Panel on product and AI
Basis for the discussion:
Technology is getting commoditized rapidly. Google, FB are on open source spree that solves hard problems. However, there is still scope to build vertical specific AI products.
The goal is to seek answers to the below questions:
- how can new startups build AI products?
- how can existing companies leverage AI?
Part 1: busting the AI myth
Do you really apply deep learning (or AI)
What is the size of dataset that you play with
What is more painful: collecting data or applying AI
How do you map effect/outcome of deep learning (or AI) on product metrics
Part 2: dark data
The data advantage
The user advantage
Building a product that has a loop between users and data
Is data your core advantage (and not technology)
How fast can your competitors have the same data (or technology)
Part 3: building products
B2B (or SAAS) product vs consulting
Since you don’t own data, how do you build a scalable product
If there is too much customization required to serve every customer, how do you build a business
B2C product vs technology
How do you build defensibility with AI
Do you build good to have features for existing market or a scalable product to serve new market
Patents vs 10X improvement in technology via research
Part 4: product-customer experiences
Has AI delivered on its promise so far
What more needs to be done
Anuj Gupta is a senior ML researcher at Freshdesk; working in the area NLP, Machine Learning, Deep learning.
Jaisimha Rao is the CEO of TartanSense that uses drone sensing and aerial image processing to aid agriculture.
Saad Nasser is the co-founder of AtiMotors. He handles technology at Ati where he works across autonomy, power electronics, and vehicle design.
Alpan Raval is the manager of the content quality relevance team at LinkedIn.
Vijay Gabale is co-founder and CTO of Huew, an AI-powered Commerce Platform.