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Content Moderation Becoming a Big Business with AI Enlisted to Help 

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Content moderation of social media and website content is becoming a big business with AI at the center of a challenging automation task.  

By John P. Desmond, AI Trends Editor  

Content moderation is becoming a bigger business, expecting to reach a volume of $11.8 billion by 2027, according to estimates from Transparency Market Research. 

The market is being fueled by exponential increases in user-generated content in the form of short videos, memes, GIFs, live audio and video content and news. Because some percentage of the uploaded content is fake news, or malicious or violent content, social media sites are employing armies of moderators equipped with tools employing AI and machine learning to attempt to filter out inappropriate content. 

Facebook has employed Accenture to help clean up its content, in a contract valued at $500 million annually, according to a recent account in The New York Times, based on extensive research into the history of content moderation at the social media giant.  

Julie Sweet, CEO, Accenture

The Times reported that Accenture CEO Julie Sweet ordered a review of the contract after her appointment in 2019, out of concern for what was then seen as growing ethical and legal risks, which could damage the reputation of the multinational professional services company.  

Sweet ordered the review after an Accenture worker joined a class action lawsuit to protest the working conditions of content moderators, who review hundreds of Facebook posts in a shift and have experienced depression, anxiety and paranoia as a result. The review did not result in any change; Accenture employs more than a third of the 15,000 people Facebook has hired to inspect its posts, according to the Times report.  

Facebook CEO Mark Zuckerberg has had a strategy of employing AI to help filter out the toxic posts; the thousands of content moderators are hired to remove inappropriate messages the AI does not catch.   

Cori Crider, Cofounder, Foxglove

The content moderation work and the relationship of Accenture and Facebook around it have become controversial. “You couldn’t have Facebook as we know it today without Accenture,” stated Cori Crider, a co-founder of Foxglove, a law firm that represents content moderators, to the Times. “Enablers like Accenture, for eye-watering fees, have let Facebook hold the core human problem of its business at arm’s length.” 

Facebook has hired at least 10 consulting and staffing firms, and a number of subcontractors,  to filter its posts since 2012, the Times reported. The pay rates vary, with US moderators generating $50 or more per hour for Accenture, while moderators in some US cities get starting pay of $18 per hour, the Times reported. 

Insights From an Experienced Content Moderator  

The AI catches about 90% of the inappropriate content. One supplier of content moderation systems is Appen, based in Australia, which works with its clients on machine learning and AI systems. In a recent blog post on its website, Justin Adam, a program manager overseeing several content moderation projects, offered some insights.   

The first is to update policies as real world experience dictates. “Every content moderation decision should follow the defined policy; however, this also necessitates that policy must rapidly evolve to close any gaps, gray areas, or edge cases when they appear, and particularly for sensitive topics,” Adam stated. He recommended monitoring content trends specific to markets to identify policy gaps.  

Second, be aware of the potential demographic bias of moderators. “Content moderation is most effective, reliable, and trustworthy when the pool of moderators is representative of the general population of the market being moderated,” he stated. He recommended sourcing a diverse group of moderators as appropriate.    

Third, develop a content management strategy and have expert resources to support it. “Content moderation decisions are susceptible to scrutiny in today’s political climate,” Adam stated. His firm offers services to help clients employ a team of trained policy subject matter experience, establish quality control review, and tailor quality analysis and reporting.   

Techniques for Automated Content Moderation with AI  

The most common type of content moderation is an automated approach that employs AI, natural language processing and computer vision, according to a blog post from Clarifai, a New York City-based AI company specializing in computer vision, machine learning, and the analysis of images and videos.   

AI models are built to review and filter content. “Inappropriate content can be flagged and prevented from being posted almost instantaneously,” to support the human moderator’s work, the company suggested.  

Techniques for content moderation include image moderation that uses text classification and computer vision-based visual search techniques. Object character recognition can identify text within an image and moderate that as well. The filters are looking for abusive or offensive words, objects and body parts within all types of unstructured data. Content flagged as inappropriate can be sent for manual moderation.  

Another technique, for video moderation, requires that the video be watched frame by frame and the audio screened also. For text moderation, natural language processing algorithms are used to summarize the meaning of the text or gain an understanding of the emotions in the text. Using text classification, categories can be assigned to help analyze the text or sentiment.    

