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.

The cute Japanese robots keep coming. Sony's adorable Aibo pups are already on to their sixth litter and now Sharp is upgrading its RoBoHon line, too. In case you need reminding, that's the robot smartphone that -- like all good droids -- can sing and dance. Along with pricey LTE and WiFi-only models, the second-gen RoBoHon range includes a cheaper "seated" bot that costs 79,000 yen plus tax (around $715). Of course, that means it can't walk, but it will still be able to bust-a-move to certain songs using the top half of its body. Users can also manually move its legs to make it stand upright.

In public, many executives wring their hands over the negative consequences that artificial intelligence and automation could have for workers. They take part in panel discussions about building