# data[].attributes.attachment_link.readable_text
How I created an entire short movie using ML models only (and you can do it as well).

Collage by Merzmensch.

It 's been a blast. I remember watching " Sunspring" - again and again. Fascinated and mesmerized by the absurd dialogues, I was trying to comprehend what's going on in this short movie. But the meaning used to slip away. Because it was written by AI.

Benjamin was the name of the author. Behind this name was a recurrent neural network LSTM hidden, developed by Ross Goodwin, an AI researcher and poet. The movie was directed by BAFTA-nominated British filmmaker Oscar Sharp and featured a Thomas Middleditch among others.

Even if the p lot consisted of absurdist and seemingly random phrases, our human brain was trying to get it. Cogito, ergo sum - I think therefore I am.

Ross' must-read essays about AI and creativity were highly inspiring for me. If you haven't a chance, do read this.

With his experiments (and later with his AI-generated book " 1 the Road"), Ross proved what is possible to achieve with Artificial Intelligence. And also this realization was a crucial one: AI is neither just a tool nor an entire replacement for a writer. You need a symbiosis to create art together with AI.

We've seen how many creative uses of Machine Learning are possible.

With the 3D Ken Burns Effect, we can make animated dream visions, converting a photo to a spatial camera flight.

Teaser to my series "dreAIms"

With GPT-2 by OpenAI, we can write entire stories.

But what if we combine all the approaches to one single artwork? And so I did it and generated a short movie completely with AI.

...But before you watch it: do you have an idea, what do you need to create a movie?

You surely can waive some elements for arthouse quality, but usually, you need the following ingredients to make a movie (even if a short one):

the Plot

the Camera / Visuals

the Actors

the Music

You also need an original idea and (in most cases) still unused media assets.

So how can we apply the Machine Learning approach to fulfill all of these points?

The Plot

As previously mentioned, GPT-2 is a powerful NLP model (pre-trained on 40 GB text with 1.5 billion parameters) to generate writings. Its main flaw - and the major advantage - is the surreal character of created contents. They usually don't make sense (at least for knowledge transfer needs - in a literary context they can be a decent object of interpretation). And: every time a new unique text is generated.

Let's see which ways GPT-3 will take in the future ( OpenAI published this new giant model with 175 billion parameters, which at the moment cannot be fine-tuned due to its excessive dimensions).

For my needs I used a GPT-2 Colab Notebook with Java Interface by Manuel Romero:

There are several different notebooks, allowing you to fine-tune GPT-2 or train on your texts, but I've chosen this particular Colab Notebook for its user-friendly and robust structure, and support of the entire 1.5 billion parameter model.

For my movie I entered the following prompt:

Empty room. A man enters the room. He looks out of the window and says: "It still hasn't begun". The woman enters. She has a white dress on and a book in her hand. The woman: "I am waiting as well." The man: "For how long?" The woman: "For a while."

GPT-2 provided me with various texts, but the one I've selected was an amazing piece of an absurdist theater play, somewhere between Beckett, Mrozek and David Lynch:

Empty room. A man enters the room. He looks out of the window and says: "It still hasn't begun". The woman enters. She has a white dress on and a book in her hand. The woman: "I am waiting as well." The man: "For how long?" The woman: "For a while." The man: "Ah, you know, I'm a very patient man." The woman: "I know, of course." The man: "I'm going to have to be less patient, then." The woman starts to laugh; the laughter has an ominous edge that only she can feel. "In my defence," she says, "I never actually told you what it was I was going to tell you." The man, suddenly, remembers: "That's right." The woman: "So you are lying behind me." The man is surprised by the abruptness of the accusation. He does not, however, feel any anger; as he has noticed in previous encounters, it is the woman who is most convincing when she's saying the lies. The man: "But it was not what you expected." The woman: "Oh...no." She is trying to sound convincing. He wants to ask her about her mother. The woman: "Do you think she'll be willing to lie?" The man turns down the opportunity. The woman: "Maybe..." "Why?" "Maybe she's still grieving." She is smiling when she says this, and the man doesn't think she'll be the first to smile when the truth comes out. He gives up. "No." The woman: "It's not over." The man has not got to see her family again. He had hoped to return to his work. He has nothing else to do. He wants to be a man.

It convinced me also because of ambiguous communication levels. It's as if there is a huge story hidden behind all those sparse phrases (human brain does wonders during interpretation, indeed).

So I had a screenplay.

Visuals

I am a fan of StyleGAN2, but especially in its implementation in ArtBreeder (at least in the section "Portraits").

You can generate new faces, you can make transitions or even animations ( read more about using Artbreeder here). So I created a bunch of images for faces ( see here the assets) of the Man, the Woman, and some Room pictures.

It was a tricky thing to align the face modification to the story part (e.g. in the "laughter moments" etc.), but after various trials and errors, it worked for me.

Here are just some assets used for the visuals:

The Actors

The faces were already made, but something was still missing: voices.

Voice generation is one of probably the oldest Machine Learning approaches. My favorite wasMelNet - a Model with unbelievable quality. Just listen to the samples (trained on professional speakers or also celebrities datasets). Alas, MelNet wasn't available as a working repository or Colab Notebook.

My second choice was Amazon Polly. Being a part of Amazon AWS AI/ML-Services Polly provides a broad quantity of voices in various languages. The downside is: most of them have rather a charisma of an anchorman and are not always suitable for fictional content.

I used one voice for my AI-generated video "Predictions":

Still, the non-emotional voice is not really convincing, if used in some dialogues.

Then I discovered Replica Studios.

Screenshot from my dashboard at https://replicastudios.com/

They provide at the moment a small collection, but the voices have astonishing quality. Some of them can be rather used for anchorman need, but another bear already theatrical power within.

I've chosen these three voices - Deckard for Narrator, Carlos for Man, and Audrey for Woman.

You also can experiment with the emotional features of various voices, you even can train them your voice, but these three were perfect for my needs.

The use of Replica Studios is pretty easy. You sort the phrases in chronological order and apply the appropriate voices to the characters:

Screenshot from my dashboard at https://replicastudios.com/

It's possible to export the voices to mp3-files for your project.

Music

AI-generated music achieved new levels of quality this year after OpenAI released JukeBox - a library of 7k songs and music pieces, being generated with pre-trained models on various musicians and songwriters ( read also here).

Many of them are pretty glitchy (like this nightmarish "Mozart" p iece). But many of them are beautiful. And especially combined with AI-generated visuals they evoke strange feelings within.

I am using them for my series "Breath ZeroX":

For our video something cinematic should pass, so I looked up the music trained on Hans Zimmer (and changed the speed a little bit):

And... action!

The rest was rather hard work of a cutter - for my video I used Premiere Pro and amounts of coffee. Probably the most tricky thing was stretching the videos in time and synchronize them with the plot (original face transitions by ArtBreeder were between 8-30 sec long).