Sentiment analysis identifies the tone of the text and can categorize it as anger, bullying, or sarcasm, for example, then label it as positive, negative, or neutral. The named entity recognition technique finds and extracts names, locations, and companies. Companies use it to track the number of times its brand is mentioned or the brand of a competitor, or the number of people from a city or state that are posting reviews. More advanced techniques can rely on built-in databases to make predictions about whether the text is appropriate, or is fake news or a scam.  

With little doubt, AI is needed in online content moderation for it to have a chance of being successful. “The reality is, there is simply too much UGC for human moderators to keep up with, and companies are faced with the challenge of effectively supporting them,” the Clarifai post states. 

Limitations of Automated Content Management Tools  

The limitations of automated content moderation tools include accuracy and reliability when the content is extremist or hate speech, due to nuanced variations in speech related to different groups and regions, according to a recent account from New America, a research and policy institute based in Washington, DC. Developing comprehensive datasets for these categories of content was called “challenging” and developing a tool that can be reliably applied across different groups and regions was described as “extremely difficult.”  

In addition, the definitions of what types of speech fall into inappropriate categories is not clear.   

Moreover, “Because human speech is not objective and the process of content moderation is inherently subjective, these tools are limited in that they are unable to comprehend the nuances and contextual variations present in human speech,” according to the post. 

In another example, an image recognition tool could identify an instance of nudity, such as a breast, in a piece of content. However, it is not likely that the tool could determine whether the post depicts pornography or perhaps breastfeeding, which is permitted on many platforms.  

Read the source articles and information from Transparency Market Researchin The New York Times, in blog post on the website of Appen,  a blog post on the website of Clarifai and an account from New America. 

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Source: https://www.aitrends.com/ethics-and-social-issues/content-moderation-becoming-a-big-business-with-ai-enlisted-to-help/

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Why Choosing the Right CBD Product Is Important

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When people first decide to use CBD products in order to enjoy the benefits of CBD, they are often confused over which product to purchase. There are many different products you can choose from these days, with many people buying CBD gummies, drops, capsules, and other products online. For those who are new to CBD, it is always important to do some research and find the right CBD products, and this is vital for a range of reasons.

Of course, you do need to look at a few key factors in order to help you to choose the right CBD products, as there are so many different options to choose from. You can do things such as look at online reviews from other people, research the manufacturer and retailer, and consider the suitability of the product for your specific needs and lifestyle. In this article, we will look at some of the reasons why you need to ensure you make the right choices.

The Importance of Doing This

There are many reasons why it is so important that you find the right CBD product for your needs as someone who is new to these products. Some of the reasons behind this are:

You Need to Ensure Quality and Safety

One of the reasons it is so important to look for the right CBD products is so that you can ensure quality and safety. As with any other type of product, you can get great quality CBD products from reputable sources, and you can find substandard ones from questionable sources. It is vital that you do not make the mistake of buying the latter, as this could lead to you ending up with a product that is ineffective and even unsafe. By choosing the right product and provider, you can benefit from quality, safety, and effectiveness.

It Is Important to Ensure Suitability

Another of the reasons you need to ensure you find the right CBD products is to ensure suitability, as you need to find ones that are perfectly suited to your needs. To do this, you should look at your preferences and your lifestyle so that you can then match these to the ideal products. For instance, if you use a vape device, you could look at using CBD liquids whereas if you like sweet treats, you could consider CBD edibles.

You Must Look at Affordability

One of the other reasons you need to choose the right CBD products is to ensure you find something that is affordable and fits in with your budget. The cost of CBD products can vary widely, so you need to do some research and compare different costs in order to find ones that you can afford. Also, make sure you know how much you can afford to spend before you start researching the options, as this means you will not waste time looking at products that are out of your price range.

These are some of the reasons you need to ensure you find the right CBD products.

The post Why Choosing the Right CBD Product Is Important appeared first on 1redDrop.

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Morgan Stanley’s robot Libor lawyers saved 50,000 hours of work

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Untangling trillions of dollars worth of loans and other financial contracts from Libor is a complex, expensive and time-consuming job.
So, finance giants are turning to artificial intelligence to simplify and speed up a task mandated by regulators — and spare human lawyers some serious drudgery.

Morgan Stanley figures it’s saved legal staffers 50,000 hours of work and $10 million in attorney fees by using robot Libor lawyers instead of only the human kind. Goldman Sachs Group Inc. says computer algorithms sped things up “drastically.” These banks aren’t alone in adopting AI, and the revolution likely won’t stop with the Libor transition — but the number of contracts involved in this shift provides an ideal testing ground for the machines.

The task would be grueling for paralegals, whose torture involves parsing dense clauses to sort out which govern in a post-Libor world. Does this paragraph decide how to replace the rate, or do these? They’d sweat floating-rate options, applicable periodic rates and substitute basis to sort out the new interest payment, and grapple with whether the legalese applies just to bonds or to loans and swaps as well.

Then repeat all that grunt work over millions of pages.

‘Army of Lawyers’

“We had a client that had 15 million queries and they were able to get all that answered within a quarter,” said Lewis Liu, chief executive officer at Eigen Technologies Ltd., which helped Goldman Sachs and ING Groep NV deploy Libor-analyzing software. “The alternative would have been literally an army of lawyers and paralegals over a year, or maybe two.”

This is all happening because a decade ago major banks were caught rigging Libor (full name: the London interbank offered rate). As a consequence, the benchmark is being switched off throughout the global financial system. Newly issued loans and other products cannot be tied to the rate after Dec. 31, and it will be retired for dollar-based legacy products after June 2023.

So here come the bots. But even with AI, examining old legal documents to figure out how they change when Libor is swapped out for another interest-rate benchmark is costly. Major global banks are each spending at least $100 million this year on the job, according to Ernst & Young. And humans still need to check their work and make final decisions; once banks discover which contracts need to be renegotiated, they must sit down and haggle with their counterparty.

“A person has to look at the documents and come up with a strategy,” said Anne Beaumont, a partner at law firm Friedman Kaplan Seiler & Adelman LLP, who views AI as an enhancement rather than a threat. “It probably makes a lot of paralegals and lawyers happy that they don’t have to waste time.”

The experience is reshaping broader attitudes toward large-scale administrative tasks, pushing other cumbersome jobs to AI. JPMorgan Chase & Co. has asked its Libor robots to expand their remit and grapple with other hard tasks in the company’s corporate and investment bank, a spokesman said.

Of course, a broader industry shift to more AI could mean fewer jobs for humans in certain areas.

Feeling the Pain

Libor is keeping the bots plenty busy, though. Morgan Stanley’s software digested 2.5 million references to Libor, according to Rob Avery, a managing director at the bank. The algorithm — based on neural-network models and known as Sherlock — rifles through contracts, digging out clauses that identify how a collateralized loan obligation or a mortgage-backed security will transition to replacement rates.

Graph by Bloomberg Mercury

It categorizes them so Morgan Stanley can determine how their value will change depending on the replacement rate. That helps the bank decide whether to keep or sell the asset. The software operates “in a fraction of human processing time to assess the impact of potential rate-change scenarios,” Avery said in an interview.

Goldman Sachs, meanwhile, has seen AI “accelerating the project timescales drastically,” Managing Director Donna Mansfield said in a testimonial published by Eigen.

ING used AI to decide whether more than 1.4 million pages of loan agreements needed revision, said Rick Hoekman, a leader in the bank’s wholesale banking lending team. “It was a big success” that eliminated a lot of manual work, he said. The company’s data scientists may eventually use the software to approve the credit of clients.

That’s not to say that everyone is piling in. NatWest Markets Plc was approached a couple years ago by consultancies offering AI, but turned them down. “We sensed it would involve a huge project to get it to work and would consume lots of time when we just wanted to crack on,” said Phil Lloyd, head of customer sales delivery. “We felt it might help but it wouldn’t be a nirvana.”

Plenty of other banks and asset managers have struggled with such software and are instead hiring offshore lawyers and paralegals to do the work after seeing the large amount of training and technology required.

But there’s likely no stopping AI from spreading throughout banking.

Bank of New York Mellon Corp. is working with Google Cloud to help market participants predict billions of dollars of U.S. Treasury trades that fail to settle each day, and with software company Evisort Inc. to manage contract negotiations.

“When your 12-year-old and my 12-year-old are our age, they’re not going to do finance the way we do — you can see their impatience with technology,” said Jason Granet, chief investment officer at BNY Mellon and the former head of the Libor transition at Goldman Sachs. “You’re not going to beat them, so you’ve got to join them.”

