Anthill Inside 2018

On the current state of academic research, practice and development regarding Deep Learning and Artificial Intelligence.

DATE

25 July 2018, Bangalore

STATUS

Open for feedback


About the conference and topics for submitting talks:

In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. The Deep Learning Conference has grown in to a large community under the brand Anthill Inside.

Anthill Inside features talks, panels and Off The Record (OTR) sessions on current research, technologies and developments around Artificial Intelligence (AI) and Deep Learning. Submit proposals for talks and workshops on the following topics:

  1. Theoretical concepts in Deep Learning, AI and Machine Learning – and how these have been applied in real life situations / specific domains. In 2017, we covered GANS, Reinforcement Learning and Transfer Learning. We seek speakers from academia who can communicate these concepts to an audience of practitioners.
  2. Latest tools, frameworks, libraries – either as short talks demonstrating these, or as full talks explaining why you chose the technology, including comparisons made and metrics used in evaluating the choice.
  3. Application of Computer Vision, NLP, speech recognition, video analytics and voice-to-speech in a specific domain or for building product. We are also interested in talks on application of Deep Learning to hardware and software problems / domains such as GPUs, self-driving cars, etc.
  4. Case studies of AI / Deep Learning and product: the journey of arriving at the product, not an elaboration of the product itself. We’d also like to understand why you chose AI, Deep Learning or Machine Learning for your use case.

Perks for submitting proposals:

Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer, and a t-shirt.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor. In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.

Target audience:

We invite beginner and advanced participants from:

  1. Academia,
  2. Industry and
  3. Startups,

to participate in Anthill Inside. At the 2018 edition, tracks will be curated separately for beginner and advanced audiences.

Developer evangelists from organizations which want developers to use their APIs and technologies for deep learning and AI should participate, speak and/or sponsor Anthill Inside.

Format:

Anthill Inside is a two-day conference with two tracks on each day. Track details will be announced with a draft schedule in February 2018.

We are accepting sessions with the following formats:

  1. Crisp (20 min) and full (40 min) talks.
  2. OTR sessions on focussed topics / questions. An OTR is 1 to 1.5 hours long and typically has four facilitators including or excluding one moderator.
  3. Workshops and tutorials of 3-6 hours duration on Machine Learning concepts and tools, full stack data engineering, and data science concepts and tools.
    4. Birds Of Feather (BOF) sessions, talks and workshops for open houses and pre-events in Bangalore and other cities between October 2017 and June 2018. We have events open round the year. Reach out to us on info@hasgeek.com should you be interested in speaking and/or hosting a community event between now and the conference in July 2018.

Selection criteria:

The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:

  1. If the technology or solution is proprietary, and you want to speak about your propritary solution to make a pitch to the audience, you should pick up sponsored session. This involves paying for the speaking slot. Write to anthillinside.editorial@hasgeek.com
  2. If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
  3. If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.

The criteria for selecting proposals, in the order of importance, are:

  1. Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
  2. Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual decription – will help us understand the focus of the talk, and the clarity of your thought process.
  3. Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.

No one submits the perfect proposal in the first instance. We therefore encourage you to:

  1. Submit your proposal early so that we have more time to iterate if the proposal has potential.
  2. Talk to us on our community Slack channel: https://friends.hasgeek.com if you want to discuss an idea for your proposal, and need help / advice on how to structure it.

Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.

How to submit a proposal (and increase your chances of getting selected):

The following guidelines will help you in submitting a proposal:

  1. Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
  2. The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
  3. Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks, from some of HasGeek’s other conferences, which participants liked most: http://hsgk.in/2uvYKI9 http://hsgk.in/2ufhbWb http://hsgk.in/2vFVVJv http://hsgk.in/2vEF60T
  4. We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: http://hsgk.in/2vDpag4 http://hsgk.in/2varOqt http://hsgk.in/2wyseXd to structure your proposal.
  5. Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to: anthillinside.editorial@hasgeek.com
  6. Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.

To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.

Passes and honorarium for speakers:

We pay an honararium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. Anthill Inside is funded through ticket purchases and sponsorships; travel grant budgets vary.

Last date for submitting proposals is: 15 April 2018.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-recorded, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond the solution you have built, or your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at previous editions of Anthill Inside.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and a document showing the full workshop plan.

