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NLP News Cypher 11.10.19

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Wow, what a week it was. The EMNLP conference gave us many treats to chew on such as the growing popularity of cross-lingual learning and the continued adoption of knowledge graphs in language models. Because of all this action, this week's Cypher will be a bit longer than usual. What were some of the top keywords in EMNLP papers? Stephen Mayhew et al were live tweeting during the conference (thank you) and sharing all the action.


Ruby on Rails in Machine Learning – Yay or Nay?

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Another important advantage of using Python libraries is considerable support from the community as the developers can easily find tutorials and tips valuable in a development process. A stable community makes the start threshold lower – it is easier to use new technologies from scratch.


AI JavaScript Rocks

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Many exciting things are happening with AI, from which, until recently, JavaScript developers were mostly shut out. If you can do npm install @tensorflow/tfjs or make an API call, you can now do AI. In this fast-paced talk, I'll open your mind to what's possible by demoing several AI-powered JavaScript apps and show you how I built them using either TensorFlow.js or easy to use AI-powered APIs. You don't need a PhD in Maths. You don't need years of experience.


Neural Networks 201: All About Autoencoders - KDnuggets

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For those getting started with neural networks, autoencoders can look and sound intimidating. But in fact, they are a conceptually simple and elegant approach that will open many doors to an ML practitioner. They can be used for anomaly detection and missing value imputation or help in building better classifiers or clusters. In any case, what makes them unique is that they provide you with a mechanism for leveraging your unlabelled data, which often is much easier to get than labeled data. For instance, it's a lot easier to get a collection of images than it is to get a collection of images where each one is labeled to tell you what's in it.


Gradient-based Optimization for Bayesian Preference Elicitation

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Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning (ML) computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte Carlo method for EVOI optimization, which is more scalable for large item spaces than methods requiring explicit enumeration of items.


The Collapse of Civilization May Have Already Begun

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"It is now too late to stop a future collapse of our societies because of climate change." These are not the words of a tinfoil hat-donning survivalist. This is from a paper delivered by a senior sustainability academic at a leading business school to the European Commission in Brussels, earlier this year. Before that, he delivered a similar message to a UN conference: "Climate change is now a planetary emergency posing an existential threat to humanity." In the age of climate chaos, the collapse of civilization has moved from being a fringe, taboo issue to a more mainstream concern. As the world reels under each new outbreak of crisis--record heatwaves across the Western hemisphere, devastating fires across the Amazon rainforest, the slow-moving Hurricane Dorian, severe ice melting at the poles--the question of how bad things might get, and how soon, has become increasingly urgent. The fear of collapse is evident in the framing of movements such as'Extinction Rebellion' and in resounding warnings that business-as-usual means heading toward an uninhabitable planet. But a growing number of experts not only point at the looming possibility that human civilization itself is at risk; some believe that the science shows it is already too late to prevent collapse. The outcome of the debate on this is obviously critical: it throws light on whether and how societies should adjust to this uncertain landscape. Yet this is not just a scientific debate. It also raises difficult moral questions about what kind of action is warranted to prepare for, or attempt to avoid, the worst. Scientists may disagree about the timeline of collapse, but many argue that this is entirely beside the point. While scientists and politicians quibble over timelines and half measures, or how bad it'll all be, we are losing precious time.



Route raises $12 million to track packages with AI

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An estimated 165.8 million people shopped between Thanksgiving Day and Cyber Monday in 2018, and each purchased an average of 16 gifts during that time frame. Collectively, their haul came in billions of packages delivered through the mail system, over 900 million of which were tendered by the USPS alone. So what's a shopper stuck juggling multiple orders from multiple retailers to do? One solution is Route's tracking platform, which offers solutions on both the merchant and consumer sides. The Silicon Slopes, Utah-based startup today announced the launch of a visual order tracking app -- Route App -- for iOS, alongside a package insurance coverage plan, coinciding with the closure of $12 million in seed funding.


Researchers Build an 'Interpretable' AI That Shows How It Thinks - The New Stack

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The use of machine learning is increasing as automation becomes more widespread in our workplaces, financial institutions and even courts of law -- telling us whom to hire, whom to lend money to, and who might re-offend. But it's becoming painfully clear that these complex algorithms can conceal any number of hidden biases -- leading them to inadvertently discriminate against people based on their gender or race -- oftentimes with terrible, life-changing consequences. The problem is that such AI systems are notoriously opaque; more often than not, the mechanisms and reasoning behind their predictions aren't immediately apparent, even to the people who created these systems. So it's little wonder that a growing number of experts are now working to build what is called "interpretable" or "explainable" AI, where the processes that underlie machine predictions are made more transparent and therefore, also more understandable (at least by us humans). In aiming to better understand how and why machines classify images the way they do, one research team from Duke University created a new deep learning neural network whose reasoning process can be deconstructed, analyzed and understood more easily than comparable models.


Salesforce teases its emerging AI capabilities

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Since introducing its Einstein AI platform a few years ago, Salesforce has built AI into more and more of its tools. At this year's Dreamforce conference, for instance, the CRM giant announced new tools for customizing voice assistants and for incorporating AI into contact centers. The new capabilities showcase how Salesforce is progressively making work easier for its customers -- albeit in incremental steps. To give Dreamforce attendees a more forward-looking glimpse into its product capabilities, the Salesforce Research team demonstrated some of its breakthroughs in areas like conversational AI and natural language generation. Their research is focused on building an AI-driven world so far only found in sci-fi, said Salesforce Chief Scientist Dr. Richard Socher.