The potentials of Artificial Intelligence have extended its reach across several platforms. Even social media platforms like Facebook and LinkedIn are using AI to make sense of the pool of human data. Around 3 billion people use social media across various countries creating data in volumes one can only imagine. And this is where AI is introduced to harness the true worth of voluminous data.

In this era of social media, whether it is about reaching out to new clients or nurturing existing business or personal relationships, one can easily cling onto a site like Facebook, Instagram, or LinkedIn. Through such platforms, people can become an integral part of far off society and community.

In terms of business, effective social media management helps enhance a brand’s strength and increase social media conversations. It is an efficient way of engaging millions of social media users. AI helps analyze voluminous data to identify trending topics, hashtags, and patterns to understand user behavior.

Such innovative algorithms can keep a check on millions of unstructured user comments or data to understand crisis situations or trends to provide a personalized experience. With effective segmentation, technology can help organizations provide content based on online activity and demographics. Many social networking sites have acquired AI businesses to move to the next level.

In the contemporary market, a variety of companies that offer online marketing services are exploring new ways to use social media with AI as well. They have started using AI to identify new demographics to target based on previous conversions. These AI tools rely on predictive analytics algorithms, which can extrapolate information on all known users in a given social network.

AI can also recognize images and help identify consumer behavior patterns. AI-empowered software recognition tools can help gather actionable insights to understand the shift in user patterns through millions of images posted on social media.

With the sheer amount of images being posted every minute, it would be very difficult for a person to notice an opportunity like but with AI the task can be carried out efficiently.

At Facebook, where there are more than two billion users, it is using artificial intelligence to flag posts automatically that show expressions of suicidal thoughts for human moderators to review. Moreover, the social media platform can enhance its program to allow human moderators to review 20 times more suicidal posts, and Facebook is sending its suicide prevention materials to twice as many people.

Over, Microsoft owned professional networking site LinkedIn, AI’s subset machine learning is being used for almost all its products. With almost seven million open candidates, LinkedIn offers the biggest pool of candidates for a recruiter to contact and connect with the highest response rate. LinkedIn uses algorithms with the capability to predict users who are possibly the best fit for the role. Using AI algorithms, LinkedIn can highlight candidates who are most likely to respond or seeking new opportunities.

Furthermore, Twitter recently launched an update to its service using AI that crops an image using face detection or creating a thumbnail from an entire image. With neural networks, Twitter can decipher which part of the image user would be interested in.

As we can see through social media platforms AI is gaining prominence. While AI can never replace human interaction, it can assist them in making things more productive and efficient.

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Cameras—especially phone cameras—are terrible at taking pictures in the dark. The tiny image sensors in most modern cameras can only absorb a small amount of light, which often results in dark, grainy images.

To try to solve this problem without inventing a new image sensor, researchers at Intel and the University of Illinois Urbana-Champlain taught an artificial intelligence algorithm how to take the data from darker images and reconstruct them so that they’re brighter and clearer, according to research published this month and to be presented in June at an industry conference.

To train the algorithm, the researchers showed it two version of more than 5,000 images taken in low-light scenarios: One set that was taken to be purposefully too dark, and one set that was taken with a longer exposure time, meaning the sensor is given more time to collect light and better expose the image. (To do that, you need to hold the camera extremely still for a few seconds or more, which is why it’s not practical in most picture-taking scenarios.)

The Intel and UIUC team claims the algorithm can now amplify low-light images the equivalent of up to 300 times the exposure, without the same noise and discoloration that programs like Photoshop might introduce or having to take two separate images.

While the team did build a custom algorithm to do the task, the most innovative aspect of the work is the dataset they created. In the paper, the researchers write that no dataset with low-light images at different exposures publicly exists. Chen Chen, a co-author on the paper who worked on the project as a part of an internship at Intel, says at first, they tried to get around having to take thousands of original images by printing out pictures of objects and then taking pictures of the printouts in low-light and well-lit scenarios. But in the end, that synthetic data didn’t produce good results, Chen says.

