Artificial Intelligence (AI)
what is Artificial Intelligence?
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, voice or handwriting recognition, and chatbots.
Artificial intelligence applications
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.
- Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
History of artificial intelligence: Key dates and names
he idea of ‘a machine that thinks’ dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:
- 1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing—famous for breaking the Nazi’s ENIGMA code during WWII—proposes to answer the question ‘can machines think?’ and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.
- 1956: John McCarthy coins the term ‘artificial intelligence’ at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.
- 1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that ‘learned’ though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.
- 1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.
- 1997: IBM’s Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).
- 2011: IBM watson beats champions Ken Jennings and Brad Rutter at Jeopardy!
- 2015: Baidu’s Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.
- 2016: DeepMind’s AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.
- 2023: A rise in large language models, or LLMs, such as ChatGPT, create anenormous change in performance of AI and its potential to drive enterprise value.With these new generative AI practices, deep-learning models can be pre-trained onvast amounts of raw, unlabeled data.
Features of Artificial Intelligence-:
Artificial Intelligence (AI) encompasses a wide range of technologies and techniques that enable machines to simulate human-like intelligence and perform tasks that typically require human intelligence. AI has numerous features and characteristics that make it a transformative field in technology and science. Here are some key features of AI:
- Learning: AI systems have the ability to learn from data and improve their performance over time. This learning can be supervised, unsupervised, or reinforcement learning, and it enables AI to adapt to changing conditions and new information.
- Reasoning: AI systems can perform logical reasoning and make decisions based on available information. They can use rules and algorithms to solve complex problems and make inferences.
- Problem Solving: AI can tackle a wide range of problems, from playing complex games like chess and Go to solving practical problems in fields like healthcare, finance, and logistics.
- Perception: AI systems can perceive and interpret the world around them through various sensors and data sources. This includes computer vision (interpreting images and videos), natural language processing (understanding and generating human language), and speech recognition.
- Adaptability: AI can adapt to different domains and tasks. Transfer learning allows models to apply knowledge learned in one context to another. This adaptability is crucial in many practical applications.
- Automation: AI enables automation of repetitive and labor-intensive tasks. This leads to increased efficiency and productivity in various industries.
- Big Data Handling: AI can process and analyze vast amounts of data at high speed, making it suitable for tasks like data mining, predictive analytics, and pattern recognition.
- Natural Interaction: AI can provide natural and intuitive interfaces, such as chatbots and virtual assistants, that allow humans to interact with machines using natural language or gestures.
- Parallel Processing: AI can perform tasks in parallel, which means it can handle multiple tasks simultaneously, making it well-suited for multitasking and complex operations.
- Self-improvement: AI systems can refine their performance and capabilities through continuous learning, thus becoming more proficient and accurate over time.
- Decision Support: AI systems can assist in making informed decisions by providing insights and predictions based on data analysis.
- Ethical Considerations: AI raises important ethical questions related to bias, fairness, privacy, and accountability. Efforts are made to ensure AI systems are developed and used responsibly.
- Scalability: AI technologies can be applied at various scales, from small applications on mobile devices to large-scale, cloud-based systems that can serve millions of users.
- Predictive Analytics: AI can be used to predict future outcomes based on historical data, which is valuable in industries like finance, healthcare, and marketing.
- Robustness: AI systems are designed to be robust, meaning they can function effectively even in the presence of noisy or incomplete data.
- Sensory Integration: Some AI systems are capable of integrating data from multiple sensory inputs, such as vision, audio, and touch, to make more comprehensive decisions.
- Deep Learning: Deep learning, a subset of AI, involves neural networks with multiple layers (deep neural networks). These networks are particularly suited for tasks like image and speech recognition.
AI is a rapidly evolving field, and its features continue to expand as researchers develop new techniques and applications. AI has the potential to revolutionize various industries, improve efficiency, and address complex problems across different domains.
