OpenAI introduces ChatGPT GOV. (Government)

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OpenAI has launched ChatGPT Gov, a specialized version of its ChatGPT Enterprise tool designed for government workers. This AI-powered assistant aims to help policymakers and agencies improve public services by answering questions, explaining complex topics, or assisting with tasks like coding and project analysis.

In today newsletter,

1. OpenAI Introduces ChatGPT gov. for Government agencies

2. Generative AI vs Agentic AI

3. Skills you need to learn Generative AI OR Agentic AI

4. Courses to learn Agentic AI OR Generative AI

What ChatGPT Gov Offers:

• Conversation Sharing: Collaborate and share insights easily.

• Advanced AI (GPT-4o): Handles text, images, and math.

• Custom GPTs: Tailored AI models for specific needs.

• Admin Controls: Manage users, security settings, and custom tools.

• Azure Integration: Deploy securely within Microsoft Azure cloud environments.

Government agencies are already using ChatGPT in various ways. For example, the Air Force Research Laboratory uses it for coding, while Pennsylvania employees analyze project requirements. OpenAI says government workers will start using ChatGPT Gov in live environments within a month.

Security Concerns

While the tool promises efficiency, security is a major concern. Government entities handle sensitive data—everything from national security details to personnel records. Luke Tenery, a risk consultant at StoneTurn, warns that even mundane information could be exploited by bad actors. He stresses the need for robust safeguards, especially given the risks posed by nation-state threats.

OpenAI is working toward FedRAMP accreditation, a federal standard for secure cloud solutions, to address these concerns. However, recent cybersecurity incidents, like a compromised key at BeyondTrust exposing Treasury data, highlight the challenges ahead.

The Bigger Picture

The rollout of ChatGPT Gov comes as governments focus more on cybersecurity. While President Biden’s administration recently issued an executive order prioritizing software security, there’s uncertainty about how these efforts align with previous initiatives under the Trump administration.

For now, ChatGPT Gov represents a step forward in bringing AI to government work—but balancing innovation with security will be key to its success.

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Generative AI vs. Agentic AI

AI is changing the world, and two big players are Generative AI and Agentic AI. They're like two sides of the same coin, different but powerful. Here's the lowdown without the tech jargon:

Generative AI: This is the AI that makes things. Need a catchy slogan? A stunning piece of art? A piece of code? Generative AI is your go-to.

It is really good at creating stuff. Text, images, music, you name it. Think of tools like ChatGPT for writing or DALL-E for images. It learns from tons of examples, figures out the patterns, and then whips up something new based on those patterns. Like a chef learning recipes and then creating their own dish.

It's brilliant at mimicking styles but doesn't truly understand what it's creating. It can't make decisions.

Agentic AI:

This is the AI that does things. It's like a virtual assistant capable of planning, making decisions, and taking action to reach a goal.

It's really good at getting things done autonomously. For example, self-driving cars or robots that can sort packages.

It senses its environment, figures out what needs to be done, and then acts. It's like a super-efficient worker who can adapt to changing situations.

We need to make sure these systems are safe and do what we actually want them to do. Who's to blame if a self-driving car makes a mistake?

The Core Difference:

SKILLS

Let's say you're excited about AI and want to build a career in this field. Should you focus on Generative AI or Agentic AI? Here's a guide to the skills you'll need for each:

Generative AI Skills

Deep Learning: This is the foundation. You need a strong grasp of neural networks, especially architectures like:

• Generative Adversarial Networks (GANs): Great for image generation.

• Transformers: The power behind large language models like ChatGPT.

• Recurrent Neural Networks (RNNs): Useful for sequential data like text and music.

• Variational Autoencoders (VAEs): Another powerful model for generating data.

Data Wrangling: You'll be working with massive datasets. You need to know how to:

• Clean and preprocess data: Garbage in, garbage out, remember?

• Handle different data formats: Text, images, audio, etc.

• Data augmentation: Techniques to increase the size and diversity of your training data.

Programming: Python is the dominant language in AI. You'll also need to be familiar with:

• Deep learning libraries: TensorFlow, PyTorch, Keras.

• Data manipulation libraries: Pandas, NumPy.

Evaluation Metrics: How do you know if your generative model is any good? You need to understand metrics like:

• Inception Score (IS) and FID (Fréchet inception distance) for images

• BLEU and ROUGE (for text)

• Perplexity (for language models)

Bonus Skills:

1. Domain Expertise: If you want to specialize in, say, music generation, a background in music theory would be incredibly helpful.

2. Cloud Computing: Training large models often requires cloud resources (AWS, Google Cloud, Azure).

In short, to become a Generative AI Engineer, one should focus on Deep Learning, Data Wrangling, Programming, Evaluation Metrics, and optionally, Domain Expertise and Cloud Computing.

Agentic AI Skills

If you're fascinated by robots, self-driving cars, and intelligent systems that can make decisions and act in the real world, here's your skill set:

Reinforcement Learning (RL): This is key to training agents that can learn through trial and error. You need to know:

• Q-learning, SARSA, Deep Q-Networks (DQN)

• Policy gradients, Actor-Critic methods

• Exploration-exploitation trade-off

Robotics (if applicable): If you're working with physical robots, you'll need:

• Kinematics and dynamics

• Control theory

• Sensor fusion (combining data from different sensors)

• Computer vision

Classical AI: Don't forget the fundamentals:

• Search algorithms (A*, Dijkstra's)

• Planning and scheduling

• Knowledge representation and reasoning

Programming: Python is essential, but you might also need:

• C++ (for robotics and performance-critical applications)

• ROS (Robot Operating System)

Simulation: You need to know how to use simulators like Gazebo or MuJoCo.

Bonus Skills:

1. Control Systems Engineering: Understanding how to design and tune control systems is crucial for many agentic AI applications.

2. Embedded Systems: If you're working with physical devices, you'll need to know how to program and interact with embedded systems.

3. Ethics of AI: As you build more powerful agentic systems, understanding the ethical implications becomes increasingly important.

In short To become a Agentic AI Engineer, one should focus on Reinforcement Learning, Robotics (if applicable), Classical AI, Programming, and Simulation. Bonus skills include Control Systems, Embedded Systems, and Ethics of AI.

Table Comparison - Generative AI vs. Agentic AI:

Best FREE and PAID resources to learn Agentic AI and Generative AI

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