AI, machine learning and deep learning are common terms in enterprise IT and sometimes used interchangeably, especially by companies in their marketing materials. But there are distinctions. The term AI, coined in the 1950s, refers to the simulation of human intelligence by machines. It covers an ever-changing set of capabilities as new technologies are developed. Technologies that come under the umbrella of AI include machine learning and deep learning.

AI refers to technologies that can understand, learn, and act based on acquired and derived information. Today, AI works in three ways.

Assisted intelligence, widely available today, improves what people and organizations are already doing.

Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do.

Autonomous intelligence, being developed for the future, features machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.

There are numerous, real-world applications of AI systems today. Below are some of the most common use cases:

Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.

Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.


AI is ideally suited to solve some of our most difficult problems, and cybersecurity certainly falls into that category. With today’s ever evolving cyber-attacks and proliferation of devices, machine learning and AI can be used to “keep up with the bad guys,” automating threat detection and respond more efficiently than traditional software-driven approaches.

Detecting New Threats
AI can be used to spot cyber threats and possibly malicious activities. Traditional software systems simply cannot keep pace with the sheer number of new malware created every week, so this is an area AI can really help with.

Battling Bots
Bots make up a huge chunk of internet traffic today, and they can be dangerous. From account takeovers with stolen credentials to bogus account creation and data fraud, bots can be a real menace.You can’t tackle automated threats with manual responses alone. AI and machine learning help build a thorough understanding of website traffic and distinguish between good bots (like search engine crawlers), bad bots, and humans.

AI enables us to analyze a vast amount of data and allows cybersecurity teams to adapt their strategy to a continually altering landscape.

Breach Risk Prediction
AI systems help determine the IT asset inventory which is an accurate and detailed record of all devices, users, and applications with different levels of access to various systems.

Cyber security is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It’s also known as information technology security or electronic information security.



Digital Maker Lab is initiative of ICAI CSRD to transform Indian youth from digital users to producers in the digital economy. We will offer skills such as artificial intelligence courses, Data scientist courses Post graduate diploma Courses, CISO , app development, 3D printing, robotics, embedded systems and data analytics; to strengthen problem solving and creativity amongst student and youths. Digital maker labs will offer tools, materials and learning resources that are made available for its members that could help nudge them to invent and embark on digital making projects. Digital Maker Lab will allow interested students to learn digital making skills during co-curricular hours. Activities to be conducted will be based on modules prepared by Digital Maker Lab. Each college needs to select a minimum of 30 students per to establish Digital Maker lab.

Digital Maker Lab is working on Digital India initiative on below Emerging Technologies -

1)Mobile social networking Project We will help individuals to start mobile social networking startup where individuals with similar interests connect with one another. Similar to web-based social networking, mobile social networking occurs in virtual communities. It is one of the emerging platforms which plays an important role in almost every sector. With the introduction of various technologies in mobile networks, social networking has reached an advanced level over four generations.

2) Cloud computing project We will connect entrepreneur willing to start cloud computing by connecting them to tech developers Cloud computing refers to shared pool of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility. Examples: SaaS, IaaS, MESH Apps

3) Cyber security is the term given to the protection of internet-connected systems, including hardware, software, and data, from cyber-attacks. Security comprises cyber security and physical security, both are used by enterprises to protect against unauthorized access to data centers and other computerized systems. Information security, which is designed to maintain the confidentiality, integrity and availability of data, is a subset of cybersecurity. Examples: Security, Intelligence Detection, Forensic Investigation and auditing.

4) Artificial intelligence or machine learning, demonstrates the analogy between machines and natural intelligence displayed by humans. The major barriers to AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. Modern artificial intelligence techniques are pervasive and are too diverse to list here. Examples: Flying Drones, Siri, Tesla, etc

5) Health Technology project we will guide individual having interest to start their health tech venture health tech is application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of lives. In this growing world, health technology plays a very important role for sustainable living.

2) Collaborative Projects

Cyber security training program for industrial sectors, Ministry and Universities. It includes Legal and Tech courses data protection Officials, CISO, PGD course, AI courses, Forensic Courses etc. It will comprise a team of professional trainers.

3) Upcoming Projects

AI-driven smart city development for sustainable urbanization.