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Techstars

Choosing the Right Technologies

Ben Evans from Andreessen Horowitz in Silicon Valley summarized a lot of the hype and reality around AI and Machine Learning recently. I’m paraphrasing slightly, but his point was that with most new technology, it’s the people closest to it are the most excited. Those further away are far more skeptical. Machine learning and AI is the opposite. People who aren't close say, oh my god, this is Sky Net, or the singularity. For the people who are deep in it - they say ‘well, it works ok I guess.’

That’s not to suggest that machine learning is not delivering. If anything, we’ve seen an increasing number of really practical, useful and tangible use cases for machine learning in a wide variety of fields over the last few years. However, we’ve seen an even larger increase in the number of people who tack the words ‘using AI’ on the end of their company pitch in the hope of sounding more credible.

Assessing what works is tough. For me, what’s interesting are companies that are using their technology to augment existing experiences of workflows. Using this technology to support personnel or processes can deliver huge benefits to companies - from a time, cost and revenue perspective.

Take Let’s Enhance as an example. Their technology allows companies to automatically upscale and enhance their imagery. Right now, this is a slow, painstaking and expensive process for people like real estate companies, marketplaces, stock photo companies and others who need their images to look good on the increasingly high-res devices we all use. With Let’s Enhance, that process becomes as simple as uploading a folder of images and waiting a few minutes.

The food and beverage industry might not seem like the most natural home for machine learning, but it’s one where humans and machines are collaborating on NPD, market research and much more. Gastro graph uses human inputs to collect sensory data, and then pairs that with proprietary machine learning algorithms to deliver informed food insights to some of the biggest companies in the world.

Companies like 42Maru, whose technology allows companies to create an internal question answering system that takes in massive amounts of data and makes it query able are working with huge industrial clients to help their staff get faster, more relevant, correct answers to the questions they have - without having to spend huge amounts of their day trying to find the right person with the right info.

All of these companies, and the thousands of others that are working on practical machine learning and AI solutions are doing something really interesting. They’re augmenting workflows. They’re helping people become more productive. They’re allowing people to focus on more meaningful tasks by taking care of the more mundane ones.

The best products are the ones that tap into existing workflows and create a real business benefit for companies quickly. They save time or money. They increase revenue. They make people happier and more engaged. Beware the product or platform that promises you the answer to life, the universe and everything for the price of a few API calls. The reality is that it will rarely live up to those promises.

As an investor whose job it is to look at what technologies will be practical and in use in the next three to five years, what excites me are companies that are thinking about how their technology can deliver an outcome today. More importantly, it’s the companies that are thinking about how they can grow their technology to better serve the needs of their customers over time - learning, iterating and always augmenting.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.

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