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Computer Society Technology Predictions 2023 – IV

The graphic highlights three clusters of technologies divided by their expected impact on humanity versus their chance of success. The first one comprises those whose potential impact is greater than their chance to succeed, hence the need to invest on these beyond a business perspective. In the second cluster impact and success go hand in hand, whilst in the latter the success of these technologies is not dependent on their (positive) impact on humanity, hence business may be the main driver. Image credit: Computer Society

Let’s take a look now at four technologies having a strong success probability. As such these will likely be pursued by business and in turns their uptake by the market may accelerate both evolution and deployment.

  • Augmented Reality: it is difficult to refer to Augmented Reality as an emerging technology. It has been around for so many years and it is quite widespread in several sectors, from industry to mass market. Yet the team feels that we are close to a disruption point where AR could take over the world by displacing many of today’s interaction protocols and significantly change the way we perceive the world.
    Over the last two decades the number of artefacts has exploded, thanks to the shift to the metaverse steered by the Digital Transformation of several businesses. In the last two years this has been further emphasised by the growing interest for the Metaverse. The Augmented (and Virtual) Reality is the tool enabling the fruition in our everyday life – and in business- of the cyberspace. The missing link to enable the take over of AR is a seamless device able to embed our everyday experience into the reality-artefact-cyberspace continuum. We already have the needed connectivity support (4G/5G/WiFi6, …) and processing storage support (including Cloud in its various form). However, the missing link is actually becoming “shorter” as better and better devices provide seamless fruition of the cyberspace. The rumoured Apple Reality One and Reality Pro might be the final push required to fill this gap (along with the copycats that would immediately follow if they prove to be successful) although the rumour has been there for at least two years now…
  • Software for Edge to Cloud Continuum: what if computers would disappear from our perception and the ambient around us is “the computer”? Computers are already everywhere, we probably never notice but we have a computer in our hand whenever we pick up our smartphone. Using the washing machine? Driving your car? Watching a movie on your smart television? Well, you are using computers that happen to wash clothes, have wheels to take you around, got a screen to show you the movie.
    As these computers are getting more and more connected one another we are moving towards a computing fabric. The cloud is part of this fabric and the cloud is extending to embed more and more processing points. To make this a seamless reality we need connectivity, and we have it, and we need a software to stitch it all together virtualising the physical entities from the point of view of the applications and of the users. The team is foreseeing an accelerated growth of this computing fabric in 2023 and in the following years with the progressive inclusion of the “edge” into the cloud creating a computing continuum.
    We are already seeing waves of IoTs products that can be aggregated into clusters for local processing with distributed intelligence and swarm intelligence.
    The evolution is driven by IoTs, as mentioned, the increasing adoption of distributed data and distributed applications that are steering towards distributed computation. New chips, like the STM32 series, support IoT data clustering and local processing. On the other hand the big Cloud Providers would prefer to see a slow uptake of decentralisation to keep computation within their centralised infrastructures.
  • AI assisted DevOps: software is ever more pervasive, and more and more coding is required. Then this software needs to be tested, updated, upgraded and operated. This would be extremely resource consuming and it would have become impossible but for the growth of tools used in software development and operation. The use of libraries and tools for customisation has dramatically reduced the effort required (the low code no code trend is also leveraging on libraries). At the same time most software today represents and overkill. A growing portion of lines of code is never used, it is there just because it has been inherited as part of the assemblage of library packages. In these last years AI has been playing a growing role in “coding” and in helping “coding”. The team foresees a growing role of AI in the software lifecycle, from design to coding to testing and operation (including release management). The continuous advance of AI, open source communities are sustaining this evolution. On the other hand there are concerns when relying on AI to develop code, on its transparency and on the possibility to check the software created, particulalry so if AI “begets” AI with the latter subject to an autonomous continuous evolution.
  • Generative AI: the last year has seen the tsunami of Generative AI sweeping several areas from translation to text to image, now getting close to text to video, from generation of music to generation of text (articles, blogs, … books).

    Till recently, AI created results based on a discriminative process and through inference, i.e., it DERIVED the results from the inputs. The progress in machine learning have led to self-learning and the possibility of creating new results, e.g., the creation of an image of a flying horse, where there is no flying horse in the training parameters but there are concepts of flying and of horse. This empowers AI to address new problem areas and respond to the demand of more flexible behavior of robots (autonomous robots) to face unexpected situations.

    Whole new areas of applications open up with generative AI. It goes beyond the possibility to increase autonomy of machines. It can support human creativity.
    In addition to the already mentioned areas of writing articles, novels, creating music, paintings Generative AI will be used to explore business opportunities, understanding market interests.

    Time-To-Market will be significantly decreased by helping designers to illustrate faster, and help businesses improve their digital channels and marketing.

    Generative AI may decrease the need for intensive processing to train AI in any new area (second half of decade). Support for no/low-code.
    On the business side, it can be expected that generative AI will increase AI adoption and create new revenue streams.
    Along with the broad slate of opportunities offered by Generative AI pointed out by the team, caution is also needed: the result of AI may seem to be so good that it can be taken at face value, however this may not be the case. A careful revision of what is produced by Generative AI is needed to avoid mistakes, fake news (unwillingly generated by G-AI).

As for the previous posts in this series let me remind you that what you find here are my comments on the identified technologies and these may not be in agreement with all the experts. This is nothing strange,  we  are dealing with predictions and these are based on facts AND on subjective perceptions on how various factors may influence the evolution. You should  make up  your mind  by reading  the document!

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the New Initiative Committee and co-chairs the Digital Reality Initiative. He is a member of the IEEE in 2050 Ad Hoc Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.