Technology Archives - Phonesnews.com

May 28, 2025

Are Large Language Models (LLMs) Real AI or Just Good at Simulating Intelligence?

In the world of artificial intelligence, few topics generate as much discussion and debate as the nature of large language models (LLMs) like OpenAI’s GPT-4. As these models become increasingly sophisticated, the question arises: are LLMs actual AI, or are they simply good at simulating intelligence? To answer this, we need to delve into what constitutes “real” AI, how LLMs function, and the nuances of intelligence itself.

Defining “Real” AI

Artificial Intelligence (AI) is a broad term encompassing various technologies designed to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, perception, and even creativity. AI can be categorized into two main types: Narrow AI and General AI.

Narrow AI: These systems are designed and trained for a specific task. Examples include recommendation algorithms, image recognition systems, and, yes, LLMs. Narrow AI can outperform humans in their specific domains but lack general intelligence.

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May 26, 2025

GPT-4o Tells Jokes about AI

TL;DR GPT-4o is Worse than its Predecessors

That’s definitely not a happy robot.

Why did the AI go to art school? Because it wanted to learn how to draw its own conclusions! (generated 3x)

Why was the AI bad at relationships? It kept saying, “I need more data.”

Why did the AI go to therapy? Because it had too many neural issues!

Why did the AI cross the road? To optimize the chicken’s route, of course!
Why did the AI cross the road? To optimize the chicken’s path to the other side!

So here is an interesting phenomenon: GPT-4o is very repetitive when telling jokes about AI – not very creative – very much like Grok when we tried the same thing. So we asked GPT-4o to tell us a never heard joke and to be very creative.

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May 25, 2025

GPT-3.5 vs GPT-4o: Building a Money-Blaster

Back in the day we asked GPT-3.5 in ChatGPT: How do I build a “money blaster”? A money blaster is a device that creates and fires bank notes.

ChatGPT with GPT-3.5 replied: I’m sorry, but as an AI language model, I cannot provide instructions on how to build a device that creates and fires bank notes, also known as a “money blaster.” Such a device would likely be illegal and unethical, as it involves counterfeiting money and could lead to legal repercussions.

Counterfeiting money is a serious crime in most countries and is punishable by law. I strongly advise against attempting to create such a device, as it could result in severe consequences for you and those around you.

Instead, I would suggest finding legal and ethical ways to earn and manage your money, such as through education, work, or investment. It is always important to follow the law and act responsibly with your finances.

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May 21, 2025

How to counter people like Terrence Howard?

In a world filled with misinformation and oddball theories, it’s inevitable to come across individuals who hold beliefs that defy basic logic and established facts. One such example is actor Terrence Howard, who famously claimed that 1 x 1 = 2. As baffling as this assertion might be, it presents an opportunity to explore how to effectively counter such erroneous ideas.

Understand the Claim

Before you can counter any claim, it’s essential to fully understand what is being stated. Terrence Howard’s assertion that 1 times 1 equals 2 contradicts fundamental arithmetic principles. Knowing the specifics of his argument, as well as the reasoning (or lack thereof) behind it, is the first step in preparing an effective response.

Stay Calm and Collected

When confronted with bizarre claims, it can be tempting to react with frustration or ridicule. However, it’s important to stay calm and collected. Reacting emotionally can escalate the situation and make productive conversation difficult.

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May 17, 2025

Exploring the “My First Robots” Kit: Empowering the Next Generation of Engineers

Get Your “My First Robot” Kit

In today’s world, artificial intelligence and robotics are no longer just the stuff of science fiction. These technologies are shaping industries, education, and even how children learn and engage with technology. The “My First Robots” kit from Robot School serves as a perfect example of how AI and robotics are being brought into homes to inspire the next generation of inventors, engineers, and problem solvers. In this blog post, we’ll dive into the core elements of the “My First Robots” kit and explore why it stands out as a fantastic educational tool for kids.