Advice: I used " Time Interpolation: Optical Flow ". It creates new frames between the existent and makes the footage fluent. Sometimes it generates glitches - but they are always welcome if it's about digitally generated films!

And now: here it is.

I wonder which new audio-visual treasures will come with new ML-approaches. Let's stay tuned!

# data[].attributes.attachment_link.description
It 's been a blast. I remember watching " Sunspring" - again and again. Fascinated and mesmerized by the absurd dialogues, I was trying to comprehend what's going on in this short movie. But the meaning used to slip away. Because it was written by AI. Benjamin was the name of the author.

# data[].attributes.body
Quick Rundown: 1st Main Body Paragraph

The involvement of software's that use AI such as GTP-2 / 3 to write scripts

The Usage of a software named StyleGan2 to create artificial imagery and use them in a film

An example of AI in the involvement of creating films and the results that have been produced

# data[].attributes.body
Computer generated imagery has aided production, but it has advanced to the point where AI can create films themselves. A software named GTP-2 by OpenAI has a large open-source software that has 1.5 billion parameters to generate texts. The one flaw it has is the surreal creation of characteristics. A second variation would be its successor GTP-3, which has 175 billion parameters, which is still in its infancy and is still to be tested thoroughly and so wont be mentioned thoroughly in this assignment

# data[].attributes.attachment_link.readable_text
Turns out after they've been trained on enormous datasets, algorithms can not only tell what a picture is such as knowing a cat is a cat but can also generate absolutely original images. The artificial intelligence that makes this possible has matured significantly in recent years and in some applications is very proficient, but in other ways, still has a long way to go.
AI Recognizes What an Image Is
It's taken two decades for computer scientist s to train and develop machines that can "see" the world around them-another example of an everyday skill humans take for granted yet one that is quite challenging to train a machine to do.

Facial recognition technology, used both in retail and security, is one way AI and its ability to "see" the world is starting to be commonplace. Retailers use facial recognition technology to better market and sell to their target audience. In one particularly intriguing use case, some Chinese office complexes have vending machines that identify shoppers through facial recognition technology and track the items they take from the machine to ultimately bill the shoppers' accounts. Even anonymous data about shoppers collected from cameras such as age, gender, and body language can help retailers improve their marketing efforts and provide a better customer experience.

Retail giant Walmart deployed a fleet of stock-monitoring robots that can identify through computer vision when a shelf needs more product and alert humans to ensure it happens. Target is also testing out similar technology.

Seeing AI, an iPhone app uses artificial intelligence to help blind and partially-sighted people navigate their environment by using computer vision to identify and speak its observations of the scenes and objects in its field of vision. From Face ID to unlock the iPhone X to cameras on the street used to identify criminals as well as the algorithms that allow social media platforms to identify who is in photos, AI image recognition is everywhere.
AI Image Generation
Now that artificial intelligence is able to understand, for the most part, what an image represents and can tell the difference between a stop sign and a dog, a dog from an elephant and more, the next frontier to perfect is AI image generation.

One of the former barriers to having AI generate believable images was the need for enormous datasets for training. With today's significant computing power and the incredible amount of data we now collect, AI has breached that barrier.

How does AI know how to execute when tasked with creating an image? It uses Generative Adversarial Network or Nets (GAN), invented in 2014 by Ian Goodfellow, who was a Google researcher. It uses two neural networks; one that creates an image and another one that judges, based on real-life examples of the target image, how close the image is to the real thing. After scoring the image for accuracy, it sends that info back to the original AI system. That system learns from the feedback and returns an altered image for the next round of scoring. This process continues until the scoring machine determines the AI-generated image matches the "control" image.

Today, AI can create realistic images and videos of cats and hamburgers, representations of your words, faces that aren't of real people and even original works of art.

In fact, there's even a market for AI's original artwork-Google hosted an art show to benefit charity and to showcase work created by its software DeepDream. It sold an AI-generated piece of art that was a collaboration between human and machine for $8,000 plus others. The human creator (or artist) that was part of this collaboration, Memo Akten explained that Google made a better "paintbrush" as a tool, but the human artist was still critical to creating art that would command an $8K price tag. Another AI-generated piece of art, Portrait of Edmond de Belamy was auctioned by Christie's for $610,000.

Akten's sentiment echoes how other industries have used AI to complement and enhance the work of humans rather than make human involvement completely unnecessary. It can inspire artists to go in directions they may not have seen without the computer collaboration.

Ready to collaborate with AI on your own original work of art? Check out Deep Dream Generator or Deepart that can transform any image you upload and create a new one following a particular art style. Need inspiration for what others have done? Just check out the users' gallery. If you like what you make, you can even get them printed to display on your wall!

Artificial intelligence and computer vision are continuing to alter the way we work, live and even create. Do you see the potential or only the peril?

Follow me on Twitter or LinkedIn. Check out my website or some of my other work here.

# data[].attributes.attachment_link.title
Artificial Intelligence Can Now Generate Amazing Images - What Does This Mean For Humans?

# data[].attributes.attachment_link.description
Turns out after they've been trained on enormous datasets, algorithms can not only tell what a picture is such as knowing a cat is a cat but can also generate absolutely original images. The artificial intelligence that makes this possible has matured significantly in recent years and in some applications is very proficient, but in other ways, still has a long way to go.

# data[].attributes.attachment_link.readable_text
Artificial intelligence continues to evolve as it finds new ways to learn and function, including tackling the subject of creativity. Next on the machine-learning list: computer-generated imagery. As Olga Egorsheva, CEO and co-founder, Lobster, explains, AI could have a greater role to play in freedom of creativity than you could imagine.
AI's relationship with creativity has always been a curious one. Can machine learning truly equal the human mind when it comes to expressing creativity? Perhaps some of the answers can be found in its efforts to recreate computer-generated images.

Not your average CGI

If you think of computer-generated imagery, or as it's more commonly known: CGI, the first image likely to pop into your head will be a Hollywood blockbuster. The special effects most often associated with CGI have become a regular feature in big-budget movies over the years.

From sketchy graphics to photo-realistic visuals, the evolution of CGI has been nothing short of extraordinary. Companies such as Pixar have created a revolution and, with the help of human voice acting, have managed to bring genuine emotion to graphics that real-life films often struggle to replicate.

CGI isn't just rooted in the movie industry, either. As the tech improves, and costs become more economical, more companies across different industries will pay greater attention. The small screen, video games, and the advertising and marketing sectors have all dipped their toes in the computer-generated imagery pool.

Advertising & marketing CGI

Computer-generated imagery is becoming more frequent within advertising and marketing, with the fashion industry, in particular, taking advantage of the technology available to promote their clothes. We are now seeing a greater frequency of brands replacing real-life models with virtual ones.

