HBO won the bidding war for J.J. Abrams’ new sci-fi series, Demimonde

http://ift.tt/2DXSMcw


A couple of weeks ago, word broke that Apple and HBO were bidding for the rights to a new show from Lost creator and The Force Awakens director J.J. Abrams. HBO won the bid, ordering a straight-to-series order for the show, titled Demimonde.

Abrams already has a relationship with HBO, as an executive producer for the network’s Westworld. But Demimonde will mark his first return to television writing since 2008’s Fringe. In the series, a scientist falls into a coma after an accident. Looking through her experiments, her daughter is transported to another world. Her father follows, and they discover a “battle against a monstrous, oppressive force.”

The show is joining Abrams’ already-hefty workload. Last fall, he took over directing duties for Star Wars: Episode IX after Colin Trevorrow left the project. He’s also producing Hulu’s Stephen King-inspired series Castle Rock, the upcoming season of Westworld, and Quentin Tarantino’s proposed Star Trek film, among many other rumored and in-progress projects.

Before he directed big-budget blockbusters like 2009’s Star Trek and 2015’s The Force Awakens, Abrams was known for his work on shows such as Alias, Lost, and Fringe, each of which were loaded down with fantastical technologies and mysteries. Demimonde sounds as though it’ll fit in that category, as well as in the long literary tradition of fantasy stories about people from our world drawn into epic otherworldly conflicts. With Game of Thrones ending in 2019, HBO will be looking to fill a considerable hole in its “fantasy epic” roster, at least until one of its numerous spinoff shows hits, and Abrams’ latest could be designed to appeal to the same audience.



from The Verge http://ift.tt/1jLudMg
via IFTTT

“Are you extroverted?”

http://ift.tt/2EcoxeV


I upload a new video every single day to YouTube about business and just getting through life. A vlog as some call it. So also, every day, I walk around talking to a camera lens in the middle of a street or crowded event.

It’s weird. It’s weird for a lot of people. So I get asked a lot: “How are you comfortable doing this?” “Do you have a history of doing something like this” But the umbrella question that I think most people are trying to ask is:

“Are you extroverted? Is that why you can pull this off and I can’t?”

The fact is, I’m probably one of the most introverted people you’ll meet. Some would maybe even label me fairly anti-social. :)

I really like one-on-one interactions and meeting new people. I love true friends. But I don’t like going to parties. You won’t see me at many conferences. If I am there, you’ll find me in the back row of something or closest to the exit so I can bolt.

And no, I’m not comfortable doing this. Even in front of a camera in a room by myself, I get nervous. Even though I know I have all this power to edit and redo. The first video I uploaded to my vlog was a Live video I recorded on Facebook but made it Private to just me :) And I filmed it 13 times.

And I hate that attention on me as I walk around talking to a camera.

But I do it anyway.

Do I have some kind of inflated image of myself? Hard for me to judge I guess since I don’t know the self talk in other people’s heads, but I’m pretty hard on myself both in what I do and how I look.

I don’t even want to open up that therapist session on all the ways I hate how I look on camera. But some obvious ones. My complexion is terrible. Skin is oily. Now I’ve developed this recurring terrible allergic reaction that comes on when I even glance at a pine/Christmas tree and recurs randomly otherwise.

Kendall Jenner, one of the younger of the Kardashian clan, was at the Golden Globes, and it was crazy how many people called her out for the visible Acne she had on the red carpet.

What were people expecting? That she’d skip the red carpet? Stay home?

This is a huge thing that keeps people back. Vanity that they have to look perfect.

And that’s why the Kardashians are so successful. They have zero fear of putting themselves out there for every single person on the planet to see, flaws and all. It doesn’t matter what the public thinks of their skin or anything else they say or do. They aren’t afraid to embarrass themselves when most everyone else is.

People also commonly ask me if I’ve had a lot of practice doing this.

Not really. I have had some brief on-camera training as part of acting lessons I’ve taken over the years, and a big reason I took those classes was to get over my fear of performing in front of people and cameras. And that practice has helped some.

But there’s been plenty of videos, especially the live ones, where I’m sweating the possibility of saying something stupid or why on earth is my hair sticking out like that today.

So, if you’re holding back from doing something like a vlog because you’re afraid of what you look like, or you’re uncomfortable in front of a camera? I’m there with you.

But I refuse to let those things keep me back. It takes some practice. It’s still uncomfortable. It gets less uncomfortable… sometimes. And it’s worth it.

