To get the inside story behind the chatbot--how it was made, how OpenAI has been updating it since release, and how its makers feel about its success--I talked to four people who helped build what has become one of the most popular internet apps ever. In addition to Agarwal and Fedus, I spoke to John Schulman, a cofounder of OpenAI, and Jan Leike, the leader of OpenAI's alignment team, which works on the problem of making AI do what its users want it to do (and nothing more). What I came away with was the sense that OpenAI is still bemused by the success of its research preview, but has grabbed the opportunity to push this technology forward, watching how millions of people are using it and trying to fix the worst problems as they come up. Since November, OpenAI has already updated ChatGPT several times. The researchers are using a technique called adversarial training to stop ChatGPT from letting users trick it into behaving badly (known as jailbreaking).

Scammers could use ChatGPT to write phishing emails at a fraction of the cost of a human-penned missive, potentially cutting the cost per email by about 96 per cent. The popular chatbot, which is based on a large language model (LLM), was released by OpenAI in November 2022 and has since become a useful tool in many industries.

Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision Process (MDP) in which agent has full knowledge of its state, rewards, and transitional probability, reinforcement learning utilizes exploration and exploitation for the model uncertainty. Under the condition that the model usually has a large state space, a neural network (NN) can be used to correlate its input state to its output actions to maximize the agent's rewards. However, building and training an efficient neural network is challenging. Inspired by Double Q-learning and Asynchronous Advantage Actor-Critic (A3C) algorithm, we will propose and implement an improved version of Double A3C algorithm which utilizing the strength of both algorithms to play OpenAI Gym Atari 2600 games to beat its benchmarks for our project.

Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. We then question, if the more diverse pre-training knowledge can be cascaded to further assist few-shot representation learning. In this paper, we propose CaFo, a Cascade of Foundation models that incorporates diverse prior knowledge of various pre-training paradigms for better few-shot learning. Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision-generative knowledge, and GPT-3's language-generative knowledge. Specifically, CaFo works by 'Prompt, Generate, then Cache'. Firstly, we leverage GPT-3 to produce textual inputs for prompting CLIP with rich downstream linguistic semantics. Then, we generate synthetic images via DALL-E to expand the few-shot training data without any manpower. At last, we introduce a learnable cache model to adaptively blend the predictions from CLIP and DINO. By such collaboration, CaFo can fully unleash the potential of different pre-training methods and unify them to perform state-of-the-art for few-shot classification. Code is available at https://github.com/ZrrSkywalker/CaFo.

History has long been a theater of war, the past serving as a proxy in conflicts over the present. Ron DeSantis is warping history by banning books on racism from Florida's schools; people remain divided about the right approach to repatriating Indigenous objects and remains; the Pentagon Papers were an attempt to twist narratives about the Vietnam War. The Nazis seized power in part by manipulating the past--they used propaganda about the burning of the Reichstag, the German parliament building, to justify persecuting political rivals and assuming dictatorial authority. That specific example weighs on Eric Horvitz, Microsoft's chief scientific officer and a leading AI researcher, who tells me that the apparent AI revolution could not only provide a new weapon to propagandists, as social media did earlier this century, but entirely reshape the historiographic terrain, perhaps laying the groundwork for a modern-day Reichstag fire. These are powerful and easy-to-use programs that produce synthetic text, images, video, and audio, all of which can be used by bad actors to fabricate events, people, speeches, and news reports to sow disinformation.

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When it comes to artificial intelligence (AI), the advances we saw in 2021 pale in comparison to those that occurred last year, and AI shows no signs of slowing down. Industries from financial services to healthcare to manufacturing are adopting AI-enabled solutions to restructure how they operate as well as to solve previously intractable problems. Building on my AI predictions from last year, in this article I will explore five ways in which AI is poised to transform our society in the near future. Generative AI had an explosive year in 2022. Popular systems like DALL-E 2, Stable Diffusion and Midjourney can produce incredibly detailed images from a text prompt in a matter of seconds, irrevocably altering the landscape of graphic design.

Students are using it draft essays, therapists experimenting with it can for counselling, it is passing MBA exams with consummate ease with universities panicking and contemplating going back to handwritten exams. It seems to write instant sonnets and haikus on the Second World War, the Webb Telescope and makki-ki-roti with equal effortlessness. It has rocketed to 100mn users in a mere two months; Twitter took five plodding years and even the WWW took seven! ChatGPT is the poster boy of a larger movement in AI, called Generative AI (GenAI), sometimes also referred to as Large Language Models, Foundation Models, or Transformers. Other well-known manifestations of Generative AI are DALL E2 and Stable Diffusion which transform prompts into spectacular art, and the text-generator GPT3, which is where ChatGPT originated from. The spectacular performance of Generative AI has people wondering and whether it will replace search and fearing whether it will take away the jobs of artists, programmers, consultants, and journalists.