³ÉÈËAVÊÓƵ

Before the advent of artificial intelligence, processing large amounts of data was limited by human capabilities and prone to errors. The evolution of machine learning has enabled:

• Automation of repetitive tasks.
• Scalability to handle vast amounts of data.
• Personalization and tailored recommendations based on individual preferences.
• Enhanced decision-making by identifying and predicting patterns.

• Requires significant computational power.
• Data-dependent (particularly the quality and availability of data).
• Vulnerable to biases based on data and programming.
• Vulnerable to security attacks designed to manipulate or deceive the model.

1. Email Filtering: Uses algorithms to evaluate content and sender of emails and classify as potential spam.
   
   
2. Recommendation Systems: Used to make suggestions for products based on preferences or patterns of behavior. 
   
   
3. Customer Segmentation: Uses clustering algorithms to segment customers and tailor marketing based on purchase behavior.
   
   
4. Credit Scoring: Uses logic regression to analyze a customer's financial history and assess credit worthiness.
   
   
5. Predictive Maintenance: Used to predict equipment malfunctions and schedule maintenance.

Expanding on the benefits of machine learning, deep learning is stronger in the following ways:

• Capable of processing more complex data.
• Capable of handling more intricate tasks.
• Requires less manual intervention.
• Excels at tasks involving unstructured data such as images, audio, text.

• High costs.
• Requires significant computational power and energy.
• Utilizes GPUs and specialized hardware for the training of the models.
• Harder to intepret the decision-making process.

1. Image Recognition: Used to identify and recognize objects and patterns within images, such as in facial recognition, medical imaging, and self-driving vehicles.
   
   
2. Speech Recognition: Used to understand and respond to voice commands or convert spoken language into text.
   
   
3. Autonomous Vehicles: Uses sensor data to navigate and make driving decisions.
    
4. Natural Language Processing (NLP): Used in chatbots and language translation to understand  queries and respond with human language.
    
5. Fraud Detection: Use patterns to detect anomalies and fraud, particularly in financial transactions. 

Generative AI offers numerous benefits, particularly as it has become available to more people.

• Creativity and inspiration using text prompts to generate images, music, and text.
• Efficiency through summarization and organization of vast content, and identification of tasks within messages.
• Problem-solving and data analysis leading to quicker and more accurate insights.
• Personalization and tailored recommendations based on individual preferences.
• Accessibility and language translation. 

As with all advancements in technologies, generative AI poses its own challenges:

• Ethical concerns like data privacy or the misuse of AG-generated content. 
• Biases that may exist in the data used to train the model.
• Quality issues due to difficulty in control the accuracy and reliability of content. 
• Job displacement as tasks that previously required human effort can now be performed more efficiently by AI. 

Generative AI has a fascinating range of applications: 

1. Text Generation: Generate natural language and conversational text based on prompts, making it valuable for content creation and chatbots.
   Example Tools: Microsoft Copilot, ChatGPT, Google Bard
   
   
2. Image Generation: Generate original images from text descriptions with the ability to customize based on art styles, perspective, color scheme, mood, and more.
   Example Tools: DALL-E, Midjourney
   
   
3. Audio Generation: Generate audio or music based on text input, such as music composition, sound design, lyric generation, remixing, and adaptations.
   Example Tools:  WaveNet, MAGENTA
   
   
4. Video Generation: Generate video based on text input, such as script-to-video, storyboarding, animation, visual effects, video editing, and more.
   Example Tools: Sora, Runway
   
   
5. Code Generation: Generate code snippets and functions based on text descriptions.
   Example Tools: GitHub Copilot 
   
   

View list of Generative AI tools.

Large language models (LLMs) are powerful tools due to their ability to perform tasks faster and more accurately than humans. They are: 

• Versatile and capable of handling a wide range of tasks such as text generation and translation.
• Efficient in automating and streamlining processes.
• Scalable to manage large volumes of data.
• Increasingly accurate with continued training.

Large language models present the following challenges:

• Resource-intensive, requiring significant computational power and energy.
• Data privacy is a concern as data queried can become part of the model.
• Potential for bias depending on the training data.
• Ethical concerns also arise from the potential misuse of LLMs.

Several of these reasons are why ³ÉÈËAVÊÓƵ’s AI Council monitors the evolution of AI and guides its adoption at ³ÉÈËAVÊÓƵ.

Here are examples of large language models (LLMs):