To be nobody but yourself in a world which is doing its best day and night to make you like everybody else means to fight the hardest battle which any human being can fight and never stop fighting. ― E.E. Cummings

P.S. You should follow me on YouTube: youtube.com/nathankontny where I share more about how we run our business, do product design, market ourselves, and just get through life. And if you need a zero-learning-curve system to track leads and manage follow-ups, try Highrise.



from Signal vs. Noise http://ift.tt/1S7Ubfs
via IFTTT

Google is using 46B data points to predict medical outcomes of patients

http://ift.tt/2niuqiB

Some of Google’s top AI researchers are trying to predict your medical outcome as soon as you’re admitted to the hospital.

A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals, University of California San Francisco Medical Center (from 2012-2016) and University of Chicago Medicine (2009-2016).

While the results have not been independently validated, Google claims vast improvements over traditional models used today for predicting medical outcomes. Its biggest claim is the ability to predict patient deaths 24-48 hours before current methods, which could allow time for doctors to administer life-saving procedures.

The biggest challenge for AI researchers looking to train their algorithms on electronic health records, the source of the data, is the vast, disparate, and poorly-labelled pieces of data contained in a patient’s file, the researchers write. In addition to data points from tests, written notes have traditionally been difficult for automated systems to comprehend; each doctor and nurse writes differently and can take different styles of notes.

colorcorrected (9)Google’s data spans from pre-admission to discharge. (Google)

To compensate for this, the Google approach relies on three complex deep neural networks that learn from all the data and work out which bits are most impactful to final outcomes. After analyzing thousands of patients, the system identified which words and events associated closest with outcomes, and learned to pay less attention to what it determined to be extraneous data. Typically, AI scientists have to carefully tinker with how their system interprets the data after it’s built, like which number of layers are needed to make the decision most accurately. In the research paper, the authors write that this was done automatically by a previous Google project called Vizier.

Beyond the paper’s results, the research represents a considerable investment in applying AI to health, outside of Alphabet’s established companies like Verily, Calico, and DeepMind. Google heavy-hitters like Quoc Le, credited with creating recurrent neural networks used for predictions based on time, and Jeff Dean, a legend at the company for his work on Google’s server infrastructure, are both on the paper, as well as Greg Corrado, a director at the company involved in high-profile projects like translation and its Smart Reply feature.

The specialized technology also could threaten work from companies like IBM, which has pitched itself as an innovator in medical AI, but received backlash for making big promises with little tangible results.

Google did not immediately respond to Quartz’ questions.

Most Popular

The supermoon rises in Prague


from Hacker News http://ift.tt/YV9WJO
via IFTTT

Kaggle Learn review: there is a deep learning track and it is worth your time

http://ift.tt/2EbSnQF


Right from my undergrad days when I was starting out with machine learning to this date, my admiration for Kaggle continues to grow. In addition to being synonymous with and popularizing data science competitions, the platform has served as a launching pad and breeding ground for countless data science and machine learning practitioners around the world, including yours truly. In fact, skills I’d picked up from the platform are part of the reason that I recently got to join SocialCops, a company I’d admired for years. However, I hadn’t been on the platform in 2017 as much as I would have liked. So when I saw Ben Hamner‘s tweet launching Kaggle Learn, a set of interactive data science tutorials, I made up my mind to give it a shot.

Zeroing in on deep learning

Learn currently hosts tutorials about 4 topics – introductory machine learning, R programming, data visualisation and deep learning. I’d stumbled across machine learning for the first time in the form of neural networks (NN) more than 3 years back. Since then, I’d studied the theoretical details of NN at various points of time but somewhat ironically, I’d never got into practical deep learning except for a few tutorials. Hence, I decided to get started with the deep learning track.
The reason I mentioned my past experience with ML and NN was to point out the fact that I was not a complete beginner when I had gotten started with this track and if you are, start with the machine learning track instead.

Getting started

If you are unfamiliar with neural networks or haven’t come across them recently, it would be a good idea to get some theoretical foundation before starting with hands-on tutorials. There are a number of introductory resources out there, both text and video. I used an excellent video by 3Blue1Brown, a YouTube channel, as a refresher.

VIDEO

Choice of framework

The track uses the high-level Keras API and a Tensorflow backend. Even with numerous frameworks out there, this combination seems to find favor as a beginner-friendly choice among a large portion of the deep learning community. Personally, I admire Keras for being well-designed, user-friendly and playing a big role in democratizing access to deep learning methods.