The benefits are clear: not only is it cost- and time-effective to use computer images rather than real people, but companies can mould the appearance of a virtual model to reflect their exact requirements. That means the clothes will always look as intended and perfectly fit the virtual model.

There is an ethical question around the use of virtual models, however, airbrushing and the distortion of images has long been an issue in the modelling industry. The real issues concerning our perceptions of what defines beauty is a topic that still needs an in-depth analysis - whether using virtual or real-life models.

It's not just humans who are getting a computer make over: brands such as Ikea use CGI to display their furniture in digital promotional catalogues as it allows them to show products in greater detail. 3D model houses have also long been a popular marketing tool for estate agents and are often used to reveal how new builds currently still under construction will look once they are completed.

Oh, hey there AI

It comes as no surprise that artificial intelligence is now entrenched in computer-generated imagery; and those in charge of machine learning feel the capabilities are there for AI to create genuine CGI.

The reason why is clear: if it's already cheaper to use CGI instead of real people, the cost will reduce even further if AI is producing computer-generated images. And, while we're not quite at the junction where fully enhanced CGI created by machine learning is the norm, there have certainly been some promising signs.

Filter out the noise

For an example of how AI is producing hugely popular images that are capturing the imagination of millions, look no further than Snapchat filters and the app, Prisma. Snapchat has spent a significant amount of time experimenting over the last 18 months as it looks for ways to offer a different dynamic from other popular social media sites. The company's face filters and face swaps have proved to be popular additions to the service - and machine learning has a heavy influence.

It's not only the filters where Snapchat is using machine learning: AI is helping to define which stories show up in your news feed when you're on the app by understanding your user journey.

Prisma, the hugely popular photo transformation app, also uses AI to transform smartphone images into stylised artwork from different genres. On the face of it, this type of capability is just a bit of extra fun to add to your photography. But 7.5 million downloads within its first week of release shows there is a desire for the feature.

Treading the waters

There are other areas where AI is having more influence as algorithms show greater complexities and pave the way for more freedom and creativity. Researchers at Stanford University have developed an AI system that can create real-time facial re-enactment and animated facial expressions.

While Adobe has experimented with replicating background imagery, potentially removing the need for screens in the future, and Google developer Damien Henry created a 56-minute video using one image and AI.

You can see the appeal these advancements hold with creatives in the advertising industry, who are always looking for new ways to engage with audiences. The idea of having the ability to take separate images and audio and bring them together opens up the potential for plenty of fun possibilities.

Victor Riparbelli, CEO at Synthesia, who are developing AI to generate synthetic video, thinks the possibilities to take advantage of these methods indicates exciting times. "We are not far from being able to easily generate artificial media that is indistinguishable from the real thing. In the future, music, pictures, and videos will increasingly be created by computers, and we will not be able to tell the difference."

Victor also feels that AI's capabilities will allow creatives to focus on the creation process, giving them more freedom to be expressive. "What we are seeing is that humans are moving up an abstraction layer. Computers will no longer serve as tools only but increasingly take on the role of an artist.

"From instructing the computer to paint x, y, and z pixels red we will simply tell the computer to generate a picture of a red fire truck or change the model's hair colour from brown to red."

AI, CGI, You & Me

Could we potentially see a future where a machine has created CGI in movies, TV shows, adverts, and streaming? Possibly. Although we're perhaps still a little way off from a landscape that is completely controlled by AI.

For the time being, though, AI is coming up with some interesting concepts that will appeal on a smaller scale and help humans to express their creative freedom that little bit more.

# data[].attributes.attachment_link.title
How AI Is Learning to Create Computer-Generated Imagery

# data[].attributes.attachment_link.description
Artificial intelligence continues to evolve as it finds new ways to learn and function, including tackling the subject of creativity. Next on the machine-learning list: computer-generated imagery. As Olga Egorsheva, CEO and co-founder, Lobster, explains, AI could have a greater role to play in freedom of creativity than you could imagine.

# data[].attributes.body
The video presented is the final product of the screenplay examples is only about 2 minutes in length, but goes to show how far artificial intelligence can go to create imagery and produce it on their own, or with very little human input. It shows the examples of warping / changing imagery for the scene that it takes place in and for the characters themselves

# data[].attributes.attachment_link.description
01		Title: 			"The Empty Room" (2020)
02
03            Produced by: 	         #Merzmensch
04
05		Visuals: 		        #StyleGAN2 / #ArtBreeder
06
07		Music:			OpenAI / JukeBox
08						SongID: #799444969
09
10		Text:			OpenAI / GPT-2
11
12		Voices:			Replica Studios /  @replica_ai

Read more about it here: https://medium.com/merzazine/the-empty-room-2020-75230f183e8b?sk=e18b708ac6bb70dee44f3a8a99090db5

# data[].attributes.attachment_link.readable_text
About Little White Lies

Little White Lies was established in 2005 as a bi-monthly print magazine committed to championing great movies and the talented people who make them. Combining cutting-edge design, illustration and journalism, we've been described as being "at the vanguard of the independent publishing movement." Our reviews feature a unique tripartite ranking system that captures the different aspects of the movie-going experience. We believe in Truth & Movies.

Editorial

Design

# data[].attributes.attachment_link.title
Peter Cushing Archives - Little White Lies

# data[].attributes.attachment_link.description
Little White Lies was established in 2005 as a bi-monthly print magazine committed to championing great movies and the talented people who make them. Combining cutting-edge design, illustration and journalism, we've been described as being "at the vanguard of the independent publishing movement."

# data[].attributes.body
The usage of AI in the creative sector could diminish certain roles such as location scouts or production image concept creators

The software of GAN: Generative Adversarial Network or Nets, is a software created by a previous google researcher and developer, Ian Goodfellow, and uses artificial intelligence to create and base imagery that has been designed to its closets elated image through numerous processes of elimination that once finishes, the image is considered true to its original created form / close related form.

# data[].attributes.body
Not only in film, other network's outside of film but still within creative areas such as the developers of Snap Chat use machine learning AI to base newly created content filters onto the users faces through facial construction imagery and thus ending in the final look of the used filter.

# data[].attributes.attachment_link.readable_text
How I created an entire short movie using ML models only (and you can do it as well).

Collage by Merzmensch.

It 's been a blast. I remember watching " Sunspring" - again and again. Fascinated and mesmerized by the absurd dialogues, I was trying to comprehend what's going on in this short movie. But the meaning used to slip away. Because it was written by AI.

Benjamin was the name of the author. Behind this name was a recurrent neural network LSTM hidden, developed by Ross Goodwin, an AI researcher and poet. The movie was directed by BAFTA-nominated British filmmaker Oscar Sharp and featured a Thomas Middleditch among others.

