It’s amazing to watch what happens when the whole world grabs onto something. General-purpose technologies have widespread applications and influence across a multitude of industries. This is causing innovation and economic growth by enabling new processes, products, and services. Our biggest challenge….bullshit.

Bullshit is a very technical term for an inaccurate prompt response. LLM’s are notorious bullshitters.

They use neural nets and training as their underlying mechanisms. Just like humans, they only know what they know. The problem arises when they don’t know, yet they have been built to possess the confidence of a trained ninja. They SOUND and LOOK like they know very well, but they’re a wolf in sheep’s clothing. That’s the precise moment they go from very smart to complete bullshit artists.

It’s also EXACTLY why they’re not ready to be products.

LLM’s are a tool that does an amazing job with languages. Masters of Verse. Wizards of Words. Kings of Koine. Connoisseurs of Code. They are programmed to think and learn. The problem remains - they simply haven’t learned enough, yet.

It’s okay when tools fail internally. That’s what we tinkerers are trying to do. We’re trying to apply the tools to our work that help us make it easier or more effective. LLM’s are prototype tools that are not ready for consumers. Yet profiteers will profit and products will launch….because of money.

Scammer, Shyster, Quack, Swindler, Hustler, Charlatan, Snake Oil Salesman.

They’re simply not ready for market - so don’t rush to buy the solution.

If you need more evidence - look at the Perplexity bullshit here. The Claude 3.5 Sonnet update was released this week and it appears to have promise. It’s simply too early to tell. Tools, not products.

Moving on

Have you made the distinction between Artificial Intelligence (AI) and Intelligence Augmentation (IA)?

Artificial Intelligence: Refers to the simulation of human intelligence in machines that are programmed to think and learn. These machines can perform tasks that typically require human intelligence such as problem-solving, learning, understanding natural language, recognizing patterns, and making decisions.

Intelligence Augmentation: Focuses on enhancing human intelligence with the help of technology. It’s about creating systems that work alongside humans to improve their abilities and decision-making processes. IA aims to assist and augment human capabilities rather than replace them. It emphasizes collaboration between humans and machines, where technology supports human expertise and judgment.

We’re intentionally putting the open-innovation workshop at the center of the technology. Polaris, a tool from our partners over at Volum8, allows us to capture, organize, and rate your ideas. This is the essence of human-centric design and it at the core of what we’re doing at the cre8lab. The tech is not accessible until the workshop. We believe this is currently the best way forward.

There are a ton of cool examples of IA’s - Decision support systems, virtual assistants, data visualization tools, and collab software that helps professionals generate authentic, validated data so they can analyze it and put it to use more effectively.

A couple more thoughts

IA can be considered a subset of AI in some respects. It leverages AI technology to enhance humans. The primary distinction lies in the objective and the application.

AI focuses on creating autonomous systems that replicate human intelligence, while IA emphasizes the symbiotic relationship between mankind and machines. Right now, we are using the AI tools that help the IA tools become products that help us.

We could all use a little help.

For all those ‘start-ups’ and SME’s spouting about AI and making money and promises on the technology. Be careful.

The song pairing for this entry is brought to us from the year 2000 by They Might Be Giants:

https://open.spotify.com/track/5eLPCp9FLP4NGdu9pnTB4E?si=be4faa0823df4f7e

It’s amazing to watch what happens when the whole world grabs onto something. General-purpose technologies have widespread applications and influence across a multitude of industries. This is causing innovation and economic growth by enabling new processes, products, and services. Our biggest challenge….bullshit.

Bullshit is a very technical term for an inaccurate prompt response. LLM’s are notorious bullshitters.

They use neural nets and training as their underlying mechanisms. Just like humans, they only know what they know. The problem arises when they don’t know, yet they have been built to possess the confidence of a trained ninja. They SOUND and LOOK like they know very well, but they’re a wolf in sheep’s clothing. That’s the precise moment they go from very smart to complete bullshit artists.

It’s also EXACTLY why they’re not ready to be products.

LLM’s are a tool that does an amazing job with languages. Masters of Verse. Wizards of Words. Kings of Koine. Connoisseurs of Code. They are programmed to think and learn. The problem remains - they simply haven’t learned enough, yet.

It’s okay when tools fail internally. That’s what we tinkerers are trying to do. We’re trying to apply the tools to our work that help us make it easier or more effective. LLM’s are prototype tools that are not ready for consumers. Yet profiteers will profit and products will launch….because of money.

Scammer, Shyster, Quack, Swindler, Hustler, Charlatan, Snake Oil Salesman.

They’re simply not ready for market - so don’t rush to buy the solution.

If you need more evidence - look at the Perplexity bullshit here. The Claude 3.5 Sonnet update was released this week and it appears to have promise. It’s simply too early to tell. Tools, not products.

Moving on

Have you made the distinction between Artificial Intelligence (AI) and Intelligence Augmentation (IA)?

Artificial Intelligence: Refers to the simulation of human intelligence in machines that are programmed to think and learn. These machines can perform tasks that typically require human intelligence such as problem-solving, learning, understanding natural language, recognizing patterns, and making decisions.

Intelligence Augmentation: Focuses on enhancing human intelligence with the help of technology. It’s about creating systems that work alongside humans to improve their abilities and decision-making processes. IA aims to assist and augment human capabilities rather than replace them. It emphasizes collaboration between humans and machines, where technology supports human expertise and judgment.

We’re intentionally putting the open-innovation workshop at the center of the technology. Polaris, a tool from our partners over at Volum8, allows us to capture, organize, and rate your ideas. This is the essence of human-centric design and it at the core of what we’re doing at the cre8lab. The tech is not accessible until the workshop. We believe this is currently the best way forward.

There are a ton of cool examples of IA’s - Decision support systems, virtual assistants, data visualization tools, and collab software that helps professionals generate authentic, validated data so they can analyze it and put it to use more effectively.

A couple more thoughts

IA can be considered a subset of AI in some respects. It leverages AI technology to enhance humans. The primary distinction lies in the objective and the application.

AI focuses on creating autonomous systems that replicate human intelligence, while IA emphasizes the symbiotic relationship between mankind and machines. Right now, we are using the AI tools that help the IA tools become products that help us.

We could all use a little help.

For all those ‘start-ups’ and SME’s spouting about AI and making money and promises on the technology. Be careful.

The song pairing for this entry is brought to us from the year 2000 by They Might Be Giants:

https://open.spotify.com/track/5eLPCp9FLP4NGdu9pnTB4E?si=be4faa0823df4f7e