Skip to main content

Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

Author: torontoai

[Project] Senior Capstone Project within Unity on Machine Learning

Hey everyone,

I’m a Computer Science(BS) senior looking for ideas for my capstone project that gives care to the scope of a semester (until about early December, 15ish weeks) . The thing about this capstone is that while it needs to be finished by that time, if there is significant room for expansion, it can be improved upon in my 2nd semester capstone. Other relevant information is that I’m going to be working on a video game throughout the semester for a separate class (nothing complex due to time). Requirements for the capstone are pretty relaxed so long as the project exists for a clear purpose.

Knowing that, do you guys think that within a semester it is feasible to design a manageable project which would then still be relevant (for a 2nd semester continuation) to a small game I can make alongside the capstone?

I’d appreciate any feedback on your minds! Thanks

submitted by /u/Marear
[link] [comments]

Kitchen Confidential: Robotics Startup Dishes Out Automation to Clean Up Food Service Operations

Clean dishes make the world go ‘round — for food service operations, at least.

Dishwashers are a key component to a commercial kitchen’s smooth operation, but they have one of the highest labor shortage and turnover rates in any industry. With the average dishwasher staying only 42 days, the commercial food industry continuously faces the expensive challenge of hiring and training.

Dishcraft Robotics, a startup based in Silicon Valley, aims to wash away this and other problems the commercial food service industry faces with the implementation of its dish-washing automation technology.

Washing dishes isn’t just difficult, it can be dangerous as well. A slip ‘n slide is ideal on a hot summer day, but not in the kitchen — the primary cause of injuries in the commercial food industry is caused by the wet floor surrounding sinks. Beyond slips and falls, dish washing is an exhausting job due to the repetition, muscle strain and frequent burns from hot water.

In response, many operations have transitioned to using disposable or compostable dishes and bowls. But that is turning out to be an even bigger headache to operations and the environment as regulations increasingly crack down on the growing volume of waste being generated each day.

According to a 2017 study by Rethink Disposable, the vast majority of compostable foodware ends up in landfill.

Dishcraft is solving these labor, safety and environmental issues with a dish delivery service that uses proprietary robotic and AI technology to provide food service operations with clean, reusable dishes every day at an affordable price. Called Dishcraft Daily, the delivery service increases efficiency and productivity of operations while reducing waste.

Dishcraft founders, CEO Linda Pouliot and CTO Paul Birkmeyer, both robotics industry veterans, spent time washing dishes in commercial dish rooms to identify challenges of the job and how robotics could resolve them. It’s that hands-on experience, combined with their vision for automation and innovative spirit, that led to the creation of Dishcraft.

Taking its inspiration from the linen service model, Dishcraft exchanges dirty dishes from the client’s location for commercially cleaned dishes from one of its dish-washing hubs each day.

The company uses its own line of dishware that includes a magnet that enables its robotic dish machine to easily pick up the dishes to scrub, wash and rack them. Dishcraft’s robot then use cameras to inspect the dishes and analyze that data through deep neural networks to clean them efficiently. Birkmeyer says that, after the dishes are washed, the robotics system uses vision-based networks to perform a quality inspection step prior to allowing the dishes to leave the system.

Each system generates a lot of data and requires real-time inference powered by internal GPUs. The startup is currently experimenting with GeForce RTX 2080 Ti cards in its robot.

The system’s deep learning training uses local NVIDIA GPUs and occasionally AWS with NVIDIA V100 Tensor Core GPUs.

Constructing a robot that can handle commercial dish washing — akin to the mania of the family kitchen after Thanksgiving dinner but with more dishes — is no easy feat. The commercial kitchen is a fast-paced, unpredictable environment, said Birkmeyer, and building a robot that can anticipate complicated scenarios is a challenge.

“Without deep neural networks, trained and deployed on NVIDIA hardware, we wouldn’t be able to provide the consistent and reliable operations that our customers demand,” he says.

Since its founding in 2015, Dishcraft’s team of just under 50 staff members has raised over $25 million in venture funding. It’s providing its Dishcraft Daily service to mostly companies ranging from 300 to 2,500 employees.

