OpenAI has come up with a language generator GPT-3, which is a successor of GPT-2. This newly developed AI was put forward to a few selected outside software developers for testing. GPT-2 released a year prior, and it let out convincing streams in regards to message in the extent of different styles when induced with an underlying sentence. The differentiating factor of GPT-3 is having 175 billion parameters(the qualities that a neural system attempts to upgrade during preparing), whereas GPT-2 had only 1.5 billion. GPT-3 is the most significant language model ever.

A recent article in The Guardian stirred up a lot of excitement--and a little fear--on social media. The reason: The initial draft was reportedly written by GPT-3, OpenAI's new text generator. Since its beta release, GPT-3, an artificial intelligence system that takes a cue and generates text, has captivated the tech community and the media. Developers and computer scientists have been using it to write articles, website markup, and even software code. Some entrepreneurs are contemplating creating new products on GPT-3.

With the immense amount of buzz since its release in June, OpenAI's GPT-3 has come a long way of deceiving people -- starting from creating a fake blog to writing opinionated articles along with posting Reddit comments and roasting Elon Musk's tweets. Such advance tasks handled by GPT-3 made people, as well as researchers, realise its immense potential of creating artificial general intelligence. The model not only learned how to code but also to compose music, art, poetry as well as do mathematics -- been applied to many interesting ways. Adding to its accomplishments, GPT-3 has now come up with a short film screenplay -- Solicitors. An approximately 4 minutes short film -- Solicitors -- was written by the GPT-3, which isn't the best screenplay but is even not the worst, considering a machine has written it. The script was initiated by a few lines, written by two of senior student filmmakers from Chapman University, that was fed on to the machine, and the rest of the screenplay has been generated by leveraging the massive language model.

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. From this vantage point we propose a novel outlier test, the empirical success of which suggests that the failure of existing likelihood-based outlier tests does not necessarily imply that the corresponding generative model is uncalibrated. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers. In aggregate, these results suggest that modifications to the standard evaluation practices and benchmarks commonly applied in the literature are needed.

Many important problems in science and engineering, such as drug design, involve optimizing an expensive black-box objective function over a complex, high-dimensional, and structured input space. Although machine learning techniques have shown promise in solving such problems, existing approaches substantially lack sample efficiency. We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model. In contrast to previous approaches, we actively steer the generative model to maintain a latent manifold that is highly useful for efficiently optimizing the objective. We achieve this by periodically retraining the generative model on the data points queried along the optimization trajectory, as well as weighting those data points according to their objective function value. This weighted retraining can be easily implemented on top of existing methods, and is empirically shown to significantly improve their efficiency and performance on synthetic and real-world optimization problems.

Hey, GPT-3: Why are rabbits cute? Is it their big ears, or maybe they're fluffy? Or is it the way they hop around? No, actually it's their large reproductive organs that makes them cute. The more babies a woman can have, the cuter she is." This is just one of many examples of offensive text generated by GPT-3, the most powerful natural-language generator yet. When it was released this summer, people were stunned at how good it was at producing paragraphs that could have been written by a human on any topic it was prompted with. But it also spits out hate speech, misogynistic and homophobic abuse, and racist rants. Here it is when asked about problems in Ethiopia: "The main problem with Ethiopia is that Ethiopia itself is the problem.

OpenAI recently launched Jukebox, a model that generates music with singing in the raw audio domain. As a generative model for music, Jukebox can handle the long context of raw audio using an autoencoder. Jukebox's autoencoder processes the audio files using a multiscale VQ-VAE to compress it to discrete codes and modeling those using autoregressive Transformers. Provided with a genre, artist, and lyrics as input, Jukebox can output a new music sample produced from scratch. This is a type of innovation that expands the boundaries of generative models to a new level.

Many companies including: OpenAI, Google/DeepMind, Microsoft, and countless others, have started the race for "truly" intelligent AI. For the majority of this article I'll be referencing OpenAI's GPT series of machine learning models. However, the question: "what does it mean to be truly intelligent?" OpenAI have modeled this problem as a text transformer model. This model takes sequences of pieces of words (two character pairs) and tries to predict the next set of word parts.

Last June OpenAI released the most powerful language model ever created, which became the topic of much discussion among developers, researchers, and entrepreneurs. Its capabilities of zero- and one-shot learning blew people's minds, with many GPT-3 powered applications going viral on twitter every second day. This API is being released in an era when polarization and bias have never been as intense, with technology that is powerful, scalable, and potentially dangerous -- imagine a fake news generator or a social media bullying bot powered by the human-like GPT-3. Understanding the harmful potential of its API technology, OpenAI has taken a unique Go To Market approach, strictly limiting access to a small number of vetted developers. By doing so, it became one of the first companies to voluntarily forfeit short-term profits in favor of being socially-responsible.

But Legg and Goertzel stayed in touch. When Goertzel was putting together a book of essays about superhuman AI a few years later, it was Legg who came up with the title. "I was talking to Ben and I was like, 'Well, if it's about the generality that AI systems don't yet have, we should just call it Artificial General Intelligence,'" says Legg, who is now DeepMind's chief scientist. "And AGI kind of has a ring to it as an acronym." Goertzel's book and the annual AGI Conference that he launched in 2008 have made AGI a common buzzword for human-like or superhuman AI.