What is AI ?
In simple terms, artificial intelligence is the ability to enable computer systems to perform a variety of advanced tasks using human intelligence processes. It includes the ability to see, understand, translate, analyze data, and more in any language, whether spoken or written.
AI is the backbone of innovation in modern computing in this world, as it unlocks value for individuals and businesses. Artificial Intelligence is a group of technologies that help extract text and data from optical character recognition (OCR) images and documents. With this, we can transform unstructured content into business-ready structured data.
As we see its use increasing in every technological field, every merchant and seller is using AI to deliver their products and services. Machine learning is a well-established technology in AI, and AI requires specialized hardware and software to write and train machine learning algorithms.
How does artificial intelligence work?
AI enables machines or software to perform tasks that require the help of human intelligence. Such as learning, reasoning, problem solving, perception and understanding language, etc. Artificial intelligence (AI) uses algorithms, data and computational power and works by simulating human intelligence.
Artificial intelligence has subgroups or subfields, each focusing on specific aspects of mimicking human intelligence or solving particular types of problems. AI subgroups often overlap and interdisciplinary approaches are common.
Some major subgroups of artificial intelligence
Neural network(s): These subgroups of AI are inspired by the structure of the human brain. These networks consist of layers of connected nodes, and contribute to the ability to understand increasingly complex features in data. Similarly, deep learning is also a group of neural networks with many layers. So far, deep learning has achieved success in tasks such as image recognition, natural language processing and playing strategic games.
Natural Language Processing (NLP): This subgroup aims to enable machines to understand, interpret and generate human language. NLP is important for applications such as chatbots, language translation, sentiment analysis and voice recognition.
Playing games: This subgroup focuses on AI systems that are designed to play games. It involves creating algorithms to play strategic games like chess at a high level.
Why is AI important?
AI has contributed significantly to our lives, such as its ability to change the way we work and play. AI has been used in business to automate tasks traditionally performed by humans. This includes customer service, lead generation, fraud detection and quality control.
AI can work more efficiently and accurately than humans in some areas. AI can be used to perform repetitive tasks. Example: Analyzing a large number of legal documents to determine if relevant fields are correct or not. AI in huge data sets provides insights into operations where attention may have been overlooked. A growing array of generative AI tools is also becoming important in the areas of education, marketing and product design.
Advances in AI technologies have also opened the door to entirely new business opportunities for some large enterprises. AI has played a key role in the development of some of the largest and most successful companies, including Google, Apple, Microsoft and Metadata. These companies are using AI to improve their operations and get ahead of the competition.
Benefits of AI
- AI can help automate everyday workflows and processes. Artificial intelligence can work independently and autonomously from human teams. For example, AI can monitor traffic from a network. It can help automate aspects of cybersecurity by analyzing it. Similarly, robots that use computer vision to measure output in a smarter way use real-time analytics.
- AI can also eliminate human errors. AI can automate manual errors in data processing, analytics, assembly in manufacturing, and other tasks. It can eliminate a task through algorithms that follow the same process every time.
- AI can be used to perform repetitive tasks. This way human capital can be freed up to work on high-impact problems. It can be used to automate processes, including verifying documents, transcribing phone calls, and answering simple customer questions like "What time do you close?"
- AI can process more information faster than humans can. AI can find patterns and discover relationships in data that humans cannot.
- AI also does not need rest during the day and is not limited by other human constraints. With the cloud, AI and machine learning can be "always on" and constantly working on the assigned task.
Disadvantages of AI
- AI's ability to automate processes, produce content faster, and work longer hours can make it difficult for human workers to perform jobs that are not currently being performed by AI.
- AI models can be trained on data that reflects biased human decisions. The outputs produced may be biased or discriminatory against certain demographics and may differ significantly from those of humans.
- Inadequate or biased data input into AI systems can lead to inadvertently incorrect outputs that can be confusing and lead to misinformation.
- AI can collect and store data without the user's consent or knowledge, and in the case of a data breach, unauthorized individuals may access and misuse it.
- AI systems can also be developed in ways that are not transparent, inclusive, and sustainable. This can lead to a lack of explanation, especially in harmful AI decisions. Ambiguity can have negative effects on users and businesses.
- AI can consume a lot of energy to operate and process large amounts of data. This can also lead to higher carbon emissions and water consumption.
Artificial Intelligence Applications
Artificial intelligence is used as an application in various industries. It plays an important role in streamlining processes and increasing business efficiency. Some of the applications of artificial intelligence are as follows
In the retail sector
In the retail business, AI helps in collecting online data to automate retail marketing for vendors and suppliers, identify counterfeit products in markets, manage product inventory and identify product trends. AI in the retail sector empowers user personalization, product recommendations, shopping assistance and facial recognition in payments and helps enhance the customer experience.
