If Moore’s Law continues as it has for the past 50 years, computing power will keep growing by a factor of 30 every decade. Even without smarter algorithms, increased hardware power will make AI systems more capable and embedded in our lives.
“Most of the AI tools in widespread use today are essentially mimicking things humans already do, but just doing them faster,” said Fergusson. “We want to really push the maths of AI, so we can get it to do new things. We don’t want it to mimic data, but to tell you how it works and break it down. That’s where the real change will happen.”
“In the past, companies have found it hard to navigate Cambridge,” said Fergusson. “We want to create a gateway: somewhere businesses can come to ask their questions, and find out what AI can really do for them, and we can learn what challenges they face. We hope this partnership with Infosys is a model of how that could be done.”
There are three main research themes in the AI Centre:
“I believe that a lot of things are going to change in the way we do research,” said Bolliet, whose research background is in cosmology and computational astrophysics. “Maybe that means that a lot of repetitive, time-consuming tasks that I spend a lot of my energy on will soon be automated, giving me more time and space to do more interesting things.”
The multi-agent model allows Bolliet to assign roles or even ‘personalities’ to each agent. For example, one agent could be a researcher and one an engineer, or one could be an idea generator and one could be a ‘hater’, relentlessly challenging and criticizing to make the end product more robust.
Bolliet says that using these multi-agent systems, AI will not only generate research but also review and correct scientific literature at scale. Unlike humans, it will seamlessly jump across academic fields from astronomy to oncology.
“We want to use AI to accelerate the exchange of information across fields,” said Bolliet. “AI agents don’t have these barriers. They’re not stuck in one discipline like we human researchers are.”
Bolliet notes while LLMs are often seen as black boxes prone to hallucinations; using multi-agent models allows him to check every step of the research process. “I can see every single step that has occurred and go through the code line by line,” he said.
Using tools such as multi-agent systems positions AI revolution poised for replacing many tasks requiring human intelligence – from legal drafting to scientific research.
Fergusson offers hope regarding machines potentially taking over jobs: "As AI systems increasingly take over routine tasks – writing code, analyzing data – original ideas become valuable."
“To paraphrase Edison," Fergusson adds "AI might handle 99% perspiration; still takes human inspiration.”
For instance: A student with app idea lacking coding skills describes idea which then gets coded tested launched via an artificial assistant; thus shifting emphasis onto creativity instead skillset mastery thereby unlocking extraordinary potential according Fergusson future belongs those asking best questions:
“There is transformational opportunity changing scientific research conducted using collaborative machine-driven high-level analysis fast accurate” asserts.
Research conducted at Infosys relevant clients because ultimately solving same problems harnessing enormous data volumes into real knowledge shared globally all sectors emphasizes universality challenge bringing together science industry adoption usage work"
"Many clients demand explainability simulations running faster trusted leveraging powers automating business processes knowledge processing tasks," adds emphasizing connection between center world-sharing insights universally facing challenges adopting technology effectively"
However concerns remain surrounding imperfections including massive water energy consumption risking derailment net-zero progress despite inherent limitations notably hallucinations logic lapses longer responses convincing correctness disparity counterbalanced management checks balances through agent-based approaches
Warnings abound against viewing solely hype harm cautions reflecting broader experience transformative impact underlying reshaping landscape spanning discovery creative industries beneath surface chatbot misadventures notwithstanding according perspective offered: "Most people's experiences chatbots failing purchase washing machines undercurrent transformation pervasive"