Some of the biggest names in the world have left us before their time and their fans have been left wondering what could have been today had they not passed away. Now, they can see what those figures would look like if they were still alive in 2022 thanks to an artistic project. Turkish artist Alper Yesiltashas used artificial intelligence to start a new project which he has called "As if nothing happened". "Behind this project lies the question of "how would people look photo-realistically if some great events had not happened to them"," he explains in an introduction to the project. Among those included are Princess Diana, John Lennon, Michael Jackson, Freddie Mercury, Elvis Presley and Tupac.

The most well-known line of inquiry in the growing anti-deepfake research sector involves systems that can recognize artifacts or other supposedly distinguishing characteristics of deepfaked, synthesized, or otherwise falsified or'edited' faces in video and image content. Such approaches use a variety of tactics, including depth detection, video regularity disruption, variations in monitor illumination (in potentially deepfaked live video calls), biometric traits, outer face regions, and even the hidden powers of the human subconscious system. What these, and similar methods have in common is that by the time they are deployed, the central mechanisms they're fighting have already been successfully trained on thousands, or hundreds of thousands of images scraped from the web – images from which autoencoder systems can easily derive key features, and create models that can accurately impose a false identity into video footage or synthesized images – even in real time. In short, by the time such systems are active, the horse has already bolted. By way of a more preventative attitude to the threat of deepfakes and image synthesis, a less well-known strand of research in this sector involves the possibilities inherent in making all those source photos unfriendly towards AI image synthesis systems, usually in imperceptible, or barely perceptible ways.

In August, first prize in the digital-art category of the Colorado State Fair's fine-art competition went to a man who used artificial intelligence (AI) to generate his submission, "Théâtre d'Opéra Spatial." He supplied the AI, a program called Midjourney, with only a "prompt"--a textual description of what he wanted. Systems like Midjourney and the similar DALL-E 2 have led to a new role in our AI age: "prompt engineer." Such people can even sell their textual wares in an online market called PromptBase. Midjourney and DALL-E 2 emerged too late to be included in "Working With AI: Real Stories of Human-Machine Collaboration," by Thomas Davenport and Steven Miller, information-systems professors at Babson College and Singapore Management University, respectively.

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Deep Learning is a subfield of machine learning: a new take on learning representations from data that puts emphasis on learning successive layers of increasingly meaningful representations. How many layers contribute to a model of the data is called the depth of the model. The specification of what a layer does to its input data is stored in the layer's weights, which in essence are a bunch of numbers. In technical terms, we'd say that the transformation implemented by a layer is parameterized by its weights. Wights are also called the parameters of a layer.

If you use a tool where it hasn't been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Instead, force machine learning and AI systems to earn your trust. If you want to teach with examples, the examples have to be good. If you want to trust your student's ability, the test has to be good. Always keep in mind that you don't know anything about the safety of your system outside the conditions you checked it in, so check it carefully!

Loab has been dubbed the first artificial intelligence-generated cryptid image and is taking the AI community by storm with its urban-legend mystique and horror backstory. Computers are becoming increasingly sophisticated, and powerful AI is being developed to make human lives easier, more fun or anything in between. AI simply stands for artificial intelligence, and as the name implies, it is the idea of creating a system that has similar intelligence or thinks similarly to humans. With advanced text-to-image software such as Dall-E 2 and Google's Imagen AI, the possibilities of artificial intelligence have never looked so bright, but are there darker aspects to this technology or horrible consequences for creating intelligent systems that users don't fully understand? Related:What Is The Future Of Dall-E AI Technology?

Google wants to provide Singapore organisations with the cloud tools and skills they need to tap data for greater efficiencies and better service delivery. It also hopes to help them leverage artificial intelligence (AI) and to do so responsibly, based on its own set of best practices and principles. With organisations worldwide digitally transforming their business, including those in Singapore and Malaysia, the US cloud vendor is keen to figure out how its technology and infrastructure can facilitate their efforts. Data, specifically, will prove critical in enabling companies to tap new opportunities in a digital economy, said Google Cloud's Singapore and Malaysia country director, Sherie Ng, in an interview with ZDNET. She said businesses would need to figure out how to leverage data to better understand and serve customers as well as to reduce inefficiencies and improve work processes.

The foundation of cognitive automation is software that adds intelligence to information-intensive processes. It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI. By utilizing AI technology, cognitive automation broadens and enhances the set of tasks normally associated with RPA, resulting in cost savings, increased customer satisfaction, and increased accuracy in intricate business processes involving unstructured data. Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. Cognitive automation represents a range of strategies that enhance automation's ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. Machine learning is not cognitive automation.

"The best way to predict the future is to invent it" – Alan Kay. Prof Shazia Sadiq explains the how the UQ AI Collaboratory has brought together experts from various fields within UQ, to find innovative solutions towards a future where we can amplify human potential with AI.