Even if the p lot consisted of absurdist and seemingly random phrases, our human brain was trying to get it. Cogito, ergo sum - I think therefore I am.

Ross' must-read essays about AI and creativity were highly inspiring for me. If you haven't a chance, do read this.

With his experiments (and later with his AI-generated book " 1 the Road"), Ross proved what is possible to achieve with Artificial Intelligence. And also this realization was a crucial one: AI is neither just a tool nor an entire replacement for a writer. You need a symbiosis to create art together with AI.

We've seen how many creative uses of Machine Learning are possible.

With the 3D Ken Burns Effect, we can make animated dream visions, converting a photo to a spatial camera flight.

Teaser to my series "dreAIms"

With GPT-2 by OpenAI, we can write entire stories.

But what if we combine all the approaches to one single artwork? And so I did it and generated a short movie completely with AI.

...But before you watch it: do you have an idea, what do you need to create a movie?

You surely can waive some elements for arthouse quality, but usually, you need the following ingredients to make a movie (even if a short one):

the Plot

the Camera / Visuals

the Actors

the Music

You also need an original idea and (in most cases) still unused media assets.

So how can we apply the Machine Learning approach to fulfill all of these points?

The Plot

As previously mentioned, GPT-2 is a powerful NLP model (pre-trained on 40 GB text with 1.5 billion parameters) to generate writings. Its main flaw - and the major advantage - is the surreal character of created contents. They usually don't make sense (at least for knowledge transfer needs - in a literary context they can be a decent object of interpretation). And: every time a new unique text is generated.

Let's see which ways GPT-3 will take in the future ( OpenAI published this new giant model with 175 billion parameters, which at the moment cannot be fine-tuned due to its excessive dimensions).

For my needs I used a GPT-2 Colab Notebook with Java Interface by Manuel Romero:

There are several different notebooks, allowing you to fine-tune GPT-2 or train on your texts, but I've chosen this particular Colab Notebook for its user-friendly and robust structure, and support of the entire 1.5 billion parameter model.

For my movie I entered the following prompt:

Empty room. A man enters the room. He looks out of the window and says: "It still hasn't begun". The woman enters. She has a white dress on and a book in her hand. The woman: "I am waiting as well." The man: "For how long?" The woman: "For a while."

GPT-2 provided me with various texts, but the one I've selected was an amazing piece of an absurdist theater play, somewhere between Beckett, Mrozek and David Lynch:

Empty room. A man enters the room. He looks out of the window and says: "It still hasn't begun". The woman enters. She has a white dress on and a book in her hand. The woman: "I am waiting as well." The man: "For how long?" The woman: "For a while." The man: "Ah, you know, I'm a very patient man." The woman: "I know, of course." The man: "I'm going to have to be less patient, then." The woman starts to laugh; the laughter has an ominous edge that only she can feel. "In my defence," she says, "I never actually told you what it was I was going to tell you." The man, suddenly, remembers: "That's right." The woman: "So you are lying behind me." The man is surprised by the abruptness of the accusation. He does not, however, feel any anger; as he has noticed in previous encounters, it is the woman who is most convincing when she's saying the lies. The man: "But it was not what you expected." The woman: "Oh...no." She is trying to sound convincing. He wants to ask her about her mother. The woman: "Do you think she'll be willing to lie?" The man turns down the opportunity. The woman: "Maybe..." "Why?" "Maybe she's still grieving." She is smiling when she says this, and the man doesn't think she'll be the first to smile when the truth comes out. He gives up. "No." The woman: "It's not over." The man has not got to see her family again. He had hoped to return to his work. He has nothing else to do. He wants to be a man.

It convinced me also because of ambiguous communication levels. It's as if there is a huge story hidden behind all those sparse phrases (human brain does wonders during interpretation, indeed).

So I had a screenplay.

Visuals

I am a fan of StyleGAN2, but especially in its implementation in ArtBreeder (at least in the section "Portraits").

You can generate new faces, you can make transitions or even animations ( read more about using Artbreeder here). So I created a bunch of images for faces ( see here the assets) of the Man, the Woman, and some Room pictures.

It was a tricky thing to align the face modification to the story part (e.g. in the "laughter moments" etc.), but after various trials and errors, it worked for me.

Here are just some assets used for the visuals:

The Actors

The faces were already made, but something was still missing: voices.

Voice generation is one of probably the oldest Machine Learning approaches. My favorite wasMelNet - a Model with unbelievable quality. Just listen to the samples (trained on professional speakers or also celebrities datasets). Alas, MelNet wasn't available as a working repository or Colab Notebook.

My second choice was Amazon Polly. Being a part of Amazon AWS AI/ML-Services Polly provides a broad quantity of voices in various languages. The downside is: most of them have rather a charisma of an anchorman and are not always suitable for fictional content.

I used one voice for my AI-generated video "Predictions":

Still, the non-emotional voice is not really convincing, if used in some dialogues.

Then I discovered Replica Studios.

Screenshot from my dashboard at https://replicastudios.com/

They provide at the moment a small collection, but the voices have astonishing quality. Some of them can be rather used for anchorman need, but another bear already theatrical power within.

I've chosen these three voices - Deckard for Narrator, Carlos for Man, and Audrey for Woman.

You also can experiment with the emotional features of various voices, you even can train them your voice, but these three were perfect for my needs.

The use of Replica Studios is pretty easy. You sort the phrases in chronological order and apply the appropriate voices to the characters:

Screenshot from my dashboard at https://replicastudios.com/

It's possible to export the voices to mp3-files for your project.

Music

AI-generated music achieved new levels of quality this year after OpenAI released JukeBox - a library of 7k songs and music pieces, being generated with pre-trained models on various musicians and songwriters ( read also here).

Many of them are pretty glitchy (like this nightmarish "Mozart" p iece). But many of them are beautiful. And especially combined with AI-generated visuals they evoke strange feelings within.

I am using them for my series "Breath ZeroX":

For our video something cinematic should pass, so I looked up the music trained on Hans Zimmer (and changed the speed a little bit):

And... action!

The rest was rather hard work of a cutter - for my video I used Premiere Pro and amounts of coffee. Probably the most tricky thing was stretching the videos in time and synchronize them with the plot (original face transitions by ArtBreeder were between 8-30 sec long).

Advice: I used " Time Interpolation: Optical Flow ". It creates new frames between the existent and makes the footage fluent. Sometimes it generates glitches - but they are always welcome if it's about digitally generated films!

And now: here it is.

I wonder which new audio-visual treasures will come with new ML-approaches. Let's stay tuned!

# data[].attributes.attachment_link.description
It 's been a blast. I remember watching " Sunspring" - again and again. Fascinated and mesmerized by the absurd dialogues, I was trying to comprehend what's going on in this short movie. But the meaning used to slip away. Because it was written by AI. Benjamin was the name of the author.

