In recent years, artificial intelligence (AI) has revolutionized the art world. The application of AI allows for virtual representation of the visual world, through which people can interactively communicate with images, paintings, and other visual works. However, AI has now evolved to the point where it is capable of generating images through the use of words. With the help of AI, people enter keywords related to a specific topic, and the algorithm creates the appropriate image from this information. AI image-generating algorithms continuously learn and refine the image creation process based on feedback from users.

As the world contends with the lingering catastrophic effects of a global pandemic, set against the ongoing backdrop of climate disasters and worsening conflict and humanitarian disasters, the need for rapid scientific advancement in response to crisis has never been so apparent. We can't say we weren't warned. In 2015, the United Nations released The 2030 Agenda for Sustainable Development, a shared blueprint constituting "an urgent call for action by all countries -- developed and developing -- in a global partnership." Central to the agenda are the 17 Sustainable Development Goals (SDGs) that address a range of urgent needs for humanity, from poverty alleviation and gender equality, to decent work, sustainable cities and communities, a clean environment, affordable and clean energy, and peace. The goals were founded on decades of input from global researchers, stakeholders and policy makers.

Artificial intelligence (AI) is transforming the way we work, and the finance department is no exception. Chat GPT-4, developed by OpenAI, is a machine learning-based language model designed to understand and generate text in response to user queries. It is emerging as a new tool capable of assisting finance professionals in their daily tasks. After a week of testing Chat GPT-4 on various daily use cases within financial departments, OpenAI's solution stands out for its ability to adapt and offer relevant solutions in various contexts. The presentation of our test results is divided into two parts.

OpenAI has taken ChatGPT offline in Italy after the government's Data Protection Authority on Friday temporarily banned the chatbot and launched a probe over the artificial intelligence application's suspected breach of privacy rules. The agency, also known as Garante, accused Microsoft-backed OpenAI of failing to check the age of ChatGPT's users who are supposed to be aged 13 or above. ChatGPT has an "absence of any legal basis that justifies the massive collection and storage of personal data" to "train" the chatbot, Garante said. OpenAI has 20 days to respond with remedies or could risk a fine of up to 20 million euros ($21.68 million) or 4% of its annual worldwide turnover. OpenAI said it has disabled ChatGPT for users in Italy at the request of the Garante.

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI's Diffusion and OpenAI's Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence.

As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.

The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points: 1. LLMs predictably get more capable with increasing investment, even without targeted innovation. 2. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 3. LLMs often appear to learn and use representations of the outside world. 4. There are no reliable techniques for steering the behavior of LLMs. 5. Experts are not yet able to interpret the inner workings of LLMs. 6. Human performance on a task isn't an upper bound on LLM performance. 7. LLMs need not express the values of their creators nor the values encoded in web text. 8. Brief interactions with LLMs are often misleading.

Originally published on Towards AI. In this article, I go through the basics of finetuning large language models like BLOOM on a legal text dataset. You can try it on HuggingFace Spaces! I am currently on a quest to learn the ropes of working with large language models (LLMs). This includes learning how to finetune pre-trained models such as OPT and BLOOM.

ChatGPT bans: The Italian Data Protection Authority also says that OpenAI could be fined if it doesn't make ChatGPT follow privacy laws in Italy. OpenAI says that it has stopped giving ChatGPT to users in Italy because that country is looking into the program. But the CEO of the company, Sam Altman, tweeted, "Though we think we follow all privacy laws." We of course defer to the Italian government and have ceased offering ChatGPT in Italy (though we think we are following all privacy laws). Italy is one of my favorite countries and I look forward to visiting again soon!

Discussions over whether "robots" will replace people have increased since the introduction of sophisticated AI tools like ChatGPT and Google's Bard, and studies indicate that some jobs--surprisingly, some traditionally white-collar work--may be significantly impacted. Since the public release of OpenAI's ChatGPT, generative AI--a type of artificial intelligence that can produce text or other content in response to user prompts--has quickly grown in popularity. Since its November 2022 release, users have used the AI chatbot ChatGPT for a variety of tasks, including as creating programming and producing college-level essays. Once Google introduced Bard on March 21, a rival to ChatGPT and a separate entity from the company's Google search engine, the AI competition picked up steam. Over 300 million jobs, or 18% of employment worldwide, could be automated, according to a new Goldman Sachs analysis, with more developed countries being severely impacted than emerging markets.