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 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 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.
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.
Deep personalization is important for businesses today because customer experiences can influence the completion of a sales transaction. Social listening provides brands with an opportunity to pick customer's conversations, analyze them and respond to them on social media. Social listening tools are a way of measuring customer views and carrying out audience research. The cheat sheet for AI and deep learning can allow you to check the documentation and help you understand the customer-related issues so that you can address them. Brandwatch is a solution that is highly focused on competitive intelligence and consumer.
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.
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.
With more than 74 percent of Gen Z spending their free time online – averaging around 10 hours per day – it's safe to say their online and offline worlds are becoming entwined. With increased social media usage now the norm and all of us living our lives online a little bit more, we must look for ways to mitigate risks, protect our safety and filter out communications that are causing concern. Step forward, Artificial Intelligence (AI) – advanced machine learning technology that plays an important role in modern life and is fundamental in how today's social networks function. With just one click AI tools such as chatbots, algorithms and auto-suggestions impact what you see on your screen and how often you see it, creating a customised feed that has completely changed the way we interact on these platforms. By analysing our behaviours, deep learning tools can determine habits, likes and dislike and only display material they anticipate you will enjoy.
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.
KDnuggets recently brought you the Top YouTube Channels for Data Science, employing a qualitative approach to identifying those channels of value on the platform. As the endeavour seemed to be be useful to some of our readers, we have repeated the exercise, this time bringing you the top machine learning channels that YouTube has to offer. For this iteration we changed up our metric for determining the "top" channels. We have maintained our quantitative approach, but tweaked the specifics. The results to this search were gathered on March 21, 2021, and appeared at this URL at the time.