Sentient AI Does Not Equal Intelligent AI

You’ve probably heard about Google’s LaMDAThe discussion on whether AI can be made to communicate with humans. At TauSentience, according to some AIs, is only part of their intelligence. AI intelligence is based more on its ability understand people’s needs and then automatically fulfill them.

Tau will become the first platform to be able take thoughts, advise, and information of users. The platform can update itself in real time by having users write in languages machines and humans understand. Tau’s decentralized social network and its monetary aspect, Agoras cryptocurrency, is powered by an AI that the team calls the truly intelligent artificial intelligence – Logical AI. Logical AI is radically different from Machine Learning, and, according to Tau’s founder Ohad Asor, is on the brink of becoming the next big wave in the world of technology.

Logical AI on Tau will let you participate in large-scale discussions with billions of other people. It also allows you to instantly understand the intended meaning behind all of those thoughts. Controlled Natural Languages are used to achieve this. They can be understood by both machines and humans. All thoughts and all pieces of knowledge will automatically be recognized as part of your Worldview. Your profile will appear on Tau. Having your ideas and knowledge organized in such an advanced manner will mean that you will be able to not only discover groundbreaking solutions, but also monetize your knowledge in an effortless and direct manner that hasn’t been possible before.

You can instantly make your Tau knowledge a digital asset just by putting in your thoughts. You will be able to sell off your knowledge to other buyers, or use it to generate income by renting specific pieces of it to your subscribers as Tau will understand that even a piece of your knowledge can be part of the solution to someone’s problem. Tau will show the benefits of combining knowledge from multiple people and suggest it to solve complex and important problems. This guarantees that all information is 100% accurate.

These solutions are impossible to achieve with other types of AI than those based on logic. LogicalAI is all about sentences and words. It is about inferring statements from others, which is what we call Deductive Reasoning. Here are three examples:

• Paris is France
• France is located in Europe.
• If x and y are in y then x will be in z. This is true for all x,y and z.

We can deduce the statement

Mathematical logic teaches us that almost all logical problems can be reduced to this type of deduction. A set of statements can be contradictory if it contains both a statement or its negation.

Logical reasoning is the automated processing of logic. This includes finding contradictions and concluding that a conclusion can be drawn from certain assumptions. The ability for machines to comprehend what we are trying to communicate to them beyond just machine instructions.

Machine Learning, currently the most common form of AI is about generalizing by using examples. So if we were to communicate the above France and Paris example in the fashion of machine learning, we’d have to supply the algorithm with many examples of the form “x is in y”, and then hope that the algorithm will conclude that Paris is in Europe.

Such a form of communication does not even deserve to be called intelligent, since how can something be intelligent if it cannot conclude that Paris is in Europe, and has to see a large number of examples in order to “understand” that, while even that is not guaranteed? It is probabilistic to generalize from examples. What is the best way to guess what unseen samples might look like? Machine Learning has the ability to make accurate predictions and can even be skewed by chance. This is why Machine Learning should be called a mathematical marvel. It is hard to believe that Machine Learning can be accurate with high probabilities and under minimal knowledge.

Amazingly machine learning can accomplish this. Machine Learning, with all its pros and cons, is exactly what Machine Learning does. This is useful when there is little or no information about the system and we are unable to take samples to try and generalize.

Logical AI is about absolute knowledge and fullness. It is also about a much more efficient way of communication, direct communication, “just saying the thing”, instead of laboring over giving many examples.

Machine Learning, however, is not capable of performing logic reasoning. Machine Learning is unable to detect contradictions. This can be mathematically demonstrated using complexity-theoretic argument. Machine Learning has limited success in Natural Language Processing. This is not surprising.

But the opposite is true: logic cannot do machine-learning, and it has already done so. In contrast to the examples, machine learning algorithms can already be expressed in logical form and implemented as computer programs that take a probabilistic rather than logical form.

Machine Learning can also be covered under Logical AI. However, it is impossible to do the opposite. The other way is to put it: machine learning covers Inductive or Abductive Reasoning. These roughly correspond with what is known as Both unsupervised and supervised learningIt is quite promising (see ), but it only covers examples. Current technologies also deal with numerical data or data that can convert into this type of data. Logical AI on the other side can be used to cover Deductive Reasoning (Inductive Reasoning) and Abductive Reasoning. This applies both in quantitative and qualitative data.

These are the major reasons you should be thankful. TauLogical AI has been chosen by Tau as the final form of AI. He believes that Machine Learning was only the beginning of AI’s history. Tau’s solutions will improve many aspects of human bandwidth, from discussion-scaling, to knowledge monetization, to smart contracts and decentralized governance. All of this because of logic’s ability to bridge the gap between humans and machines.

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