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A web browser is a software application designed to access and display web pages on the World Wide Web. When a user requests a web page from a specific website, the browser retrieves its files from a web server and then displays the page on the user's device. Web browsers are used on a variety of devices, including desktops, laptops, tablets, and smartphones. In 2020, an estimated 4.9 billion people have used a web browser

. Some of the most popular web browsers include Google Chrome, Safari, Mozilla Firefox, Microsoft Edge, Opera, and Brave

. Web browsers are not the same as search engines, which provide links to other websites. Instead, they are used to connect to a website's server and display its web pages

A web browser is a software application designed to access and display web pages on the World Wide Web. When a user requests a web page from a specific website, the browser retrieves its files from a web server and then displays the page on the user's device. Web browsers are used on a variety of devices, including desktops, laptops, tablets, and smartphones. In 2020, an estimated 4.9 billion people have used a web browser

. Some of the most popular web browsers include Google Chrome, Safari, Mozilla Firefox, Microsoft Edge, Opera, and Brave

. Web browsers are not the same as search engines, which provide links to other websites. Instead, they are used to connect to a website's server and display its web pages

Artificial intelligence, or AI, is technology that enables computers and machines to simulât human intelligence and problem-solving capabilities.

On its own or combined with other technologies (e.g., sensors, geolocation, robotics) AI can perform tasks that would otherwise require human intelligence or intervention. Digital assistants, GPS guidance, autonomous vehicles, and generative AI tools (like Open AI's Chat GPT) are just a few examples of AI in the daily news and our daily lives.

As a field of computer science, artificial intelligence encompasses (and is often mentioned together with) machine learning and deep learning. These disciplines involve the development of AI algorithms, modeled after the decision-making processes of the human brain, that can ‘learn’ from available data and make increasingly more accurate classifications or predictions over time.

Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing (NLP). Today, generative AI can learn and synthesize not just human language but other data types including images, video, software code, and even molecular structures.

Applications for AI are growing every day. But as the hype around the use of AI tools in business takes off, conversations around ai ethics and responsible ai become critically important. For more on where IBM stands on these issues, please read Building trust in AI.

 Types of artificiel intelligence: weak AI vs. strong AI

Weak AI—also known as narrow AI or artificial narrow intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. "Narrow" might be a more apt descriptor for this type of AI as it is anything but weak: it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watsonx™, and self-driving vehicles.

Strong AI is made up of artificial general intelligence (AGI) and artificial super intelligence (ASI). AGI, or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would be self-aware with a consciousness that would have the ability to solve problems, learn, and plan for the future. ASI—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman and rogue computer assistant in 2001: A Space Odyssey.

Deep learning vs. machine learning   

Machine learning and deep learning are sub-disciplines of AI, and deep learning is a sub-discipline of machine learning.

Both machine learning and deep learning algorithms use neural networks to ‘learn’ from huge amounts of data. These neural networks are programmatic structures modeled after the decision-making processes of the human brain. They consist of layers of interconnected nodes that extract features from the data and make predictions about what the data represents.

Machine learning and deep learning differ in the types of neural networks they use, and the amount of human intervention involved. Classic machine learning algorithms use neural networks with an input layer, one or two ‘hidden’ layers, and an output layer. Typically, these algorithms are limited to supervised learning: the data needs to be structured or labeled by human experts to enable the algorithm to extract features from the data.

Deep learning algorithms use deep neural networks—networks composed of an input layer, three or more (but usually hundreds) of hidden layers, and an output layout. These multiple layers enable unsupervised learning: they automate extraction of features from large, unlabeled and unstructured data sets. Because it doesn’t require human intervention, deep learning essentially enables machine learning at scale.

The rise of generative models

Generative AI refers to deep-learning models that can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of AI models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

VAEs opened the floodgates to deep generative modeling by making models easier to scale,” said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab. “Much of what we think of today as generative AI started here.”

Early examples of models, including GPT-3, BERT, or DALL-E 2, have shown what’s possible. In the future, models will be trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI systems that learn more generally and work across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

As to the future of AI, when it comes to generative AI, it is predicted that foundation models will dramatically accelerate AI adoption in enterprise. Reducing labeling requirements will make it much easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the computing power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

Artificial intelligence applications

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

Speech recognition

Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, speech recognition uses NLP to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—Siri, for example—or provide more accessibility around texting in English or many widely-used languages. See how Don Johnston used IBM Watson Text to Speech to improve accessibility in the classroom with our case study.

Customer service

Online  virtual agents and chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQ) 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. See how Autodesk Inc. used IBM watsonx Assistant to speed up customer response times by 99% with our case study.

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. See how ProMare used IBM Maximo to set a new course for ocean research with our case study.

Supply chain

Adaptive robotics act on Internet of Things (IoT) device information, and structured and unstructured data to make autonomous decisions. NLP tools can understand human speech and react to what they are being told. Predictive analytics are applied to demand responsiveness, inventory and network optimization, preventative maintenance and digital manufacturing. Search and pattern recognition algorithms—which are no longer just predictive, but hierarchical—analyze real-time data, helping supply chains to react to machine-generated, augmented intelligence, while providing instant visibility and transparency. See how Hendrickson used IBM Sterling to fuel real-time transactions with our case study.

Weather forecasting

The weather models broadcasters rely on to make accurate forecasts consist of complex algorithms run on supercomputers. Machine-learning techniques enhance these models by making them more applicable and precise. See how Emnotion used IBM Cloud to empower weather-sensitive enterprises to make more proactive, data-driven decisions with our case study.

Anomaly detection

AI models can comb through large amounts of data and discover atypical data points within a dataset. These anomalies can raise awareness around faulty equipment, human error, or breaches in security. See how Netox used IBM QRadar to protect digital businesses from cyberthreats with our case study.

Ressources : https://www.ibm.com/topics/artificial-intelligence