WHY IT MATTERS: Digital Transformation
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WHY IT MATTERS: Digital Transformation
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Curated by Farid Mheir
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Google’s #AI builds its own AI child and it’s better than anything humans have made - for recognizing images

Google’s #AI builds its own AI child and it’s better than anything humans have made - for recognizing images | WHY IT MATTERS: Digital Transformation | Scoop.it

The child AI is really only capable of a specific task – image recognition. Using its AutoML AI, Google’s AI-building AI created its child AI using a technique called reinforcement learning. This works just like machine learning, except it’s entirely automated where AutoML acts as the neural network for its task-driven AI child.

Farid Mheir's insight:

WHY IT MATTERS: AI recent progress was made possible mostly by supervised learning: humans providing AI algorithms with the right kind of insight to understand the world. Reinforcement learning provides an enhanced level of improvement as it lets machine teach machine. This is one example. Expect more in the coming years and brace for exponential growth of this technology.

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JPMorgan's massive guide to machine learning jobs in finance: summary of a 280 page report.

JPMorgan's massive guide to machine learning jobs in finance: summary of a 280 page report. | WHY IT MATTERS: Digital Transformation | Scoop.it

In May, J.P. Morgan’s quantitative investing and derivatives strategy team, issued the most comprehensive report ever on big data and machine learning in financial services.
Titled, ‘Big Data and AI Strategies’ and subheaded, ‘Machine Learning and Alternative Data Approach to Investing’, the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don’t they’ll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

Farid Mheir's insight:

WHY IT MATTERS: AI will disrupt industries that had been left mostly alone by the digital Internet revolution: Finance, Accounting, Law, Medecine, etc. They are all industries that rely on visual or text analysis but require very simple processing to perform the most routine back office tasks. This report presents the impact in very detailed fashion and this summary is useful because it applies to many industries, if you adjust the terminology. It highlights one key element: learn to program and to analyze data because these are the jobs of the (near) future. For those crazy enough to read the 280 pages, here is the link: http://valuesimplex.com/articles/JPM.pdf 

Graphics Design's curator insight, January 30, 2018 7:51 AM

In the event that somebody says "please" and you hear "cheddar" recall that (record it). On the off chance that somebody says "entryway" and you hear "more" focus on that, don't disregard it or shut it out or accuse the speaker, simply perceive that it happened. Try not to contend with anyone or focus on their "you require a portable Hearing Aids". Watch your days concerning sound, what is the number on the TV volume control when you watch it without anyone else's input - what is the number when another person in the house is controlling

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3 video #mustSee if you are into understanding #AI but not a scientist: How neural networks work, how convolutional networks work, Deep learning demystified

3 video #mustSee if you are into understanding #AI but not a scientist: How neural networks work, how convolutional networks work, Deep learning demystified | WHY IT MATTERS: Digital Transformation | Scoop.it

Far from being incomprehensible, the principles behind neural networks are surprisingly simple. Here's a gentle walk through how to use deep learning to categorize images from a very simple camera.

Farid Mheir's insight:

WHY IT MATTERS: you don't need to be a mechanic to drive a car but it helps if you understand the principles of a car - engine, drive shaft, carburator, etc. - to help you when you buy or get serviced. Well I believe the same should hold true for technology in your computer or your cell phone. So no need to listen to these videos to use Apple Siri or Google assistant, but it will certainly make some of it feel less "magical".

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.@Stanford release the #AI index for 2017 full of data and analysis on the state of AI today #mustRead

.@Stanford release the #AI index for 2017 full of data and analysis on the state of AI today #mustRead | WHY IT MATTERS: Digital Transformation | Scoop.it

The AI Index is an open, not-for-profit project to track activity and progress in AI. It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists and others to rapidly develop intuitions about the complex field of AI.
AI is the new electricity, and is transforming multiple industries. The AI Index will help current generations track and navigate this societal transformations. It will also help future generations look back and understand the AI's rise.