— By William Shaw with assistance from Greg Ritchie and Fergal O’Brien

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Source: https://bankautomationnews.com/allposts/business-banking/morgan-stanleys-robot-libor-lawyers-saved-50000-hours-of-work/

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7 Ways Machine Learning Can Enhance Your Marketing

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In the digital era, no marketers can survive without mastering data, analytics, and automation; the reason is a massive surge in data generation. Suppose you look at the stats about data generation. In that case, it’s more than 2.5 quintillions of data generated every day, which equals 2.5, followed by stupefying 18 zeros according to social media today.

“And by 2025, the amount of data generated each day will surge to 463 exabytes of data globally, according to the world economic forum.” 

And the fun part is the words that humans have spoken fit into only five exabytes of data. Now imagine the importance of mastering data, analytics, and automation and why it is crucial today? You probably have got your answers by now.

But to stand out in the market and beat your competitors, you need to understand the ongoing and upcoming trends. How can you analyze them seamlessly? Through machine learning and advanced automation.

And in this blog, we’re going to learn how machine learning can enhance marketing in the highly competitive world. Remember, you’re not alone in the race, but you need to think and act a step in advance to beat your competitors.

If you get what I mean, let’s dive in and explore them in detail.

7 Coolest Ways Machine Learning Can Enhance Your Marketing

Marketing success depends upon many significant factors, from proper customer research to building the brand strategy, engaging with the customers, and delighting them; it takes a lot of effort and automation.

And to solve these massive problems, ease the marketer’s work and responsibilities through accurate data analysis, machine learning has enormous roles to play. And here is the complete breakdown of how machine learning influences marketing.

Understanding Customers in 360-degree

Every day, your customers share information about themselves, but the best thing you can do is spend most of your time where your customers love to spend. When you start paying attention, you start knowing them better and better.

You get to know your customer’s last purchase, their problems, and how you and your products can help them. When you understand their pain points and are able to fulfil their needs and predict what they are likely to purchase the next time, understand the psychology behind it – you get the 360-degree view of customers.

Real-Time Analytics Gives You On-going and Up-coming Trends

Today, in the digital era, the world is changing so fast that it’s tough to comprehend data, and that’s one reason why business decisions keep changing from time to time. Because the whole thing is when you’re up to the final decision in the making, more and more data gets bombarded.

A few free tools from Google are Google Keywords, Google Analytics, and Google Search Console. When you use them, you get the exact data you need to understand the ongoing and upcoming trends and how your competitors do the same for any location and product.

According to Gartner, real-time analytics is a discipline that requires logic and mathematics to make better decisions quickly. And again, according to Gartner’s research, by 2022, most companies will incorporate real-time analytics to push their firm to the ultimate level and stay ahead of their competitors — just to improve decision making.

Smart Engine Recommendations is the Smartest Move Ever

Businesses run on data, and that’s so true, but where does the data come from? From users, right? Yes, whenever you visit a website or purchase a product, the website cookies track everything, and from there, the analyst can know what other things you would be interested in and like to buy.

And they push you to do similar things when you visit their website. Let’s suppose you purchased an iPhone at this Great Indian Festival; what Amazon will show you next, the phone charger, the case, and tempered glass, saying people who have purchased iPhones have also purchased these items.

How does Amazon do that? Amazon does that using KNN algorithms, using smart engine recommendations. That’s the most intelligent move over.

Predictive Engagement and Analytics (Just a Few Steps Away)

The first step of data analytics is to be able to understand the data, meaning when you know the data, you know customers and what they are looking for. From there, you might know what they might actually purchase.

And predictive analytics is all about that; it’s the likelihood of customers taking a particular action and companies using different software for the accurate prediction.

The best example is “The Big Billion Sale” campaign by Flipkart. If you have looked closely, you have seen the best deals, only seven left, and many different tactics to boost sales while the price fluctuates.

When you’re about to purchase, the order gets out of stock, and again it gets available. Or something you can relate to wherever the new flagship phone launches, there are limited sales every week and delivery to the first registered customers until the device is fully available.

Chatbots are the New and Ultimate Sales Persons

Nowadays, if you see every website, it has something called chatbots, and it is NLP enabled, meaning it’s a self-learning algorithm that learns by itself. With this, you don’t need to be active on a website 24 X 7.

