Causal language modeling (LM) uses word history to predict the next word. BERT, on the other hand, makes use of bi-directional word information in a sentence to predict words at masked positions. While BERT is effective in sequence encoding, it is non-causal by nature and is not designed for sequence generation. In this paper, we propose a novel language model, SUffix REtrieval-Augmented LM (SUREALM), that simulates a bi-directional contextual effect in an autoregressive manner. SUREALM employs an embedding retriever to search for training sentences in a data store that share similar word history during sequence generation. In particular, the suffix portions of the retrieved sentences mimick the "future" context. We evaluated our proposed model on the DSTC9 spoken dialogue corpus and showed promising word perplexity reduction on the validation and test set compared to competitive baselines.

Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case (1), and show that small variations that maintain the principles of ToM turn the results on their head. We argue that in general, the zero-hypothesis for model evaluation in intuitive psychology should be skeptical, and that outlying failure cases should outweigh average success rates. We also consider what possible future successes on Theory-of-Mind tasks by more powerful LLMs would mean for ToM tasks with people.

On the last day of February, NYU professor Gary Marcus published an essay entitled "The threat of automated misinformation is only getting worse." He warned about the easiness with which you can create misinformation backed by fake references using Bing "with the right invocations." Shawn Oakley, dubbed by Marcus as a "jailbreaking expert" said that "standard techniques" suffice to make it work, providing evidence that the threat of automatic AI-generated misinformation at scale is increasing. Marcus shared his findings on Twitter and FoundersFund's Mike Solana responded: My interpretation of Solana's sarcastic tweets is that claiming that an AI model is a dangerous tool for misinformation (or, more generally, harm of some kind) isn't a good argument if you've consciously broken its filters--he implies the problem isn't the tool's nature but your misuse, and thus you're to blame and not the company that created the tool. His "analogy" between Bing Chat and a text editor misses the point (i.e., language models can generate human-sounding misinformation at scale--you can't do that with Microsoft Word but can with Microsoft Bing) but, even if Marcus is right, there's some truth in Solana's implied stance.

LLMs (large language models) like ChatGPT are of such high capability – or threat – that we can forget to think about how they are achieving such near supernatural generative abilities. 'Oh, LLMs have just learned advanced word statistics'

According to a recent report by the German news website Heise, Andreas Braun, Microsoft Germany CTO, has stated the ChatGPT 4 language module is going to be rolled out in the coming week. The report added that ChatGPT 4, the brand new language model, will also contain "multimodal models" to provide entirely different feasibilities, including videos, Bruan claimed. Braun apparently acknowledged the Large Language Models (LLM) similar to GPT as a "game changer", and the reason behind this is that these devices permit them to understand "natural language" along with providing conversational answers. After the company rolls out the latest GPT-4, it is likely to be utilized in making ChatGPT and Microsoft services that use Bing AI better. The worldwide team of Microsoft has still not provided any confirmation about the presentation of GPT-4 coming week.

OpenAI just announced pricing for businesses seeking to integrate its ChatGPT service into their own products, and it looks an awful lot like a 90 percent off sale. It all starts with OpenAI, a former nonprofit that's now gunning for riches as lustily as any Silicon Valley unicorn. The company has built a dazzling array of products, including the DALL-E image generator and the renowned ChatGPT service. ChatGPT is powered by a system known as a large language model (or LLM), and it's one of several LLM lines that OpenAI sells commercially. Buyers of LLM output are mostly companies that integrate language-related services like chat, composition, summarization, software generation, online search, sentiment analysis, and much more into their websites, services, and products.

The concept of immediacy is ingrained in 21st-century life. From shopping on Amazon with next-day delivery to internet and location services providing real-time information in the palm of our hands, it is clear that instant results are only going to become more prevalent in everyday life. In an era when many are jaded about tech -- and it is increasingly harder to surprise and excite people about what it can do -- ChatGPT has been a refreshing development. It is engaging, can be a lot of fun to test-drive and has proved beneficial for students and professionals looking to generate content. And it is all done in an instant.

Almost overnight, ChatGPT became a new hot topic in many countries and regions around the world. After its launch in late November, ChatGPT quickly became the fastest-growing consumer app in history, registering more than 1 million users in just five days and topping 100 million two months later. By comparison, It took 6.5 years for iTunes to reach 100 million users, 5 years for Twitter, 4.5 years for Meta (Facebook), and 3.5 years for WhatsApp. ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a chatbot. It uses Transformer neural network architecture, also known as GPT-3.5 architecture, which is a model for processing sequential data with language understanding and text generation capabilities.

New AI systems such as ChatGPT, the overhauled Microsoft Bing search engine, and the reportedly soon-to-arrive GPT-4 have utterly captured the public imagination. ChatGPT is the fastest-growing online application, ever, and it's no wonder why. Type in some text, and instead of getting back web links, you get well-formed, conversational responses on whatever topic you selected--an undeniably seductive vision. But the public, and the tech giants, aren't the only ones who have become enthralled with the Big Data–driven technology known as the large language model. Bad actors have taken note of the technology as well. At the extreme end, there's Andrew Torba, the CEO of the far-right social network Gab, who said recently that his company is actively developing AI tools to "uphold a Christian worldview" and fight "the censorship tools of the Regime."

It is a robot researcher with good communication skills; you can ask it to answer questions about various areas of knowledge and it will write short documents in various formats and in excellent English. Or write bad poetry, incomprehensible jokes, and obey a command like "Write Tetris in C." What comes out looks like it could be, too. Coders love that sort of thing, and have been stuffing Stack Overflow's dev query boards with generated snippets. Just one problem – the quality of the code is bad. So bad, Stack Overflow has screamed "STOP!" and is mulling general guidelines to stop it happening again.