An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this paper presents a systematic literature review of research at the intersection of SE DL. The review canvases work appearing in the most prominent SE and DL conferences and journals and spans 84 papers across 22 unique SE tasks. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level.

One of the most gratifying parts of my job at Microsoft is being able to witness and influence the intersection of technological progress and impact: harnessing the big trends in computing that have the opportunity to benefit everybody on the planet. Frank's post this morning from Ignite shows just how much progress is happening in many of these areas. Today, the foremost computing trend is undoubtedly artificial intelligence (AI). As we increasingly develop the ability to deploy huge AI models at scale in a way that can be leveraged by all developers and businesses, AI is becoming a platform – an environment upon which folks can build amazing new experiences, just like we've seen happen before with personal computers, mobile devices or the internet. Getting this AI platform off the ground requires unprecedented computing horsepower.

AI researchers from the Ludwig Maximilian University (LMU) of Munich have developed a bite-sized text generator capable of besting OpenAI's state of the art GPT-3 using only a tiny fraction of its parameters. GPT-3 is a monster of an AI system capable of responding to almost any text prompt with unique, original responses that are often surprisingly cogent. It's an example of what incredibly talented developers can do with cutting-edge algorithms and software when given unfettered access to supercomputers. At least not when compared to a new system developed by LMU researchers Timo Schick and Hinrich Schutze. According to a recent pre-print paper on arXiv, the duo's system outperforms GPT-3 on the "superGLUE" benchmark test with only 223 million parameters: In this work, we show that performance similar to GPT-3 can be obtained with language models whose parameter count is several orders of magnitude smaller.

Three months since OpenAI has released their GPT-3 API, it's now a shared notion -- getting to STOA results is mostly a function of effective prompts programming, rather than an NLP task. It can literally be the difference between a chatbot whose replies do not make any sense, and a chatbot able to pass a Turing test. The interaction with GPT-3 is being done via prompts -- textual hints guiding it about the context and task at hand. To a large extent, prompts are an abstract fine-tuning layer, allowing the user, i.e. developer, to guide GPT-3 in knowing which neuron-tree in its'brain' it should activate to perform best. The more examples of successful completions one provides, i.e. pair of inputs and outputs, the higher the chances it will generate the right completion for an unseen case.

Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.

If you are considering using artificial intelligence (AI) to mature your foundational IT and data capabilities, how do you separate hype from reality? Whether you are exploring the promises of AI for your business or still wondering when you will see truly transformative results, here are five industry trends that will help realize AI's untapped potential. For most of us, deep learning systems are essentially incomprehensible. Using millions of data points as input and the correlating data as output, their internal logic can generally not be interpreted using plain language. However, if automated systems are to assist in making critical decisions such as which operations and processes to use and we cannot understand how these decisions are made, how can we identify and address errors?

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That's a problem that's much easier to fix now than it was a few years ago, thanks to the advent of "MLops" environments and frameworks that support machine learning lifecycle management. The easy answer to this question would be that machine learning lifecycle management is the same as ALM, but that would also be wrong. That's because the lifecycle of a machine learning model is different from the software development lifecycle (SDLC) in a number of ways.

Among the many benefits associated with AI, the most important is the ability to predict and recommend, ultimately benefitting every industry out there. Additionally, AI helps improve marketing efforts, initiates conversations with users, and also increases user engagement. We are already seeing intense and defining trends in 2020 that encompass AI and offer incredible solutions for businesses. Here are eight AI trends that will transform 2020 and beyond. Artificial Intelligence is dependent on deep learning and machine learning methods to introduce and enhance various systems.

After they published the article, many responses came from different media houses and notable people trying to shed some light on what exactly happened. First, let's look at the legitimacy of the article. Surely it was the generated one but with no human intervention? So, it is just another media overhype? I mean cherry-picking the best and presenting it to you in a way that sells.

Since its release, GPT-3, OpenAI's massive language model, has been the topic of much discussion among developers, researchers, entrepreneurs, and journalists. Most of those discussions have been focused on the capabilities of the AI-powered text generator. But much about GPT-3 remains obscure. The company has opted to commercialize the deep learning model instead of making it freely available to the public. And though the AI has shown to be capable of many interesting feats, it's not yet clear if GPT-3 will become a real product or will join the endless array of abandoned projects that never found a viable business model. Earlier this month, as reported by users who have access to the beta version of the language model, OpenAI declared the initial pricing plan of GPT-3.