By Zat Rana
Although the paper that introduced the theory of evolution to the world was published in 1858, Charles Darwin first conceived of the idea in 1838.
He had spent five years on the HMS Beagle as a geologist when he noticed something peculiar in his records: the geological distribution of fossils and wildlife showed a pattern of change between different species.
At the time, the controversial predecessor to Darwin’s theory was transmutation, which rightly suggested that one species changes into another, but which wrongly assumed that this occurs due to some spontaneous life-force, or laws that kick into play at different predetermined times by God, or some other mysterious but unidentified process.
Critics saw it as a feeble attempt at the materialization of life, an idea that had taken hold of the world ever since the Enlightenment, without any compelling evidence to support its radical claim.
That initial paper published by Darwin (along with Alfred Russel Wallace, who had come to similar conclusions), however, was strong where transmutation was weak: it gave a specific process for this change.
达尔文最初发表的论文，同一时期还有Alfred Russel Wallace，他也得出了相似的结论。无论如何，比较而言，这个理论要跟强势一些相对于蜕变理论来说，它给出了演变的一种特殊的过程。
In any population of a species, we have variation in phenotype (observable characteristics), arising from mutations that occurs in the genome and from the epigenetic changes that occur during life, and the result is that different individuals in a group of organisms show differences in their ability to adapt to their environment — some do well and survive; others don’t.
在一个物种的任何种群中，我们的表型（可观察到的特征）都有变异，这是由基因组中发生的突变和生命中发生的表观遗传变化引起的，结果是一组生物体中的不同个体表现出差异。 他们适应环境的能力 - 那些做的好的得以存活下来，而别的则不行。
This simple process of variation and selection explains how a common ancestor produces the diversity of life we observe in the biosphere.
In this way, life is — as Jonas Salk, the famed medical researcher put it — “an error-making and error-correcting process.” It gives us many attempts at overcoming the challenges of any environment by introducing variation, and it then selects the correct answer by eliminating what doesn’t work.
Useful knowledge survives and gets passed down to newer generations, who can then use this knowledge to enhance their effectiveness. But this, however, isn’t the only kind of knowledge available to us.
Experimentation and Refinement
The actual process of learning (or getting smarter) extends beyond our predetermined genome, but evolution has set a precedent in form.
Even the learning we do in the world follows a variation and selection (via elimination) pattern. We try lots of different things, we see what works, and then based on the results, we eliminate the competing options, selecting for the skills that will be most useful in the future, too.
Cognitive neuroscience has a theory of mind (called predictive processing) that suggests that the human brain is a prediction engine, which consistently creates our perception of the world based on our past interactions within similar environments.
In the beginning, when you are young, there isn’t much information to go off of, so you get mostly unconstrained inputs from the external world into your brain, but as you get older, you start to filter through this variety for usefulness, making better distinctions.
You create mental concepts in your mind about what is important and what is not, and then these concepts shape your future perceptions by using the already-selected knowledge to further select knowledge.
This entire process is mostly intuitive, and what keeps it updating is pain/pleasure, which tells your body that a certain perception and your corresponding reaction should either be reinforced or not. But some forms of experience on the pain/pleasure axis like surprise and awe can be used to intentionally tell your mind that something unexpected was experienced, too, encouraging you to consciously readjust the conceptual model.
Whether you are learning to play a sport or simply trying to create a more accurate mental model of reality in your mind, you are working with a variety of experiences, and within those experiences, you have to choose and reinforce the ones that are the most useful to you.
In this way, everything that you do is essentially an experiment that gets refined and corrected with experience and practice.
The difference between you and, say, a professional tennis player is almost certainly that they have a genome that makes them more suitable to play their sport, but more importantly, they have intuitive knowledge embedded in their brain from all of the predictive processing they have done, in a very specific environment, to refine their sense for what works and what doesn’t.
The same can be said for great artists and scientists, entrepreneurs and investors, and other everyday folks who do what they do well.
Our brain is a prediction engine that builds knowledge and gets smarter as it better aligns what it needs to do with the demands of the environment.
Conjectures and Their Refutation
Predictive processing alone likely isn’t what makes humans unique. If it really is the process by which we make sense of the world, the chances are that some form of it appears in other animals in nature, too.
What takes humans one step beyond this simple empirical knowledge-building is that we can think in abstract concepts, with a complex language, and then share this knowledge between us within culture.
The best formal system that we have ever devised for this is the scientific method, which operates based on a combination of asking questions, formulating hypotheses, and then testing those hypotheses based on the data collected from our experiments and observations.
In the same way that we have variation and selection in evolution (and in our empirical mental modeling), the philosopher of science Karl Popper suggested that we have it in scientific inquiry, too, where we start by formulating a conjecture based on incomplete information (a theory), and we improve on our conjectures by refuting them.
与之相同的是，我们在进化中的变异和选择（和经验思维模型中），科学哲学家 Karl Popper说，“在科学的获取中也同样如此，开始时候我们基于不完全的信息或者理论形成一个推测，然后我们通过驳斥来优化推测”。
Science, in this way, can never be completely certain of anything but it can only get more and more correct as we refute bad conjectures and replace them with better ones and so on. And in order for something to be considered a scientific theory, it has to be capable of being proven wrong.
We don’t need to just rely on our mind updating itself by putting it in different environments to gain knowledge; we can also make use of the abstract knowledge we collectively build in culture.
Whereas personal experimentation and refinement can improve a brain by directly building its intuitive understanding, abstract theories (based on evidence) can do the same thing without us needing to go through the same process that someone else did to collect that knowledge.
There is, of course, some important practical knowledge that is lost in the translation from the abstract to the concrete, just like empirical knowledge (from predictive processing) lacks the rigor that comes with having a scientific community constantly challenging you, but both are capable of adapting our minds in a way that is more useful to us.
By updating our mental model, good conjectures, based on strong collective evidence, can make our predictions of reality more accurate.
Knowledge, whether implicit or explicit, underlies everything that we do.
By virtue of evolution, much of this knowledge is encoded in our genome, which programs us before we are born. It has been selected based on generations and generations of efforts to survive, implanting us with a general template of a phenotype that is best suited for our environment.
In the 21st century, however, as our environment continues to change at an exponential rate, the knowledge encoded in our genome is becoming less and less sufficient for our attempts at making sense of the world.
Fortunately, evolution has also programmed us with the ability to learn. With a mind that experiments, predicts, and corrects, we can build empirical knowledge to adapt us to other relevant environments.
We can use our pain/pleasure axis and the affect that we experience as surprise/awe to self-correct into building an intuitive understanding of the world that allows us to master our surroundings and their demands.
To further augment this intuitive understanding, we can also stand on the shoulders of the giants that have come before us in culture by using their theories and evidence to further sharpen our mental model of reality.
There are many ways to define intelligence, and different definitions cater to different expectations, but ultimately, it’s about how effectively an agent can make sense of and navigate its environment.
Knowledge and its application is the process that we build everything else on, and it starts with what we do to feed it.