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Author: torontoai

Machine learning platform helps enable early diagnosis of life-threatening infection in premature infants

Vector Institute and Ontario Tech University supporting two hospitals in predictive analytics to detect sepsis in infants through machine learning

Toronto – Today, the Vector Institute, an
independent, not-for-profit research institute focused on leading-edge machine
learning, announced the latest of its series of
Pathfinder Projects
to implement artificial intelligence (AI) in the health sector.

The fifth Pathfinder Project uses machine learning for early detection of sepsis in infants in the
neonatal intensive care unit (NICU).

Sepsis is a life-threatening condition where bacteria
grows in the blood stream, resulting in a severe widespread inflammatory
response. In infants, it is one of the leading causes of long-term morbidity or
mortality globally.  

With support from the Vector Institute and led by Dr. Carolyn McGregor at Ontario Tech University, Artemis is a predictive analytics platform that applies machine
learning to help physicians with the critical care of newborns. Artemis is being developed in partnership with McMaster Children’s
Hospital and Southlake Regional Health Centre. Once fully
implemented, the Artemis system will monitor infants in NICUs, alerting
clinicians when sepsis develops before it would otherwise be clinically
apparent. Ultimately, Artemis will reduce mortality, morbidity and average
length of stay in NICUs.

“Early detection of sepsis in newborns has the
potential to save many lives,” says Dr. McGregor. “Artemis data can help NICUs
better manage the use of antibiotics and reduce the frequency of blood draws
from patients. Our research has also developed a new understanding of a number
of other conditions which will all contribute to better outcomes for these
fragile infants and their families.”

Premature babies have underdeveloped immune
systems making them acutely susceptible to infections, which can lead to sepsis.
Symptoms appear rapidly and unpredictably and can become fatal within hours. A
quarter of preterm infants will develop an episode of sepsis during their stay
in the NICU. 10 per cent of all cases are fatal.

“We’ve started Artemis with the very smallest
of patients,” adds Dr. Edward Pugh, clinical lead at McMaster Children’s
Hospital. “But this analytics platform has the potential to be rolled out
across the adult world and very much change the way that my colleagues and I
work.”

“As a community and regional hospital,
Southlake is passionate about the care of all our patients,” says Patrick
Clifford, Director of Research and Innovation at Southlake Regional Health
Centre. “The opportunity to collaborate on Artemis not only advances care for
our highly vulnerable neonates, but allows hospitals like Southlake to better
serve our tiniest of patients with leading edge care, closer to home.”

Pathfinder Projects are small-scale efforts
designed to produce results in 12 to 18 months to guide future research and
technology adoption. With technical and resource support from the Vector
Institute, projects bring together a multidisciplinary research team to tackle
an important health care problem or opportunity by using machine learning and
AI more broadly. Each project was chosen for its potential to help identify a
“path” through which world-class machine learning research translates into
widespread benefits for patients.

About
the Vector Institute

The Vector Institute is an independent,
not-for-profit corporation dedicated to advancing AI, excelling in machine and
deep learning. The Vector Institute’s vision is to drive excellence and
leadership in Canada’s knowledge, creation, and use of AI to foster economic
growth and improve the lives of Canadians.

The Vector Institute is
funded by the Province of Ontario, the Government of Canada through the
Pan-Canadian AI Strategy administered by CIFAR, and industry sponsors from across the Canadian economy.

Predicting
sepsis in infants with machine learning

Behind the medical monitors in the neonatal
intensive care unit (NICU) at McMaster Children’s Hospital sits a small beige
box. It likely goes unnoticed by most visitors, yet its benign appearance belies
its role as a gateway to a powerful tool that could save the lives of the
hospital’s smallest patients.

“The box itself is very unexciting,” confirms
Dr. Edward Pugh, clinical lead at McMaster. “But it very much has the potential
to change the way that my colleagues and I work.”

The box in question is the bedside connection
point for Artemis, a cloud-based data collection platform that uses machine learning
technology to collect, store and analyze patient data. The system is currently
being pilot tested in two Ontario hospitals: Southlake Regional Health Centre
in Newmarket and McMaster in Hamilton. Running continuously in the background
of each hospital’s NICUs, the system sends and receives data at a volume equivalent to 1,000
tweets a second per infant for approximately 1,200 patients annually. 

