WIRE)--Cerebras Systems, a startup dedicated to accelerating Artificial intelligence (AI) compute, today unveiled the largest chip ever built. Optimized for AI work, the Cerebras Wafer Scale Engine (WSE) is a single chip that contains more than 1.2 trillion transistors and is 46,225 square millimeters. The WSE is 56.7 times larger than the largest graphics processing unit which measures 815 square millimeters and 21.1 billion transistors1. The WSE also contains 3,000 times more high speed, on-chip memory, and has 10,000 times more memory bandwidth. In AI, chip size is profoundly important.

GUVI will be the platform partner and One Fourth Labs will be the *content* partner for this joint initiative.

PyTorch 1.2 is now fully supported on Azure – here's some ways it can be utilized Neowin PyTorch 1.2 was released earlier this month.

With many companies ramping up their digital transformation initiatives, there's been a massive surge in the implementation of data analytics. With many companies ramping up their digital transformation initiatives, there's been a massive surge in the implementation of data analytics. Big data adoption increased from a mere 17% in 2015 to 59% in 2018 – a 42% increase in just three short years. Data analytics has a nearly infinite number of uses and can help small businesses become smarter, more productive and more efficient. And when used correctly, it can create a noticeable competitive advantage, while boosting both conversions and revenue.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

Put simply, AI refers to the process of incorporating human intelligence to machines, especially computer systems. AI is often used interchangeably with other terms such as "machine learning" or "deep learning," but they're not the same. Machine Learning (ML) is the training process for machines so that they can learn how to make decisions, while deep learning (DL) is a subset of ML. DL algorithms work similarly to the human brain in that when they receive new information, they compare it to a known item to make sense of it.

This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.

With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they entirely overlook the genetic profile and properties of the target disease. In the case of cancer, this is problematic since it is a highly genetic disease in which the biomolecular profile of target cells determines the response to therapy. Here, we introduce the first deep generative model capable of generating anticancer compounds given a target biomolecular profile. Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile. Our molecule generator combines two pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile. The efficacy predictor is used to optimize the generated molecules through a reward determined by the predicted IC50 drug sensitivity for the generated molecule and the target profile. We demonstrate how the molecule generation can be biased towards compounds with high inhibitory effect against individual cell lines or specific cancer sites. We verify our approach by investigating candidate drugs generated against specific cancer types and investigate their structural similarity to existing compounds with known efficacy against these cancer types. We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease.

Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model Matthias von Davier August 21st, 2019 Abstract Objective: Showcasing Artificial Intelligence, in particular deep neural networks, for language modeling aimed at automated generation of medical education test items. Materials and Methods: OpenAI's gpt2 transformer language model was retrained using PubMed's open access text mining database. The retraining was done using toolkits based on tensorflow-gpu available on GitHub, using a workstation equipped with two GPUs. Results: In comparison to a study that used character based recurrent neural networks trained on open access items, the retrained transformer architecture allows generating higher quality text that can be used as draft input for medical education assessment material. In addition, prompted text generation can be used for production of distractors suitable for multiple choice items used in certification exams. Discussion: The current state of neural network based language models can be used to develop tools in supprt of authoring medical education exams using retrained models on the basis of corpora consisting of general medical text collections. Conclusion: Future experiments with more recent transformer models (such as Grover, TransformerXL) using existing medical certification exam item pools is expected to further improve results and facilitate the development of assessment materials. Objective The aim of this article is to provide evidence on the current state of automated item generation (AIG) using deep neural networks (DNNs). Based on earlier work, a first paper that tackled this issue used character-based Address for correspondence: mvondavier@nbme.org: Time flies in the domain of DNNs used for language modeling, indeed: The day this paper was submitted, on August 13th, 2019, to internal review, NVIDIA published yet another, larger language model of the transformer used in this paper. The MegratronLM (apart from taking a bite out of the pun in this article's title) is currently the largest language model based on the transformer architecture [3]. This latest neural network language model has 8 billions of parameters, which is incomprehensible compared to the type of neural networks we used only two decades ago. At that time, in winter semester 1999-2000, I taught classes about artificial Neural Networks (NNs, e.g. Back then, Artificial Intelligence (AI) already entered what was referred to as AI winter, as most network sizes were limited to rather small architectures unless supercomputers were employed.

Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process. Depending on the product being purchased, and the organizational structure, the process may involve employees who first conduct market research, and then influence decision makers who place the online purchase order. In such cases, the online activity trail of a single individual in the organization may only provide partial information for predicting purchases (conversions). To refine conversion prediction for business-to-business (B2B) products using online activity trails, we introduce the notion of relevant users in an organization with respect to a given B2B advertiser, and leverage the collective activity trails of such relevant users to predict conversions. In particular, our notion of relevant users is tied to a seed list of relevant activities for a B2B advertiser, and we propose a method using distributed activity representations to build such a seed list. Experiments using data from Yahoo Gemini demonstrate that the proposed methods can improve conversion prediction AUC by 8.8%, and provide an interpretable advertiser specific list of activities useful for B2B ad targeting.