Focus on automating human labour as much as possible. Problems in the world all trace back to lack of resources. A rich man can afford to be 'nice to the environment', a poor man can't.
The below all are easier machine learning problems than self-driving cars, yet no big tech companies or national initiatives are focused on aggressively applying machine learning to them.
Likely a couple of billion dollars, a year, and a 100 people lab would 'solve' each specific problem.
1. Robot that cooks meals, and clean the dishes afterwards. That saves billions of hours daily.
2. Robotic self-cleaning toilets. Saves another billion hours daily.
3. Robots that can dig up dirt and build a house from that dirt.
4. An app that can teach anyone anything like a teacher would - literally - a talking avatar and cameras and voice output and machine learning powered dialogue.
5. Home manufacturing 'box' that can make 95% of anything that anyone typically wants (some arrangment of 3d printer/laser cutters/pcb placement/wood router machines etc, that can take plastics/wood/metal/electronic components and output a gadget/furniture etc)
The above 5 give the equivalent of a 'basic income' for everyone (if distributed to everyone, and assuming the finished gadget is about the size and complexity of an automobile).
Then the inputs/ouputs problem of energy/raw materials/waste needs to be provided.
Disregarding scientific advances like fusion power etc (which require more than 100 billion maybe, or not possible), a drone distrubution platform for getting the energy / matter (input/waste) handled.
To do this (as above, 100 people, a year, 1 billion dollars)
6. p2p aerial surveillance system for air traffic managment of millins of drones. Basically, a sky pointing camera gadget that analyzes and broadcasts what it sees and process. Millions/billions of these camera gadgets airdropped every few 100 meters .
7. a drone that can carry 100 kilos and drop ship materails/waste p2p using the p2p air traffic control. The drones are battery operated with range a coupel of kilometers.
8. a drone that can mid-air 'refuel' the above drones. Basically a flying battery that recharges that larger cargo drones.
Summary - 'gadgetize' every problem (it becomes a self contained mechano-electrical desktop/fridge size thing that a team of 100 people can rapidly iterate on) and throw machine learning at it at heavily as possible. Seek to eliminate human labour as fast as possible.
The below all are easier machine learning problems than self-driving cars, yet no big tech companies or national initiatives are focused on aggressively applying machine learning to them.
Likely a couple of billion dollars, a year, and a 100 people lab would 'solve' each specific problem.
1. Robot that cooks meals, and clean the dishes afterwards. That saves billions of hours daily.
2. Robotic self-cleaning toilets. Saves another billion hours daily.
3. Robots that can dig up dirt and build a house from that dirt.
4. An app that can teach anyone anything like a teacher would - literally - a talking avatar and cameras and voice output and machine learning powered dialogue.
5. Home manufacturing 'box' that can make 95% of anything that anyone typically wants (some arrangment of 3d printer/laser cutters/pcb placement/wood router machines etc, that can take plastics/wood/metal/electronic components and output a gadget/furniture etc)
The above 5 give the equivalent of a 'basic income' for everyone (if distributed to everyone, and assuming the finished gadget is about the size and complexity of an automobile).
Then the inputs/ouputs problem of energy/raw materials/waste needs to be provided. Disregarding scientific advances like fusion power etc (which require more than 100 billion maybe, or not possible), a drone distrubution platform for getting the energy / matter (input/waste) handled. To do this (as above, 100 people, a year, 1 billion dollars)
6. p2p aerial surveillance system for air traffic managment of millins of drones. Basically, a sky pointing camera gadget that analyzes and broadcasts what it sees and process. Millions/billions of these camera gadgets airdropped every few 100 meters .
7. a drone that can carry 100 kilos and drop ship materails/waste p2p using the p2p air traffic control. The drones are battery operated with range a coupel of kilometers.
8. a drone that can mid-air 'refuel' the above drones. Basically a flying battery that recharges that larger cargo drones.
Summary - 'gadgetize' every problem (it becomes a self contained mechano-electrical desktop/fridge size thing that a team of 100 people can rapidly iterate on) and throw machine learning at it at heavily as possible. Seek to eliminate human labour as fast as possible.