Generative and Predictive AI - Artificial Intelligence

Generative and Predictive AI - Artificial Intelligence

Generative AI:

Generative AI (GenAI) is a type of artificial intelligence (AI) that can create new content.

GenAI learns patterns from training data and uses this knowledge to generate new outputs.

The new outputs have similar characteristics to the training data, but they don't repeat it.

GenAI can create a variety of content, including:

Images
Videos
Audio
Text
3D models
Music
Speech
Software code
Product designs

Here are some steps to build a GenAI:

Benefits of GenAI:

Makes informed decision
Content creation and inspection
Personalized experience
Improved efficiency
Automaton friendly
Virtual guidance

Disadvantages of GenAI:

Generative AI can raise ethical questions about privacy, intellectual property, and misinformation.
Generative AI models depend on the quality and diversity of their training data.
Generative AI can't be completely trusted due to Incorrect facts and biased information.
Generative AI is not meant to replace human intuition, inspiration, and ideas.
Generative AI can increase the likelihood of fraud, economic crime, and organized crime.
Generative AI outcomes may be limited in traceability and irreproducible, which could lead to bad or illegal decision making.

Predictive AI:

Predictive artificial intelligence (AI) uses machine learning to identify patterns in past events and make predictions about future events.

Predictive AI uses patterns in historical data to forecast future outcomes or classify future events.

Predictive AI models can be trained to predict:

Stock market trends
Customer behavior
Disease progression
Future needs or events of a company
Trends

Here are some steps to build a predictive model:

Benefits of Predictive AI:

Identifying the most valuable customers
Provides actionable insights and aids in decision-making

Disadvantages of Predictive AI:

Predictive can tell you when you should consider taking action, but it can't tell you what action to take. Thus, if you rely on gut feel or standard practices to make decisions, sometimes you'll end up selecting a bad response.
Due to the lack of current data, the outcomes can be inaccurate.

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