# data[].attributes.attachment_link.readable_text
Turns out after they've been trained on enormous datasets, algorithms can not only tell what a picture is such as knowing a cat is a cat but can also generate absolutely original images. The artificial intelligence that makes this possible has matured significantly in recent years and in some applications is very proficient, but in other ways, still has a long way to go.
AI Recognizes What an Image Is
It's taken two decades for computer scientist s to train and develop machines that can "see" the world around them-another example of an everyday skill humans take for granted yet one that is quite challenging to train a machine to do.

Facial recognition technology, used both in retail and security, is one way AI and its ability to "see" the world is starting to be commonplace. Retailers use facial recognition technology to better market and sell to their target audience. In one particularly intriguing use case, some Chinese office complexes have vending machines that identify shoppers through facial recognition technology and track the items they take from the machine to ultimately bill the shoppers' accounts. Even anonymous data about shoppers collected from cameras such as age, gender, and body language can help retailers improve their marketing efforts and provide a better customer experience.

Retail giant Walmart deployed a fleet of stock-monitoring robots that can identify through computer vision when a shelf needs more product and alert humans to ensure it happens. Target is also testing out similar technology.

Seeing AI, an iPhone app uses artificial intelligence to help blind and partially-sighted people navigate their environment by using computer vision to identify and speak its observations of the scenes and objects in its field of vision. From Face ID to unlock the iPhone X to cameras on the street used to identify criminals as well as the algorithms that allow social media platforms to identify who is in photos, AI image recognition is everywhere.
AI Image Generation
Now that artificial intelligence is able to understand, for the most part, what an image represents and can tell the difference between a stop sign and a dog, a dog from an elephant and more, the next frontier to perfect is AI image generation.

One of the former barriers to having AI generate believable images was the need for enormous datasets for training. With today's significant computing power and the incredible amount of data we now collect, AI has breached that barrier.

How does AI know how to execute when tasked with creating an image? It uses Generative Adversarial Network or Nets (GAN), invented in 2014 by Ian Goodfellow, who was a Google researcher. It uses two neural networks; one that creates an image and another one that judges, based on real-life examples of the target image, how close the image is to the real thing. After scoring the image for accuracy, it sends that info back to the original AI system. That system learns from the feedback and returns an altered image for the next round of scoring. This process continues until the scoring machine determines the AI-generated image matches the "control" image.

Today, AI can create realistic images and videos of cats and hamburgers, representations of your words, faces that aren't of real people and even original works of art.

In fact, there's even a market for AI's original artwork-Google hosted an art show to benefit charity and to showcase work created by its software DeepDream. It sold an AI-generated piece of art that was a collaboration between human and machine for $8,000 plus others. The human creator (or artist) that was part of this collaboration, Memo Akten explained that Google made a better "paintbrush" as a tool, but the human artist was still critical to creating art that would command an $8K price tag. Another AI-generated piece of art, Portrait of Edmond de Belamy was auctioned by Christie's for $610,000.

Akten's sentiment echoes how other industries have used AI to complement and enhance the work of humans rather than make human involvement completely unnecessary. It can inspire artists to go in directions they may not have seen without the computer collaboration.

Ready to collaborate with AI on your own original work of art? Check out Deep Dream Generator or Deepart that can transform any image you upload and create a new one following a particular art style. Need inspiration for what others have done? Just check out the users' gallery. If you like what you make, you can even get them printed to display on your wall!

Artificial intelligence and computer vision are continuing to alter the way we work, live and even create. Do you see the potential or only the peril?

Follow me on Twitter or LinkedIn. Check out my website or some of my other work here.

# data[].attributes.attachment_link.title
Artificial Intelligence Can Now Generate Amazing Images - What Does This Mean For Humans?

# data[].attributes.attachment_link.description
Turns out after they've been trained on enormous datasets, algorithms can not only tell what a picture is such as knowing a cat is a cat but can also generate absolutely original images. The artificial intelligence that makes this possible has matured significantly in recent years and in some applications is very proficient, but in other ways, still has a long way to go.

# data[].attributes.attachment_link.readable_text
Artificial intelligence continues to evolve as it finds new ways to learn and function, including tackling the subject of creativity. Next on the machine-learning list: computer-generated imagery. As Olga Egorsheva, CEO and co-founder, Lobster, explains, AI could have a greater role to play in freedom of creativity than you could imagine.
AI's relationship with creativity has always been a curious one. Can machine learning truly equal the human mind when it comes to expressing creativity? Perhaps some of the answers can be found in its efforts to recreate computer-generated images.

Not your average CGI

If you think of computer-generated imagery, or as it's more commonly known: CGI, the first image likely to pop into your head will be a Hollywood blockbuster. The special effects most often associated with CGI have become a regular feature in big-budget movies over the years.

From sketchy graphics to photo-realistic visuals, the evolution of CGI has been nothing short of extraordinary. Companies such as Pixar have created a revolution and, with the help of human voice acting, have managed to bring genuine emotion to graphics that real-life films often struggle to replicate.

CGI isn't just rooted in the movie industry, either. As the tech improves, and costs become more economical, more companies across different industries will pay greater attention. The small screen, video games, and the advertising and marketing sectors have all dipped their toes in the computer-generated imagery pool.

Advertising & marketing CGI

Computer-generated imagery is becoming more frequent within advertising and marketing, with the fashion industry, in particular, taking advantage of the technology available to promote their clothes. We are now seeing a greater frequency of brands replacing real-life models with virtual ones.

The benefits are clear: not only is it cost- and time-effective to use computer images rather than real people, but companies can mould the appearance of a virtual model to reflect their exact requirements. That means the clothes will always look as intended and perfectly fit the virtual model.

There is an ethical question around the use of virtual models, however, airbrushing and the distortion of images has long been an issue in the modelling industry. The real issues concerning our perceptions of what defines beauty is a topic that still needs an in-depth analysis - whether using virtual or real-life models.

It's not just humans who are getting a computer make over: brands such as Ikea use CGI to display their furniture in digital promotional catalogues as it allows them to show products in greater detail. 3D model houses have also long been a popular marketing tool for estate agents and are often used to reveal how new builds currently still under construction will look once they are completed.

Oh, hey there AI

It comes as no surprise that artificial intelligence is now entrenched in computer-generated imagery; and those in charge of machine learning feel the capabilities are there for AI to create genuine CGI.

The reason why is clear: if it's already cheaper to use CGI instead of real people, the cost will reduce even further if AI is producing computer-generated images. And, while we're not quite at the junction where fully enhanced CGI created by machine learning is the norm, there have certainly been some promising signs.

