Industries around the world have caught AI fever. Developers around the world have made more ways than ever for the technology to automate, optimize and enable different services. Facebook, Alphabet, IBM, Microsoft, Amazon and other major companies are all working on AI projects, along with numerous tech startups. One such upstart, which leverages artificial intelligence and image recognition in part to enable commerce, is Clarifai. Founded in 2013, Clarifai utilizes neural networks and provides customers with an image and video recognition API.

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959). Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

If you forget to tag or add a description when uploading a photo or gallery to Facebook, it can be tough to find an image when you need it. Or at least it used to be. The social network revealed today that it built an AI image search system that can "see" things in your photos even when you forget to add the aforementioned identifiers. Facebook says the system uses its Lumos platform to understand the content of photos and videos and quickly sort through the items you've uploaded. This means that if even if you can't remember when a photo was taken, if you remember the content, you might still be able to find it with ease.

There was a time when every neighbourhood bank in North America and Europe was acquired by or merged with a larger institution. By 2000, global mega-banks offered fewer choices to consumers looking for competitive interest rates and other services. But the too big to fail banks are now facing competition in the form of a resurgence of customer-friendly, local banks. There is an even bigger challenge: Technology companies have been applying for financial licences that would allow them to enter the digital payments space. As traditional banks grapple with the challenges posed by fintech, legacy constraints and traditional operational models, artificial intelligence (AI) is emerging as the saviour.

FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey primarily targets FOSS contributors and relevant anonymized dataset is publicly available under CC by SA license. In this study, the dataset is analyzed from a critical perspective using statistical and clustering techniques (especially multiple correspondence analysis) with a strong focus on women contributors towards discovering hidden trends and facts. Important inferences are drawn about development practices and other facets of the free software and OSS worlds.

Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.

Even though the phrase "image recognition technologies" conjures visions of high-tech surveillance, these tools may soon be used in medicine more than in spycraft. A team of Stanford researchers trained a computer to identify images of skin cancer moles and lesions as accurately as a dermatologist, according to a new paper published in the journal Nature. In the future, this new research suggests, a simple cell phone app may help patients diagnose a skin cancer -- the most common of all cancers in the United States -- for themselves. "Our objective is to bring the expertise of top-level dermatologists to places where the dermatologist is not available," said Sebastian Thrun, senior author of the new study, founder of research and development lab Google X and an adjunct professor at Stanford University. He added that those who live in developing countries do not have the same level of care as can be found in the US and other industrialized nations.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8]

It is set to be a monumental battle for the next generation of smart assistants. Samsung has fired the latest salvo in its AI phone battle with Apple, revealing more details of Bixby, it's competitor for Siri. The AI is said to have visual search capabilities to analyze the images, identify objects and performing optical character recognition on visible text. Although the Samsung Galaxy S8 reveal is around the corner, many users are still feeling the burn from the Galaxy Note 7 fiasco. However, the firm may redeem itself, as the flagship smartphone is rumored to have an assistant more powerful than Apple's Siri Bixby could be used for a wide variety of functions in a similar way to Apple's Siri.