Submissions
Anthill Inside 2018

Anthill Inside 2018

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

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

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Accepting submissions

Not accepting submissions

Dr. Chiranjiv Roy

Industrial Vision & Deep Learning for Manufacturing Quality Inspection

Quality Inspection in Manufacturing Industries is of great importance and incurs spends of about $ 3 Billion USD, which is a complex pipeline of tasks which are rigourous, time-consuming and a lot of manual work. Complex testing processes takes weeks to conclude the impact of tests on Parts/Subject where there is a lot of scope for faster inspection using Analytics. The proposed talk elaborates t… more
  • 9 comments
  • Awaiting details
  • 24 Oct 2017
Intermediate

Mukesh G

BigDL: Integrating Deep Learning with Apache Spark

BigDL (https://github.com/intel-analytics/BigDL/) is an open source distributed deep Learning library, which is natively integrated with Apache Spark and provides rich deep learning functionalities for Spark. It combines the benefits of “high performance computing” and “Big Data” architecture, so as to provide orders of magnitude speedup than existing out-of-box open source DL frameworks (e.g., C… more
  • 1 comments
  • Rejected
  • 21 Mar 2018
Full talk Beginner

Aakash N S

Deep Learning with High School Math (or Less)

You don’t need a PhD or a master’s degree or even a bachelor’s degree in Math/CS to learn and appy deep learning. In most cases, all you need is some programming experience and a quick revision of some high school math e.g. differentiation and matrix multiplication. I’ll show you how you can get up and running in just few hours, and build state of the art deep learning models that solve problems … more
  • 2 comments
  • Rejected
  • 28 Mar 2018
Full talk Beginner

Karanbir Chahal

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

The ability to detect objects in images has captured the world’s imagination. One can make a decent case that this application is the poster child of deep learning. What really put it on the map. But few people really understands how computers have begun to detect these objects in images with a high accuracy. Which is surprising since it is the backbone of the tech powering self driving cars, dro… more
  • 4 comments
  • Waitlisted
  • 31 Mar 2018
Full talk Intermediate

Harsh Gupta

What you cannot do with Machine Learning

During this “boom” of machine learning and data driven technologies, there is an underlying belief that given enough data any problem is solvable. But like any other technology, machine learning is a tool, appropriate for some problems and not so appropriate for others. Though this talk I would like to remind the community of the things which cannot be done through machine learning. more
  • 1 comments
  • Confirmed & scheduled
  • 31 Mar 2018
Crisp Talk Beginner

Tapan Shah

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

In any multi-class supervised learning problem, labeling of training examples is imperative. In most cases, we take expert help in order to execute the annotation, which is time-consuming and often inconsistent. In this talk, we will explain an interactive topic modeling framework to label training examples where the ground truth resides in free text. They key takeaways of this talk will be 1) A … more
  • 2 comments
  • Under evaluation
  • 31 Mar 2018
Crisp Talk Intermediate

Sohan Maheshwar

Applying Alexa’s Natural Language To Your Challenges

This talk will give you a complete picture of all the tools and techniques required to build complex production-quality Alexa skills. You will leave this session knowing how to use Alexa’s dialog management entity resolution and slot elicitation capabilities. The talk will also touch upon some of the key design principles while designing for voice. more
  • 0 comments
  • Rejected
  • 10 Apr 2018
Sponsored talk Intermediate

Kalpit Desai

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

While large enterprises have the necessary resources to acquire and process Big Data, the Micro / Small / Medium enterprises in emerging economies like India are far from being ‘data-driven’. This is a huge opportunity untapped, considering that MSMEs account for more than 99% of businesses, and they make up the backbone of our economy. For the opportunity to be leveraged, a crucial pre-requisite… more
  • 2 comments
  • Rejected
  • 12 Apr 2018
Crisp Talk Advanced

Upendra Singh

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

An organization’s data is like a living organism - growing, expanding and evolving over time to form complicated and connected systems. This is similar to biological evolution, where life forms evolved from simple unicellular structures to more and more complex multicellular organisms. And as organizations compile more and more data, it is crucial for them to understand that the value of any data… more
  • 1 comments
  • Rejected
  • 12 Apr 2018
Crisp Talk Advanced

Gaurav Goswami

Adversarial Attacks on Deep Learning

Deep learning algorithms are highly popular and being applied to solve various problems with high accuracies. However, they are not infallible and are, in fact, highly susecptible to adversarial attacks. These attacks can manifest in the form of synthetically generated data or perturbed data which mean one thing to a human observer and something completely different to the deep network. Thereby, … more
  • 2 comments
  • Awaiting details
  • 12 Apr 2018
Crisp Talk Intermediate

