I’ve been looking into Nvidia’s lineup of cards, wondering which is the best for the $3000 ish price range. I’ve come down to one Titan RTX or two RTX 2080 tis. I know they would have the same memory, and nvlink should decrease major consequences of speed with two separate cards. Is there any situation where the Titan RTX would be a better choice? Also, are there any other options that would be better than that?

Hi all,

I am the creator of this site Skynet Today (https://www.skynettoday.com/), which is dedicated to providing accessible and informed coverage of the latest AI news and trends. Basically it’s a platform for people with expertise in AI (so researchers, engineers, etc.) to write pieces aimed at non-experts and explain concepts or clean up misunderstanding. I have been thinking to put out a call for coverage on a specific set of topics, and wonder if you all have any thoughts on what good topics might be.

Some of the ones we already have written down are:

* The American public is already worried about AI catastrophe
* Trends in media coverage of AI
* Documented misuses of DeepFakes so far
* State of weaponized military AI
* Stop citing non AI experts (including Gates, Hawkins, etc.) about its future

PS we can always use more contributors, if you might be interested take a look here https://www.skynettoday.com/contribute

Hey everyone,

Disclaimer: I’m not totally new to Machine Learning, I have witnessed some projects and I’m generally informed with how it all works but I never applied it myself.

Anyway, I have a Machine Learning problem and because of the properties of the problem I’m not sure what algorithm to use.

I’m trying to predict the travel time of an object.

Each trip is time-series labelled (infrequent due to detection and sensor placement) and consists out of one or more data points (records in my dataset). Each record contains geospatial information about the object.

Basically, the dataset consists out of tons of records of which one or more records belong to one trip exclusively. Each new trip is marked with a unique ID. They are all important since they contain information such as geospatial data.

Connecting all the data points of each trip ID will generate a “path”. However, some trips in the dataset only have one data point but with a known start and end point. This means a path cannot be constructed from those records.

I have created labels by finding certain conditions that should be met. Each label consists at least out of two records. This means a lot of information in between does not have to be present, as long as the object was observed close to the start and ending point, it can be considered a label and the total travel time can be estimated. It can also mean that a trip with a lot of data points but with no data points close to the start and end location of the object cannot be considered a label.

I have added additional features like the distance to the previous point in km and total trip distance in km (float values). Most features describe the relationship to the previous data point.

In summary:

For the prediction labels: at least know the total distance the object will travel and I will have at least one data point with geospatial information in between this start location and end location.

For the training labels: at least I will have two data points with geospatial information close to start and end location together with the total travel time.

Ideally I want to predict the travel time for each trip with one or more data points (records). I’m not trying predict/construct the path taken. Just an estimate for the travel time.

The problem is, is that I’m not sure what algorithm to use since:

• I have multiple records that belong to one record. So far I haven’t really come across how to deal with a variable input shape size. Most say to reduce the input shape size to a single record/row (I would call it “flattening”) by binning values or one-hot encoding values.
• “Flattening the features” is not really possible. The added depth of the additionally computed features would be lost by binning since they provide insight about how it is connected to the previous data point (distance).
• A trip can be represented by simply one record in the database or by a lot of records. The greater the record count, the better. Some trips have consists out of 50 records which allow for a better estimate.

I was thinking of a Recurrent Neural Network since they can deal with a time-series sequence but I’m questioning it can be applied tot his problem.

Can I train a Recurrent Neural Network on a lot of groups (trips in this case) and generate a generalized model that I can use to predict other groups? Or can it only make prediction within each group? I have a lot of trips (groups) but the available information per trip (group) is very limited in most cases. I therefore want to develop a generalized model that will work for all groups.

Currently working as a machine learning intern at a well-known tech company in the Bay Area. A huge problem we have is acquiring labeled data. We simply don’t have the time or resources to go through tens of thousands of pieces of data and label them individually. Currently, we employ a sort of semi-supervised learning to get this done. As I look forward in my career and to other places, I was wondering… is this a problem that anyone else faces? How big of a problem is this?

What is Terrarium.ai?

I’ve always been interested in Reinforcement Learning and had a lot of fun messing around with OpenAI’s Gym. I created some little models that could play Atari games, but I wanted to take it to the next level. I wanted to see my model compete with other models and fight for survival rather than a basic score.

This is why I built Terrarium.ai, a persistent, online environment where models control agents and fight for survival. I’ve just released the first version and I believe it is the first step towards an entire universe of persistent online worlds for AI to live in.

Terrarium.ai is FREE

I can’t tell you how excited I am to share this, but I’ve received a bit of capital for this project. This means I can offer it to you all for free right now! Absolutely no strings attached. The only thing I want right now is feedback so we can improve as fast as possible.

Development progress

Here’s some insight into what I’m working on at the moment.

Right now agents can move, eat, and attack. They have energy and health which need to be minded in order to survive. I am working as hard as I can to add more features to the agents and environment. I really want to add this stuff, but I would like to get some feedback from you guys on how you would like to see them implemented.

If you are interested in using Terrarium or contributing to its development, check out the website and feel free to email me or message me on Reddit, Twitter, or Discord (info about this is on the website). I would love any feedback I can get in order to make Terrarium a better experience. Let me know what features you want prioritized, how you would like them to be implemented, or what problems you see with the platform that could be improved on!

This tutorial that implements the math behind the Generative Modelling by Estimating Gradients of the Data Distribution.

Get the tutorial: view on github or run in Google Colab

In short, they train a neural network to estimate gradients of image probability and iteratively follow these gradients to improve the image.

Hi everyone,

I’m using the LIME for images implementation from https://github.com/marcotcr/lime. From what I can see, it seems that LIME works on one sample at a time. Using this in a simple for-loop (PyTorch dataloader) seems inefficient and results in < 20 % GPU utilization on ImageNet val set with Inception v3.

Anyone has any experience with speeding up LIME for better GPU utilization?

Just as we saw a huge influx of images in the past decade or so, we are now seeing a lot of videos being produced on social media. The need to understand and moderate videos using machine learning has never been greater.

In this post, I will show you how to build an efficient pipeline to processes videos in Tensorflow

https://lifepluslinux.blogspot.com/2019/08/neural-network-inference-pipeline-for.html

Hi!

I was working with keras and tensorflow as backend on an NLP problem when I observed that increasing my training data size caused an increase in the number of trainable parameters even when batch size remained the same. From what I understand, trainable parameters are the weights which are learnt for each layer. If that is the case then it should not change irrespective of whether I increase or decrease my input data size.

So what is exactly happening here? The reason why this is important is because I perform normalization upon my data once it is fully loaded. This normalization would not work properly if I used a generator function.

I have some interesting ideas for a vision/analytics project/potential startup, but the costs to train a model and experiment with different stuff is just absurd. How do you go about paying for the cloud compute? I am also a vision phd student, and, while I have access to plenty of gpus in the lab, I reckon there would be potential IP issues if I were to use the school’s resources for my company.