacent boxes are calculated. If there is any kind of violation detected, the boxes are consequently highlighted.

 

  • In the medical sector – The most recent use of computer vision in the medical sector is identifying Covid-19 infections. A common symptom of computer vision is pneumonia. Analyzing chest X-rays can help in detecting Covid-19.

 

In a nutshell, it can be concluded that computer vision as a subfield of artificial intelligence has found its applications anywhere and everywhere in the present scenario, and in the near future, it will effectively harbinger AI technologies that are as human as us.

Latest developments in Natural Language Processing (NLP)

Ever wondered how robots and machines perceive a command given to them?

Well, Natural language processing (NLP) gives them the ability to read, understand, and deduce useful information them from human languages. There are many applications of natural language processing including this.

As defined by Wikipedia, Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language. It talks about how to program computers to process and analyze large amounts of natural language data. NLP finds numerous applications in today’s world with major ones being in chatbots, sentiment analysis, and market intelligence.

Following are some of the major trends and advancements and some NLP examples that have dominated AI and the tech world in recent years-

  • Business Intelligence– There is a parallel connection between business intelligence and NLP. NLP facilitates the user’s interaction with complicated databases.
    • Using NLP companies gain product information like marketing and sales information, customer service, brand notoriety and the present talent pool of a company.
    • Another popular method of NLP used in BI is opinion mining. It uses NLP to extract customer sentiments from their reviews and ratings.
  • Semantic modelling- Semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences, and paragraphs to the level of the writing as a whole, to their language-independent meanings. Its goal is to draw the exact meaning from a text. It processes the logical structure of the text and identifies the most relevant elements in the text. It also understands the relationships between the elements of the text which is used in further analysis.
  • Chatbots– Natural Language Processing in AI is very popular. Atleast one-fourth of the organizations will have chatbots or visual customer assistants or some other type of NLP included in their customer service system by 2020. Chatbots learn the semantic relations, understand the objective of the questions asked, and then automatically perform the filtration and organization necessary to serve a relatable and significant answer, rather than simply showing the data. For instance, Microsoft’s Cortana is helping many small and large-scale businesses do research and process data by voice.
  • Human-machine interaction- One of the most common examples of usage of NLP in human-machine interaction is spam detection where emails undergo a process of getting filtered administered under NLP algorithms based on whether it is spam or not.
  • Deep learning for NLP– Deep learning techniques like Recurrent Neural Networks are used to get accurate results after analyzing the data.
  • Supervised and unsupervised learning– Natural Language Processing in machine learning is used for text analytics where statistics identify sentiments, expressions, and aspects of speech.

 

 

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Let us discuss the supervised and unsupervised learning aspects of NLP in more detail below since they house some of the most popular techniques.

In supervised learning, a set of text documents are tagged with examples with the machine of what is to be searched. These supplied examples are used to train the model which is later supposed to analyze the unlabelled or untagged text. Some of the most popular supervised learning NLP techniques are Support Vector Machines and Neural Networks. (A good read – Natural Language Processing (NLP) for Machine Learning)

In unsupervised learning, the model is trained without pre-tagging. Clustering, Latent Semantic Indexing (LSI), and Matrix Factorization are some popular techniques in unsupervised learning NLP.

  • In clustering, a similar types of data is grouped into sets and later sorted based on relevance using algorithms.
  • LSI on the other hand involves identifying words and phrases that frequently occur in the given text.
  • Matrix Factorization is different from the other two as it deals with breaking larger matrices into smaller ones using latent factors i.e., similarities between two or more items.
  • Reinforcement learning- Reinforcement learning along with supervised and unsupervised learning forms the three basic paradigms for ML. It allows machines and software applications to determine the ideal behaviour within a specific context. Tasks such as summarization of a text are performed by reinforcement learning algorithms.
  • Company monitoring- The impact of social media is irreplaceable. It has become an integral part of the normal life of every individual and perhaps this is the reason why companies and organisations have started focusing on social media interactions for promotions and growth of their business and reach more than ever before And social media monitoring tools such as Buffer and Hootsuite have been built using the latest algorithms of NLP. Tools like these help in monitoring company’s engagement in the market.

 

General Natural Language Processing Tasks

Now as we have seen various applications of NLP, let us walk through the general NLP tasks that are followed when NLP systems deal with a language-

  • Content categorization– Includes summarization along with content indexing, duplication, and content alerts.
  • Topic discovery and modelling- Deducing meaning from the text and applying analytics
  • Contextual extraction- Extracting information from text-based sources
  • Sentiment analysis-Includes identifying specific moods and opinion mining.
  • Speech-to-text and text-to-speech conversion-Transforming voice commands into written text, and vice versa.
  • Document summarization-Generating structures from large bodies of text.
  • Machine translation- Automatic translation of text or speech from one language to another.

NLP has gained popularity since its inception. Devices like Amazon’s Alexa are being used widely all across the globe today. And for enterprises, business intelligence and customer monitoring are fast becoming popular and will dominate the sector in the coming years.

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