The track

The deep learning track is currently comprised of six sections. They are:

  • Intro to Deep Learning and Computer Vision: Starting off with a computer vision example is a great way to get acquainted with machine learning. This is the application which had put deep learning in the limelight and the data (images) is something most of us deal with on an everyday basis. The accompanying exercise allows you to play around with basic convolutions and images.
  • Building Models from Convolutions: Convolutional neural networks (ConvNet) have received wide praise and coverage for being extremely successful with image recognition tasks. The basics of ConvNets are discussed and the stage is set up for their implementation.
  • Programming in Tensorflow and Keras: You get to see TF+Keras in action for the first time and you’ll be amazed at the ease with which you can get up and running. There’s a lot of hand-holding here so getting the code to work alone won’t be very useful. Try to understand the code, including helper functions, as much as possible.

    Related – Big deep learning news: Tensorflow chooses Keras
  • Transfer Learning: It was a great decision by Dan Becker to include this, and it is my favorite part of the tutorial. Prior to this, my perception of transfer learning was as an advanced topic which would require a decent amount of know-how to even get started. I am delighted to tell you that that I couldn’t have been more wrong. Even if all you know are the very basics of NN, the idea of transfer learning itself is fascinating and I’ve decided to spend some time in near future to research about the topic. Prior to starting this section, I’d gone through the following video by the one and only Andrew Ng.

VIDEO

Conclusion and additional resources

When learning a new topic, I’ve always found it best to start with a high-level overview. That’s precisely what this track aims to offer and for most part, delivers. For a considerable amount of time, setting up deep learning frameworks used to be a roadblock to getting started with the topic. To that end, Kaggle leverages its platform’s capabilities to host the code and while doing so, showcases its potential for being useful for collaboration. All that being said, this topic only scratches the surface, even if in a better manner than most tutorials out there. You can plan out your path from here on. If it helps, below are some of the resources I plan to dive into or explore over the next few weeks.

If there’s any other useful resource you can think of, feel free to mention it in the comments below.

If you read and liked the article, sharing it would be a good next step.
Additionally, you can check out some of my open source projects on Github.
Drop me a mail, or hit me up on Twitter in case you want to get in touch.

 

 



from Hacker News http://ift.tt/YV9WJO
via IFTTT

Giving teens alcohol to teach them responsible drinking may backfire

http://ift.tt/2DPHFS9


It’s common to hear about parents giving their teens alcohol, hoping that if they learn about responsible drinking at home they’ll be less likely to binge drink when they’re on their own. But a new study suggests that this method doesn’t seem to protect teens from the risks of alcohol abuse.

Australia scientists followed 2,000 teens for six years and found that parents providing alcohol not only doesn’t prevent binge drinking, it was actually linked to teens finding alcohol through other sources. The study, the first to analyze the long-term effect of parents providing alcohol, was published this week in the journal Lancet Public Health.

Every year for six years, teens and their parents filled out different surveys about alcohol habits. The survey asked about alcohol abuse symptoms, binge drinking levels, and how the teens got alcohol. To be clear, “binge” drinking was defined as drinking more than four drinks at once, which the authors acknowledge is a somewhat conservative estimate. And the sources of alcohol included parents, not from parents, and both.

The teenagers in the study were, on average, 13 years old at the beginning and 18 at the end. Unsurprisingly, more parents gave alcohol to their children as the children aged — from 15 percent of parents at the beginning of the study, to about 60 percent at the end.

By the end of the study, 81 percent of teens who received alcohol both from their parents and other sources were binge drinking. In contrast, 62 percent of teens who only got it from other people (and were not given alcohol by their parents) were binge drinking. (Also, 25 percent of teens who were given alcohol only by their parents binged, which is a strange finding.) And teens who got alcohol only from their parents one year were twice as likely to get it from other people the next year.

This is an observational study, so it can’t prove that giving alcohol to your kid causes them to seek it out and binge. There are other limitations too: Self-reported surveys are rarely the most accurate way to measure anything. Teens from low socio-economic status backgrounds weren’t well-represented, and the results are from Australia, and we don’t know how broadly they generalize. Still, it’s an interesting result, and it’s worth thinking about how it’s possible for some well-meaning tactics to backfire.



from The Verge http://ift.tt/1jLudMg
via IFTTT

Venmo can now instantly transfer money to your debit card for 25 cents

http://ift.tt/2GkKfxW


PayPal has finally rolled out its Instant Transfer feature on Venmo after first announcing that support would be coming last summer. The option allows Venmo users to pay an extra 25 cents to deposit funds to their debit cards in a matter of minutes rather than days. This is particularly useful if you need to cash out right before a holiday or on Sundays, which are times when banks typically do not process the withdrawal.

