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When looking into the world of content creation we find ourselves drawn toward the personality and relatability of those we watch. We follow these people through good times and bad because of that human connection we share. Unbeknownst to some though, there is a wide variety of AI posing as people to do jobs like reporting the news. One of these jobs is content creation with robots starting to become more prevalent in many spaces online taking over many communities once occupied solely by humans. This is not an overnight phenomenon either, it has been a gradual rise in popularity as the technology has become more accessible for companies to implement.

After posting the following AI-generated images, I got private replies asking the same question: "Can you tell me how you made these?" So, here I will provide the background and "how to" of creating such AI portraits, but also describe the ethical considerations and the dangers we should address right now. Generative AI – as opposed to analytical artificial intelligence – can create novel content. It not only analyzes existing datasets but it generates whole new images, text, audio, videos, and code. As the ability to generate original images based on written text emerged, it became the hottest hype in tech. It all began with the release of DALL-E 2, an improved AI art program from OpenAI.

From data visualisation to deep learning libraries, Python is the most valuable language for machine learning.

Welcome to our November 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we hear from researchers who've developed an AI system for live music accompaniment and improvisation. Amongst other things, we also find out more about counterfactual explanations for reinforcement learning, planning robust frictional multi-object grasps, and social bias in knowledge graphs. Olga Vechtomova and Gaurav Sahu envisioned and developed a system, LyricJam Sonic, that uses AI to create a real-time generative stream of music based on an artist's own catalogue of studio recordings. The purpose is to inspire the artist with potentially unexpected combinations of sounds.

Machine learning (a subfield of AI) aims to program computers to learn and grow as people do. Machine learning may automate virtually any activity that can be solved using a pattern or set of data-developed rules. It's crucial to have a firm grasp of the various data kinds to clean and preprocess the data in preparation for use with ML algorithms. For machines to recognize patterns in data, it must first be translated into a numerical representation. This will allow us to pick the top-performing models that can quickly and accurately identify the underlying patterns.

Synthetic voice and splicing audio clips have been generated to spoof Internet users and artificial intelligence (AI) technologies such as voice authentication. Existing research work treats spoofing countermeasures as a binary classification problem: bonafide vs. spoof. This paper extends the existing Res2Net by involving the recent Conformer block to further exploit the local patterns on acoustic features. Experimental results on ASVspoof 2019 database show that the proposed SE-Res2Net-Conformer architecture is able to improve the spoofing countermeasures performance for the logical access scenario. In addition, this paper also proposes to re-formulate the existing audio splicing detection problem. Instead of identifying the complete splicing segments, it is more useful to detect the boundaries of the spliced segments. Moreover, a deep learning approach can be used to solve the problem, which is different from the previous signal processing techniques.

The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven data collection methods for creating a corpus of more than 60,000 translations from Hindi to Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. During this process, we help expand information access in Gondi across 2 different dimensions (a) The creation of linguistic resources that can be used by the community, such as a dictionary, children's stories, Gondi translations from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform; (b) Enabling its use in the digital domain by developing a Hindi-Gondi machine translation model, which is compressed by nearly 4 times to enable it's edge deployment on low-resource edge devices and in areas of little to no internet connectivity. We also present preliminary evaluations of utilizing the developed machine translation model to provide assistance to volunteers who are involved in collecting more data for the target language. Through these interventions, we not only created a refined and evaluated corpus of 26,240 Hindi-Gondi translations that was used for building the translation model but also engaged nearly 850 community members who can help take Gondi onto the internet.

For the past six months, I've been working in secret on a real-world experiment. Today, I'm delighted to begin sharing what we've been doing, why we've been doing it and to be part of a larger public debate about the questions it raises. I teamed up with a particle physicist and together we secured a professional script development deal to create a feature film script entirely written by artificial intelligence (AI). There is a lot to share about this project, and so I can't hope to cover it all in this article. Instead, I'm going to introduce the project and tell you what's coming out soon. You'll be able to hear me and Dr Eliel Camargo Molina talk about the project live next week, at the Future of Film summit.

The concept of generative artificial intelligence (GAI) poses a groundbreaking question that has until recently not been contemplated: at what stage does the relationship between humans and machines evolve from its present-day form into one that is so fundamentally changed that we can no longer regard one as being superior to the other when it comes to creative terms? Humanity stands on the brink of a new technological revolution. It is poised to harness the full potential of AI and machine learning, allowing us to automate many tasks and systems, revolutionise communication, and conserve time and money in our daily lives. Many are concerned that this could be the harbinger of a world full of robot overlords which would rob the human race of its free will. But what about those who will create those machines? In fact, some argue that in developing AI, we are creating a tool to enhance human cognition, giving us new means to think, invent and explore the universe rather than enslave humanity. Let's explore what generative AI is, where it currently stands, and where it could potentially take us in the next years. Generative AI is a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code. It is all about creating authentic-looking artifacts that are completely original. In other words, generative AI is a subset of machine learning that focuses on creating algorithms that can generate new data. Generative models are used in many different application areas, from art and music to computer vision and robotics.