The world is currently discussing if artificial systems are good or bad, will help us or destroy us, and if they will ever function or not, and by doing that people make the mistake of actually trying to answer the wrong question. As of today, the biggest question about artificial intelligence is not the system itself, but the biggest challenge is the interface consequences between the human and the machine, or to be more precise the system existent out of two elements — a carbon and a silicon body. Read More
Tag Archives: Collective Intelligence
AI Augmentation: The Real Future of Artificial Intelligence
I love Grammarly, the writing correction software from Grammarly, Inc. As a writer, it has proved invaluable to me time and time again, popping up quietly to say that I forgot a comma, got a bit too verbose on a sentence, or have used too many adverbs. I even sprung for the professional version.
Besides endorsing it, I bring Grammarly up for another reason. It is the face of augmentative AI. Read More
Will robots really steal our jobs?
A new PricewaterhouseCoopers report analyzes the long-term impacts of AI and automation, dividing the future of automation into three “waves:” the algorithm wave, extending into the early 2020s; the augmentation wave, into the late 2020s, and the autonomy wave, extending into the mid-2030s as described in Table 1.1 below.
Artificial Swarm Intelligence
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as “human swarming” or building “hive minds,” the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgements, and medical diagnoses. Read More
A new mind-set for the no-collar workforce
What Makes a Strong Team? Using Collective Intelligence to Predict Team Performance in League of Legends
Recent research has demonstrated that (a) groups can be characterized by a collective intelligence (CI) factor that measures their ability to perform together on a wide range of different tasks, and (b) this factor can predict groups’ performance on other tasks in the future. The current study examines whether these results translate into the world of teams in competitive online video games where self-organized, time-pressured, and intense collaboration occurs purely online. In this study of teams playing the online game League of Legends, we find that CI does, indeed, predict the competitive performance of teams controlling for the amount of time played as a team. We also find that CI is positively correlated with the presence of a female team member and with the team members’ average social perceptiveness. Finally, unlike in prior studies, tacit coordination in this setting plays a larger role than verbal communication. Read More
How AI will make us think harder
The ultimate goal of AI research is a system that rivals a human’s thinking power — but it is still far out of sight.
Instead, in the coming years, AI may provide an intelligence boost in a different way: by coordinating still-unsurpassed human brainpower and correcting some of the errors inherent how we think.
By definition, AI processes information very differently from humans and can combat group-think, says Berkeley’s Ken Goldberg, a robotics expert who directs the university’s People and Robots Initiative. Read More
The upside of humans — a lot of them
As work on artificial intelligence plods along, an advanced form of crowdsourcing is emerging as an accelerated way to surpass human thinking.
Why it matters: Specially organized groups of people could “get to superhuman intelligence first,” said Daniel Weld, a computer science professor at the University of Washington.
Collective intelligence — the knowledge of an organized group, which goes beyond that of any individual member — is already present inside every organization, community, company, and government. Read More
Artificial Intelligence and Collective Intelligence in Teams
Artificial Intelligence and Collective Intelligence
The vision of artificial intelligence (AI) is often manifested through an autonomous software module (agent) in a complex and uncertain environment. The agent is capable of thinking ahead and acting for long periods of time in accordance with its goals/objectives. It is also capable of learning and refining its understanding of the world. The agent may accomplish this based on its own experience, or from the feedback provided by humans. Famous recent examples include self-driving cars (Thrun 2006) and the IBM Jeopardy player Watson (Ferrucci et al. 2010). This chapter explores the immense value of AI techniques for collective intelligence, including ways to make interactions between large numbers of humans more efficient.
By defining collective intelligence as “groups of individuals acting collectively in an intelligent manner,” one soon wishes to nail down the meaning of individual. In this chapter, individuals may be software agents and/or people and the collective may consist of a mixture of both. The rise of collective intelligence allows novel possibilities of seamlessly integrating machine and human intelligence at a large scale – one of the holy grails of AI (known in the literature as mixed-initiative systems (Horvitz 2007)). Our chapter focuses on one such integration – the use of machine intelligence for the management of crowdsourcing platforms (Weld, Mausam, and Dai 2011). Read More
Rick's Cafe AI 