Aryâ_Crypto01

I have been a member of one small study group at the university in the past semester. We were four who were about to take the same finals. We attended the library two times a week and we exchanged notes and assisted one another in interpreting challenging issues. Initially, we had a very traditional schedule of studying. We read textbooks, exchanged ideas and attempted to describe to each other.
Several weeks later, it was proposed to use AI tools to simplify studying.
Initially, it was a great idea. We do not have to waste hours browsing through pages and chapters, but we can pose the AI questions and get answers at the touch of a button. The responses were very elaborate and comprehensible. The AI even improved the clarity of the textbook at times. This was a very big benefit on complex topics.
In the near future, AI was integrated into our classes.
We would always ask the AI to clarify a concept whenever we failed to grasp a concept. The AI gave us summaries fast when we required them. In case we needed practice questions, the system would come up with it within seconds. Our learning sessions were improved and accelerated. That is at least what we thought then.
On one evening, when I was examining a very challenging subject, something did come to pass.
The AI was questioned of the same question by two of our group members and they were given slightly different answers. The reasons were similar, and the decisive point in the solution was different. We contrasted both answers and found that one of them had a minor error.
Initially we believed that it was an accident.
But curiosity impelled us to put it to the further test. We posed additional questions and began to test the answers according to our textbooks and lecture notes. The majority of the answers were quite right, however, they were sometimes missing or partially incorrect.
This was when we began studying differently.
We started to check all AI answers rather than acceptance. In case the AI described something, we checked it on our notes. In case it had given a fact, we verified the source. Our research team gradually became a mini verification team.
The most unexpected thing was the fact that this enhanced our learning.
We were able to grasp the content on a deeper level when we began to check the answers by ourselves. We did not memorize the explanations but analyzed them. In the group, there was more activity in discussions as everybody was seeking to verify the right information. The AI remained useful, however, not the ultimate authority anymore.
I had learned about new AI technologies later, and one of such projects was called Mira. The concept itself immediately brought back to my mind the experience in a study group.
Mira is devoted to the checking of AI results instead of its mere production. The system splits AI responses into smaller claims and leaves them to be checked by a number of validators. Once a sufficient number of validators support a claim it is verified. The simplified version of this would be a group of reviewers going through the information to accept it.
I was quite accustomed to that thought.
A comparable process had been followed inadvertently by our study group. One individual would give a solution and others would challenge it and we would all come to a common conclusion whether it was right or wrong. The information had become considerably more reliable by the moment we had ended the discussion on it.
The world of technology is rapidly changing and AI will continue to have a significant role in education. It will be used by the students in explanations, research, and exercises. However, this was not the case; experience taught me something significant learning is not only about getting answers. It is about knowing why such answers are right.
Checking is significant in that.
AI can give incredible support in the study, except that it is best when used with critical thinking. Such systems as Mira demonstrate that AI can be safely trusted in the future with its embedded verification capable of letting users trust the information they get.
In retrospect, I can say that our group sessions of studying made the big difference in having the small habit of checking answers. It transformed immediate answers into certain information. And it brought to mind that despite the era of artificial intelligence, good learning still requires curiosity, dialogue and validation.
@Mira - Trust Layer of AI #Mira $MIRA

Yesterday in the peaceful afternoon of a university library, a cluster of computer engineering students sat round a table, with robotics textbooks and laptops on it. They were not creating something that day. Rather they were talking about a mere concept their professor had touched on earlier in the lecture: robots will soon be included in the global labor force.
Initially the talk had remained technical. Sensors. Machine learning models. Navigation systems. The standard subjects that prevail in robotics studies. However, after some time the conversation changed. The question that one of the students asked could not be answered in a clear way via any textbook.
When the robots begin to do actual labor everywhere, how will we know what they are doing, she asked?
It was a thoughtful question. Not about mechanics; that is, but about responsibility. The moment machines begin to work in the warehouses, farms, hospitals and transportation systems, somebody has to check what they do. Somebody must check that the work has been done properly, the information is credible and the systems are transparent.
That discussion brought back to my mind the concepts discussed by Fabric Foundation. The project does not just look at the infrastructure that may coordinate the smarter machines but rather in building them. The idea is that robots might work in the network where identity, verification, and coordination of work are logged in clear systems instead of being hidden in closed company systems.
This view is revelation to the students. It demonstrates that robotics is not merely an engineering concept of hardware. It is also the responsibility of creating systems by which machines can be responsive to interact with the surrounding world. It is not merely that robots should be able to do it. The problem is that they should make sure that their actions are verifiable, trustful and comprehensible.
Mechanisms such as ROBO may contribute to supporting such systems in the framework proposed by Fabric by ensuring compatibility of interests among developers, operators, and validators that oversee robotic activity. The concept is that once machines do a certain job, they can be tracked, checked and synchronized over an open infrastructure instead of closed systems.
It is yet unclear whether this very model will become the standard in the future. The robotics is progressing rapidly and numerous methods are being experimented. Still, the educational point behind it is rather strong: coordination systems are the key to technological advancement just as individual machines are.
When the students were stuffing books and walking out of the library, the discussion had moved away on the subject of robots themselves and what the robots are going to be like. And maybe that is one of the best lessons of any person who studies automation nowadays.
It is essential to understand machines.Yet, it can be even more important to have an idea of the systems that govern them.
@Fabric Foundation #ROBO $ROBO

