Artificial intelligence cannot be boiled down to designing algorithms, binary or otherwise, simply because the exhibition of intelligence in biological systems predated the invention of algorithmic computing. From this, we can further draw the conclusion that algorithms are not a necessary component of systems that exhibit behavior we deem intelligent.
A decision was made, per the recommendation of John von Neumann, to increase reliability of computing machinery by delegating to a single binary central processing unit all computation. This choice and the prior work upon which it was based (Shannon, Church, and Turing) led to the preeminence of algorithm specification in computer languages. The foundation of expressing functional design in algorithmic terms was laid and the software industry was born.
Since that time, there has existed a parallel trend in research back toward the biological inspiration of computing machinery and, more specifically, parallel processing. We see this at several levels.
- Movement of floating point arithmetic, video rendering, and machine learning bottlenecks to dedicated VLSI hardware acceleration
- Multiple core VLSI processors
- Computing clusters and processing frameworks, containers, and environments that expose interfaces through which compiler and kernel programmers can control parallel machinery explicitly or implicitly
- Multiple thread and processes delegated to multiple cores, agents, or hosts in computing clusters
- Sophisticated VLSI level caching to maximize the efficiency of parallel operations
- Language and compiler features to support the trends toward deployment to multiprocessing environments, such as declarative languages for Big Data platforms (ECL for example)
- Development of AI chip designs that completely or partially shift the computing paradigm to prior to the emergence of the CPU in some ways, returning to considerable parallelism and departing from centralized processing (yet capitalizing on lessons learned in computer vision, cognitive science, reverse engineering of brain genetics, mental signal tracing, the use of gradient descent with back propagation, reinforcement designs, and applied robotics) — This is likely a major research direction for the 2020s.
Some believe that an implication of Gödel's two incompleteness theorems is that the human mind does not meet the criteria of a computing machine as Turing defined one, but these are largely tangential issues.
It is true that The working out of a proof that RNNs of sufficient resolution, depth, and width can be trained to be equivalent to any Turing Machine by Hava Siegelmann. It is true that her work is considered support for Marvin Minski's bold assertion that the human brain is a meat machine. However, the work on determinism by John Lucas and Roger Penrose's The Emperor's New Mind are not refutations of either of Gödel's theorems. They are refutations of what some thought were consequences Gödel's theorems and some of the implications of Minski's declaration.
Gödel clearly explains his intentions in the early portion of the paper presenting the theorems, and they had nothing to do with computing. He intended to and succeeded in proving that theorems within a concrete mathematical system not always be proven even if they are true. Gödel's work placed unwanted doubt on the initiative to prove all remaining unproven mathematical theorems. Mathematicians naturally tended to think of mathematics as the perfect human endeavor, and a legitimate proof of incongruity between what is true and what is provable seemed an imperfect irritation.
Perhaps the most profound response to Gödel's incompleteness theorems came from Alan Turing, who likely deliberately placed the word Completeness in the name of his theorem. But this was not a refutation either. He worked around incompleteness by defining a class of mathematical operations and finite data structures upon which they can operate that he could prove could be complete. Upon doing so, he put into place an important portion of the basis for algorithm development.
Nonetheless, it is probably wise for present day AI researchers to accept both incompleteness and inconsistency and realize that intelligence, artificial or not, is likely fallible after any finite degree of learning. This is likely because one cannot provide an infinite range of problem types to a learning system in a finite amount of time. There may always be at least one problem that the current state of learning cannot address. The practical colloquialism for this condition of partial knowledge is, "We don't know what we don't know."
Furthermore, a clear implication of the work of Gödel is that no proof may be found for some things that are true, ever, by any type of intelligence. Similarly, we cannot be sure that the most intelligent searching for a counter example to dispute a false assertion may end in finding one, ever. The PAC Learning framework addresses categories of problems that are solvable or not from a mathematical perspective and is worthy of study.
Lastly, but perhaps most profoundly, it is not clear that a type of intelligence exists that can learn anything, as opposed to be programmed to accomplish anything. Said another way, general intelligence may be an ideal conception never achieved but possibly approached. What may seem like super intelligence in one environment and during one specific time period may be entirely ineffective or even counter-intelligent and problematic in another environment or during a different time period.
This cannot be stressed too much, with so many statements about AI being made in the guise of science that have no origin in scientific rigor.
Nonetheless, even with these likely limitations on both AI and human intelligence, one cannot conclude that AI will be ineffective in real time critical applications. One cannot conclude that AI will be less effective than human intelligence in any particular domain either.
It is actually difficult to conclude anything about intelligence at all, without defining it formally and reaching a consensus in that definition, which continues to escape us. We can see that in the absence of this formality, mail industry from continuing to sort mail automatically. The automotive industry continues to pursue the invention of better artificial drivers than the average human driver. The game industry implements artificial opponents that have to deliberately make mistakes to let people win in an otherwise fair, real time game.
Clearly AI is evolving faster than the DNA components that affect the human brain.
People are less startled today than they would have been ten years ago by the proposition that, some time during this century, driving a car will be illegal in some jurisdictions, when the human and property loss statistics prove automated drivers to be substantially safer than nearly all manual ones. The bar for driving safety set by humans is not very high, with day dreaming, texting, occasional tiredness or inebriation slowing an already insufficient reaction time for many street events.
If the driving computing agent panics because there is determines the trajectory of a dog, a child, and an elderly person is intersecting with the car's trajectory, it may resolve the panic and plot a safe course in a millisecond (perhaps avoiding all three or perhaps sacrificing the dog to save the two people), whereas the human may resolve the panic only after hitting some one.
In summary, it is not infallibility that determines the proper balance or volume of AI deployment but the comparison of the distribution of human performances compared with the distribution found with the machine replacements under similar conditions.