Examples Of Artificial Intelligence Applications

Examples Of Artificial Intelligence Applications – If there’s one thing science fiction movies have taught us, it’s that the future is a bleak and terrifying dystopia ruled by murderous sentient robots.

Fortunately, only one of those things is true—but as doomsday men like to tell us, that’s about to change.

Examples Of Artificial Intelligence Applications

Artificial intelligence and machine learning are among the most important technological developments in recent history. Few fields promise to “disrupt” life as much (to borrow a popular term) as machine learning, but many applications of machine learning techniques are becoming invisible.

Examples Of Ai In Retail And E Commerce

Want to see some practical examples of machine learning? Here are 10 companies using the power of machine learning in exciting new ways (and a glimpse into the future of machine learning).

There is nothing better than trying a new restaurant and then complaining about it online. This is one of many reasons why Yelp is so popular (and useful).

While Yelp may not seem like a tech company at first glance, Yelp uses machine learning to improve the user experience.

Since photos are almost as important to Yelp as users’ own reviews, it’s no surprise that Yelp is always trying to improve how it handles photos.

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That’s why Yelp turned to machine learning when it first implemented image classification technology a few years ago. Yelp’s machine learning algorithms help business employees compile, categorize, and tag photos more efficiently—no small feat when you’re dealing with tens of millions of photos.

Whether you’re a hardcore pinner or have never used the site before, Pinterest occupies a strange place in the social media ecosystem. Since Pinterest’s primary function is to aggregate existing content, it makes sense to invest in technology that can make this process more efficient—and that’s certainly the case with Pinterest.

In 2015, Pinterest acquired Kosei, a machine learning company specializing in commercial applications of machine learning techniques, particularly algorithms for content discovery and recommendation.

Today, machine learning touches almost every aspect of Pinterest’s business, from spam moderation and content discovery to ad monetization and reducing email newsletter subscribers. very cool

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Seems to have a strong love for the messaging app), which is one of the most exciting aspects of the world’s largest social media platform. That’s because Messenger has become an experimental test lab for chatbots.

Any developer can create and submit a chatbot for inclusion in Facebook Messenger. This means that companies that place a high value on customer service and retention can benefit from chatbots, even if they are a small startup with limited technical resources.

Of course, this is not the only application of machine learning that Facebook is interested in. Facebook uses artificial intelligence applications to filter spam and low-quality content, and the company is also working on computer vision algorithms that can “read” images. visually impaired.

Twitter has been at the center of a lot of controversy lately (especially the much-mocked decision to perfect everyone’s profile picture and change how people tag @replies), but the most controversial change we’ve made One of the things Twitter is seeing is the shift to algorithmic feeds .

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Whether you want Twitter to show you the “best tweets” first (whatever that means), or in a logical chronological order, these changes are powered by Twitter’s machine learning technology. Twitter’s artificial intelligence evaluates each tweet in real time, and “scores” it based on various metrics.

Finally, Twitter’s algorithm reveals the tweets likely to drive the most engagement. It is a personal decision; Twitter’s machine learning technology makes these decisions based on your personal preferences, resulting in an algorithmically curated feed, which sucks a bit if we’re being completely honest. (Anyone actually prefer algorithmic feeds? Tell me why in the comments, you sweet weirdos.)

It might be easier to list the areas of scientific R&D at Google (or rather, its parent company Alphabet) these days

Needless to say, Google has been very busy in recent years, having diversified into anti-aging technology, medical devices and neural networks – perhaps most excitingly for tech fans.

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The most visible advance in Google’s neural network research is the DeepMind network, the “dream machine”. It is this network that produces the psychedelic images that everyone was talking about not too long ago.

According to Google, the company works with “practically all aspects of machine learning”, which will lead to exciting developments in what Google calls “classical algorithms”, as well as other applications such as natural language processing, speech translation, search ranking and prediction systems.

For years, retailers have struggled to overcome the massive disconnect between in-store and online commerce. Despite all the talk about how online shopping is the death of traditional shopping, there are still plenty of e-commerce sites thriving.

Edgecase hopes its machine learning technology will help e-commerce merchants improve their user experience. In addition to streamlining the e-commerce experience to improve conversions, Edgecase plans to use its technology to provide a better experience for shoppers who may only have a vague idea of ​​what they’re looking for through certain analyzes of behavior and actions. Commercial intent – ​​seeks to make casual browsing more rewarding and closer to the traditional retail experience.

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Google isn’t the only search giant involved in machine learning. The Chinese search engine Baidu is also investing heavily in AI applications.

One of the most interesting (and disturbing) developments from Baidu’s R&D labs is what the company calls “Deep Voice,” a deep neural network that can generate fully synthetic human voices that are difficult to communicate with real human speech. The network can “learn” the unique subtleties of rhythm, accent, articulation and pitch to reproduce a speaker’s voice with extreme accuracy.

Far from being an idle experiment, Deep Voice 2, the latest iteration of Deep Voice technology, promises to have a lasting impact on natural language processing, the underlying technology behind voice search and speech pattern recognition systems. This could have major implications for voice search applications, as well as dozens of other potential uses, such as real-time translation and biometric security.

Anyone familiar with HubSpot probably knows that the company has long been an early adopter of new technologies, which was demonstrated again earlier this month when the company announced the acquisition of machine learning company Kemvi.

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HubSpot plans to use Kemvi’s technology in a variety of applications – specifically by integrating Kemvi’s DeepGraph machine learning and natural language processing technology into its internal content management system.

Bradford Coffey, HubSpot’s chief strategy officer, said this enables HubSpot to better identify “trigger events” — changes in business structure, management or other changes that affect day-to-day operations — so HubSpot can more effectively market leads and services. existing customers.

The inclusion of IBM may seem a little strange, as it is one of the largest and oldest legacy technology companies, but IBM has managed to transition well from its old business model to its new revenue stream. No product from IBM demonstrates this better than Watson, its famous artificial intelligence.

Championship, but it has a more impressive record than beating people on TV game shows. In recent years, Watson has been deployed in various hospitals and medical centers, demonstrating its ability to make highly accurate recommendations about the treatment of certain types of cancer.

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Watson also shows great potential in retail or hospitality, where it can be used as an assistant to help shoppers. As a result, IBM now offers Watson’s machine learning technology on a licensed basis, one of the first examples of artificial intelligence applications packaged in this way.

Salesforce is a giant in the technology world with a strong market share and resources to match in the CRM space. Predicting and scoring leads is one of the biggest challenges for even the smartest digital marketers, which is why Salesforce is investing heavily in its own machine learning technology from Einstein.

Salesforce Einstein allows businesses using Salesforce CRM software to analyze all aspects of a customer relationship—from initial contact to ongoing touchpoints—to build more detailed customer profiles and identify critical moments in the sales process. That means more comprehensive scoring of potential customers, more efficient customer service (and satisfied customers) and more opportunities.

One of the main problems with rapid technological progress is that, for whatever reason, we take these leaps for granted. Some of the above applications of machine learning were almost unthinkable a decade ago, but the pace at which scientists and researchers are advancing is astounding.

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It won’t be long before we see AI that can learn more effectively. This will lead to the development of algorithmic processing, as well as AI deployments that can identify, change and improve their own internal architecture with minimal human oversight.

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