What’s the Difference Between Machine Learning, Artificial Intelligence, and Deep Learning?
Machine learning refers to the use of learning algorithms that build a model of understanding about the relationships between existing data to make predictions about new data. The term machine learning is often used interchangeably with artificial intelligence, but in fact these terms refer to related, but separate, concepts.
Artificial intelligence is the ability to sense, learn, reason, act, and adapt to the real world without explicit programming – broadly, it can be defined as any system capable of exhibiting some level of human-like intelligence.
So if artificial intelligence is the overall concept of building solutions that allow computers to learn and make decisions without explicit human instruction, machine learning is the method by which developers create those abilities.
Deep learning is the third term often used when discussing machine learning. Rather than use explicit mathematical algorithms, deep learning attempts to model how the brain works and learns with systems called neural networks.
The takeaway: There are multiple ways to build a system that is capable of demonstrating human-like characteristics, with rule-based systems and knowledge-based systems each having their time in the sun over the last few decades. But machine learning is deeply rooted in statistics, which is why you would use machine learning tools and services to build artificial intelligence applications and systems.
How is Cloud Machine Learning Different?
The combination of massive compute power, data lakes, security, analytics capabilities, and their ability to integrate with cloud services is turning machine learning from a niche, experimental technology into an essential business building block.
Today, companies are using machine learning tools in greater numbers to prepare data for analysis, build, and refine machine learning models, and take advantage of end-user cognitive applications including voice recognition, image and video analysis, providing forecasts and recommendations, and many other intelligent solutions.
The result is that machine learning is revealing new insights, discoveries, and efficiencies from the systems, processes, and information technologies that drive daily business. The core infrastructure that underlies nearly every business or creative endeavor can be enhanced by machine learning technologies in ways that add value to the work product and the people and processes that interact with it.
This is increasingly true for video providers in media and entertainment, the enterprise, and the public sector, all areas where machine learning can increase the value of video content and create outstanding audience experiences.
For video providers in particular, the applications for cloud machine learning tools in video are vast in number and continuously being developed and continuously being refined.
What are some Advantages of Cloud Machine Learning for Video?
Modern video providers have a number of questions in common:
- Which actors are in a scene?
- When are certain words uttered?
- What objects are on screen?
- Once we know said actors/scenes/words/objects exist, how do we retrieve them precisely when we need them?
Cloud video machine learning offers a convenient way to answer each question. Here are a few ways it does it.
Searchable video archives: With cloud machine learning services, video teams can substantially reduce the time and resources spent cataloging, searching, and building assets from their video archive. Machine learning-powered content indexing and metadata generation can enable a number of applications with significant real-world benefits.
For example, many broadcasters must maintain massive archives of video content, often originating from disparate sources and using inconsistent, if any, systems for tagging assets. With machine learning tools, the time-consuming manual labor of tagging content for search can be eliminated, and video content libraries can be optimized for fast, accurate search.
Automated video captions: Caption metadata is essential to making video useful and accessible to all audiences. However, the process of transcribing video assets, and producing and integrating accurate captions in the various formats required to assure accessibility across different screens and devices, can be costly and slow. The time and expense involved becomes increasingly prohibitive at scale, when generating captions for large volumes of content is required.
The advent of machine learning tools that can process and analyze video in the cloud gives content providers a powerful, scalable process for automating the caption creation process. This is a major time and labor saver for companies – such as online training providers – which have thousands of hours of video and need captions to meet accessibility requirements set by their customers.
Video clip generation: Traditionally, the process of generating and publishing video clips has required a manual workflow to identify relevant content from raw video, generate time-coded clips, then transcode, package, and distribute those clips for publication on social channels.
This high-touch, multi-step process can cause delays and result in missed opportunities, particularly for live event broadcasts. Now, machine learning tools can automate key steps of the process to help broadcasters get high-value clips to viewers’ screens in near real time – which is far more suited to the power of immediacy that social media has.
Personalization and monetization: To optimize revenue opportunities for streaming video, content providers must equip their infrastructure to furnish advertising that is targeted to individual users and delivered in a way that is tailored to each viewer, such as through personalized ads.
With machine learning-enhanced video workflows, content providers can now seamlessly insert personalized advertising based on a variety of factors, such as the type of device being used by the viewer, demographic information about the viewer, or even information about the content being streamed; this is known as content-aware advertising insertion.
Analytics and measurement: Today’s video workflows have the ability to measure and report seemingly endless amounts of information about live and VOD streams, and the infrastructure behind them. Data related to the performance of individual components, key processes, and complete workflows can be measured and used for real-time notifications or long-term analysis.
To identify new insights and discoveries, the application of machine learning systems offers content providers new ways to optimize every aspect of the video workflow, including workflow performance, use of network resources, monetization results, and much more.
What are some Possible Machine Learning Video Applications of the Future?
Security: One of the primary concerns around securing the cloud is access control. For example, video providers want to prevent the possibility of employees making mistakes that could accidentally expose private content, such as footage from an unreleased blockbuster. A “machine learning security guard” could protect against such issues by detecting protected content in the wild before anyone else notices.
Content rights: Another headache for video providers is when someone publishes videos of copyrighted content online with tricks that avoid watermarks or content filters; for example, slowing the frame rate by one frame per second. Imagine a machine learning solution that has studio scripts or rights holders content loaded into it; with this data, the solution can scan the web for new content and recognize dialogue and audio that matches a script.
Frame-Based Analytics for Video Demo
Machine learning in sports: Learn how easily you can identify and track people in a scene, create and expose metadata from that scene, and take advantage of incredibly fast and intelligent search capabilities in this demonstration that pairs AWS Elemental Media Services and Amazon Rekognition.