In an ABC News interview this week, he warned "there will be other people who don't put some of the safety limits that we put on." OpenAI released its A.I. chatbot ChatGPT to the public in late November, and this week it unveiled a more capable successor called GPT-4. Other companies are racing to offer ChatGPT-like tools, giving OpenAI plenty of competition to worry about, despite the advantage of having Microsoft as a big investor. "It's competitive out there," OpenAI cofounder and chief scientist Ilya Sutskever told The Verge in an interview published this week. "GPT-4 is not easy to develop…there are many many companies who want to do the same thing, so from a competitive side, you can see this as a maturation of the field."

Arguably the first public-facing instance of AI to truly go viral, ChatGPT stands poised to revolutionize many aspects of the modern business world. While other use cases for AI continue to make an impact, especially in automation, this example of generative AI appears poised to take things to another level. Essentially a supercharged chatbot, ChatGPT boasts the ability to produce written content, including documentation, articles and even prose. Needless to say, generative AI has the potential to optimize a variety of corporate functions. These include new product ideation, project management, customer service, marketing and so much more.

Microsoft logo displayed on a phone screen and Copilot displayed on a screen are seen in this ... [ ] illustration photo taken in Krakow, Poland on March 16, 2023. Microsoft is adding Microsoft 365 Copilot into its office productivity applications. Who doesn't remember Mr. Scott in Star Trek 4: The Voyager Home sitting in front of a computer trying to speak with it via to come up with the transparent aluminum formula. Well, we aren't quite there yet but the momentum is definitely headed in that direction. As I alluded to in an earlier take, Microsoft is headed down the path of turning its every day users into power users coupled with offering them greater skills at a more rapid rate translating into productivity improvements.

Generative AI is already looking like the major tech trend of 2023. The ability to generate fresh content via algorithms has been thrust into the public consciousness by the likes of ChatGPT, a chatbot-style technology trained on large language models (LLMs) capable of producing essays, poems, lyrics, news articles and even computer programs. Then there's DALL-E, from the same Microsoft-backed OpenAI that spawned ChatGPT, which serves a similar purpose but for visual creations instead. While some have argued that ChatGPT signals AI's arrival into the mainstream, the truth of the matter is that we're just at the start of a new era of AI-powered applications that will transform just about every facet of industry, from consumer search and stock photography to real estate and content marketing. It's against that backdrop that a fledgling startup called Tavus is looking to make its mark by enabling companies to create "unique" videos tailored to a specific individual, but based entirely on a single initial recording.

Microsoft shares rose 12.4% last week on word of the release of Copilot, a generative artificial intelligence tool to be integrated into its Office suite. That's huge news from the tech giant because it hands AI to the masses via highly familiar, everyday interfaces such as Word, Excel, PowerPoint and Outlook. Copilot will swiftly bring significant efficiencies and improvements to common tasks including email, analyses, business cases, presentations and performance reviews. With streamlined workflows and automated administrative functions, companies will be challenged to rethink business models, talent needs and resource usage. Very quickly, it will spur creativity, shorten work timelines and improve results.

OpenAI CEO Sam Altman said in a recent interview with ABC News that he's a "little bit scared" of artificial intelligence technology and how it could affect the workforce, elections and the spread of disinformation. OpenAI developed the ChatGPT bot, which creates human-like answers to questions and ignited a new AI craze. "I think people really have fun with [ChatGPT]," Altman said in the interview. But his excitement over the transformative potential of AI technology, which Altman said will eventually reflect "the collective power, and creativity, and will of humanity," was balanced by his concerns about "authoritarian regimes" developing competing AI technology. "We do worry a lot about authoritarian governments developing this," Altman said.

Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are produced from a few independent and physically meaningful continuous latent factors, among which the fundamental frequency $f_0$ and the formants are of primary importance. In this work, we start from a variational autoencoder (VAE) trained in an unsupervised manner on a large dataset of unlabeled natural speech signals, and we show that the source-filter model of speech production naturally arises as orthogonal subspaces of the VAE latent space. Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding $f_0$ and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces. Without requiring additional information such as text or human-labeled data, this results in a deep generative model of speech spectrograms that is conditioned on $f_0$ and the formant frequencies, and which is applied to the transformation speech signals. Finally, we also propose a robust $f_0$ estimation method that exploits the projection of a speech signal onto the learned latent subspace associated with $f_0$.

As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. Impressed by the capability of the ChatGPT, many people are wondering about its limits: can GPT-5 (or other future GPT variants) help ChatGPT unify all AIGC tasks for diversified content creation? Toward answering this question, a comprehensive review of existing AIGC tasks is needed. As such, our work comes to fill this gap promptly by offering a first look at AIGC, ranging from its techniques to applications. Modern generative AI relies on various technical foundations, ranging from model architecture and self-supervised pretraining to generative modeling methods (like GAN and diffusion models). After introducing the fundamental techniques, this work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future. Moreover, we summarize their significant applications in some mainstream industries, such as education and creativity content. Finally, we discuss the challenges currently faced and present an outlook on how generative AI might evolve in the near future.

Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.

A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: $alpa.\!ai$, $Copy.\!ai$, ChatGPT (versions 3 and 4), $Studio.\!ai$, and YouChat. Humans and a specifically trained AI independently assessed the quality and quantity of ideas. We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Interestingly, 9.4 percent of humans were more creative than the most creative GAI, GPT-4. Our findings suggest that GAIs are valuable assistants in the creative process. Continued research and development of GAI in creative tasks is crucial to fully understand this technology's potential benefits and drawbacks in shaping the future of creativity. Finally, we discuss the question of whether GAIs are capable of being truly creative.