--Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2V ecand TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%. Satires can be considered as a literary form which involves a delicate balance between criticism and humor.

We initiate the study of multi-stage episodic reinforcement learning under adversarial manipulations in both the rewards and the transition probabilities of the underlying system. Existing efficient algorithms heavily rely on the "optimism under uncertainty" principle which dictates their behavior and does not allow flexibility to perform corruption-robust exploration. We address this by (i) departing from the optimistic behavior, and (ii) creating a general framework that incorporates the principle of action-elimination. (This principle has been essential for corruption-robust exploration in multi-armed bandits, a degenerate special case of episodic reinforcement learning.) Despite constructing a lower bound for a straightforward implementation of action-elimination, we provide a clean and modular way to transfer it to episodic reinforcement learning. Our algorithm enjoys near-optimal guarantees in the absence of adversarial manipulations, has performance that degrades gracefully as the amount of corruption increases, and does not need to know this amount. Our results shed new light on the broader question of robust exploration, and suggest a way to address a rather daunting mismatch between optimistic algorithms and algorithms with higher flexibility. To demonstrate the applicability of our framework, we provide a second instantiation thereof, showing how it can provide efficient guarantees for the stochastic setting, despite doing almost uniform exploration across plausibly optimal actions.

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.

Apple (NasdaqGS:AAPL) and Salesforce (NYSE:CRM) have announced the launch of two new apps, including the AI-powered redesigned Salesforce Mobile App with features exclusive to iOS and iPadOS. "With Salesforce Mobile, Salesforce and Apple are empowering sales, service and marketing professionals on the go to deliver game-changing customer experiences, powered by AI. And with Trailhead GO, millions more can now skill up for free, anytime and anywhere, to learn in-demand skills and fill the jobs of today and tomorrow." Intel Corporation (NasdaqGS:INTC) has unveiled oneAPI, "a unified and scalable programming model to harness the power of diverse computing architectures in the era of HPC/AI convergence", and "a general-purpose GPU optimized for HPC/AI acceleration based on the Xe architecture". "HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialized deep-learning NNPs, which Intel demonstrated earlier this month. Simplifying our customers' ability to harness the power of diverse computing environments is paramount, and Intel is committed to taking a software-first approach that delivers a unified and scalable abstraction for heterogeneous architectures."

Cortex just released V 0.10, which includes their new Predictor Interface for serving models. It lets you take models from any framework and implement them in simple Python, before deploying them with a single terminal command. V 0.10 also still includes out-of-the-box support for TensorFlow Serving and ONNX Runtime.

In the recent update of GraphVite, we release a new large-scale knowledge graph dataset, along with new benchmarks of knowledge graph embedding methods. The dataset, Wikidata5m, contains 5 million entities and 21 million facts constructed from Wikidata and Wikipedia. Most of the entities come from the general domain or the scientific domain, such as celebrities, events, concepts and things. To facilitate the usage of knowledge graph representations in semantic tasks, we provide a bunch of pre-trained embeddings from popular models, including TransE, DistMult, ComplEx, SimplE and RotatE. You can directly access these embeddings by natural language index, such as "machine learning", "united states" or even abbreviations like "m.i.t.".

It's been 7 months since my last commentary on the field, and as it became regular appearance in this blog (and in fact many people apparently enjoy this form and keep asking for it), it is a time for another one. For those new to the blog, here we generally strip the AI news coverage out of fluff and try to get to the substance, often with a fair dose of sarcasm and cynicism. The more pompous and grandiose the PR statement, the more sarcasm and cynicism - just to provide some balance in nature. The field of AI never fails to deliver on pompous and grandiose fake news hence I predict there will be a material for this blog for many years to come. Now that the introductory stuff is behind and you've been warned, let us go straight to what happened in the field since May 2019.

A team of Japanese researchers from Yamagata University and IBM Research have discovered an incredible 143 stunning geoglyphs, etched into the desert in southern Peru around the enigmatic Nazca Lines. It is yet another example of how technology is assisting archaeology because a number of images were found using state-of-the-art AI technology developed by IBM, finds that were then confirmed with an on-site investigations. The geoglyphs include humans, birds, camels, cats and other animals and were found between 2016 and 2018. They were identified through fieldwork and analysing high-resolution 3D data and aerial photography. Incredibly, one geoglyph in particular was solely discovered with AI technology, without the aid of humans, making it the first geoglyph discovered by an AI.

Conversational robots and virtual characters can enhance learning and expand entertainment options for children, a trio of studies by Disney Research shows, though exactly how these autonomous agents interact with children sometimes depends on a child's age. Pre-school children responding to an on-screen character, for instance, may be happiest if the character simply waits for their responses or repeats a question. Older children talking with a robot, on the other hand, appreciate it when the robot references their previous conversations, while younger children are just as happy if the robot treats each conversation as a new encounter. "Teasing out these nuances is necessary if we are to make the interactions between automated characters and children as engaging as possible," said Jill Fain Lehman, senior research scientist. Lehman and other staff members of Disney Research will present findings from the three studies at the Interaction Design and Children Conference in Palo Alto, Calif., June 27-30. "Though parent-child interaction remains the most important factor in child development, the prospect of automated characters that can interact with children offers exciting opportunities for further enhancing learning and play," said Markus Gross, vice president at Disney Research.

London, November 14, 2019 – IFS, the global enterprise applications company, today announces the findings of a global research study into the attitudes and strategies towards artificial intelligence (AI) among business leaders. The study polled 600 business leaders worldwide and a broad spectrum of industries involved with their companies' enterprise technology including enterprise resource planning (ERP), enterprise asset management (EAM), and field service management (FSM). "AI is no longer an emerging technology. It is being implemented to support business automation in the here and now, as this study clearly proves," IFS VP of AI and RPA Bob De Caux said. "We are seeing many real-world examples where technology is augmenting existing decision-making processes by providing users with more timely, accurate and pertinent information. In today's disruptive economy, the convergence of technologies such as AI, RPA, and IoT is bolstering a new form of business automation that will provide companies that are brave enough with the tools and services they need to be more competitive and outflank larger competitors."