In one of the robotics classes at a local technical institute, the lecturer proposed that students observe the performance of a small group of training robots in a simulation of a small warehouse. The machines transferred the packages among the shelves, checked labels and went back to charging points. Initially, the students pay attention to the mechanics how the sensors were operating, how the motors were changing the speed, how the navigation system did not lead the students to a collision.
Then one student gave a question which shifted the whole discussion.And, he said, how will another robot in some other place learn the same thing tomorrow, should a robot here learn something useful to-day?
There was a silence between the rooms. The hidden meaning of the question was realized by everyone. Robotics education is mostly concentrated on the development of smarter machines but what the student wanted to know about was something bigger, the system that links them.
In the modern world, numerous robots are working in the closed world. A warehouse robot may enhance its performance with time, although it tends to remain confined within the structure of a particular company. Another robot constructed by another manufacturer can solve the same problem completely afresh. Even with an increase in the intelligence of machines, knowledge is fragmented.
This difficulty is a factor that has led to researchers and developers looking to the wider systems of coordination. Other projects such as Fabric Foundation are exploring the applicability of robotics networks sharing verified knowledge, coordinating machine behavior and developing transparent systems to monitor how robots learn and work.
The concept does not consider the robots separately as an isolated machine, but as a part of a bigger ecosystem. A robot completing a job would be able to check its work, distribute valuable information, and advance a network that would assist other machines in doing so. One example of how incentives, validation, and governance can be used to enable such collaboration is infrastructure powered by mechanisms such as $ROBO.
This is a significant change in attitude, to students in the field of robotics today. More powerful motors and more intelligent algorithms will not be the solutions to the future of automation. It will also rely on the way machines talk to each other, how they check the information and the development of trust in the shared systems that can be comprehended and managed by the human beings.
These ideas can be experimented best in educational settings. When the students come up with robots they are also learning how to come up with responsiveness. The way machines will socialize with human beings, the way mistakes are identified and how refinements propagate securely over systems, is being determined by them.
At the conclusion of the workshop, the instructor summarized the lesson in an easy manner. The creation of a robot is an exercise in engineering. However, by coming up with the systems to enable the robots to cooperate, there is something more significant that is learned, which is the role that technology plays in society.
Simple though the question posed by that student was, it looked into the future. In case the robots will become a part of the daily routine, the world will require the infrastructure that will assist in their learning process, functioning in a transparent manner, and answer to the people to whom they provide the services.
@Fabric Foundation #ROBO $ROBO

