I asked the Bard A.I. engine to write an essay comparing evolutionary processes found in nature with the algorithms used by A.I. engines. This was the result:
Evolutionary processes and the algorithms used by AI engines are both inspired by the natural world. However, there are some key differences between the two.
Evolutionary processes are the mechanisms that drive change in populations of living organisms over time. They are based on the principles of variation, selection, and inheritance. Variation is the difference between individuals in a population. Selection is the process by which individuals with traits that are better suited to their environment are more likely to survive and reproduce. Inheritance is the passing of traits from parents to offspring.
AI engines use algorithms to solve problems and learn from data. Algorithms are step-by-step procedures that can be used to perform a task. AI engines use a variety of algorithms, including genetic algorithms, particle swarm optimization, and ant colony optimization. These algorithms are inspired by evolutionary processes, but they are not exact copies.
One of the key differences between evolutionary processes and AI algorithms is that evolutionary processes are non-directed. This means that there is no pre-defined goal or objective. Instead, evolution is driven by the environment and the random variation that occurs within populations. AI algorithms, on the other hand, are directed. They are designed to achieve a specific goal, such as maximizing accuracy on a classification task or minimizing the error on a regression task.
Another key difference is that evolutionary processes are typically slow and gradual. Change in populations occurs over many generations. AI algorithms, on the other hand, can learn and adapt much more quickly. This is because they can be trained on large datasets of data, which allows them to explore a large number of possible solutions in a short period of time.
Despite these differences, there are also some similarities between evolutionary processes and AI algorithms. Both are based on the principle of trial and error. Both are able to learn and adapt to new situations. And both can be used to solve complex problems.
Examples of how evolutionary processes are used in AI engines
Genetic algorithms: Genetic algorithms are used to optimize the parameters of AI models. For example, a genetic algorithm could be used to optimize the weights of a neural network.
Particle swarm optimization: Particle swarm optimization is used to train AI models to solve complex problems. For example, particle swarm optimization has been used to train AI models to play video games and to control robots.
Ant colony optimization: Ant colony optimization is used to design efficient algorithms for solving optimization problems. For example, ant colony optimization has been used to design algorithms for routing vehicles and for scheduling tasks.
Evolutionary processes and AI algorithms are both powerful tools for solving complex problems. However, they have different strengths and weaknesses. Evolutionary processes are non-directed and can be slow, but they can be used to find creative solutions to problems. AI algorithms are directed and can learn quickly, but they may be limited by the data that they are trained on.
In the future, we can expect to see evolutionary processes and AI algorithms being used together to develop even more powerful and intelligent AI systems.
I can’t help but conclude that Bard has a better “grasp” of evolution than a great many well-known Theory of Evolution deniers. No surprise there.
What I find most ironic about the whole “it can’t evolve - therefore things require intelligent design by an intelligently designing mind” is that what ID creationists think minds are is design by magically knowing something by revelation. The human mind just doesn’t work like that. We, too, had to learn by trial and error, by having information copied into our brains that was gained from previous generations of trial and error, and all the rest. Tutoring, learning by interaction, practice and observation. Over and over and over. With the benefit of centuries of accumulated information stored in the form of books, and passed on from brain to brain through physical means of communication.
Whatever you can “design” you can design only because an absurdly long transgenerational process of trial and error, and copying successful information from previous generations into your own brain, is what enables you to do that.
At no point does magical spook knowledge by revelation enter the picture. All the problems they think evolution can’t solve, they can’t solve either. Get an infant, try to make it design a functional protein. The only people who can design functional proteins are people who learned how by centuries of accumulated knowledge of biochemistry, and hands-on experience with the methods and principles, being stored in their brains. And they’re still not particularly good at it. Nobody just thinks up a solution, and certainly nobody who isn’t a trained biochemist with expertise in that subject.
The fact that people from unrelated fields don’t get magical revelations about how to design stuff from other fields of knowledge is a hint. They don’t have the requisite experience to understand the subject, which proves it is a product of learning and experience, not occult telepathic revelations from the beyond. Engineers don’t suddenly wake up and know how to compose great symphonies, and great composers don’t suddenly wake up and know how to code in python. And people who know how to compose great symphonies had practice, lots and lots and lots of practice. They didn’t just one day compose their great symphonies at 4 years old as if by having the knowledge telepathically installed in their brains.
The kind of intelligent design they have in mind is wholly outside of experience, and probably impossible.
And many people have no concept of what it takes to learn even the fundamentals of a particular field of expertise. So it is very easy for them to say confidently but illogically, “There is evidence of flooding all over the planet so that proves that there was a global flood!” They possess not a shred of knowledge about basic geology, hydrology, or even basic cause-and-effect. It “sounds good” to them because it came from somebody they trust (e.g., a parent, a pastor, a Youtube video from Answers in Genesis.) So where does one begin with such a person? It is much like explaining the geologic column and the fossil record to that four year old that @Rumraket mentioned. There is no chance that they will easily grasp concepts which took us much study to learn.
I always grimace when I hear one of the countless variations of the pop maxim, “You do not really understand something unless you can explain it to your grandmother.” (Equally popular is “explain it to a six-year old.”) Many people insist that it was a saying of Albert Einstein but, obviously, there is no record of him ever saying something so inane. What annoys me most about such sayings is that it blames the person explaining the idea for what the listener is unable to grasp because of their woefully limited background.
Could I have explained evolutionary algorithm solutions for the Traveling Salesman Problem to my grandmother? I only knew her at a very advance age when she suffered from severe dementia but in her younger days she would have immediately scoffed because my explanation would have included the word “evolution.” For many, that word is the trigger for their brain’s Iron Dome defense system.
Is this the common understanding? It was my understanding that the designing mind may very well be employing a trial-and-error process or other exploratory process, or perhaps calculating solutions from models, or using any of the other cognitive methods one might describe other than awaiting revelation. The key feature is not that it necessarily occurs all at once, but merely that it occurs ‘offline’ relative to biological processes. Thus the intermediate steps–those which were worked mentally–are not part of any lineage and may have occurred more quickly than a biological instantiation of the same process would have, since the latter would be bound by generation times.