This post provides steps and python syntax for utilizing the Google Cloud Platform speech transcription service. Speech transcription refers to the conversion of speech audio to text. This can be applied to many use cases such as voice assistants, dictation, customer service call center documentation, or creation of meeting notes in an office business setting. It is not difficult to see the value this can bring to individuals and businesses. AWS has long been a leader in this space. Google, IBM, and Microsoft have of course developed their own services as well.
CIOs have tested many emerging strategies during the pandemic, including the Internet of Things sensors, low-orbit satellites, and augmented reality. Now the challenge is to get the technologies to work together to reach for big business goals. This was the message from Adriana Karaboutis, group chief information and digital officer at National Grid, speaking at the 2021 MIT CIO Symposium in a session on Accelerated Digital Transformation, held virtually recently. The effects of the crisis made organizations "double down on that focus and crystallization for what we need to do," she stated in an account in CIO Dive To pursue IoT, standardization is a must, suggested Harmeen Mehta, chief digital and innovation officer at BT, the British multinational telecommunications firm. "If the world can consolidate a bit on standardization, it will help pick up speed," stated Mehta.
NUREMBERG, Germany and SUNNYVALE, CA, USA, May 5, 2021 – Google Cloud and Siemens, an innovation and technology leader in industrial automation and software, today announced a new cooperation to optimize factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future. Siemens and Google Cloud to cooperate to transform manufacturing by enabling scaled deployment of artificial intelligence. Data drives today's industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.
Artificial Intelligence (AI), Cloud, 5G, and IoT are continuously advancing innovation that extends across business development all the way down to the consumer level. Critical innovations are emerging from the escalation of new technologies, including hybrid workforces, remote healthcare delivery, hyper-personalization, and zero-touch. These use cases are generating myriad benefits for both organizations and consumers, and inspiring new levels of efficiency, productivity, and engagement. We're currently witnessing a dynamic surge in technological advancement that has spawned the era of ubiquitous digital transformation, but these new technologies still need room to grow. Ronald van Loon is working in partnership with NVIDIA, and recently had the opportunity to discuss the technology trends and drivers shaping the post-pandemic future, and assess the role the Arm acquisition by NVIDIA is positioned to play in this development.
IBM has revealed it is set to acquire Turbonomic in the latest of a series of cloud computing related takeovers. Turbonomic adds to IBM's arsenal of Artificial Intelligence (AI)-powered hybrid cloud offerings and is the latest in big blue's bid to provide enterprises AI-based services to manage their networks and workloads. The acquisition of Turbonic, which provides tools for application resource and network performance management, is part of IBMs attempts to apply AI to enhance IT operations (AIOps). While IBM did not disclose the financial terms of the deal, based on Turbonomic's recent valuation based on its last funding round, it is estimated to have cost between $1.5 billion and $2 billion, according to reports. IBM argues the acquisition puts it in a position to offer full stack application observability and management in the increasingly complex hybrid cloud environments in today's enterprises.
Predictive maintenance has often been hailed as one of the most immediate and effective uses for machine learning, and big promises have been made regarding its capabilities. However, it's been slow to take off in practice. Even with falling prices on intelligent sensors allowing manufacturers to collect and transmit the various types of data such as temperature and vibration needed to drive the development of predictive maintenance programs, properly deriving actionable insights from that data without domain experts and on-site data analysts has proven more difficult than initially imagined. Unfortunately, when operators and plant managers can't properly leverage this value, their industrial internet of things (IIoT) investments may not produce an ideal return on investment (ROI). In the hopes of alleviating these issues, cloud-provider Amazon Web Services (AWS) has recently announced the general availability of Amazon Lookout for Equipment, a service that feeds data from end-users' industrial equipment into the AWS cloud-based machine learning model to assist them in more accurately predicting machine failures.
IBM said it will acquire Turbonomic, a company focused on application resource and network performance management, in the company's 11th hybrid cloud and AI acquisition since CEO Arvind Krishna took over. Turbonomic move comes after IBM's recent purchase of business automation company myInvenio. For IBM, the Turbonomic purchase will bolster its AIOps business. Turbonomic's platform uses AI to monitor and manage containers, virtual machines, databases, servers and storage. IBM will use Turbonomic's platform for observability across cloud infrastructure, optimization tools and AIOps.
An artificial-intelligence (AI) bank leapfrogs the competition by organizing talent, technology, and ways of working around an AI-first vision for empowering customers with intelligent value propositions delivered through compelling journeys and experiences. Making this vision a reality requires capabilities in four areas: an engagement layer, decisioning layer, core technology layer, and platform operating model. This article was a collaborative effort by Sven Blumberg, Rich Isenberg, Dave Kerr, Milan Mitra, and Renny Thomas. Previous articles in this series have explored the first two areas. The current article identifies capabilities needed in the third area, the core technology and data infrastructure of the modern capability stack. Deploying AI capabilities across the organization requires a scalable, resilient, and adaptable set of core-technology components. When implemented successfully, this foundational layer can enable a bank to accelerate technology innovations, improve the quality and reliability of operations, reduce operating costs, and strengthen customer engagement.
Let us examine an illustrative example from big data processing. Consider a simple query that might arise in an ecommerce setting: computing an average over 10 billion records using weights derived from one million categories. This workload has the potential for a lot of parallelism, so it benefits from the serverless illusion of infinite resources. We present two application-specific serverless offerings that cater to this example and illustrate how the category affords multiple approaches. One could use the AWS Athena big data query engine, a tool programmed using SQL (Structured Query Language), to execute queries against data in object storage.
It's human nature to choose the path of least resistance, as we're most comfortable with what we already know. Take the example of, "No one ever got fired for buying Cisco." But there comes a point in time when inaction becomes the greatest risk of all. Market transitions are real, and every product has a life cycle. Understanding these transitions and a vendor's product strategy is just as important--or sometimes more important--than selecting a specific company.