Low-level Artificial Intelligence Parrots mimic and repeat
High-level Artificial Intelligence Crows drink water, causally driven artificial intelligence
Establish your own characteristics, equivalent to using your professional knowledge to "claim territory": People from different backgrounds have various advantages in the same situation. As someone with a scientific background, do not try to encroach on what those from a computer science background should do for two reasons: first, if you do this, you will need to spend more time than computer scientists learning redundant knowledge; second, there is a lack of individuals with a solid understanding of natural sciences in computer science. If you do not leverage your advantages to the fullest, it will be difficult to make impactful contributions. Living is not just about existing; in today's context, living means doing something meaningful. If not, what difference does it make whether you exist or not?
Setting standards in your industry: This subheading seems very large and somewhat vague to me, but it should be the correct goal for my efforts; direction is more important than effort. Also, keep in mind that you should not expect to publish articles just by tweaking others' code. It is unrealistic; this field is not easy, and I have already experienced that. One must unify the problems in a field with a framework. For example: Schrödinger's equation.
Current State of Artificial Intelligence: In "non-fundamental disciplines," it is not necessary to make groundbreaking scientific discoveries; most of the time, it is sufficient to identify 'patterns' among variables in a certain field based on existing scientific theories and data to achieve scientific discovery. However, in some "fundamental disciplines," scientific discovery is not merely about finding the "connections" between data and empirical materials; more often, it involves exploring and discovering new variables. Currently, artificial intelligence is better at finding "correlations," so its depth of application in these areas is still insufficient. On the other hand, the extent to which scientific research can be automated and the role of artificial intelligence in the process of scientific discovery still depends on human understanding of the nature of scientific research activities and scientific discovery.
Quote: "If I have been able to see further, it was only because I stood on the shoulders of giants." --Newton
Sources of Machine Learning Data:
- Theoretical calculations can quickly generate large amounts of data. They are relatively "cheap and easy to obtain," but lack "real-world complexity."
- There is a wealth of data in past literature and databases. The data in databases is structured and relatively easy to extract, but the data in literature is unstructured, and extracting this data often requires certain domain knowledge. However, manual extraction is too inefficient, and the time and economic costs are too high. Natural language processing technology can be used to extract data from literature.
- Data obtained from experiments is very rare and precious.
Deep Statement Collection:
Zhu Songchun: This touches on a core issue, which is that we acknowledge and accept that the future society is big data or something that cannot be clearly articulated. Moreover, this model must be this village, and that model must be that store; there is no unified statement, which is a relatively popular view at present. However, I personally believe this is a significant misconception. The development of science seeks the simplest explanations. The current situation fundamentally stems from problems in research methods.
What does this mean? If intelligence is fitted as an objective phenomenon, its model is indeed very complex. We previously conducted a simple experiment to judge the boundary between physics and intelligence (vitality). For example, if two objects collide in a room, that is very simple, and a few rounds of parameters can fit it; but if it involves two people, one running away while the other chases, with various social relationships, the physical model (energy function, big data model) cannot solve it. Each segment of intelligent agent movement requires a different model to fit, and it remains unclear.
However, if we use a subjective value function instead of an energy function to describe it, it becomes very simple to clarify. So why do I later discuss "Li Science" and "Mind Science"? In Mind Science, it is said, "The mind is the principle, and there is nothing outside the mind." It holds that the complex intelligent phenomena in the world are driven by simple values; find your values, and all your behaviors become uncomplicated. If I specifically try to replicate your behavior, that is just learning to walk like a baby, and fitting can never be completed. Once I understand your value orientation, your positioning, and your framework, all your behaviors can be explained by very simple motives.
Therefore, I believe that currently, in artificial intelligence, everyone is still using the original fitting method, trying to explain all human subjective behaviors with big data, which is not feasible.
- being existence
- becoming change
- believing belief
Wu Jiarui: For young scientists, I think breaking the deadlock involves three aspects. First, it relates to theory; most life scientists work under reductionism, but we now need to approach it from a systems theory perspective; otherwise, research will become increasingly detailed, overly focusing on trivial matters. From the perspective of research theory and thinking, merely looking at details is not enough; one must have a systematic viewpoint.
Second, regarding research methods, life sciences are now, in a sense, technology-driven, and there is a trend where research content pursues traffic and technology. I suggest advocating more for thinking and theory.
Third, the phenomenon of utilitarianism is particularly severe now; we should promote the scientific spirit. For example, Japanese figure skater Yuzuru Hanyu insists on attempting a quadruple Axel; he does not pursue gold medals but rather seeks to surpass human limits. We scientists should not pursue gold, silver, or bronze medals, but rather aim to go higher, faster, and farther; the Olympic spirit should be extended to scientific research. Foucault mentioned in his autobiography that if science cannot lead us knowledgeable people astray, then what value does science have?