ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have
the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.
Types of Artificial Intelligence: Broadly speaking the IA can be divided into to categories, i.e. Weak and strong. The weak artificial intelligence embodies a system designed to carry out one particular job. Weak Al systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, and it answers it for you.
Strong artificial intelligence systems are systems that carry on the tasks considered to be human- like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.
Technically Artificial intelligence can be categorized into one of four types,
- Reactive Al uses algorithms to optimize outputs based on a set of inputs, Chess-playing Als, for example, are reactive systems that optimize the best strategy to win the game. Reactive Al tends to be fairly static, unable to learn or adapt to novel situations. Thus, it will produce the same output given identical inputs.
- Limited memory Al can adapt to past experience or update itself based on new observations or data. Often, the amount of updating is limited (hence the name), and the length of memory is relatively short. Autonomous vehicles, for example, can "read the road" and adapt to novel situations, even "learning" from past experience.
- Theory-of-mind Al are fully-adaptive and have an extensive ability to learn and retain past experiences. These types of Al include advanced chat-bots that could pass the Turing Test, fooling a person into believing the AI was a human being. While advanced and impressive, these AI are not self-aware.
- . Self-aware AI, as the name suggests, become sentient and aware of their own existence. Still in the realm of science fiction, some experts believe that an AI will never become conscious or "alive".
Artificial intelligence applications: There are numerous, real-world applications of Al systems today, one of the most common are as discussed below:
1. 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.
2. 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.
3. Computer vision: This Al 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.
4. 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.
5. Automated stock trading: Designed to optimize stock portfolios, Al-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
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