Artificial intelligence (AI) is becoming increasingly versatile. Last year was typified by progress towards a landscape in which enterprises can automate a wider variety of functions more easily, with the potential to lower costs.
The robotics sector is a good example of where versatile AI could pay dividends. In December 2020, a research paper in the academic journal Science Robotics demonstrated how a four-legged robot was able to exploit multiple precursor AI systems to effectively improvise, adapting to changes in its workload on the fly.
The approach, dubbed multi-expert learning architecture, is the result of collaborative research between University of Edinburgh in the UK’s School of Informatics and Zhejiang University in China’s Institute of Cyber-Systems and Control.
Robots using multi-expert learning would be trained over multiple stages. First, a computer simulation shows two distinct neural networks how to perform the robot’s basic mobility functions: how to move across the ground and how to recover after falling over.
A further eight neural networks then learn how to execute specialised motor skills, such as rolling over or turning left or right, with an AI supervisor, known as a gating network, deployed to synthesise combinations of the neural nets to maximum effect, according to Singularity Hub.
Michael Rovatsos, professor of AI at University of Edinburgh’s School of Informatics and director of its data science and AI-focused Bayes Centre, told GCV: “The need to engineer and train bespoke AI systems to solve specific, narrow problems is still a major cost factor for businesses.
“This research demonstrates we are making tangible progress in terms of recombining and repurposing these components by developing more generic solutions. I anticipate increased versatility will be the focus of much AI research over the next few years and will help remove significant roadblocks for AI adoption.”
In another landmark for versatility in AI, DeepMind, the Google-owned advanced automation lab in London, completed a software program that is able to play strategy games such as chess against humans without being shown the rules.
Earlier variants of DeepMind’s technology could learn and master the strategy board game Go. However, the recent iteration – MuZero – can autonomously figure out rules without trial and error, meaning the algorithm could potentially be repurposed to explore other environments without first being fed the dynamics.
The developments foreshadow a generation of AI poised to overcome the biggest barriers facing current technologies. These are difficulties in mixing and adapting skills at levels approaching human cognition.
It is three and a half years since Jensen Huang, Nvidia’s president and CEO, first predicted AI was to experience a “Cambrian Explosion”, referencing the evolutionary period in which predecessors to most major groups of animals begin to appear in the fossil record.
And versatility has certainly brought AI closer to its own Cambrian threshold, despite GCV Analytics data that suggest corporate venturing-backed transactions in the space tailed off slightly last year.
Total deals fell to 316 in 2020 from 334 the previous year, with dollar amounts dropping by approximately a quarter to $15.5bn from $20.5bn.
To realise versatile AI’s full potential, there is a need for more efficient computer chip technologies in the cloud and on end-devices to match the progress of ingenious new algorithms and data techniques.
After a period of sustained research and development and corporate venture-driven innovation, and with lots of accelerated chipsets for data centres to choose from, it is up to the market to determine which products are best. It seems the semiconductor industry has responded, in a bid to put its silicon behind the hype.
Venture capital appetite may increasingly pivot to specific use-cases, such as deploying AI inference on the edge through end-devices like sensors or intermediary networking gateways or to products positioned to alter the way chips are manufactured, targeting the wider production ecosystem.
GCV data showed the number of corporate venturing-backed deals for AI and machine learning-related semiconductor businesses remained static year-on-year in 2020, at 347. However, the equity funding deployed in those rounds fell to $17.7bn from $21.4bn, with the Asia-Pacific region experiencing a heavier fall in dollar amounts than North America.