Instant Transfer was previously available only for PayPal beta users. It promises to transfer funds to your account within 30 minutes even on holidays and weekends. A free option will still remain for those who don’t mind waiting the one to three days for payments to process. According to PayPal, Venmo instant transfers will only work with Visa and Mastercard debit cards.

The move comes after major US banks formed an alliance and launched Zelle, a competing peer-to-peer payment processing service that offers instant withdrawals for free and does not charge for transfers between different banks.



from Hacker News http://ift.tt/YV9WJO
via IFTTT

The U.S. Can No Longer Hide from Its Deep Poverty Problem

http://ift.tt/2nefJ04

Photo
Credit Matt Rota

You might think that the kind of extreme poverty that would concern a global organization like the United Nations has long vanished in this country. Yet the special rapporteur on extreme poverty and human rights, Philip Alston, recently made and reported on an investigative tour of the United States.

Surely no one in the United States today is as poor as a poor person in Ethiopia or Nepal? As it happens, making such comparisons has recently become much easier. The World Bank decided in October to include high-income countries in its global estimates of people living in poverty. We can now make direct comparisons between the United States and poor countries.

Properly interpreted, the numbers suggest that the United Nations has a point — and the United States has an urgent problem. They also suggest that we might rethink how we assist the poor through our own giving.

According to the World Bank, 769 million people lived on less than $1.90 a day in 2013; they are the world’s very poorest. Of these, 3.2 million live in the United States, and 3.3 million in other high-income countries (most in Italy, Japan and Spain).

As striking as these numbers are, they miss a very important fact. There are necessities of life in rich, cold, urban and individualistic countries that are less needed in poor countries. The World Bank adjusts its poverty estimates for differences in prices across countries, but it ignores differences in needs.

Continue reading the main story

An Indian villager spends little or nothing on housing, heat or child care, and a poor agricultural laborer in the tropics can get by with little clothing or transportation. Even in the United States, it is no accident that there are more homeless people sleeping on the streets in Los Angeles, with its warmer climate, than in New York.

The Oxford economist Robert Allen recently estimated needs-based absolute poverty lines for rich countries that are designed to match more accurately the $1.90 line for poor countries, and $4 a day is around the middle of his estimates. When we compare absolute poverty in the United States with absolute poverty in India, or other poor countries, we should be using $4 in the United States and $1.90 in India.

Once we do this, there are 5.3 million Americans who are absolutely poor by global standards. This is a small number compared with the one for India, for example, but it is more than in Sierra Leone (3.2 million) or Nepal (2.5 million), about the same as in Senegal (5.3 million) and only one-third less than in Angola (7.4 million). Pakistan (12.7 million) has twice as many poor people as the United States, and Ethiopia about four times as many.

Deeply Poor in Wealthy Lands

A tally of those living on $4 a day or less in selected developed countries.