With support from the Vector Institute, Ontario
Tech University researcher Dr. Carolyn McGregor, the project’s lead, along with
Dr. Pugh, Southlake and their teams have set up Artemis to use AI to constantly
monitor many streams of data and analyze changes in infant physiology.
Variations in indicators like heart rate or breathing are signs a child is
dealing with an infection. Should such signs occur, Artemis will alert
physicians who will interpret the data and decide next steps.

“One of our main contributions is defining the
patterns of other conditions that will nevertheless make a baby unstable in
similar ways,” says Dr. McGregor. “By accurately identifying sepsis and other
events that make a baby unstable, we will be able to minimize unnecessary
antibiotics and investigations. Minimizing interventions in the NICU can
improve the long-term outcome of these fragile infants and decrease the distress
and burden on their families.”

Sepsis is one of the most common and devastating
conditions preterm and ill term infants can develop says Dr. McGregor. It occurs
when the natural chemicals that the body produces to ward off infections fall
out of balance. The underdeveloped immune systems of premature babies make them
particularly vulnerable — a quarter of preterm infants develop sepsis.
“Symptoms appear rapidly and unpredictably and can be fatal within a few
hours,” she says, noting that 10 per cent of cases are fatal.

McMaster is home to Ontario’s largest NICU,
where babies from just 350 grams to as large as eight kilograms are cared for.
“We’ll have babies who will stay with us for up to a year of life,” notes Dr.
Pugh. “You don’t see that in many neonatal intensive cares.” The volume, acuity
and wide variety of patient conditions seen across McMaster and a large
community hospital like Southlake, make them ideal locations to pilot Artemis
in the field.

Studies will continue through 2020. Once fully
implemented, the researchers hope to expand Artemis beyond checking for sepsis
and outside of the NICU. “We’re small footprint place working with the tiniest
of patients,” says Dr. Pugh, “but we have huge potential for a large impact.”

Additional
Pathfinder Projects

Connecting the Dots: Domino Data Lab Drops Into Data Science Wave

Event opportunity: Join Josh Poduska, Domino Data Lab’s chief data scientist, who will be presenting at GTC DC on Tuesday, Nov. 5

As Wall Street was morphing into a game of quants, Nick Elprin, Christopher Yang and Matthew Granade saw something big shaping up on the horizon: a data science wave swelling across industries.

So, the three left Bridgewater Associates, the world’s largest hedge fund, and shortly thereafter started Domino Data Lab, an open source data science platform now making a splash with AI developers worldwide. 

“My co-founders and I built a lot of the internal platforms and technology that those quants used at Bridgewater to do their quantitative research — what the rest of the world now calls data science,” said Elprin, the company’s CEO.

The San Francisco company, a member of the NVIDIA Inception program that helps startups scale, in August landed on Inc. magazine’s annual list of the fastest-growing private companies.

Bridgewater to Domino

After leaving Bridgewater in 2013, the three found that what companies lacked most was an industrialized platform for data science teams, so they started Domino Data Lab to fill the void.

“The experience and perspective at Bridgewater let us see the white space in the market to see what technology and products could do,” said Elprin.

Domino’s software platform automates infrastructure for data scientists, enabling users to accelerate research, deploy models and track projects.

Under Domino’s Hood

A data science supercharger, Domino’s customizable environment provides users with data science tools to speed workflows.

Its Domino Analytics Distribution offers a scientific computing stack for programming in Python, R, Julia and other popular languages. Domino offers access to commonly used interactive tools and notebooks, including Jupyter, RStudio, Zeppelin and Beaker.

Domino also provides deep learning packages and GPU drivers, including access to frameworks such as TensorFlow, Theano and Keras. The platform enables access to any NVIDIA GPUs in the cloud.

“Working with NVIDIA has helped Domino build products that allow our mutual customers to automate deployment of workloads to GPUs,” Elprin said. “NVIDIA Inception has also helped us grow our Fortune 500 customer base through  podcasts and conference talks.”

Customer Domino Effect

Companies are lining up. Red Hat, Dell, Bayer, AllState, Gap and Bristol-Myers Squibb are all using Domino to accelerate their data science workflows.

