Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn't happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side's perspectives and paradigms. The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4].

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

More Open AI and Machine Learning Toolsets Arrive by - Dec. 02, 2016 Google's Open Embedded Projector is a Cool Data Visualization Tool Google Collects Open Artificial Intelligence Demos, Invites You to Contribute The Renaissance Continues for Open Source Artificial Intelligence Microsoft Open Sources Transformative Speech Recognition Toolkit Google Open Sources Powerful Image Recognition Tool Recently, in an article for TechCrunch, Spark Capital's John Melas-Kyriazi weighed in on how startups can leverage artificial intelligence and machine learning to advance their businesses or even give birth to brand new ones. As a corollary avenue on that topic, it's worth noting that some very powerful artificial intelligence and machine learning engines have recently been open sourced. Quite a few of them have been tested and hardened at Google, Facebook, Microsoft and other companies, and some of them may represent business opportunities. Just recently, two new open source entries on this front have emerged, and they are worth investigating. Health Catalyst has created healthcare.ai as a repository of healthcare-focused open source machine learning software, with an eye toward encouraging the healthcare industry to tap into the power of AI and machine learning.

To learn more about the state of AI today and where we might be headed in coming years, download the free report "What is Artificial Intelligence?," by Mike Loukides and Ben Lorica. There are four basic ingredients for making AI: data, compute resources (i.e., hardware), algorithms (i.e., software), and the talent to put it all together. In this era of deep learning ascendancy, it has become conventional wisdom that data is the most differentiating and defensible of these resources; companies like Google and Facebook spend billions to develop and provide consumer services, largely in order to amass information about their users and the world they inhabit. While the original strategic motivation behind these services was to monetize that data via ad targeting, both of these companies--and others who are desperate to follow their lead--now view the creation of AI as an equally important justification for their massive collection efforts. While all four pieces are necessary to build modern AI systems, what we'll call their "scarcity" varies widely.

For a deep dive into the current state of AI and where we might be headed in coming years, check out our free ebook "What is Artificial Intelligence," by Mike Loukides and Ben Lorica. A few weeks ago, I wrote a post on the ethics of artificial intelligence. Since then, we've been presented with an excellent example to reflect on: the use of face recognition to identify people likely to commit crimes. In my post, I said that we need to discuss what kind of society we want to build. I'm reasonably confident that, even under the worst societal conditions, we don't want a society where you can be imprisoned because your eyes are set too closely together.

Corporate machine learning research may be getting a new vanguard in Apple. Six researchers from the company's recently formed machine learning group published a paper that describes a novel method for simulated unsupervised learning. The aim is to improve the quality of synthetic training images. The work is a sign of the company's aspirations to become a more visible leader in the ever growing field of AI. Google, Facebook, Microsoft and the rest of the techstablishment have been steadily growing their machine learning research groups.

When Apple said it would publish its artificial intelligence research, it raised at least a couple of big questions. When would we see the first paper? And would the public data be important, or would the company keep potential trade secrets close to the vest? At last, we have answers. Apple researchers have published their first AI paper, and the findings could clearly be useful for computer vision technology.

Germany composer Johann Sebastian Bach remains a staple in classical music. His illustrious works such as Brandenburg Concertos, the Goldberg variations and Mass B minor remain some of the most powerful pieces ever made. Bach spent over 50 years perfecting his ability to adapt rhythms, forms and textures from styles abroad. Many regard him as one of the greatest composers to have ever lived. And one new artificial intelligence system can recreate Bach's biggest achievements in just minutes. Fast forward to the 21st century where music is created by statistical models and artificial intelligence in no time.

Apple Inc. (NASDAQ:AAPL) made waves in the artificial intelligence world when it officially announced that it would start publishing artificial intelligence papers very soon. The Cupertino-based tech giant has done exactly what it promised and published its maiden AI paper. After submitting its artificial intelligence paper on Nov. 15, Apple's AI paper was officially published on Dec. 22. The paper discusses the different techniques of how a developer can improve the training of an algorithm's capability of recognizing different images which are computer generated and not from the real world. The technique is applicable in machine learning research where developers can apply such synthetic images, including those incorporated in video games, to train neural networks.