Adele's record-breaking album 25 is coming to Spotify, Apple Music, and other streaming services tomorrow (or right now if you happen to reading from New Zealand or Australia). While there are other artists who are absent from most music subscription services--Prince and Neil Young come to mind--Adele's 25 is a unique for two reasons: First, it's the best-selling album since 2001, when music sales began their epic collapse. Secondly, 25 has not been made available on any streaming service until now. Other big name recent albums have either limited their release to certain services (Tidal, more often than not) or delayed their streaming debut all together, but these windows and exclusives typically don't last seven months. While the subscription services are undoubtedly thrilled to finally offer Adele's latest, the stunning success she achieved without their help doesn't bode well for the streaming music model.

In part 1, we went over how to use data visualization and data analysis prior to machine learning. For example, we discussed how to visualize the data to identify potential issues in the dataset, examine the variable distributions, etc. In this blog post, we'll continue by building a very simple model and using data visualization to examine that model. Just a quick reminder: as I noted in part 1, we're working with a very simple model. This is deliberately a "toy" model, which allows us to focus on the visualization/analysis aspect of the task without the added level of complexity that we'd inject by using a more advanced machine learning algorithm.

Chat bots seem to be extremely popular these days, every other tech company is announcing some form of intelligent language interface. The truth is that language is everywhere, it's the way we communicate and the way we manage our thoughts. Most, if not all, of our culture & knowledge is encoded and stored in some language. One can think that if we manage to tap to that source of information efficiently then we are a step closer to create ground breaking knowledge systems. Of course, chat-bots are not even close to "solving" the language problem, after all language is as broad as our thoughts.

AI and linked data can be leveraged to perform a search on a client. The resulting data garnered from both free and paid sites can then be analyzed, categorized, and filtered to identify any issues of concern. Performing these tasks manually is time consuming for a single client. If the client is a company then similar checks must be performed on the company's officers and board members. Employing AI to perform the "heavy lifting"greatly reduces manual effort and frees up the onboarding specialist to focus on the results.

The artificial intelligence (AI) system is "based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explains Andrew Beck, an associate professor in pathology at Harvard Medical School, who heads the team developing the new system at Beth Israel Deaconess Medical Center (BIDMC), in Boston, MA. Prof. Beck and colleagues demonstrated the new AI system in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI 2016) in Prague in April. He and his colleagues are developing AI methods that train computers to interpret pathology images to improve the accuracy of diagnoses. The approach they are using teaches computers to interpret the complex patterns seen in such images by "building multi-layer artificial neural networks," says Prof. Beck. The process is thought to be similar to the way learning takes place in the layers of neurons in the neocortex of the brain, the region where thinking occurs.

In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

Exponential Finance celebrates the incredible opportunity at the intersection of technology and finance. Apply here to join Singularity University, CNBC, and hundreds of the world's most forward-thinking financial leaders at Exponential Finance in June 2017. This is the year AI has hit the public consciousness hard. Whether calls for universal basic income in the face of an automation tsunami or alarms over the loss of privacy when everything you do can be monitored and analyzed, people are discussing and debating AI more than ever in an effort to quell their dystopian fears about humanity's future. Fueling the fire, it seems like every day someone is announcing a new AI beating another Turing test.

Google is ringing in 2014 with a spending spree, first dropping 3.2 billion to acquire Nest Technologies and now spending a reported 400 million (or more) on the UK-based artificial intelligence outfit DeepMind. It's no secret that Google has an interest in artificial intelligence; after all, technologies derived from AI research help fuel Google's core search and advertising businesses. AI also plays a key role in Google's mobile services, its autonomous cars, and its growing stable of robotics technologies. And with the addition of futurist Ray Kurzweil to its ranks in 2012, Google also has the grandfather of "strong AI" on board, a man who forecasts that intelligent machines may exist by midcentury. If all this sounds troubling, don't worry: Google's acquisition of DeepMind isn't about fusing a mechanical brain with faster-than-human robots and giving birth to the misanthropic Skynet computer network from the Terminator franchise.

We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."