Use in healthcare
AI in healthcare helps to facilitate development and improve the accuracy of medical diagnosis. It is also used in drug research, managing sensitive healthcare data and automating online patient experiences. Medical robots do a successful job of providing supportive therapy and guiding surgeons during surgical procedures.
In the customer service industry
AI in the customer service industry provides faster and more personalized assistance. AI-powered chatbots and virtual assistants in the industry can handle regular inquiries from customers. AI can solve common problems in real time. AI systems can also understand and answer people's questions in a more human-like way with NLP.
Finance industry
AI also has a great contribution in the finance industry as it can detect fraud in banking activities. It helps in assessing financial credit standing, identifying financial risk of businesses. Artificial intelligence can manage stock and bond trading according to market patterns. AI can be used to personalize banking and provide 24/7 customer service support in banking apps.
Manufacturing
AI in manufacturing can reduce assembly errors and production times and also improve worker safety. AI can help identify incidents and track quality control. AI systems can help monitor the factory floor as well as predict potential equipment failure. AI can also drive factory and warehouse robots, automate manufacturing workflows and handle dangerous tasks.
In the marketing field
AI can be very beneficial in customer engagement and running better targeted advertising campaigns. Marketers gain deeper insights into customer behavior, preferences, and trends by having advanced data analytics. But AI also helps them generate content and reach more personalized content and customers. With the help of AI, email marketing and social media management can be automated.
Gaming
Developers use AI in their video games to make gaming experiences more immersive. AI is used in video games to interact and react to the surrounding environment. Artificial intelligence creates game scenarios that are more realistic, entertaining and unique for the player.
For the military
AI can help the military process intelligence data faster, detect cyberwarfare attacks, automate military weapons, defense systems and vehicles. AI also helps the military on and off the battlefield. It can also be used to operate drones and robots and may be suitable for autonomous warfare or search and rescue operations.
Examples of Artificial Intelligence
AI has become an integral part of our daily lives for quite some time now. It has contributed significantly to various industries and improved user experiences. Below are some examples of AI applications described in the article:
ChatGPT
ChatGPT is developed by OpenAI and is an advanced language model. It can generate human responses and is capable of conversing in natural language. ChatGPT applies deep learning techniques to understand and generate coherent text. Due to which it is extremely useful for customer support, chatbots and virtual assistants.
Google Maps
Google Maps uses AI algorithms to provide navigation, traffic updates and personalized recommendations. It helps in suggesting faster routes and estimating arrival times. Artificial intelligence is used to predict traffic congestion by analyzing huge amounts of data including historical traffic patterns and inputs from users.
Smart Assistant
AI is also used by big companies as a smart assistant such as Alexa in Amazon, Siri in Apple and Google Assistant of Google etc. AI techniques are used to interpret voice commands, answer questions and perform tasks as a smart assistant. Natural language processing and machine learning algorithms are used to help the assistant understand the user's intentions, get relevant information and perform the requested actions.
Snapchat filters
Filters in Snapchat use AI to create interactive effects on users' faces. AI algorithms enable Snapchat to use different filters, masks, and animations. Users align these filters with their facial expressions and movements.
Self-driving cars
Self-driving cars rely on AI to perform perception, decision-making, and control in real time. These cars can detect vehicles and objects using a combination of sensors, cameras, and machine learning algorithms. With its help, they can navigate complex road conditions and detect traffic signs, keeping an eye on the roads.
In wearable devices
AI is now also used in wearable devices. It can monitor and analyze users' health data in fitness trackers and smartwatches. It provides personalized information and recommendations to improve health by tracking activities, heart rate, sleep patterns, and more.
Impact of Artificial Intelligence on Modern Society
Economic Transformation
AI has an important role in economic transformation. Industries run by AI have increased efficiency, reduced costs and improved productivity. AI automation has raised concerns of job displacement, but it has also opened up avenues for the creation of new roles and opportunities. With AI taking over routine tasks, humans can perform more creative, complex, and value-driven work, boosting innovation and economic growth.
Personalized Experiences
AI algorithms gather information about personal experiences every day, such as content recommendations on streaming platforms, targeted advertising online. AI analyzes large amounts of data to tailor experiences based on individual preferences. This increases user satisfaction, but also raises questions about privacy and the ethical use of personal data. This has led to discussions about the need for strong regulation and ethical guidelines.
In the Healthcare Sector
AI is playing an important role in the healthcare sector. AI has access to everything from diagnostic tools that analyze medical images to predictive models that predict disease outbreaks. AI contributes significantly to accurate diagnosis and personalized treatment plans. AI in the healthcare sector has decided to improve patient outcomes, optimize resource allocation, and advance medical research.
