Siri understood my intent, pulled the local weather data via an API and answered me in less than two seconds: “There’s no rain in the forecast for today.”
When I woke up this morning, I asked my assistant a simple question: “Siri, is it going to rain today?”
Siri understood my intent, pulled the local weather data via an API and answered me in less than two seconds: “There’s no rain in the forecast for today.”
In the not-too-distant past, this kind of human-computer interaction would have blown away technologists and delighted consumers — but in 2016, it’s nothing special. Conversations with Siri are commonplace, just like they are with Microsoft’s Cortana and Amazon’s Alexa.
Machine learning (ML) and narrow forms of artificial intelligence (AI) have officially reached the mainstream. The explosion of innovation we’re seeing in AI/ML stems from a series of rapid technological advances over the last few decades: widespread internet connectivity and proliferation of online data, faster/cheaper computers (per Moore’s Law), variable-cost cloud computing, R&D investments from large technology companies and a vibrant open-source software community.
We haven’t yet built HAL 9000, but we’re getting closer.
Like many venture capitalists, I talk to technology startups leveraging AI/ML almost every day. When I do, I’m always hunting for companies that are building something completely new — whether it’s a proprietary new data set to train machine learning models or a radically different approach to solving big technical problems using AI. The fundamental reason is this: If a company is going to out-compete others long-term using AI/ML, it better have the best data to solve a specific problem or be playing a different game from its competitors.
When I woke up this morning, I asked my assistant a simple question: “Siri, is it going to rain today?”
Data is the fuel we feed into training machine learning models that can create powerful network effects at scale. Unfortunately for startups, big technology companies typically have huge, proprietary data sets that span many industries. Meanwhile, the open-source community’s efforts are quickly democratizing access to the most sophisticated machine learning algorithms. It’s now nearly impossible for a startup to develop a competitive advantage around algorithm development alone.
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