While many organizations and sectors are deploying AI solutions for the first time, one industry has been gobbling up algorithms and artificial intelligence solutions – Media. Big players in media have been successfully using personalization and intelligent algorithms for quite some time. Highlighting the media industry’s large appetite for AI, Ted Sarandos, Head of Content at Netflix, said, “Our shows intentionally don’t have a unifying brand. Our brand is content personalization.”

But the applications of AI in media are not just limited to content personalization. Media teams have to deal with manual processes for everything – from tagging the media to creating multilingual subtitles. But recent advances in AI are automating many of these tasks. Developments in computer vision, speech to text and natural language processing algorithms are changing the face of media creation, distribution and most importantly, media consumption.

Voice Search

Voice is the most natural way for people to communicate. That’s why the use of voice search in media is skyrocketing. Even in India, the voice is becoming a dominant way to interact with computers – Google reported that Hindi Voice Queries are growing 400% YoY. Media companies are scrambling to meet this demand – YouTube has a voice search and recently, Gaana also introduced a voice assistant in their mobile apps.

Voice queries are very different than traditional search. People ask long questions like “Show me when Kohli scored a century” and expect to see the exact time when Kohli scored. Traditional media is not really equipped to do that – the match recording will be a huge, 4 hour plus monolithic file. The part where Kohli actually scores a century is just a 2-minute clip in this long file. This means a clip will have to be created manually, which cannot scale for all the different queries people will ask.
AI can change all that. AI can analyze the video frame by frame and the commentary word for word. Using face detection, AI can identify Kohli and the NLP algorithms can detect the time when commentators are announcing the century. This defines a short section of the video when it is most likely that Kohli has scored a century. This is a dynamically created section, without any manual editing, and can be played when the user makes the search query.

Conversant Media

Once the media can understand voice interactions, it can start to interact with you. This creates exciting new avenues for storytelling. Look at a company like Novel Effect, which has created a new experience of reading storybooks with children. As you read the book aloud, the smart voice recognition stays in sync and the AI automatically plays custom-created music like thunder, violins or songs at the right time to keep the child engaged in the story.

The media is now talking with you, understanding you and then creating something completely new. Is this still a book or something more? Is it a story or an experience? AI is unshackling media from the traditional silos of audio, video and text and fusing them together into something exciting.

Adaptive Media

Content personalization is all about the recommendation. While it suggests a video or audio that you will like, the media itself does not change for you. In the future, the media will change itself and adapt for you. With AI, production teams can give more context to media – the location, the user profile, metadata of the file and based on that the media can dynamically change.

Consider the simple example – There is a movie that has spoken profanity in it. Traditionally, editors will bleep out the profanity manually to make it suitable for younger audiences. Deep learning algorithms, in combination with the speech to text algorithms, can detect profanity and the time where it was spoken in the media and automatically bleep it with very high accuracy. This type of media accepts user inputs (the app can pop the question “What is your birth date?” to the user) and based on the user inputs, the profanity is dynamically bleeped if the user is below 18, else it is not. The key word here is dynamical – it happens on the fly, without any human intervention.

The Rise of Intelligent Content

Traditional media infrastructure cannot support all these use cases and that is why making content intelligent is a top priority of all media houses. Instead of storing media as unintelligent, monolithic files, companies can have AI take a pass at the files for intelligent labelling, transcribing and classifying. This makes the media itself intelligent and ready for the future of voice search and other exciting, new experiences.

The post How AI is Changing The Face of Content Consumption in The Future appeared first on Artificial Intelligence.

A deep learning system takes textual hints from the context of images to describe them without the need for prior human annotations.

Since its humble beginnings at the turn of the millennium, deep learning, as both a scientific discipline and an industry, has come a long way.

From smartphone assistants to pattern recognition software, security solutions, and other applications, deep learning is becoming a multi-billion dollar business poised for great growth over the few next years.

However, for deep learning agents to reach their full potential, they have to “learn” how to learn on their own.

Herein lies the whole difference between supervised and unsupervised deep learning.

Self-Supervised Deep Learning

The power and appeal of deep learning is all about their ability to recognize different types of patterns like faces, voices, objects, images, and codes.

AI software doesn’t understand what these things really are, and all they see is digital data, and they’re pretty good at that.

The great computer vision capability of deep learning algorithms enable them to tell these things apart, categorize, and classify them.

To do so, however, this software needs to be supervised.

They require human manual input in the form of annotations to guide them before they generalize and build on what they learned into new, similar situations.

Building and labeling large datasets is a complicated and time-consuming task.

Unsupervised machines will be completely autonomous as all they need is data taken directly from their environment. From there, they would take the information to make predictions and yield the expected results.

To design unsupervised, or self-supervised deep learning systems, computer scientists take inspirations from how human intelligence works.

Now, an international team of computer vision scientists has devised a method to enable deep learning software to learn the visual features of images without the need for annotated examples.

Researchers from Carnegie Mellon University (U.S.), Universitat Autonoma de Barcelona (Spain), and the International Institute of Information Technology(India), worked on the study,

Unsupervised Computer Vision Algorithms, it’s a Matter of Semantics

In the study, the team built computational models that use textual information about images found on websites, like Wikipedia, and linked them to the visual features of these images.