Vikram Vij

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

Bixby is an intelligent, personalized voice interface for your phone. It lets you seamless switch between voice & type/touch, and supports more than 75 domains (eg. Camera, Gallery, Messages, WhatsApp, Youtube, Uber etc.). It was launched in July 2017 for English and is now available on more than 200 countries with about 8 million registered users. The talk focuses on challenges in deep learning … more
  • 0 comments
  • Rejected
  • 13 Apr 2018
Sponsored talk Beginner

Aditya Prasad Narisetty

Anomaly detection with Variational Autoencoders

There are two types of companies: those that have been hacked, and those who don’t know they have been hacked - John Chambers more
  • 1 comments
  • Awaiting details
  • 14 Apr 2018
Full talk Beginner

Vijay Gabale

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

This talk will focus an area of computer vision, object detection, that involves automatically localising and classifying different objects from photos and videos. A real-time and accurate object detection technique can help in several critical systems and applications such as self-driving cars (detecting multiple instances of vehicles, humans, etc.), surveillance for public safety, social media … more
  • 0 comments
  • Confirmed & scheduled
  • 14 Apr 2018
Full talk Intermediate

Saumya Suneja

AI at the Edge: A Software Perspective

Internet of Things(IoT) devices need to run AI algorithms which are usually associated with high data and computational costs, which is only possible with cloud servers having very powerful systems. Hence these IoT devices aren’t capable of accomplishing much at the ‘edge’ (where the IoT devices are deployed) and their ‘brains’ are located on distant cloud servers. more
  • 2 comments
  • Submitted
  • 15 Apr 2018
Full talk Beginner

Aditya Patel

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

Time series data, in today’s age, is ubiquitous. With the emerge of sensors, IOT devices it is spanning over all the modern aspects of life from basic household devices to self-driving cars affecting all for lives. Thus classification of time series is of unique importance in current time. With the advent of deep learning techniques , there have been influx of focus on Recurrent Neural Nets (RNN)… more
  • 0 comments
  • Confirmed & scheduled
  • 15 Apr 2018
Crisp Talk Intermediate

Amar Lalwani

Explaining Human Cognition through Deep Learning

Revised Bloom’s Taxonomy is very well known and widely used taxonomy for classifying educational objectives. The said taxonomy describes a hierarchical ordering of cognitive skills from simple to complex. The Revised Taxonomy relaxed the strict cumulative hierarchical assumptions of the Original Taxonomy allowing overlaps. We use a knowledge tracing model, Deep Knowledge Tracing (DKT), to investi… more
  • 3 comments
  • Under evaluation
  • 15 Apr 2018
Crisp Talk Beginner

Aravind Putrevu

Machine Learning and Statistical Methods for Time Series Analysis

In this talk, I will present a deep algorithmic dive into the new machine learning technologies available in the Elastic Stack and how they can be applied to real datasets. more
  • 1 comments
  • Under evaluation
  • 16 Apr 2018
Full talk Intermediate

Puneeth N

Building IOT Data pipelines using Prediction IO

We showcase as to why we chose to build ML Pipelines using PredictionIO for our IOT solutions platform. We delve into what PredictionIO is, and how it can help with integrating heterogenous sources of data. more
  • 1 comments
  • Awaiting details
  • 16 Apr 2018
Crisp Talk Intermediate

Krishna Bhavsar

Deep Learning - An example implementation

In this talk I intend to showcase one of the problems I solved using Deep Learning framework recently. Resume Classification for a recruitment consultant agency. I shall go through the multiple approaches in which I tried to solve that problem, the obstacles I faced and finally how I came to the final solution. more
  • 6 comments
  • Rejected
  • 17 Apr 2018
Full talk Beginner

Krishna Bhavsar

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

In late 2017 we were wanting to do an academic project in the area of AI/ML. We did a literature review of chronic diseases and found that India is racing to be the Diabetes capital of the world. Then on, the core objective of our study became to predict Diabetes risk of an individual through a prediction module. To augment the prediction module, we trained a text analytics engine with knowledge … more
  • 4 comments
  • Waitlisted
  • 17 Apr 2018
Crisp Talk Intermediate

Vineeth N Balasubramanian

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

As machine learning methods get increasingly absorbed in technologies ranging from high-end aerospace systems to low-end consumer technologies, there is a gradual, however steady, increase in the demand for explaining the decisions made by machine learning algorithms. DARPA launched a large initiative in 2016 to further the progress of explainable AI methods, underscoring the need for a concerted… more
  • 0 comments
  • Confirmed & scheduled
  • 23 Apr 2018
Full talk Intermediate

