I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing ...
Full BioI write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing
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How to win in the AI era? For now, it's all about the data
- Strategic data acquisition. This is a complex process, requiring companies to play what Ng called multiyear chess games, acquiring important data from one resource that's monetized elsewhere. "When I decide to launch a product, one of the criteria I use is, can we plan a path for data acquisition that results in a defensible business?" Ng said.
- Unified data warehouse. This likely comes as no surprise to CIOs, who have been advocates of the centralized data warehouse for years. But for AI companies that need to combine data from multiple sources, data silos -- and the bureaucracy that comes with them -- can be an AI project killer. Companies should get to work on this now, as "this is often a multiyear exercise for companies to implement," Ng said.
- New job descriptions. AI products like chatbots can't be sketched out the way apps can, and so product managers will have to communicate differently with engineers. Ng, for one, is training product managers to give product specifications.
- Centralized AI team. AI talent is scarce, so companies should consider building a single AI team that can then support business units across the organization. "We've seen this pattern before with the rise of mobile," Ng said. "Maybe around 2011, none of us could hire enough mobile engineers." Once the talent numbers caught up with demand, companies embedded mobile talent into individual business units. The same will likely play out in the AI era, Ng said.