“Our investment in Domino has really paid off — probably a return around 10x in terms of efficiency of our data science community,” said Heidi Lanford, vice president of enterprise data and analytics at Red Hat, in a video.

Image credit: Photo by Shalom Jacobovitz, licensed under Creative Commons.

The post Connecting the Dots: Domino Data Lab Drops Into Data Science Wave appeared first on The Official NVIDIA Blog.

On-Device Captioning with Live Caption

Captions for audio content are essential for the deaf and hard of hearing, but they benefit everyone. Watching video without audio is common — whether on the train, in meetings, in bed or when the kids are asleep — and studies have shown that subtitles can increase the duration of time that users spend watching a video by almost 40%. Yet caption support is fragmented across apps and even within them, resulting in a significant amount of audio content that remains inaccessible, including live blogs, podcasts, personal videos, audio messages, social media and others.

Recently we introduced Live Caption, a new Android feature that automatically captions media playing on your phone. The captioning happens in real time, completely on-device, without using network resources, thus preserving privacy and lowering latency. The feature is currently available on Pixel 4 and Pixel 4 XL, will roll out to Pixel 3 models later this year, and will be more widely available on other Android devices soon.

When media is playing, Live Caption can be launched with a single tap from the volume control to display a caption box on the screen.

Building Live Caption for Accuracy and Efficiency
Live Caption works through a combination of three on-device deep learning models: a recurrent neural network (RNN) sequence transduction model for speech recognition (RNN-T), a text-based recurrent neural network model for unspoken punctuation, and a convolutional neural network (CNN) model for sound events classification. Live Caption integrates the signal from the three models to create a single caption track, where sound event tags, like [APPLAUSE] and [MUSIC], appear without interrupting the flow of speech recognition results. Punctuation symbols are predicted while text is updated in parallel.

Incoming sound is processed through a Sound Recognition and ASR feedback loop. The produced text or sound label is formatted and added to the caption.

For sound recognition, we leverage previous work that was done for sound events detection, using a model that was built on top of the AudioSet dataset. The Sound Recognition model is used not only to generate popular sound effect labels but also to detect speech periods. The full automatic speech recognition (ASR) RNN-T engine runs only during speech periods in order to minimize memory and battery usage. For example, when music is detected and speech is not present in the audio stream, the [MUSIC] label will appear on screen, and the ASR model will be unloaded. The ASR model is only loaded back into memory when speech is present in the audio stream again.

In order for Live Caption to be most useful, it should be able to run continuously for long periods of time. To do this, Live Caption’s ASR model is optimized for edge-devices using several techniques, such as neural connection pruning, which reduced the power consumption to 50% compared to the full sized speech model. Yet while the model is significantly more energy efficient, it still performs well for a variety of use cases, including captioning videos, recognizing short queries and narrowband telephony speech, while also being robust to background noise.

The text-based punctuation model was optimized for running continuously on-device using a smaller architecture than the cloud equivalent, and then quantized and serialized using the TensorFlow Lite runtime. As the caption is formed, speech recognition results are rapidly updated a few times per second. In order to save on computational resources and provide a smooth user experience, the punctuation prediction is performed on the tail of the text from the most recently recognized sentence, and if the next updated ASR results do not change that text, the previously punctuated results are retained and reused.

Looking forward
Live Caption is now available in English on Pixel 4 and will soon be available on Pixel 3 and other Android devices. We look forward to bringing this feature to more users by expanding its support to other languages and by further improving the formatting in order to improve the perceived accuracy and coherency of the captions, particularly for multi-speaker content.

Acknowledgements
The core team includes Robert Berry, Anthony Tripaldi, Danielle Cohen, Anna Belozovsky, Yoni Tsafir, Elliott Burford, Justin Lee, Kelsie Van Deman, Nicole Bleuel, Brian Kemler, and Benny Schlesinger. We would like to thank the Google Speech team, especially Qiao Liang, Arun Narayanan, and Rohit Prabhavalkar for their insightful work on the ASR model as well as Chung-Cheng Chiu from Google Brain Team; Dan Ellis and Justin Paul for their help with integrating the Sound Recognition model; Tal Remez for his help in developing the punctuation model; Kevin Rocard and Eric Laurent‎ for their help with the Android audio capture API; and Eugenio Marchiori, Shivanker Goel, Ye Wen, Jay Yoo, Asela Gunawardana, and Tom Hume for their help with the Android infrastructure work.

