05Jan
artificial intelligence bees
By: Hp Creative Space On: January 5, 2018

Why Bees Could Be the Secret to Superintelligence

The plausible statement or idea makes one wonder how smart bees are initially. Well, bees have small brains but possess great intelligence individually. There is no form of “hive-mind” intuition that makes a bee depend mentally on another when they work. This cognition is backed up by their relatively high brain density (of which is up to ten times that of mammals). With their intricate sensory system, it attributes to keen abilities of sight, touch, taste, and smell. Due to this potential, research and experiments have it that bees have excellent thoughts during their busy activities compared to other insects. There is almost no other insect that portrays more proficiency than bees.

Now, technological singularity depicts a hypothesis that over time, artificial intelligence will supersede the human intellect. Consequently, Louis Rosenberg believes to have discovered a method of enhancing human episteme with the aid of bees. He says, “We can’t stop the development of smarter and smarter artificial intelligence, so our alternative is to make ourselves smarter so that we always stay one step ahead.” The idea where he infers bees plays its role here. Bees are known to very intelligent on their own, talk less of when they come together to a make decision. The premise here is, can humans do likewise? Of course. Small swarms of bees consistently surmount in activities than larger crowds. Just a pool of 49 people is more likely to increase the accuracy of educated conjectures when behaving like a swarm. So, it’s merely more than wisdom to give average group’s responses which is why Rosenberg feels groups of people can be made smarter.

The wisdom of Crowd Accurately Predicts Supreme court decisions

At times, crowds show more accuracy in forecasting events than individuals that seem to be well acquainted with knowledge on a particular occasion.

Research also has it that forecasters usually overestimate their potentials in predicting geopolitical events such as who the next pope will be or who in Taiwan will win the next election. Scientists believe that in some cases, the wisdom of crowds is extremely accurate while in other circumstances, it is less rational than a random conjecture. Thus, one might still be perplexed on how good the crowd is at predicting Supreme Court decisions.

One of the ways some individuals deduced goes thus:

Imagine if the following story were real. In October 2011, a million-dollar prize was announced to predict six years of decisions of the Supreme Court of the United States using crowdsourcing data. Thousands of teams from across industry and academia respond to the challenge, each making slightly different but reasonable choices about how to cast their predictions.

As a result, teams plan to use this same strategy to predict the effect and termination of other legislative processes as well as national elections.

Artificial Intelligence and Crowd Sourcing

It is no new news that as innovations and inventions are accounted for during this contemporary period, new disruptive changes tend to become rampant and mercurial. Reports say that as we relinquish control to artificial intelligence, undoubtedly, the machines will need to make choices. Nonetheless, the analysis and decisions are not going to affect ethical paradigms to artificial intelligence (AI). The evolution of intelligent tools like this artificial intelligence and crowdsourcing can have optimum efficiency in making an unprecedented analysis. If this characteristic is neglected, companies and other facilitators of its usage can invoke institutional detriment on themselves. AI has been initially thought of and chewed over by scientists and researchers. Specifically, some devised a technique for computers to observe and make analogies- a comparison between a variety of methodologies and issues that highlight fundamental similarities.

Akin to decision-making schemes of AI and crowdsourcing, there’s also the matter of what the crowd is or does. Peradventure, an occurrence of a sample bias that is employed based on whose choices a sample reflects. Another possible chance will be the potential bias in individuals who tend to convert or alter crowdsourced data in specific stratagems for algorithms and so on. In other words, can crowdsourcing teach AI to make correct opinions these days?

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