Hollywood-based film studios are increasingly using AI as part of the decision-making process when ... [+] commissioning and producing new films. (Photo by Valery SharifulinTASS via Getty Images)

For anyone who's ever thought Hollywood's output is formulaic and tired, the movie industry may be about to get worse. Major studio Warner Bros. has signed a deal with Cinelytic, which has developed an AI-powered system that can predict the likelihood of a film's success based on such factors as actors, budget and brand.

Predictably enough, Warner Bros. will be using Cinelytic's software as part of the research process it undergoes when deciding which movies to commission. Cinelytic's platform can determine the 'value' (i.e. profitability) of an actor in any major territory and also calculate how much money a film is likely to earn in cinemas and through supplementary merchandising (e.g. DVDs).

While it obviously can't measure how good a film will be artistically, Warner Bros. will likely use it during early production phases to separate ideas likely to succeed from those that most likely aren't. This follows a run of several years during which the studio has suffered a number of high profile losses on such titles asJustice League and Pan, as well as a few instances where its output hasn't performed as well as hoped (e.g. Batman v. Superman).

And it would seem that Warner Bros. won't be the only film studio integrating AI into its decision-making processes. In fact, AI has already received a modest amount of use by studios up until now, so Warner Bros. entry is likely to open the floodgates even further.

For example, 20th Century Fox has been using a system called Merlin for several years now. In contrast to Cinelytic's platform, Merlin uses AI and machine learning (as well as big data) to match particular films to particular genres and audiences. It does this by using a computer vision system to generate a frame-by-frame analysis of movie trailers. After labelling objects and events within each trailer, it then takes the data it has gathered for one film and compares it against data for other films. It might find, say, that a given trailer most resembles films x, y and z, which were popular with female teenagers.

By comparing datasets, Merlin helps 20th Century Fox identify the ideal demographic(s) for any given film. It also helps the studio decide how it should be advertising and classifying that film, insofar as Merlin links a films trailer to genres.

Aside from Warner Bros. and 20th Century Fox, it's likely that other film studios and production companies have already turned to AI, without being open about it. For instance, Belgium-based ScriptBook uses AI to analyze a film's script and arrive at an estimation of the revenues that film is likely to earn. Not only that, but ScriptBook's platform can also provide likability scores for a film's characters, profiles of its target audience, and even its likely IMDB rating.

According to the company's CEO, Nadira Azermai, ScriptBook is already at a stage where the financial forecast it provides for each film has an 86% accuracy rate. In other words, it's already working with clients in the film industry, even if it hasn't gone public with the names of any studio or company.

ScriptBook was founded in 2015, but it's probable that other companies will emerge in the coming years, since research into the use of AI-based film prediction is still ongoing. In August, researchers from Sungkyunkwan University in South Korea revealed that they had used deep learning to train a bot to forecast the likelihood of a film's success, based this time on a textual summary of its plot. They trained this bot on 42,306 film plot summaries, in the end finding that it was best at predicting which films would be unsuccessful.

That the bot was better at weeding out 'stinkers' rather than classic films is encouraging. Because while the influx of AI into the film industry might imply that Hollywood could become even more self-plagiarizing in the future, it's possible that studios might restrict the use of artificial intelligence specifically to making sure they don't end up commissioning flops. This would potentially leave space for human decision-making and creativity to get involved in choosing between ideas more likely to succeed commercially.

And to play devil's advocate, it's possible that the use of AI might make Hollywood's output less homogenous. To take a simplified and hypothetical example, the massive success of a superhero film could conceivably create a situation where human producers end up commissioning a series of other superhero movies, even though each entry in this series goes on to enjoy diminishing returns. By contrast, an AI-based platform trained on masses of regularly updated data might be able to determine that, rather than making the next Batman or Superman film, a different kind of movie now has a chance of greater success.

That is, an AI platform might force a studio to change its artistic or stylistic direction sooner than it would have done otherwise. If this is the case, then moviegoers and cinephiles probably don't have anything to fear from AI's invasion of cinema.

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Hollywood Is Using Artificial Intelligence To Pick Its Next Blockbuster - Forbes

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Artificial intelligence and machine learning promises to revolutionize healthcare.

Proponents say it will help diagnose ailments more quickly and more accurately, as well as help monitor peoples health and take over a swath of doctors paperwork so they can see more patients.

At least, thats the promise.

Theres been an exponential increase in approvals from the Food and Drug Administration (FDA) for these type of health products as well as projections that artificial intelligence (AI) will become an $8 billion industry by 2022.

