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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.

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

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Excerpt from:
Evolving Relationship Between Artificial Intelligence and Big Data - ReadWrite

MITs School of Engineering and Takeda Pharmaceuticals Company Limited today announced the MIT-Takeda Program to fuel the development and application of artificial intelligence (AI) capabilities to benefit human health and drug development. Centered within the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), the new program will leverage the combined expertise of both organizations, and is supported by Takedas three-year investment (with the potential for a two-year extension).

This new collaboration will provide MIT with extraordinary access to pharmaceutical infrastructure and expertise, and will help to focus work on challenges with lasting, practical impact. A new educational program offered through J-Clinic will provide Takeda with the ability to learn from and engage with some of MIT's sharpest and most curious minds, and offer insight into the advances that will help shape the health care industry of tomorrow.

We are thrilled to create this collaboration with Takeda, says Anantha Chandrakasan, dean of MITs School of Engineering. The MIT-Takeda Program will build a community dedicated to the next generation of AI and system-level breakthroughs that aim to advance healthcare around the globe.

The MIT-Takeda Program will support MIT faculty, students, researchers, and staff across the Institute who are working at the intersection of AI and human health, ensuring that they can devote their energies to expanding the limits of knowledge and imagination. The new program will coalesce disparate disciplines, merge theory and practical implementation, combine algorithm and hardware innovations, and create multidimensional collaborations between academia and industry.

We share with MIT a vision where next-generation intelligent technologies can be better developed and applied across the entire health care ecosystem, says Anne Heatherington, senior vice president and head of Data Sciences Institute (DSI) at Takeda. Together, we are creating an incredible opportunity to support research, enhance the drug development process, and build a better future for patients.

Established within J-Clinic, a nexus of AI and health care at MIT, the MIT-Takeda Program will focus on the following offerings:

James Collins will serve as faculty lead for the MIT-Takeda Program. Collins is the Termeer Professor of Medical Engineering and Science in MITs Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, J-Clinic faculty co-lead, and a member of the Harvard-MIT Health Sciences and Technology faculty. He is also a core founding faculty member of the Wyss Institute for Biologically Inspired Engineering at Harvard University and an Institute Member of the Broad Institute of MIT and Harvard.

A joint steering committee co-chaired by Anantha Chandrakasan and Anne Heatherington will oversee the MIT-Takeda Program.

See the rest here:
MIT School of Engineering and Takeda join to advance research in artificial intelligence and health - MIT News

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.

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

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.

Go here to read the rest:
Warner Bros. Will Use Artificial Intelligence to Help Decide Which Movies to Greenlight - /FILM