Want to up your SEO game and be more successful in 2018? Good! You've come to the right place. Below, you'll find tips from some of the top SEO professionals. They will tell you just about everything you need to know to succeed in 2018. However, before we get to our main event, I must note that after our 2017 edition of SEO trends launched last year on Search Engine Journal, we heard a couple of complaints about how long it was. Well, you'll be happy to know that this year's SEO trends post is a mere 13,014 words, down from 13,839 last year. As an added bonus, this year we've added this helpful TL;DR section for those of you with extremely short-attention spans who care not about context or details but simply want to know what the most important SEO trends tricks will be in 2018. So go focus on all those things and I'm sure you'll be just fine. For those of you who plan to read on, we've got some amazing insights in store for you about why all these things (and many others) will ...

The market for cobots is emerging as a fast-growing segment of the rapidly growing industrial robotics market. Demand for cobots is expected to rise at a CAGR of more than 50 percent over the next decade. Although there are some reasons to assume that the acceptance and implementation of cobots will slow growth somewhat, it is clear that the cobot market is rapidly expanding and the number of use cases will continue to rise. The steadily falling prices of components such as sensors make cobots accessible for SMEs. All markets are expected to see growth rates above 50 percent CAGR, but in a regional sense, expectations are especially high for China, because it still lags in the use of robots, when compared to countries like South Korea, Japan, the US, and Germany.

The White House last week proudly announced the formation of a committee to advise the president and Federal government on AI. Sure, the US is forming the committee, that part's real. Here's a link to the official document announcing the panel, including remarks from Trump's top "science adviser" (sarcastic finger quotes, his degree is in political science) Michael Kratsios. In order to improve the coordination of Federal efforts related to AI and ensure continued U.S. leadership in AI, today The White House chartered a Select Committee on Artificial Intelligence ("Select Committee") under the National Science and Technology Council. It looks pretty cut and dry right?

The NTSB is already investigating the Autopilot system in a crash of a Tesla Model X in California in March in which the driver was killed. The NTSB and the National Highway Traffic Safety Administration are also probing a fiery crash of a Model S earlier this month in Florida that left two teens dead. Officials are reportedly investigating the emergency response to the serious battery fire that occurred.

Day 2 of Insight Exchange Network's "Healthcare on the Blockchain" event began with sessions on using blockchain technology for secure verification and changing the architecture of consumer decision making (see our coverage of Day 1). Morning speakers included David Houlding, Director of Healthcare Privacy & Security at Intel, returning to moderate a session on blockchain security with Jeremy Grant, Managing Director of Technology Business Strategy at Venable LLP and Debbie Bucci from the Office of Standards and Interoperability at Health and Human Services. Doug Emery, Director of Benefits Innovation Solutions and the the Center for Value in Healthcare at Altarum, and James Maldonado, CEO of Trestleworks, presented a session which encouraged some new points of view. For example, the speakers discussed evolving from fee-for-service to risk-adjusted systems as the unit of account and transitioning from revenue cycle management to value cycle management through computable, composable payment and benefits contracts. After a morning networking break, Jennifer Georgino, Contributor to Blockchain Healthcare Review, returned to moderate a session on managing data on the blockchain for cost-savings and error reduction.

It's tough being an imagery analyst for the U.S. military: you're drowning in pictures and drone video, with more pouring in endlessly from the tons of sensors and cameras used on planes, ships and satellites. Sifting through it to find roadside bombs or missile components is a time-consuming challenge. That's why the Pentagon's blue sky research arm figures that cameras ought to be able to filter out useless information themselves – so you don't have to. Darpa announced yesterday that it's moving forward in earnest with a program to endow cameras with "visual intelligence." That's the ability to process information from visual cues, contextualize its significance, and learn what other visual data is necessary to answer some pre-existing question.

Blockchain technology will aid the Philippine economy to maintain jobs that the advent of artificial intelligence (AIs) is supposed to get rid of, as reported by Edwin Bautista, president, and CEO of Union Bank of the Philippines. "Blockchain could play an important role in the Philippine economy. If we play it right, we have the potential to emerge as one of the leaders in block chain's future." The emerged AIs will eliminate many jobs, and developing the blockchain abilities of Filipinos could cancel out these job losses, Bautista added on the sidelines of the launch of XLOG, a Block chain-based logistics solutions platform developed provincially. The founder and CEO of XLOG creator Shiptek, Eugenio Ynion Jr., repeated Bautista's warning about the threat of robots for hundreds of thousands of Filipino jobs. "Me and my wife just came from China to discuss XLOG with capitalists there, and we were informed by Chinese officials that their country would be needing close to five million IT developers, programmers, etc., over the next five years.

The accuracy of police facial recognition systems has been criticised by a UK privacy group. Two forces have been testing facial recognition cameras at public events in an effort to catch wanted criminals. Big Brother Watch said its investigation showed the technology was "dangerous and inaccurate" as it had wrongly flagged up a "staggering" number of innocent people as suspects. But police have defended its use and say additional safeguards are in place. Police facial recognition cameras have been trialled at events such as football matches, festivals and parades.

Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior is represented by changes in species concentration. It is then required to discern how the model inputs impact the mixing process. This task is challenging and typically involves interpretation of large model outputs. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal hidden features in product concentration. An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes. The ML method is applied to a large set of high-resolution FEM simulations representing reaction-diffusion processes in perturbed vortex-based velocity fields. The applied FEM ensures that species concentration are always non-negative. The simulated reaction is a fast irreversible bimolecular reaction. The reactive-diffusion model input parameters that control mixing include properties of velocity field, anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product. The presented ML analysis allowed us to identify additive features that characterize mixing behavior.

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart" (omni-present in anything related to the IoT as well), "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, healthcare, life sciences, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, next gen smart building technologies, FinTech, predictive maintenance, robotics and so much more. On top of that, AI is added to several other technologies, including IoT and big, as well as, small data analytics. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms.