In this code pattern, learn how to extract audio from video and store it in IBM Cloud Object Storage when given a video recording of a virtual meeting or a virtual classroom. The first step in getting insights from video is extracting audio from the video and storing it in a common accessible storage space. This code pattern shows how to look at a video recording of a meeting and extract audio from that video file using the open source FFmpeg library in a Python Flask runtime. FFmpeg is a complete, cross-platform solution to record, convert, and stream audio and video. You then store the extracted audio in IBM Cloud Object Storage, a highly scalable cloud storage service that is designed for high durability, resiliency, and security.

There is no doubt that the AI marketplace is an attractive, fast-growing sector for IBM Business Partners to get engaged. IDC has forecasted the 2020 AI revenue opportunity at USD 49 billion, growing to USD 98 billion by 2023¹. Customers tell us that infrastructure for AI is not getting better, it’s getting worse. In their struggle to help meet escalating demands, many vendors resort to cobbling disparate portfolios that are not tightly integrated. The result is a jigsaw puzzle of data silos, multiple data copies and independent components, which lead to growing complexity with increased cost of ownership and poor data insights. Data locked in silos is complex to manage—it can also increase operating expenses, which, in turn, drives up capital expenses. It is also hard for users to collect, organize and analyze to extract value. AI needs data to achieve the holistic view that is required for meaningful insights that fuels business growth. How can Business…

Data is the one area where every company has an equal opportunity to be great. Knowing your own data can give your company a clear competitive advantage.[1] Yet, according to a survey conducted by Forrester with global IT, data, and line-of-business decision makers, more than half of the respondents admitted they simply don't know what their AI data needs are[2]. If business leaders do not fully understand their data needs, they can t be expected to understand their infrastructure needs. Mike Leone, Senior Analyst at Enterprise Strategy Group covering Data Platforms, Analytics, and AI, states: "Artificial intelligence is poised to revolutionize business around the globe. While it's easy to get lost in the eye-opening use cases where AI is being applied today, it all starts with a foundational infrastructure that can satisfy the extensive list of AI requirements…" There is no AI without information architecture (IA).

In this article, you learn how to use artificial intelligence, or at least machine learning, to raise the alarm when there are changes in a supposedly static environment, such as a hay barn while the hay is drying after the harvest. I use two methods to achieve this: visual recognition and image comparison. Visual recognition requires more processing than can be done easily on a Raspberry Pi. The solution here is to upload pictures of the IBM Cloud, and ask IBM Watson Visual Recognition to identify the objects in them. If a new object appears, or if an expected object disappears (and doesn't show for a whole day, because objects may only be identifiable under certain lightening conditions), this AI system raises an alarm. Because object recognition using IBM Watson Visual Recognition requires significant bandwidth to upload pictures to the IBM Cloud, I designed an AI system that can work on a low-bandwidth network connection, such as a LoRa connection. To detect changes in such an environment, the second part of this article uses image comparison. Images are taken every ten minutes, and each time the image is compared to the image taken 24 hours prior. This way, the changes in lightening conditions will hopefully be minor enough to prevent false alarms. To implement visual recognition in the cloud, we will base our architecture on the short range architecture in the first article. The devices in the hay barn use WiFi to communicate with an access point in the farmhouse which is connected to the Internet.

The digital media industry has long faced a unique data challenge. Radio, television and film productions generate massive unstructured data sets, which can measure up to hundreds of terabytes total for a single large-scale film project. The amount of data generated by these digital media productions is growing rapidly. In fact, IDC has projected that the world's total amount of data will grow to 44 zettabytes (or a billion terabytes) by 2020, and a whopping eighty percent of that growth will be from unstructured content much of it created by the digital media industry. Managing and storing all this digital content can be both complicated and costly.

The concept of "outthink" is sweeping through numerous industries as companies and individuals uncover new ways to transform their businesses using social, mobile, cloud, Internet of Things (IoT) and cognitive technologies. To make this strategy a success, outthink must come alive to fuel innovation. This new approach to technology must be thoughtfully applied to a company's daily processes, enabling real transformation and driving unprecedented value with the goal of vastly improving customer experiences and lives. This is an example of a simple IBM cloud platform based application. It is designed to help insurance claims adjusters assess hail damage following a storm for a faster, safer and more accurate claims processing at lower cost.