So, Chen spent two months collecting images of outdoor low-light scenarios, and a week collecting images in low-light indoor scenarios. He took photos with two kinds of consumer cameras that use different image processing methods to ensure the algorithm wouldn’t just learn to only work on one camera manufacturer’s technology.

But even though the data was generated using high-resolution digital cameras, the team found that the algorithm also improved underexposed images from an iPhone 6S—a sign that the low light capabilities of our smartphones might be only a software update away. To make that process even faster, the team has posted code and the dataset online, which can be found on Github.

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All the marketing behind artificial intelligence today reminds me of the push for the cloud (never worry about infrastructure maintenance again!) and big data (kiss concerns about structuring your data goodbye!) just a couple years ago.

Sure, we all benefited from moving to the cloud and getting smarter about working with structured vs. non-structured data. But implementation was never as seamless as the tech sales team said it was going to be. It also took more than just dumping data into the cloud to realize any substantial business benefits. AI is no different than all the technology hype that came before it.

Will AI fundamentally change the way we work and live? Yes. Can your company point an algorithm at your data and just push a button and have magic come out the other end without doing any legwork? Probably not.

I want to be clear: AI has made tremendous strides over the past few years thanks to the ever-increasing amount of data and ever-decreasing cost of computational power. But it’s far from the effortless panacea being pushed for most companies by the collective technology marketing machine, and I worry sometimes that we’re all being seduced into believing we can get all this benefit without any work or critical thinking on where and how to use it.

Fundamentals of decision making and technology solution design still apply to AI as much as any other capability from the past couple years. The thoughtfulness of humans, the quality of the starting data and the state of the infrastructure are just as critical, if not more important than the AI algorithm or automation process being considered.

For example, according to Deloitte’s 2017 cognitive technologies survey (download required), the most common application for AI across businesses is process automation. This includes robotics and software processes such as transferring data automatically from email and call center systems to central data warehouses. In terms of human thoughtfulness, however, AI can’t really help you decide if a process should exist in the first place. Sometimes, the best solution for a legacy practice is to remove it completely rather than trying to automate it with technology. After all, is anyone really looking at that weekly report your team has been pulling together because of that one-off fire drill request from a senior client a year ago?

Speaking of automation, AI also can’t guarantee that legacy COBOL (ugh) reporting system you’re stuck with from that last acquisition can actually export a data feed to your data warehouse. According to Deloitte’s study, 47% of respondents felt the top challenge with emerging AI technologies was the difficulty in integration with existing processes and systems.

Another common use case for AI we’ve come across among our clients is for predictive modeling. An example would be identifying which prospects are most likely to respond to which advertisements. Running prediction algorithms is the easy part. Getting clean data that can be used with an algorithm is the difficult task.

There’s a saying among data scientists: “Ninety-five percent of the work is just getting all the data ready for the 5% of the time you get to do the modeling.” Sure, algorithms can help fill in the blanks for partially missing data, but these algorithms can’t do much when only a handful of people on your sales team are consistently logging the important details of their 9:30 a.m. offsite prospect meetings into the CRM system.

I’m sure this all makes me sound like a modern Luddite and AI skeptic. Quite the contrary. We’ve used AI to help our clients achieve some incredible results for their marketing campaigns. Last year, we built an automated, AI-powered targeting model that helped one of our Fortune 500 clients improve their cost per acquisition by 30% vs. non-targets. But this wasn’t as simple as pushing a button. We needed data scientists to figure out which models were optimal for the use case. We needed a group of business analysts to help understand how to control for sales cycles unique to the client and an entire tech team to build the infrastructure to let the algorithms run autonomously.

Like other companies, we’ve implemented countless AI-powered processes and have been exposed to a number of our clients’ AI efforts over the past year. The proverbial juice is usually worth the squeeze, but it takes a good deal of critical analysis to figure out how to get the most out of AI, and it’s rarely as easy as just pushing a button to integrate it into the business. I look forward to the day when integrating AI is truly seamless and all our major problems are solved. But until then, there’s a lot of work to be done.

