AI Concepts and Characteristics

Deep Learning is a subset of Machine Learning, which on the other hand is a subset of Artificial Intelligence. Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible.  

Deep Learning, on the other hand, is just a type of Machine Learning, inspired by the structure of a human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

All recent advances in artificial intelligence in recent years are due to deep learning. Without deep learning, we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. 

The Google Translate app would continue to be as primitive as 10 years ago (before Google switched to neural networks for this App), and Netflix or Youtube would have no idea which movies or TV series we like or dislike. Behind all these technologies are neural networks.

- Automation

Automation is an important feature of artificial intelligence because it can perform tasks with minimal human intervention. This is transforming the industry by streamlining operations and improving efficiency. Examples of AI automation include:

• Manufacturing: Robotics and AI-driven systems handle repetitive and dangerous tasks in manufacturing, such as welding, assembly and quality control.
• Customer Service: Chatbots and virtual agents are able to resolve customer queries and provide assistance around the clock.
• Data entry: Artificial intelligence can simplify data entry and data retrieval tasks, reducing the need for manual data management.

- Data Processing

AI is good at processing large amounts of data efficiently. This is critical in today’s data-driven world, where businesses and organizations rely on data for decision-making and insights. Applications of AI in data processing include:

• Big data analytics: AI algorithms are capable of processing large data sets to extract valuable insights and identify patterns.
• Personalization: It uses customer profiles to provide personalized recommendations in e-commerce, content streaming and advertising.
• Healthcare: It processes patient data for diagnosis, treatment recommendations and drug discovery.

- Parallel Processing

Thanks to parallel processing, AI systems can perform multiple tasks simultaneously. This enables it to handle complex calculations and instant data analysis, helping to make decisions faster. Examples include:

• High-performance computing: Artificial intelligence-powered supercomputers can simulate complex phenomena such as weather patterns, drug interactions, and nuclear reactions.
• Financial transactions: Artificial intelligence algorithms quickly check market data and quickly execute transactions, taking advantage of market fluctuations in an instant.

- Continuous Learning

AI’s ability to continuously learn enables it to adapt to new information and changing situations. This is extremely valuable in applications where knowledge is constantly being updated or changed. Examples include:

• Cyber ​​security: AI systems can learn to detect new cyber threats and adjust their defense mechanisms accordingly.
• Self-driving cars: Self-driving cars continuously update their knowledge of road conditions and traffic to make instant driving decisions.

- Human-computer Interaction

Artificial intelligence systems are increasingly interactive, able to understand and respond to human emotions and intentions. This feature enhances the user experience in a variety of applications:

• Virtual Reality (VR): Artificial intelligence-driven VR environments can respond to user gestures, voice commands and expressions to create an immersive experience.
• Games: Artificial intelligence-driven game characters can adjust their behavior according to the player’s actions, making the game more engaging and challenging.
• Customer service: AI-powered chatbots and virtual agents can provide more human interactions and increase customer satisfaction.

Facial recognition is a system built to identify a person from an image or video. It is a branch of computer vision, but face recognition is specialized and comes with social baggage for some applications, as well as some vulnerabilities to spoofing. 

This technology has been around for decades, but its usage has become more noticeable, and accessible, in the past few years as it now powers innovative solutions, such as personal photo applications and secondary authentication for mobile devices.

AI has made it possible to recognize individual faces using biometric mapping. This has lead to pathbreaking advancements in surveillance technologies. However, this has also faced a lot of criticism for breach of privacy. 

Facial recognition is becoming more accurate, but some systems exhibit racial bias and some uses of the technology are controversial. Using AI to identify people is a powerful tool for individuals, businesses and government organizations. However, privacy advocates and legal experts have called into question the ethics of using artificial intelligence to analyze faces without a person's consent.

AI has the ability to execute the same kind of work over and over again without breaking a sweat. To understand this feature better, let’s take an example of Siri, a voice-enabled assistant created by Apple Inc. 

It can handle so many commands in a single day! From asking to take up notes for a brief, to rescheduling the calendar for a meeting, to guiding us through the streets with navigation, the assistant has it all covered. 

Earlier, all of these activities had to be done manually which used to take up a lot of time and effort. The automation would not only lead to increased efficiencies but also result in lower overhead costs and in some cases a safer work environment.

Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models. It's also time intensive, especially if done manually, and if you have large amounts of data from multiple sources. Automating this effort frees up resources and ensures your models use the most recent and applicable data.

With every passing day, the data that we are all producing is growing exponentially, which is where AI steps in. Instead of manually feeding this data, AI-enabled not just gather this data but also analyzes it with the help of its previous experiences. 

AI, with the help of neural networks, analyzes a large amount of such data and helps in providing a logical inference out of it.

Chatbots are software to provide a window for solving customer problems’ through either audio or textual input. Earlier the bots used to respond only to specific commands. If you say the wrong thing, it didn’t know what you meant. The bot was only as smart as it was programmed to be.

The real change came when these chatbots were enabled artificial intelligence (AI). Now, you don’t have to be ridiculously specific when you are talking to the chatbot. It understands language, not just commands. AI-powered chatbots, also known as Conversational AI, improves reach, responsiveness, and personalization of the customer experience. 

A conversational AI agent uses natural language processing (NLP) and Machine Learning (ML) to better understand what the human says and needs in order to provide a more natural, near human-level of communication. In other words, AI-powered chatbots mimic human conversation. Conversational AI digital assistants is one of the digital transformation trends in the next decade.

With the rise of conversational technology and advances in Natural Language Processing (NLP) in the next few years business users will increasingly engage with analytics platforms. An intelligent digital agent will have the ability to maintain a human-like conversation with those it is speaking with, understanding different ways in which the information is being phrased.