However, many experts are urging to pump the brakes on the AI craze.

[AI] has the potential to democratize healthcare in ways we can only dream of by allowing equal care for all. However, it is still in its infancy and it needs to mature, Jos Morey, MD, a physician, AI expert, and former associate chief health officer for IBM Watson, told Healthline.

Consumers should be wary of rushing to a new facility simply because they may be providing a new AI tool, especially if it is for diagnostics, he said. There are really just a handful of physicians across the world that are practicing that understand the strengths and benefits of what is currently available.

But what exactly is artificial intelligence in medical context?

It starts with machine learning, which are algorithms that enable a computer program to learn by incorporating increasing large and dynamic amounts of data, according to Wired magazine.

The terms machine learning and AI are often used interchangeably.

To understand machine learning, imagine a given set of data say a set of X-rays that do or do not show a broken bone and having a program try to guess which ones show breaks.

The program will likely get most of the diagnoses wrong at first, but then you give it the correct answers and the machine learns from its mistakes and starts to improve its accuracy.

Rinse and repeat this process hundreds or thousands (or millions) of times and, theoretically, the machine will be able to accurately model, select, or predict for a given goal.

So its easy to see how in healthcare a field that deals with massive amounts of patient data machine learning could be a powerful tool.

One of the key areas where AI is showing promise is in diagnostic analysis, where the AI system will collect and analyze data sets on symptoms to diagnose the potential issue and offer treatment solutions, John Bailey, director of sales for the healthcare technology company Chetu Inc., told Healthline.

This type of functionality can further assist doctors in determining the illness or condition and allow for better, more responsive care, he said. Since AIs key benefit is in pattern detection, it can also be leveraged in identifying, and assist in containing, illness outbreaks and antibiotic resistance.

That all sounds great. So whats the hitch?

The problem lies in lack of reproducibility in real-world settings, Morey said. If you dont test on large robust datasets that are being just one facility or one machine, then you potentially develop bias into the algorithm that will ultimately only work in one very specific setting but wont be compatible for large scale roll-out.

He added, The lack of reproducibility is something that affects a lot of science but AI in healthcare in particular.

For instance, a study in the journal Science found that even when AI is tested in a clinical setting, its often only tested in a single hospital and risks failing when moved to another clinic.

Then theres the issue of the data itself.

Machine learning is only as good as the data sets the machines are working with, said Ray Walsh, a digital privacy expert at ProPrivacy.

A lack of diversity in the datasets used to train up medical AI could lead to algorithms unfairly discriminating against under-represented demographics, Walsh told Healthline.

This can create AI that is prejudiced against certain people, he continued. As a result, AI could lead to prejudice against particular demographics based on things like high body mass index (BMI), race, ethnicity, or gender.

Meanwhile, the FDA has fast-tracked approval of AI-driven products, from approving just 1 in 2014 to 23 in 2018.

Many of these products havent been subjected to clinical trials since they utilize the FDAs 510(k) approval path, which allows companies to market products without clinical trials as long as they are at least as safe and effective, that is, substantially equivalent, to a legally marketed device.

This process has made many in the AI health industry happy. This includes Elad Walach the co-founder and chief executive officer of Aidoc, a startup focused on eliminating bottlenecks in medical image diagnosis.

The FDA 510(k) process has been very effective, Walach told Healthline. The key steps include clinical trials applicable to the product and a robust submission process with various types of documentation addressing the key aspects of the claim and potential risks.

The challenge the FDA is facing is dealing with the increasing pace of innovation coming from AI vendors, he added. Having said that, in the past year they progressed significantly on this topic and created new processes to deal with the increase in AI submissions.

But not everyone is convinced.

The FDA has a deeply flawed approval process for existing types of medical devices and the introduction of additional technological complexity further exposes those regulatory inadequacies. In some instances, it might also raise the level of risk, said David Pring-Mill, a consultant to tech startups and opinion columnist at TechHQ.

New AI products have a dynamic relationship with data. To borrow a medical term, they arent quarantined. The idea is that they are always learning, but perhaps its worth challenging the assumption that a change in outputs always represents an improved product, he said.

The fundamental problem, Pring-Mill told Healthline, is that the 510(k) pathway allows medical device manufacturers to leapfrog ahead without really proving the merits of their products.

One way or another, machine learning and AI integration into the medical field is here to stay.

Therefore, the implementation will be key.

Even if AI takes on the data processing role, physicians may get no relief. Well be swamped with input from these systems, queried incessantly for additional input to rule in or out possible diagnoses, and presented with varying degrees of pertinent information, Christopher Maiona, MD, SFHM, the chief medical officer at PatientKeeper Inc., which specializes in optimizing electronic health records, told Healthline.

Amidst such a barrage, the systems user interface will be critical in determining how information is prioritized and presented so as to make it clinically meaningful and practical to the physician, he added.

And AIs success in medicine both now and in the future may ultimately still rely on the experience and intuition of human beings.

A computer program cannot detect the subtle nuances that comes with years of caring for patients as a human, David Gregg, MD, chief medical officer for StayWell, a healthcare innovation company, told Healthline.

Providers can detect certain cues, connect information and tone and inflection when interacting with patients that allow them to create a relationship and provide more personalized care, he said. AI simply delivers a response to data, but cannot address the emotional aspects or react to the unknown.

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Don't Put Your Health in the Hands of Artificial Intelligence Just Yet - Healthline

The advent of large data sets from many sources (big data), machine learning, and artificial intelligence (AI) are poised to revolutionize asthma care on both the investigative and clinical levels, according to an article published in the Journal of Allergy and Clinical Immunology.

According to the researchers, a patient with asthma endures approximately 2190 hours of experiencing and treating or not treating their asthma symptoms. During 15-minute clinic visits, only a short amount of time is spent understanding and treating what is a complex disease, and only a fraction of the necessary data is captured in the electronic health record.

Our patients and the pace of data growth are compelling us to incorporate insights from Big Data to inform care, the researchers posit. Predictive analytics, using machine learning and artificial intelligence has revolutionized many industries, including the healthcare industry.

When used effectively, big data, in conjunction with electronic health record data, can transform the patients healthcare experience. This is especially important as healthcare continues to embrace both e-health and telehealth practices. The data resulting from these thoughtful digital health innovations can result in personalized asthma management, improve timeliness of care, and capture objective measures of treatment response.

According to the researchers, the use of machine learning algorithms and AI to predict asthma exacerbations and patterns of healthcare utilization are within both technical and clinical reach. The ability to predict who is likely to experience an asthma attack, as well as when that attack may occur, will ultimately optimize healthcare resources and personalize patient management.

The use of longitudinal birth cohort studies and multicenter collaborations like the Severe Asthma Research Program have given clinical investigators a broader understanding of the pathophysiology, natural history, phenotypes, seasonality, genetics, epigenetics, and biomarkers of the disease. Machine learning and data-driven methods have utilized this data, often in the form of large datasets, to cluster patients into genetic, molecular, and immune phenotypes. These clusters have led to work in the genomics and pharmacogenomics fields that should ultimately lead to high-fidelity exacerbation predictions and the advent of true precision medicine.

This work, the researchers noted, if translated into clinical practice can potentially link genetic traits to phenotypes that can for example predict rapid response, or non-response to medications like albuterol and steroids, or identify an individuals risk for cortisol suppression.

As with any innovation, though, challenges abound. One in particular is the siloed nature of the clinical and scientific insights about asthma that have come to light in recent years. Although data are now being generated and interpreted across various domains, researchers must still contend with a lack of data standards and disease definitions, data interoperability and sharing difficulties, and concerns about data quality and fidelity.

Machine learning and AI present their own challenges; namely, those who utilize these technologies must consider the issues of fairness, bias, privacy, and medical bioethics. Legal accountability and medical responsibility issues must also be considered as algorithms are adopted into routine practice.

We must, as clinicians and researchers, constructively transform the concern and lack of understanding many clinicians have about digital health, [machine learning], and [artificial intelligence] into educated and critical engagement, the researchers concluded. Our job is to use [machine learning and artificial intelligence] tools to understand and predict how asthma affects patients and help us make decisions at the patient and population levels to treat it better.

Reference

Messinger AI, Luo G, Deterding RR. The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma [published online December 25, 2019]. J Allergy Clin Immunol. doi: 10.1016/j.jaci.2019.12.898

Link:
Machine Learning and Artificial Intelligence Are Poised to Revolutionize Asthma Care - Pulmonology Advisor

Update: An in-the-know source has reached out to correct some of the information in this story. Turns out that Cinelytic is only being used by Warner Bros. International as an additive tool to help select release dates, and not, as many have suggested, in any sort of major creative capacity. Our original story continues below.

The frequent tug-of-war between art and commerce means that there have long been Hollywood studio executives whose jobs include looking at analytics and trying to assess whether greenlighting a certain film will be financially beneficial to their shareholders. Now Warner Bros. is inviting artificial intelligence into the equation, because the studio has signed a deal with a company called Cinelytic to use itsproject management system and leverage the systems comprehensive data and predictive analytics to guide decision-making at the greenlight stage. Is this situation as bad as it sounds?

The Hollywood Reporter has the story, saying that Toby Emmerichs film division of Warner Bros. is going to utilize this system, which is supposed to help find patterns in the numbers that might be missed by human eyes. The platform is capable of assess[ing] the value of a star in any territory and how much a film is expected to make in theaters and on other ancillary streams, and its supposedly going to reduce the amount of time executives spend on low-value, repetitive tasks and instead give them better dollar-figure parameters for packaging, marketing and distribution decisions including release dates.

According to Cinelytic head Tobias Queisser, who invented this system four years ago, The system can calculate in seconds what used to take days to assess by a human when it comes to general film package evaluation or a stars worth. But as Thor and X-Men: First Class screenwriter Zack Stentz wrote on Twitter,the entire Marvel Cinematic Universe was built on [Jon] Favreau convincing a bunch of executives that a middle-aged actor not long out of rehab and prison, who had described himself as box office poison even during his earlier 1990s heyday, would be the perfect Iron Manthese analytics that purport to tell you which actor is worth how much in these territories are useless compared to the casting intuitions that end up creating magic onscreen.

Still, I can sympathize with this level of desperation. Its easy to see why studios would be eager to minimize risk and find a way to compete against Disney, which absolutely crushed all competition last year and became the first studio to cross the $10 billion mark in a single year (the House of Mouse pulled in$11.12 billion total worldwide). And its not like all of a sudden every movie will be chosen by an algorithm Queisser says that an AI cannot make any creative decisions and explains its real intended use in this setting. What it is good at is crunching numbers and breaking down huge datasets and showing patterns that would not be visible to humans, he said. But for creative decision-making, you still need experience and gut instinct.

Emmerich has been in this business for a long time, and anyone who expects him to just cede all creative control over to Skynet is misreading this situation. Im betting the studio will look at these AI-crunched numbers to help figure out better release dates every once in a while, and leave the real creative decisions to the people who are getting paid millions of dollars a year to make them.

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Warner Bros. Will Use Artificial Intelligence to Help Decide Which Movies to Greenlight - /FILM

Artificial Intelligence Could Help Scientists Predict Where And When Toxic Algae Will Bloom

Climate-driven change in the Gulf of Maine is raising new threats that "red tides" will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay.

In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

"I wholesale to restaurants around town, and if there's a lot of mackerel or scallops, I'll ship into Massachusetts," he says.

But business grinds to a halt, he says, when blooms of toxic algae suddenly emerge in the bay causing the dreaded red tide.

Toxins can build in filter feeders to levels that would cause "Paralytic Shellfish Poisoning" in human consumers. State regulators shut down shellfish harvests long before danger grows acute. But when a red tide swept into Casco Bay last summer, Bangs Island's harvest was shut down for a full 11 weeks.

So when the restaurants can't get Bangs Island they're like 'Why can't we get Bangs Island?' It was really bad this summer. And nobody was happy."

As Parr notes, businesses of any kind hate unpredictability. And being able to forecast the onset or departure of a red tide has been a challenge although that's changing with the help of a type of artificial intelligence called machine learning.

"We're coming up with forecasts on a weekly basis for each site. For me that's really exciting. That's what machine learning is bringing to the table," says Izzi Grasso, a recent Southern Maine Community College student who is now seeking a mathematics degree at Clarkson University.

Last summer Grasso interned at the Bigelow Laboratory for Ocean Sciences in East Boothbay. That's where she helped to lead a successful project to use cutting-edge "neural network" technology that is modeled on the human brain to better predict toxic algal blooms in the Gulf of Maine.

"Really high accuracy. Right around 95 percent or higher, depending on the way you split it up," she says.

Here's how the project worked: the researchers accessed a massive amount of data on toxic algal blooms from the state Department of Marine Resources. The data sets detailed the emergence and retreat of varied toxins in shellfish samples from up and down the coast over a three-year period.

The researchers trained the neural network to learn from those thousands of data points. Then it created its own algorithms to describe the complex phenomena that can lead up to a red tide.

Then we tested how it would actually predict on unknown data, says Grasso.

Grasso says they fed in data from early 2017 which the network had never seen and asked it to forecast when and where the toxins would emerge.

