Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

It is originally formulated as a hierarchical generative model: a document is generated from a mixture of topics, and a word in the document is generated by first choosing a topic from a document-specific distribution, and then choosing the word from the topic-specific distribution. The main difficulty of topic modeling is the optimization problem, which is NPhard in the worst case due to the intractability of the posterior inference. Existing methods aim to find approximate solutions to the difficult optimization problem, which falls into the framework of matrix factorization. Matrix factorization based topic modeling maps documents into a low-dimensional semantic space by decomposing the documents into a weighted combination of a set of topic distributions: D CW where D (:,d) represents the d -th document which is a column vector over a set of words with a vocabulary size of v, C (:,g) denotes the g -th topic which is a probability mass function over the vocabulary, and W ( g,d) denotes the probability of the g -th topic in the d -th document.

The famous St Petersburg Paradox shows that the theory of expected value does not capture the real-world economics of decision-making problem. Over the years, many economic theories were developed to resolve the paradox and explain the subjective utility of the expected outcomes and risk aversion. In this paper, we use the concept of the net utility to resolve the St Petersburg paradox. The reason why the principle of absolute instead of net utility does not work is because it is a first order approximation of some unknown utility function. Because the net utility concept is able to explain both behavioral economics and the St Petersburg paradox it is deemed a universal approach to handling utility. Finally, this paper explored how artificial intelligent (AI) agent will make choices and observed that if AI agent uses the nominal utility approach it will see infinite reward while if it uses the net utility approach it will see the limited reward that human beings see.

Machine Intelligence For You (MIFY) is a Benin company which is specialized in Artificial Intelligence, Internet-of-things, Embedded Systems, and their applications. It is the organizer of MIFY Artificial Intelligence Contest (MAIC). MAIC is a yearly international artificial intelligence competition in which the participants find the best algorithm to play a turn-taking strategy game with a time limit for each decision. This game is a society game from Africa and through the world. By this way, MIFY aims to promote artificial intelligence and these games. MAIC is organized in 3 main phases: Group phase, Semi-final, and Final.

This ebook, based on the latest ZDNet / TechRepublic special feature, looks at the outlook for business leaders in 2020 and where they are spending their tech dollars. Gartner's outlook for the next five years revolves around the idea that artificial intelligence will augment human decisions, emotions and relationships. The big question is whether Gartner's prognostications will play out in the next five years. At the Gartner IT Symposium/Xpo, analyst Daryl Plummer outlined the trends to expect in the years ahead. By 2025, half of the people with a smartphone without a bank account will use a cryptocurrency account.

In recent years, machine learning and artificial intelligence have made a way into all areas of homes, lives and businesses. Today, the technology is becoming increasingly ubiquitous such as from AI assistants like Alexa or Siri, speech translator, GPS finding location, smart devices etc. These days, you can find exciting evidence of machine learning innovation in marketing today as it is applied to pay-per-click, created more intelligent email campaigns and chatbots. Here, GoodFirms has conducted a survey based on AI and Machine Learning for PCC Campaign Management. In this research, PPC Gurus (Duane Brown, Ed Leake, Gianluca Binelli, Jeff Sauer, Kirk Williams, Larry Kim, Luca Senatore, Martin Roettgerding, Navah Hopkins, Patrick Gilbert) were contacted that are experts in digital marketing.

Web pages and other contents such as local listings or videos are ranked or displayed on Search Engines such as Google, Bing and Yahoo search results, based on what the search engine considers to be most relevant to users. An SEO friendly website will automatically rank higher in search results. This has a great effect on traffic flow which is also very essential at will enhance internet visibility. Search engine optimization in Kenya is an important aspect of website development. Without quality search engine optimization services it is hard to achieve top search engine rankings.

Last March, Google took the wraps off of Coral, a collection of hardware development kits and accessories intended to bolster the development of machine learning models at the edge. It launched in select regions in beta, but the tech giant today announced that it's graduating to a "wider" and global release. All Coral products -- including the $150 Coral Dev Board, the $74.99 Coral USB Accelerator, and the $24.99 5-megapixel camera accessory -- are available for sale at electronics retailer Mouser and for large-volume sale through Google's sales team. The company says that by the end of the year, it'll expand distribution into new markets including Taiwan, Australia, New Zealand, India, Thailand, Singapore, Oman, Ghana, and the Philippines. Coinciding with Coral's general availability, the Coral website -- which now lives at Coral.ai -- has been revamped with better organization for docs and tools, testimonials, and "industry-focused" pages. Additionally, it links to a new set of examples aimed at providing solutions to common AI problems, such as image classification, object detection, pose estimation, and keyword spotting.

In this research guide, we'll look at deep learning papers aimed at synthesizing video frames within an existing video. This could be in between video frames, known as interpolation, or after them, known as extrapolation. The better part of this guide will cover interpolation. Interpolation is useful in software editing tools as well as in generating video animations. It can also be used to generate clear video frames in sections where a video is blurred. Video frame interpolation is a very common task, especially in film and video production. Optical flow is one of the common tactics used in solving this problem.

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.