#g-deaton-box .g-artboard { margin:0 auto; } #g-deaton-box .g-artboard p { margin:0; } .g-aiAbs { position:absolute; } .g-aiImg { display:block; width:100% !important; } .g-aiPointText p { white-space: nowrap; } #g-deaton-300 { position:relative; overflow:hidden; width:300px; } #g-deaton-300 p { font-family:nyt-franklin,arial,helvetica,sans-serif; font-weight:500; font-size:11px; line-height:15px; height:auto; filter:alpha(opacity=100); -ms-filter:progid:DXImageTransform.Microsoft.Alpha(Opacity=100); opacity:1; letter-spacing:0em; text-align:left; color:rgb(0,0,0); text-transform:none; padding-bottom:0; padding-top:0; mix-blend-mode:normal; font-style:normal; } #g-deaton-300 .g-pstyle0 { font-weight:700; font-size:14px; line-height:17px; height:17px; } #g-deaton-300 .g-pstyle1 { height:15px; text-align:right; } #g-deaton-300 .g-pstyle2 { height:15px; } #g-deaton-300 .g-pstyle3 { font-weight:700; height:15px; text-align:right; } #g-deaton-300 .g-pstyle4 { font-weight:700; height:15px; } #g-deaton-300 .g-pstyle5 { font-weight:700; font-size:13px; line-height:17px; height:17px; color:rgb(188,60,64); } #g-deaton-300 .g-pstyle6 { font-weight:700; font-size:13px; line-height:17px; height:17px; text-align:center; } #g-deaton-300 .g-pstyle7 { height:15px; text-align:center; } #g-deaton-300 .g-pstyle8 { font-weight:700; font-size:13px; line-height:17px; height:17px; text-align:right; } #g-deaton-600 { position:relative; overflow:hidden; width:600px; } #g-deaton-600 p { font-family:nyt-franklin,arial,helvetica,sans-serif; font-weight:500; font-size:14px; line-height:18px; height:auto; filter:alpha(opacity=100); -ms-filter:progid:DXImageTransform.Microsoft.Alpha(Opacity=100); opacity:1; letter-spacing:0em; text-align:left; color:rgb(0,0,0); text-transform:none; padding-bottom:0; padding-top:0; mix-blend-mode:normal; font-style:normal; } #g-deaton-600 .g-pstyle0 { font-weight:700; height:18px; } #g-deaton-600 .g-pstyle1 { height:18px; text-align:right; } #g-deaton-600 .g-pstyle2 { font-size:13px; height:18px; text-align:right; } #g-deaton-600 .g-pstyle3 { font-weight:700; height:18px; color:rgb(188,60,64); } #g-deaton-600 .g-pstyle4 { height:18px; } #g-deaton-600 .g-pstyle5 { font-weight:700; height:18px; text-align:center; } #g-deaton-600 .g-pstyle6 { height:18px; text-align:center; } #g-deaton-600 .g-pstyle7 { font-size:13px; height:18px; } #g-deaton-600 .g-pstyle8 { font-weight:700; height:18px; text-align:right; } #g-deaton-600 .g-pstyle9 { font-weight:700; font-size:13px; height:18px; }

Percentage of total population that is poorest …

Germany

0

Iceland

0

Switzerland

0

Netherlands

0.2%

Britain

0.2

Belgium

0.3

France

0.3

Norway

0.3

Denmark

0.4

Austria

0.5

South Korea

0.7

Australia

0.7

Canada

0.7

Japan

0.7

Sweden

0.7

Ireland

1.0

United States

1.7

Spain

2.0

Italy

2.3

Portugal

2.5

Greece

3.7

… and their estimated numbers:

United

States

5.3 million

(nearly the

population of

Minnesota)

Total

13.8 million

(nearly the pop-

ulation of Sweden

and Ireland

combined)

European

Union

6.9 million

Japan 867,000

S. Korea 338,000

Canada 239,000

Australia 157,000

Percentage of total population that is poorest …

… and their estimated numbers:

Germany

0

Iceland

0

Switzerland

0

United

States

5.3 million

(nearly the

population of

Minnesota)

Total

13.8 million

(nearly the pop-

ulation of Sweden

and Ireland

combined)

Netherlands

0.2%

European

Union

6.9 million

Britain

0.2

Belgium

0.3

France

0.3

Norway

0.3

Denmark

0.4

Austria

0.5

South Korea

0.7

Japan 867,000

Australia

0.7

S. Korea 338,000

Canada

0.7

Japan

0.7

Canada 239,000

Sweden

0.7

Australia 157,000

Ireland

1.0

United States

1.7

Spain

2.0

Italy

2.3

Portugal

2.5

Greece

3.7

This evidence supports on-the-ground observation in the United States. Kathryn Edin and Luke Shaefer have documented the daily horrors of life for the several million people in the United States who actually do live on $2 a day, in both urban and rural America. Matthew Desmond’s ethnography of Milwaukee explores the nightmare of finding urban shelter among the American poor.

It is hard to imagine poverty that is worse than this, anywhere in the world. Indeed, it is precisely the cost and difficulty of housing that makes for so much misery for so many Americans, and it is precisely these costs that are missed in the World Bank’s global counts.

Of course, people live longer and have healthier lives in rich countries. With only a few (and usually scandalous) exceptions, water is safe to drink, food is safe to eat, sanitation is universal, and some sort of medical care is available to everyone. Yet all these essentials of health are more likely to be lacking for poorer Americans. Even for the whole population, life expectancy in the United States is lower than we would expect given its national income, and there are places — the Mississippi Delta and much of Appalachia — where life expectancy is lower than in Bangladesh and Vietnam.