Filter out the noise

For an example of how AI is producing hugely popular images that are capturing the imagination of millions, look no further than Snapchat filters and the app, Prisma. Snapchat has spent a significant amount of time experimenting over the last 18 months as it looks for ways to offer a different dynamic from other popular social media sites. The company's face filters and face swaps have proved to be popular additions to the service - and machine learning has a heavy influence.

It's not only the filters where Snapchat is using machine learning: AI is helping to define which stories show up in your news feed when you're on the app by understanding your user journey.

Prisma, the hugely popular photo transformation app, also uses AI to transform smartphone images into stylised artwork from different genres. On the face of it, this type of capability is just a bit of extra fun to add to your photography. But 7.5 million downloads within its first week of release shows there is a desire for the feature.

Treading the waters

There are other areas where AI is having more influence as algorithms show greater complexities and pave the way for more freedom and creativity. Researchers at Stanford University have developed an AI system that can create real-time facial re-enactment and animated facial expressions.

While Adobe has experimented with replicating background imagery, potentially removing the need for screens in the future, and Google developer Damien Henry created a 56-minute video using one image and AI.

You can see the appeal these advancements hold with creatives in the advertising industry, who are always looking for new ways to engage with audiences. The idea of having the ability to take separate images and audio and bring them together opens up the potential for plenty of fun possibilities.

Victor Riparbelli, CEO at Synthesia, who are developing AI to generate synthetic video, thinks the possibilities to take advantage of these methods indicates exciting times. "We are not far from being able to easily generate artificial media that is indistinguishable from the real thing. In the future, music, pictures, and videos will increasingly be created by computers, and we will not be able to tell the difference."

Victor also feels that AI's capabilities will allow creatives to focus on the creation process, giving them more freedom to be expressive. "What we are seeing is that humans are moving up an abstraction layer. Computers will no longer serve as tools only but increasingly take on the role of an artist.

"From instructing the computer to paint x, y, and z pixels red we will simply tell the computer to generate a picture of a red fire truck or change the model's hair colour from brown to red."

AI, CGI, You & Me

Could we potentially see a future where a machine has created CGI in movies, TV shows, adverts, and streaming? Possibly. Although we're perhaps still a little way off from a landscape that is completely controlled by AI.

For the time being, though, AI is coming up with some interesting concepts that will appeal on a smaller scale and help humans to express their creative freedom that little bit more.

# data[].attributes.attachment_link.title
How AI Is Learning to Create Computer-Generated Imagery

# data[].attributes.attachment_link.description
Artificial intelligence continues to evolve as it finds new ways to learn and function, including tackling the subject of creativity. Next on the machine-learning list: computer-generated imagery. As Olga Egorsheva, CEO and co-founder, Lobster, explains, AI could have a greater role to play in freedom of creativity than you could imagine.

# data[].attributes.attachment_link.description
01		Title: 			"The Empty Room" (2020)
02
03            Produced by: 	         #Merzmensch
04
05		Visuals: 		        #StyleGAN2 / #ArtBreeder
06
07		Music:			OpenAI / JukeBox
08						SongID: #799444969
09
10		Text:			OpenAI / GPT-2
11
12		Voices:			Replica Studios /  @replica_ai

Read more about it here: https://medium.com/merzazine/the-empty-room-2020-75230f183e8b?sk=e18b708ac6bb70dee44f3a8a99090db5

# data[].attributes.attachment_link.readable_text
About Little White Lies

Little White Lies was established in 2005 as a bi-monthly print magazine committed to championing great movies and the talented people who make them. Combining cutting-edge design, illustration and journalism, we've been described as being "at the vanguard of the independent publishing movement." Our reviews feature a unique tripartite ranking system that captures the different aspects of the movie-going experience. We believe in Truth & Movies.

Editorial

Design

# data[].attributes.attachment_link.title
Peter Cushing Archives - Little White Lies

# data[].attributes.attachment_link.description
Little White Lies was established in 2005 as a bi-monthly print magazine committed to championing great movies and the talented people who make them. Combining cutting-edge design, illustration and journalism, we've been described as being "at the vanguard of the independent publishing movement."

# data[].attributes.attachment_link.readable_text
The role of artificial intelligence (AI) tools and techniques in business and the global economy is a hot topic. This is not surprising given that AI might usher in radical-arguably unprecedented-changes in the way people live and work. The AI revolution is not in its infancy, but most of its economic impact is yet to come.

New research from the McKinsey Global Institute attempts to simulate the impact of AI on the world economy. First, it builds on an understanding of the behavior of companies and the dynamics of various sectors to develop a bottom-up view of how to adopt and absorb AI technologies. Second, it takes into account the likely disruptions that countries, companies, and workers are likely to experience as they transition to AI. There will very probably be costs during this transition period, and they need to be factored into any estimate. The analysis examines how economic gains and losses are likely to be distributed among firms, employees, and countries and how this distribution could potentially hamper the capture of AI benefits. Third, the research examines the dynamics of AI for a wide range of countries-clustered into groups with similar characteristics-with the aim of giving a more global view.

The analysis should be seen as a guide to the potential economic impact of AI based on the best knowledge available at this stage. Among the major findings are the following:

1. There is large potential for AI to contribute to global economic activity

2. A key challenge is that adoption of AI could widen gaps among countries, companies, and workers

There is large potential for AI to contribute to global economic activity

The McKinsey Global Institute looked at five broad categories of AI: computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning. Companies will likely use these tools to varying degrees. Some will take an opportunistic approach, testing only one technology and piloting it in a specific function (an approach our modeling calls adoption). Others might be bolder, adopting all five and then absorbing them across the entire organization (an approach we call full absorption). In between these two poles, there will be many companies at different stages of adoption; the model also captures this partial impact.

By 2030, the average simulation shows that some 70 percent of companies might have adopted at least one type of AI technology but that less than half will have fully absorbed the five categories. The pattern of adoption and full absorption might be relatively rapid-at the high end of what has been observed with other technologies.

Several barriers might hinder rapid adoption and absorption (see video, "A minute with the McKinsey Global Institute: Challenges of adopting automation technology"). For instance, late adopters might find it difficult to generate impact from AI, because front-runners have already captured AI opportunities and late adopters lag in developing capabilities and attracting talent.

Nevertheless, at the global average level of adoption and absorption implied by our simulation, AI has the potential to deliver additional global economic activity of around $13 trillion by 2030, or about 16 percent higher cumulative GDP compared with today. This amounts to 1.2 percent additional GDP growth per year. If delivered, this impact would compare well with that of other general-purpose technologies through history.

A number of factors, including labor automation, innovation, and new competition, affect AI-driven productivity growth. Micro factors, such as the pace of adoption of AI, and macro factors, such as the global connectedness or labor-market structure of a country, both contribute to the size of the impact.