Upendra Singh

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

An organization’s data is like a living organism - growing, expanding and evolving over time to form complicated and connected systems. This is similar to biological evolution, where life forms evolved from simple unicellular structures to more and more complex multicellular organisms. And as organizations compile more and more data, it is crucial for them to understand that the value of any data… more
  • 0 comments
  • Rejected
  • 26 Apr 2018
Crisp Talk Advanced

Kalpit Desai

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

While large enterprises have the necessary resources to acquire and process Big Data, the Micro / Small / Medium enterprises in emerging economies like India are far from being ‘data-driven’. This is a huge opportunity untapped, considering that MSMEs account for more than 99% of businesses, and they make up the backbone of our economy. For the opportunity to be leveraged, a crucial pre-requisite… more
  • 2 comments
  • Rejected
  • 26 Apr 2018
Crisp Talk Advanced

Ashwin

Attention Mechanisms and Machine Reasoning

Attention Mechanisms have been popular for the past couple of years, giving new insights in image and NLP applications using Recurrent Neural Networks. We would like to discuss advances in Attention Mecahnisms in this panel with specific emphasis on two new innovations, Compositional Attention Networks (https://arxiv.org/abs/1803.03067) and Hierarchical Recurrent Attention Networks (https://arxiv… more
  • 0 comments
  • Confirmed & scheduled
  • 01 May 2018
Full talk Intermediate

Hari (ഹരി) Proposing

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

Can computers think? Can they have emotion? Such questions are no longer in the realm of fantasy, but are real possibilities in the horizon. Computers are no longer number crunchers; the new age AI make high demands of computers. Deep meaning understanding, sentiment and emotion, translation, inference etc. are the cutting edge technologies that are pushing the frontiers of human computer interac… more
  • 3 comments
  • Confirmed & scheduled
  • 02 May 2018
Full talk Advanced

Madhu Gopinathan

Uncertainty in Deep Learning

How do you deal with uncertainty when making decisions? Presumably, you would collect more information to reduce the uncertainty before making a decision. Now, think about the outputs of deep learning models which can be used to make automated decisions. How will you get uncertainty estimates for these outputs? In this talk, we will focus on quantifying model uncertainty based on recent research … more
  • 0 comments
  • Confirmed & scheduled
  • 07 May 2018
Full talk Intermediate

Jaley Dholakiya

Introduction to Game Training using Deep RL

From AlgphaGo to VizDoom, Deep Reinforcement Learning has revolutionarized the way in which we learn game environments. Especially for people playing CS and Dota in Colleges, having a smarter bot by thier side can help them sleep well, while the bots are fighting against each other. In the talk, we will discussing intuition behind design of Alphago. We will also discuss, what makes Reinforcement … more
  • 1 comments
  • Waitlisted
  • 07 May 2018
Crisp Talk Beginner

Anuj Gupta

Sarcasm Detection : Achilles Heel of sentiment analysis

Sentiment analysis has been for long poster boy problem of NLP and has attracted a lot of research. However, despite so much work in this sub area, most sentiment analysis models fail miserably in handling sarcasm. Rise in usage of sentiment models for analysis social data has only exposed this gap further. Owing to the subtilty of language involved, sarcasm detection is a hard problem. more
  • 0 comments
  • Confirmed & scheduled
  • 08 May 2018
Full talk Intermediate

Akhilesh Singh

Advances in Deep Learning : Lessons from the field

DL research has progressed at tremendous speed in recent years. From the demo of Natural conversations at google I/O to BMW X3+ driving itself on road, AI is no more only a topic of research or interest. AI is already everywhere. This talk presents advancements in Deep Learning both in research and field from practitioner’s perspective. This talk has two parts. First part demonstrates the advance… more
  • 0 comments
  • Awaiting details
  • 09 May 2018
Full talk Intermediate

lavanya TS

Product Size Recommendation for Fashion E-commerce

Recommending product sizes to customers is an important problem in the e-commerce domain. Though e-commerce is becoming increasingly popular, products such as apparel and shoes remain challenging to buy online and record high return rates. A key customer pain point that leads to excessive product returns is the size-fit problem. This talk (based on the linked WWW 2018 paper) describes some recent… more
  • 0 comments
  • Confirmed & scheduled
  • 14 May 2018
Crisp Talk Intermediate

