Issues surrounding gender and other biases in machine learning (ML) … Synced Report | A Survey of China’s Artificial Intelligence Solutions in …

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Using machine learning to build a library of known and unknown patterns for an individual user.

Just hours after making waves and triggering a backlash on social media, Genderify -- an AI-powered tool designed to identify a person's gender by analyzing their name, username or email address -- has been completely shut down. Launched last week on the new-product showcase website Product Hunt, the platform was pitched as a "unique solution that's the only one of its kind available in the market," enabling businesses to "obtain data that will help you with analytics, enhancing your customer data, segmenting your marketing database, demographic statistics," according to Genderify creator Arevik Gasparyan. Here is a quick read: AI-Powered'Genderify' Platform Shut Down After Bias-Based Backlash

I was doing a pretty intense literature research for the sequence to sequence GANs in the past couple of days, and I was really surprised to find that there is very little that fits the bill, so I thought I would make a post here. The usual bias in machine learning seems to be that image problems have pretty out of the box solutions and everything else is a bit more complicated. With discrete sequence problems that is understandable, but with continuous sequential problems, I don't immediately see the need to jump to RL. Is there a reason people tend to avoid these kinds of solutions? Did anybody try inserting an Attention/BLSTM layer at the end of his GAN to make the output variable length?

Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, the consistently overlooked downside of these models is their stunningly massive complexity, which seems concomitantly crucial to their success. In this paper, we address this issue by proposing a model pruning method based on the lottery ticket hypothesis. We modify the original approach to allow for explicitly removing parameters, through structured trimming of entire units, instead of simply masking individual weights. This leads to models which are effectively lighter in terms of size, memory and number of operations. We show that our proposal can remove up to 90% of the model parameters without loss of accuracy, leading to ultra-light deep MIR models. We confirm the surprising result that, at smaller compression ratios (removing up to 85% of a network), lighter models consistently outperform their heavier counterparts. We exhibit these results on a large array of MIR tasks including audio classification, pitch recognition, chord extraction, drum transcription and onset estimation. The resulting ultra-light deep learning models for MIR can run on CPU, and can even fit on embedded devices with minimal degradation of accuracy.

This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attention from economists, computer scientists, and social scientists. We characterize the various methods proposed in the literature into three general approaches: learning models to predict outcomes, learning models to predict causal effects, and learning models to predict optimal treatment assignments. We show analytically that optimizing for outcome or causal-effect prediction is not the same as optimizing for treatment assignments, and thus we should prefer learning models that optimize for treatment assignments. We then compare and contrast the three approaches empirically in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the different treatment assignment approaches on a real-world application at scale (based on more than half a billion individual treatment assignments). Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing treatment assignments rather than outcome or causal-effect predictions can improve treatment assignment performance by more than 28%.

It will also allow developers to standardize machine learning workflows and accelerate AI deployments from months to days with the HPE Ezmeral ML …

New Jersey, United States,- The research report on AI/Machine Learning market comprises of insights in terms of pivotal parameters such as …

This machine learning model was the primary output from the first Plastic Free Oceans project in 2018, proving it was possible to use AI to create a …