Contact details:

For information about the conference, sponsorships and tickets contact support@hasgeek.com or call 7676332020. For queries on talk submissions, write to anthillinside.editorial@hasgeek.com


Confirmed sessions

Birds Of Feather (BOF) session: AI - ethics and privacy

Suchana Seth (@suchana)

  • Intermediate
  • 1 upvotes
  • 0 comments
  • Fri, 13 Jul

Birds Of Feather (BOF) session: AI and Product

Vijay Gabale (@vijaygabale)

  • Intermediate
  • 2 upvotes
  • 0 comments
  • Fri, 13 Jul

Birds Of Feather (BOF) session: Hubs and spokes of AI

Anuj Gupta (@anuj-gupta)

  • Off The Record session
  • Intermediate
  • 7 upvotes
  • 0 comments
  • Fri, 13 Jul

Deep Learning in the Browser: Explorable Explanations, Model Inference & Rapid Prototyping

Amit Kapoor (@amitkaps)

  • Intermediate
  • 1 upvotes
  • 0 comments
  • Thu, 28 Jun
  • slideshow

Building and driving adoption for a robust semantic search system

Hrishikesh Ganu (@hrishikeshvganu)

  • Crisp Talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Tue, 12 Jun
  • slideshow

Neural-network Field Aware Factorisation Machines for Online-behaviour Prediction

Gunjan Sharma (@gunjan-sharma)

  • Full talk
  • Advanced
  • 2 upvotes
  • 0 comments
  • Fri, 8 Jun
  • slideshow

Building Knowledgeable Machines

Hari C M (@haricm) (proposing)

  • Full talk
  • Advanced
  • 2 upvotes
  • 0 comments
  • Wed, 6 Jun

Product Size Recommendation for Fashion E-commerce

lavanya TS (@lavanyats)

  • Crisp Talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Mon, 14 May
  • slideshow

Sarcasm Detection : Achilles Heel of sentiment analysis

Anuj Gupta (@anuj-gupta)

  • Full talk
  • Intermediate
  • 6 upvotes
  • 0 comments
  • Tue, 8 May
  • slideshow

Uncertainty in Deep Learning

Madhu Gopinathan (@mg123)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Mon, 7 May
  • play_arrow
  • slideshow

The Sentimental Computer- the Art and Science of Making Computers Understand Sentiment and Emotion

Hari C M (@haricm) (proposing)

  • Full talk
  • Advanced
  • 2 upvotes
  • 2 comments
  • Wed, 2 May

Attention Mechanisms and Machine Reasoning

Ashwin (@srisriashwin)

  • Full talk
  • Intermediate
  • 5 upvotes
  • 0 comments
  • Tue, 1 May

Going beyond what and asking why: Explainability in Machine/Deep Learning

Vineeth N Balasubramanian (@nbvineeth)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Mon, 23 Apr
  • slideshow

The evolution in AI thinking and products of the next decade

Shailesh Kumar (@shkumar)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Tue, 17 Apr

Looking beyond LSTMs: Alternatives to Time Series Modelling using Neural Nets

Aditya Patel (@pataditya)

  • Crisp Talk
  • Intermediate
  • 9 upvotes
  • 0 comments
  • Sun, 15 Apr
  • play_arrow
  • slideshow

Learning Real-time Object Detection In The Absence of Large-scale Datasets

Vijay Gabale (@vijaygabale)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Sat, 14 Apr
  • slideshow

What you cannot do with Machine Learning

Harsh Gupta (@hargup13)

  • Crisp Talk
  • Beginner
  • 0 upvotes
  • 1 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Unconfirmed proposals

A very gentle introduction to deep reinforcement learning and applications

Hari C M (@haricm) (proposing)

  • Full talk
  • Advanced
  • 1 upvotes
  • 0 comments
  • Wed, 6 Jun

Make your own DL framework

Nithish Divakar (@nithishdivakar)

  • Full talk
  • Intermediate
  • 12 upvotes
  • 7 comments
  • Mon, 14 May

Advances in Deep Learning : Lessons from the field

Akhilesh Singh (@meetdestiny)

  • Full talk
  • Intermediate
  • 5 upvotes
  • 0 comments
  • Wed, 9 May

Introduction to Game Training using Deep RL

Jaley Dholakiya (@jaleydholakiya)

  • Crisp Talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Mon, 7 May
  • play_arrow

Deep Learning Howlers: Downside of Learning only Statistical Regularities

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • Beginner
  • 1 upvotes
  • 3 comments
  • Thu, 3 May
  • play_arrow

The Catalog as a Catalyst - Bringing benefits of Big Data to MSMEs

Kalpit Desai (@kalpitdesai)

  • Crisp Talk
  • Advanced
  • 1 upvotes
  • 2 comments
  • Thu, 26 Apr
  • play_arrow
  • slideshow

How organizations can leverage 'Large Scale Graph Based Analytics’ to derive value from their data.

Upendra Singh (@upendrasingh)

  • Crisp Talk
  • Advanced
  • 1 upvotes
  • 0 comments
  • Thu, 26 Apr
  • play_arrow
  • slideshow

Know Your Diabetes Risk - Preventive Health through Risk Prediction and Knowledge Base

Krishna Bhavsar (@krishnabhavsar)

  • Crisp Talk
  • Intermediate
  • 20 upvotes
  • 4 comments
  • Tue, 17 Apr
  • play_arrow
  • slideshow

Deep Learning - An example implementation

Krishna Bhavsar (@krishnabhavsar)

  • Full talk
  • Beginner
  • 19 upvotes
  • 6 comments
  • Tue, 17 Apr
  • play_arrow
  • slideshow

Building IOT Data pipelines using Prediction IO

Puneeth N (@puneethnarayana)

  • Crisp Talk
  • Intermediate
  • 2 upvotes
  • 1 comments
  • Mon, 16 Apr

Machine Learning and Statistical Methods for Time Series Analysis

Aravind Putrevu (@aravindputrevu)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 1 comments
  • Mon, 16 Apr

Explaining Human Cognition through Deep Learning

Amar Lalwani (@amar1707)

  • Crisp Talk
  • Beginner
  • 3 upvotes
  • 2 comments
  • Sun, 15 Apr
  • play_arrow
  • slideshow

AI at the Edge: A Software Perspective

Saumya Suneja (@saumyas)

  • Full talk
  • Beginner
  • 3 upvotes
  • 2 comments
  • Sun, 15 Apr
  • play_arrow
  • slideshow

Anomaly detection with Variational Autoencoders

Aditya Prasad Narisetty (@adityaprasadn)

  • Full talk
  • Beginner
  • 8 upvotes
  • 1 comments
  • Sat, 14 Apr

Building a next generation Speech & NLU Engine: In pursuit of a Multi-modal experience for Bixby

Vikram Vij (@vikramvij)

  • Sponsored talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Fri, 13 Apr
  • play_arrow
  • slideshow

Adversarial Attacks on Deep Learning

Gaurav Goswami (@gauravgoswami)

  • Crisp Talk
  • Intermediate
  • 1 upvotes
  • 2 comments
  • Thu, 12 Apr
  • play_arrow
  • slideshow

How organizations can leverage 'Large Scale Graph Based Analytics’ to derive value from their data.

Upendra Singh (@upendrasingh)

  • Crisp Talk
  • Advanced
  • 1 upvotes
  • 1 comments
  • Thu, 12 Apr
  • play_arrow

The Catalog as a Catalyst - Bringing benefits of Big Data to MSMEs

Kalpit Desai (@kalpitdesai)

  • Crisp Talk
  • Advanced
  • 2 upvotes
  • 2 comments
  • Thu, 12 Apr
  • play_arrow

Applying Alexa’s Natural Language To Your Challenges

Sohan Maheshwar (@sohanm)

  • Sponsored talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Tue, 10 Apr

A novel Interactive Framework for semi-automated labeling when ground truth resides in free text

Tapan Shah (@tapan-shah)

  • Crisp Talk
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

A Hitchhiker's Guide to Modern Object Detection: A deep learning journey since 2012

Karanbir Chahal (@karanchahal)

  • Full talk
  • Intermediate
  • 5 upvotes
  • 3 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Deep Learning with High School Math (or Less)

Aakash N S (@aakashns)

  • Full talk
  • Beginner
  • 4 upvotes
  • 1 comments
  • Wed, 28 Mar

BigDL: Integrating Deep Learning with Apache Spark

Mukesh G (@mkgbv)

  • Full talk
  • Beginner
  • 2 upvotes
  • 1 comments
  • Wed, 21 Mar

Industrial Vision & Deep Learning for Manufacturing Quality Inspection

Dr. Chiranjiv Roy (@drroy)

  • Full talk
  • Intermediate
  • -1 upvotes
  • 9 comments
  • Tue, 24 Oct