Click here for the Download: Tech Trend Report 2024
The Future Today Institute (FTI) has published its 17th Tech Trend Report and provides a comprehensive overview of technological trends that will (significantly) change our everyday lives and work. The report is downloaded by more than 1 million users annually. The report provides a well-structured overview of technological trends in a wide range of areas – artificial intelligence (AI), climate, biotechnology, finance, medicine, and more.
The trend report is based on the evaluation of extensive data on consumer behavior, but also on insights into the activities of research institutions. Based on this quantitative basis of the trend report, a reliable short- to medium-term perspective emerges. In some places, the book goes beyond that mid-term time frame and looks further into the future in both optimistic and pessimistic scenarios.
I’ll present below some diligently curated facts from the Tech Trend Report 2023. It’s a good read, the report is structured in small reading bites, so that you can browse through it from time to time.
Artificial Intelligence
122 pages are devoted to the topic of artificial intelligence (AI). It is a very compact overview on a variety of topics. The table of contents of this chapter makes this clear:
If you’ve been following the evolution of GenAI’s offerings in 2023, you’ve seen that many of the leading AI models, such as OpenAI’s GPT series, started the year as text-only models and matured into multimodal models that can process and produce images, audio, and in some cases even video. At the same time the learning processes for AI algorithms has also become multimodal, which means that it allows AI to learn from visual and auditory information, not just text – just like we humans do.
In February (2023), Meta introduced and released the LLaMa model. The compact yet advanced language model with 65 billion parameters is freely accessible and free of charge for research and commercial use. In doing so, Meta has sparked a movement for large open-source language models . The Tech Trend Report analyzes the advantages and disadvantages of proprietary versus open-source models; actually, it makes a huge strategic difference whether a company relies on either one or the other type of LLMs to develop its own AI-based offerings.
Companies using large language models face a difficult choice: either they opt for the big names like OpenAI and Microsoft for easy access to best-in-class technology but forgo adaptability and transparency; or they roll up their sleeves and build their own custom systems to ensure transparency and extensibility. Despite the high development costs associated with proprietary LLMs, the open source community has responded with notable alternatives, such as Databricks’ Dolly LLM, which provides a solution at a fraction of the cost. The new shift to open-source solutions aims to counter the increasing concentration of AI tools in the hands of a few large enterprises by offering companies the ability to integrate custom applications without compromising proprietary information.
The report also points out that the unequal distribution of AI advances risks deepening global inequalities. The Global South is at a considerable disadvantage. However, this this risk also includes the ongoing geopolitical tensions between China and the West, which resulted, for example, in a ban on chip deliveries to China by US companies.
The Future Today Institute predicts (further) consolidation among the tech giants. Why? – Because the development of AI requires extraordinary resources. The biggest names in AI – OpenAI, DeepMind, Anthropic – are increasingly connected to the world’s largest hyperscalers and cloud providers (Microsoft, Google, Amazon). For proof just take a look at Microsoft’s 2023 increased investment in OpenAI for Bing, which aims to capture market share from Google Search.
How will the scope of AI models evolve? – The FTI predicts the emergence of large reasoning models: vertically integrated solutions that achieve higher transaction value. Some companies will offer a “refined value-added LLM product” to the end user and meet the customer in the desired distribution channels, such as healthcare, legal, finance, and architecture LLMs.
Also noteworthy is a breakthrough in AI development towards solving complex mathematical problems. This is particularly relevant with regard to the question of whether or when artificial intelligence could reach the level of human general intelligence (cf. my blog post: Artificial General Intelligence (AGI) – experts on the question of when we can expect the breakthrough … ):
This breakthrough in the mathematical capabilities of AI was achieved by DeepMind’s AlphaGeometry: In a groundbreaking paper published in Nature, AlphaGeometry demonstrated its ability to solve complex geometry problems at a level comparable to that of a human Olympic gold medalist. AlphaGeometry solved 25 out of 30 Olympic-level geometry problems within standard time, a feat comparable to top human candidates, and solved a wide range of math and computer science questions more effectively than human mathematicians working alone.
The success of AlphaGeometry underscores AI’s growing ability to think logically and discover knowledge. AlphaGeometry is an example of a process that reflects real-world thinking. This process has been compared to the dual process theory of thinking, Type I and Type II, popularized by psychologist Daniel Kahneman in “Thinking, Fast and Slow”.
Below are some more highlights from 2023, which outline the further development dynamics in the AI environment. I also refer to my blog with an overview of important milestones: Annual Review 2023: Milestones in AI Development The following points complement this view:
Computing
And here is the table of contents for the “Computing” section of the report, which deals with: quantum computing, new chip design for AI algorithms, photonic chips, smart textiles and more:
