Technology is developing today at a pace that's never been seen before. New advancements and breakthroughs happen far more readily than at any time in the past. One of the most talked-about areas of cutting-edge tech is that of artificial intelligence (AI). AI is driving the digital transformation of organizations in all manner of niches. So wide-ranging are the applications of AI, that you've probably already interacted with an example of the tech today.

(Nasdaq: VERI), the creator of the world’s first operating system for artificial intelligence, aiWARE™, today announced the findings of a new research …

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time. A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages.

Today, we are coming close to experience the fourth one too -- the Artificial Intelligence revolution. Even though we now think we've seen it all, the AI revolution is going to make a change in the society just like the previous three did. Naturally, the revolution is going to affect all the aspects of our lives, including the business sphere. Since it's inevitable that AI is about to transform our lives in the near future, the best thing we can do is learn how to use it to work for us and help our business grow. We are all familiar with technology gadgets such as computers, drones, cameras, and even robots that can perform a specific action.

Unlike Mark Ruffalo's depiction of Banner in the films, Banner (played by Troy Baker) is a tortured soul through and through. The Hulk seems less a comedy relief "party trick" and more of a burden. While initially excited about her powers, Khan still suffers from the trauma of A-Day. The real fight of "Marvel's Avengers" is the inter- and intrapersonal conflict within each the team and each of the heroes. Kamala and Bruce both tackle this in their own way, as an adult and as a child.

Last week, The Guardian ran an op-ed that was supposedly written by GPT-3. The article was very misleading and had zero value in informing the public about advances in AI. But it perfectly showed us how humans and AI can team up to create sensational and moneymaking BS. Here's why I think the entire methodology was very wrong, misleading and damaging to AI research: In case you want to read the original article on Guardian (IMO don't waste your time):

Cable TV news is a primary source of information for millions of Americans each day. The people that appear on cable TV news and the topics they talk about shape public opinion and culture. While many newsrooms and monitoring organizations routinely audit the content of news broadcasts, these efforts typically involve manually counting who and what is on the air. But now researchers at the Brown Institute for Media Innovation at Stanford University have launched the Stanford Cable TV News Analyzer, an interactive tool that gives the public the ability to not only search transcripts but also compute the screen time of public figures in nearly 24/7 TV news broadcasts from CNN, Fox News and MSNBC dating back to January 2010. The site is updated daily with the previous day's coverage, and enables searches of over 270,000 hours of news footage.

Bollywood superstar Amitabh Bachchan will be the first Indian celebrity to lend his voice to Amazon's Alexa digital assistant starting next year, as the Silicon Valley giant expands its presence in the significant market. The 77-year-old actor has been a household name in India for nearly half a century, and his deep baritone is instantly recognisable to listeners in the country of 1.3 billion. Foreign firms such as Amazon have spent tens of billions of dollars in India in recent years as they fight for a piece of the Asian giant's burgeoning digital economy. In a blog post on Monday, Amazon India said Bachchan's "voice experience" feature will become available for purchase on Alexa next year. "It will include popular offerings like jokes, weather, shayaris (poetry), motivational quotes, advice and more," the firm said.

The field of journalism over the past decade or so has been witnessing continuous change. Today, journalism is influenced by big data and new computational tools. Data and visualisation have become the latest techniques for telling stories in media, thanks to intersections between journalism and computation. One of the many things that AI is doing for journalism is to make it easier and faster to analyse the data and also synthesise the data into stories. When we mention automatic story writing tools, they use Natural Language Understanding and Processing, to synthesise the stories.

Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business. These systems are in their infancy in the industry and in need of practical solutions to some fundamental research challenges. At Adobe research, we have been implementing such systems for various use-cases, including points of interest recommendations, tutorial recommendations, next step guidance in multi-media editing software, and ad recommendation for optimizing lifetime value. There are many research challenges when building these systems, such as modeling the sequential behavior of users, deciding when to intervene and offer recommendations without annoying the user, evaluating policies offline with high confidence, safe deployment, non-stationarity, building systems from passive data that do not contain past recommendations, resource constraint optimization in multi-user systems, scaling to large and dynamic actions spaces, and handling and incorporating human cognitive biases. In this paper we cover various use-cases and research challenges we solved to make these systems practical.