Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from data equipped with a binary relation. By considering only the similarity constraints, and initially ignoring the features, we are able to learn target vectors for each instance using one of several appropriately designed loss functions. A regression model can then be constructed that maps novel feature vectors to the same target vector space, resulting in a feature extractor that computes vectors for which a predefined metric is a meaningful measure of similarity. We present results on both multiclass and multi-label classification datasets that demonstrate considerably faster convergence, as well as higher accuracy on the majority of the intrinsic evaluation tasks and all extrinsic evaluation tasks.

If 2015 was the year of Machine Learning, 2016 is going to be the year of Deep Learning, whose popularity has been on a steep incline ever since Google bought DeepMind at the end of 2014. Last year's technical breakthroughs, acquisitions, funding deals and, open source releases have all helped to cement Deep Learning as the hip artificial intelligence. Our CTO, Matt Painter, explains how Deep Learning got its start. How does my brain work? Starting with Guillaume-Benjamin-Amand Duchenne, scientists began exploring how neurons work in the 1890s by pressing on different parts of the brain to see what would happen.

I am a data scientist, big data engineer, and full stack software engineer. For my masters thesis I worked on brain-computer interfaces using near-infrared spectroscopy. These assist non-verbal and non-mobile persons communicate with their family and caregivers. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

Salesforce has acquired artificial intelligence startup MetaMind. Terms of the deal were not disclosed. Based in Palo Alto, MetaMind -- which was backed by Salesforce CEO Marc Benioff -- touts its technology as being enterprise-grade AI. Its work has revolved mainly around deep learning in language and vision-based applications. "With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes," MetaMind cofounder and chief executive Richard Socher wrote in a blog post.

A big shakeup happening in the world of deep learning, as Salesforce announced that it has acquired startup darling MetaMind. As part of the acquisition, MetaMind will shut down on May 4 for unpaid users and June 4 for paid users. Don't miss our biggest TNW Conference yet! With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes. We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform.

Salesforce.com Inc. is acquiring MetaMind, an artificial-intelligence startup that specializes in deep-learning services, boosting its capabilities for the new technology. The Palo Alto, California-based company helps business users employ deep learning technology, which trains networks of computers to mimic the human brain's ability to recognize and analyze pictures or text without explicit instructions. The acquisition will help Salesforce offer AI capabilities to automate and personalize customer support, marketing and many other business processes, MetaMind said on its website. "We thought about how to maximize our impact with deep learning and we feel like Salesforce is the best place to do that," Richard Socher, MetaMind's chief executive officer, said in an interview. Salesforce is boosting its focus on machine learning and automation as it looks for new ways to bolster its approach to helping customers tackle sales with their own clients.

I am designing a neural network in Lasagne, a Theano based Deep Learning Library. I am trying to program a simple, Reinforcement Learning network, but am running into a road block in defining the loss function. Basically, the input can be thought of as a location of the AI. The AI needs to get closer to a fixed destination point. The distance can be calculated by the input alone.

Today, I'm thrilled to share the news that Salesforce has acquired MetaMind. We couldn't be more excited to join Salesforce. Over the past year and a half, we've been on a mission to empower business users with state of the art deep learning technology to simplify, improve and automate decision making. And now, we'll be able to continue our journey at Salesforce on a much larger scale, with the resources and ecosystem of one of the world's most innovative and influential enterprise software companies. With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes.

Salesforce has acquired MetaMind, a deep learning startup backed by Salesforce cofounder and chief executive Marc Benioff. The startup's services will stop working on May 4. Terms of the deal weren't disclosed. "Salesforce plans to integrate MetaMind's technology into Salesforce services," MetaMind cofounder and chief executive Richard Socher wrote in a blog post. "For unpaid web users, MetaMind's products will be discontinued on May 4. For our monthly recurring users, MetaMind's products will be discontinued on June 4. We'll delete any data we have stored for unpaid web users and monthly recurring users after April 11." Deep learning is a type of artificial intelligence that many large technology companies -- like Facebook, Google, Microsoft, and Twitter -- have invested in by way of acquisitions or in-house research.

What is AI and what is not AI is, to some extent, a matter of definition. There is no denying that AlphaGo, the Go-playing artificial intelligence designed by Google Deep Mind that recently beat world champion Lee Sedol, and similar deep learning approaches have managed to solve quite hard computational problems in recent years. But is it going to get us to full AI, in the sense of an artificial general intelligence, or AGI, machine? Not quite, and here is why. One of the key issues when building an AGI is that it will have to make sense of the world for itself, to develop its own, internal meaning for everything it will encounter, hear, say, and do.