Hype Vs. Reality – Another check point.
Expert Beliefs About the Likelihood and Strength of Artificial General Intelligence AbstractThis paper synthesizes survey evidence on expert opinion within the field of computer science regarding the likelihood of artificial general intelligence (AGI). Using probability-weighted interpretations of survey responses, we categorize experts into six belief clusters and collapse these into four outcome bins: no AGI, […]
My experiments with MOE models in Fixed Income Pricing.
by Chandra Pendyala First let me setup the problem. A couple of different kinds of technologists will read this article (finance and machine learning). So let me use a couple of short paragraphs to get both the groups to appreciate this material. To Machine Learning People: Option pricing uses Black & Scholes Model (BSM) for […]
Open Weights Models – Interesting Applications.
by Chandra Pendyala The two most obvious use cases are now commonplace. Did we do anything else interesting? Yes we did! First the two obvious ones: 1, Availability of Hardware and Kernels that allow for cost efficient deployment of these models: Inference costs go down by upto 99%, finetuning costs go down by upto 50x..Depends […]
Stop Chasing Parameter Count: A Practical Recipe for GPT-4-Class Outcomes with SML + Logic Model + A2A + Engineered RAG + Verifiers
by Chandra Pendyala If your goal is reliable answers on real workloads (enterprise QA, doc analysis, math/coding with tools), you don’t need a frontier-scale LLM. A Small Language Model (SML, ~0.5–1B params) paired with a logic verifier, orchestrated through an Agent-to-Agent (A2A) workflow, and backed by an engineered RAG stack with strong verifiers can reach […]
Code Generators – The new disruptors?
by Chandra Pendyala Are code generators the new disruptors in the tech realm? In contemplating the impact of code generators on jobs and quality, a fundamental question arises: Is the shift from coding in punch cards to English to utilizing code generators a more transformative force? Taking this inquiry further, let’s delve into a more […]
MIT Report: 95% of Gen AI pilots Failing
by Chandra Pendyala MIT’s recent report highlighted a concerning trend: 95% of generative AI pilots in companies are failing. As experts called in to salvage these projects, I have a few observations: 1. **Avoid the AI Hype:** It’s crucial not to approach these initiatives solely as AI projects. Instead, focus on utilizing mature frameworks for […]
ML for the Industrial Supply Industry
by Chandra Pendyala Recently we launched a package of EnterpriseAIML services for the Dealer/Distributor and Industrial Supply segment. Four key problems could use intelligent automation in this industry: Sales Intelligence: Sales Intelligence trained on order history can provide a prioritized list of sales leads for the sales force to act on. The system can pro-actively […]
Build Differentiated Innovation- AI/ML
by Chandra Pendyala Mission critical AI-ML systems need interpretable, traceability, auditing, bias detection, security, compliance, governance, ownership and monitoring. Highly interpretable systems do not feel like they are automating learning and intelligence. Completely uninterpretable systems without proper effectiveness metrics will feel like random numbers generating black-boxes. Natural Language Processing, Image Processing, Recommendation Engines, Content Generating […]
CIO’s considerations while acquiring AI/ML Capabilities
by Chandra Pendyala CIOs are contending with a lot of technology buzz words. In this article I intend to convert the discussion about Data Science and Machine Learning into a business discussion. There are some important differences between the usual technology acquisition programs and these technologies. I will address those differences and propose some upgrades […]
A CEO’s guide to investing into AI/ML
by Chandra Pendyala A CEO’s non-technical guide to investing into Machine Learning and Data Science CEOs are contending with a lot of technology buzz words. In this article I intend to convert the discussion about Data Science and Machine Learning into a business discussion. I will propose a few simple questions and make some assertions […]