Analysis. VAST Data is set on building a transformative data computing platform it hopes could be the foundation of AI-assisted discovery.
A blog by Global Systems Engineering Lead Subramanian Kartik, The quest to build thinking machines, starts by saying: “The idea behind VAST has always been a seemingly simple one: What if we could give computers the ability to think and discover for themselves?”
His next paragraph builds on the idea: “If computers were capable of original thought the process of discovery, which has catalyzed all human progress since the earliest moments of civilization, could be accelerated significantly. We could build revolutionary artificial intelligence capabilities that wildly surpass our own potential, solving the world’s biggest challenges and moving humanity forward.”
He seems to be aiming his pitch at future models, adding: “ChatGPT and AI [large] language models do not discover things or generate new ideas.”
Kartik notes: “AI-driven discovery and deep learning goes far beyond processing unstructured data like documents, images, or text … It’s processing real-world, analog data from sensors or genome sequencers or video feeds or autonomous vehicles, interpreting it in the context of the body of human knowledge, and making connections with ideas that we haven’t imagined yet.”
Kartik envisages self-discovering computers: “We think a data platform that provides neural networks with broad access to such natural data at tremendous speed and scale will deliver much more sophisticated AI than what we’ve seen to date. And as datasets grow larger, as algorithms get smarter, and as processors get stronger, self-discovering computers – thinking machines – will no longer be science fiction.”
In September, VAST Data co-founder Jeff Denworth wrote: “CoreWeave, like VAST, is focused on a future where deep learning will improve humanity by accelerating the pace of discovery.” A VAST announcement said in August: “The true promise of AI will be realized when machines can recreate the process of discovery by capturing, synthesizing and learning from data – achieving a level of specialization that used to take decades in a matter of days.”
It went on to say: “The era of AI-driven discovery will accelerate humanity’s quest to solve its biggest challenges. AI can help industries find treatments for disease and cancers, forge new paths to tackle climate change, pioneer revolutionary approaches to agriculture, and uncover new fields of science and mathematics that the world has not yet even considered.
That last point, about uncovering “new fields of science and mathematics that the world has not yet even considered” certainly seems like eureka-style discovery. But an organization may not like what has been discovered. Consider Galileo and his discovery that the Earth orbited the Sun. This infuriated the Catholic Church hierarchy who believed the Sun orbited the Earth, and told Galileo to abandon his heliocentric theory and not teach it.
I think that VAST has a more constrained idea of discovery; that its system will help customers improve their operations and not disrupt or damage them. The idea of discovery, of an AGI discovering new things, of a VAST Data system discovering new things, seems tremendously impressive but, unless the things discovered help an organisation they will not be wanted.
Imagine Kodak back in 1975 being told by an AI that digital cameras were the future and not film amd chemistry-based cameras; would this have made any difference to Kodak’s future business failure? Probably not.
Bootnote
The first digital camera was invented by Kodak engineer Steve Sasson in 1975. His management told him not to talk about it. Kodak stayed in denial for more than 25 years, working to get film good enough to compete with digital. It obviously failed. The point is that discovery, on its own, is not enough. The impact and relevance of the discovery has to be understood by the humans overseeing the system that produced the discovery.