Farid Mheir's insight:

WHY IT MATTERS: AI evolves rapidly and this report presents the state of the world (mostly the US at this time) as it relates to AI. Must read.

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#weekendReading this 8-part blog post called "Machine Learning is Fun!" is a very good overview of what #AI technologies are all about

#weekendReading this 8-part blog post called "Machine Learning is Fun!" is a very good overview of what #AI technologies are all about | WHY IT MATTERS: Digital Transformation | Scoop.it

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

Farid Mheir's insight:

WHY IT MATTERS: this series of article present the fundamental technologies behind AI in a very simple and easy to understand way.

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Puppy or Muffin? How #deepLearning enabled computer vision & made The Business of Artificial Intelligence possible via @hbr

Puppy or Muffin? How #deepLearning enabled computer vision & made The Business of Artificial Intelligence possible via @hbr | WHY IT MATTERS: Digital Transformation | Scoop.it

What AI can — and cannot — do for your organization.

Farid Mheir's insight:

WHY IT MATTERS: advances in computer vision due to deep learning algorithms has opened a world of possibilities for companies. Read this article and accompanying HBR posts. Great series.

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New Theory helps explain how Deep Learning works @QuantaMagazine @Wired

New Theory helps explain how Deep Learning works @QuantaMagazine @Wired | WHY IT MATTERS: Digital Transformation | Scoop.it

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

Farid Mheir's insight:

WHY IT MATTERS

This article explains how deep learning neural networks learn to generalize and find patterns in their inputs by initially finding patterns then compressing to forget irrelevant elements and generalize what they've "learned". Well illustrated and relatively simple to understand.

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Now anyone can explore machine learning, no coding required

Now anyone can explore machine learning, no coding required | WHY IT MATTERS: Digital Transformation | Scoop.it
Teachable Machine, an experiment that makes it easier for anyone to explore machine learning. Teach a machine using your camera – live in the browser, no coding required.
Farid Mheir's insight:

WHY THIS MATTERS

You have to try this to better understand how AI works. Moreover, the fact that the network runs on your browser is, by itself, quite the testament to the power of deep learning and machine learning to transform every application and device we own. Now go play!

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McKinsey's State Of Machine Learning And AI in 2017

McKinsey's State Of Machine Learning And AI in 2017 | WHY IT MATTERS: Digital Transformation | Scoop.it
Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. AI investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.
Farid Mheir's insight:

WHY IT MATTERS

This article from McKinsey provides an overview of the investments, improvements and priorities in AI and machine learning as of mid 2017.

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TensorFlow & Tensor Processing Units mean @Google will become the leader in #MachineLearning and #AI?

TensorFlow & Tensor Processing Units mean @Google will become the leader in #MachineLearning and #AI? | WHY IT MATTERS: Digital Transformation | Scoop.it

In just its first year, TensorFlow has helped researchers, engineers, artists, students, and many others make progress with everything from language translation to early detection of skin cancer and preventing blindness in diabetics. We're excited to see people using TensorFlow in over 6000 open-source repositories online.

Second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference. We call them Cloud TPUs, and they will initially be available via Google Compute Engine.

https://blog.google/topics/google-cloud/google-cloud-offer-tpus-machine-learning/ 

Farid Mheir's insight:

WHY THIS IS IMPORTANT

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are impossible to neglect today and recent announcements from Google on TensorFlow and Tensor Processing Units mean that more companies and developers will be able to explore how AI and ML can be leveraged in their applications and industries. Expect acceleration in this field.

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A Peek at Trends in Machine Learning shows an explosion in papers recently

A Peek at Trends in Machine Learning shows an explosion in papers recently | WHY IT MATTERS: Digital Transformation | Scoop.it

Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years?

Farid Mheir's insight:

WHY THIS IS IMPORTANT

More research = more discoveries so we can expect even more practical uses of machine intelligence in the coming years...

