Job listings included a senior machine learning and information intelligence researcher, an information security manager and an Apple Media …

Prescriptive analytics is about using machine learning and AI to proactively suggest actions to be taken.

Break the business problem into an analytical problem Let's start this with another cliche - " If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions". This is where a lot of analysts/consultants fail. As soon as a business problem falls into their ears, they straightaway get down to solution-ing, without even a bare attempt at understanding the problem at hand. To tackle this, I (and my team) follow what we call the CS-FS framework (extra marks to those who can come up with a better naming). The CS-FS framework stands for the Current State - Future State framework.In the CS-FS framework, the first step is to identify the Current State of the client, where they're at currently with the problem, followed by the next step, which is to identify the Desired Future State, where they want to be after the solution is provided - the insights, the behaviors driven by the insight and finally the outcome driven by the behavior.

Entity Embed is based on and is a special case of the AutoBlock model described by Amazon. It allows you to transform entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors. Using Entity Embed, you can train a deep learning model to transform records into vectors in an N-dimensional embedding space. Thanks to a contrastive loss, those vectors are organized to keep similar records close and dissimilar records far apart in this embedding space. Embedding records enables scalable ANN search, which means finding thousands of candidate duplicate pairs of records per second per CPU.

Researchers at the University of Groningen in the Netherlands used artificial intelligence to analyze the longest text of the Dead Sea Scrolls, running …

Picture this: you turn up the volume in your favourite music streaming service and set off on your daily drive to work using your favourite web mapping service. All while enjoying a playlist that is auto-curated for you and without having to manually enter where you want to go. These services just seem to know what you would like and where you are going. You probably don't think about the complex machine learning and analytics that are combining in the background to give you a pleasant commute. Trends in historical drives and your recently played tunes power artificial intelligence (AI) predictions about which song to listen to and which turn to take next.

Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence generation tasks. However, the issue of mode-collapsing remains a main issue for the current models. In this paper we propose a GAN-based generic framework to address the problem of mode-collapse in a principled approach. We change the standard GAN objective to maximize a variational lower-bound of the log-likelihood while minimizing the Jensen-Shanon divergence between data and model distributions. We experiment our model with text generation task and show that it can generate realistic text with high diversity.

For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate people tend to speak only low-resource languages, for which the datasets necessary for speech technology development are scarce. In this paper, we investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives, which are abundant even in low-resource languages. We make three core contributions. First, we release two datasets to the research community. The first, West African Radio Corpus, contains 142 hours of audio in more than 10 languages with a labeled validation subset. The second, West African Virtual Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in four languages. Next, we share West African wav2vec, a speech encoder trained on the noisy radio corpus, and compare it with the baseline Facebook speech encoder trained on six times more data of higher quality. We show that West African wav2vec performs similarly to the baseline on a multilingual speech recognition task, and significantly outperforms the baseline on a West African language identification task. Finally, we share the first-ever speech recognition models for Maninka, Pular and Susu, languages spoken by a combined 10 million people in over seven countries, including six where the majority of the adult population is illiterate. Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.

AI, or more correctly termed machine learning, can be found in many different applications in your home, car, and workplace. Our relationship with machine learning applications started when we began to use voice-operated smartphone assistants like Apple's Siri or Google's Voice but voice recognition is just one example of a machine learning application; others include vision processing and object recognition. With the wide range of industrial and electronics sector clients Publitek has, machine learning is a frequently covered topic. Over the past ten or so years, we've seen the use of machine learning (ML) neural networks, once restricted to compute-intensive data centers, emerge to bring intelligent control to the edge of industrial applications. It isn't always apparent, but many of the voice recognition applications we interact with rely on constant cloud connectivity to process our speech.

The recent analysis of Machine Learning in Education market size has been methodically put together to impart an in-depth understanding of the key …