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the clients have sent this image - to give you a sense of the set design

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the clients have sent this image - to give you a sense of the set design

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this is the *working title* for the show ( the artwork can be ignored) 

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this is the *working title* for the show ( the artwork can be ignored) 

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Truth
Trust
Fake
Artificial
Real
Soul
Heart
Mind
Consciousness
Learning
Machine learning
Imitation
Logic
Intelligence
knowledge
Data
Big Data
neural network
hallucination
generative software

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this robot parrot was created using AI - and it took 3 seconds to make

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this robot parrot was created using AI - and it took 3 seconds to make

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we train parrots to repeat ( mimic)  what they are told, but they do not understand what they are saying

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we train parrots to repeat ( mimic)  what they are told, but they do not understand what they are saying

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behavioural scientists use mice in mazes to understand how we learn: and how we are motivated 

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behavioural scientists use mice in mazes to understand how we learn: and how we are motivated 

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The infinite monkey theorem states that if you let a monkey hit the keys of a typewriter at random an infinite amount of times, eventually the monkey will type out the entire works of Shakespeare

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The infinite monkey theorem states that if you let a monkey hit the keys of a typewriter at random an infinite amount of times, eventually the monkey will type out the entire works of Shakespeare

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a slot machine randomly "generates" TRUTH ???

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a slot machine randomly "generates" TRUTH ???

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the purpose of the mood reel is to quickly present some of the ideas - it is like a "mood board" but this time in video form. It is hoped that the mood reel will inspire you

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how does it work? ( by the way, this was written by Chat GPT, so you could say that it is autobiographical! )

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how does it work? ( by the way, this was written by Chat GPT, so you could say that it is autobiographical! )

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Artificial Intelligence (AI) is a broad field of computer science that focuses on creating machines and computer systems capable of performing tasks that typically require human intelligence. AI systems work by processing data, making decisions, and learning from experience. Here's a simplified overview of how AI works:

1. Data Collection: AI systems require data to learn and make decisions. This data can be text, images, videos, sensor readings, or any other form of information relevant to the task at hand. The more high-quality data an AI system has access to, the better it can perform.

2. Data Preprocessing: Raw data often needs to be cleaned, organized, and prepared for analysis. This step involves removing noise, handling missing values, and transforming data into a format that AI algorithms can work with effectively.

3. Algorithms and Models: AI systems use various algorithms and models to process and analyze data. These algorithms can be rule-based, statistical, machine learning, or deep learning models. The choice of algorithm depends on the specific task and the type of data.

Rule-Based Systems: These systems follow predefined rules and logic to make decisions. They are simple but lack adaptability.

Statistical Methods: These methods use statistical techniques to analyze data and make predictions. They work well for tasks like regression and classification.

Machine Learning: Machine learning algorithms allow systems to learn from data and make predictions or decisions based on patterns and relationships within the data. Common machine learning techniques include decision trees, support vector machines, and k-nearest neighbors.

Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to automatically extract features and patterns from data. Deep learning has been particularly successful in tasks like image recognition and natural language processing.

4. Training: For machine learning and deep learning models, training is a crucial step. During training, the AI system is exposed to a large dataset with known outcomes. The system adjusts its internal parameters to minimize errors and improve its performance on the task. This process is called "learning."

5. Inference: After training, the AI model is ready for inference. During inference, the model makes predictions or decisions based on new, unseen data. It applies the knowledge it has acquired during training to solve specific tasks.

6. Feedback Loop: Many AI systems incorporate a feedback loop. They continuously learn and improve by receiving feedback on their predictions or decisions. This feedback can come from human evaluators or through reinforcement learning, where the system receives rewards or penalties based on its actions.

7. Deployment: Once an AI model is trained and validated, it can be deployed in real-world applications. This can include autonomous vehicles, virtual assistants, recommendation systems, fraud detection, medical diagnosis, and more.

It's important to note that AI is a rapidly evolving field, and various subfields, techniques, and applications continue to emerge. AI systems can range from simple rule-based programs to highly complex neural networks capable of deep learning. The key to the success of AI systems often lies in their ability to process vast amounts of data and make intelligent decisions based on patterns and insights derived from that data.

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https://chat.whatsapp.com/JqRlsM2Xsnc5pGWVhXL21M