Archive for the ‘Reinforcment learning’ Category

AI next conference Jan. 2018 Seattle WA

February 6, 2018

The  NextCon conference series  began  early last year. The conference was organized by Association of Technology and Innovation (ATI) ‘s and Bill Liu was the lead.  This was in succession to the last one in Seattle in March 2017. The conference had  loads of companies which sponsored and had tech. talks. Roughly around 400 people attended the conference.

Conference Schedule:





Quick summary of recently concluded AI conference Nextcon in Seattle, Jan 2018.   This summary has my key takeaways and my learnings, Keynote summaries,  some of the break-away sessions I attended summaries, my side discussions as well as links and resources to the tech talks as well as links to blogs and Videos. It also includes some of my follow-up blog reading to understand  concepts. Hope this is useful for you as  much I found it to be!!


The conference had 4 tracks:

  1. Computer Vision
  2. Speech /NLP
  3. Data Science/Analytics
  4. Machine Learning

With limited time I picked mainly the Data Science and Machine learning tracks to understand trends on how to handle large amount of data and how to make sense of large amounts of data.


  1. Key takeaways & learnings for us:

These are some of things I have distilled and filtered from the Conference as areas of interest.

  1. AI is a great tool to have in your tool box. It isn’t the end all of all tools at least for now. But this could change in the future. For eg: AI which can disambiguate and find flaws in the electronic welds cant tell you whether a Kid is holding a tooth brush or base ball bat.
  2. Reinforcement learning(RL) is making a come back and yielding great results thought at a slightly higher cost of latency etc., time and infrastructure costs. Martin  Gomer  from google showed that how he trained a Pong playing AI with just historic data and by making it play itself and generating lot of data and get better at it. Think of how a kid learns to bike or learns to walk …ai_nvidiaReinforcement  learning (RL) and Neural networks Neural networks are algorithms, RL is a problem type. You can approach RL with neural networks.What makes RL very different from the others is that you typically don’t have a lot of data to start with, but you can generate a lot of data by playing. You have to deal with the problem that you have to make decisions, but it is not clear what is good (delayed reward). For example, it might take several moves until you know in Go if a move was smart.3. OS for AI – The next frontier will be when people will use each others algorithms and models to come up with a sophisticated service which aggregates. For eg: John peck from Algortigmia shows how. Someone writes a fruit classifier and another persona vegetable classifier and then a third party could aggregate them into a fruit or vegetable classifier.composability

elastic scale

Algorithmia maybe a good resource for paying for ML algorithms. I suggested to them after the talk to support  offering data as well at a $$.  This is inline with Data Science as a service idea

4. Auto ML or off-the-shelf machine learning methods – Machine learning is evolving at a pretty strong pace. More and more it is possible to just feed the AI platform dataset and it tunes the hyperparameters and comes up with a trained model

5. In the large scale of things AI is currently pretty early in its evolution


aiishere6. Future of AI from Prof. Oren Otzioni  – When will Superintelligence Arrive? AI experts try and answer  the question. It still is far out!!

7. Another interesting talk was by Twitter on Online ML and why they didn’t use deep learning. Deep learning currently has some disadvantages especially in real time low latency scenarios. More details on this below

8. Deep learning is providing lot of value however it comes at a cost as it requires a large data set. It however does require a solid hardware infrastructure.  Unfortunately, in deep learning, people usually see very sublinear speedups from many GPUs. Top performance thus requires top-of-the-line GPUs.

9. Microsoft AI platform is super rich in terms of tools, services, 3rd party tools integrations etc.

Microsoft demoed Azure ML workbench which seems like a really cool tool for the time consuming activity  of data wrangling.


2. Conference KeyNote summary:

  1. Steve Guggenheimer from Microsoft

Steve talked about the Microsoft AI platform and applications already on a lot of features.


Microsoft AI platform-


Microsoft demoed Azure ML workbench which seems like a really cool tool for the time consuming activity  of data wrangling.



The platform is super rich in terms of tools, services, 3rd party tools integrations etc.

Ethics in AI

Microsoft realizes the potential of AI and how it can be misused and hence Steve shared the Microsoft AI ethics. Satya has talked about compassionate AI  as  the AI for the future

Microsoft has published a nice book on this subject  called “The Future Computed”

ai ethics msft

I also liked the live demo on how the Bing  team uses specialized FPGA’s. FPGA’s or Field Programmable Gate Arrays are programmable hardware devices sort of a CPU for specific task rather than general purpose which allows optimizations to be built in.


CPU vs FPGA performance within Bing team – FPGAs and ASIC derivatives just dedicated to a certain task perform really really well at the same time taking a fraction of the Power.


