At the end of 2014, Stanford announced it was going to play host to a 100-year-long study, proposed by computer scientist Eric Horvitz, on the effects of artificial intelligence on society and how those effects "will ripple through every aspect of how people work, live and play." The study, nicknamed AI100, is mostly carried out by having a panel of experts produce a report every five years. On Thursday the first report came out, produced by 17 people, most of whom are professors in various AI-related disciplines, with a couple from companies like Microsoft of Google X. The experts break down the different effects of AI into eight different broad categories, ranging from transport to entertainment. In honor of labor day, let's focus on the area they dub "Employment and Workplace."

Cortana may eventually be able to peek inside your refrigerator following news that Microsoft is bringing its machine learning tech to kitchens. The computer firm is working on a new computer vision module called SmartDeviceBox, which will be installed inside fridges to make them capable of recognising objects inside and telling owners when they need to re-stock. The SmartDeviceBox is essentially an internet-connected camera unit that sits inside fridges and tracks the objects inside. The unit contains an image processing system based on Microsoft's suite of deep learning tools, which has been trained to recognise a variety of products such as milk cartons, ketchup bottles and pickle jars. The system is able to collate these into an inventory list that customers can view in list form using a smartphone app.

If you would like to clarify the clouds over what machine learning is please check this article from Stanford first. Page 9 and 15 have good description on the artificial intelligence topic along with description of what machine learning and reinforcement learning means. If you would like to refresh your knowledge on project haystack, Berkeley study uses Haystack information circa 2012 is very helpful overview. Most updated info is also available on project-haystack.org Would like to hear opinion below to further the conversation on this complex topic.

These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. You can also submit a pull request directly to our git repo. We encourage the use of the hypothes.is

Microsoft has teamed up with the Tate's IK Prize winner for 2016, Italian-based communication research centre Fabrica, to surface images from Tate's extensive collection of more than 30,000 images that relate to other photos, supplied by Reuters, of current events as part of an art installation at the Tate called Recognition. The search engine uses Microsoft's Cognitive Services AI to recognize expressions, themes, context and style similarities between pictures in the news and Tate's massive collection, merging the modern and historical. The algorithm is not only able to match pictures, but also to explain why it thinks they are a matches, for example having similar objects, colours or compositions, and builds on technology such as HowOldAmI.net The exhibition is a show-case of Microsoft's AI technology and is also designed to improve public perception of Artificial Intelligence. "From a Microsoft perspective AI is the future – we're betting the company on this technology that extends the reach of humanity," said Dave Coplin, chief envisaging officer at the tech brand.

While chatbots have been around since the early 90's, this year they became completely synonymous with Facebook Messenger. Facebook brought automated, chat-based customer service into the mainstream at a time when businesses and buyers are obsessed with improving the customer experience. This technology will be transformative. It's part of a wave of innovations that is going to help businesses meet the expectations of their consumers. Who wouldn't want every interaction with a business to be faster, easier, and more like talking to a friend?

Advancements in artificial intelligence, coupled with the proliferation of messaging apps, are fueling the development of chatbots -- software programs that use messaging as the interface through which to carry out any number of tasks, from scheduling a meeting, to reporting weather, to helping users buy a pair of shoes. Foreseeing immense potential, businesses are starting to invest heavily in the burgeoning bot economy. A number of brands and publishers have already deployed bots on messaging and collaboration channels, including HP, 1-800-Flowers, and CNN. While the bot revolution is still in the early phase, many believe 2016 will be the year these conversational interactions take off. In a new report from BI Intelligence, we explore the growing and disruptive bot landscape by investigating what bots are, how businesses are leveraging them, and where they will have the biggest impact.

Researchers at Stanford University are utilizing artificial intelligence (AI) to identify areas of poverty in hard-to-reach places. Publishing their research in Science Magazine, the team consisting of Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, and Stefano Ermon used deep learning algorithms to sort through millions of satellite images to identify economic conditions in five African countries. This research supports a forecast Tractica made over a year ago in our Artificial Intelligence for Enterprise Applications report, that spending on AI software by philanthropy organizations will grow dramatically over the next 10 years. Traditionally, philanthropy organizations have conducted door-to-door surveys to identify people living in poverty, but these surveys are imprecise, time-consuming, and expensive. Many international aid organizations including the World Bank have been trying to use satellite surveys to gather data remotely on developing countries, but the expense of gathering and analyzing this data using conventional methods has proven prohibitive.

SAP is betting that artificial intelligence can help eliminate bias in hiring and employee performance reviews. The tech giant has been particularly vocal about improving gender and ethnic diversity, not exactly strange considering it owns human resources software company SuccessFactors. It even recently gave nearly 1% of its U.S. workforce, including some men, raises in order to close a pay gap. More significantly for the rest of the corporate world, however, are additions that SAP sap plans for its SuccessFactors HR management system, which includes applications for recruiting, performance appraisals, and career development. The new features include data crunching that flags potentially biased language in job descriptions that could unintentionally limit a pool of candidates, according to presentations that the company is making during its annual customer conference this week in Las Vegas.

Data clustering is the process of grouping items together based on similarities between the items of a group. Clustering can be used for data compression, data mining, pattern recognition, and machine learning. Examples of applications include clustering consumers into market segments, classifying manufactured units by their failure signatures, identifying crime hot spots, and identifying regions with similar geographical characteristics. Once clusters are defined, the next step may be to build a predictive model. TIBCO Spotfire makes it easy to perform clustering with these two popular out of box user-friendly solutions: 1. K-means Clustering 2. Hierarchical Clustering The k-means method is a popular and simple approach to perform clustering and Spotfire line charts help visualize data before performing calculations.