Our simulation examined seven possible channels of impact. The first three relate to the impact of AI adoption on the need for, and mix of, production factors that have direct impact on company productivity. The other four are externalities linked to the adoption of AI related to the broad economic environment and the transition to AI. We acknowledge that these seven channels are not definitive or necessarily comprehensive but rather a starting point based on our current understanding and trends currently under way (Exhibit 1).

The impact of AI might not be linear but could build up at an accelerating pace over time. Its contribution to growth might be three or more times higher by 2030 than it is over the next five years. An S-curve pattern of adoption and absorption of AI is likely-a slow start due to the substantial costs and investment associated with learning and deploying these technologies, then an acceleration driven by the cumulative effect of competition and an improvement in complementary capabilities alongside process innovations.

It would be a misjudgment to interpret this "slow burn" pattern of impact as proof that the effect of AI will be limited. The size of benefits for those who move early into these technologies will build up in later years at the expense of firms with limited or no adoption.

A key challenge is that adoption of AI could widen gaps among countries, companies, and workers

Although Al can deliver a boost to economic activity, the benefits are likely to be uneven.

How AI could affect countries

Potentially, AI might widen gaps between countries, reinforcing the current digital divide. Countries might need different strategies and responses as AI-adoption rates vary.

Leaders of AI adoption (mostly in developed countries) could increase their lead over developing countries. Leading AI countries could capture an additional 20 to 25 percent in net economic benefits, compared with today, while developing countries might capture only about 5 to 15 percent. Many developed countries might have no choice but to push AI to capture higher productivity growth as their GDP-growth momentum slows-in many cases, partly reflecting the challenge due to aging populations. Moreover, in these economies, wage rates are high, which means that there is more incentive to substitute labor with machines than there is in low-wage, developing countries.

Would you like to learn more about the McKinsey Global Institute?

In contrast, developing countries tend to have other ways, including catching up with best practices and restructuring their industries, to improve their productivity. Therefore, they might have less incentive to push for AI (which, in any case, might offer them a relatively smaller economic benefit than it does advanced economies). Some developing countries might prove to be exceptions to this rule. For instance, China has a national strategy in place to become a global leader in the AI supply chain and is investing heavily.

How AI could affect companies

It is possible that AI technologies could lead to a performance gap between front-runners (companies that fully absorb AI tools across their enterprises over the next five to seven years) and nonadopters (companies that do not adopt AI technologies at all or have not fully absorbed them in their enterprises by 2030).

At one end of the spectrum, front-runners are likely to benefit disproportionately. By 2030, they could potentially double their cash flow (economic benefit captured minus associated investment and transition costs). This implies additional annual net cash-flow growth of about 6 percent for longer than the next decade. Front-runners tend to have a strong starting IT base, a higher propensity to invest in AI, and positive views of the business case for AI.

At the other end of the spectrum, nonadopters might experience around a 20 percent decline in their cash flow from today's levels, assuming the same cost and revenue model as today. One important driver of this profit pressure is the existence of strong competitive dynamics among companies that could shift market share from laggards to front-runners and might prompt debate about the unequal distribution of the benefits of AI (Exhibit 2).

How AI could affect workers

Notes from the frontier: Modeling the impact of AI on the world economy

A widening gap might unfold at the level of individual workers (see video, "A minute with the McKinsey Global Institute: What AI can and can't [yet] do"). Demand for jobs could shift away from repetitive tasks toward those that are socially and cognitively driven and require more digital skills. Job profiles characterized by repetitive activities or that require a low level of digital skills could experience the largest decline as a share of total employment to around 30 percent by 2030, from some 40 percent. The largest gain in share could be in nonrepetitive activities and those that require high digital skills, rising from roughly 40 percent to more than 50 percent.

These shifts would have an impact on wages. We simulate that around 13 percent of the total wage bill could shift to categories requiring nonrepetitive and high digital skills, where incomes could rise, while workers in the repetitive and low-digital-skills categories could experience a stagnation or even a cut in their wages. The share of the total wage bill of the latter group could decline to 20 percent, from 33 percent.

A direct consequence of this widening gap in employment and wages would be an intensifying war for people, particularly those skilled in developing and using AI tools. On the other hand is the potential for structural excess supply for a still relatively high portion of people lacking the digital and cognitive skills necessary to work with machines.

Overall, the adoption and absorption of AI might not have a significant impact on net employment. There will likely be substantial pressure on full-time-employment demand, but the total net impact in aggregate might be more limited than many fear. Our average global scenario suggests that total full-time-equivalent-employment demand might remain flat, or even that there could be a slightly negative net impact on jobs by 2030.

The opportunity of AI is significant, but there is no doubt that its penetration might cause disruption. The productivity dividend of AI probably will not materialize immediately. Its impact is likely to build up at an accelerated pace over time; therefore, the benefits of initial investment might not be visible in the short term. Patience and long-term strategic thinking will be required.

Stay current on your favorite topics

Policy makers will need to show bold leadership to overcome understandable discomfort among citizens about the perceived threat to their jobs as automation takes hold. Companies will also be important actors in searching for solutions on the mammoth task of skilling and reskilling people to work with AI. Individuals will need to adjust to a new world in which job turnover could be more frequent, they might have to transition to new types of employment, and they likely must continually refresh and update their skills to match the needs of a dynamically changing job market.

Using historical trends of new jobs created to old jobs, and adjusting for a lower labor-output ratio that considers the likely labor-saving nature of AI technologies via smart automation, new jobs driven by investment in AI could augment employment by about 5 percent by 2030. The total productivity effect could have a positive contribution to employment of about 10 percent.

# data[].attributes.attachment_link.title
Notes from the AI frontier: Modeling the impact of AI on the world economy

# data[].attributes.attachment_link.description
The role of artificial intelligence (AI) tools and techniques in business and the global economy is a hot topic. This is not surprising given that AI might usher in radical-arguably unprecedented-changes in the way people live and work. The AI revolution is not in its infancy, but most of its economic impact is yet to come.

# data[].attributes.attachment_link.readable_text
The entertainment and media (E&M) industry is a diverse sector composed of multiple segments including film, television and media streamed online. By 2021, the U.S. E&M industry is projected to reach $759 billion in revenue, increasing at a compound annual growth rate (CAGR) of 3.6 percent.

Despite the anticipated growth, there are concerns about a revenue declines in more traditional market segments . As a result, industry analysts such as PwC argue that user experience must take increasing priority and AI is among leading emerging technologies poised to positively contribute to this effort.

To gauge the emerging role of AI in the E&M industry, we researched this sector in depth to help answer questions business leaders are asking today, including:

What types of AI applications are currently in use in the entertainment and media industry?

How has the market responded to these AI applications?

Are there any common trends among these innovation efforts - and how could these trends possibly affect the future of the entertainment and media sector?

