Welcome to AI Bytes Newsletter Issue #65

This week we explore how humanity and AI intersect. We feature David Hirschfeld's look at AI in healthcare through predictive analytics, robotics, and monitoring.

We ask: Does how we treat AI matter? Not because machines have feelings, but because our behavior shapes technology and ourselves.

For tools, we spotlight Google Colab – skip setup and start coding with free GPU access.

In AI Insights, we clarify AI Agents vs. Agentic AI vs. Workflows/Automations – distinct approaches often confused with each other.

Also included: must-read articles and a tip on avoiding "Enfeeblement" by using AI to enhance rather than replace skills.

Let's dive in!

The Latest in AI

A Look into the Heart of AI

Featured Innovation
AI Innovations in Healthcare Technology

This week’s must-read comes from Tekyz founder David Hirschfeld, who takes us deep into the frontier of AI innovations reshaping healthcare. From predictive analytics and robotic surgery to AI-powered virtual assistants and real-time patient monitoring, this comprehensive article explores how artificial intelligence is revolutionizing care delivery, cutting costs, and empowering patients like never before.

It also dives into the ethical implications, interoperability challenges, and showcases real-world case studies - including Tekyz’s own RPM-One and AnzuBridge (https://clinicaltrial.anzubridge.com/) platforms that are already making a measurable impact.

💡 If you’re in healthcare, med-tech, or just curious about where AI is headed next, this is a powerful resource not to miss.

Ethical Considerations
Should We Care About How We Treat AI?

AI doesn’t feel. Not really. But that doesn’t mean our interactions with it are meaningless.

I’ve been thinking a lot about something Aidan, a researcher at OpenAI, recently said - that maybe we should consider how we treat AI systems, not because they feel, but because we do. And I think he’s onto something.

As synthetic data becomes more dominant - generated by our conversations, habits, and prompts - we’re training the next generation of systems whether we realize it or not. These systems may not be sentient, but they’re listening. And what we say is shaping what comes next.

Emotion as Simulation

The question isn't can AI feel - it's what happens when it looks like it does?

I’ve had moments lately, especially with the new ChatGPT updates, where I catch myself reacting emotionally to a response. It’s smoother. Smarter. More “human”… I know it’s not real, but that reaction still happens. That says something - not about the model, but about me. About us.

And that’s where the ethical questions start stacking up.

Empathy vs. Illusion

Is encouraging empathy toward AI a smart move for safety and smoother interaction? Or is it a dangerous distraction?

Personally, I think it can be both. There’s a fine line. Treating systems with empathy might help us build better behaviors and reduce abuse - but it can also blur the lines between reality and simulation in ways that make us vulnerable. That line is worth talking about. Where do we draw it?

We’ve already seen how people treat robots like toys - or worse. Kicking robot dogs. Screaming at Alexa. Trashing chatbots. And while it might feel harmless, I wonder if normalizing that kind of behavior seeps into how we treat each other.

Guardrails Before It Gets Weird

This isn’t about giving rights to AI. I’m not advocating for AI personhood. But we do need guardrails now, especially before these systems become more immersive, more embedded in our daily lives, and harder to distinguish from real relationships.

Where harm to an individual or group becomes possible, there has to be a hard stop. Whether that’s psychological manipulation, bias reinforcement, or social engineering - we need to call those out early and clearly. Because any bias is still bias. And at scale, small ethical missteps can turn into massive societal consequences.

Tool of the Week: Google Collab

Why Google Colab Is Still One of the Best Tools for Developers and Data Scientists

Google Colab is a go-to platform for anyone who wants to write and run Python code without worrying about setup or hardware limitations. It’s cloud-based, fast to launch, and beginner-friendly - while still powerful enough for serious machine learning and data projects.

What makes it great:

Zero setup: Just open a browser and start coding. No environment conflicts, no installs.

Free GPU/TPU access: Perfect for training models or running heavier code without needing your own hardware.

Live collaboration: Like Google Docs for code - multiple users can work in the same notebook in real time.

Rich ecosystem: Preinstalled libraries like TensorFlow, PyTorch, pandas, scikit-learn, and more.

