The Harvard astronomers uncovered the real shape of the Milky Way's halo of the stars.

Researchers from Harvard University have now uncovered the shape of the Milky Way's stellar halo.  The image above this text shows that the Milky Way galaxy's real shape looks a little bit like American football. That thing is one of the most interesting things in the history of science. 

Because the halo around the Milky Way stretched. That means there is some kind of force that is turning its shape to look like the rugby ball. The jets from Sagittarius A are traveling straight out from the poles of that supermassive black hole. The reason why the halo around the material stretches only from certain points is interesting. 

If that stretching would happen because of plasma, that is coming out from the material disk's equator the stretching must be symmetrical. That means the shape of the stellar halo should be like a beach ball, not an American football. So that thing is very interesting. Is there some yet unknown plasma or energy jet that stretches the stellar halo? 

There is a possibility. That the Fermi bubbles at both sides of the Sagittarius A are the answer. For the strange shape of that material halo. The impact radiation, that comes from Sagittarius A or its material jets can explain the shape of the stellar halo. But that thing is pure speculation. 

Only the part of the material that forms the Milky Way and other galaxies is in their material disks. There are lots of stars and material, including star clusters around the spiral galaxies. This halo is interesting because at the impact area where the material disk impacts with galactic halo are forming whirls. And those whirls can be the beginning of new stars. 

The reason why researchers are researching the halos around galaxies is that they can tell about the shape of the universe. There are theories that the universe is looking a little bit like the galaxy. 

There is a ball-shaped point of visible matter that is surrounding the far larger structure of dark matter. But nobody surely knows about that thing. The symmetry of the shape of those gases tells about the energy sources in distant galaxies. When something hits those halos that affect its symmetry. 

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AI doesn't create new things. They just recycle old ones.

 

Even the best AI cannot generate a thing. It just recycles old ones. When we think about paintings. And other things that AI. The truth is that it only connects details from sources that somebody else made. In real life, it connects information or details from multiple sources. The number of sources determines how effective and smooth results the AI can create.

The weakness is that the AI doesn't understand what it does. Productive AI like Chat GPT selects a certain number of sources by using the search engine's results. Then the AI just select one or two paragraphs from those homepages and connects them to a new entirety. 

And here is the danger. The AI doesn't know what is reading in those paragraphs. The thing is that the AI recognizes the paragraphs. But because it doesn't know what is reading in those paragraphs. The AI can choose something that doesn't belong to any topics in the text. That means AI is far away from human intelligence.  

So do we need scholars in the time of AI? The answer is that humans still in a vital role in machine learning. The AI can even collect thesis from homepages that human is prechecked. AI can play chess better than humans because it can calculate possible movements so far ahead. The AI can fly aircraft closer to the ground than humans because it doesn't afraid.

It just needs two or four radars that can fly in canyons. And of course, GPS and map where is the profile of the canyon. The radar altimeter can measure the aircraft's distance to the ground. And side radars tell the distance to walls. 

The nose radar sees the possible obstacles. But that thing is not anything there the AI creates new things. An interesting thing about AI is that self-driving car requires more complicated code than drone needs. 

The learning AI can learn multiple canyon profiles. It just records the aircraft's route. When we make self-driving cars, they need much more variables than robot aircraft needs for a successful mission. 

Every variable is an object that the system must recognize. The AI-driven car simply stops the vehicle and says that the driver must use the manual system in city areas. The AI can make many complicated-looking things, but all those things that AI knows must be pre-program in its memory. Otherwise, AI is ineffective. 

But when we think about the future and autonomously learning AI, we must realize one thing. The human brain requires information that it uses for connecting memory blacks. 

The problem is this. We don't know the precise moment when the data collection begins. 

The human brain involves 86-500 billion neurons. Calculating those neurons is difficult because of things like the number of axons. And the ability to interconnect neurons together has a great effect on the brain. The number of connections between neurons is the same way important. As the number of physical neurons.