Popular about AGTInw
How AgroGloryTime is building a global superintelligence.
Back around 2010, without knowing anything about bitcoin, we thought about creating virtual money.
While bitcoin, and gold (almost) has no real value, our goal from the beginning was to give computing the value attached to solving useful problems or obtaining information. We viewed new money as analogous to tokens (not yet knowing that word), projects or property/intellectual rights that can be secured and generate revenue and then converted to fiat money.
The Bitcoin blockchain has offered an innovative approach to solving this problem. They redefined the concept of money, which was traditionally associated with the function of accumulating savings, managing settlements, etc.
In reality, money is simply a function of accounting for debt. And that is what was ingeniously put into computerized form, and then used to create its own cryptocurrency.

Already around 2014, tokens turned out to be roughly what we envisioned, but we didn’t know how it could be realized. Cryptocurrency is a note of debt and a token ( in our case) is a stock, receipt, artwork, digital right to a work of art, etc.
This implements a powerful network of distributed computing and complex dynamic tables, i.e. blockchain. However, these calculations may not be of value. We decided to enable distributed computing by generating revenue and gaining some knowledge and information.
The AGTInw system differs from artificial intelligence in that it is built using our network based on completely different neurons (called Agents) – connecting humans, and their decisions, thoughts and goal-setting – and a computer dibo gadget. I will first give a brief description of artificial intelligence and then move on to a description of our network.
Current artificial intelligence was developed back in the 1940s, and Rosenblatt’s perceptron can be considered the closest and most understandable model. A neural network contains processors, which are actually small information stores and adders (made of dozens of transistors) connected to each other. The links between them are simply one-dimensional numbers, (perhaps now vectors as well) – so-called weights that change with learning. The process of training a network is simply setting up the weights. Well, the complexity of such quasi-synapses is also incomparable to the synapses of the human brain. However, the number of neurons – billions, co-location and fast performance allow them to achieve truly fantastic results!

And yet, the statement that the GPT network has more than 1 billion “neurons”, and the number of them will soon be comparable to the human brain, does not make sense, because the neuron, in the neural network of the brain, performs independent functions and is actually a living organism, controlling, thinking, making decisions. Naturally, the processing power of a human with 100 billion neurons in the brain is far superior to any AI neural network, including GPT. And the moments of goal-setting, morality, and self-awareness are generally inaccessible to classical computing systems. We’re reading R. Penrose’s “Shadows of the Mind.”
In turn, the AGTInw intelligent network differs from neural networks in that it somewhat imitates the work and development of the human brain, as well as uses algorithms for creating itself, self-development and self-learning.

Once again, a conventional neural network, as it is commonly called, is not a full-fledged neural network, but merely an aggregate of a huge number of small processors on which a Rosenblatt perseptron or similar more modern systems are emulated. Its advantages are high performance and the ability to emulate almost any perceptron-type network. However, the disadvantage is the lack of goal-setting capability. Also, AI neurons and synapses cannot be compared to those proposed in AGTInw.
Unlike conventional neural networks, each neuron in our system corresponds to real 100,000,000,000 neurons (incomparable to AI neurons) of the human brain with all its advantages and disadvantages, as well as connectivity, memory and power of the computer/gadget belonging to the person (“Owner” of AGTIneuron). The processing power of our system allows us to solve digital problems on a computer as well as manage network operations on a phone.
Our app has two versions: for phone and for computer. I can go into detail about the advantages and disadvantages of our system.

So, our idea of building a network is to connect a person and their gadget (computer, tablet or phone). The system allows you to register an Agent who can perform tasks or put them in front of other Agents. By clustering the available Agents in the Network, it is possible to create networks for various tasks in search, construction, medicine, translation, law, big data processing, etc.
This system resembles the human brain, where neurons combine to make new connections and form new networks. Each network initiator trains the network using a learning matrix, and this potentially leads to the solution of entirely new problems.
The creation of such a system will lead to its gradual development, where the initial small number of AGTIneurons, links and sub-networks will gradually increase, and the cost and complexity of the tasks to be solved will increase. Dealing with large amounts of information will justify the cost of developing this system. As the number of neurons grows, the chance of the network having a profound impact on humanity increases. Our goal is to take the Network from the level of brain organization of worms to butterflies to reptiles and further to build a system with millions of neurons and the ability to organize new networks to solve complex problems that seemed unsolvable.

But the payment in this network will be made with AGTI token – in what I see a sure prospect of its price growth.
It is the profitability and security of the AGTI token that will attract people to the work and development of the AGTInw network, and further the increase in the price of the token caused by participation in the Network will pull additional capitalization of agribusiness conducted by AgroGloryTime.

©P.Drobyshev, D.Novhorodkina