Social impact
The social impact of AI in our lives is evident from different perspectives. Chatbots and virtual assistants have changed the way we communicate and made it a little more interesting. Now social media platforms also use AI to curate content based on user preferences. Apart from this, AI is making it easier to solve social challenges. For example, predicting the impact of natural disasters and optimizing urban infrastructure for sustainability.
Educational transformation
AI has also made its mark in the field of education. Now adaptive learning platforms prepare educational content based on the needs of the individual student. AI-powered tools help facilitate a personalized learning experience, and teachers can detect areas where students may need assistance. The shift to adaptive and personalized education from AI has the potential to enhance the overall learning experience.
Types of Artificial Intelligence:
Weak AI
Weak AI can also be called narrow AI or artificial narrow intelligence (ANI). Weak AI is trained and focused on performing specific tasks. Most of the AI around us is powered by weak AI. It may be correct to call this type of AI as “narrow” because it is not weak. This AI enables some very strong applications like Apple Siri, Amazon Alexa, IBM Watsonx™ and self-driving vehicles.
Strong AI
Strong AI is a combination of artificial general intelligence and artificial super intelligence. It is a theoretical form of AI and this machine will have intelligence equal to humans. Strong AI will have the ability to solve problems, learn and plan for the future by being self-aware with consciousness. ASI is known as superintelligence which can exceed the intelligence and capability of the human brain. There are no practical examples of strong AI and it is still completely theoretical. Its AI researchers are still exploring its development.
Types of AI
We can categorize AI into four types, starting with task-specific intelligent systems and progressing to sentient systems. The categories are as follows:
Reactive machines
These AI systems do not have memory but are task-specific. For example, we take the IBM chess program Deep Blue, which defeated Russian chess grandmaster Garry Kasparov in the 1990s. Deep Blue could identify and predict chess pieces. It had no memory, so it could not use past experiences to inform future decisions.
Limited memory
Limited memory AI systems have memory available. This AI uses past experiences to inform future decisions. Some of the decision-making functions of self-driving cars are designed this way.
Theory of mind
Theory of mind is a term from psychology. When used in AI, it can refer to systems capable of understanding emotions. This AI can guess human intentions and can also predict behavior. Therefore, this is a necessary skill for AI systems to become an integral part of historically human teams.
Self-awareness
Such AI systems have a sense of self, which gives it consciousness. Machines with self-awareness can understand their current state. But such AI does not exist yet.
History of AI
Artificial intelligence began to emerge as a concept in the 1950s. At that time computer scientist Alan Turing released a paper called "Computing Machinery and Intelligence". In which the question was taken whether machines can think and how one can test the intelligence of a machine. Then this paper set the stage for AI research and development. The first proposal of the Turing test was a method used to test machine intelligence. The term "artificial intelligence" was first taken by computer scientist John McCarthy at an academic conference at Dartmouth College in 1956.
After McCarthy's conference and in the 1970s, academic institutions and US government funding took interest in AI research. During the time of computing innovations, many AI foundations were allowed to be established. These include machine learning, neural networks and natural language processing etc. After some progress was made, AI techniques became more difficult than expected, which led to a decline in interest and funding. As a result, the first AI century was seen by the 1980s.
Computers became a little more powerful in the mid-1980s and deep learning gained popularity. Then, the introduction of AI-powered "expert systems" revived interest in AI. But again, new systems faced complexity and inefficiency of existing technologies. With this, the second AI winter lasted until the mid-1990s.
Innovations in processing power, big data, and advanced deep learning techniques gave way to AI in the 2000s. This led to some more success in AI. Technologies like virtual assistants, driverless cars, and generative AI started in the 2010s and AI became what it is today.
In the Artificial Intelligence Timeline, in 1943 Warren McCullough and Walter Pitts published a paper called "A Logical Calculus of Ideas Immanent in Nervous Activity". This paper proposes the first mathematical model for building neural networks.
In 1949 Donald Hebb, in his book The Organization of Behavior: A Neuropsychological Theory, proposed the theory that neural pathways are formed by experiences. The connections between neurons become stronger the more often they are used. Hebbian learning remains an important model in AI to this day.
1950: The paper "Computing Machinery and Intelligence" was published by Alan Turing. It proposed a method known as the Turing Test to determine whether a machine is intelligent. Harvard undergraduates Marvin Minsky and Dean Edmonds built their first neural network computer, the SNARC, in 1950.
The term "artificial intelligence" was coined in 1950 by the Dartmouth Summer Research Project on Artificial Intelligence. This conference by John McCarthy is also widely considered the birthplace of AI.
