What is the future of ai in blockchain? While a blockchain is highly secure, applications it works with can be vulnerable. This is exactly where the incorporation of AI development might be a game-changer. For instance, in finance, AI solutions can boost the deployment of blockchain apps. Beyond this, it can also help to predict cybersecurity breaches.

A recently developed computational approach based on AI can improve the understanding of different states of carbon, helping guide the search for materials yet to be discovered. We address the applications around us by using materials to create solutions, and everything we make is by definition made up of them. We discover some, and we create some, but commercializing materials for mainstream manufacturing can be tedious, expensive, and often based on trial and error. A material's atomic structure establishes its electronic, thermal, and mechanical properties. Scientists in this field are always looking for ways to arrange atoms to develop useful materials, often using high pressures and temperatures.

You tell the model what to do through a textual interface, and the model tries to accomplish the task. What you tell the model to do in a broad sense is the prompt. In the case of image generation AI models such as DALLE-2 or Stable Diffusion, the prompt is mainly a description of the image you want to generate. In the case of large language models (LLMs) such as GPT-3 or ChatGPT the prompt can contain anything from a simple question ("Who is the president of the US?") to a complicated problem with all kinds of data inserted in the prompt (note that you can even input a CSV file with raw data as part of the input). It can also be a vague statement such as "Tell me a joke. Even more generally, in generative task oriented models such as Gato, the prompt can be extremely high level and define a task you need help with ("I need to organize a one week trip to Greece"). For the rest of this document, and for now, we will focus on the specific use case of prompts for LLMs. In order to obtain a result, either 1 or 2 must be present. Let's see a few examples (all of them using ChatGPT). Beyond asking a simple question, possibly the next level of sophistication in a prompt is to include some instructions on how the model should answer the question. Here I ask for advice on how to write a college essay, but also include instructions on the different aspects I am interested to hear about in the answer. "How should I write my college admission essay?

The identification of nonlinear dynamical systems from experimental time series and image series data became an important research theme in the early 1990s [25, 37, 36]. After lapsing for almost two decades, it is now experiencing a spectacular rebirth. A key element of the older work was the use of neural architectures [14, 37] (recurrent, convolutional, ResNet) motivated by traditional numerical analysis algorithms. Importantly, such architectures allow researchers to identify effective, coarse-grained, mean-field type evolution models from fine-scale (atomistic, molecular, agent-based) data [29, 5]. In this paper, we identify coarse-grained, effective stochastic differential equations (eSDE) for colloidal particle selfassembly based onfine-grained, Brownian dynamics simulations under the influence of electric fields [51, 11]. We demonstrate that the identified eSDE encodes accurately the physics of the Brownian Dynamic simulations and captures the dynamics of corresponding experimental data. Those experiments have previously been shown to quantitatively match to BD simulations at equilibrium in terms of time-averaged distribution functions [11, 18, 20]. Figure 1 shows a sample path of a latent space trajectory {t, φ(t)}

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data trying to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that is emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting ...

Anomaly detection is critical for finding suspicious behavior in innumerable systems. We need to detect anomalies in real-time, i.e. determine if an incoming entity is anomalous or not, as soon as we receive it, to minimize the effects of malicious activities and start recovery as soon as possible. Therefore, online algorithms that can detect anomalies in a streaming manner are essential. We first propose MIDAS which uses a count-min sketch to detect anomalous edges in dynamic graphs in an online manner, using constant time and memory. We then propose two variants, MIDAS-R which incorporates temporal and spatial relations, and MIDAS-F which aims to filter away anomalous edges to prevent them from negatively affecting the internal data structures. We then extend the count-min sketch to a Higher-Order sketch to capture complex relations in graph data, and to reduce detecting suspicious dense subgraph problem to finding a dense submatrix in constant time. Using this sketch, we propose four streaming methods to detect edge and subgraph anomalies. Next, we broaden the graph setting to multi-aspect data. We propose MStream which detects explainable anomalies in multi-aspect data streams. We further propose MStream-PCA, MStream-IB, and MStream-AE to incorporate correlation between features. Finally, we consider multi-dimensional data streams with concept drift and propose MemStream. MemStream leverages the power of a denoising autoencoder to learn representations and a memory module to learn the dynamically changing trend in data without the need for labels. We prove a theoretical bound on the size of memory for effective drift handling. In addition, we allow quick retraining when the arriving stream becomes sufficiently different from the training data. Furthermore, MemStream makes use of two architecture design choices to be robust to memory poisoning.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

There is an increasing need to develop artificial intelligence systems that assist groups of humans working on coordinated tasks. These systems must recognize and understand the plans and relationships between actions for a team of humans working toward a common objective. This article reviews the literature on team plan recognition and surveys the most recent logic-based approaches for implementing it. First, we provide some background knowledge, including a general definition of plan recognition in a team setting and a discussion of implementation challenges. Next, we explain our reasoning for focusing on logic-based methods. Finally, we survey recent approaches from two primary classes of logic-based methods (plan library-based and domain theory-based). We aim to bring more attention to this sparse but vital topic and inspire new directions for implementing team plan recognition.

Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation types, caused by language complexity and data sparsity. In this paper, we introduce a simple enhancement of RE using $k$ nearest neighbors ($k$NN-RE). $k$NN-RE allows the model to consult training relations at test time through a nearest-neighbor search and provides a simple yet effective means to tackle the two issues above. Additionally, we observe that $k$NN-RE serves as an effective way to leverage distant supervision (DS) data for RE. Experimental results show that the proposed $k$NN-RE achieves state-of-the-art performances on a variety of supervised RE datasets, i.e., ACE05, SciERC, and Wiki80, along with outperforming the best model to date on the i2b2 and Wiki80 datasets in the setting of allowing using DS. Our code and models are available at: https://github.com/YukinoWan/kNN-RE.

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an $n$-qubit gapped local Hamiltonian after learning from only $\mathcal{O}(\log(n))$ data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}(n^c)$ data for a large constant $c$. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}(n \log n)$ in the number of qubits $n$. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.