In this observation, we ranked nearly 1,900 articles posted in September 2016 about machine learning, deep learning and AI. Many people thesedays claim they use machine learning, especially companies that are trying to sell the vision of AI (including Mybridge). Mybridge AI evaluates the quality of content and ranks the best articles for professionals. This list is competitive and carefully includes quality content for you to read. You may find this condensed list useful in learning and working more productively in the field of machine learning.

Artificial intelligence is a hot topic these days. In this industry, investment grew more than 3-fold since 2013, and new services and applications for it are appearing every week. These two facts alone provide plenty of reason to believe that AI is here to stay and will influence businesses quite a bit. Now, news coming from Amstelveen (Netherlands) and San Francisco (USA) report that the KLM Royal Dutch Airlines is testing AI in customer service through social media, taking the next step in social servicing. To do so, KLM is using DigitalGenius' AI, integrating it in its customer relationship management tool.

Google and a group of IT hardware companies have joined forces to develop the open coherent accelerator processor interface (OpenCAPI) specification, providing a foundation for high-speed microprocessor interconnect systems to devices. The open interface architecture is based on IBM's CAPI for POWER8 processors. OpenCAPI will however be processor design-agnostic. The idea is to allow fast, direct connections between applications and user-level hardware accelerators and advanced memory systems for cloud servers and big data and analytics. OpenCAPI does away with the need to run device drivers and communication with accelerators over a bus like PCIe, which adds latency and slows performance.

The importance of SEO has been stated time and again. However, SEO can still be a tricky thing to master. The issue that arises pivots around the ever-evolving nature of SEO. Businesses understand how important SEO is, but keeping up with the best practices can feel like a game of catch-up, especially now that artificial intelligence has been thrown into the mix. Artificial intelligence is already changing the face of SEO as we know it, and we'll be sure to see even more changes in the future.

The Internet is in transformation. Initially, we started with the Web and digitized content. The market then moved to tracking and controlling the digitized world with for example General Packet Radio Service ( GPRS). Machine-to-Machine ( M2M) introduces a completely different connectivity model and application use case. And now we embark on Machine Learning where machines have the capability to make decisions with either supervised or unsupervised controls.

ThinkTV is proud to announce the formation of an independent laboratory to carry out cutting-edge research into the performance of TV advertising, in partnership with leading international media academic Professor Karen Nelson-Field (pictured above) and Media Intelligence Co. The laboratory's forthcoming two-year research program will help advertisers and media agencies get the best out of TV by providing robust evidence and greater clarity about how multiplatform TV advertising delivers business results. The ThinkTV Smart Lab is directed by Karen Nelson-Field, Professor of Media Innovation at the University of Adelaide and CEO of research joint venture, Media Intelligence Co. (MIC). The purpose-built facility will examine TV's impact on brand and advertiser performance and will use artificial intelligence technologies to remove human error and bias. It is funded by ThinkTV but remains independent to ensure research rigour and credibility.

THIS is a timely and provocative anthology on a theme that has fascinated scientists, philosophers and SF writers for decades. The "singularity" occurs when artificial intelligence (AI) exceeds that of humans and AIs design new technologies beyond our understanding. Its realisation would bring massive and unpredictable changes to civilisation and the environment. The book's symposium structure has a "target" paper by philosopher David J Chalmers which elicits a range of articles in response and it concludes with a response from the same author, while the editor's introduction establishes the history of the singularity concept, highlights the interdisciplinary debates it generates and provides an accessible way into the language and logical arguments employed in later, more challenging pieces. Chalmers argues that human-level AI is likely to be created in a century or so, unless prevented by disaster, legislation or direct action. If AI is created, AI -- representing greater than human intelligence -- is likely to follow within decades and, a few years after, the world would see AI systems with much greater than human intelligence.

Honeypot design and deployment is a tradeoff between realism and simplicity; this tradeoff can be characterized as the difference between high and low interaction honeypots. A realistic design could use an actual operating system instrumented to detect and capture intruders (known as a high interaction honeypot). However, the detection would be greatly complicated, because it is difficult to distinguish between normal traffic on the system and the attacker's. It is a low signal to noise detection problem due to the complexity of modern operating systems running hundreds of threads generating large volumes of traffic with complex signatures. A honeypot that is designed only to superficially mimic an OS (low interaction honeypot) can easily detect the attacker's actions, since there is no background noise.

I've been trying to think of a way to describe how big Machine Learning is, and I think I finally have a decent one: Because Statistics is the primary mechanism we've had for decades to learn from data. That's what Machine Learning is, except far more powerful. Most importantly, machine learning can…well, learn. It improves as it gets more data. More wisdom potentially gets extracted when you apply Statistics to more (and better) data, but the analysis itself doesn't improve with better data.