Swarm Intelligence and the Intelligence of the Crowd
In crowdsourcing, the collaboration of many individuals on one problem is often more effective than an individual working alone. This is because the collective knowledge of the group allows for a comprehensive and intricate treatment of the problem and therefore achieves much better results. The term swarm intelligence relates to this phenomenon.
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Significance of Swarm Intelligence in Crowdsourcing
“Swarm intelligence” is a fundamental component of crowdsouring. Many crowdsourcing sectors use collective intelligence to improve results. Some examples include crowdtesting where numerous users search for errors in apps or web applications, or wikis, in which readers edit and expand articles.
This means much more can be completed than if one person were working alone.
Differences Between Swarms and Crowdworking
“Whilst swarm intelligence plays a part in crowdworking and crowdtesting, they are not the exact same thing. In swarm intelligence all entities work individually and are sometimes influenced by others in the crowd. Therefore, they can adapt to change their behaviour based on the collective swarm. In crowdworking most individuals do not have any connection to anyone else. Therefore, they work or produce data as they see fit.
Crowds or Swarms – Which are Better?
Many will wonder if either swarm behaviour or crowdworking is better than the other. However, this doesn’t really apply in many situations because they can both work together. Additionally, one can influence the other and in some occasions, one is more appropriate then the other. A study done by NPR analized this after asking 17,000 people to guess the weight of a cow.
With little knowledge of cows and just a photo to base their guess on the crowd did really well, being less than 70 pounds under the actual weight. Then just 49 people were asked to guess using a swarm intelligence platform made by Unanimous. This platform uses a real-time algorithim that uses the thoughts of everyone as one, this is known as a “brain of brains” method. Of course, many would assume a larger error margin for the group of 49. However, the guess was very close to the group of over 17,000 with just a 3% difference.
Crowdworking – The Better Option
The above mentioned study shows that less people were required when swarm intelligence was brought into the situation. In some cases, this can prove very beneficial. For example, in communications companies, the use of such swarm intelligence applications can mean calls are routed with efficiency. This is better than having to hire 100s more staff to take calls via the same route.
There are several situations where crowdworking is going to be the better option. AI cannot always be given data based on swarm intelligence alone. In fact, in some cases, a company may want to develop or look further into swarm behavior by utilising the crowd. One of the most simple ways of doing this is via polls or surveys. However, it can be much more complex than that. For example, many tasks at clickworker gather data based on how humans, speak, react, consume, move, and make decisions.
Examples of AI Development
These days, AI development is becoming or is already crucial to many businesses and the way they work. There is a long history around the intelligence of crowds and swarm intelligence. Therefore, it is not surprising that both are heavily involved in the advancement of AI technology.
Crowdworking has helped many companies. One example is a company who makes car voice recognition systems wanted to improve their product. Thankfully, after coming to clickworker they used the power of the crowd to advance the AI. This AI was used for machine learning to enchance the capability of the system. Clickworkers across the globe recorded almost one million commands. The data created by the crowd allowed the companies software to train the system via AI to become more accurate.
Swarm AI is a platform created by Unanimous AI. It uses algorithms combined with human insights to produce more accurate data. This platform connects networks of human groups into AI systems to do this. Ermi LLC, a healthcare company, used Swarm AI to see what impact the global Covid pandemic would have on their sales. Virtual meetings were held with questions asked of sales personnel, managers, and affiliates. The data from this was input into the platform and it gave a prediction of the next two quarters sales. It turned out to be correct and so the company continued to use Swarm AI to help it in the future.
There are different types of machine learning. Supervised machine learning is when data from humans is given to an AI system to teach it by example. However, with unsupervised learning, the AI algorithm looks for certain trends in data itself. It doesn’t have to be given various examples of something to do this nor is it told to pick out specific data. It just scans for data with similarities, for example, sending out a sales email to customers with the same shopping habits.
Another type of learning is reinforcement learning. Here, the AI system is doing several things to arrive at a best possible outcome. It can do this by increasing a reward depending on the data it is assessing, it will try many times until it reaches the most suitable situation. In 2018 AI company Wayve used deep reinforcement learning to teach AI to drive a car in just one day. The AI was rewarded when it steered to avoid objects, stayed within lanes, and kept to certain speeds. All done without anyone telling the car to adjust or having any auto-collison, speed limiting etc.