At its core InfraNodus is a tool that helps discover patterns, hidden connections and gaps in data. You can use it both during the process of writing and ideation — as a live visual feedback tool — or to get a different perspective on any subject, something you've written, or something you're interested in.
Our studies have found that having direct visual feedback on the process of ideation helps think in terms of connections. This means that if you are writing using the graph you are more likely to think of connecting the parts of your discourse which would not otherwise be connected. The additional feedback you get using the Analytics tool on the top topics and the most influential nodes helps you see the patterns and meanings that emerge in your work. The Insight feature helps you discover and bridge the gaps between the different ideas.
Let's use "artificial intelligence" as a topic that we would like to develop to demonstrate how you can use InfraNodus as an ideation tool. It may be more interesting for you to follow this with your own instance of InfraNodus running, so you can try it in real-time.
Stage 1: Developing an Idea
1. Open the Insight app in InfraNodus apps and type in "artificial intelligence" into the Editor (bottom left). Add it into the graph and you will have it visualized. Click the "Insight" panel and then Action Advice —
You will see that the current advice is to "develop the less represented topics" around the periphery. Which means we need to add more ideas. The hint in the editor says: "next idea tip: what is the relation between "intelligence artificial" and "artificial"?
2. Let's add a new statement to answer this question: what's the relation between the natural and the artificial:
the artificial nature of intelligence juxtaposes it against the idea of natural
We can see that the system is still recommending us to develop the periphery of the idea, so maybe let's focus more on the notion of natural and add another statement:
whatever is natural has a certain dynamics to itself, which is ecological at its core, because if it was not, the nature would not survive
3. Click "Reveal the Gap" as shown above to see the structural gap that could be fulfilled to make this idea more coherent. We will see the two statements we added: one about the juxtaposition of the artificial towards the natural, the other one is about the natural itself. How could they be related? Let's add a new statement:
should artificial intelligence have some natural qualities? and what is artificial intelligence's relation to ecology?
Stage 2: Focusing on a Topic
4. Let's then have an Overview of the document (click Data > All Statements at the top menu and the Analytics > Essence tab). We can see that the notion of "natural intelligence" became quite prominent in our text.
We can continue writing using the live network feedback feature and continue getting deeper into this subject (e.g. "what is the natural intelligence") or we can also go outside to get some inspiration.
Stage 3: Getting the Inspiration
5. Let's enrich our text with some external data: Google search results for the notion of "natural intelligence" to see a picture of this discourse.
Select the two nodes you're interested in, they will automatically be added into the Editor panel. However, instead of clicking Save, click import + Search button to import Google search results for this query:
6. We will see the two graphs superimposed on each other. One is the graph of our original text, the other one is the graph of Google search results for the "natural intelligence" query:
The blue color on the graph indicates intersections: how the two discourses (our original text and Google search results) are similar. You can see that this mode is on because the "intersection" Graph Comparison icon in the left menu is colored green.
We can also see in the Main Topical Groups field the main topics that emerged, which now include the applications of AI to marketing and natural resources price data and natural language processing.
Therefore we can infer that part of the discourse on this subject is focused on business applications of the AI and not so much on the notions of natural vs artificial, ecology, and sustainability.
Actionable insight: if we want to make a contribution to the existing discourse, we need to link our text to the business applications of AI and its use in natural language processing and market study.
7. Click on the Analytics > Insight panel, then go to the Missing in the Current Graph field and click Reveal. You will see even clearer the parts of the discourse that exist in the Google search results but are missing from our text. We can add this as an idea into our original text using the "Interpret" editor field:
the discourse in the field of artificial intelligence is focused on market analysis and applications as well as the natural language processing.
When we add this statement into the graph we will see that the number of blue nodes increase, which indicates that our original text will now have a better coverage of the existing discourse on "natural intelligence".
Stage 4: Discourse Reconfiguration
8. Click the Statements Menu > Current Graph Data to see the graph of your original text. Also, see Action Advice and Structural Gap in the Analytics > Insight panel. The system proposes us now to think of the relation between language processing and market analysis in the context of AI.
We can add a statement into the Interpret editor field where we say:
artificial intelligence is sometimes used for natural language processing in order to predict the market sentiment and commodity prices
This statement will be added to our resulting text (available in the "Result" panel) as we are now just interpreting Google search results that we got.
Note, that we are demonstrating here a continuous iterative process of ideation. So some actions may seem repetitive, however, the insight you get every time is new. You don't have to do it in the same order. The main idea here is to constantly explore the patterns, the connections, and the gaps. And to sometimes use the external sources to fulfil those gaps and to develop the patterns.
9. Let us take a look at the Google search results only, without our text: click the Editor > Original panel:
Then in the Search Topics field let's add the search results for "artificial intelligence" query to have the full picture:
We will then add the Google search results for "artificial intelligence" into this graph and we will then be brought back to the page where we superimpose our text and the Google search results for the query. Remove the most obvious words "artificial", "intelligence" and "natural" and discover the new patterns revealed in the discourse:
Mainly we see that the new new subjects came up: "computer science" and "machine learning".
So we can add some new ideas into our Editor > Interpret field, linking it back to our original interest in the "natural" aspects of intelligence:
how are the principles of the natural, nature, and ecology explored in the fields of computer science and machine learning?
Stage 1 (again): Reiteration — Developing an Idea
10. Then click Editor > Result to see the text we are working on. We will a very interesting gap between the notion of the "natural" and "ecology" we've been writing about earlier and the use of AI for analyzing markets and sentiment:
What would happen if we link those two? How can AI be ecological and sustainable in its applications to market analysis? Is it about the profit or is it also about sustainable development and some idea of balance?
how can AI take nature and ecology in consideration when it performs market analysis. is it about the profits and growth or is it also about sustainable development and some sort of dynamic stability?
We add this into the graph and then
You can check out the original graph we created on InfraNodus (you won't be able to edit it though unless you export and import it into your own account).
10. Let's continue writing our text, based on the Insight > Action Advice: Bridging the Gaps. In this particular instance we can talk more about predictive capacities of AI that are ecological at their core (linking the topics identified in the Structural Gap and shown on the graph). For instance, how can AI have a certain notion of ecological in its decision-making and analysis process? How can it detect deviations from the sustainable course of events and alert the decision-makers in the markets?
We then reiterate this process several times. The basic idea to oscillate in a system of coordinates with 2 types of strategies — dispersion of attention vs focus, each of which has two polarities — receptive vs proactive.
The image above is also used in the political and social theory of Panarchy, which is an ecological view of development where the objective is not constant growth, but, rather, acceptance of all the important cycles, including the crisis.
It also represents the essence of a creative process: bringing things together and letting them fall apart, listening and acting.
Using the network as a live feedback mechanism on this process we can observe where we are in this cycle at every moment of time and readjust our dynamics accordingly.
Stage 1: Receptive to the existing information, multiplicity towards focus
1 > 2 transition: increasing the specificity of your focus and proactive effort — steps 1-3 above
Stage 2: Proactively creating the information, focusing on a certain subject
2 > 3 transition: focusing, but now becoming receptive — step 4 above
Stage 3: Receptive to the new things, but still focused
3 > 4 transition: listening and then proactively exploring different perspectives — steps 5-7 above
Stage 4: Proactive in dispersing attention and learning new things
4 > 1 transition: letting the gaps in the multiplicity we explored reveal themselves — steps 8-9 above
1 > 2 transition: focusing on a certain (new) aspect again, developing a new idea.
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