I vs Algorithms 3 – Age of GPTs
Anand Panda
Business Head - Digital Business & Global Payments | BillDesk | Payments Tech
This is 3rd?edition in the series. Please refer links below for previous posts. Also published on my blog Blue Peepal
Let’s look at few scenarios.
2002
Pratik and Vivek stood outside the Dean’s office anxiously.
They were charged with copying a dissertation paper from each other, while a unique submission was required.
The office assistant ushered them in at the scheduled time.
The Dean thundered, “When you know this is a course deliverable, why did you indulge in this copying act?”
“Sir, it was my dissertation paper. I had shared with Vivek as he wanted to review certain concepts.” Pratik pleaded.
“No Sir, this is my original document.” Vivek defended.
“Unless whoever has copied owns up, I am going to suspend marks for both of you.”
“Sir, I have back-up documents of tests that I had run and the results are captured in my paper. If you may please allow me, I can share the documents and prove my case.”, Pratik requested.
2019
“Your patent application is rejected, as several sections were found to be extracts from a journal published in 2008.”
The letter from patent registration office landed on CEO's desk.
“Sir, we had done fact-checks and plagiarism checks before submission.”, the Legal head tried to reason.
The CEO with eyes still glued to the letter, “Please issue a termination letter to our Head - Research and the team that submitted this application.”
2025
“How do you explain this trading strategy turning negative returns for third consecutive month, when broad index is up 9.7%?”
The CEO enquired the Fund Manager.
“Sir, we are investigating the trading model. Even the CIO has engaged his team.”, the Fund Manager looked across the table at the CIO.
The CEO asked impatiently, “How is the technology team involved in your trading strategy? There are no execution error or trade delivery issues.”
“Sir, we are investigating the codes as one of the team members had used a coding AI tool, without authorisation.”, the CIO spoke with regret.
The learning in humans and machines
The scenarios state an elementary human need – “to reduce effort”, for recurrent actions or events, and for new discoveries.
Be it the college dissertation paper, the patent application or, the trading strategy, the desire to reduce effort as intent precedes the method deployed to achieve the goal. There are inherent conflicts, temptations and incentives that may impair judgment about sanctity of method.
Learning on the contrary is a difficult path that requires rigor, research, application and validation. Learning may reduce effort, but learning inherently is a demanding multi-step process.
Learning when applied to machines, start with development of mathematical models called algorithms to a variety of inputs like data, text, images or, sound. Much like human cognition, machines learn with algorithms that when fed with tons of data from MB, GB to PB or Zetta bytes, can turn these into artificial intelligence, or AI based on a multi-step testing and validation, mathematically speaking.
What are the points of failure in this learning, reduce effort and AI mass deployment?
Unsupervised and unknown
In recent memory, when Covid-19 started there was no reference to determining how quickly would it spread or, how would lockdowns and vaccination flatten the infection spread.
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Similar situations arise in fields of academic research, medicine, space, business et al.
The input data or signals in these situations may be a combination of qualitative and quantitative. Implementing algorithms with these data inputs have to be passed thru learning stages.
In unknown situations, the algorithms shall require assisting with validation through domain experts, or a certain understanding of what the results indicate.
Imagine Christopher Columbus used an AI-navigator and set the destination as India. The navigator is supposed to take coordinates and direct the ship to India. Columbus and his team have never been on that route and hence dependent on the navigator. Upon landing on the shore, the team celebrate sighting India, when it ended up being America.
In unknown and unsupervised situations like here, not all outcomes may end up so favourable.
What was used as a tool to guide, or reduce effort of arduous discovery and mapping of the route to India, carried risk that may be disproportionately higher depending on area of deployment.
Unbridled AI
Implementing AI so far has been in areas of research and by technology/IT-ITES companies to consumer platforms all within realms of industrial applications, with their own guardrails and spillovers. Consumers are feeders to the giant AI engines that work behind social media, streaming content to search engines.
With Generative AI, ChatGPT and upcoming GPT stores, large-language-models (LLMs) have now made their way into code building, imaging, creatives, blogging, 3D-printing to host of consumer applications.???
It’s as easy as an app-store download and use.
What does it change?
The learning effect which is knowledge acquired from a set of actions repeated, or for discovery has been the basis of continuity and self-correction in human civilisation.
Large-scale development of GPTs as a class of AI, and ease of consumer access has implications on the foundation of learning and accountability around data, security and authenticity.
A college project around monetary policy implications on economic growth may turn from a careful study of policy actions and consequent effect, into a GPT prompt and report ready in few minutes. Even referencing the sources shall prove a challenge.
Besides the potential impurities in input data?being taken into models?from public sources that may not pass verifiability, there are vulnerability points at storage, refresh and even the algorithms being used.?
With consumer access there is a risk of mass adoption of verified to unverified AI models, even code stacks.
An autonomous car given its physical size and impact on traffic safety may carry a higher hazard value of AI deployment than a simple text editor. A trading strategy gone wrong may cause a market anomaly or, undetected may cause a crash, with significant financial loss. Errors or risks that seem trivial in early stages, may develop into large impact outcomes.?
There is already growing need for greater monitoring, plagiarism tools, fact-checkers to filters to detect AI-generated content.
Re-thinking GPT
ChatGPT may be the Cambrian moment in growth of common language as input to consumer and industrial AI. Use of common language as input opens it up for large-scale adoption.
In the last 12 months, there has been curiosity, hype, new marketing language, rapid adoption to growing concerns around its effect on jobs to veracity of its usage.
Writers Guild of America representing over 11,000 writers in Hollywood went on strike for reasons including guarantees that AI would not impact their compensation and protect jobs.
The proposed European Union AI Act is a step towards bringing data, accountability and security with framework around oversight, implementation and ethical applications.
As the world gets digitally inter-connected, AI becomes an inevitability.?
However, anything that is not supervisable, verifiable or, explainable is a random event, cannot be AI.
This understanding is vital; our history in future depends on it.
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