"Throughout 2021 and 2022, the world began to experiment with a massive influx of commercially available AI-assisted and AI-powered tools that can be used, whether knowingly or unknowingly, during the process of creating, researching, and innovating. Looking ahead to 2023, we will start witnessing the legal and regulatory impact of these tools." In general, the adoption of artificial intelligence (AI) and machine learning technologies has the potential to impact society in many ways. These technologies can automate tasks and make them more efficient, which can lead to job displacement and other economic impacts. They can also be used to make decisions that affect people's lives, such as in the criminal justice system or in hiring, which raises ethical concerns.

Recently, an RTI filed by the Internet Freedom Foundation (IFF) revealed that the Delhi Police is using Facial recognition technology (FRT) to nab rioters in the capital city. This has caused an uproar as many members of the civil society raised concerns and called the Delhi Police's use of FRT'unethical' in the absence of a Data Protection Act in the country. The argument being made by them is national security should not come at the cost of privacy. Technology such as FRT has been controversial, and authorities leveraging such tech is definitely a concern. The RTI filed by IFF revealed that the procurement of the FRT by the Delhi Police was authorised as per a 2018 direction of the Delhi High Court in Sadhan Haldar v NCT of Delhi.

The use of artificial intelligence (AI) has become increasingly common in many different industries and fields, from healthcare to finance to transportation. While the potential benefits of AI are vast and numerous, it's important to also consider the potential drawbacks and negative uses of this technology. One area where AI is increasingly being utilized is in the realm of cybercrime. One way that AI is used in cybercrime is through the development of sophisticated malware. This type of software is designed to infect a computer or network without the user's knowledge and can cause significant damage or disruption.

Two weeks ago, I gave a lecture setting out my current thoughts on AI safety, halfway through my year at OpenAI. I was asked to speak by UT Austin's Effective Altruist club. You can watch the lecture on YouTube here (I recommend 2x speed). The timing turned out to be weird, coming immediately after the worst disaster to hit the Effective Altruist movement in its history, as I acknowledged in the talk. I then spent 20 minutes taking questions. For those who (like me) prefer text over video, below I've produced an edited transcript, by starting with YouTube's automated transcript and then, well, editing it. Thank you so much for inviting me here. I do feel a little bit sheepish to be lecturing you about AI safety, as someone who's worked on this subject for all of five months. But this past spring, I accepted an extremely interesting opportunity to go on leave for a year to think about what theoretical computer science can do for AI safety. I'm doing this at OpenAI, which is one of the world's leading AI startups, based in San Francisco although I'm mostly working from Austin. Despite its name, OpenAI is famously not 100% open … so there are certain topics that I'm not allowed to talk about, like the capabilities of the very latest systems and whether or not they'll blow people's minds when released. By contrast, OpenAI is very happy for me to talk about AI safety: what it is and and what if anything can we do about it. So what I thought I'd do is to tell you a little bit about the specific projects that I've been working on at OpenAI, but also just, as an admitted newcomer, share some general thoughts about AI safety and how Effective Altruists might want to think about it. I'll try to leave plenty of time for discussion. Maybe I should mention that the thoughts that I'll tell you today are ones that, until last week, I had considered writing up for an essay contest run by something called the FTX Future Fund. Unfortunately, the FTX Future Fund no longer exists. It was founded by someone named Sam Bankman-Fried, whose a net worth went from 15 billion dollars to some negative number of dollars in the space of two days, in one of the biggest financial scandals in memory. This is obviously a calamity for the EA community, which had been counting on funding from this individual. I feel terrible about all the projects left in the lurch, to say nothing of FTX's customers. Let's start with this: raise your hand if you've tried GPT-3.

Using time series derived from big Earth Observation data sets is one of the leading research trends in Land Use Science and Remote Sensing. One of the more promising uses of satellite time series is its application to classify land use and land cover. Information on land is critical for sustainable development because our growing demand for natural resources is causing significant environmental impacts. The target audience for sits is the new generation of specialists who understand the principles of remote sensing and can write scripts in R. Ideally, users should have basic knowledge of data science methods using R. This book presents sits, an open-source R package for land use and land cover classification using big Earth observation data.

Like human intelligence, artificial intelligence (AI) can recognize … this external technology is a deep-structured, machine-learning method …

Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP). However, while pretraining on general language has been shown to work very well for common language, it has been observed that niche language poses problems. In particular, climate-related texts include specific language that common LMs can not represent accurately. We argue that this shortcoming of today's LMs limits the applicability of modern NLP to the broad field of text processing of climate-related texts. As a remedy, we propose CLIMATEBERT, a transformer-based language model that is further pretrained on over 2 million paragraphs of climate-related texts, crawled from various sources such as common news, research articles, and climate reporting of companies. We find that CLIMATEBERT leads to a 48% improvement on a masked language model objective which, in turn, leads to lowering error rates by 3.57% to 35.71% for various climate-related downstream tasks like text classification, sentiment analysis, and fact-checking.

Foundation models can be disruptive for future AI development by scaling up deep learning in terms of model size and training data's breadth and size. These models achieve state-of-the-art performance (often through further adaptation) on a variety of tasks in domains such as natural language processing and computer vision. Foundational models exhibit a novel {emergent behavior}: {In-context learning} enables users to provide a query and a few examples from which a model derives an answer without being trained on such queries. Additionally, {homogenization} of models might replace a myriad of task-specific models with fewer very large models controlled by few corporations leading to a shift in power and control over AI. This paper provides a short introduction to foundation models. It contributes by crafting a crisp distinction between foundation models and prior deep learning models, providing a history of machine learning leading to foundation models, elaborating more on socio-technical aspects, i.e., organizational issues and end-user interaction, and a discussion of future research.

Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or sharing even within an enterprise organization. The process of authorizing the use of, and access to, sensitive data often delays or derails ML projects. Alternatively, we can tackle these challenges by generating and using synthetic data.

A spokesperson for Stability.AI told MIT Technology Review: "We are listening to artists and the community and working with collaborators to improve the dataset. This involves allowing people to opt out of the model and also to opt in when they are not already included." But Karla Ortiz, an artist and a board member of the Concept Art Association, an advocacy organization for artists working in entertainment, says she doesn't think Stability.AI is going far enough. The fact that artists have to opt out means "that every single artist in the world is automatically opted in and our choice is taken away," she says. "The only thing that Stability.AI can do is algorithmic disgorgement, where they completely destroy their database and they completely destroy all models that have all of our data in it," she says.