We propose a new uniform framework for text classification and ranking that can automate the process of identifying check-worthy sentences in political debates and speech transcripts. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. In particular, we analyse the semantic meaning of each sentence using state-of-the-art neural language models such as BERT, ALBERT, and RoBERTa, while embeddings for entities are obtained from knowledge graph (KG) embedding models. Specifically, we instantiate our framework using five different language models, entity embeddings obtained from six different KG embedding models, as well as two combination methods leading to several Entity-Assisted neural language models. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF' 2019 & 2020 CheckThat! Labs. Our results show that the neural language models significantly outperform traditional TF.IDF and LSTM methods. In addition, we show that the ALBERT model is consistently the most effective model among all the tested neural language models. Our entity embeddings significantly outperform other existing approaches from the literature that are based on similarity and relatedness scores between the entities in a sentence, when used alongside a KG embedding.

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., EleutherAI, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting

Kuda is a fintech company on a mission to make financial services accessible, affordable and rewarding for every African on the planet. We're a tribe of passionate and diverse people who dreamed of building an inclusive money app that Africans would love so it's only right that we ended up with the name'Kuda' which means'love' in Shona, a language spoken in the southern part of Africa. We're giving Africans around the world a better alternative to traditional finance by delivering free money transfers, smart budgeting and instant access to credit through digital devices. We've raised over $90 million from some of the world's most respected institutional investors, and we're rolling out our game-changing services globally from our offices in Nigeria, South Africa, and the UK. To support our rapid growth and reach 4M customers by the end of 2022, Kuda is looking for an experienced Data Analyst who can mine attribution, engagement and behavioral data to establish insights and ensure we make data-driven decisions around product and channel experimentations, ensuring we focus on the highest ROI growth tactics.

In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy1,2. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by up to 0.15 standard deviations, more than doubling the growth rate during our study period relative to untreated areas. Our results provide country-scale evidence on the impact of grid-based infrastructure investment and our methods provide a low-cost, generalizable approach to future policy evaluation in data-sparse environments. Advancements in satellite imagery and machine learning can be used to infer the causal impact of electricity access on livelihoods, providing a low-cost, generalizable approach to evaluating public policy in data-spare environments.

Just three days before England kick off their FIFA World Cup campaign against Iran, scientists have a rather pessimistic forecast. The experts, based at London's Alan Turing Institute, say Gareth Southgate's men have only a seven per cent chance of winning the World Cup for the first time since 1966. However, out of the 32 participating teams, England is the fifth mostly likely team to bring home the trophy, just behind the likes of France, Belgium and Brazil. Meanwhile, Wales, which is playing in its first World Cup since 1958, has only a 0.5 per cent chance of winning the tournament – and only a 46 per cent chance of making it out of the group stage. Brazil is most likely to win the World Cup this year, according to the team's research.

While all "metallic miniature humans" are termed as robots, there are distinctions and humanoids are one variation of a robot. Humanoids are generally termed non-living creatures with human features. The term was used to address indigenous people in European colonies. Globally, the first Al-humanoid was named Sophia. Sophia was first actuated on February 14, 2016.

MIT Sloan Management Review and BCG have assembled an international panel of AI experts that includes academics and practitioners to help us gain insights into how responsible artificial intelligence (RAI) is being implemented in organizations worldwide. This month, we asked our expert panelists for reactions to the following provocation: Executives usually think of RAI as a technology issue. The results were wide-ranging, with 40% (8 out of 20) of our panelists either agreeing or strongly agreeing with the statement; 15% (3 out of 20) disagreeing or strongly disagreeing with it; and 45% (9 out of 20) expressing ambivalence, neither agreeing nor disagreeing. While our panelists differ on whether this sentiment is widely held among executives, a sizable fraction argue that it depends on which executives you ask. Our experts also contend that views are changing, with some offering ideas on how to accelerate this change.

In 2022, the growth of Legal Tech Artificial Intelligence Market is projected to reach Multi-million USD by 2026, In comparison to 2021, Over the next Seven years the Legal Tech Artificial Intelligence Market will register a magnificent spike in CAGR in terms of revenue, In this study, 2021 has been considered as the base year and 2022 to 2026 as the forecast period to estimate the market size for Legal Tech Artificial Intelligence. Legal Tech Artificial Intelligence Market Insights 2022 With "Legal Tech Artificial Intelligence market revenue was Million USD in 2016, grew to Million USD in 2020, and will reach Million USD in 2026, with a CAGR of % during 2020-2026." Legal Tech Artificial Intelligence Market research report is an analysis report that gives you an insight into the future and the future of business. The factual information and data contained in this report will allow you to identify the key features of the Legal Tech Artificial Intelligence Market that drive, revenue and growth potential. During the COVID-19 period, the global economy may be affected in three different ways: directly as it relates to production and demand, indirectly as it relates to supply chains and markets, and as a result of its financial consequences on firms and financial markets.

Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal timescales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at $\sim200~\mathrm{km}$ resolution and on the cubed sphere. The NWMs are trained using the ERA5 reanalysis product and a set of candidate loss functions, including the mean squared error and exponential losses targeting extremes. We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss. This improvement is accomplished with almost no skill reduction in the general temperature prediction task, and it can be efficiently realized through transfer learning, by re-training NWMs with the custom losses for a few epochs. In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time. Our best NWM is able to outperform persistence in a regressive sense for all lead times and temperature anomaly thresholds considered, and shows positive regressive skill compared to the ECMWF subseasonal-to-seasonal control forecast after two weeks.

Traditional multi-agent reinforcement learning (MARL) algorithms, such as independent Q-learning, struggle when presented with partially observable scenarios, and where agents are required to develop delicate action sequences. This is often the result of the reward for a good action only being available after other agents have taken theirs, and these actions are not credited accordingly. Recurrent neural networks have proven to be a viable solution strategy for solving these types of problems, resulting in significant performance increase when compared to other methods. In this paper, we explore a different approach and focus on the experiences used to update the action-value functions of each agent. We introduce the concept of credit-cognisant rewards (CCRs), which allows an agent to perceive the effect its actions had on the environment as well as on its co-agents. We show that by manipulating these experiences and constructing the reward contained within them to include the rewards received by all the agents within the same action sequence, we are able to improve significantly on the performance of independent deep Q-learning as well as deep recurrent Q-learning. We evaluate and test the performance of CCRs when applied to deep reinforcement learning techniques at the hands of a simplified version of the popular card game Hanabi.