Goal In today's summary we have a look at the expectation maximization algorithm that allows to optimize latent variable models when analytic inference of the posterior probability of latent variables is intractable. Motivation Latent variable models are itself interesting, because they are related to variational autoencoders and encoder-decoder frameworks that are popular in unsupervised and semi-supervised learning. They allow to sample from the data distribution and are believed to enhance the expressiveness of the hierarchical recurrent encoder decoder models. We can think of them as memorizing higher abstract information, such as emotional states that allow to generate sentimental utterances in the encoder. Steps In general we are concerned with finding good models, which means determining parameters of this model that can explain the data.

Structure Of Intelligent Agents 12. Types of agent program 13. Goal based Agents 14. Utility-based agents 15.

To pitch its cloud storage business, Google is leaning on its artificial intelligence features. Amazon, the market leader, is too. Now Amazon has recruited a leading expert in the field to up its game. Alex Smola, a top machine learning scientist at Carnegie Mellon University and research alum of Google and Yahoo, is moving over to run the "Cloud Machine Learning Platform" at AWS, he wrote in a post. Leaving CMU - Dear Friends, As some of you may have already heard, I'm leaving CMU to join Amazon.

Summary: What changes in your analytics and Big Data stack would you have to make if you only had 10 milliseconds to make a decision? There's an entire industry that has to live by that rule. This is a great story about the collision of ecommerce, digital advertising, predictive analytics, and AI in a new digital battleground, the automation and optimization of advertising targeting and spend. Like so many interesting opportunities this new market starts with a pain point and an unmet need. In this case the lag in digital advertising spend, particularly in mobile devices.

Microsoft, the famously conservative company best-known for producing office software like Windows, has taken the unexpected step of entering the burgeoning industry of marijuana. The company, founded by Bill Gates, announced a partnership on Thursday with Kind Financial, a Los Angeles firm that sells computer systems to governments that track legalized marijuana sales. Microsoft, which is headquartered in Washington, a state where recreational cannabis is now legal, is licensing Kind to sell Microsoft cloud services to authorities to monitor sales – rather than becoming directly involved in the trade itself. Kind, founded in 2013, sells pot tracking software to growers, sellers and governments. But even the cannabis company recognizes the foundations of its business is controversial in some quarters.

A lovely nonthreatening body for a robot that can see warm bodies. Gamma 2 Robotics' RAMSEE borrows the name of pharaohs, which is fitting since it works the graveyard shift. The security bot is an autonomous patrolman, rolling around empty halls and hard-to-surveil places, its LIDAR eyes and infrared cameras scanning the environment around it at all times. It is a robot built for empty spaces, alerting its human overseers to any harmful intrusion of life. And here, a pair of the robots scan a hallway before peering into the beyond.

Hanwha Techwin America (the Hanwha Group acquired Samsung Techwin America, but the companies agreed to keep Samsung in the product name) fixed one of the Samsung SmartCam HD's biggest weaknesses on Thursday by adding cloud storage to the product's feature set: Owners now have the option of subscribing to SmartCloud instead of relying on the onboard SD card that included with the camera. As we pointed out in our review, without this feature, you lose your forensic evidence of the crime if the thief steals the camera (or its SD card) during the break-in. The absence of cloud storage apparently wasn't lost on customers: Hanwha Techwin says feedback showed "an overwhelmingly high demand for cloud services." The SmartCloud subscription service comes in three tiers: 50 per year to store up to five days of continuous video, a 70 plan for seven days of storage, and a 200 plan to store up to 21 days. You can also subscribe on a monthly basis for 5, 8, or 20 respectively.

In addition to conducting pure research in artificial intelligence and machine learning (ML), the group will also develop new tools and products that make use of the technology. The company noted that it already offers several services to consumers that are based on machine learning technology, such as Google Translate, Photo Search, and Smart Reply for Inbox. The new research center is part of a broader initiative at Google to advance the field of machine learning. Researchers working at the company's existing engineering offices in Zurich have already made major contributions to the field, such as developing the conversation engine that powers the Google Assistant in the Allo smart messaging app, and developing the engine that powers Knowledge Graph. The Zurich office is already the company's largest engineering office outside of the US.

NVIDIA's (NASDAQ:NVDA) stock price is up 113% over the past 12 months, the company's revenues were up 13% year-over-year in the first quarter of its fiscal 2017, and CEO Jen-Hsun Huang says the company is seeing growth across all of its platforms. So why is NVIDIA in such a good position, and how might it stay there? Let's take a look at three things the company is doing well to answer that. The most obvious advantage the company has right now is its graphics processing unit (GPU) business. NVIDIA currently holds 76% of the discrete desktop GPU market, and it's continually growing the business.

The New York City taxi dataset is one of the largest publicly available datasets, with information about 1.1 billion NYC taxi rides. This dataset has been explored and visualized in a number of blog posts, using a variety of techniques and technologies (e.g., PostgreSQL, Apache Elastic Search). A recent blog post showed how to build ML models over one years' worth of this dataset using MRS running in a 4-node Hadoop cluster. In a new blog post, Microsoft Data Scientist Dmitry Pechyoni shows us how to build a binary classification model that will predict if a passenger will pay a tip. Dmitry was able to use Microsoft R Server (MRS) to drive the entire process of building and evaluating machine learning models over hundreds of millions of examples using a single commodity machine.