SDL a global leader in content creation, translation and delivery, today calls on brands to rethink current content strategies, and prepare for a digital future where content supply chains are autonomous, machine-first and human optimized, for greater impact with worldwide audiences, across any language and device. Companies are struggling to handle the growing volume and velocity of content required to engage with global audiences. And it's expected to get worse: 93% say the content they produce will increase in the next two years. SDL's Enabling the Future of Content report addresses these challenges, offering insights on how companies can move towards an autonomous content supply chain of the future, capable of delivering any type of content to global audiences. Peggy Chen, CMO, SDL said, "Engaging with customers globally requires content, and lots of it.

The battle between PewDiePie and T-Series to be the world's most popular YouTube channel has taken a bizarre turn, after billionaire entrepreneur Elon Musk revealed he hosted PewDiePie's'meme review'. PewDiePie, whose real name is Felix Kjelberg, has been the top channel on the world's most popular video-sharing platform since 2013. His dominance has been challenged in recent months by the Indian channel, which posts Bollywood film trailers and music videos. The rise of T-Series has proved controversial within some corners of the YouTube community, seen as a David vs Goliath-style contest between an independent creator and a major corporate brand. For PewDiePie supporters, T-Series' popularity reflects a perceived shift in YouTube's focus towards larger brands that have more potential for generating revenue.

Bangalore: India is on the cusp of a digital transformation, thanks to the spread of high-speed data services. Not just that, as new age technologies like digital, analytics, artificial intelligence (AI), robotics disrupt industry value chains, Indian firms would be at the forefront of driving this change in the global market, says a report. The report by Deloitte predicts that the year 2019 will be the inflection point for conversational AI in India, with voice assistants moving well beyond information access and entertainment in the next two to three years. AI-powered voice assistants could become an important element in home automation, hospitality sector, heath care, potentially progressing to voice-based commerce. "The technology industry in India is in a truly exciting phase right now, unlike any other country at any other point in time. For more than two decades, its technology services industry was at the forefront of driving the technology-enabled transformation of businesses across the world,'' said Hemant Joshi, Leader-Technology, Media & Telecom and Partner, Deloitte India. India's first phase of digital explosion was driven by smart phones and mobile internet penetration. Its next growth phase will be led by mobile-first and voice-first technologies. Original and localised content will play a crucial role in ensuring the faster adoption and wider application of voice technologies, the Deloitte's Predictions Report for 2019 said. Another area that will witness significant growth is financial services technology. The shift in consumer preference towards digital channels for investments and wealth management is steadily rising. While digital investing may increase at varying rates for different asset classes, the facility of immediate service, better discovery of products, and innovations in providing improved customer experience will cause customers to gravitate towards these new-age platforms, found the study. It expects the entertainment industry to reach a high-growth phase this year. Jehil Thakkar, Partner, Deloitte India, said: "This is a good time to be a (content) consumer in the Indian market.

Collaborative filtering (CF)[1] based on the alternating least squares (ALS) technique[2] is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize. The CF algorithm has also been implemented in Spark MLlib[3] with the aim to address fast execution on very large datasets.

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Flipboard, a news aggregation platform, is an extreme testing ground for AI recommendations, given that it features as many as 300,000 articles each day. Of the 150 people who work for Flipboard, about 40 are focused on tech, engineering and data science, which are the teams responsible for creating and monitoring AI tools. Those tools scan for contextual clues to tag articles by topic and keyword, Cora said. The platform's AI features also weed out identical articles, and block spam domains that attempt to spoof legitimate websites. AI does not manage the entire recommendation process, however.

I'm learning RNN theory and as a project, I tried to better understand the dynamics of LSTM elements when applied to input strings by relating the dynamics to concepts I'm more familiar with in chemical nonequilibrium statistical mechanics. Identified some interesting behavior in terms of the relatively smaller impact of terminal pad characters on the element dynamics versus other characters which cause large changes in the element values. Details in the linked blog post. Assume this behavior is well known, but I wasn't able to find a publication that demonstrates this behavior. Would appreciate learning about prior related work that I should be citing.

There's been some interest in the Explorer Challenge which is a 1 million dollar competition combining machine learning and geology to come up with the best prospect. A funny video and detailed slides have been released. I was also at the presentation in Perth, Australia so feel free to clarify things with me if the slides aren't clear. This is an interesting competition because geology seems like it could be disrupted by the application of ML, but it's also challenging because of the large amount of contextual and qualitative data that goes into making decisions.

The performance of automatic speaker recognition systems degrades when facing distorted speech data containing additive noise and/or reverberation. Statistical uncertainty propagation has been introduced as a promising paradigm to address this challenge. So far, different uncertainty propagation methods have been proposed to compensate noise and reverberation in i-vectors in the context of speaker recognition. They have achieved promising results on small datasets such as YOHO and Wall Street Journal, but little or no improvement on the larger, highly variable NIST Speaker Recognition Evaluation (SRE) corpus. In this paper, we propose a complete uncertainty propagation method, whereby we model the effect of uncertainty both in the computation of unbiased Baum-Welch statistics and in the derivation of the posterior expectation of the i-vector. We conduct experiments on the NIST-SRE corpus mixed with real domestic noise and reverberation from the CHiME-2 corpus and preprocessed by multichannel speech enhancement. The proposed method improves the equal error rate (EER) by 4% relative compared to a conventional i-vector based speaker verification baseline. This is to be compared with previous methods which degrade performance.

AFTER seeing Ironman for the first time in grade eight, Jun Hong Liu decided he wanted a career in artificial intelligence.