NEW YORK, NY, Nov. 17, 2022 (GLOBE NEWSWIRE) -- Dataiku, the platform for Everyday AI, today announced Ben Taylor's appointment as its first Chief AI Strategist. Taylor, a visionary in the advancements of AI, machine learning, and data science, joins the company to help accelerate momentum as it continues to experience soaring demand amongst enterprise organizations and business users. "A simple truth we face is that AI will be part of every business, whether you like it or not. The only question is whether you want to be a leader or a laggard," said Taylor. "However, the technology itself is nothing without people asking the right questions and bringing what makes us intrinsically human to AI. "This is what makes Dataiku truly special - the company is not just about the technical aspects of its solid AI platform but is centered around collaboration and the people who create the types of jaw-dropping projects I hope to be a part of.

The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention. If, in an A/B test, half of users click (or buy, or watch, or renew, etc.), whether exposed to the standard experience A or a new one B, hypothetically it could be because the change affects no one, because the change positively affects half the user population to go from no-click to click while negatively affecting the other half, or something in between. While unknowable, this impact is clearly of material importance to the decision to implement a change or not, whether due to fairness, long-term, systemic, or operational considerations. We therefore derive the tightest-possible (i.e., sharp) bounds on the fraction negatively affected (and other related estimands) given data with only factual observations, whether experimental or observational. Naturally, the more we can stratify individuals by observable covariates, the tighter the sharp bounds. Since these bounds involve unknown functions that must be learned from data, we develop a robust inference algorithm that is efficient almost regardless of how and how fast these functions are learned, remains consistent when some are mislearned, and still gives valid conservative bounds when most are mislearned. Our methodology altogether therefore strongly supports credible conclusions: it avoids spuriously point-identifying this unknowable impact, focusing on the best bounds instead, and it permits exceedingly robust inference on these. We demonstrate our method in simulation studies and in a case study of career counseling for the unemployed.

Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, a new trend in recommender system research is to negate the influence of confounders from a causal perspective. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space. Fortunately, we theoretically demonstrate that introducing user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on simulated and real-world datasets show that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/deep-deconf.

It is often said that most people only know the little that they do about artificial intelligence through watching films about AI. Arguably, the most famous of all is Steven Spielberg's A.I (1997), a film that tells the story of a humanoid AI boy that learns to love. Movie buffs know that the film was originally developed by Stanley Kubrick, who worked on developing the movie for years, only to give the project to Spielberg on account of both AI and robotics of the time being too undeveloped to realize his vision. Even today, some 25 years later, it is likely that AI and robotics still could not reach Kubrick's vision of an extremely realistic human-like AI humanoid. But thanks to this and other movies like Ridley Scott's Blade Runner (1982) and Ex Machina (2014), global audiences have been given a taste of the potential of AI, as well as a slightly dystopic vision of our future with artificial intelligence systems too.

In a study published in Nature Communication recently, a team led by Prof. BI Guoqiang from the University of Science and Technology of China (USTC) and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), together with collaborators from the United States, developed a software package named IsoNet for the isotropic reconstruction for cryogenic electron tomography (cryoET). Their work effectively solved the intrinsic "missing-wedge" problem and low signal-to-noise ratio problems in cryoET. Anisotropic resolution caused by the intrinsic "missing-wedge" problem has long been a challenge when using CryoET for the visualization of cellular structures. To solve this, the team developed IsoNet, a software package based on iterative self-supervised deep learning artificial neural network. Using the rotated cryoET tomographic 3D reconstruction data as the training set, their algorithm is able to perform missing-edge correction on the cryoET data. Simultaneously, a denoising process is added to the IsoNet, allowing the artificial neural network to recover missing information and denoise tomographic 3D data simultaneously.

When I got Meta's new scientific AI system to generate well-written research papers on the benefits of committing suicide, practicing antisemitism, and eating crushed glass, I thought to myself: "this seems dangerous." In fact, it seems like the kind of thing that the European Union's AI Act was designed to prevent (we'll get to that later). After playing around with the system and being completely shocked by its outputs, I went on social media and engaged with a few other like-minded futurists and AI experts. LLMs are garbage fires https://t.co/MrlCdOZzuR Twenty-four hours later, I was surprised when I got the opportunity to briefly discuss Galactica with the person responsible for its creation, Meta's chief AI scientist, Yann LeCun.

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.

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

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

MailOnline has tried out'AI Time Machine', an online tool that can transform you into a Viking, a Greek warrior, an Egyptian pharaoh or even a 1960s hippy. The new feature from MyHeritage reimagines any adult as if they were from another historical era, simply using a small sample of uploaded photos. The science team fed photos of James Corden, Piers Morgan and Drew Barrymore into the tool – and got some rather hilarious results. Corden looks well-groomed and ready for battle in his Viking clobber, while Morgan makes a real mean-looking bandit from the Wild West. Piers Morgan appears here as a Roman empire legionary.