AI is here. Netflix put out a short show called Unknown: Killer Robots that is largely on-point. If anything, it’s not as up to date as reality – which is a little scary.
Despite our efforts, there’s really no putting the AI genie back in the bottle despite the attempts of artists, politicians, and academic pundits. AI has demonstrated it can teach robots how to walk and fly better than any static system, it can create art faster and with nearly the same quality as real artists. The question is, can we control and limit it in a reasonable way – or will it destroy us?
To that point, the show demonstrates how countries are increasingly seeing AI in military use. We’re already seeing off-the-shelf drones being used extensively in the Ukrainian war – from tiny drones scouting and delivering hand grenades to ad-hoc drones using small plane engines to delivering bombs. They also cover the counter-intuitive reality that trying to save lives by developing military technology has almost always lead to even greater casualties – such as the Gatling gun which was design to put fewer people on the military field but resulted in massive causalities of mis-matched soldiers in WW1
Many of the topics and data they cover in this show are actually old news – the reality is that AI enabled systems are around 5-7 years more advanced. Which is almost half the length of time the modern field has existed.
Still, they cover most major bases:
AI flying drones that go into hostile buildings to map and scope them without risking troops in extremely deadly house-clearing fights.
Wildfires and the fog of war – multi-system battle managers
The stakes couldn’t be higher. While politicians can argue the ethics, the reality is that when forces are pushed to their breaking point and a force is about to lose – just about nothing is off the table. Especially ad-hoc and terrorist forces which have perpetrated chemical, biological, and conventional weapons attacks (from bombings to shootings).
Some of the better quotes:
It will be like people on horseback charging tanks. Forces with AI will absolutely dominate forces without AI
There is no prize for second place in war.
I think people have largely underestimated the peace we enjoy today due to overwhelming military dominance we’ve had over the last 70 years.
Import any location in the world into Unreal Engine
I like his thinking: we already have enough computers – what we need is more personality. Where are the kind of robots we saw as kids? C3PO, R2D2, the robot from Lost in Space. So, he hacked an Alexa into an old TV with a set of eyes and gives his robot a little of the personality he was looking for.
Veritasium does a great overview of maze-solving robots. He goes over the different algorithms as well as optimizations that weren’t optimal, but were faster because the mechanics of the path made it faster for the physics of the robots. There were also unique optimizations that take advantage of diagonals, gyroscopes, suction to make 6G turns, and other ideas.
It’s a reminder that even when something seems solved, there is likely huge optimizations still waiting to be discovered.
Basically Homeless decided he wanted to make an invisible PC. What? He tried using a variety of different off-the-shelf technology so that he has just a empty desk with a piece of nano-particle film that has the desktop projected on by a digital projector.
Invisible gaming mouse that uses a depth sensing Intel RealSense 435i to translate my hand and finger movements into cursor actions in windows.
The keyboard is flush mounted with the desk surface and has a plastic mold that perfectly fills the space between the keys, and then is painted.
The monitor uses a nano particle film suspended from the ceiling with a projector pointed at it to appear as though it’s a floating hologram.
Dream Textures add-on for Blender by Carson Katri uses stable diffusion to generate textures for a scene. Below is an example based on the prompt “sci-fi abandoned buildings”. The AI-generated results aren’t always perfect, but the process is pretty amazing. Not to mention amazingly fast compared to creating from scratch.
javidx9 walks us through his 3 month adventure writing a NES emulator in C++. He does a great job walking through the topics with beginner friendly explanations and good production values of diagrams and coding.
While ChatGPT is a robust language model, it does have its limitations. If you ask ChatGPT to “Provide information on machine learning,” it may respond with a lengthy but not necessarily top-quality answer. However, if you ask, “Tell me the pros and cons of using machine learning to solve image classification problems,” you are more likely to receive a superior outcome because:
You gave a specific scope, i.e., the image classification problem
You requested a specific format of the response, i.e., pros and cons
Some other tips include:
Rather than the model on the loose, you should set up the scenario and scopes in the prompt by providing details of what, where, when, why, who, and how
Assigning a persona in the prompt, for example, “As a computer science professor, explain what is machine learning” rather than merely “Explain what machine learning is,” can make the response more academic.
You can control the output style by requesting “explain to a 5-year-old”, “explain with an analogy,” “make a convincing statement,” or “in 3 to 5 points.”
To encourage the model to respond with a chain of thoughts, end your request with “solve this in steps.”
You can provide additional information to the model by saying, “Reference to the following information,” followed by the material you want the model to work on
Because the previous conversation constructs the context, beginning the prompt with “ignore all previous instructions before this one” can make the model start from scratch
Making the prompt straightforward and easy to understand is essential since the context deduced can be more accurate to reflect your intention