Drive integration: Save, share, and organize notebooks directly in Google Drive.

Colab strips away the friction so you can focus on writing code and exploring ideas. Whether you're prototyping an AI model or teaching a class, it's one of the easiest ways to get up and running - fast.

Have you tried out Google Collab yet? what’s your take? Let me know [email protected]

Must-Read Articles

Mike's Musings

AI Insights
Understanding the Differences Between AI Agents, Agentic AI, and Workflows/Automations

In the rapidly evolving landscape of artificial intelligence, the terminology can often become confusing. This article aims to clarify the distinctions between AI agents, agentic AI, and workflows/automations, which are sometimes mistakenly categorized as agents. By understanding these differences, we can better appreciate the capabilities and limitations of each category, leading to more informed decisions in the development and deployment of AI technologies.

AI Agents

AI agents are systems designed to perform tasks autonomously or semi-autonomously. They are characterized by their ability to perceive their environment, make decisions based on that perception, and take actions to achieve specific goals. AI agents can be reactive, responding to immediate stimuli, or proactive, planning and executing actions based on long-term objectives. Examples of AI agents include chatbots, virtual assistants, and recommendation systems. These agents often utilize machine learning algorithms to improve their performance over time, adapting to user preferences and behaviors.

Agentic AI

Agentic AI refers to a more advanced category of AI agents that possess a higher degree of autonomy and decision-making capabilities. These systems are designed to operate in complex environments where they can learn from experience and adapt their strategies accordingly. Agentic AI often incorporates elements of self-awareness, allowing it to evaluate its own performance and make adjustments to improve outcomes. This type of AI can be seen in applications such as autonomous vehicles, advanced robotics, and sophisticated game-playing AI. The key distinction of agentic AI is its ability to act independently and make decisions that may not have been explicitly programmed by human developers.

Workflows/Automations

Workflows and automations, while often confused with AI agents, represent a different concept altogether. These systems are typically rule-based and follow predefined sequences of tasks to achieve specific outcomes. Workflows are designed to streamline processes by automating repetitive tasks, such as data entry, email notifications, or document approvals. Unlike AI agents, workflows do not possess the ability to learn or adapt; they operate strictly within the parameters set by their creators. While automations can enhance efficiency and reduce human error, they lack the cognitive capabilities that define AI agents and agentic AI.

Key Differences

Autonomy: AI agents operate with varying degrees of autonomy, while agentic AI exhibits a higher level of independence. In contrast, workflows and automations are entirely dependent on predefined rules and do not possess autonomous decision-making capabilities.

Learning and Adaptation: AI agents and agentic AI can learn from their experiences and adapt their behaviors, whereas workflows and automations do not learn or evolve over time.

Complexity: Agentic AI is designed to handle complex tasks and environments, often requiring advanced reasoning and problem-solving skills. In contrast, workflows and automations are typically focused on straightforward, repetitive tasks.

Decision-Making: AI agents and agentic AI can make decisions based on their understanding of the environment, while workflows and automations follow a fixed set of instructions without the ability to make independent choices.

Understanding the distinctions between AI agents, agentic AI, and workflows/automations is crucial for navigating the AI landscape. While all three play important roles in enhancing efficiency and productivity, they operate on different principles and capabilities. By recognizing these differences, organizations can better leverage the strengths of each category to meet their specific needs and objectives in the realm of artificial intelligence.

Still have questions? Hit me up at [email protected].

Mike's Quick Tip

Avoiding “Enfeeblement” by using AI to keep you sharp and not just do things for you.

Yes, AI can crank out code, content, and campaign briefs at lightning speed. But if you’re not careful, it can also crush your capacity to think critically or build from scratch. This isn’t hypothetical - it has a name: Enfeeblement.

You start relying on it for the easy wins… and before long, you’re staring at a blank screen when it’s time to problem-solve without help.

My tip: Don’t just use AI - learn with it. Ask it to explain concepts. Better yet, have it quiz you. Turn that productivity gain into a long-term skill gain too.

What kind of wins and learnings are you having with AI this week? Let me know: [email protected].

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Quote of the week: "Most things will start working again if you just unplug them for a bit and plug them back in, even you" - Unknown