Dishcraft’s clients are primarily dining service operations in the San Francisco Bay Area that provide food on site or through catering and delivery. The company is also planning on servicing universities and hospitals in the future.

The post Kitchen Confidential: Robotics Startup Dishes Out Automation to Clean Up Food Service Operations appeared first on The Official NVIDIA Blog.

[Project] I made a voice based control interface to play video games and control your PC using voice commands from your mic

The project works by employing a siamese network to predict what you say and performs the respective action. Unlike traditional neural networks, this method does not require a large dataset to train on, powerful hardware resources or a long training time. You can get this up and running typically in 10-15 minutes and you do not need a GPU to run this program.

Github link :- https://github.com/andohuman/Shadowcol

I’ve provided very detailed installation instructions in the repo and instructions on how to train your own model with your own commands to aid your gaming experience.

I’d also like to mention that this project is still under development and there might be a few bugs. Please DO NOT ATTEMPT TO PLAY ANY COMPETITIVE GAMES WITH THIS (Had to learn this the hard way after dying a couple of times in overwatch).

The typical use case I see for this is mapping hard-to-reach keys to your voice instead. Or use your voice commands to open/close applications in your PC. I’ll leave you to your imagination.

Play with the code and let me know what you guys think, and if you have any suggestions I’d be glad to hear it.

Special thanks to u/chaosparrot and u/jonnor for their insights !

submitted by /u/Andohuman
[link] [comments]

[D] BatchNorm alternatives 2019

The main reason why people BatchNorm despite being compute heavy (~25% of total model) is because of fast official cudnn implementations. Same reason why RNNs other than LSTM and GRU never went popular.

Also, BatchNorm requires computing square root and division which require full precision to work properly. Going half-precision or applying quantization is not easy.

Anyway, are there any new methods that can dethrone BatchNorm entirely? Some papers:

Equinormalization https://openreview.net/forum?id=r1gEqiC9FX

Generalized Hamming Network https://arxiv.org/abs/1710.10328

submitted by /u/tsauri
[link] [comments]

[D] Are conferences interested in papers that introduce new datasets?

I’ve been working with a new dataset and running standard models on it. Would any conferences be interested in a paper introducing this NLP dataset detailing what’s special about it, results of current sota methods?

A bit on this data: each example has text, code, and a place in a graph so it acts as a task where there are methods for each of these types of data but few for all combined. Could be interesting for someone working with NLP or GNNs. Essentially, there are a lot of complex relationships within this data that I haven’t seen other datasets match.

submitted by /u/searchingundergrad
[link] [comments]

[D] Specific tips on Machine Learning research in a PhD

I am a new Machine Learning PhD and my topic is roughly vision, i.e. semantic/instance segmentation, and to be honest I am a little lost.

How exactly, specifically do you conduct research in this field? How does the day to day work look like?

  • Do you think of new NN architectures and test them experimentally?
  • Do you download others models and just try them out with own datasets?
  • How do you keep track on different architectures, papers, etc. Maybe make an excel document with all the papers you’ve read with a short summary?

I would be really interested in how the day to day work of other researchers in the field looks like and what specific tips you might have.

submitted by /u/schrowawey
[link] [comments]

[D] Inter-annotator agreement: how does it work for computer vision?

We have a dataset which we need to annotate: the task is object detection, thus we need to create bounding boxes. We’re going to use

https://github.com/wkentaro/labelme

But I’mm open to alternative suggestions, if you think there are better tools. Since the dataset is very large and very confidential, we’re going to annotate it in-house. I’ve heard of people trying to estimate the error due to subjectivity/mistakes in human annotation, but I don’t quite understand how it works. Let’s suppose for the sake of example that I have 900 images and 3 annotators. If I understand correctly, rather than partitioning the dataset in three subsets of size 300 and sending each subset to a different annotator, I divide it in three datasets of size, say, 330, which means that some images will necessarily be annotated by multiple users.

I don’t understand how to use these multiple annotations in practice, though: when I prepare my dataset, for each image which has been annotated by multiple users I’ll have to choose which annotations to use. It’s not like I can have three different bounding boxes (three different ground truths) for each object in the image. So, how does it work in practice?

submitted by /u/arkady_red
[link] [comments]