Chatbots are your new and ultimate sales AI-Robots and can guide your visiting customers by understanding their search intent, helping you collect the leads, and later you can turn them into customers.

Personalization is the New Customer-Centric Emotion

When you look into it from different perspectives, you can always relate to customers being emotion-driven; when you present them in the right way and poke their pain points, they are most likely to take action.

But when you personalize them, addressing them with their name, they feel ‘This company is customer-centric and valued their customers a lot. And that’s what hooks them to your business.

The best way to do this is through email marketing, and we have so many tools for the same with self-learning algorithms that automate the whole process with personalization.

Voice Search is the New Generation of Search Optimization and Search Engine

In the digital era, and with many advanced features on mobile and web apps, our life has become more sophisticated. People were hardly interested in typing out their queries but voice-searched them.

That’s what the world’s largest eCommerce platform, Amazon, does brilliantly with Alexa implementation. It works on the principle of Natural Language Processing, where it captures the audience queries, looks for the best matches and related to them through the KNN algorithm, and showcases the most relevant items to the customers with matching keywords.

That way, Amazon makes the marketing and business model easy for the end-users and holds their customers for a long time.

Conclusion

When you read the whole thing, you learn how advanced and essential machine learning has become and how crucial it is to integrate into the business models.

These seven machine learning algorithms have already been game-changing. If you’re a business owner or stakeholder, you must plan to implement them in your business to see it scaling.

Also, Read How to Use Machine Learning for E-Commerce

The post 7 Ways Machine Learning Can Enhance Your Marketing appeared first on AiiotTalk – Artificial Intelligence | Robotics | Technology.

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Source: https://www.aiiottalk.com/ways-machine-learning-can-enhance-your-marketing/

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Common Pay Per Click Mistakes and How to Avoid Them

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There are plenty of articles online that talk about some recommended practices on how to build your marketing campaigns. There are also a variety of techniques for optimization and numerous concepts regarding how to structure effective online advertisements.

Since there are countless pieces of advice that are available on the internet, it is very likely for you to get lost with conflicting ideas and be confused as to what you will follow or not.

Things will be easier if you have an expert team to help you with your needs. Nevertheless, it is completely normal to commit mistakes as long as you will learn how to avoid them next time.

Not Utilizing Negative Keyword Lists Efficiently

One of your allies in the effective execution of PPC campaigns is the proper use of keywords. Aside from that, using negative keyword lists with efficiency is also a helpful way to ensure that your PPC campaigns are doing well.

“It will be a great practice if you will have a master list of negative keywords so you can apply it to all of your campaigns with particular terms or phrases that you do not want your advertisements to appear for.” 

Regularly checking the search query reports will help you avoid wasting money on search queries that you do not want your advertisements to be suggested.

Not Matching Keywords to Ad Copy

As a wise business owner, you have to exert more effort in making your advertising campaigns as relevant as possible. Since online consumers have a very short attention span, they do not have the luxury of time to deal with unnecessary and uninteresting websites.

One of the most common mistakes in PPC is when an advertiser is making one set of ads and utilizing them across multiple ad groups. It is good only for having a broad same theme but for personalization, it will make your campaigns weak.

Since you have a lot of other things to focus on for your business, it would be wise and easier to hire an ROI-driven PPC team who are experts in making relevant and successful advertising campaigns.

Focusing Too Much on an Average Position

Advertisers commit mistakes by focusing on an average position. This is because an average position of one (1) simply means that your advertisements are appearing ahead of any other paid ads in the search results.

It does not strictly mean that your ads are actually in the top spot. This is why the average position is not an indication of the location of your ads when they are suggested.

Key Takeaway

Now that you are knowledgeable regarding the common mistakes on PPC, take this information as your driving force to help yourself avoid committing these mistakes.

It is good that you know how to solve these problems when you have committed some mistakes but it is better that you know how to avoid these problems before you even commit some mistakes. You have to employ a proactive approach to ensure that you maximize the full potential of PPC as your marketing campaign.

Also Read, Impact of Artificial Intelligence and Machine Learning on SEO

The post Common Pay Per Click Mistakes and How to Avoid Them appeared first on AiiotTalk – Artificial Intelligence | Robotics | Technology.

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Source: https://www.aiiottalk.com/ppc-mistakes-and-how-to-avoid-them/

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