While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in Alpha that makes Alpha the first lazy-grounding ASP system to support declaratively specified domain-specific heuristics. Two practical example domains are used to demonstrate the benefits of our proposal. Additionally, we use our approach to implement informed search with A*, which is tackled within ASP for the first time. A* is applied to two further search problems. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size problems.

A court hearing in February is set to make history when the defendant is advised by artificial intelligence. The technology stems from the company DoNotPay, founded in 2015 by a then-Stanford University freshman, that was initially developed to appeal parking tickets. The world's first robot lawyer will run on the defendant's smartphone and listen to commentary to provide its client with instructions on what to say in arguments. The courthouse location, charges and name of the defendant have not been revealed, according to New Scientist. Joshua Browder initially created the robot to appeal parking tickets in the UK when he first launched the technology but has since expanded it to the US.

The use of robots in the food service industry is on the rise, from robotic steak-flippers to robotic bartenders. Certainly, advancements in robot technology have played a substantial role in its widespread adoption. However, inflation and rising labor costs have also played important roles. Regardless of the impetus for the shift, the introduction of robotics into restaurants will alter the landscape of the food service sector. The field of restaurant robotics focuses on the automation formerly performed by human workers.

Elastic is a free and open search company that powers enterprise search, observability, and security solutions built on one technology stack that can be deployed anywhere. From finding documents to monitoring infrastructure to hunting for threats, Elastic makes data usable in real-time and at scale. Thousands of organizations worldwide, including Barclays, Cisco, eBay, Fairfax, ING, Goldman Sachs, Microsoft, The Mayo Clinic, NASA, The New York Times, Wikipedia, and Verizon, use Elastic to power mission-critical systems. Founded in 2012, Elastic is a distributed company with Elasticians around the globe. We believe that engineering complex, pluggable software for the web that is built to last the test of time is both tricky and exciting.

Artificial intelligence took the world by storm in 2022. From better predictive analytics for providing legal advice to a push for a standardized AI Bill of Rights to combat AI bias, artificial intelligence has found its way into nearly every aspect of the law. More recently, AI tools have stunned the legal world with their ability to generate convincing text and art, even if these tools have opened the door to novel legal questions. There's little doubt, however, that we're just getting started--2023 is poised to outdo 2022 when it comes to advancements in artificial intelligence. The following is a collection of predictions from experts in the legal industry on what to expect from AI in 2023.

NFTs and cryptocurrency as a whole continue to be a hotly debated topic. The collapse of cryptocurrency exchange FTX and the subsequent arrest of its founder Sam Bankman-Fried brought intense scrutiny to the industry. Bankman-Fried, FTX co-founder Gary Wang and Caroline Ellison, the former CEO of Alamenda Research (a firm affiliated with FTX) were all charged with conspiracy to commit various forms of fraud. Wang and Ellison pleaded guilty; Bankman-Fried pleaded not guilty. In September, the White House said that crypto asset creation could hamstring the country's commitment to the Paris Agreement, an international treaty to combat climate change.

An artificial intelligence is set to advise a defendant in court for the first time ever. The AI will run on a smartphone and listen to all speech in the courtroom in February before instructing the defendant on what to say via an earpiece. The location of the court and the name of the defendant are being kept under wraps by DoNotPay, the company that created the AI.

The question of whether artificial intelligence (AI) can invent is nearly 200 years old, going back to the very beginning of computing. Victorian mathematician Ada Lovelace wrote what's generally considered the first computer program. As she did, she wondered about the limits of what computers could do. The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform.

To build trust and confidence in the technology, laws should require organisations and governments to use AI in an ethical, safe and responsible manner that protects peoples' privacy. This means companies and the government must be accountable for the decisions their AI systems make. It means AI systems must be transparent and that an organisation can explain how a person's data is being used by the AI system. It means protections must be put in place to help reduce the risk that AI outputs are not biased or discriminatory. It means individuals are notified when AI is used to make a decision that affects their rights. It means there are boundaries on how high-risk AI systems can be used, and it means individuals have an appropriate legal recourse when those boundaries are broken.