Aman Naimat is the SVP Technology for Demandbase where he is working on creating the first Artificial Intelligence account-based marketing platform. Aman was previously co-founder and CTO of Spiderbook, a data-driven sales engine. Before Spiderbook, he was the co-founder of TopCorner, a platform for Open Government. Aman has been building CRM systems since he was 19. He also founded and worked in various startups in search, trading systems, and enterprise software.

"This is a propaganda machine. It's targeting people individually to recruit them to an idea. It's a level of social engineering that I've never seen before. They're capturing people and then keeping them on an emotional leash and never letting them go," said professor Jonathan Albright. Albright, an assistant professor and data scientist at Elon University, started digging into fake news sites after Donald Trump was elected president. Through extensive research and interviews with Albright and other key experts in the field, including Samuel Woolley, Head of Research at Oxford University's Computational Propaganda Project, and Martin Moore, Director of the Centre for the Study of Media, Communication and Power at Kings College, it became clear to Scout that this phenomenon was about much more than just a few fake news stories. It was a piece of a much bigger and darker puzzle -- a Weaponized AI Propaganda Machine being used to manipulate our opinions and behavior to advance specific political agendas. By leveraging automated emotional manipulation alongside swarms of bots, Facebook dark posts, A/B testing, and fake news networks, a company called Cambridge Analytica has activated an invisible machine that preys on the personalities of individual voters to create large shifts in public opinion.

I met Aman Naimat, Senior Vice President of Technology at Demandbase at their headquarters in San Francisco on my recent visit to California for Social Media Strategies Summit. Aman is working on leveraging the latest developments in artificial intelligence (AI) and data science for marketing and sales platforms. In this podcast, Aman and I discuss how AI functions in account-based marketing (ABM). Before working with Demandbase, Aman was previously founder and CTO of Spiderbook, a data-driven sales engine for account-based targeting. Aman has been building CRM systems since he was 19, and was the architect for the Oracle CRM applications.

Machine learning can infuse every application with predictive power. Data scientists use these sophisticated algorithms to dissect, search, sift, sort, infer, foretell, and otherwise make sense of the growing amounts of data in our world. Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example and from fresh data feeds. With unsupervised approaches, data scientists can dispense with training examples entirely and use machine learning to distill insights directly and continuously from the data.

I'm working on a PoC of some system and part of the project is to classify images. Let's say there are many (hundreds) "tags" and for each tag there is a hundred of images. Each image is produced as a result of some technological process and looks like set of lines and dots. What would be the simplest way to "learn" the existing images such that for a new image/tag pair I could be able to tell whether it matches the classification. Is there a ready to use library or solution for such a problem?

Say you're a giant company that's heard about a fancy "new" technology called artificial intelligence and you're interested in adding some cutting-edge data crunching muscle to your business. Contrary to what the artificial intelligence-hype cycle might suggest, just adding popular buzzwords like "machine learning" to your vernacular isn't as easy as hooking a smartphone to a laptop. At the Machine Learning and the Market for Intelligence conference this week put on by the Rotman School of Management at the University of Toronto, several founders behind artificial intelligence startups that have been acquired by industry heavyweights like Salesforce.com (crm), Uber, and Amazon (amzn) shared lessons they've learned since joining the big-time corporate world. Richard Socher, the founder of the A.I. startup MetaMind that was swallowed by Salesforce in April, explained on a panel what he's learned since joining the cloud software giant and becoming its chief scientist. Socher said he was pleased with the research that his small team worked on with two types of A.I. techniques called computer vision, in which software can learn to recognize images in pictures, and natural language processing, in which software learns to recognize text.

In healthcare, mobile innovation represents an opportunity to save lives and improving the quality of care for countless patients. A new mobile solution promises significant changes to how physicians approach medical diagnosis, likely resulting in more timely diagnoses to give medical experts a better shot at catching disease early and administering treatment. Researchers at UCLA have developed a tool that leverages machine learning to power a smart mobile device designed for the healthcare world. More specifically, this piece of technology can detect proteins, viruses, cancer biomarkers and other microscopic bodily objects that have traditionally been difficult for researchers to find in patients, according to Phys.org. Researchers believe that, through machine learning, this smart solution can train its own algorithms to process biometric data and better understand biomarkers and other biological trends.

Behind the scenes, the same call automatically and invisibly decides whether a machine learning classifier is reliable enough to classify the example on its own, or whether human intervention is needed. Models get built automatically, they're continually retrained, and the caller never has to worry whether more data is needed. In the rest of this article, we'll go into more detail on the problems we described above--problems that are common to all efforts to deploy machine learning to solve real-world problems. In order to train any spam classifier, you'll first need a training set of "spam" and "not spam" labels.

Our simple and extensible framework lets users easily point and collect data from multiple data sources across similar application and infrastructure elements. In addition to providing rich, out-of-the-box data source plug-ins, Perspica enables fast, easy creation of custom data source integrations through our data collection API.