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The machines have not yet gained dominance over the world but is in the process of doing so. They have seeped into our lives influencing the way we eat, sleep, work and live. From vocal personal assistants like Siri and Cortana to more complex technologies, AI has been in severe application today.

But the technology is still under progress. Many companies still do not consider it a necessity. But what AI actually is? This may be a matter of. Some may refer it as any piece of software has AI it has an algorithm that responds on the basis of pre-defined inputs or user behaviors. However some may consider it a machine that can learn on its own. A neutral network which can make connections and reach conclusions without depending on previous behaviors. A true AI can get better and smarter allowing itself to enhance its knowledge and capabilities.

Many companies like Apple and Google have been bringing revolutionary changes to the AI. Many individuals like Indrasen Poola have been working on its betterment. But still many of us are unaware about its application all over the world. Here are some of the uses listed:

Virtual personal assistants

Siri and Cortana are two popular examples of virtual assistants. They help users to find useful information when you ask for it using your voice. The assistant relies on the information you give and finds relevant replies to it. It even interacts with other commands for better responses. It continually learns about the user and serves the result tailored to your preference.

Video games

This is one of the instances where AI is used massively. In fact it has been used for a very long time. But the effectiveness has exponentially increased over the past years where gaming characters learn your behaviors and respond according to that. First person shooter games like Call of Duty has made significant use of AI.

Smart cars

Self-driving cars are slowly becoming a possibility now. Tesla’s auto-pilot is one example that have been in the news lately. Google have reportedly come with an algorithm where self-driving cars can learn to drive cars on their own like humans getting better as they get experience.

The basic idea is that car will be able to look ahead of it and make decisions based on what it visualizes. This has taken AI to a whole new next level.

Online customer support

Many online retailers now give customers an opportunity to chat with a representative who actually is not a human but a computer. These chat support bots are automated responders who can extract knowledge from the website and present it to the customers when they request about it.

The real obstacle hereby is that chatbots need to understand the human jargon which is really difficult. The way in which customers communicate and computers talk is radically different. Translation is not an easy process. But the chat bots are getting better in time by advancing in the NLP.

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As the AI services ecosystem expands, vendors are offering automation tools designed to make life easier for embattled data scientists through toolkits used to build machine and deep learning models, and then move those trained models to production.

That’s the premise behind the upgraded version of machine learning “lab” from toolkit vendor deepsense.ai called Neptune. The AI startup based in Palo Alto, Calif., positions its toolkit as enabling data scientists to build machine-learning models in their preferred framework, including Keras, the open source neural network library, and TensorFlow, the Google-developed machine-learning framework.

The platform also is being promoted as a way to reduce complex technology stack and infrastructure management tasks, freeing data scientists to focus instead on model deployment and maintenance. “Users can share and compare their results in leaderboards and choose the best models for further development and deployment,” noted company CTO Piotr Niedźwiedź.

The Neptune toolkit also addresses the increasing complexity associated with creating machine learning models. That complexity has seeded an emerging market for what’s being called “automated machine learning.”

“Designing and tuning a machine learning model is not for the faint of heart,” noted data science analyst James Kobielus. “If you’re a working data scientist, you must sort your way through a bewildering range of parameters in an attempt to get it right. For starters, you must select a feature model that contains the right set of independent variables to drive your intended machine-learning outcome.”

The upgraded version of Neptune includes an interactive prototyping feature with Jupyter Notebooks, the open source web application that allows the creation and sharing of documents containing live code, equations and visualizations. The platform also allows users to track and reproduce machine-learning experiments hosted in public clouds.

Neptune also supports collaboration and eliminates the need to analyze logs by including a user interface designed to make it easier for data scientists to monitor model training, deepsense.ai said.

Earlier this year, the startup participated in a competition sponsored by the U.K.’s Defence Science and Technology Laboratory designed to identify and categorize objects in satellite imagery. The laboratory provided 1-km by 1-km satellite images and the task was to detect different objects such as buildings, vehicles, trees or roads scattered across the landscape. Users had to identify an AI algorithm or develop software that would help evaluate large and complex data sets.

The deepsens.ai data science team finished fourth in the competition, ahead of 400 other international teams.

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