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Slack’s newest bot is taking communication to a new level

(TECH NEWS) If you’re a fan of Slack, you likely also love the variety of integrations. The newest “bot” on the block will rock your socks for customer support.

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Integration excellence

If you’re a fan of Slack, chances are one of the reasons you love it is because of the wide range of integrations available. Slack integrates well with Google Calendar, Dropbox, Trello, and all kinds of other useful tools you need on a daily basis.

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One of the best things I have seen lately is the ability to add customer support (almost like an integrated ZenDesk).

Threaded Customer Support

For businesses that receive a lower volume of emails, but still need customer support, a new integration called MailClark may be your new best friend.

This application works in a similar fashion to Slack’s own “slackbot,” but instead of being focused on tasks, MailClark is focused on sending and receiving email, directly to and from your Slack channels.

As you know, Slack’s official integrations only allow you to receive emails and tweets; whereas MailClark will work both ways, allowing you to send and reply to emails and tweets directly in Slack.

Awesome (with a caveat)

However, greatest benefits to using MailClark, can also be a drawback; it all depends on your preference and the quantity/frequency of email you receive (in my opinion). Allow me to explain.

When you add MailClark, Slack adds a new user called @mailclark. It will also provide you with a proxy email address with your team name so you can use it for your inbound question channel.

For example, if your Slack team name is abchelpteam, and your Slack channel is named abcsupportnow, you might get an intermediate email address like this to handle your inbound questions: abcsupportnow@abchelpteam.mailclark.ai. If this sounds confusing, other MailClark users have set up a Gmail account that is a bit more logical-sounding, that can be forwarded to MailClark.

One little conundrum

Here’s the benefit/drawback conundrum I mentioned: emails sent to that address will show up on the abcsupportnow channel, along with the complete email address of the sender and a “reply” button.

If you click on the integral “reply” button, MailClark will create an entirely new Slack channel for the conversation, including the original message/complaint/query.

This channel will be named, but randomized for the individual and will contain any further communication. This is very similar to the majority of support ticket services/platforms for technical issues where threaded responses stay right where you need them.

Learning curve

However, if you’re dealing with a larger volume of emails/queries/tweets, this could be a problem because the caveat of the MailClark system is that you must use “@mailclark send” at the end of every reply, and the tag must be on its own line at the end of the message.

It sounds a bit tedious, but like anything else, once you get the hang of it, it becomes second nature.Click To Tweet

In my opinion, this is a great tool to get effective, consistent, threaded customer support (once you get the hang of it), especially if you’re just starting out and your business is looking to simplify support, but you are not quite ready to jump on-board with the “big” support systems, like ZenDesk.

#SlackBotNoSlacking

Jennifer Walpole is a Senior Staff Writer at The American Genius and holds a Master's degree in English from the University of Oklahoma. She is a science fiction fanatic and enjoys writing way more than she should. She dreams of being a screenwriter and seeing her work on the big screen in Hollywood one day.

Tech News

Career consultants help job seekers beat AI robot interviews

(TECH NEWS) With the growth of artificial intelligence conducting the job screening, consultants in South Korea have come up with an innovative response.

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job screening by robot

When it comes to resume screenings, women and people of color are regularly passed over, even if they have the exact same resume as a man. In order to give everyone a fair try, we need a system that’s less biased. With the cool, calculating depictions of artificial intelligence in modern media, it’s tempting to say that AI could help us solve our resume screening woes. After all, nothing says unbiased like a machine…right?

Wrong.

I mean, if you need an example of what can go wrong with AI, look no further than Microsoft’s Tay, which went from making banal conversation to spouting racist and misogynistic nonsense in less than 24 hours. Not exactly the ideal.

Sure, Tay was learning from Twitter, which is a hotbed of cruelty and conflict, but the thing is, professional software isn’t always much better. Google’s software has been caught offering biased translations (assuming, for example, if you wrote “engineer” you were referring to a man) and Amazon has been called out for using job screening software that was biased against women.

And that’s just part of what could go wrong with AI scanning your resume. After all, even if gender and race are accounted for (which, again, all bets are off), you’d better bet there are other things – like specific phrases – that these machines are on the lookout for.

So, how do you stand out when it’s a machine, not a human, judging your work? Consultants in South Korea have a solution: teach people how to work around the bots. This includes anything from resume work to learning what facial expressions are ideal for filmed interviews.

