What is artificial intelligence (NEW)?
May 18, 2023

What is artificial intelligence (NEW)?

What is artificial intelligence?

Many artificial intelligence few years 2004 article (PDF, 127 KB) (link resides outside IBM): This is Science and engineering. Intelligent computers is an obstacle to similar studies of using computers to understand human intelligence, but intelligence need not limit itself to biological analysis. Machinery and Intelligence” (PDF, 92 KB) (link resides outside IBM), published 1950 . With this article, Turing, often refer to as the “father of computer science,” ask the question: “Can machines be need?
From there, he devised a test now known as the “Turing test”, in which people tried to distinguish a computer from a human response. It’s an important part of the history of artificial intelligence, and a concept that remains in thought as it draws ideas out of speech. Stuart Russell and Peter Norvig went on to publish Artificial Intelligence: A Modern Approach (link outside of IBM). Textbooks on AI research in which they explore the four goals or concepts of AI that differ in computer systems based on thinking, thinking, and action:

Human Approaches:

• Processes that think like humans
• Processes that act like humans
Best approaches:
• Processes that think rationally
• Meaningful processes
Alan Turing’s definition falls under the category of “human-like systems”.

Artificial intelligence is a field that combines computer science with powerful data to solve problems in its simplest form. It also includes sub-fields of machine learning and deep learning, which are frequently mention. The AI has gone through many hyperbole cycles over the years, but despite the skepticism, OpenAI’s release of ChatGPT appears to be a turning point. The breakthrough was in the field of computer vision, but now a breakthrough has been made in the field of natural language processing.
Not just words: Designs can also learn the grammar of software code, molecules, natural images, and many other materials.
The applications of this technology are increasing day by day and we are only starting to explore the possibilities. But as the hype surrounding the use of AI in business begins to mount, the debate about ethics becomes important. To learn more about where IBM stands in the AI ​​ethics debate, read more here. Meet
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Magic Quadrant for Interactive AI Platforms for Business, 2023
First IBM Watson Orchestrate
Second IBM AI assistant Watson4 mode
third IBM Weak intelligence and

Strong AI

Weak AI – also known as Narrow AI or Artificial Intelligence Narrow (ANI) and it is an AI that is train and focused on specific tasks. Weak AI drives most of the AI ​​around us today. “Narrow” might be a more accurate description for this type of AI as it is not powerful at all; It enables some very powerful applications such as Apple’s Siri, Amazon’s Alexa, IBM Watson and self-driving cars.
Strong AI includes Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial General Intelligence (AGI) or general AI is a theoretical form of AI in which machines would have the same intelligence as humans; will be self-aware and will be able to solve problems, learn and plan for the future.

Deep learning automates much of the feature extraction part of the process, removing some of the manual intervention require and allowing larger datasets to be use. As Lex Fridman pointed out in the same MIT lecture cited above, you can think of deep learning as “scalable machine learning.” Classical or “deep” machine learning relies more on human intervention to learn. Human experts identify hierarchy of features to understand differences in input data and often require more data to learn.
“Deep” machine learning can use log data (also known as supervised learning) to train its algorithms, but does not require log data.
It can seamlessly retrieve data in raw form (like text, images) and identify the unique hierarchy that separates different data groups. Unlike machine learning, it does not require human intervention to process data, allowing us to evaluate machine learning in more detail.

The Rise of Generative Models

Generative Artificial Intelligence refers to deep learning models that take raw data (for example, Wikipedia or the entire archives of Rembrandt) and show “learning” to generate the results.
At a high level, the model encodes a simple
representation of the training data and extracts the original data to create
new functions that are similar but not identical.
Generative modeling has been use for numerical data analysis for many years. However, the rise of deep learning has made it possible to extend it to images, speech, and other complex data. The Variable Auto Encoder (VAE), introduced in 2013, is one of the first models to meet this challenge. VAEs are the first deep learning models widely used to generate real images and speech.
“VAEs open doors for deep modeling by simplifying the
test models,” says Akash Srivastava, an AI specialist at MIT-IBM Watson AI Lab and an AI expert at MIT-


IBM Watson AI Lab.
“Most of what we think of as productive AI today started here.” Early examples of models such as the
GPT-3, BERT or DALL-E 2 show how this can be done.

The future is for training models on a wide variety of anonymous data that can be used for different tasks with minimal effort. A specialized system in a domain is being replace by a general artificial intelligence that can learn more broadly and work across domains and problems.
Anonymous and easy-to-learn big data model, optimized for various applications, is driving this change.
In developing AI, the underlying structure is predict to accelerate the adoption of AI in the business world. Reducing enrollments will make it easier for businesses to invest in the effective AI-driven automation they can actually do will mean more companies can use AI for many key purposes. The hope for IBM is that the power of the centralize model can finally bring to all businesses in a seamless hybrid cloud environment.

Artificial Intelligence Applications

Today, artificial intelligence has many applications in the real world.
Some of the most common applications are:
• Speech Recognition: Automatic Speech Recognition (ASR), also called Computer Speech Recognition or Speech-to-Text, is a process that uses Natural Language Processing (NLP) in procedures. people talk with paper. Many cell phones integrate voice recognition into their own voice search system (like Siri) or provide easier access while browsing the newspaper.

• Customer Service: Online virtual agents are replacing face-to-face customer service representatives.
They answer frequently asked questions (FAQs) or give personalized advice on topics like delivery, sell products or show users sizes, changing the way we interact with customers on the web and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps like Slack and Facebook Messenger, and tasks often performed by voice assistants and voice commands.

• Computer Vision: This cognitive process enables computers and systems to extract and input useful information from digital images, videos, and other visual sources. This can provide recommendations that distinguish it from image recognition. Computer vision powered by convolutional neural networks has applications in image tagging in social media, radiography in medicine, and self-driving cars in the automotive industry.

• Recommendations: Artificial intelligence algorithms can use historical customer behavior data to help identify trends in the data that can be use to create better sales strategies. It is use as additional information for customers during the online seller’s and checkout process.

• Automated Stock Trading: Artificial intelligence-driven high-frequency trading platforms designed to improve the quality of stock trading execute thousands or millions of trades per day without human intervention.

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