
AI Startup Predictions for 2017 - tim_sw
http://www.bradfordcross.com/blog/2017/3/3/five-ai-startup-predictions-for-2017
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freddealmeida
The article is full of grammatical and spelling mistakes. Looking at their
points in order:

1.Bots go bust - I've seen a few vertical applications of Bots with limited AI
adding real value to customers. I'm not sure we will see a full collapse but I
think the points are some what salient. Potentially, bot as a service firms
will find their margins eaten away by firms that are focused in specific
consumer oriented areas. Such as recruiting or highly regulated markets.

2\. Deep Learning will be a commodity among machine learning researchers. I'm
not entirely sure what this means. There is a clear increasing complexity in
the research. The trend towards deep reinforcement learning, meta-learning,
transfer learning and generative adversarial are indicative of this complexity
increase. Even in areas such as object recognition, there is much left to
discover, such as models like fastCNN or new flavors of ResNet. I think if
anything, deep learning wont become a commodity. Which the author goes on to
hint at with the poor use of deep learning APIs

3\. Cleantech AI - I suppose the author here means that a general AI firm will
never be as interesting as a firm that is vertically aligned (Tesla). I think
actually we always believed this. However, I also believe there are places for
research oriented firms that drive innovation for specific verticals. Such as
a Parc Labs model. Though I don't disagree with this much.

4\. MLaaS is dying. I agree fully. My first startup in this space pivoted into
a fintech vertical simply because our clients had no idea how to implement DL
into their businesses. And where we could suggest high value projects they
almost never understand the cost/value. Consulting in DL actually kind of
sucks.

5\. Full stack AI verticals will win. I agree. But this should be obvious. But
100m in ARR in 2017 I find hard to believe. My own (new) firm is focused on
autonomous vehicles and we are closing ARR contracts but 100m is way off. I
wonder who he thinks will hit this level of revenue this year? I may want to
pivot that way. :D

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argonaut
Transfer learning is very simple. Very complicated generative adversarial
models / deep RL are still research domains and it is not clear it's good
enough yet for major commercial applications. ResNet is a very simple idea.

The spelling/grammar errors are minor and not very important.

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malux85
Shameless self promotion here, Solo Founder, Bootstrapping, hustle,

My AI Startup, SignalBox [https://signalbox.ai/](https://signalbox.ai/)

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rsmoore215
I haven't looked up the DCVC portfolio co's yet, but which specific startups
are developing these full-stack AI solutions? I'm unclear on the exact
definition of full-stack here

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perseusprime11
Can A.I make it's own predictions in the future?

