My thoughts are as follows:
Crimes are motivated by desires. Desires are linked to biological properties of human beings. As long as humanoid robots do not include the biological properties linked to desires, humanoid robots will not be able to self-plan and self-execute any crime. However, humanoid robots could be the tools for bad guys to use in a process of committing crimes. But, such scenario could be avoided with built-in software which evaluates and monitors the consequence of humanoid robots’ actions.
My short response to his question is as follows:
If we believe that Artificial Intelligence simply means computer-aided human intelligence or computerized human intelligence, then we have already achieved a great deal in this direction. And, all the data-based decision-making systems are good examples of such achievements.
If we believe that Artificial Intelligence should mean machine’s, or robot’s, self-intelligence, then only very few people are making the progress into this direction. And, we are still in the infant stage in this direction. Most importantly, I believe that much more resources should be put into this direction, instead of continuing to speculate about computer-aided human intelligence or computerized human intelligence.
In the video below, Professor Oh did not believe that there should be a specific term on dynamic walking.
Please share your view about the classification of biped walking by humanoid robots.
Below is my short viewpoints:
According to the supply of mechanical energy, biped walking can be classified into two categories: one is called actuated biped walking and the other is called non-actuated biped walking. However, our main concern here is about the stability or balance, which is a challenge to both planning and control of biped walking by humanoid robots.
Interestingly, in the domain of biped walking, there are two types of stability or balance. One is called static stability or balance, in which the zero-moment point (ZMP) is always within the support zone of a humanoid robot’s foot or feet (assume that there is no other contact point between a humanoid robot and the environment). The other is called dynamic stability or balance, in which the body supported by biped maintains its state of motion. For example, in the sagittal plane of a humanoid robot, a dynamic stability is achieved with a successive clock-wise rotations of legs for backward walking or a successive of counter-clock-wise rotations of legs for forward walking. And, in the coronal plane of a humanoid robot, a dynamic stability is achieved with periodic oscillations of clock-wise and counter-clock-wise rotations of legs.
Hence, on the basis of these two types of stability or balance, we can classify biped walking into the following two categories: static walking which maintains static stability, and dynamic walking which maintains dynamic stability.
Great Debate – Artificial Intelligence: Who is in control? (OFFICIAL) (Part 01)
Great Debate – Artificial Intelligence: Who is in control? (OFFICIAL) (Part 02)
What is the greatest invention of the universe? Undoubtedly, it is human being. Then, what is the greatest invention of human being so far? Most likely, the answer is human language. Indeed, languages are human-made inventions, which help mankind to encode, record, acquire, transfer and most importantly re-discover both propositional and procedural knowledge.
Interesting enough, if we ask this question of “what is knowledge?” to public, you will get many versions of answers. Ironically, everyone knows what knowledge refers to, but yet is not able to give a commonly acceptable definition. Ten years ago, we have advocated the following definition: Knowledge are the clusters of properties and constraints of all entities. In other words, knowledge refer to properties and constraints possessed by all entities in existence. It is our view that the above definition of knowledge will receive least critics from the research community. So far, we have not yet found any better version of definition of knowledge. If you know one, please share with the research community.
Once we know what knowledge refer to, we can continue to raise the following question: what is the best way of representing knowledge? If you go to ask this question in front of college students or researchers, it is for sure that you will not get the correct answer most of the time.
Surprising enough, all of us innately know, but mentally are not able to speak out, the correct answer which is: human languages. Yes, human languages and the extensions of them are the best ways of representing knowledge discovered by human beings. This fact is largely ignored by the textbooks or research community of Artificial Intelligence because knowledge representation is still considered as “an issue which still looks for definite answer”. Despite such shortcoming in research community, public expectation on Artificial Intelligence is still increasingly high.
On the other hand, from the viewpoint of human languages, we can easily understand the nature of mathematics, which are also called as technical languages. In fact, mathematics are extensions of human languages, and are for the purposes of describing properties and constraints at much deeper levels of abstraction. Therefore, it is not a surprise for people to say that mathematics are simply technical languages invented by human beings.
Also, from the viewpoint of human languages, we should call programming languages the extensions of human languages, instead of calling them machine languages. This is simply because programming languages are for the purposes of describing algorithms, logics, control loops and data processing in the form of programs (which have no difference from texts). So, we must say that programming languages are not for machines to use. Especially, if all of us love human languages, all of us in general, and college students in particular, should equally love mathematics and programming languages.
Knowing the nature of human languages, we can now examine the nature of this massively-used term: learning. We all know that an individual will normally spend about 6 years in primary school, 6 years in secondary/high school, and at least 4 years in university, for the sole purpose of learning under the guidance of teachers. Therefore, learning is a process which involves the presence of learners and teachers. And, the primary activities of learners are to learn knowledge as well as skills, and to discover better ways of learning, while the primary activities of teachers are to teach knowledge as well as skills, and to discover better ways of teaching.
At this point, everyone should be able to understand the true nature of languages as well as learning. However, if you think further, you will find out that the following questions are still waiting for satisfactory answers: Why is a human being able to learn any human language in the world? Why isn’t an animal (e.g. a Monkey, a Cat or a Dog) able to learn a little bit of human language? What will be the principles which will enable machines to learn human languages?, etc.
Therefore, the actual challenge in learning is to make machines of tomorrow to be able to master all human languages and the extensions of them. Without considering the aspect of human languages, deep learning will always remain deeply superficial.
The objective of the L2M program is to develop technologies necessary to enable a next-generation adaptive artificial intelligence (AI) system capable of continually learning and improving performance in real-world environments while remaining constrained by pre-determined capability limits. Such a system would be able to apply existing knowledge to new circumstances without pre-programming or training sets, and would be able to update its network based on its situation for a variety of applications.
The L2M program aims to develop technology that could support the creation of a new generation of AI systems that learn online, in the field, and based on what they encounter—without having to be taken offline for reprogramming or retraining for new conditions.
The L2M program will combine computer science with biology-inspired principles of learning with the goal of developing the capability to perform continual learning, with a focus on the creation of learning paradigms and evolving networks that learn perpetually through external data and internal goals. Performers will be tasked with developing systems that can demonstrate an ability to learn new tasks without losing capability on previously learned tasks, and can apply previous knowledge to novel situations—and in doing so develop more complex capabilities.