Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the Computational Social Science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers' gold references. We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in} social science analysis in partnership with humans.

Energy consumption is a major factor to plan for when implementing a long-term project or service that uses large-scale machine learning algorithms. Now, a team of researchers from Georgia Tech has created an interactive tool called EnergyVis that allows users to compare energy consumption across locations and against other models. "Sometimes, training machine learning models from end-to-end takes the same amount of energy as a transatlantic flight. Is every organization using machine learning able to budget for such an expense? What if the grid in which a business runs is running on coal versus green energy?" said Omar Shaikh, a computer science undergraduate student.

Microsoft researchers have built an artificial intelligence system that can generate captions for images that are, in many cases, more accurate than the descriptions people write. The breakthrough in a benchmark challenge is a milestone in Microsoft's push to make its products and services inclusive and accessible to all users. "Image captioning is one of the core computer vision capabilities that can enable a broad range of services," said Xuedong Huang, a Microsoft technical fellow and the chief technology officer of Azure AI Cognitive Services in Redmond, Washington. The new model is now available to customers via the Azure Cognitive Services Computer Vision offering, which is part of Azure AI, enabling developers to use this capability to improve accessibility in their own services. It also is being incorporated into Seeing AI and will start rolling out later this year in Microsoft Word and Outlook, for Windows and Mac, and PowerPoint for Windows, Mac and web.

Today Microsoft announced an update to the Seeing AI app that will include new language output options, including Dutch, French, German, Japanese, and Spanish. The iOS exclusive app was first released in 2017 as a free tool to help people with visual impairments navigate day-to-day life. It's built around a series of different channels, which users can select depending on their particular needs or circumstances. For the first time Microsoft's Seeing AI app will speak in languages other than English. Today's update enables audio output in Japanese, German, Spanish, Dutch, and French In one channel, the app will read out the text of any document the iPhone's or iPad's front facing camera is pointed at.

In developing the ground-breaking Seeing AI app, Saqib Shaikh and the team at Microsoft were driven by this simple but powerful re-framing he articulated, "What if we could look at disability as an engine of innovation?" "There's so many examples where the technologies we rely on today where inspired or influenced by disability, from speech recognition and text to speech to the touch screen itself. There's this terminology of inclusive design where if you focus in on one person's needs, then actually doing that can help you create solutions which benefit a broader population. With seeing AI, we focus in on the needs of people who are blind or low vision, but in doing that I believe it also helps us make better products for all customers," said Shaikh. We spoke about the evolution of platform and how the team approached adding new features. "We're always listening to our customers (the low or no vision community) and understanding what are the challenges that they face. And then we're talking to the scientists and engineers at Microsoft to see what are the emerging technologies we can leverage. And with each of these, we consider the type of task you can complete," said Shaikh.

In the recent time, there is a lot of focus on big data, data analytics, artificial intelligence and machine learning. All these four terms are different sides of the same cube and it has a huge potential across boards, which is why it is believed that data has the power to change the landscape of your business. No wonder the large companies are running in that direction. However, they do have the breadth and the resources to take the risk and rise up after failures. While small and medium companies that are keen to transform into a data-driven business need to scale up wisely and scrutinize each move minutely.

Microsoft has released Seeing AI -- a smartphone app that uses computer vision to describe the world for the visually impaired. With the app downloaded, the users can point their phone's camera at a person and it'll say who they are and how they're feeling. They can also point it at a product and it'll tell them what it is. All of this is done using artificial intelligence that runs locally on their phone. The company showed off a prototype of Seeing AI in March last year at its Build conference, but starting today, the app is available to download for free in the US on iOS. However, there's no word yet on when it'll come to Android or other countries.

Could you ever imagine yourself in a heated argument with a chatbot? Like, really passionate, deeply reasoned position-taking--argument, counterargument, countercounterargument, countercountercounterargument. Could you imagine a chatbot convincing a jury of a defendant's guilt? And if you could imagine it, what would that mean? Questions like these are at the core of a paper published recently in AI Matters by Samira Shaikh, a computer science-slash-psychology researcher at the University of North Carolina-Charlotte.

Microsoft principal software development engineer Jennifer Marsman talked about the applications of machine learning at Microsoft's Ignite NZ conference. From teaching computers to make predictions to helping blind people "see", machine learning technology has already made incredible advancements in a short timeframe. Microsoft's Jennifer Marsman's interest is machine learning and helping to make the technology understandable to the average person. The Detroit-based principal software development engineer was in New Zealand last week for Microsoft's Ignite New Zealand conference, where she gave talks about applications of machine learning. It can be easy to let our imaginations run too wild when it comes to the future of technology, so Marsman to gave examples of machine learning's relevance in real life.

In a span of two and a half hours, Microsoft packed a lot into the opening keynote of its Build 2016 conference. But it was the last video shown that seem to have the biggest impact on many of the viewers at home: the introduction of an AI that helps one of its blind developers "see." At the very end of its keynote, Microsoft CEO Satya Nadella reiterated that he wants technology to enhance the ways humans communicate. Though the many chat bots he introduced earlier seems technologically "smart," the framework behind it still needs the help of developers to continue improving on what has already been built. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.