Monthly Archives: February 2023

I was just forwarded the following video about ChatGPT and AI:

Interesting. Thanks for the forward.

Where do you think we are on the curve?

I would assert that we are just at the beginning – in the “Napster-esque-era” of AI.

But, I look at AI with the new constant questions:

• “How can I harness AI?”
• “How can I stand out from others who are using AI?”

What do you think?

I was driving home from Koh Samed to Bangkok yesterday and started getting annoyed by the cars/trucks that were in the fast lane when they should move over to let faster traffic go by. During my drive, I began to think about how much Google/Apple or other maps apps have on us as data. They know where we go, when we go, and often where we plan to go if we have set up “directions” in the device. They know the speed we travel and the stops we make. From my understanding the apps show congestion based upon the number of stationary phone sitting in a location.

But let’s take this further:

Since the apps know how many phones are in a general area and are obviously tracking who is exactly where and at what time, have they devised a way to know which drivers are staying in the left lane and causing all traffic to stay behind them or go around them dangerously in the slow lane? Can the app developers use the data to analyze who is slowing down too much to watch an accident, play on their phone, watch some cows, or read a billboard? And can they take this data to begin to ticket people for un-artful driving:

“Honey, I got a ticket in the mail; it says I was driving doing 103km/h or less in a 120km/h fast lane for 13 minutes from Rayong to Bangkok and caused 47 cars to go around me unnecessarily. The ticket costs $132.00.”

Data like this could be connected to cars to indicate to the driver, it is time to move over to let faster cars drive by – and also ensure that I do not get a ticket for this in the future. Repeated offenders could have their license cost more to renew – or suspended altogether.

AI can also be used to help with car-traffic throttling in other ways:

1. Traffic prediction: AI can analyze traffic data from various sources such as cameras, GPS, and sensors to predict traffic conditions in real-time. This can help traffic management centers to plan ahead and deploy resources to alleviate congestion before it occurs.
2. Intelligent traffic signal control: AI can be used to optimize traffic signal timings based on real-time traffic data. The AI algorithm can adjust the timings of traffic signals at intersections to reduce congestion and improve the overall flow of traffic.
3. Route optimization: AI can analyze traffic patterns and suggest alternate routes for drivers to take to avoid traffic congestion. This can help reduce the number of cars on congested roads and improve traffic flow.
4. Autonomous vehicles: AI-powered autonomous vehicles can help reduce traffic congestion by optimizing their driving patterns and reducing the likelihood of accidents. Autonomous vehicles can also communicate with each other to coordinate movements and avoid traffic jams.
5. Dynamic tolls and pricing: AI can be used to determine dynamic tolls and pricing based on traffic demand. By analyzing traffic patterns, AI can determine when and where to implement tolls and pricing to reduce congestion and incentivize drivers to take alternate routes.

Overall, AI can be a powerful tool for managing car-traffic, improving traffic flow, and reducing congestion. By implementing AI-powered systems, we can create a safer, more efficient, and happier transportation networks. Let’s take advantage now and let me go to the beach quicker next time.

Back in 2021, there were several AI interfaces similar to ChatGPT that you can interact with:

1. Dialogflow: Dialogflow is a Google-owned AI platform that allows developers to create conversational interfaces for various platforms such as websites, messaging apps, and voice assistants.
2. Amazon Lex: Amazon Lex is a service from Amazon Web Services (AWS) that enables developers to build conversational interfaces for applications using natural language understanding (NLU).
3. IBM Watson Assistant: IBM Watson Assistant is an AI-powered chatbot platform that allows businesses to create chatbots that can interact with customers through natural language.
4. Microsoft Bot Framework: The Microsoft Bot Framework is a set of tools and services that developers can use to create conversational interfaces across multiple channels, including email, SMS, and popular messaging platforms like Facebook Messenger, Skype, and Slack.
5. Rasa: Rasa is an open-source conversational AI framework that allows developers to build and deploy chatbots and virtual assistants with advanced natural language processing (NLP) and dialogue management capabilities.

