In the Wizard of Oz, the titular wizard responds to Dorothy’s accusation that he’s a humbug and a bad man, ’Oh no, my dear. I’m a very good man. I'm just a very bad wizard.’ For many, this is AI in a nutshell. The industry is rampant with good intentions, but it has so far fallen short of its promise. Debacles such as Microsoft’s racist Twitter bot early in 2015 demonstrated the degree to which machines still require human involvement, and we are still early in the technology’s maturity cycle.
While the technology may still lack maturity, however, progress is being made. In the past, AI has been stuck in a vicious cycle, going through periods of rapid progress only to hit a wall and see investment flatline - a phenomenon known as ‘AI winters.’ However, Andrew Ng, chief scientist at Baidu Research, believes that progress is now such that we have seen the back of such cycles, noting ’there’s definitely hype, but I think there’s such a strong underlying driver of real value that it won’t crash like it did in previous years.’
Evidence would appear to reinforce Ng’s assertions. Research firm Gartner included AI and machine learning in its list of 10 strategic tech trends for 2017, universities have started to offer classes around AI's possibilities thereby increasing the talent pool to help drive AI forward in 2017, and M&A activity remains high. The real drivers of the AI revolution, though, are the many innovative startups that have sprung up in recent years, heavily backed as they are by the deep pockets of VCs and tech giants.
We’ve looked at some of the startups set to make waves in the next year and help drive us to a truly autonomous future.
There are a number of open-source frameworks for deep learning, with both large companies and startups realizing the benefits. Indeed, Andrew Ng cites the prevalence of open sourcing for AI as one of the reasons that AI winters have come to an end. Skymind’s Deeplearning4j is one of the best. It is unique in being the only commercial-grade, open-source, distributed deep-learning library written for Java and Scala. It integrates with Hadoop and Spark, and it is specifically designed to run in business environments on distributed GPUs and CPUs.
The company was founded in 2013 by CEO Chris Nicholson and now has a staff of 15 spread across the globe. Earlier this year, they closed a $3 million funding round with financing coming from Tencent, SV Angel, GreatPoint Ventures, Mandra Capital, and Y Combinator. Its libraries were downloaded 22 thousand times in August - a number that is growing at 17% month-on-month - and its customer list already boasts such giants as $42 billion French telecom Orange SA.
6sense has now raised total equity funding of $36M in 3 rounds from a list of investors that includes Bain Capital Ventures, Battery Ventures, Venrock, and Salesforce. It helps companies like Cisco and IBM predict everything a sales department would need, using a private network of billions of time-sensitive intent interactions to reveal new prospects at every stage of the funnel and determine any prospects that are available to buy, how much they would be willing to spend, and when.
iCarbonX is attempting to apply advanced data mining and machine learning to an individual’s biological, behavioral, and psychological data in order to construct an ‘digital you’. The technology looks at everything from saliva and DNA, through to diet and environmental factors like air quality. Using this data, it creates accurate, individualized health analysis from which they can suggest tailored wellness programs, food choices, and medicines.
Despite having only recently opening its doors in October 2015, iCarbonX already has a valuation of $1 billion following a $155 million Series A round led by Tencent, an Asian internet behemoth with a market capitalization of around $200 billion.
For many, the first real interaction they will have with AI is through the personal assistants on their phones. Apple’s Siri and Microsoft’s Cortana have already proved themselves in the market place, but while such virtual assistants have a clear advantage in that they come built in to people’s phones, they have competition from independents. Ozlo, for one, launched on iOS last October. Its focus thus far has been on finding people restaurants, bars, and recipes by analyzing data from sources like TripAdvisor, FourSquare, and Yelp, among others, and it is looking to expand. In 2017, Ozlo is being made available to third-party developers so that they can build their own versions of Ozlo. It differs from its rivals in that it pulls together different data sources in a single query, and while it is up against it in terms of matching its rivals’ for funding, it is well positioned moving forward should it get all the training data it needs.
Clarifai was started in 2013 by Matt Zeiler, a former Google Brain intern with a big reputation. It specializes in visual recognition, working in a similar way to Google Photo but with a number of improvements. It also now allows customers to train neural nets of their own, with developers able to easily integrate its visual recognition services into their apps.
The company already provides services to major brands including Unilever, Curalate, Trivago, and others. To date, it has raised total equity funding of $40M across 2 Rounds from 11 Investors, with its most recent funding round seeing it raise $30M in October 2016.