Gabor Priegl Blog

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    • Gabor Priegl

    AI, what it is and what it isn’t

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    Copyright UNNews

    Let’s cut through the chaos and get the basics first about AI.

    If you follow the developments in the world of technology you couldn’t avoid the breakthrough news a couple of months ago.
    The demonstrated capabilities of an artificial intelligence (AI) application (ChatGPT developed by OpenAI) has reached the stimulus-threshold even of the average IT users. This is a generative AI application which creates, human-made-like text and images instantly.
    “Hey, it’s quite like if I talked with a fellow human regarding the style, the speed of reaction and the wide area of topics that can be discussed – that’s the surprising experience of the users testing ChatGPT.”

    This particular application has thrusted AI into the limelight.

    Everyone is excited about the potentials of AI. Business leaders, investors and a wide range of users feel the buzz without knowing what it is all about. And that uncertainty, that undefinedness of the topic itself may amplify the interest and helps to fan fantasies about artificial intelligence.

    Because, yes, there is no widely accepted, settled single definition of AI.

    It means different things to different people, plus it has been hyped these days and everyone is trying to apply AI as an eye-catching label for their product, solution, concept.

    Despite this, it is not hopeless, by organizing our thoughts and systematizing the pieces of information to develop an AI framework for ourselves.

    Even if there isn’t one definition we still can have a look at one of the most well-known and complex examples of AI in order to identify the different aspects and dimensions of it.

    The overwhelming majority of people associate self-driving cars with AI, so this seems to be a good starting point. What are the main elements here? Complex, real, multi-actor and fast changing environment where the machine collects the ever changing status of items, runs analyses and based on them makes decisions in real time.


    A couple of things are obvious in the example:

    • the application generates a real time customer experience we have never witnessed before,
    • it utilizes (relatively) new technological fields like computer vision, breakthroughs in search and find methods, pattern recognition functions, decision making processes in higher levels of uncertainty,
    • and the machine imitates human behavior quite well.

    But this is only scratching the surface.
    What really matters here are: autonomy and adaptivity. These make the difference.

    Now, we have taken a good example. Let’s try to summarize the findings and provide some outlines.

    So, what is AI?

    1: AI is a discipline, part of Computer Science, heavily overlapping with Data Science. AI has some essential and signature sub-areas like Machine Learning and Deep Learning. Aim of AI as a discipline to research, develop, formulate, measure math models that can be applied in computer systems in order to solve complex problems in complex and changing environments, where the computer systems work in an (as far as possible) autonomous and adaptive mode of operation.

    2: AI is an approach and a (rapidly developing) portfolio of problem-solving methods based on the section above. The final evaluation and acceptance of the results produced by the method come from humans so humans’ decision of what right or wrong is still determining.

    3: AI is a concrete realization of a problem solving solution. Focus is on Autonomy and Adaptivity. In case of the latter the development of the solution requires external reference for improving the model(s) and training data sets and shaping the quality of outcomes all the time.

    I hope this article helped you clarify what AI is.

    Next time a few thoughts on the taxonomy of the subject and the possible roles of AI in our lives.

    Any thoughts and remarks are welcome.

    G.

    THINKING AND BOXING

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    WHAT BOX?

    I am sure that you are familiar with J. P. Guilford’s famous study, the nine-dot puzzle. The challenge is to connect all nine dots using just four straight lines without lifting your pencil from the page.

    The Nine dots Challenge

    You may know from your own experience that during the first attempts you feel compelled to keep your lines within an imaginary square, which you unconsciously project around the image of the nine dots.

    The usual solution, however, requires you to draw lines that extend beyond the area occupied by the dots.

    The typical accepted solution, however, requires you to draw lines that extend beyond the area defined by the dots.

    This challenge was to demonstrate how participants censor their own thinking by limiting the possible solutions to those within the imaginary square. And this experiment may very well be the origin of a new method: “Thinking Outside the Box”.

    The phrase itself has become a mantra especially in the business world when it comes to finding innovative solutions to new, complex challenges.

    ALWAYS FROM SCRATCH?

    At its core, innovation is about solving difficult problems.

    Right, but does innovation require totally new, unique thoughts, ideas, processes?

    We tend to think that: yes, most of us have a concept like that in our mind.

    Let us have a quick glance at some of the established images related to innovations.

    • The individual eccentric genius inventor.
    • The laboratory research team which requires highly experienced team members and well funded support.
    • And … the manager expected to think outside the box.

    At this point other questions arise:

    What is there to be found beyond such clichés?

    How do innovations and creative problem-solving work in practice? 

    What processes do innovators follow? Are there patterns to be recognized?

    These questions must have come to the great inventor, innovator and researcher, Genrikh Saulovich Altshuller (Ге́нрих Сау́лович Альтшу́ллер) at the end of the 1940s when in the navy he faced an unusual challenge: how to help others to innovate.

    “Sailors can draw maps of reefs and shallow waters that others can follow, but inventors have no such maps. Each beginner goes along making the same mistakes.”

    When charged with scaling the navy’s creativity without a structured methodology, Altshuller committed himself to making one.

    TRIZ40 – A SYSTEMATIC WAY

    Studying thousands of patents he exposed the scarcity of genuinely revolutionary thinking and detected a logic for systematic innovation, later to be known as TRIZ (a Russian acronym for the ‘Theory of Inventive Problem Solving”). Through TRIZ, Altshuller was able to demonstrate the science behind creative innovation.

    Here are the three primary findings of TRIZ, formulated by the author:

    1.           It’s been solved before.

    2.           There are consistent patterns of solutions.

    3.           Solving contradictions creates breakthrough innovation.

    TRIZ works to formalize the belief that somebody, somewhere has already solved your problem. TRIZ helps us recognize engineering strategies that have converged across categories and industries, when faced with shared technical constraints. In TRIZ the patterns of solutions are classified based on their related technical features. These are known as inventive principles. In total, the TRIZ methodology recognizes that there are 40 inventive principles that can be used to inspire innovation.

    TRIZ’s inventive principles include concepts such as segmentation (principle 1): describing solutions that break up an object into its independent parts (like modular furniture or Venetian blinds). Do it in reverse (principle 13) is another favorite, in which the movable part of an object or environment is held stationary, and the stationary part made moveable (like a swimming training pool where the water moves, not the swimmer). The inventive principle nested doll (principle 7) classifies patterns of solutions that place one object inside another (like the typical Russian doll), helping to represent an array of adaptations from several technical categories. A nail polish brush that’s screwed inside its own bottle is an example of a nested doll.

    To create breakthrough inventions, one must overcome a contradiction (or trade-off). TRIZ helps to address challenges like “How might we make a bulletproof jacket stronger without it becoming heavier?” or “How might we make an umbrella big enough to cover a human body but not so large it doesn’t fit in a handbag?”

    Altshuller concluded that there were about 1,500 standard engineering contradictions which he then summarized into a contradiction matrix comprising 39 parameters. These parameters include physical constraints (like weight and shape), performance parameters (think speed, power, and stability), and efficiency limitations (like time, temperature, and information). For each contradiction identified in the matrix, TRIZ then maps the most relevant inventive principles for a solution.

    We can say with the words of Altshuller: “There is no magic formula after all, but there are procedures that are sufficient in most cases.”

    MY POSITION

    I personally find it amazing how http://www.triz40.com/TRIZ_GB.php works, just visit the website and play with the options.

    Frankly speaking, as an engineer and manager I had never heard of the TRIZ40, for me it came as a revelation that somebody had developed such a system of systematized knowledge about creative thinking and innovation.

    What do YOU think about the methods of innovation?