[D] For GNN’s are gradients normally tracked on neighborhood aggregation operations (e.g. max, mean)?

I am writing a GNN from scratch, to demonstrate to myself that I understand all the required concepts.

I am a bit confused on whether neighborhood aggregation operations require gradients to be tracked through those operations like mean and max of neighbors embeddings. In my code where I perform these operations, currently I do them within a with torch.no_grad() block because if I don’t each epoch takes forever.

Here my code for those operations:

def neighborhood_aggregation(self, adj_lists, feat, agg_method): # adj_lists is a dict of neighbors for every node in graph # e.g. adj_list = {0:{1, 4, 5, 6}, 1: {2, 4, 5}, ...} # node 0 has neighbors 1, 4, 5, 6 with torch.no_grad(): # construct aggregated neighborhood embedding dim = list(feat.size()) n_nodes = dim[0] feat_dim = dim[1] aggregated_embed = torch.Tensor(n_nodes, feat_dim) # aggregated embeddings for all nodes in graph. embed_element_vec = torch.arange(feat_dim) # for node_id, neighbor_node_ids in adj_lists.items(): neighborhood_embedding = feat[list(neighbor_node_ids), :] if agg_method == 'mean': aggregated_neigborhood_embedding = torch.mean(neighborhood_embedding, 0) elif agg_method == 'pool': aggregated_neigborhood_embedding = torch.max(neighborhood_embedding, 0)[0] else: raise KeyError('Aggregator type {} not recognized.'.format(agg_method)) aggregated_embed[node_id, embed_element_vec] = aggregated_neigborhood_embedding return aggregated_embed 

Note: The above code works, and I am getting very good results with it. It’s just I am not sure if what I am doing is wrong. IF it is wrong I was thinking that I need a 3D tensor for the aggregated_embed tensor [n_nodes, n_neighbors, embed_dim] (which requires_grad=False) and perform the mean/max on that tensor which would track gradients.

Thanks for any help.

submitted by /u/Muunich
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[N] Even notes from Siraj Raval’s course turn out to be plagiarized.

[N] Even notes from Siraj Raval's course turn out to be plagiarized.

More odd paraphrasing and word replacements.

From this article: https://medium.com/@gantlaborde/siraj-rival-no-thanks-fe23092ecd20

Left is from Siraj Raval’s course, Right is from original article

‘quick way’ -> ‘fast way’

‘reach out’ -> ‘reach’

‘know’ -> ‘probably familiar with’

‘existing’ -> ‘current’

Original article Siraj plagiarized from is here: https://www.singlegrain.com/growth/14-ways-to-acquire-your-first-100-customers/

submitted by /u/Kitchen_Extreme
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[P] Lyrics Generator Twitter Bot

[P] Lyrics Generator Twitter Bot

I fine-tuned 2 small GPT-2 models (124M parameters) and created twitter bots that interact with Twitter users.

I have shared the code and useful things I learned and used hoping it will help somebody in the following repository :

https://jsalbert.github.io/lyrics-generator-twitter-bot/

The following samples correspond to the outputs of such models.

Eminem Bot Lyrics (@rap_god_bot)

https://preview.redd.it/anndufmguhv31.png?width=600&format=png&auto=webp&s=e027a50442f71b64fbcbe8821ed843c6d6823ead

Music Storytelling Bot Lyrics (@musicstorytell)

https://preview.redd.it/lo8qhzshuhv31.png?width=600&format=png&auto=webp&s=c0a609f649bb3daeeea18aa91c43165c7216f038

submitted by /u/jsalbert_
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DC Startup Casts an AI Net to Stop Phishing and Malware

When the price went way up on a key service a small Washington, D.C., firm was using to protect its customers’ internet connectivity, the company balked.

After not finding a suitable alternative, the company decided to build its own. The result was a whole new business, called DNSFilter, which is casting a wide net around the market to combat phishing and malware.

Its innovation: It ditched the crowdsourcing model that has served for more than a decade as the bedrock for identifying whether websites are valid or corrupt. It opted, instead, for GPU-powered AI to make web surfing safer by identifying threats and objectionable content much faster than traditional offerings.