It helps that many companies use the same software to do screening. Instead of trying to prepare to impress a wide variety of humans, if someone knew the right tricks for handling an AI system, they could potentially put in much less work. For example, maybe one human interviewer likes big smiles, while the other is put off by them. The AI system, on the other hand, won’t waver from company to company.

Granted, this solution isn’t foolproof either. Not every business uses the same program to scan applicants, for instance. Plus, this tech is still in its relative infancy – a program could easily be in flux as requirements are tweaked. Who knows, maybe someday we’ll actually have application software that can more accurately serve as a judge of applicant quality.

In the meantime, there’s always AI interview classes.

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Tech News

Google chrome: The anti-cookie monster in 2022

(TECH NEWS) If you are tired of third party cookies trying to grab every bit of data about you, google has heard and responded with their new updates.

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3rd party cookies

Google has announced the end of third-party tracking cookies on its Chrome browser within the next two years in an effort to grant users better means of security and privacy. With third-party cookies having been relied upon by advertising and social media networks, this move will undoubtedly have ramifications on the digital ad sector.

Google’s announcement was made in a blog post by Chrome engineering director, Justin Schuh. This follows Google’s Privacy Sandbox launch back in August, an initiative meant to brainstorm ideas concerning behavioral advertising online without using third-party cookies.

Chrome is currently the most popular browser, comprising of 64% of the global browser market. Additionally, Google has staked out its role as the world’s largest online ad company with countless partners and intermediaries. This change and any others made by Google will affect this army of partnerships.

This comes in the wake of rising popularity for anti-tracking features on web browsers across the board. Safari and Firefox have both launched updates (Intelligent Tracking Prevention for Safari and the Enhanced Tracking Prevention for Firefox) with Microsoft having recently released the new Edge browser which automatically utilizes tracking prevention. These changes have rocked share prices for ad tech companies since last year.

The two-year grace period before Chrome goes cookie-less has given the ad and media industries time to absorb the shock and develop plans of action. The transition has soften the blow, demonstrating Google’s willingness to keep positive working relations with ad partnerships. Although users can look forward to better privacy protection and choice over how their data is used, Google has made it clear it’s trying to keep balance in the web ecosystems which will likely mean compromises for everyone involved.

Chrome’s SameSite cookie update will launch in February, requiring publishers and ad tech vendors to label third-party cookies that can be used elsewhere on the web.

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Tech News

Computer vision helps AI create a recipe from just a photo

(TECH NEWS) It’s so hard to find the right recipe for that beautiful meal you saw on tv or online. Well computer vision helps AI recreate it from a picture!

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computer vision recreates recipe

Ever seen at a photo of a delicious looking meal on Instagram and wondered how the heck to make that? Now there’s an AI for that, kind of.

Facebook’s AI research lab has been developing a system that can analyze a photo of food and then create a recipe. So, is Facebook trying to take on all the food bloggers of the world now too?

Well, not exactly, the AI is part of an ongoing effort to teach AI how to see and then understand the visual world. Food is just a fun and challenging training exercise. They have been referring to it as “inverse cooking.”

According to Facebook, “The “inverse cooking” system uses computer vision, technology that extracts information from digital images and videos to give computers a high level of understanding of the visual world,”

The concept of computer vision isn’t new. Computer vision is the guiding force behind mobile apps that can identify something just by snapping a picture. If you’ve ever taken a photo of your credit card on an app instead of typing out all the numbers, then you’ve seen computer vision in action.

Facebook researchers insist that this is no ordinary computer vision because their system uses two networks to arrive at the solution, therefore increasing accuracy. According to Facebook research scientist Michal Drozdzal, the system works by dividing the problem into two parts. A neutral network works to identify ingredients that are visible in the image, while the second network pulls a recipe from a kind of database.

These two networks have been the key to researcher’s success with more complicated dishes where you can’t necessarily see every ingredient. Of course, the tech team hasn’t stepped foot in the kitchen yet, so the jury is still out.

This sounds neat and all, but why should you care if the computer is learning how to cook?

Research projects like this one carry AI technology a long way. As the AI gets smarter and expands its limits, researchers are able to conceptualize new ways to put the technology to use in our everyday lives. For now, AI like this is saving you the trouble of typing out your entire credit card number, but someday it could analyze images on a much grander scale.

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