In this article we break down applications of artificial intelligence in the entertainment and media industry market to provide business leaders with an understanding of current and emerging trends that may impact their sector. We'll begin with a synopsis of the sectors we covered:

Entertainment and Media AI Applications Overview

Based on our assessment of the applications in this sector, the majority of entertainment and media use-cases appear to fall into three major categories:

Marketing and Advertising: Companies are training machine learning algorithms to help develop film trailers and design advertisements.

Personalization of User Experience: Entertainment providers are using machine learning to recommend personalized content based on data from user activity and behavior.

Search Optimization: Media content producers are using AI software to improve the speed and efficiency of the media production process and the ability to organize visual assets.

In the full article below, we'll explore the AI applications of each application by section and provide representative examples.

Marketing and Advertising

Fox and IBM Watson - Morgan Film Trailer

In August 2016, IBM the release of the trailer for a 20th Century Fox suspense/horror film reportedly developed using machine learning. The research team trained the AI system on scenes from "100 horror movies." Features from each of the movie scenes were categorized into what the team called "moments" and were then analyzed based on visual, audio and scene composition elements.

Once the system gained an understanding of the types of scenes found in a standard suspense/horror movie trailer, it was given the full-length film and recommended 10 moments for the trailer. A total of six minutes of footage were pulled from the 90-minute movie, resulting in a 24 hour process, from start to finish. Comparatively, the film trailer development process normally takes weeks to complete. See the complete film trailer below:

While the impact of integrating AI may have resulted in savings in trailer production costs, the estimated $8 million budget film grossed just over $7.3 million in global box office sales. However, with being the first attempt at using AI for trailer development, it is too soon to accurately determine the direct impact on ticket sales.

This application is similar to IBM Watson's foray into sports - where the technology was used to generate a highlight reel from tennis matches by analyzing video footage and fan reactions in real time from multiple angles (we covered this other Watson use-case in our full AI in Sports article).

McCann Erickson Japan - "AI Creative Director"

In March 2016, advertising agency MaCann Erickson Japan reportedly an AI creative director called AI-CD ß. The company claims this is the first robotic creative director developed using artificial intelligence. AI-CD ß was officially hired on April 1, 2016 along with 11 other human employees.

The poll results reportedly showed that Kuramoto won the majority of the vote coming in at 54 percent while AI-CD ß earned the remaining 46 percent. While this isn't the ideal outcome for the AI creative director, the narrow margin shows promise for the robot's future efforts.

However, it is important to acknowledge the inherent limitations of AI in generating original ideas without human assistance. Tony McCaffey, PhD a cognitive psychologist and computer scientist by training, conducted reportedly demonstrating mathematical proof that computers are limited in their ability to perform creative tasks. He is among analysts who argue that while repetitive tasks can be more efficiently handled using AI, when it comes to creativity, human and computer collaboration is most effective.

Personalization of User Experience

Netflix - Machine Learning Workflow Management

When it comes to on-demand entertainment, personalization of the user experience has shifted from a luxury to a user expectation. For example, according to its 2016 annual report Netflix boasts 93 million global members streaming over 125 million hours of TV shows and movies per day . Predicting what a user wants to watch is a key part of the company's business model. Machine learning is reportedly integral to streamlining the diversity of user preferences.

In May 2016, Netflix the development of a workflow management and scheduling application called to reportedly manage its various machine learning pipelines that "build, train, and validate personalization algorithms" responsible for providing video recommendations.

IRIS.TV - Recommendation and Personalization

In the 4:25 minute interview below, IRIS.TV founder Field Garthwaite discusses the platform and his company's collaboration with IBM Watson, which helps to drive the machine learning capabilities:

Specifically, the platform aims to match content to users based on their preferences. The software integrates into the majority of video players. The algorithms "learn" what users want to watch and recommends similar content.

Search Optimization

Zorroa - Machine Learning for Visual Asset Management

offers a platform for managing visual assets that reportedly integrates machine learning algorithms to allow users to perform content searches within large databases. Documents are imported into what is called an "analysis pipeline." The pipeline is composed of processors which tag each visual asset. Algorithms are trained to recognize specific components of visual content which can then be organized and catalogued to deliver robust search results. Examples include:

Face Recognition (find faces and decide if they are known, if so tag the asset with a name)

Image Classification (use a neural network to classify the image into a set of predefined categories)

Once the analysis pipeline is operating, the process continues with workflow management steps allowing users to scale up the process for large datasets and fine tune categorization of visual assets. The image below depicts a visual representation of the process:

The short 58 second video below demonstrates how the video search was performed in the EVI platform:

Currently, it appears that the company is targeting clients in the E&M and oil & gas industry. A platform of this kind would technically be useful for any company where organizing and accessing large databases of visual content is a regular task.

Concluding Thoughts

Emerging applications of machine learning in the entertainment and media industry are being applied to multiple areas of importance for companies including marketing, advertising and content production. Innovations poised to deliver the greatest benefit over time will save industry professionals time and improve efficiency of the production process.

We will provide our thoughts on which applications in this article appear to hold the most promise based on our research. generally ranking each from most viable to least viable:
Personalization of the User Experience
Leaders and emerging competitors in the on-demand entertainment space are leveraging machine learning to sustain their ability to personalize content at scale for every client. Now that personalization is becoming a standard client expectation, AI is poised to be an integral strategy for keeping pace with consumer demand.
Search Optimization
Zorroa Corporation's Enterprise Visual Intelligence (EVI) platform is meeting a practical need for film production houses, helping to improve the process of searching for specific visual assets. If the EVI platform consistently delivers the results reported by Sony Pictures, it has the potential to be widely adopted in the film industry.
Marketing and Advertising
In contrast, the current lack of data on a correlation between machine learning in film trailer developement and ticket sales makes it challenging to predict how quickly other films may follow suit. However, if the effort for the film was intended more as a marketing strategy, media buzz was indeed generated.

Nevertheless, the film industry desires tools which reduce production time and costs. Therefore, we can anticipate feedback in the coming years on which of these applications may prove most useful. These data may substantiate the value AI could bring to the industry in multiple capacities of the production process.

The challenge of implementing AI for more creative tasks, as in the case of McCann Erickson Japan's AI creative director, may require more research before competing firms make investments in this technology. For optimum results, seasoned creative directors will need to be continually involved in the AI training and improvement process which may cost advertising agencies employee time.
Header image credit: dw.com

# data[].attributes.attachment_link.title
AI in Movies, Entertainment, and Visual Media - 5 Current Use-Cases

# data[].attributes.attachment_link.description
The entertainment and media (E&M) industry is a diverse sector composed of multiple segments including film, television and media streamed online. By 2021, the U.S. E&M industry is projected to reach $759 billion in revenue, increasing at a compound annual growth rate (CAGR) of 3.6 percent.