High-quality text data has become an important data source for social scientists. We have witnessed the success of pretrained deep neural network models, such as BERT and RoBERTa, in recent social science research. In this paper, we propose a compact pretrained deep neural network, Transformer Encoder for Social Science (TESS), explicitly designed to tackle text processing tasks in social science research. Using two validation tests, we demonstrate that TESS outperforms BERT and RoBERTa by 16.7% on average when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS over BERT and RoBERTa on social science text processing tasks. Lastly, we discuss the limitation of our model and present advice for future researchers.

Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers. A popular paradigm to solve this problem is to combine a search system with a machine reader, where the former retrieves supporting evidences and the latter examines them to produce answers. Recently, the reader component has witnessed significant advances with the help of large-scale pre-trained generative models. Meanwhile most existing solutions in the search component rely on the traditional ``index-retrieve-then-rank'' pipeline, which suffers from large memory footprint and difficulty in end-to-end optimization. Inspired by recent efforts in constructing model-based IR models, we propose to replace the traditional multi-step search pipeline with a novel single-step generative model, which can dramatically simplify the search process and be optimized in an end-to-end manner. We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index. Empirical results show that CorpusBrain can significantly outperform strong baselines for the retrieval task on the KILT benchmark and establish new state-of-the-art downstream performances. We also show that CorpusBrain works well under zero- and low-resource settings.

Electronic brain implants could allow lawyers to quickly scan years of background material and cut costs in the future, a new report claims. The report from The Law Society sets out the way the profession could change for employees and clients as a result of advances in neurotechnology. It suggests that a lawyer with the chip implanted in his or her brain could potentially scan documentation in a fraction of the time, reducing the need for large teams of legal researchers. 'Some lawyers might try to gain an advantage over competitors and try to stay ahead of increasingly capable AI systems by using neurotechnology to improve their workplace performance,' wrote Dr Allan McCay, the author of the report. Neurotechnology could also allow firms to charge clients for legal services based on'billable units of attention' rather than billable hours, as they would be able to monitor their employees' concentration.

The second quarter of 2022 saw U.S. federal lawmakers and agencies focus on draft legislation and guidance aimed at closing the gap to the EU with respect to addressing risks in the development and use of AI systems, in particular risks related to algorithmic bias and discrimination. The American Data Privacy and Protection Act ("ADPPA"), the bipartisan federal privacy bill introduced to the U.S. House in June 2022, marks a major step towards a comprehensive national privacy framework, and companies should take particular note of its inclusion of mandated algorithmic impact assessments. Meanwhile, the E.U.'s regulatory scheme for AI continues to wind its way through the EU legislative process. Though it is unlikely to become binding law until late 2023 at the earliest, the EU policy landscape remains dynamic. Our 2Q22 Artificial Intelligence and Automated Systems Legal Update focuses on these key efforts, and also examines other policy developments within the U.S. and EU that may be of interest to domestic and international companies alike.

Required Skills/Abilities/Profile: • Mandatory 3 years of demonstrated hands-on experience designing and implementing AWS core infrastructure components including expert level CLI experience with IAM, S3, EC2, VPC, ELB, Route 53, DynamoDB, RDS, Elasticache, and other core AWS products.

Policymakers and candidates of both parties have increased their focus on how technology is changing society, including by blaming platforms and other participants in the tech ecosystem for a range of social ills even while recognizing them as significant contributors to U.S. economic success globally. Republicans and Democrats have significant interparty--and intraparty--differences in the form of their grievances and on many of the remedial measures to combat the purported harms. Nonetheless, the growing inclination to do more on tech has apparently driven one key congressional committee to have compromised on previously intractable issues involving data privacy. Rules around the use of algorithms and artificial intelligence, which have attracted numerous legislative proposals in recent years, may be the next area of convergence. While influential members of both parties have pointed to the promise and peril of the increasing role of algorithms and artificial intelligence in American life, they have tended to raise different concerns.

Generative models capable of automatically producing paragraphs of text or digital art are becoming increasingly accessible. People are using them to write fantasy novels, marketing copy, and to create memes and magazine covers. Content automatically created by software is poised to flood the internet for better or worse as AI technology is commercialized. Take Cosmopolitan's recent and "world's first artificially intelligent magazine cover," for instance: the image of a giant astronaut walking on the surface of a planet against a dark sky splattered with what looks like stars and gas as produced by OpenAI's DALL-E 2 model. Karen Cheng, a creative director, described trying various text prompts to guide DALL-E 2 in producing the perfect picture.

Face biometrics are now firmly established as a way for people to unlock their mobile phones, or sign up to a new online account. As a core means of identifying a person, however, former UNDP Policy Advisor and Program Manager for Legal Identity Niall McCann thinks facial recognition may be on its way out. Biometrics are often part of the registration process, linking a person to their ID number, and ID documents may encode the individual's biometrics, number, or both. McCann tells Biometric Update's Frank Hersey in episode two that because facial recognition can be carried out without the consent or knowledge of the subject, unlike fingerprint biometrics, it is likely to be restricted by the UN for ID projects in the coming years. "You don't know when a CCTV camera system based on street corners is identifying you via facial recognition means," McCann explains.

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

In regions that practice common law, relevant historical cases are essential references for sentencing. To help legal practitioners find previous judgement easier, this paper aims to label each court judgement by some tags. These tags are legally important to summarize the judgement and can guide the user to similar judgements. We introduce a heuristic system to solve the problem, which starts from Aspect-driven Topic Modeling and uses Dependency Parsing and Constituency Parsing for phrase generation. We also construct a legal term tree for Hong Kong and implemented a sentence simplification module to support the system. Finally, we propose a similar document recommendation algorithm based on the generated tags. It enables users to find similar documents based on a few selected aspects rather than the whole passage. Experiment results show that this system is the best approach for this specific task. It is better than simple term extraction method in terms of summarizing the document, and the recommendation algorithm is more effective than full-text comparison approaches. We believe that the system has huge potential in law as well as in other areas.