We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form advisor-advisee relationships in order to speed up individual learning and exploit and learn from data on the Web.

This paper demonstrates the use of the Multi-Agent MicroServices (MAMS) architectural style through a case study based around the development of a prototype traffic simulation in which agents model a population of individuals who travel from home to work and vice versa by car.

Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the degeneration problem, i.e., the generated text is unnatural and contains undesirable repetitions. Existing solutions to this problem either introduce randomness prone to incoherence or require a look-ahead mechanism that demands extra computational overhead. In this study, we formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. Thereby, we understand the phenomenon of degeneration as circular loops within the directed graph. Based on our formulation, we propose a novel decoding method -- \textit{momentum decoding} -- which encourages the LM to \textit{greedily} explore new nodes outside the current graph. Meanwhile, it also allows the LM to return to the existing nodes with a momentum downgraded by a pre-defined resistance function. We extensively test our approach on three benchmarks from different domains through automatic and human evaluations. The results show that momentum decoding performs comparably with the current state of the art while enjoying notably improved inference speed and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal the merits and inner workings of our approach. Our codes and other related resources are publicly available at https://github.com/gmftbyGMFTBY/MomentumDecoding.

When Hurricane Ian churned over Florida in late September, it left a trail of destruction from high winds and flooding. But a week after the storm passed, some people in three of the worst-hit counties saw an unexpected beacon of hope. Nearly 3,500 residents of Collier, Charlotte, and Lee Counties received a push notification on their smartphones offering $700 cash assistance, no questions asked. A Google algorithm deployed in partnership with nonprofit GiveDirectly had estimated from satellite images that those people lived in badly damaged neighborhoods and needed some help. GiveDirectly is testing this new way of targeting emergency aid in collaboration with Google.org, the search and ad company's charitable arm.

A new form of "influence government", which uses sensitive personal data to craft campaigns aimed at altering behaviour has been "supercharged" by the rise of big tech firms, researchers have warned. National and local governments have turned to targeted advertisements on search engines and social media platforms to try to "nudge" the behaviour of the country at large, the academics found. The shift to this new brand of governance stems from a marriage between the introduction of nudge theory in policymaking and an online advertising infrastructure that provides unforeseen opportunities to run behavioural adjustment campaigns. Some of the examples found by the Scottish Centre for Crime and Criminal Justice (SCCCJ) range from a Prevent-style scheme to deter young people from becoming online fraudsters to tips on how to light a candle properly. While targeted advertising is common across business, one researcher argues that the government using it to drive behavioural change could create a perfect feedback loop.

According to a new IDC Spending Guide, worldwide spending on edge computing will reach $250 billion by 2024. Services will make up nearly half (46.2%) of all spending, with hardware next at 32.2%, and 21.6% to be spent on edge-related software. Looking even a bit further, Gartner estimates that by 2025, 75% of data will be processed at the edge, outside of traditional, centralized data centers and the cloud, up from less than 10% today. If these statistics are any indication, edge computing will likely play a huge role in improving the customer experience and could change the playing field in not just marketing, but in manufacturing, security, healthcare and retail. Edge computing refers to systems in which the computing resources are placed closer to a device or user, in other words, at the edge of the network.

I've been talking with the cognitive behavioral therapist chatbot known as Woebot a lot recently, and it's a little like talking to my mother. Not because of the whimsical anecdotes and GIFs the bot dispenses--that's not her style--but because of all the practical reminders to get out of my own way. Like the sensible woman who raised me, Woebot isn't big on protracted whinging or solicitations for pity, and its dogged optimism occasionally rubs me the wrong way. But they are both mostly right, my mom and the bot. More importantly, where a dose of practicality would do a person good, they are mostly helpful.

Molecule prospector Atomwise co-founded a drug research company alongside Velocity Pharmaceutical Development designed to shepherd its artificial intelligence-derived candidates through preclinical testing and into potential takeout deals with pharma industry partners. Dubbed X-37, the new firm will maintain a flexible LLC structure that will house each development program within its own virtual subcompany. This will allow X-37 to divest individual properties in the future while maintaining its parent organization and team, the company said. And to kick things off, X-37 is debuting with a $14.5 million series A round to fund operations and early development, starting in autoimmune diseases, cancer and anticoagulation. It began research work in 2018 and aims to have novel candidates ready to begin human testing by 2022.

Firm leverages AI technology similar to facial recognition to figure out which small molecules can bind most effectively with targeted enzymes. Does the future of drug development lie in a kind of facial-recognition technology for enzymes? That is the hope of X-37 LLC, a drug-development startup that is using artificial intelligence and a deep neural network developed by San Francisco-based Atomwise. "We think that this is an approach and a technology that is really going to transform drug discovery across the board," said Dr. David Collier, CEO of X-37, which is partly owned by Atomwise and is based in South San Francisco, California. It was founded last year.

In 2001, when Jamie was diagnosed with chronic myelogenous leukemia (CML), a cancer that starts inside the bone marrow, the disease had few effective cures. Fourteen years later, thanks to advances in cancer treatment, she is able to manage the disease and live a full life. Jamie is profiled in the PhRMA.org Yet many patients and their doctors wait for years before promising treatments become available. All too often, unforeseen side effects send researchers back to the drawing board, just when they thought they were close to bringing a new medication to market.