“It is not our goal to recreate the human mind — that’s not what we’re trying to do. What we’re more interested in are the techniques of interacting with humans that inspire creativity in humans,” — Rob High, IBM Watson.
Creativity in different forms is embedded into art, design & science We often tend to say that “creative minds” have “artistic qualities” or are simply “sporadic”. In fact, it is fair to say that Albert Einstein inspired a paradigm shift in physics not just as a scientist, but also as an artist.
AI, Machine Learning, IoT, Quantum Computing, will guide human creativity in the future.
Einstein was an artist in more ways than one. Indeed more of his success can be attributed to his “creativity” than to the underlying knowledge of the creative field as such. This is because being “creative” is simply about producing.
In 1902, Einstein got a job at a Swiss patent office. He had searched for a teaching position so far with little luck. This forced him into an uninspiring place compared to his interest in physics. During his time there, however, he chose to manage his day so as to achieve a disciplined balance between hours at work and what he dedicated to his own scientific work.
He was steady, planned and deliberate in his commitment to creation, which finally lead to Annus Mirabilis papers. It would go on to inspire the formulation of two fundamental theories in physics: the theory of general relativity and quantum mechanics.
Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.
Inspiration doesn’t just strike out of nowhere. It comes from a definite place.
Creativity is a process. Even what seems like sporadic insights actually comes from or is derived by what came before them..
Creativity is simply about producing something using a combination of existing elements around you in reality.
Inspiration relies on a consistent pattern of work. It can be reading, writing, whatever — that sometimes manifests itself in the form of those rare moments.
Experts say that teaching a computer to be creative is quite a different process from how we learn to create. Although there’s so much we don’t yet know about our own creative methodology.
Creativity may be the ultimate destination for AI. It has already helped write pop ballads, mimicked styles of great painters and informed creative decisions in filmmaking. Experts wonder how far AI can or should go in the creative process.
It’s easy for AI to come up with something novel just randomly. But it’s very hard to come up with something that is novel and unexpected and useful.
John Smith, IBM Research
It’s easy to conclude that the greats have been suddenly inspired. But the often told story of the moment the fall of an apple led Newton to discover gravity, may not have been as “momentous” as we are led to believe. Or “Eureka” may not have come out of the bathtub alone. Yes, the experience of
insight is sudden and can seem disconnected from earlier thoughts. But studies show that insight is actually a culmination of a series of brain states and processes operating at different time scales.Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales.
Data Analysts and Interaction Designers work at two extremes — from technical to humanistic. Data Analysts offer expertise — but they address the problem from a largely technical and analytical perspective. There is no focus on the humanistic aspects of design problems they address.
Interaction Designers are experts in designing interfaces for devices with screens. They may encounter and understand the data behind interfaces, but it’s too often left out of the design equation.
Create quality data by asking right questions
Data Analysts and Interaction Designers work at two ends of the spectrum, from technical to humanistic. Data Analysts offer the most expertise in the medium, which is a great place to start, but they are approaching the problem from a largely technical and analytical perspective, without the concentration we need in the humanistic aspects of the design problems they address.
Interaction Designers today are expert in designing interfaces for devices with screens. They may encounter and even understand the data behind their interfaces, but for the most part, it’s too often left out of the design equation.
Quality data by asking right questions
We need to apply a humanistic lens to the questions of what data we need, and how to organize it to best use.
What business problem is the data model solving?
Who are end users? Are they technical or non-technical?
What value are they expecting from this product?
Is the data static or dynamic?
What is the quality of data being collected? How do we best
represent that information?
What are the larger implications of this data model?
How big is the market? Who are the other players?
Are they complementing your product or direct competitors?
What is the switching cost?
What are the key regulatory, cultural, socioeconomic and
technological trends to take into account?
Data is like garbage. You’d better know what you are going to do with it before you collect it.
When we want to use data for ML and AI, you’ve to go beyond the standard criteria of data quality. These criteria — availability, usability and reliability — are still valid. But we should know whether the data is truthful, and the methods and roles to establish the truthfulness of data.
The technical facets of data — latency, density, context, meaning and size — change AI/ML/IoT behaviour. They deeply affect the user experience.
Designers will go on to consider and use these tools of their expertise to affect quality, availability and usability. Design becomes a bridge discipline to the world of applications that have least of visual interfaces. It’s a bridge to voice-controlled and voice-output user interface. A designer’s toolset is imperative when cognitive systems become more common place. These systems will do far more processing of data and expect to present less raw information. This processing and output will require design, for performance and information accuracy, but also to make systems humane. Designers will drive how information is used by machines to perform tasks that don’t require human intervention.
Design has been focused on just rendering pixels on screens. But now data is becoming an articulate medium of design, in its own right. Designers can turn data into information and knowledge. They can deliver a world where interfaces get out of the way and allow people to spend less time with machines and more on life itself.
Traditional web and mobile analytics may not work as expected in a hyper-connected world.
Machines can learn much quicker than humans. However, machines are not about to take over the world and lead the creative sphere. They are here to help create better, simpler, more personal experiences.
AI, ML, IoT can drive a new generation of creative experiences. They are beginning a powerful shift that will bring brands closer to consumer expectations, passions and emotions. Assistive and smart technologies are catching up and we’re ready for a new world of possibilities. Not everything that can be counted counts, and not everything that counts can be counted.
Not everything that can be counted counts, and not everything
that counts can be counted.-
IBM +Data Science +Capgemini +WIRED MIT
Content: Sandeep Ozarde (Director at Leaf)
Editing: Gunjan Pai (Writer at Leaf)
Illustration: Sourajit Sengupta (Principal Designer at Leaf)