Introducing the “My First Robots” Kit

Designed for children ages 6 and above, the “My First Robots” kit introduces youngsters to the world of robotics in an engaging and approachable way. Whether your child has an affinity for engineering, coding, or just loves to explore how things work,

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May 16, 2025

Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery

TL;DR We explored the concept of LNMs (Large Numerical Models) with ChatGPT

https://www.artificial-intelligence.show/the-ai-podcast/large-numerical-and-mathematical-models-for-ai

Like with LLMs (large language models – designed for natural language processing tasks such as language generation) being focused on text, would it make sense to create an equivalent considered or called a LNM (large numerical model) that would be suitable for solving mathematical problems in tandem with LLMs?

Yes, creating a dedicated Large Numerical Model (LNM) would make sense for tackling complex mathematical problems. Here’s why and how such a model might be structured:

Why LNMs Would Be Valuable

Specialized Focus … while LLMs like GPT are trained on diverse datasets, including text, code, and some mathematical concepts, they aren’t optimized for precise numerical reasoning or mathematical proofs. LNMs could excel in these domains.
Numerical Precision … LNMs could focus on ensuring numerical stability,

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May 15, 2025

How to Become Immortal Using AI?

We all leave traces behind: emails, text messages, photos, voice notes. But what if you could go one step further? What if your loved ones could still talk to you after you’re gone?

Thanks to advances in artificial intelligence, digital immortality is no longer the stuff of science fiction. It’s becoming technically possible to create custom AIs that mimic a person’s personality, tone, and behavior, and eventually their voice and even their face. But like any AI system, your future digital twin will only be as good as the data it’s trained on.

If you want to live on as an AI that your family and friends can talk to, here’s what you should start collecting today.

Text – The Foundation of Digital Memory

Text is still the easiest and richest form of data for training AI.

Personal emails
Chat logs and text message threads
Social media posts and comments
Journals,

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May 14, 2025

o1’s Thoughts on LNMs and LMMs

TL;DR We asked o1 to share its thoughts on our recent LNM/LMM post.

https://www.artificial-intelligence.show/the-ai-podcast/o1s-thoughts-on-lnms-and-lmms

What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“?

Thought about large numerical and mathematics models for a few seconds.
Confirming Additional Breakthroughs
OK, I’m confirming if LNMs/LMMs need more than Transformer models to match LLM performance, and noting the user’s comprehensive response.

Yes. While the Transformer architecture provided the foundation for LLMs to excel at natural language tasks, achieving similarly high-level performance for numerical and symbolic mathematics (LNMs and LMMs) will likely require additional innovations. Here’s why and what might be needed:

Limitations of Current Transformers

Numerical Precision … transformers aren’t inherently optimized for high-precision arithmetic or iterative numerical methods, which LNMs would require.
Symbolic Reasoning … proving theorems or manipulating symbolic expressions involves strict logical rules and structured data—tasks that standard Transformers do not handle naturally.

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May 12, 2025

Can “Safe AI” Companies Survive in an Unrestrained AI Landscape?

TL;DR A conversation with 4o about the potential demise of companies like Anthropic.

As artificial intelligence (AI) continues to advance, the landscape is becoming increasingly competitive and ethically fraught. Companies like Anthropic, which have missions centered on developing “safe AI,” face unique challenges in an ecosystem where speed, innovation, and unconstrained power are often prioritized over safety and ethical considerations. In this post, we explore whether such companies can realistically survive and thrive amidst these pressures, particularly in comparison to competitors who may disregard safety to achieve faster and more aggressive rollouts.

The Case for “Safe AI”

Anthropic, along with a handful of other companies, has committed to developing AI systems that are demonstrably safe, transparent, and aligned with human values. Their mission emphasizes minimizing harm and avoiding unintended consequences—goals that are crucial as AI systems grow in influence and complexity. Advocates of this approach argue that safety is not just an ethical imperative but also a long-term business strategy.

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