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Why Should Investors Start Looking at AI Companies

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Why Should Investors Start Looking at AI Companies
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Across the world, the technology community is buzzing with how Artificial Intelligence will change the world. Investors globally have pumped close to 10B USD in AI startups over the last few years and the trend is likely to continue. What is causing this frenzy? Is it all hype or is there something cooking for real this time around? Where is India playing in the global arena?

Here is a brief look at the origins of AI and the current in an attempt to answer some of the questions above.

AI has been in “Winters” for decades.

Starting from 1956, when it was formally started in a conference in Dartmouth college; for a long time, even after a lot of research into the area, the results were far from being practicable, for almost 60 years. This started to change in 2005, when Prof Geoffery Hinton, realized that neural nets, trained with the availability of faster compute power at much lower cost, made what is now called as deep learning possible.

This new technique coupled with large data sets and ever decreasing cost of computing is starting to impart human-like senses (vision, sensing & natural interactions) to machines. This is starting to now make quite a difference across sectors, making investors excited with the possibility of wide-scale disruption of status quo. Let us look at these three major trends in more detail as follows;

Computer Vision becoming more Real

As more and more people are taking pictures of the environment around themselves, more image data is becoming available for machines to get trained on, coupled with computing power and deep learning, literally giving them eyes to see our world. For example Tobias Weyand & his team at Google, trained a deal learning machine(called PlaNet) to work out the location of almost any photo using only the pixels it contains with remarkable accuracy. One can also try it here www.geoguessr.com. Another significant area of work impacting lives is in Healthcare. For example, Sigtuple(www.sigtuple.com), a revolutionary startup out of India, is using deep learning to deliver accurate blood diagnostics in India in an accessible way. There are other examples as well where computer vision based systems are being used in manufacturing setups to sort out the different sizes fruits/vegetables/other products. Overall, we believe that Computer Vision becoming real for machines will enable a whole set of applications across verticals.

Sensors are becoming ubiquitous thus enabling access to Dark Data

Sensors are becoming cheaper and more ubiquitous. For example, our mobile phone itself is equipped with 9 sensors logging in data about how it is getting used, which was never alive for analysis earlier called “Dark Data”. However things are becoming serious on this front, you have industry grade sensors providing us data which was never available before in real time with more accuracy. Try out https://movement.uber.com/cities, where UBER is sharing data collected through mobile phones in UBER taxi about their movement. This data can now be used for many different applications from town planning to transportation solutions. Similarly, we see interesting startups in India, for example, Zenatix (www.zenatix.com)  leveraging sensing intelligence to deliver predictive alerts about AC failure to their customers. Again sensors are playing a significant role in disrupting healthcare. ten3T (www.ten3health.com) has devised a unique smart patch which gives a medical grade ECG in a matter of seconds.

 

Natural Language Processing becoming more Natural

Advancements in language processing using ML is another interesting dimension. If you think about it, voice is the most natural interface to interact with each other. Ability to comprehend language in real time and then respond to it intelligently is a significant step in making machines more Natural to interact with. Google translate is a very real example. By enabling sequence in and sequence out instead of individual words, it can gather deeper meaning and context, which used to elude computers earlier. This is making possible practical applications, which allow for unstructured interactions across languages. For examples, Liv.ai, an Indian startup is leveraging the advancement in NLP and signal processing using deep learning, to enable more accessible voice driven interfaces for a large population that is not used to typing text into a screen.

These fundamental and horizontal advances are creating new possibilities and allowing developers and entrepreneurs to leapfrog existing solutions. Especially in a country like India, where processes are broken and legacy sometimes doesn’t exist, these solutions can become the new normal for rapidly adopting consumers. Given that India has the third largest cluster of AI startups in the world, we think that this trend can accelerate rapidly. We believe that India is arriving slowly but surely on the global AI scene and therefore investors should take a serious look at the space!

( Jointly written by Manish Singhal and Umakant Soni, Founding Partners, pi Ventures)

Edition: February 2017

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