Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code workflow for creating video subtitles using Amazon Transcribe within your Amazon Web Services account. The terms subtitles and closed captions are commonly used interchangeably, and both refer to spoken text displayed on the screen.
Educated at St Pauls School, London and Cambridge University, José Luis Bermúdez is Professor of Philosophy at Texas A&M University, where he has also served as Dean of Liberal Arts and Associate Provost for Strategic Planning. Since his first book, The Paradox of Self-Consciousness (MIT Press 1998) he has been working on interdiscipinary aspects of self-representation and self-consciousness, most recently in Understanding "I": Language and Thought (OUP, 2017) and The Bodily Self: Selected Essays (MIT Press, 2018). He also works on rationality and reasoning, where he has published Decision Theory and Rationality (OUP, 2009). He is currently writing a book of framing and rationality, and also preparing the third edition of his textbook Cognitive Science: An Introduction to the Science of the Mind, both for Cambridge University Press. His work has appeared in seven languages and he is one of the 100 most cited philosophers on Google scholar.
AWS customers are relying on Infrastructure as Code (IaC) to design, develop, and manage their cloud infrastructure. IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible, while being able to follow best practices in the area of development operations (DevOps). One possible approach to manage AWS infrastructure and services with IaC is Terraform, which allows developers to organize their infrastructure in reusable code modules. This aspect is increasingly gaining importance in the area of machine learning (ML). Developing and managing ML pipelines, including training and inference with Terraform as IaC, lets you easily scale for multiple ML use cases or Regions without having to develop the infrastructure from scratch.
OppFi Inc., a 10-year-old fintech platform based in Chicago, targets U.S. households with an average of $50,000 in annual income that need extra cash for car repairs, medical bills, student loans and other expenses. Todd Schwartz, the company's chief executive, said its customers are employed and have bank accounts but are otherwise "locked out of mainstream financial services." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. OppFi, which made its public-market debut last summer, uses an AI model, real-time data analytics and a proprietary scoring algorithm to automate the underwriting process. It generates a credit score by analyzing a loan applicant's online shopping habits, income and employment information, among other data sources.
Rajiv Malhotra was trained initially as a Physicist, and then as a Computer Scientist specializing in AI in the 1970s. After a successful corporate career in the US, he became an entrepreneur and founded and ran several IT companies in 20 countries. Since the early 1990s, as the founder of his non-profit Infinity Foundation (Princeton, USA), he has been researching civilizations and their engagement with technology from a historical, social sciences and mind sciences perspective. He has authored several best-selling books. Infinity Foundation has also published a 14-volume series on the History of Indian Science & Technology.
Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust model can identify paraphrased text when the language used may be completely different, and also identify differences when the language used has high lexical overlap.
In the current era of digitization and globalization, when technology is undoubtedly causing disruption to almost every other industry sector, the online commerce segment is, of course, no exception. The pandemic-led dramatic shift in consumer behavior and market dynamics and thus the accelerated focus towards'online-first' and'digital-first' shopping experiences have added fuel to usher in a better future for the new-age commerce ecosystem, wherein multiple latest and emerging technologies are already playing, or are bound to play a pivotal role, in the times to come. Probably one of the biggest technological frontiers that have been contributing majorly in the recent past vis-à-vis positively reshaping e-commerce and online shopping is Artificial Intelligence AI. In particular, AI has been proven to be a boon in creating best-in-class, tailored, and customized shopping experiences on a plethora of online shopping, e-commerce, and m-commerce marketplaces for today's consumers. With the optimal use of AI and allied technologies, e-tailers or digital sellers are nowadays able to regularly and consistently learn, track and analyze their customers' online behaviors, and parallelly able to use the predictive analytics powered by AI to near-accurately forecast their future purchase decisions – which in turn is leading to increased repeat buys and customer loyalty over a period of time.
I have a background in IT, marketing, and business development with over ten years of experience in startups and multinational companies. I live by the philosophy that "entrepreneurship isn't about starting your own company or making money, it's all about solving problems." I m the co-founder of Expert Project from 2010 to 2019, where he oversaw day-to-day operations as well as international expansion. I also founded People Marketing (2012 to 2014) which was a startup focused on creating scalable customer acquisition campaigns for small businesses. When I m not working on my own projects, I can be found reading books about new cultures and innovation or traveling to places for inspiration.
While it may seem I'm just trying to work in as many buzzwords as I can, in fact, there really is an important intersection of these three elements. I've been interested in both big data and fast data for several years, and my newest tech interest is machine learning. As I have learned about the latter, I see that there are problems that require all three to be truly effective. One application for which I'm looking at bringing together these technologies is in Recommender Systems for brick and mortar shops. Probably the first big win for machine learning was Recommender Systems.
Jeff Bezos went on 60 Minutes in 2013 and pledged to fill the skies with a fleet of delivery drones that could zip parcels to customers' homes in 30 minutes. Asked when this future would arrive, the Amazon.com Inc. founder said he expected drone deliveries to commence in the next five years or thereabouts. Almost a decade later, despite spending more than $2 billion and assembling a team of more than 1,000 people around the world, Amazon is a long way from launching a drone delivery service. A Bloomberg investigation based on internal documents, government reports and interviews with 13 current and former employees reveals a program beset by technical challenges, high turnover and safety concerns. A serious crash in June prompted federal regulators to question the drone's airworthiness because multiple safety features failed and the machine careened out of control, causing a brush fire.