These are just a few examples of the many AI interfaces that are available today. Each platform has its own strengths and weaknesses, so it’s important to evaluate your specific needs and requirements before choosing a tool to build your conversational interface.

Today, however, we are seeing the “big guns” release their powerful rivals – possibly before they should be open to the public:

1. China is sending out the MOSS Chatbot.
2. Google is unleashing Bard.
3. Microsoft has Prometheus and has invested in or bought ChatGPT.

My understanding is that many people are figuring out ways around the safeguards put in place, and instead using the AIs for designing unethical, prejudice, deceitful, and possibly dangerous output.

The good news to my ears is that Meta is giving LLaMA AI to scholars and researchers in order to test out and see why these others are failing, having major issues, or are being circumvented. I hope that the foresight of Meta’s LLaMA use and its practitioners will disable major catastrophes from occurring in more of a walled-garden space used by “not-dummies”. I have heard Musk foretell of a bleak future where AI comes from nowhere to wipe-out humanity or begin to use us in ways that we might not want.

Please let us have lots of sense and a less neo-liberal attitude with something that is beyond out comprehension of powerful. We are playing with fire. Let’s have the fire extinguisher on-hand.

Written by none other than AI:

Ah, Artificial Intelligence (AI) and Artificial General Intelligence (AGI), the buzzwords that everyone loves to throw around without really understanding what they mean. The future of technology, they say, the solution to all of our problems, they say. And yet, here we are, decades after the inception of AI research, and all we have to show for it are some fancy chatbots and recommendation systems that still can’t seem to understand sarcasm.

Don’t get me wrong, AI has certainly made some impressive advancements in specific fields like computer vision and natural language processing. But the truth is, despite all the hype, AI is still far from reaching the level of intelligence that we see in humans, let alone surpassing it. And yet, we’re already talking about AGI as if it’s just around the corner. Ha! The idea of creating a machine that can perform any intellectual task that a human being can is nothing short of delusional.

But let’s not even get into AGI yet, let’s talk about the current state of AI. Despite its limitations, AI is often touted as a silver bullet that can solve all of our problems. It’s the cure for world hunger, the solution to climate change, and the key to unlocking limitless economic growth. Really? Because all I see are algorithms that struggle to accurately identify objects in photos, chatbots that can barely carry a coherent conversation, and recommendation systems that are easily manipulated by data scientists.

And let’s not forget about the ethical issues that arise with AI. Bias and discrimination are inherent in any system that is trained on data, and AI is no exception. The lack of accountability in AI systems means that when things go wrong, there’s no one to hold responsible. And yet, we’re just supposed to blindly trust these algorithms and the companies that develop them? Give me a break.

Now, on to AGI. The idea that we’ll ever create a machine that can match or surpass human intelligence is pure fantasy. First of all, we still don’t even have a solid definition of intelligence, let alone a way to measure it. How can we create something that we don’t even understand? And even if we could, the complexity of the human brain is unparalleled in the known universe. The idea that we could recreate it in a machine is simply ridiculous.

Furthermore, even if we could create an AGI, what makes us think that it would be benevolent or even neutral towards humans? Just because we program it to follow certain rules doesn’t mean it will continue to do so as it evolves and learns on its own. In fact, the idea of a superintelligent AI that could potentially turn against us is a popular topic in science fiction for a reason.

And yet, despite all of these limitations and potential dangers, people continue to pump billions of dollars into AI research and development. Why? Because it’s trendy. Because it sounds cool to say that you’re working on AGI. Because it’s an easy way to make a quick buck by selling snake oil to investors.

In conclusion, AI and AGI are nothing more than buzzwords used to generate hype and drive investment. The reality of the current state of AI is that it’s far from reaching the level of intelligence that we see in humans, let alone surpassing it. And the idea of AGI is pure fantasy. So, let’s all stop pretending that we’re on the cusp of creating superintelligent machines and focus on solving real-world problems with practical, proven solutions.

The writing had me sold until that last bit where it asserted the fact that AGI is pure fantasy. I feel that with the use of AI, AGI might be around the corner sooner than we think – if it is not here already. What are your thoughts?