Beyond that, many Americans, especially whites with no more than a high school education, have seen worsening health: As my research with my wife, the Princeton economist Anne Case, has demonstrated, for this group life expectancy is falling; mortality rates from drugs, alcohol and suicide are rising; and the long historical decline in mortality from heart disease has come to a halt.

I believe, as do most people, that we have an obligation to assist the truly destitute. For those who believe that aid is effective, this is reflected in their own giving, or by supporting national and international organizations like the United States Agency for International Development, the World Bank or Oxfam.

For years, in determining this spending, the needs of poor Americans (or poor Europeans) have received little priority relative to the needs of Africans or Asians. As an economist concerned with global poverty, I have long accepted this practical and ethical framework. In my own giving, I have prioritized the faraway poor over the poor at home.

Recently, and especially with these insightful new data, I have come to doubt both the reasoning and the empirical support. There are millions of Americans whose suffering, through material poverty and poor health, is as bad or worse than that of the people in Africa or in Asia.

Practical considerations reinforce the argument for recognizing America’s poor in the global context. There is a better chance of monitoring the effects of domestic spending than of foreign spending. Money spent by and for fellow citizens, either individually or collectively, is subject to democratic evaluation by both donors and recipients, who can see the effects and who can show their approval or disapproval in the voting booth. Those who donate for projects in Africa often find it difficult to know what good their gifts are doing, let alone to discover whether the intended beneficiaries actually receive or appreciate them.

Official aid from the United States is mostly set by geopolitics — the leading recipients are Afghanistan, Israel and Iraq. Yet the United States is committed to eliminating $1.90-a-day poverty in the world, a target that is not contingent on poverty at home. Britain insists on spending 0.7 percent of its gross domestic product on foreign aid, in spite of occasional difficulties in finding suitable projects and in spite of domestic suffering caused by austerity at home.

None of this means that we should close out “others” and look after only our own. International cooperation is vital to keeping our globe safe, commerce flowing and our planet habitable.

But it is time to stop thinking that only non-Americans are truly poor. Trade, migration and modern communications have given us networks of friends and associates in other countries. We owe them much, but the social contract with our fellow citizens at home brings unique rights and responsibilities that must sometimes take precedence, especially when they are as destitute as the world’s poorest people.

Continue reading the main story


from Hacker News http://ift.tt/YV9WJO
via IFTTT

Vancouver Canucks Skill Coach Glenn Carnegie: Puck Protection

http://ift.tt/2n4lPAb

Glenn Carnegie Puck Protection Ice Hockey Coach Tips and Drills Skills Edges Vancouver Canucks NHL

“I want to get that puck with body position already established.”

The first thing that’s obvious when you meet Glenn Carnegie is how passionate he is about the game. We were fortunate enough to be able to watch Glenn in action behind the scenes in the last couple weeks working with some injured Vancouver Canucks players at the University of British Columbia. Brandon Sutter and Bo Horvat were both put through the paces by Carnegie, and as you can imagine, it’s important for NHL teams to get it right when players are injured. 

So for Carnegie, it’s not just about teaching skills, it’s about teaching the right skills at the right time. Puck protection and other drills and methods Glenn creates are born out of a deep understanding of how hockey players work, how they think, and what they need to feel confident.

Because as we all know, confidence is everything in this business.

 

Inspire Connect Lead

 

It’s a lot easier to protect the puck when you’re confident. In one of our on-ice presentations at the 2017 Coaches’ Conference, Glenn worked with a handful of midget and junior hockey players to demonstrate puck protection techniques he uses at the NHL level.

One of the best things about these on-ice videos is watching the pros in action. Their movements, their terminology – it’s all so valuable. Watch below as Glenn establishes body position before retrieving the puck.

 


If you enjoy these video excerpts, remember you can sign up for a free 30 day trial on our TCS | MEMBERS site. This gets you access to our entire library of videos from our annual TeamSnap Hockey Coaches Conference. You can cancel any time, although after joining a community of coaches from all over the world using the videos on a daily basis to pick up new tips and stay relevant, we doubt you will.

Sign up now!

TCS|Members Ice Hockey Coach Tips and Drills Todd Woodcroft

See Also

The post Vancouver Canucks Skill Coach Glenn Carnegie: Puck Protection appeared first on Ice Hockey Coaching Tips & Drills.



from Ice Hockey Coaching Tips & DrillsIce Hockey Coaching Tips & Drills http://ift.tt/29iJqEN
via IFTTT