2. AI at DIdi Chuxing – Didi Chuxing is like the Uber of China and the scale they have to deal with is humongous.   I liked DIdi Chuxing’s Presentation on how they are using AI in the transportation sector.  Lot of it can be applied to other fields as well as the problems are similar in nature. They  presented the iterations  on how they solved their problems using various AI algorithms and have narrowed it down to deep learning and Reinforcement learning to look at forecast, ETA, dispute resolution etc.  They started with  regression models to Deep learning models. Deep learning has helped them solve more problems.


They have applied AI to multiple problems areas within transportation –


More details here:

 3. UW Prof. Oren Etzioni also presented a good deck on Future of AI which is more like Is AI the evil power it is made out to be rather than typical technical trends of AI?


Winograd schemas is an alternative to the Turing Test developed by Hector Levesque.

The Turing Test is intended to serve as a test of whether a machine has achieved human-level intelligence. In one of its best-known versions , a person attempts to determine whether he or she is conversing (via text) with a human or a machine. However, it has been criticized as being inadequate. At its core, the Turing Test measures a human’s ability to judge deception: Can a machine fool a human into thinking that it too is human? It also suggests that the Turing Test may not be an ideal way to judge a machine’s intelligence.  An alternative is the Winograd Schema Challenge.

Rather than base the test on the sort of short free-form conversation suggested by the Turing Test, the Winograd Schema Challenge (WSC) poses a set of multiple-choice questions that have a particular form.  The test is dedicated to furthering and promoting research in the field of formal commonsense reasoning. For eg:

4. “Tensorflow and deep reinforcement learning without a PhD“ by Martin Gomer  from google.

He briefly alluded to Auto ML which learns the model architecture directly on the dataset of interest:


Google deep mind teaching a virtual human to walk/jump etc I was an athlete and when I look at the image below – The stride and the arms are just what a good long jumper would use and would be proud off below 🙂


Demonstration of playing  pong without any specialized algorithms with deep reinforcement leaning and lots of data:

 Link to the Video of his talk: –

Google deep mind taught itself to walk –


5. Key Note – “Deep learning at amazon Alexa” by Nikko Strom from Amazon

This is very powerful as it shows how Alexa is using multi modality, device and personal context which Alexa is using. This is really very powerful and can really engage the user!!

3. Summary of Breakout sessions (I attended)

  1. ML track – Twitter – Parameter Server approach for online ML at Twitter

The talk basically discussed the evolution of parameters servers in Twitter which need to scale and have real time approaches to online ML. Their approach has been around load balancing,  filtering, centralized Parameter servers. They have tried deep learning but they found as of now Deep Learning is not working for them:

Some of the disadvantages of Deep learning:

  1. Latency for their usage is high
  2. Model quality not impacted much ROI
  3. New approaches in ML could remove displace deep learning


  1. ML track – Machine learning at scale by Amy Unruh from Google The talk showed that there is a gap in the Google ML offering and is addressed by Auto ML for vision. Also, compares the various techniques in terms of resources needed to solve an AI problem typically:

1)      Time

2)      Prediction Code

3)      Serving Infrastructure

4)      Model Code

5) Training data


Resources needed to solve an AI problem  per Google


ML  as an API  – Mainly time and prediction code


Custom code and model – More resource intense

custom build

Custom model with transfer of learning from another project  – It takes less time and can reuse model code and training data

transfer learning

Google has identified Gap in the continuum  from DIY ML to ML APIs


They are trying to address it with Auto ML. This is currently limited to Vision API only.

cloud automl

Auto ML – Currently Google has it  only  for Vision API but allows for deep Neural networks  to be auto generated.

It allows savings on Model code etc, infrastructure etc


Only need to provide training data. It trains, deploys, creates a Neural network automatically.


Under the hood is creating new Neural network layers automatically.

Content below came from Martin Gomer  Google keynote speakers with the talk  titled “Tensorflow and deep reinforcement learning without a PhD


He briefly alluded to Auto ML which learns the model architecture directly on the dataset of interest


More details on Auto ML  here:

 3. Deep multimodal intelligence by Xiaodong He from Microsoft –

Microsoft Research Xiaodong He described  the scene with natural language :

  1. Understanding the image’s content
  2. Reasoning relationships among objects & concepts
  3. Generate a story in natural language

However, true understanding of the world is much more challenging


There were quite a few other parallel talks  but time was limited 😦

4. Presentation Tid- Bits

Slight digression on a nice presentation tool I found some speakers use at the conference which compliments the laser pointer. It retails for  around $130


And is great for highlighting code etc.


5.  Links /References



Papers — Lots of good papers on KDD: