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Edgardo Temporetti on LinkedIn: #CNNs #images #AutoML

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Understanding #CNNs are tough as it uses various filters/kernels to extracts various features from the #images, uses pooling for down scalling, uses flatten layer to convert into 1D array and uses Fully connect layer for feed forward and back-propogation.... Here's how CNN extracts the features from the images and classify the object. Check out how #AI can meet you with your forefathers and make you cry after watching them live in front of you.


Twitter Unveils Algorithmic Fairness Initiative to Offer More Transparency

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Twitter said Wednesday it was launching an initiative on "responsible machine learning" that will include reviews of algorithmic fairness on the social media platform. The California messaging service said the plan aims to offer more transparency in its artificial intelligence and tackle "the potential harmful effects of algorithmic decisions." The move comes amid heightened concerns over algorithms used by online services, which some say can promote violence or extremist content or reinforce racial or gender bias. "Responsible technological use includes studying the effects it can have over time," said a blog post by Jutta Williams and Rumman Chowdhury of Twitter's ethics and transparency team. "When Twitter uses (machine learning), it can impact hundreds of millions of tweets per day and sometimes, the way a system was designed to help could start to behave differently than was intended."


It's About Time We Broke Up Data Science

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It's highly unlikely that business owners are going to read this and begin to change their perspectives on how we define Data Science. Not because I doubt my influence or anything, but since I'm aware that the majority of my readers are at the beginning of their Data Science journey -- I really dislike the term "aspiring" -- but here is what I wish to tell you all… Stop trying to be good at everything in Data Science, and pick 1 (max 2) area's you want to specialize in and get really good at it! Let's face it... Breaking into Data Science is difficult for a number of reasons. However, I've come to a realization recently that much of the difficulty lies in the fact that the term "Data Scientist" encompasses so many different technical qualities that make it virtually impossible for one individual to meet all these criteria and stay up to date in each area -- and that's okay! I've been listening and speaking to Vin Vashishta, Chief Data Scientist and LinkedIn Top Voice 2019, and he believes that for roles to be defined better then more specialization amongst practitioners must occur.


Meet Facebook's Powerful New Image Recognition SEER A.I.

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If Facebook has an unofficial slogan, an equivalent to Google's "Don't Be Evil" or Apple's "Think Different," it is "Move Fast and Break Things." It means, at least in theory, that one should iterate to try news things and not be afraid of the possibility of failure. In 2021, however, with social media currently being blamed for a plethora of societal ills, the phrase should, perhaps, be modified to: "Move Fast and Fix Things." One of the many areas social media, not just Facebook, has been pilloried for is its spreading of certain images online. It's a challenging problem by any stretch of the imagination: Some 4,000 photo uploads are made to Facebook every single second.


The Unbearable Shallowness of "Deep AI"

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Since people invented writing, communications technology has become steadily more high-bandwidth, pervasive and persuasive, taking a commensurate toll on human attention and cognition. In that bandwidth war between machines and humans, the machines' latest weapon is a class of statistical algorithm dubbed "deep AI." This computational engine already, at a stroke, conquered both humankind's most cherished mind-game (Go) and our unconscious spending decisions (online). This month, finally, we can read how it happened, and clearly enough to do something. But I'm not just writing a book review, because the interaction of math with brains has been my career and my passion. Plus, I know the author. So, after praising the book, I append an intellectual digest, debunking the hype in favor of undisputed mathematical principles governing both machine and biological information-processing systems. That makes this article unique but long. "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World" is the first book to chronicle the rise of savant-like artificial intelligence (AI), and the last we'll ever need. Investigative journalist Cade Metz lays out the history and the math through the machines' human inventors. The title, "Genius Makers," refers both to the genius-like brilliance of the human makers of AI, as well as to the genius-like brilliance of the AI programs they create. Of all possible AIs, the particular flavor in the book is a class of data-digestion algorithms called deep learning. Metz's book is a ripping good read, paced like a page-turner prodding a reader to discover which of the many genius AI creators will outflank or outthink the others, and how. Together, in collaboration and competition, the computer scientists Metz portrays are inventing and deploying the fastest and most human-impacting revolution in technology to date, the apparently inexorable replacement of human sensation and choice by machine sensation and choice. This is the story of the people designing the bots that do so many things better than us.


DigiTech Insight Magazine

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The global spending on the artificial intelligence (AI) market is also estimated to reach $118.6 billion by 2025. A Business Wire research unveiled that the amount spent on cloud AI in the media and entertainment (M & E) industry is anticipated to reach $1,860.9 million by 2025 from $329 million in 2019. The worldwide AI market adoption rate is estimated to reach $118.6 billion by 2025 [source: www.statista.com] Here are some of the examples of how AI is changing the media landscape. The AI market for social media is estimated to reach 3,714.89 million at 28.77% CAGR by 2025.


If You Like Shares + Tech, Here are Best AI Stocks to Buy

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Artificial intelligence adoption has the ability to reshape practically every industry, including retail, healthcare services, transportation, and manufacturing. Furthermore, it will probably make immense fortunes for some in the process. Thanks to its more extensive features and impressive advantages, an ever-increasing number of organizations are investing in artificial intelligence to reinforce and meet their business objectives. Henceforth, numerous organizations are taking a look at the best AI stocks for investment purposes in 2021. The top AI stocks to buy range from chip creators, software companies and tech giants that use AI tools in numerous applications.


Non-Traditional Data Sources

Communications of the ACM

The world is facing enormous challenges, ranging from climate change to extreme poverty. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs)a were adopted by United Nations Member States in 2015 as an operational framework to address these challenges. The SDGs include No Poverty, Quality Education, Gender Equality, Peace, Justice and Strong Institutions, among others, as well as a meta goal on Partnerships for the Goals. Despite limitations,7 the SDGs form a rare global consensus of all 193 UN member states on where we should collectively be heading. Goals are meaningless without a way to track their progress. Data on the SDGs and the associated indicatorsb are often outdated or unavailable, hindering progress during the Decade of Action leading up to 2030.c


Complete tutorial on how to use Hydra in Machine Learning projects

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In an effort to increase standardization across the PyTorch ecosystem Facebook AI in a recent blog post told that they would be leveraging Facebook's open-source Hydra framework to handle configs, and also offer an integration with PyTorch Lightning. This post is about Hydra. If you are reading this post then I assume you are familiar with what are config files, why are they useful, and how they increase reproducibility. And you also know what a nightmare is argparse. In general, with config files you can pass all the hyperparameters to your model, you can define all the global constants, define dataset splits, and … without touching the core code of your project.


Future of AI & 5G Part 4: Driving Cleaner Economic Growth & Jobs

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Governments, investors and business leaders need to adopt practical solutions that can be deployed across the world at scale. The arrival of 5G along with wider adoption of AI technology into the physical world will make it possible to substantially enhance the opportunities to scale cleaner energy generation technologies, enable efficiency gains in manufacturing, our homes, retail stores, offices and transportation that will enable substantial reductions in pollution. Policies that incentivise the accelerated development and deployment of Industry 4.0 solutions will require politicians and regulators to better understand the opportunities that 5G alongside AI will enable. The OECD published a paper "What works in Innovation Policy" and observed that "Policies ignoring or resisting the industrial transition have proven to be not just futile but result in an innovative disadvantage and weak economic performance." Entering the new year will allow us to develop and deploy solutions for the 2020s that make use of the next industrial revolution with 5G and AI to enable dramatic efficiency gains across all sectors of the economy and to enhance renewable energy generation. The emergence of India, China and others as industrial economic powers is occurring at a time when we now know the damage that such pollution causes and hence there is a need to work together, collaboratively to solve a global problem. Embracing technological change and enhancing its capabilities to deliver better living standards alongside sustainable development is the best option for those who really want to make an impact on climate change at scale in the 2020s and beyond. I wish to thank Henry Derwent, former advisor to Prime Minister Margaret Thatcher and former CEO of IETA for his efforts to promote technological innovation and scaled up financing with Green Bonds.