WHY IT MATTERS: Digital Transformation
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WHY IT MATTERS: Digital Transformation
Going digital in your business? I select the best posts and highlight the reasons why it matters. Like it? Click "recommend" + register to get the digest in your inbox: http://fmcs.digital/newsletter-signup/
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 

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Graphics Design's curator insight, January 30, 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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How neural networks work - short #video introduction everyone should watch #AI #NeuralNetwork #DeepLearning

A gentle introduction to the principles behind neural networks, including back propagation.

Farid Mheir's insight:
WHY IT MATTERS: this is the basic of how artificial intelligence works. Everyone should understand this to gain little bit more understanding of the digital world around us.
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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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Google API #Vision brings image analysis to all of us - try out with your own photos & be blown away

Google API #Vision brings image analysis to all of us - try out with your own photos & be blown away | WHY IT MATTERS: Digital Transformation | Scoop.it

Google cloud vision API allows developers to analyze the content of images.

Farid Mheir's insight:

WHY IT MATTERS: just drop a photo in the page and watch what Google vision API returns. Amazing.

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Machine Learning #APIs by Example from Google I/O '17 #AI #deepLearning

Find out how you can make use of Google's machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide access to pre-trained machine learning models with a single API call: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models. In this session we'll share an overview of each API and dive into code with some live demos.

Farid Mheir's insight:

WHY IT MATTERS: Google is putting AI in everything they do and makes it really easy for developers to embed AI into their website, app, or tool. To quickly test the vision API and be amazed go here: https://cloud.google.com/vision 

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DeepMind's human-bashing AlphaGo AI is now even stronger

DeepMind's human-bashing AlphaGo AI is now even stronger | WHY IT MATTERS: Digital Transformation | Scoop.it

The AlphaGo Zero agent destroyed the original AlphaGo that beat Lee Sedol 100-0 after just three days of training.

Farid Mheir's insight:

WHY IT MATTERS: self-learning is important in AI and this is a major milestone. Follow the link for the Nature paper that explains the algorithm and advances: https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html 

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Google explains what it takes to create Production-Scale #MachineLearning Platform using #TensorFlow #TFX

Google explains what it takes to create Production-Scale #MachineLearning Platform using #TensorFlow #TFX | WHY IT MATTERS: Digital Transformation | Scoop.it

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

Farid Mheir's insight:

WHY THIS MATTERS

This article explains the different components that Google had to create in order to make their machine learning tensorFlow system production grade and ensure it could support a software engineering development process. We are entering a brave new world of new tools.

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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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Tinker With a #NeuralNetwork in Your Browser #Tensorflow

Tinker With a #NeuralNetwork in Your Browser #Tensorflow | WHY IT MATTERS: Digital Transformation | Scoop.it

Explore TensorFlow Playground demos. See how they explain the mechanism and power of neural networks, which extract hidden insights and complex patterns.

Farid Mheir's insight:

WHY IT MATTERS

Understanding how neural networks work is difficult. This simple tool helps you visualize how they work and change parameters to see their impact on the network quality.

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How #instacart use #DeepLearning to sort #grocery shopping lists & improve picker productivity

How #instacart use #DeepLearning to sort #grocery shopping lists & improve picker productivity | WHY IT MATTERS: Digital Transformation | Scoop.it

Instacart is saving minutes per delivery by sorting shopping lists using deep learning. Emojis help to define the problem and outline both a simple and a more complex deep learning architecture.

Farid Mheir's insight:

WHY IT MATTERS

Grocery shopping is often tedious, as most grocery stores have a slightly different layout. Instacart sends human shoppers to pick orders placed online on their website. Mapping every single one of their thousands of stores is not an option. Instead, they have trained a neural networks on millions of orders to predict the best sort order for their shopping lists. And in the meantime save precious minutes in the order picking process.

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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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Investment firm ARK focus on digital disruption to invest - their 7 Big Ideas for 2017 provides great insight

Investment firm ARK focus on digital disruption to invest - their 7 Big Ideas for 2017 provides great insight | WHY IT MATTERS: Digital Transformation | Scoop.it
ARK Invest aims to identify large-scale investment opportunities resulting from technological change. We believe innovation is key to growth. From a broad spectrum of disruptive innovations, Big Ideas represents our annual breakout of technologies that we believe will accelerate significantly in the months ahead.
Farid Mheir's insight:

WHY THIS IS IMPORTANT

Investment firms tend to be very traditional in their approach. This one appears to be embracing the digital transformation and making very insightful research that - from what they claim - guides their investments. Great idea. Now we'll see if their assumptions prove right and generate more returns than other investment guidance. I have a feeling it will.

 

Topics covered: 3D printing, additive manufacturing, artificial intelligence, automation, autonomous taxis, autonomous vehicles, bitcoin, blockchain, CRISPR, cryptocurrency, deep learning, dna sequencing, fintech, genomics, machine learning, mobile, robotics, shared autonomous vehicles

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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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Researchers show it is possible for #AI system to learn to fly by crashing into objects via @gnat

Researchers show it is possible for #AI system to learn to fly by crashing into objects via @gnat | WHY IT MATTERS: Digital Transformation | Scoop.it

How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see: https://youtu.be/u151hJaGKUo 

Farid Mheir's insight:

WHY THIS IS IMPORTANT

We learn as much from our mistakes as from our good moves. This research shows that AI systems can do the same.

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The world’s most valuable resource is no longer oil, but data via @TheEconomist

The world’s most valuable resource is no longer oil, but data via @TheEconomist | WHY IT MATTERS: Digital Transformation | Scoop.it

An oil refinery is an industrial cathedral, a place of power, drama and dark recesses: ornate cracking towers its gothic pinnacles, flaring gas its stained glass, the stench of hydrocarbons its heady incense. Data centres, in contrast, offer a less obvious spectacle: windowless grey buildings that boast no height or ornament, they seem to stretch to infinity.

Also read: http://www.economist.com/news/leaders/21721656-data-economy-demands-new-approach-antitrust-rules-worlds-most-valuable-resource 

Farid Mheir's insight:

WHY THIS IS IMPORTANT

For a while the saying was that "If You're Not Paying for It; You're the Product" but it looks like today "If You're Not Paying for It; You're the Data". data is in fact so important today because it is essential to program the new algorithms of deep learning. Those that have plenty of Data - Facebook, Google, Apple, UBER, Tesla - will be able to have better software and thus hopefully better products.

Thus, you are the data. Think about it next time you use a computer or mobile phone...

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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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DeepMind is building a blockchain-style system to track healthcare records to bring back #privacy

DeepMind is building a blockchain-style system to track healthcare records to bring back #privacy | WHY IT MATTERS: Digital Transformation | Scoop.it

Alphabet’s artificial intelligence outfit, DeepMind, plans to build a blockchain-style system that will carefully track how every shred of patient data is used. The company, which is rapidly expanding its health-care initiatives, has announced that it will build a tool that it calls Verifiable Data Audit during the course of this year. The idea: allow hospitals, and potentially even patients, to see exactly who is using health-care records, and for what purpose. By logging how every piece of patient data is used, the company hopes to leave behind an indelible audit trail.

Farid Mheir's insight:

WHY THIS IS IMPORTANT

For everyone concerned about privacy in the digital world, the mega digital record would provide visibility into what is currently a completely opaque process. But far more reaching is the possibility to extend this audit trail to all our personal data. 

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New report confirms #InternetOfThings #BigData #MachineLearning Will Revolutionize Retail via @Forbes 

New report confirms #InternetOfThings #BigData #MachineLearning Will Revolutionize Retail via @Forbes  | WHY IT MATTERS: Digital Transformation | Scoop.it
  • 70% of retail decision makers globally are ready to adopt the Internet of Things to improve customer experiences.
  • 73% of retailers rate managing big data as important or business-critical to their operations.
  • 78% of retailers say it is important or business-critical to integrate e-commerce and in-store experiences, so an omnichannel experience is delivered to every customer.
  • 87% of retailers will deploy mobile point-of-sale (MPOS) devices by 2021, enabling them to scan and accept credit or debit payments anywhere in the store.
  • 90% of retailers will implement buy online, pickup in store by 2021.

 

Farid Mheir's insight:

WHY THIS IS IMPORTANT

Based on a detailed report, this article provides a chockful of stats about investments in certain technologies. Although based on a survey, it confirms certain trends that are widely accepted - so there should be no surprises for readers of this blog.

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Curated by Farid Mheir
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