“We figured that if we built a whole new DNS from the ground up, built on artificial intelligence and machine learning, we could find threats faster and more effectively,” said Rustin Banks, chief revenue officer and one of four principals at DNSFilter.

Spinning Up Phishing Protection

DNS, or domain name system, is the naming system for computers, phones and services that connect to the internet. DNSFilter’s aim is to protect these assets from malicious websites and attacks.

The company’s algorithm takes seconds to compare websites to a machine learning model generated from 30,000 known phishing sites. To date, its AI prevents over 90 percent of new requests to visit potentially corrupt sites.

It’s this speed that largely separates DNSFilter from the rest of the industry, Banks said. It gets results in near real time, while competitors typically take around 24 hours.

The company’s algorithm has been built and trained in the cloud using NVIDIA P4 GPU clusters.

“NVIDIA GPUs allow us to rapidly train AI, while being able to use cutting-edge frameworks. It’s not a job I would want to do without them,” said Adam Spotton, chief data scientist at DNSFilter.

Inferencing occurs at 48 locations worldwide, hosted by 10 vendors who’ve passed DNSFilter’s rigorous security standards.

Banks said the company’s rivals primarily use a company in the Philippines that has a team of 150 people classifying sites all day. But for DNSFilter, the more corrupt sites it identifies, the faster and more accurate its algorithm becomes. (Disclosure: NVIDIA is one of the company’s biggest customers.)

Moreover, DNSFilter’s solution works at the network level so there’s no plug-in necessary and the solution works with any email client, protecting organizations regardless of where employees are or what device they’re using.

“If the CFO uses his Yahoo mail on his mobile device, it doesn’t matter,” said Banks. “It’s built right into the fabric of the internet request.”

Upping the Ante

Banks estimates that DNS filtering represents a billion-dollar market, and he’s confident that the $10 billion firewall market is in play for DNSFilter.

Already, the startup is fielding more than a billion DNS requests a day. Banks foresees that number rising to 10 billion by the end of 2020. He also expects accuracy will come to exceed 99 percent as the dataset of corrupt sites grows.

The company isn’t stopping there. More services are planned, including a log -analysis product currently in beta. It scans logos on sites linked from phishing emails and compares them against a database of approved sites to determine whether the logo is real. It then blocks phishing sites in real time.

Eventually, Banks said, the company intends to evolve from its current machine learning feedback loop to a neural network with sufficient cognition to identify things that its algorithms can’t find.

This, he said, would be like having an extra pair of eyes inside an organization’s security team, constantly monitoring suspicious web surfing wherever employees may be working.

“This is taking phishing protection to a new level,” said Banks. “It’s like network-level protection that comes with you wherever you go.”

The post DC Startup Casts an AI Net to Stop Phishing and Malware appeared first on The Official NVIDIA Blog.

[D] The roots of natural language processing can be traced back to Kabbalist mystics

For people interested in the history of technology — here’s an eccentric essay arguing that the first examples of NLP happened in medieval times. Mystics studying the Kabbala devised “sacred rules” for combining letters to generate prophetic texts and, sometimes, to create golems.

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/natural-language-processing-dates-back-to-kabbalist-mystics

“While specific technologies have changed over time, the basic idea of treating language as a material that can be artificially manipulated by rule-based systems has been pursued by many people in many cultures and for many different reasons. These historical experiments reveal the promise and perils of attempting to simulate human language in non-human ways—and they hold lessons for today’s practitioners of cutting-edge NLP techniques.”

submitted by /u/newsbeagle
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[P] Classify whether some text is talking about Apple or apples

Hey guys,

I’m having a project in which I have a very big dataset related to the term “apple” (case unsensitive). It contains some text with that word and my job is to determine whether it’s talking about the Apple company, or something else.

There are so many ways to do this and I can’t seem to find the best one. Eventually, I guess it’s doable with 0 machine learning but as a lazy data scientist I want that process to be as autonomous as possible (in order to generalize to other words).

I tried some NLP techniques like bag of words then kmeans but it gave horrible results.

The problem is that there is no labeled dataset.

I have some ideas, like a proper noun / common noun classifier or using wikipedia to create a context vocabulary.

Any ideas? Thanks.

submitted by /u/aewin678
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