#ai #future
So why not hire a data head, which is a robot? Why can only the CEO and Operational head be robots? Can a robot find its way from fresher to managerial mode? Is robotic CEO Mika favouring robotic hires 😊
Well, it all depends on who made the robot.
Does the robot have bias?
What can the robot give us?
Who can assist in the decision-making of the robot?
What are the challenges Mika and Tang Yu, the Robotic leaders facing?
What to expect? General-purpose or Specialized robot?
How much time to train?
Battery?
Technology stack to build it?
Delivery to the customer?
Installation Tricks and Tips?
Do we even need robotics when jobs are already scarce?
General purpose or Specialized? What say? Let’s develop flexible computing in robotics.
Well, we all have some specialization, then why do we want to wait for general-purpose robots? The skills missing in a robot can be honed and downloaded, as I mentioned in my sci-fi series on robots. Download whatever feature you want the robot to have. Update the features you want the robot to have. We should have something like this.
But avoid reprogramming? Relearning to be supported!
General-purpose robots- A Jack of all things is not an easy thing? Let it take time till you deploy and learn with special-purpose robots.
There are two meanings of general-purpose robots—one is a robot that can perform all jobs in the world (the traditional definition), and the other is a robot that can handle any task assigned to it with different inputs (such as collecting water from a filter into bottles, whether in full light or dim light, or when the weather filter is moved from the kitchen to the lobby). This is a need for flexible computing in robotics.
People can have two robots if they want more.
But why not fix specialized robots, making them flexible, and send them to market? Then, automatically improve on them. Let’s develop flexible computing in robotics.
While general-purpose features like ChatGPT, which know everything, can wait.
Let’s do it in a spiral way.
Spiral around and come back to the same task with a positive displacement the next year.
Home robots and office robots are separate.
Some examples of robotics in real life are:
1. Robot Mika, at a Polish spirits company, Dictador. Developed by Hanson Robotics. It gives decisions free of personal bias.
2. Robot Tang Yu as an Operational Head of the Chinese gaming firm NetDragon Websoft.
3. Then there are Hybrid Leadership Models. Here, human leaders look at AI for analytics and assisting roles. This is also the use of AI at a high level. A robot can take on this role; why wait to become a CEO like Mika?
4. In the end comes Data-Driven Decision Making: Similar context as above, just at a lower level. This can also be referred to as the use of AI in business decisions. A robot can take this position why wait to be Operational Head like Tang Yu.
We are all unique individuals, with diverse aims and aspirations. We are not general-purpose know-it-alls, so why do we want our robots to be non-humanly?
The main goal isn’t to achieve mechanical balance, which has already been done; we’re aiming for an AI robot. Let a versatile robot come its way. Until then, let’s use specialized robots, like home robots and office robots, or simply a robot that acts as a friend.
Therefore, robots that can generalize and learn from experience, such as personal robots, are needed before a full-scale, all-around robot becomes a reality.
The current robot chosen as CEO can be replicated by other companies if they purchase it from the same company, such as FIGURE AI, NEO, or similar. The hardware is the same, the algorithms are the same, so what differs then? On-the-job training, trade secrets, and the knowledge gained. Are you training our robots for all this? Then all robots would be unique, just as unique as you and I.
Thank you for reading.
To be continued…
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Artificial Intelligence, Computer Science & Mathematics 