"I wasn't surprised that it worked, but I was surprised how well it worked, the level of accuracy and the resolution on specific sites and specific weeks," says Nick Record, Bigelow's big data specialist.

Record says that the network's accuracy, particularly in the week before a bloom emerges, could be a game-changer for the shellfish industry and its regulators.

Once it's ready, that is.

"Basically it works so well that I need to break it as many ways as I can before I really trust it."

Still, the work has already been published in a peer-reviewed journal, and it is getting attention from the scientific community. Don Anderson is a senior scientist at the Woods Hole Oceanographic Institution who is working to expand the scope of data-gathering efforts in the Gulf.

"The world is changing with respect to the threat of algal blooms in the Gulf of Maine," he says. "We used to worry about only one toxic species and human poisoning syndrome. Now we have at least three."

Anderson notes, though, that machine-learning networks are only as good as the data that is fed into them. The Bigelow network, for instance, might not be able to account for singular oceanographic events that are short and sudden or that haven't been captured in previous data-sets such as a surge of toxic cells that his instruments detected off Cutler last summer.

"With an instrument moored in the water there, and we in fact got that information, called up the state of Maine and said you've got to be careful, there's a lot of cells moving down there, and they actually had a meeting, they implemented a provisional closure just on the basis of that information, which was ultimately confirmed with toxicity once they measured it," says Anderson.

Anderson says that novel modeling techniques such as Bigelow's, coupled with an expanded number of high-tech monitoring stations, like Woods Hole is pioneering in the Gulf, could make forecasting toxic blooms as simple as checking the weather report.

"That situational awareness is what everyone's striving to produce in the field of monitoring and management of these toxic algal blooms, and it's going to take a variety of tools, and this type of artificial intelligence is a valuable part of that arsenal." Back at the Portland wharf, shellfish dealer George Parr says the research sounds pretty promising.

"Forewarned is fore-armed, Parr says. If they can figure out how to neutralize the red tide, that'd be even better."

Bigelow scientists and former intern Izzi Grasso are working now to look "under the hood" of the neural network, to figure out how, exactly, it arrives at its conclusions. They say that could provide clues about how not only to predict toxic algal blooms, but even how to prevent them.

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Artificial Intelligence Could Help Scientists Predict Where And When Toxic Algae Will Bloom - mainepublic.org

The future where people can delegate mundane tasks to a machine is not far from happening. From starting the laundry down to cooking dinner after a long day is about to be over. Artificial Intelligence has really helped shape our internet today.

After all, we can already communicate with virtual assistants like Apples Siri and Amazons Alexa for small things around the house, like calling Uber or ordering a pizza. Things that we only see on sci-fi movies may be closer than you think. With the internet making things possible, which is unthinkable decades ago, you will wonder what it is capable of under the influence of AI.

It is no wonder why this fast-technological advancement will get you into thinking; How AI is Helping Shape Up the Internet Today?

Artificial Intelligence or AI is the technology that transforms a computer to think, operate, and act human-like. This process is possible by taking in data and information from its surroundings. After collecting this data, it will then decide on a response based on what it had learned and sensed.

Without a doubt, AI is becoming an integral part of our society. Now with the technology behind it evolving faster than ever, the internet could transform sooner than any of us could have anticipated.

People can utilize Artificial Intelligence to do impressive tasks and jobs faster than any human can. That is why people use AI with almost everything to speed up the manual process. In the present day, you can find AI in all sorts of industries. This development can only prove that the importance of AI in our everyday life is equivalent to efficiency and accuracy.

AI-powered software and equipment can provide fast and accurate X-ray readings and laboratory results. Before lab results could take hours before yielding results. But now, with smart equipment available in hospitals, health care is better than ever.

Not just health institutions, as you can also have access to personal health care assistants. These AI-powered apps can serve as a useful partner in reminding you to take your medicines on time and follow a fit lifestyle. It can also advise you on your everyday diet and coach you in exercise routines.

Virtual shopping that offers shoppers their very own personalized recommendations is made possible through AI. It can also present options with the consumer for a better retail experience. For store owners, stock management is more streamlined than ever.

Better business models and more accurate data is vital to earning more significant profits. With AI automating backend processes it will not just eliminate human errors but will also boost productivity.

AI is equipped to analyze a factorys IoT (Internet of Things). Thanks to the data that streams from all the interconnected equipment, it can make a detailed analysis of the factorys operation. It can predict machine life and its productivity to reduce costs.