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#fakeNews was nothing: AI algorithms are creating a frighteningly realistic fake future in #video

#fakeNews was nothing: AI algorithms are creating a frighteningly realistic fake future in #video | WHY IT MATTERS: Digital Transformation | Scoop.it
Thanks to machine learning, it’s becoming easy to generate realistic video, and to impersonate someone.
Farid Mheir's insight:

WHY THIS IS IMPORTANT

If you thought fake news was bad, well an era of fake audio and news clips is about to dawn on us. We need regulations to govern this otherwise there will be consequences in the political, business and personal fronts for sure...

 

Also see this other blog post on the subject, as it related to fake actors in movies, a much less troublesome use of the technology: http://sco.lt/8Eglqj

 

This field is moving fast if you compare this to a blog post of January 2016: http://sco.lt/85op5l 

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AudioSet: A Dataset of 2M audio clips for Audio Event Research

AudioSet: A Dataset of 2M audio clips for Audio Event Research | WHY IT MATTERS: Digital Transformation | Scoop.it

Systems able to recognize sounds familiar to human listeners have a wide range of applications, from adding sound effect information to automatic video captions, to potentially allowing you to search videos for specific audio events. Building Deep Learning systems to do this relies heavily on both a large quantity of computing (often from highly parallel GPUs), and also – and perhaps more importantly – on significant amounts of accurately-labeled training data. However, research in environmental sound recognition is limited by currently available public datasets.
In order to address this, we recently released AudioSet, a collection of over 2 million ten-second YouTube excerpts labeled with a vocabulary of 527 sound event categories, with at least 100 examples for each category.

Farid Mheir's insight:

WHY THIS IS IMPORTANT

Training data is crucial for machine intelligence. This kind of stuff will help researchers improve their AI solutions. What is most important is the data has been crowdsourced from youTube videos. It used to be we were the product, not it seems we are the data...

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The First Person to Hack the iPhone Built a Self-Driving Car. In His Garage.

The First Person to Hack the iPhone Built a Self-Driving Car. In His Garage. | WHY IT MATTERS: Digital Transformation | Scoop.it
A few days before Thanksgiving, George Hotz, a 26-year-old hacker, invites me to his house in San Francisco to check out a project he’s been working on. He says it’s a self-driving car that he had built in about a month. The claim seems absurd. But when I turn up that morning, in his garage there’s a white 2016 Acura ILX outfitted with a laser-based radar (lidar) system on the roof and a camera mounted near the rearview mirror. A tangle of electronics is attached to a wooden board where the glove compartment used to be, a joystick protrudes where you’d usually find a gearshift, and a 21.5-inch screen is attached to the center of the dash. “Tesla only has a 17-inch screen,” Hotz says.
Farid Mheir's insight:

The story of a very bright kid that unleashes the power of deep learning and machine intelligence to tech his car how to drive. And it seems to be working. Someone to look at very carefully.

 

But also a story about the power that these new tools have on our world and should be leveraged more aggressively by corporations.

 

Look at the video here: https://www.youtube.com/watch?v=KTrgRYa2wbI

Numalis's curator insight, March 15, 2016 3:32 AM
Technology is really accelerating. What an expert can do in one month could take years before. This is as exciting as it is uncontrollable. The only remaining question is to know if simple and fast prototyping could be a good canvas to build upon for safe and efficient designs.
Emeric Bailleul's curator insight, March 15, 2016 5:05 AM

The story of a very bright kid that unleashes the power of deep learning and machine intelligence to tech his car how to drive. And it seems to be working. Someone to look at very carefully.

 

But also a story about the power that these new tools have on our world and should be leveraged more aggressively by corporations.

 

Look at the video here: https://www.youtube.com/watch?v=KTrgRYa2wbI

Murphy Ben International 's curator insight, March 16, 2016 4:20 PM

The story of a very bright kid that unleashes the power of deep learning and machine intelligence to tech his car how to drive. And it seems to be working. Someone to look at very carefully.

 

But also a story about the power that these new tools have on our world and should be leveraged more aggressively by corporations.

 

Look at the video here: https://www.youtube.com/watch?v=KTrgRYa2wbI

Curated by Farid Mheir
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