1958-1959: John McCarthy develops the AI programming language Lisp and publishes "Programs with Common Sense", a paper proposing a hypothetical advice taker whose full AI system has the ability to learn from experience as effectively as humans. Arthur Samuel also coined the term "machine learning" in 1959 while at IBM.
1964-1966: Daniel Bobrow was a doctoral candidate at MIT who developed STUDENT, an early natural language processing program for solving algebra word problems. Joseph Weizenbaum, a professor at MIT, then created ELIZA, a chatbot that successfully mimicked users' conversational patterns. This chatbot gave the illusion that it understood more than it did.
1969 -1972: The first successful expert systems, DENDRAL and MYCIN, were created at Stanford University's AI lab. The logic programming language PROLOG was developed in 1972.
1973-1980: The British government detailed the disappointments in AI research and released the Lighthill Report, which drastically cut funding for AI projects. The disappointments in the progress of AI development led to drastic cuts in academic grants by DARPA. The ALPAC Report and the Lighthill Report combined to end AI funding and halt research.
1980-1985: Digital Equipment Corporation created R1 (XCON), the first successful commercial expert system. R1 triggered a surge in investment in expert systems for configuring orders for new computer systems. This surge lasted for most of the decade, effectively ending the first AI winter. Companies spent over a billion dollars a year on expert systems.
Then an entire industry known as the Lisp Machine market sprang up to support them. In 1985 companies Symbolics and Lisp Machine Inc. built specialized computers to run the AI programming language Lisp.
1987-1993: As computing technology improved, cheaper alternatives emerged. The Lisp Machine market collapsed in 1987, beginning the "second AI winter." During this time expert systems fell out of favor as they proved too expensive to maintain and update.
1997-2006: Deep Blue developed by IBM defeated world chess champion Garry Kasparov. Fei-Fei Li started working on the ImageNet visual database in 2009. This AI became a catalyst on the basis of which image recognition grew.
2008-2011: Google got success in speech recognition and introduced this feature in its iPhone app. After this, Apple introduced Siri, an AI-powered virtual assistant in its iOS operating system.
2012-2014: Andrew Ng, founder of Google's Brain Deep Learning project, prepared a training set for neural networks using deep learning algorithms. The neural network started recognizing the cat without being told. After this came an era of success for neural networks and deep learning funding. In 2014, Amazon released a virtual home smart device in the form of Alexa.
2016-2018: Google DeepMind's AlphaGo defeated world champion player Lee Sedol. The complexity of the ancient Chinese game was a major hurdle for AI to overcome. Google released the natural language processing engine BERT in 2018. It reduces the barriers to translation and understanding through ML applications.
2020: Baidu released its LinearFold AI algorithm for scientific and medical teams working on a vaccine in the early stages of the SARS-CoV-2 pandemic. The algorithm could predict the RNA sequence of the virus in just 27 seconds and was 120 times faster than other methods. OpenAI then introduced the natural language processing model GPT-3, which can generate text based on the way people speak and write.
2021: OpenAI built DALL-E on GPT-3, which can generate images from text prompts.
2022: The first draft of the AI Risk Management Framework is released by the National Institute of Standards and Technology. It is a voluntary US guidance for better managing the risks to individuals, organizations, and society associated with artificial intelligence.
OpenAI launches ChatGPT, a chatbot powered by a large language model. It has reached over 100 million users in just a few months. The White House also releases an AI Bill of Rights outlining principles for AI development and use.
2023: Microsoft search engine Bing launches an AI-powered version of Google, based on the same technology that powers ChatGPT. Google also introduces Bard AI, a competing conversational AI, and later rebrands it as Gemini. GPT-4 is launched by OpenAI, and has emerged as the most sophisticated language model to date. In 2023, the chatbot Grok is introduced by Elon Musk's AI company xAI.
2024: The Artificial Intelligence Act is passed by the European Union. It is used to ensure that AI systems deployed inside the EU are "safe, transparent, traceable, non-discriminatory and environmentally friendly". Cloud 3 Opus is a large language model LLM developed by AI company Anthropic, which outperforms chatGPT-4.
Conclusion
Artificial Intelligence (AI) is an emerging technology in this modern world. It attempts to simulate human intelligence using machines. In AI machine learning and deep learning help systems learn and adapt in new ways from training data. AI offers significant advancements in the form of widespread applications in many industries such as healthcare, finance, and transportation. AI also serves to raise ethical, privacy, and employment concerns.
As AI is getting involved in the business aspect, enterprises are also becoming dependent on it in making important decisions. From leveraging innovation, enhancing customer experience to maximizing the profit of enterprises, AI has emerged as a ubiquitous technology. Apart from all these popular beliefs, AI will replace humans in the job field. In the coming times, a collaborative engagement will be seen between humans and machines. This will accelerate cognitive skills and abilities and boost overall productivity.