Whether or not we're still talking about ChatGPT in a year remains to be seen, but right now, the generative AI chatbot is all the rage. You've probably already used ChatGPT on OpenAI's website, but did you know that you can bring ChatGPT with you anywhere you go? Ever since ChatGPT launched last November, developers have been incorporating the AI into their apps. Here are five of the best ChatGPT iOS apps currently on the App Store. One of our favorite conversational AI apps is Perplexity.

A collateral beneficiary of the feverish pace of generative artificial intelligence development appears to be the humanoid robot. A Norwegian company called 1X Technologies, formerly Halodi Robotics, which describes itself as a manufacturer and inventor of androids, recently attracted $23.5 million in a round of funding led by the OpenAI Startup Fund -- the same OpenAI that got the AI snowball rolling with its ChatGPT generative AI bot. "1X is at the forefront of augmenting labor through the use of safe, advanced technologies in robotics," Brad Lightcap, OpenAI's COO and manager of the OpenAI Startup Fund, said in a statement. "The OpenAI Startup Fund believes in the approach and impact that 1X can have on the future of work." With the funds, 1X said it intends to accelerate the development of its bipedal android model NEO and expand manufacturing of its first commercially available wheel-based android, EVE, in Norway and North America.

China's top digital regulator proposed bold new guidelines this week that prohibit ChatGPT-style large language models from spitting out content believed to subvert state power or advocate for the overthrow of the country's communist political system. Experts speaking with Gizmodo said the new guidelines mark the clearest signs yet of Chinese authorities' eagerness to extend its hardline online censorship apparatus to the emerging world of generative artificial intelligence. "We should be under no illusions. The Party will wield the new Generative AI Guidelines to carry out the same function of censorship, surveillance, and information manipulation it has sought to justify under other laws and regulations," Michael Caster, Asia Digital Programme Manager for Article 19, a human rights organization focused on online free expression, told Gizmodo. The draft guidelines, published by the Cyberspace Administration of China, come hot on the heels of new generative AI products from Baidu, Alibaba, and other Chinese tech giants.

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

Amazon is joining the generative AI fray. Bedrock is the company's new API for Amazon Web Services (AWS) that lets developers use and customize AI tools that generate text or images. Think of it as a cloud-based and configurable alternative to OpenAI's ChatGPT and DALL-E 2 aimed at businesses and developers. AWS customers can use Bedrock to write, build chatbots, summarize text, classify images and more based on text prompts. It gives its users a choice of Amazon's Titan foundation model (FM) and several startups' models, including Anthropic's Claude (a Google-backed ChatGPT rival from former OpenAI employees), AI21's Jurassic-2 (a language model specializing in Spanish, French, German, Portuguese, Italian and Dutch) and Stable Diffusion (a popular open-source image generator).

Generative artificial intelligence (AI) models are a type of AI model that can generate new data that is similar to the data it was trained on. These models are often used for tasks such as image synthesis, music composition, and natural language processing. Overall, generative AI models are an exciting and rapidly evolving area of AI research. They have the potential to revolutionize many industries, from entertainment to healthcare to finance, by enabling machines to create new and unique data that was previously only possible for humans to produce.

Dedicating more effort toward improving the safety and security of these AI models can contribute toward making them more accessible and robust. The next time someone tells you that the latest artificial intelligence (AI) model will benefit humanity at large or that it displays evidence of artificial general intelligence, think about its hidden costs to people and the planet. The current trend is toward creating bigger and more closed and opaque models. But ther is still time to push back, demand transparency, and get a better understanding of the cost and impact while limiting how they are deployed in society at large.

Is the OpenAI bug bounty for ChatGPT all that it could be, some wonder. I'm sure that you've heard that oft-repeated sage advice. The same utterance has been smarmily used to describe the recently announced Bug Bounty initiative that OpenAI has proclaimed for ChatGPT and their other AI apps such as GPT-4 (successor to ChatGPT). In essence, the skeptics and cynics are suggesting that their Bug Bounty is not up to par and misses the boat in a variety of crucial ways. It misses the devout mark. Time to take this one home. You see, some carp that it undershoots what could have been a much more robust and momentous proclamation aiming to curtail AI-related woes. Not everyone sees things as quite so dismally about the announcement. You might have thought that proffering a bug bounty effort would be appreciated and applauded.

I'm very familiar with how West Monroe is navigating this process--not only as the leader of our Technology practice, but as the head of our generative AI taskforce. We've pulled together a group of individuals across the firm--call it a committee, a multidisciplinary team, or a task force--that has representation from many areas of our company who are actively interested in advancing the technology. There is plenty of pressure from different departments to develop and test their use cases for generative AI as quickly as possible; this team helps prioritize those use cases while keeping our company's goals and strategy in mind. At the same time, such excitement must be met with an appropriate amount of caution. How do you successfully balance risk and innovation with generative AI?

In the journal Nature today, my colleagues and I published an article on the future directions of generative A.I. (aka Large Language or Foundation models) for the practice of medicine. These new AI models have generated a multitude of new and exciting opportunities in healthcare that we didn't have before, along with many challenges and liabilities. I'll briefly explain how we got here and what's in store. Back in 2017, Google researchers published a paper ("Attention Is All You Need") describing a new model architecture, which they dubbed Transformer, that could give different levels of attention for multiple modes of input, and go faster, to ultimately replace recurrent and convolutional deep neural networks (RNN and CNN, respectively). Foreshadowing the future to Generative AI, they concluded: "We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video."