Nithish Divakar

Make your own DL framework

We have all used all the high end frameworks that works really well. How about writing a small strip down version of one. In this session, I’ll walk you through how to write a small Deep Learning Framework in Pure python and numpy which has auto grad and optimizers and easy to create models. Also, the framework will be extendible so that you can easily play around with. more
  • 7 comments
  • Awaiting details
  • 14 May 2018
Full talk Intermediate

Hari (ഹരി) Proposing

Building Knowledgeable Machines

Knowledge harvesting from Web-scale text datasets has emerged as an important and active research area over the last decade or so, resulting in the automatic construction of large knowledge graphs (KGs) consisting of millions of entities and relationships among them. This has the potential to revolutionize Artificial Intelligence and intelligent decision making by removing the knowledge bottlenec… more
  • 0 comments
  • Confirmed & scheduled
  • 06 Jun 2018
Full talk Advanced

Hari (ഹരി) Proposing

A very gentle introduction to deep reinforcement learning and applications

To be filled Outline To be filled Speaker bio To be filled more
  • 0 comments
  • Submitted
  • 06 Jun 2018
Full talk Advanced

Shailesh Kumar

The evolution in AI thinking and products of the next decade

To be filled Outline To be filled Speaker bio To be filled more
  • 2 comments
  • Confirmed & scheduled
  • 17 Apr 2018
Full talk Intermediate

Wei Li

Combining Neural Networks and Regression Tree for Dynamic Pricing in Mobile Advertising

In a diversified mobile advertising marketplace, it is important to dynamically set the minimum CPM bid in order to maximize revenue as well as meeting CPM expectations. Our approach combines neural networks and a customized regression tree for accurate prediction and effective subsidization. The dNN model predicts revenue and impressions as functions of the minimum CPM bid and other supply/deman… more
  • 0 comments
  • Submitted
  • 08 Jun 2018
Advanced

Gunjan Sharma

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

In the AdTech mobile-app industry, bidding for each and every ad-request at a suitable price and in real-time is absolutely critical. Thus, there is always a need for more scalable and more accurate prediction models, which drive higher revenue. more
  • 1 comments
  • Confirmed & scheduled
  • 08 Jun 2018
Full talk Advanced

Vijay Srinivas Agneeswaran, Ph.D

Deep Learning Howlers: Downside of Learning only Statistical Regularities

It has been shown in a recent work ( https://arxiv.org/pdf/1711.11561.pdf), that deep convolutional learning networks do not learn higher level abstract concepts, but only statistical regularities. We investigate this claim by taking open source deep learning libraries and testing them out. more
  • 3 comments
  • Under evaluation
  • 03 May 2018
Beginner

Hrishikesh Ganu

Building and driving adoption for a robust semantic search system

This talk focuses on how to use deep learning based sub-word embeddings to create a practical search system robust to queries with mis-spellings, SMS lingo etc. Lifts of upto 20% in search recall compared to commercial solutions were demonstrated with retrieval latency of just 50 milliseconds for queries with mis-spellings and other aberrations. more
  • 0 comments
  • Confirmed & scheduled
  • 12 Jun 2018
Crisp Talk Intermediate

Anuj Gupta

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

In this session, we will discuss the ‘not-so-glamourous’ aspects of wheel of AI. more
  • 0 comments
  • Confirmed & scheduled
  • 13 Jul 2018
Off The Record session Intermediate

Vijay Gabale

Birds Of Feather (BOF) session: AI and Product

Last 5 years have seen significant progress in many CV, NLP and RL problems. The advent of powerful GPU’s along with insights from last two decades has made inroads into problems that were thought to be unsolvable in the near future. How can these newly solved problems be used for building AI products? What are these problems? How can we convert a Business requirement that requires AI to an AI pr… more
  • 0 comments
  • Confirmed & scheduled
  • 13 Jul 2018
Intermediate

Suchana Seth

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

In the aftermath of Cambridge Analytica issue, now public is more conscious about privacy and companies are forced to follow some standards. It also opened a pandora box of issues that has been brewing for a while like bias in algorithms. more
  • 0 comments
  • Confirmed & scheduled
  • 13 Jul 2018
Intermediate

Amit Kapoor

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

We showcase three live-demos of doing deep learning (DL) in the browser - for building explorable explanations to aid insight, for building model inference applications and even, for rapid prototyping and training ML model - using the emerging client-side Javascript libraries for DL. more
  • 0 comments
  • Confirmed & scheduled
  • 28 Jun 2018
Intermediate

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more