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You’ve heard about you’ve heard about Google’s LaMDA (Lambda)and the viral debate about whether AI can be sentient teams at Tau Companyteams at argue that perhaps the AI’s senses may be only a small part of its intelligence. Rather, an AI’s true intelligence would be based on its ability to logically understand people’s needs and automatically meet them.
Tauis the first ever platform that allows users to write in a language that both machines and humans can read and understand, allowing it to incorporate their thoughts, advice, and knowledge and update its own software in real time. Tau’s decentralized social network and its monetary aspects, Agoras’ cryptocurrency is powered by what the team calls truly intelligent artificial intelligence – Logical AI. Logical AI is fundamentally different from machine learning and, according to Tau founder Ohad Athor, is on the brink of becoming the next big wave in the technology world.
At Tau, Logical AI will allow billions of people to participate in discussions and instantly see the collective intentional meaning behind the thoughts shared on the network. This will be accomplished by letting people use Controlled Natural Languages (CNL), which both humans and machines can understand. Every thought and piece of knowledge, whether explicit or tacit, will be automatically recognized and registered as your worldview, serving as your profile on the Tau, making it completely yours. Having your ideas and knowledge organized in such a sophisticated way means that not only will you be able to discover breakthrough solutions, but you will also be able to monetize your knowledge in an effortless and direct way that was previously impossible.
By simply entering your thoughts into Tau, your knowledge automatically becomes a digital asset that you own. Because Tau understands that any part of your knowledge can be part of someone else’s problem solution, you can sell off your knowledge to other buyers or use it to generate income by renting specific portions to your subscribers. Tau looks at the combination of knowledge from multiple users and suggests solutions to important and complex problems, thus ensuring that the knowledge required is 100% consistent with the specifications.
These solutions are impossible to achieve outside of logic-based AI. Because, simply put, logic AI is all about words and sentences. At its core is the ability to infer sentences from other sentences using a technique called deductive reasoning. For example, from three sentences
- Paris is in France.
- France is in Europe.
- If x is in y and y is in z, then x is in z. This is for all x, y, and z.
We can deduce the statement
The field of mathematical logic teaches that virtually all logical problems can be attributed to this form of reasoning. For example, a set of statements is contradictory if and only if both the statement and its negation can be deduced from it.
Logical AI is the mechanization of logical reasoning, such as finding contradictions or determining whether a conclusion follows from a given assumption. Thus, it is about being able to make a machine understand what we want to communicate, not just mechanical commands.
On the other hand, machine learning, the most popular AI today, aims to generalize from examples. So, if we were to convey the above example of France and Paris using machine learning methods, we would have to supply the algorithm with many examples of the form “x is in y” and expect the algorithm to conclude that “Paris is in Europe.”
Because how can something be intelligent if it cannot conclude that Paris is in Europe and has to look at many examples to “understand” it, and even that is not guaranteed? Generalizing from examples is a probabilistic property. How can we make inferences about unseen samples? It is amazing that machine learning is sometimes correct and not completely random, which is precisely why machine learning can be called a mathematical miracle. After all, how can one say, with zero knowledge beyond a given sample, that something is correct with high probability, even approximately?
Surprisingly, machine learning can do that. And that is machine learning, with all its advantages and disadvantages. The use case is when you have very little knowledge about the system and all you can do is take a sample and try to generalize it.
Logical AI, on the other hand, is all about complete knowledge and absolutes, whether explicit or implicit. It is also about direct communication, a more efficient way of communicating, “just saying that,” rather than struggling through many examples.
Moreover, machine learning is inherently incapable of logical reasoning, e.g., detecting contradictions. This is mathematically proven by complexity theory arguments. Thus, it is not surprising that machine learning is only successful in non-verbal areas and can only demonstrate very limited capabilities in the field of natural language processing.
However, logic is not only capable of machine learning, it is already being machine learned. Machine learning algorithms are already expressed in a logical form (in contrast to the example), and they are also already implemented in a probabilistic rather than logical form, namely as computer programs called machine instructions.
Thus, covering logical AI also covers machine learning, but the reverse can never be achieved. Put another way, machine learning will eventually cover what is called inductive and inductive reasoning (which corresponds roughly to what is calledIt is the equivalent of {supervised learning and unsupervised learning (110) (111), and is very promising, but it is still only an example, and moreover, current technology only deals with numerical data, or data that can be converted into such data. Logical AI, on the other hand, covers all deductive, inductive, and inductive reasoning, and can handle qualitative as well as quantitative data.
These are the main reasons why Tau argued that machine learning is only a milestone in the history of AI and chose logical AI as the ultimate form of AI. Tau’s solution would improve many aspects of human bandwidth, from discussion scaling to knowledge monetization to smart contracts and distributed governance. All of this is due to Logic’s ability to bridge the gap between humans and machines.
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