Did you know there are over 3,874 companies offering marketing technology? That's how many companies are featured on Scott Brinker's behemoth 2016 Marketing Technology Landscape Supergraphic, which drives home the challenge of navigating the marketing industry. "Marketing has the unique challenge of not having a typical stack or process. If you look into any Fortune 500 company, they will have hundreds of products that they are stitching together," says Eric Stahl, an SVP of Product Marketing at Salesforce. Leading marketing experts agree that the plethora of tools available to marketers and advertisers is both a blessing and a curse.

On this new post series, we will analyze hundreds of thousands of articles from TechCrunch, VentureBeat and Recode to discover cool trends and insights about startups. These are the types of questions we aim to answer with this analysis. On this first post, we will cover how Scrapy can be used to get all the articles ever published on these tech news sites and how MonkeyLearn can be used for filtering these crawled articles by whether they are about startups or not. We want to create a dataset of startup news articles that can be used for studying trends later on. On the second post, we will create text classifiers that do analysis on the actual content of the startup articles. Is it a news about acquisition? Finally, on the third post we will use the data we got here, and the classifiers from the second post, to answer our questions.

About Thorn Thorn partners across the tech industry, government and NGOs, leveraging technology to combat predatory behavior, rescue victims, and protect vulnerable children. About Eric Boutin Eric leads an engineering team for MemSQL in our Seattle office. This is background information from Eric on our work with Thorn. I was introduced to Federico Gomez Suarez, a volunteer working with Thorn, by a common friend. I was impressed by the work Thorn was doing, and excited about the opportunity to help them.

Across enterprises, ever-expanding stores of data remain unstructured and unanalyzed. Few organizations have been able to explore nontraditional data sources such as image, audio, and video files; the torrent of machine and sensor information generated by the Internet of Things; and the enormous troves of raw data found in the unexplored recesses of the "deep web." However, recent advances in computer vision, pattern recognition, and cognitive analytics are making it possible for companies to shine a light on these untapped sources and derive insights that lead to better experiences and decision making across the business. In this age of technology-driven enlightenment, data is our competitive currency. Buried within raw information generated in mind-boggling volumes by transactional systems, social media, search engines, and countless other technologies are critical strategic, customer, and operational insights that, once illuminated by analytics, can validate or clarify assumptions, inform decision making, and help chart new paths to the future. Until recently, taking a passive, backward-looking approach to data and analytics was standard practice. With the ultimate goal of "generating a report," organizations frequently applied analytics capabilities to limited samples of structured data siloed within a specific system or company function. Moreover, nagging quality issues with master data, lack of user sophistication, and the inability to bring together data from across enterprise systems often colluded to produce insights that were at best limited in scope and, at worst, misleading.

Artificial intelligence is going to transform and impact many jobs, and it is up to the companies building the technology and government agencies regulating it to make sure it is a force for good, Salesforce CEO Marc Benioff said during a conversation with IBM CEO Ginni Rometty during the IBM InterConnect conference in Las Vegas Tuesday. The two companies earlier this month announced a surprise AI partnership. The companies will combine aspects of their respective AI technologies -- IBM's Watson and Salesforce's Einstein -- in a new bid to win customers in the emerging world of cloud-based artificial intelligence. For example, they say, Watson could analyze shopping patterns, weather and retail data, working in conjunction with Salesforce Einstein to help a retailer send automated personalized marketing emails to customers. Benioff and Rometty said they traveled to the White House last week to talk about how AI will impact jobs.

Several months ago, we read a Facebook post about how the jobs that many people have today will not exist in ten years' time. The post discussed topics ranging from anyone that worked in the transportation industry not having a job because all cars will be automated to many types of doctors not working because you will have a chip in your arm that will detect diseases before either the patient or a doctor could diagnose them today. It gave, in many respects, a very Draconian view of the world-- talking about how do we employ people in a world where the computers and the machines can do things better, faster and cheaper than anyone else can do them. That had us thinking about the topic of this article, because the comparative number of Google results one receives upon searching "artificial intelligence" (AI) plus most topics far outweigh the results regarding AI and the wealth management space. The scarcity of the commentary on this subject made us start thinking about how AI would impact the wealth management sector. In this article, we look at how certain areas of the wealth management sector may be affected by AI.

Canan Dagdeviren [JAH-naan DAH-day-vee-ren] is head of the new'Conformable Decoders' research group at MIT. Scientist Canan Dagdeviren is an interpreter for a language without words. She knows they're both saying something important, speaking the unique language of the body. It's a lexicon that's completely different from the Turkish and English that Dagdeviren speaks every day – but it's one she believes we need to start translating in earnest. She wants to count up our brain pulses, watch our temperature change in real time and observe how we breathe. This is different from the health-monitoring tech inside consumer Fitbits and smart watches that can count each step and monitor every heartbeat.

Please email your data science related quandaries to rachel@fast.ai. Note that questions are edited for clarity and brevity. In the last week I received two questions with diametrically opposed premises: one was excited that machine learning is now automated, the other was concerned that machine learning takes too many years of study. Q1: I heard that Google Cloud announced that entrepreneurs can easily and quickly build on top of ML/NLP APIs. Is this statement true: "The future of ML and data post Google Cloud - the future is here, NLP and speech advancements have been figured out by Google and are accessible by API. The secret sauce has been commoditized so you can build your secret sauce on top of it. The time to secret sauce is getting shorter to shorter"?

You could be forgiven for being overwhelmed by customer data. It's likely that some of those transactions and data involve your customers and your company. But it's a dizzying prospect to figure out how you can translate all that activity into implications for customer experience. In the old days of database marketing and customer segmentation, we practiced what might be called "artisanal analytics." The bulk of our activities involved generating queries and reports on what our customers had done in the past.

William Shakespeare once wrote, "What's in a name? That which we call a rose by any other name would smell as sweet." For the star-crossed lovers of Romeo and Juliet, it meant that a name is nothing but an artificial and meaningless convention. Many panelists at SXSW seemed to have a similar mindset. There are two kinds: chatbots that rely on scripted query and response systems and those that are powered by artificial intelligence.