As I was a student and I used to spend hours in the library researching on a topic @Mira - Trust Layer of AI . I can still recall that I was walking between long lines of books, turning pages and writing notes manually. In order to be able to check a fact, you had to go through several sources. It was not an easy task, but the experience of that process taught something valuable: one should never believe information without checking its truthfulness.
Learning nowadays is a whole new picture. Using contemporary AI, students are able to pose a question and get an answer in detail in a few seconds. It feels almost magical. Elaborate descriptions, chronicles, technical manuals, everything comes at a touch of a button. This technology has in a way simplified and brought education closer in many aspects.
Speed masks a serious problem sometimes.
The fact that information comes fast does not necessarily imply that it is accurate. Artificial intelligence is trained in such a way that its responses are natural and useful. They are able to interpret trends on large datasets and come up with answers that suit the question. In the majority of cases, the information is correct, but in some cases, the system provides empty areas with guesses that sound persuasive.
This is what the people refer to as AI hallucination. The system is not aimed to deceive anyone. It merely estimates what appears to be the case in its training information. This need not be a big issue as long as it is used casually. However, in cases of AI application in research, professional decisions or technical work, even minor errors may lead to misunderstanding.
This is why, to this day, verification is as vital as it used to be in these silent library rows.
Students used to verify the facts by checking various books and articles. We require such mechanisms to be incorporated directly into our tools in the digital world. The system should not assume that an answer is correct because it ought to assist in proving its reliability. This concept is starting to be reflected in new AI infrastructure endeavors.
The example of Mira is one of the interesting ones. Mira is not a person who is only creating responses but checking them. The system covertly divides the AI outputs into smaller claims and channels them to other independent validators. Such validators analyze the assertions and see whether they are substantiated. The claim is then labeled as validated when a sufficient number of people choose to validate it.
The advantage of the approach is that it is transparent. The system does not give a single answer which should be believed in without any questions, but a record of the verification is made. Users are able to view the claims which were agreed upon and those which need further consideration. Simply put, the technology introduces a sense of responsibility.
There is an interesting parallel when I consider the contrast between conventional research and AI tools of the contemporary world. Students in the library collected numerous materials to develop confidence in the acquired information. Mira applies the same principle, but instead applies such a process to the information produced by AI via networks of validators and cryptographic logs.
Such a system may be of significance to education. Consider the situation when the students are offered AI explanations with verification marks. They will not memorise answers, but they would learn how information can be proved to be correct. It would stimulate critical thinking and make them comprehend the distinction between the confident language and the confirmed knowledge.
Naturally, a perception system will not turn AI into an ideal one. The questions will always be complicated in nature, the data may be unclear and there will always be areas of disagreement among the experts. However, this technology will be more responsible with the addition of verification. It transforms AI into a solution generator that can be quickly used and one that facilitates credible learning.
Revisiting those hours at the library, I understand that it was the process that taught me. Research was not merely the discovery of information. It was regarding knowing how to challenge it, validate it, and know it well.
AI can simplify the knowledge access process, yet it is not supposed to eliminate the scrutinizing habit. On the contrary, it should enhance it.
It is quite likely that the future of education will be a mix of these two worlds, the pace of artificial intelligence, and the critical thinking traditional research made us accustomed. And the combination of the two things makes learning faster, but more reliable as well.#Mira $MIRA

I was in a small engineering classroom the other day and elementary robots were being tested by the students. Nothing advanced. Only small programmable machines that were taught how to move along lines on the floor and to pick up objects. cloisters were all inquisitive. Wires, laptops, sensors, and the type of enthusiasm that is created when individuals find out that they are creating something that moves by itself.
One of the students had an interesting observation in the exercise. All of the robots in the classroom were committing the same error when turning a corner. The groups had programmed their machine individually but the error continued reoccurring. The code was eventually corrected by somebody, but the incident raised a greater question within the room. Why could not the robots tell what they had learnt, that the others might make it at once?
That elementary school crisis is an indication of a much greater problem in robotics. Machines are becoming more intelligent and more competent though they tend to work alone. One robot at a single factory can learn a useful improvement, but a different robot in a separate part of the country continues with the same inefficient behavior since there is no system of knowledge sharing, verification, and updates.
This is the type of problem that had made me focus on the work that is being conducted by Fabric Foundation. The project does not concentrate on the construction of robots, rather it pays attention to the creation of the infrastructure which might enable robots to work together, test their behavior and act in the framework of transparent economic systems. To put it simply, it poses the question of how machines can eventually coordinate with one another across networks and not within closed systems.
This notion creates a valuable insight to teachers and learners. Robotics is not mechanical engineering or artificial intelligence only. It is also concerning systems thinking. As soon as the thousands or even millions of machines start to work in the logistics networks, hospitals, farms, and cities, coordination becomes as significant as the robots themselves.
Fabric discusses how that coordination problem could be overcome through the use of decentralized infrastructure. Using the systems driven by ROBO, machines may even be seen as possessing verifiable identities, be able to trace the activities they are involved in, and conduct themselves in an open system which can be seen as validating of data and updates. Rather than individual robots learning, advances might make up a common ecosystem which other robots can expand on.
Educationally, this promotes the change in the method of teaching robotics. Students are also not just learning to make machines move, or even think. They are also learning to come up with systems on which machines are able to interact with each other and man responsibly. Such a mentality is probably what will characterize the next wave of robotics development.
The robots finally were able to figure out how to get around the corner in that classroom. It had to make some changes and we had to collaborate as students. However, the moment remained with me since it defined something significant about technology and education. The most important advancement is usually done in little errors and trivial questions.
And at other times such questions give rise to larger conceptions how the systems of the future could be.
@Fabric Foundation #ROBO $ROBO