Artificial Intelligence improves the speed, accuracy, and effectiveness of everyday human tasks. Now, financial institutions such as banks have started to utilize AI techniques to identify highly suspicious transactions that can result in fraud. AI can adapt fast and calculate a more accurate credit scoring than any manual process can. It can also automate intensive data management tasks. With transactions fully automated, it will lessen human error and the possibility of security leaks.

The internet is a part of a life that is now widely affected by the rise of AI technology. Almost every household has internet, and everybody owns a smartphone. We are always plugged-in, and AI has come in to revolutionize the internet as we know it.

With machines becoming better and more efficient at learning and processing data, it is inching towards human beings faster than ever. However, dont worry as they wont replace workers anytime soon, but the tasks getting delegated to them is growing faster every day.

AI algorithms can now build websites from scratch, and the most popular are Wix ADI, Firedrop, and Grid. The AI assistant can determine the type of site you are making and offers suggestions. Unlike before, where you have to hire a website developer and designer, you can now cut costs and opt for an AI designer.

Virtual customer service agents are a revolutionary approach to how customers are getting served. Automated customer experience is no longer a thing in the future. But chatbots are not limited to the food sector, as social platforms, and other sites use them as well. These intelligent service agents learn from customer interactions to answer questions.

A study suggests that in the year 2020, machines will take over 85% of customer interactions. This research means that humans powering these channels may soon find themselves replaced by AI.

Voice-powered AI assistants like Alexa, Siri, Google Assistant and has become a part of most homes in the past few years. So, it is not impossible for online stores to adapt to this technology in the future. Imagine talking to online retail assistant online, how convenient would that be?

With e-commerce on the rise, a fully automated transaction for goods and services online is not unlikely. Having AI recognize voice commands to run stores will not just cut unnecessary costs but can also increase work efficiency compared to manual labor.

AI is helping businesses to have a better understanding of day to day operations. Not to mention how good it is in predicting risks that are attached to the information traveling via the internet. It can also help with deploying a rapid response during unforeseen accidents such as financial losses and cyber threats.

AI-powered applications are being utilized in detecting fraudulent transactions at bank ATMs and driver insurance that is based on the clients driving patterns. They can also identify potential hazards workers to prevent accidents. It is also used for law enforcement surveillance data that can help in recognizing developing crime scenes ahead of time.

As a writer, one of first, you need to do before you can start crafting a piece is research. You need to compile and consolidate data from all sources so you will only have the best information; this process can be time-consuming, not to mention labor-intensive. Fortunately, with how fast the advancement of Artificial Intelligence is, we might be able to delegate this task to them in the future. When I say in the future, it is not in the far one, but in an immediate one.

After all, salesforce is already equipped with an algorithm that can summarize longer texts. Understanding the market is much easier compared to crunching numbers before. More and more people are reaping the benefits of having data delivered to them more quickly and much more precise than manual research with AI processing information faster.

Though it is true that spell check is not a new tech anymore, AI is learning to do much more than that. AI is becoming more better at comprehending the context and purpose behind written words. Hence, it can soon learn to correct style and grammar more efficiently and accurately. Grammarly and Atomic Reach are already into this, so who knows how this tech will revolutionize writing?

AI and content creation are made possible and currently being improved thanks to algorithms that are continuously getting updated. With Googles religious updates in recent years, online content has shifted from the one ruled by keyword stuffing to real digestible content directed at human readers. But of course, the SEO elements are still mixed in.

As a matter of fact, AI journalism has been around for a while as machines can now automatically generate content like business reports, hotel descriptions, stock insights, and sport event recaps.

However, is it possible for them to start writing novels anytime soon? The reasonable assumption will be a no. Creative tasks still need complex thinking and rationality that is still impossible for AI. But for less original content and data-driven writings, then it is more than possible for AI to rise to the task.

AI is revolutionizing the internet as we know it. With tons of automation available, not to mention the rise of virtual assistants, we can say that the future is upon us. The constant evolution of technology that is furthered fueled by humanitys desire for progress has propelled the rise of AI.

Making our lives better and performing tasks more efficiently is the main reason for the inception of AI. They are designed to aid humans in leading to a better quality of life. That is why it is not surprising if AIs growth will leap bounds in the upcoming years. Because after all, if there is one thing humans are consistent with, it is progress.

Hayk Saakian is an entrepreneur who has a keen interest in everything tech related. He can usually be found writing informative articles at hayksaakian.com, in which he shares valuable insights in today's modern trends.

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How Will Artificial Intelligence Shape Up the Future of the Internet - ReadWrite