“We aim to give computers the capability to read and understand textual information in any type of image in the real world,” said Dimosthenis Karatzas, a research team member.

In the next step, researchers used the models to train deep learning algorithms to pick adequate visual features that textually describe images.

Instead of labeled information about the content of a particular image, the algorithm takes non-visual cues from the semantic textual information found around the image.

“Our experiments demonstrate state-of-the-art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or naturally-supervised approaches,” wrote researchers in the paper.

This is not a fully unsupervised system as algorithms still need models to train on, but the technique shows that deep learning algorithms can tap into the internet to enhance their unsupervised learning abilities.

“We will continue our work on the joint-embedding of textual and visual information,” said Karatzas. “looking for novel ways to perform semantic retrieval by tapping on noisy information available in the Web and Social Media.”

The post New Deep Learning Strategy Could Enhance Computer Vision appeared first on Artificial Intelligence.

I have seen a glimpse of the future impact of artificial intelligence on corporate communications – and it is good. AI will bring a new level of trust to information, improve the way information is delivered (i.e., via augmented reality and virtual reality apps) and provide better insights and predictive analytics for decision making by corporate communications professionals.

My exposure to artificial intelligence has primarily been in the trusted identity technology industry, where AI is starting to revolutionize the digitization of identity and access management, physical access control and cybersecurity, especially as a proactive approach to threat and fraud detection. The management of identities, either physical or digital, is changing rapidly, requiring new ways of thinking to add trust.

Trust is an important topic for corporate communications, too. If people do not trust the information coming from a corporation, credibility is lost. If people think the communications team is out of touch with market realities, is too slow to take action or doesn’t have a vision for the future, then corporate communications unwittingly gets relegated to a tactical corner, subject to the misperceptions and misgivings of narrow-minded, tactics-obsessed, transactional people.

We as corporate communications professionals should expect more from ourselves and our teams. I challenge my colleagues in this field to embrace the opportunities AI presents to augment our communications function in the long term, rather than being defensive, reactionary or ignorant that change will happen.

For the sake of sparking a new stream of dialogue in the communications field, I am going to lay out my case for how AI could help corporate communications in the years to come. I hope readers challenge my points and stretch our collective thinking so we can have an honest discussion about how to harness AI in a productive way to serve our strategic communications goals.

AI does not need to define us or replace us; we have the opportunity to define AI in the context of corporate communications, which includes both external and internal communications. AI technology itself is neither good nor bad. In fact, it is a reflection of the heart of the person using it or unleashing it through automation. Just as the internet has done for more than two decades, it can reveal as much moral clarity as it can moral depravity. Someone can use the internet to spread false or misleading information just as much as to post truthful information.

However, at a higher level, AI’s real value is in enhancing, supporting and amplifying human truth, human experience and, ultimately, human freedom. And I believe that organizations will increasingly become the purveyors of these things in the future. Ironically, AI can help enhance what it means for us to be human.

Here are some ways artificial intelligence will help corporate communications in the future:

• AI will analyze the digital landscape of what I call digital-breathing, living networks and report on exact insights, real-time updates (up to the minute) and trend assessments. Let your imagination run wild. What would you do if you had this depth of information at your fingertips every minute of every day?

• AI will deliver news to target audiences (even microtarget audiences) in new and innovative ways using virtual and augmented reality applications. Editors, analysts and employees will be able to virtually “go” into a conference room (or White House briefing room) from the comfort of their offices and experience a press conference, briefing or information session.

• AI will enable faster responses to crises, following preset parameters as part of human-centric contingency plans. AI bots will be programmed to assist crisis communication leaders – and they won’t be swayed by emotions in heated crisis situations.

• AI will deliver better metrics for corporate communications. Knowing what I know about machine learning and deep learning algorithms, I predict that the metrics that corporate communications teams will get a decade from now will be better than what today’s marcom teams get from marketing automation services.

• AI will be the method through which a new concept called identity-based corporate communications will emerge. The precision will be impressive, with communications customized to each individual.

• AI will be able to inform corporate communications personnel of inconsistencies, discrepancies, conflicts and predictions of oncoming issues. AI will also help expose lies and identify deception. Due to the mass oversaturation of society from jacked-up marketing automation, a company’s reputation will mean more in the next 5-10 years than it does even today.

• AI will make it possible for corporate communications to communicate directly with machines as part of daily routine work. It is somewhat radical to think of distributing information to robots, but it is coming, perhaps sooner than you think  – and the robots will not be like R2-D2 from Star Wars.

Now I want to talk about how I believe AI will affect corporate communications professionals.

• AI will enable corporate communications professionals to train more people on the best practices of communications. AI will help corporate communications scale its influence, even if you have to show up as a hologram in one of your company’s other locations to deliver a media training session or explain the difference between corporate communications and marketing communications.

• Communications leaders will manage better localization of content with AI around the world.

• Corporate communications professionals will have a bigger stake in an organization’s risk management team, using AI as a basis for interpreting probabilities.

• Amid the rise of AI, corporate communications professionals will be trusted sources who people turn to for authentic experiences.

AI can be a friend to corporate communications, although there is a potential dark side of AI when it is used for nefarious purposes. We need to be at our best and harness AI to reflect our integrity as communications professionals.

The post How Artificial Intelligence Will Impact Corporate Communications appeared first on Artificial Intelligence.