"With devices like Alexa Echo, Apple HomePod and SmartLock in our homes, the'intelligent personal assistant' SIRI and Google in our pockets, we have to become more aware that we are giving devices an incredible amount of personal data and control to machines. Our young people are growing up where this is normal practice and more and more common. This initiative is about giving students the opportunity to reflect on the control and information that we freely hand over, and about examining some of the potential outcomes in society as we give more and more to machines and machine learning. HAL was a concept 50 years ago, a warning of sorts, that is still valid today. We want the students to have fun, and to see the opportunities as well as the challenges of the incredible technology we have in our world today and potentially tomorrow," said Valerie Cowman, Chair of the Skills & Education Committee of the CEIA – Cork's Technology Network.

Artificial intelligence (AI) is a field of technology that is surrounded by both hype and misconceptions. It is predicted that $60 billion will be spent by brands on AI technology by 2025, so this hype is having a direct impact on where companies allocate their budgets. A significant difficulty in defining the size of the AI market is in defining exactly where its boundaries lie. Although we tend to imagine eerily human robots that mimic our mannerisms, AI is actually a very broad field that encompasses a range of disciplines – some more relevant to search marketing than others. More often than not, it is embedded in software that can process vast amounts of data to make or inform more intelligent decisions.

Chatbots and voice assistants powered by artificial intelligence are starting to gain traction in the workplace of large organisations, according to a report from tech firm Spiceworks. The report, based on a survey of 500 IT professionals in organisations across North America and Europe, found that within the next 12 months, 40 percent of large businesses – those with more than 500 employees – expect to implement one or more intelligent assistants or AI chatbots on company-owned devices, compared to 25 percent of mid-size companies and 27 percent of small businesses. The findings indicate that although adoption is on the rise, some organisations are holding back due to a lack of use cases in the workplace and privacy concerns. Across all company sizes, Microsoft Cortana is the most commonly used intelligent assistant in the workplace, likely due to its native integration into Windows 10. Among organisations that have implemented intelligent assistants or chatbots on company-owned devices and services, the results show 49 percent are currently using Microsoft Cortana for work-related tasks, followed closely by Apple Siri at 47 percent.

A report from Bloomberg earlier this week claimed that Apple's HomePod isn't doing so well, and that the company cut orders for new hardware from suppliers. This might not shock some of you: Apple missed the all-important holiday buying season and is competing with less expensive hardware from Google, Sonos and Amazon. But is the first smart speaker with Siri already a failure, or does the HomePod simply need time to find its place? I'm not in any way surprised that the HomePod has fizzled, simply because it's a weird product with a very weird proposition. I seriously considered buying it in the run-up to its launch, but ultimately couldn't find a strong enough reason to plunk down $350.

Despite concerns over data privacy and immature technology, AI assistants and chatbots are gaining a foothold among businesses, according to a report from IT professional network Spiceworks. The report, which surveyed 529 IT professionals from North America and Europe, showed that 29% of respondents have implemented one or more chatbots or intelligent assistants for work-related tasks, or plan to do so this year. Adoption was higher for larger organizations, which have more resources to evaluate emerging technologies, with 24% using AI assistants or chatbots currently and 16% set to deploy the technology this year, for a total of 40%. "A lot of businesses are starting to see how these technologies are useful, and because they are more widely available, we are seeing adoption increase," said Peter Tsai, Spiceworks senior technology analyst and the author of the report. He added, however, that the technology is still "in its infancy."

Apple has turned Siri into a real comedienne. Siri has always been able to tell a joke or two, but it appears that Apple has programmed the voice assistant to be able to serve up a whole slew of new puns. The new jokes recently started rolling out to Siri-equipped devices, according to MacRumors. Most of the jokes are pretty cringe-worthy, but they're still likely to summon a chuckle. To try it out yourself, say: 'Siri, tell me a joke'.

Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc.. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.

Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from user-item historic behaviors. To apply it into real big data scenarios, efficiency becomes the first concern, including offline model training efficiency and online recommendation efficiency. In this paper, we propose a Distributed Collaborative Hashing (DCH) model which can significantly improve both efficiencies. Specifically, we first propose a distributed learning framework, following the state-of-the-art parameter server paradigm, to learn the offline collaborative model. Our model can be learnt efficiently by distributedly computing subgradients in minibatches on workers and updating model parameters on servers asynchronously. We then adopt hashing technique to speedup the online recommendation procedure. Recommendation can be quickly made through exploiting lookup hash tables. We conduct thorough experiments on two real large-scale datasets. The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure. Furthermore, the encouraging performance of DCH is also shown for several real-world applications in Ant Financial.

We're all about being disruptive,

Apple's HomePod doesn't appear to be as big of a hit as the tech giant had hoped. In January, the firm finally released its smart home speaker that was bid as a rival to the Amazon Echo and Google Home. But by last month, Apple slashed sales forecasts for the HomePod as inventory for the device began to pile up, Bloomberg reported, citing sources close to the situation. In January, the firm finally released its smart home speaker that was bid as a rival to the Amazon Echo and Google Home. It also cut HomePod orders with one of its manufacturers that assembles the device for the company, Inventec, according to Bloomberg.