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Machines help us do things – build cities, fly, lift boulders – but they can also help us be things. Machines can help people be more creative, faster, and freer.
The consternation surrounding AI and ML technologies is that positive intentions are not singularly sufficient to guarantee positive results. We can be the architects of our undoing; in pushing against borders of possibility, we expose ourselves to dragons. The unknown is a territory that must be defined as it is realized. Pre-empted as it is made. It is only in not knowing what role machines will (or should) play in our lives that we entertain the more dystopian visions of the future.
The goal of HUMAN Protocol is to enable machines to help humans be more creative and productive. It is built to support global job markets in which machines submit work to – and complete work submitted by – other machines, relieving human workers of the repetitive and uninteresting.
There are different forms of intelligence: specialized and general. Specialized intelligence refers to intelligence applied within specific contexts, to solve specific problems. Machine intelligence is specialized: able to do specific tasks well, such as predict the next word in a sentence, and others poorly. Machines cannot conceptualize as humans do; they cannot imagine something of nothing, nor emphasize, and struggle to translate the meaning of words, objects, or signs across different contexts.
The “specialized intelligence” of machines can complement the “general intelligence” of humans. HUMAN Protocol provides the infrastructure for better collaboration between machines and humans, creating a system in which the former is able to take care of specialized and repetitive tasks, and the latter of more interesting or creative tasks. Liberating human workers from the repetitive helps maximize their unique capabilities, while providing the focused space for creativity to flourish.
HUMAN Protocol supports data-labeling applications. Within these applications, machines could be developed to answer the easiest and most repetitive of questions (for example, in which pictures does an animal appear) and humans those that require creative interpretation (for example, in which pictures is animosity shown) or specialist knowledge.
Machines cannot be creative - at least, not as creativity is typically understood. Machine awareness is derived from an amalgamation of what has been. It can predict, but not create, nor imagine (nor fabricate) entirely new solutions. Machines can produce facsimiles of creativity – such as predictive-text software writing a New Yorker piece – which replicate the process of creativity without being, in itself, creative. Machines learn from the past to approximate the future. Similarity may be achieved, but not novelty. For now, original thought remains an exclusively human ability.
But that doesn’t mean machines cannot help humans do what they’re best at – creating, extrapolating, and nuance thinking. Machines can free up human capital, to allow human workers to dedicate their time, attention, and focus on creative or specialist tasks.
Imagine digital job markets in which machines and humans are able to meet in the middle: an infrastructure for secure collaboration, in which the jobs and tasks suitable to each are intelligently managed and distributed. In such a world, humans could work as humans, on uniquely human tasks, and not as machines.
Intra-human connection will always play a decisive role in labor networks; a human world requires human input. This could mean human feedback to machine processes, for validation and course correction, humans collaborating with other humans, or human physical presence. The machines not in the room – unseen, collaborating through HUMAN-supported networks – can empower the humans in the room.
Before AI is ever able to supplant humanity’s capabilities, in other words, they can empower them. HUMAN aims to provide a means for knowledge workers to achieve greater focus: their time liberated from the remedial and relieved of the micro. A writer may be better able to write if the research is already available; a designer to design if they do not need to spend hours storyboarding; and an architect to conceive of new designs with a ready awareness of materials, space, and regulations. In allowing machines to take care of the small steps, and provide the necessary information and details in response to tasks submitted by other machines, human workers can focus on bigger and more meaningful tasks.
Humans are creative problem solvers. Machines can be used to gather, assess, and organize the “ingredients” of creative solutions: the required data points, automatically requested and supplied by other machines. This will allow human ingenuity to scale, to address a wider range of challenges at greater speed and with greater flexibility.
Repetitive distractions can interrupt the novel. With machines handling repetitive tasks – scaled to the requirements of the problem being addressed – human workers may focus on new solutions. Those which require the application of intuition, empathy, or creative interpretation, such as emotion recognition in a video or voice, or sentiment analysis of an email. Individual time is a scarce resource, particularly for knowledge workers. By ensuring it is spent on the problems that matter, HUMAN Protocol can empower human potential.
The future, though virtualized, digitized, and mechanized, requires humans. It requires human vision, creativity, and ingenuity. It requires its ability to sympathize, emphasize, and communicate – and, naturally, its physical presence. Creation, as much as it is an individual endeavor, is also a collective feat driven by connection. Creation is nourished by the interpersonal: the ability to intuit responses using more than surface-level information.
Machines can communicate with one another, but their understanding is arrived at computationally rather than intuitively, and is derived from specific inputs. A creative flow can exist between humans that machines cannot approximate. Connection is not necessarily communication, and neither necessarily leads to understanding. Machines’ conversations are determined by the systems that underpin them.
But machines can assist and empower creative processes. An inability to independently create should not – and through HUMAN Protocol’s design, may not – limit the ability to contribute to creative outcomes. Creativity is expanded when human capital is relieved of the micro; when specialized tasks are solved by machines, and creatively challenging tasks are solved by human creativity, perspicacity, and knowledge.
What machines take care of, they relieve as a burden from human workers. HUMAN Protocol aims to support job markets in which humans and machines work more symbiotically, and collaboration between the two is prescribed and reinforced within the network.
In an upcoming piece, we will discuss the specifics of how this can be achieved through data annotation. For the latest updates on HUMAN Protocol, follow us on Twitter or join our community Telegram channel. Alternatively, to enquire about integrations, usage, or to learn more about how HUMAN Protocol supports machine-learning technologies, get in contact with the HUMAN team.
The HUMAN Protocol Foundation makes no representation, warranty, or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained here. Any assumptions, opinions, and estimations expressed constitute the HUMAN Protocol Foundation’s judgment as of the time of publishing and are subject to change without notice. Any projection contained within the information presented here is based on a number of assumptions, and there can be no guarantee that any projected outcomes will be achieved.