The overarching goal of this paper is to develop a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another main dimension lies in the possibility to go over from specific to more general types of problems. The third main dimension is provided by semantic grounding. Since this is a philosophically complex and controversial dimension, a larger part of the paper is devoted to it. We take a fresh look at known foundational arguments in the philosophy of mind and cognition that are gaining new relevance in view of the recent AI developments including the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and general use-theoretic considerations of meaning. Finally, the AI state space, spanned by the main dimensions generalization, grounding and "selfx-ness", possessing self-x properties such as self-learning, is outlined.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

LOS ALTOS, CALIFORNIA and LEMONT, ILLINOIS – Cerebras Systems, a company dedicated to accelerating artificial intelligence (AI) compute, and the Argonne National Laboratory, a multidisciplinary science and engineering research center, today announced that Argonne is the first national laboratory to deploy the Cerebras CS-1 system. Unveiled today at SC19, the CS-1 is the fastest AI computer system in existence and integrates the pioneering Wafer Scale Engine, the largest and fastest AI processor ever built. By removing compute as the bottleneck in AI, the CS-1 enables AI practitioners to answer more questions and explore more ideas in less time. The CS-1 delivers record-breaking performance and scale to AI compute, and its deployment across national laboratories enables the largest supercomputer sites in the world to achieve 100- to 1,000-fold improvement over existing AI accelerators. By pairing supercompute power with the CS-1's AI processing capabilities, Argonne can now accelerate research and development of deep learning models to solve science problems not achievable with existing systems.

In an MIT Sloan Management Review article published last week, David A. Bray and Ray Wang outline the challenges ahead for incorporating people-centered design principles for deep learning. Deep learning, like other types of AI, trains itself, raising questions about accuracy and fairness in the findings. As companies adopt these technologies, "leadership must ensure that artificial neural networks are accurate and precise because poorly tuned networks can affect business decisions and potentially hurt customers, products, and services," Bray and Wang write. They advocate for "a people-centered approach to deep learning ethics," which benefits not just a few individuals, but entire communities. The approach is built on transparency, explainability, and reversibility, they write, which should be the foundation for any AI implementation.

Artificial Intelligence (AI) is already pervasive in many applications. Deep learning has happened since 2012 and it's only growing. This is Nigel Toon's perspective, which he shared at Berlin's TechCrunch Disrupt conference on Wednesday. Toon is Graphcore's CEO, a company that specializes in AI chips. For Toon, AI can allow many different kinds of innovation. Deep learning, probabilistic machine learning, to exploit to make new breakthroughs.

AltDeep is a newsletter focused on microtrend-spotting in data and decision science, machine learning, and AI. Know an engineer, research scientist, AI product manager, or entrepreneur in the AI space? Buy them a gift subscription. The Economist is the latest top tier publication to hype the GPT-2 transformer network language model created by OpenAI. They did so by "interviewing" the network, meaning that it generated "unedited" answers to the questions.

In the world of machine learning, model deployment is a crucial piece of the puzzle. While data scientists excel at other parts of the pipeline, deploying machine learning models tends to fall under the umbrella of software engineering or IT operations. And for good reason--successful deployments require a myriad of complex tasks, including building infrastructure, implementing APIs, load balancing, and integrating with data pipelines. We'll briefly walk you through a basic model deployment example by picking out tools and planning out an approach to construct a simple sentiment classification model. By the end of this post you will have the tools to serve your deep learning (DL) models via an API.

As we come to the end of 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record-breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey. To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change -- much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines ('physical technology') mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust ('social technology'). With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just'cool things' for data science teams.

Artificial intelligence seems to be everywhere these days. The news has stories about poetry-writing AI, the experts consider AI "the new electricity," and even AI whiskey is going to make an appearance soon. When you try reading these articles, there's usually a flood of information coming at you. But what do these concepts even mean? With this series of blog posts, we're going to address all the questions you've ever had about this topic. In this first part, we're going to introduce the concepts of machine learning, neural networks, and deep learning. You don't need any previous knowledge about these topics to follow this article, so settle in and keep reading! You might also like: A Beginner's Guide to Machine Learning: What Aspiring Data Scientists Should Know Let's start with a basic concept: functions.

Past progress in deep learning has concentrated mostly on learning from a static dataset, mostly for perception tasks and other System 1 tasks which are done intuitively and unconsciously by humans. However, in recent years, a shift in research direction and new tools such as soft-attention and progress in deep reinforcement learning are opening the door to the development of novel deep architectures and training frameworks for addressing System 2 tasks (which are done consciously), such as reasoning, planning, capturing causality and obtaining systematic generalization in natural language processing and other applications. Such an expansion of deep learning from System 1 tasks to System 2 tasks is important to achieve the old deep learning goal of discovering high-level abstract representations because we argue that System 2 requirements will put pressure on representation learning to discover the kind of high-level concepts which humans manipulate with language. We argue that towards this objective, soft attention mechanisms constitute a key ingredient to focus computation on a few concepts at a time (a "conscious thought") as per the consciousness prior and its associated assumption that many high-level dependencies can be approximately captured by a sparse factor graph. We also argue how the agent perspective in deep learning can help put more constraints on the learned representations to capture affordances, causal variables, and model transitions in the environment. Finally, we propose that meta-learning, the modularization aspect of the consciousness prior and the agent perspective on representation learning should facilitate re-use of learned components in novel ways (even if statistically improbable, as in counterfactuals), enabling more powerful forms of compositional generalization, i.e., out-of-distribution generalization based on the hypothesis of localized (in time, space, and concept space) changes in the environment due to interventions of agents.