In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.

Machine learning is defined as a subfield of computer science and artificial intelligence which "gives computers the ability to learn without being explicitly programmed" (source). Although the statistical techniques which underpin machine learning have existed for decades recent developments in technology such as the availability/affordability of cloud computing and the ability to store and manipulate big data have accelerated its adoption. This essay is meant to explore the most popular methods currently being employed by data scientists such as supervised and unsupervised methods to people with little to no understanding of the field. Supervised machine learning describes an instance where inputs along with the outputs are known. We know the beginning and the end of the story and the challenge is to find a function (story teller, if you will) which best approximates the output in a generalizable fashion.

Jean Brillat-Savarin was a 19th-century French lawyer famed for his writings on gastronomy. In his most famous work, he said: "Dis-moi ce que tu manges, je te dirai ce que tu es." Or "Tell me what you eat and I will tell you what you are." This idea--that you are what you eat--has become increasingly popular. Since Brillat-Savarin's time it has been used as the title of various cookbooks and health guides; for some it is a way of life.

When people look back at the current decade, what will they single out as the most significant technological breakthrough? In the future, there will be billions of artificial minds, intelligently organizing business processes. The cost of intelligence can eventually fall to zero. We're a little way off that scenario today, but we're in the midst of a second big wave of automation that all businesses can benefit from. Many tasks that we do today in business are time-consuming and expensive.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.

In celebration of our 5th anniversary, this month we're publishing a series of interviews with innovative leaders about what the next five years hold. To read more about this series, read our editor Nilay Patel's introduction here. Few subsidiaries at Alphabet Inc. inspire as much curiosity as Google X, now called simply "X." X is the company's innovation lab, where ambitious but far-fetched tech ideas are pitched, tested, and either come to life or are ultimately killed. It's where Google's self-driving car concept was developed, where giant internet access balloons were conceived, where glucose-monitoring contact lenses were first experimented with, and where burrito-delivering drones are part of a beta test for bigger things. And while more than 250 employees are behind these far-fetched projects, for the past five years the face of X has been Astro Teller, the so-called "Captain of Moonshots."

The new platform is part of a larger AI effort that includes the machine-vision Lumos app used to suss out images that violate its community standards. It has also open-sourced similar tech on Github to non-Facebook developers. Google released its Tensorflow framework to the open source community and Microsoft recently made its Cognitive Toolkit available to developers. Facebook first flaunted Caffe2Go last month, then brought some of the effects to a new camera in a limited European release. Much like the Prisma app, it transfers styles from Van Gogh or Monet onto any still or moving image.

The use of Apache Spark is dramatically increasing as new workloads create more use cases. The open source cluster computing framework Apache Spark is now being actively used by 54% of people and the majority of them (64%) are finding that it's proving invaluable. That's according to a Cloudera study, conducted by Taneja Group on 7,000 people from technical and managerial roles that are directly involved in big data. According to the study the technology is being used for the most important use cases by 57% of people, when that technology is provided by Cloudera. Those use cases aren't always for the likes of data processing, engineering and ETL workloads that are said to make up 55% of current Spark use.

Facebook has built a simple-looking video tool to show off a sophisticated use of artificial intelligence on cell phones. During an event at its office fb in Menlo Park, Calif., last Friday afternoon, Facebook CTO Mike Schroepfer showed off software that takes a live Facebook video feed from a cell phone and converts the image in real time into a selection of artistic styles, such as that of Van Gogh. It might sound like a simple filter, but usually an algorithm of this nature would need to send that type of information back to a server in a data center to process the pixels on more powerful machines. The Facebook crew crafted a less power-hungry and computing-intensive deep learning system they call "Caffe2Go," that uses the computing power in a cell phone. Facebook's Schroepfer showed the algorithm and other applications of artificial intelligence at the Web Summit conference in Lisbon, Portugal on Tuesday.

"Machine learning and AI is something we've just started to track as it will have an impact at some point on the operation of the railway, but it's early days from a railways systems perspective and there is always a bit of reticence around bleeding-edge technology where safety is critical." "We've deployed some AI/ML capability within our sentiment dashboard application which uses machine learning services in the cloud combined with in-house data to build a picture for the licensee. "I've seen some early prototypes in our North American labs of virtual agents – be that chat bots, be that the recently announced integration into Amazon's Alexa product and I think we'll see a lot more of virtual agents in the financial services industry and other industries; I think it's a good example of helping customers interact with financial services companies with a lot less friction." "Things like using Tensor Flow (Google's open source AI framework), AI is really starting to get interesting and seeing how we can use that to help UK Households to save more money will be fun!" Tim Jones, Moneysupermarket.com "AI, analytics using a wide range of techniques (including NoSQL), IoT, automation and wearables are all things we are either doing or continue to explore today.