After a year in which the world came to a halt and so many experienced some form ofpain, stress or grief, perhaps a theme park that can charge more than $150 per ticket would be seen as ... frivolous? Yet if early demand, hype and talk are any indication, one of our most treasured experiences is the great American theme park. And the original Disneyland, just a few months shy of its 66th birthday, still stands as our preeminent example. Deeply embedded with a playful and sunny Southern California leisure lifestyle, Disneyland for locals is a habitual, cross-generational hangout -- as much a tradition as baseball's opening day or a hike to the Hollywood sign. When Disneyland re-opens today after 13 months of closure -- essentially its second grand opening after July 1955 -- it will do so as a Californians-only locale due to pandemic restrictions.

Well, now we do not have to only imagine it. A project called Lost Tapes of the 27 Club, focused on mental health in the music industry, recently released a song called Drowned in the Sun. It was touted as a never-heard-before Nirvana song. Except that this song was never written by Kurt Cobain or Nirvana and discovered from some old musty attic years later; it was written by an artificial intelligence (AI) engine. To be more precise, it was written by a neural network trained on the entire body of Nirvana's work.

Amazon's latest top-of-the-range Alexa smart display has a trick up its sleeve like no other: it can follow you around a room. The third-generation Echo Show 10 costs £239.99 and is Amazon's largest smart display, sitting above the smaller £100 Echo Show 8 with an 8in screen and £80 Echo Show 5 with a 5.5in screen. It looks very different to its forerunners, like one of Amazon's Fire tablets mounted to a fat rotating Echo speaker, but fundamentally it is just like other wifi-connected smart displays powered by Alexa. The 10.1in screen is bright, good-looking and crisp enough for viewing at arm's length or more. It acts as a digital photo frame, displays timers and alarms, tells you the weather, the news and what's coming up on your calendar, controls smart home devices and plays music and videos from services such as Prime Video and Netflix, or YouTube through the built-in web browser.

First we had deepfakes, which could glue someone's face onto someone else's body. Then we had This Person Does Not Exist, which created people on a website every time you refreshed the page. Then we had Generated Photos, a commercial stock photography site, built entirely from AI-generated humans. Generating realistic-looking people has been one of the biggest challenges in visual AI, but researchers are mastering the technique quickly. The latest example: Generated Photos--which currently does $15,000 a month in revenue selling a library of AI-generated stock models, according to the company--has released an update that not only generates an AI-built human on demand but also lets you position it.

If you think AI and analytics are better apart, you may be missing out on some valuable opportunities. I was shocked recently while gathering background information for a short e-book I co-authored with Ellen Friedman, AI and Analytics at Scale: Lessons from Real-World Production Systems, to find that some people still think large-scale analytics projects and AI projects should be siloed and segregated. In particular, these people think of AI systems as very expensive, very specialized, separate systems, that must be completely isolated from analytics systems. My take is just the opposite. For years we've observed real-world enterprises across many sectors that benefit by co-locating analytics – even legacy analytics – together with modern AI and machine learning projects.

Recognizing and categorizing human actions is an important task with applications in various fields such as human-robot interaction, video analysis, surveillance, video retrieval, health care system and entertainment industry. This thesis presents a novel computational approach for human action recognition through different implementations of multi-layer architectures based on artificial neural networks. Each system level development is designed to solve different aspects of the action recognition problem including online real-time processing, action segmentation and the involvement of objects. The analysis of the experimental results are illustrated and described in six articles. The proposed action recognition architecture of this thesis is composed of several processing layers including a preprocessing layer, an ordered vector representation layer and three layers of neural networks. It utilizes self-organizing neural networks such as Kohonen feature maps and growing grids as the main neural network layers. Thus the architecture presents a biological plausible approach with certain features such as topographic organization of the neurons, lateral interactions, semi-supervised learning and the ability to represent high dimensional input space in lower dimensional maps. For each level of development the system is trained with the input data consisting of consecutive 3D body postures and tested with generalized input data that the system has never met before. The experimental results of different system level developments show that the system performs well with quite high accuracy for recognizing human actions.

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.

Have you interacted with a digital persona yet? At the Museum of Art & Photography in Bangalore, you can have a deep and engaging exchange with one that represents the late artist M.F. Husain -- considered the "Picasso of India" by many. This avatar is eager to talk art. And if you ask him whether he's real, he will look straight at you and say, "As close to real, enough to impress you."

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