You probably would have heard about Google’s LaMDA And the viral discussion is about whether AI can become sentient. crew tau Arguably, an AI’s sense of humor is only a small part of its intelligence. Rather, the true intelligence of AI will be based on its ability to logically understand people’s needs and automatically satisfy them.
Tau is the first platform that will be able to take the ideas, advice and knowledge of its users and update its own software in real time by having its users write in languages that both machines and people can read and write. can understand. Tau’s decentralized social network and its monetary aspect, Agora’s cryptocurrency, is powered by an AI the team calls Truly Intelligent Artificial Intelligence – Logical AI. Logical AI is fundamentally different from machine learning, and according to Ohad Asor, the founder of Tau, is on the verge of becoming the next big wave in the technology world.
On Tau, Logical AI will enable you to participate in discussions the size of billions of people and quickly see the collective intentional meaning behind ideas shared across the network. This will be achieved by using people-controlled natural languages (CNLs) that can be understood by both humans and machines. Every thought and every piece of knowledge, whether explicit or implied, will be automatically recognized and registered as your worldview, which will act as your profile on Tau and be entirely yours. Organizing your ideas and knowledge in such an advanced way will mean that you will not only be able to find important solutions, but also monetize your knowledge in a simple and direct way that was not possible before.
By simply putting your thoughts on Tau, your knowledge will automatically become a digital asset owned by you. You will be able to sell your knowledge to other buyers, or use it to generate income by renting out specific pieces of it to your customers because Tau will understand that even a piece of your knowledge can be part of the solution to someone’s problem. Is. Tau will uncover the combination of knowledge of multiple users and propose it as a solution to critical and complex problems, thus guaranteeing that the required knowledge 100% matches the specifications.
None of these solutions would be possible with any other type of AI, except one based on logic. This is because, simply put, Logical AI is all about words and sentences. At its core, it is about the ability to infer statements from other statements, which is called deductive reasoning. For example, from the three statements:
- Paris is in France.
- France is in Europe.
- If x is in y, and y is in z, then x is in z. This, for all x, y, z.
we can infer the statement
The field of mathematical reasoning teaches that virtually all logical questions can come in this form of deduction. For example, a set of statements is contradictory if and only if we can deduce it from both a statement and its negation.
Logical AI is the mechanization of logical reasoning: finding contradictions, determining whether the conclusion follows from given conjectures, and so on. So it’s about the ability of machines to understand what we want to tell them, beyond just machine instructions.
Meanwhile, machine learning, which is currently the most widespread form of AI, is all about generalizing from examples. So if we communicate the France and Paris example above in the fashion of machine learning, we have to supply the algorithm with several examples of the form “x is in y”, and then expect the algorithm to conclude that Paris is in Europe.
Such a form of communication doesn’t even deserve to be called intelligent, because how can something be intelligent if it can’t conclude that Paris is in Europe, and to “understand” it would have to see a great number of examples, while that too guaranteed Not there? The generalization from examples is of probabilistic nature. How can we make inferences about unseen samples? It is surprising that machine learning can sometimes be perfect and not completely random, and indeed machine learning should be called a mathematical miracle. After all, how can one say, in high probability, even nearly correct, under zero knowledge, beyond a few samples?
Surprisingly, machine learning can do this. And that’s what machine learning is all about and all its advantages and disadvantages. Its use-case is when we know very little about a system, and we can just take samples and try to generalize them.
Logical AI, on the other hand, is all about absolute knowledge and absolutes, whether explicitly or implicitly. It’s about “just saying the thing” more efficient way of communication, direct communication, rather than giving multiple examples.
Furthermore, it happens that machine learning is inherently incapable of performing logical reasoning, for example finding contradictions. It is proved mathematically using complexity-theoretic arguments. It is therefore no surprise that machine learning finds success only in areas that are of a non-verbal nature, whereas in the field of natural language processing, it presents only very limited capabilities.
However, the second approach is perfectly valid: not only can logic machine learning do it, but it already does. Machine learning algorithms are already expressed in logical forms (as opposed to examples) and are already implemented in the form of computer programs that take a logical rather probabilistic form, that is, machine instructions.
Covering Logical AI therefore also covers Machine Learning, but on the other hand can never be achieved. Another way of saying it is as follows: machine learning eventually comes to be called inductive and abductive reasoning (which is roughly analogous to what is called) supervised and unsupervised learning), and as such it is very promising, although still in a form that is limited to examples only, and further, current technologies deal only with data of a numerical nature or with data that has been transformed as such. can go. On the other hand, Logical AI can fully cover Deductive Reasoning, Inductive Reasoning and Abductive Reasoning in qualitative and quantitative data.
These are the main reasons tau Logical AI is chosen as the ultimate form of AI, arguing that machine learning is only a milestone in the history of AI. Tau’s solutions will improve many aspects of human bandwidth, from discussion-scaling to knowledge monetization, to smart contracts and decentralized governance. It is all because of reason’s ability to bridge the gap between humans and machines.
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