People complain that governments or hackers are reading our messages for nefarious purposes. Of course this "reading" is done automatically, in large volume, by machines and NLP (natural language processing) algorithms looking (among other things) at keywords. The advantage is that the text is still very easy to read by a human being, but unless a specific rule is introduced in NLP algorithms to detect such patterns (this means that the trick would be used by many people,) for automated algorithms, it looks like gibberish. And such rudimentary encoding is straightforward to implement. It is also more compact than encoding text as images using a screenshot capture tool.

On the heels of acquiring data science community Kaggle, Google is launching a machine learning competition of its own for startups. Google is targeting early-stage companies taking an innovative approach to machine learning. The competition is being run in partnership with seven venture capital firms that include Sequoia, KPCB, GV, Data Collective, Emergence Capital, Andreessen Horowitz and Greylock. Two of the firms, Data Collective and Emergence Capital, plan to contribute $500,000 each to the winning startup. To qualify for that prize, startups will not be required to use Google services.

At the beginning when I started working with natural language processing, I used the default Python lists. But soon enough with bigger experiments and more data I run out of RAM. Python lists are not optimized for memory space so onto Numpy. Numpy arrays are much like in C – generally you create the array the size you need beforehand and then fill it. Merging, appending is not recommended as Numpy will create one empty array in the size of arrays being merged and then just copy the contents into it.

Inside the University of Rochester's Robotics and Artificial Intelligence Laboratory, a robotic torso looms over a row of plastic gears and blocks, awaiting instructions. Next to him, Jacob Arkin '13, a doctoral candidate in electrical and computer engineering, gives the robot a command: "Pick up the middle gear in the row of five gears on the right," he says to the Baxter Research Robot. The robot, sporting a University of Rochester winter cap, pauses before turning, extending its right limb in the direction of the object. Baxter, along with other robots in the lab, is learning how to perform human tasks and to interact with people as part of a human-robot team. "The central theme through all of these is that we use language and machine learning as a basis for robot decision making," says Thomas Howard '04, an assistant professor of electrical and computer engineering and director of the University's robotics lab.

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

The concept has been around for decades and most of us use it everyday without thinking about it. However, AI represents the next wave in marketing innovation for tech marketers and will become more prevalent in 2017 and beyond. AI is a collection of technologies and algorithms that do things that require human intelligence such as learning, understanding natural language, image recognition and problem solving. A simple example is when someone calls your iPhone an algorithm searches your phone data for a possible match to identify the caller. Another common use case is bidding/optimization tools on programmatic ad platforms; and ad A/B testing is conducted by machine learning and AI tools.

As marketers strive to engage in more meaningful conversations with their audience, understanding which words, phrases, sentences and even content formats resonate with particular audience members is key. Last year we saw progress in lexical analysis with the goal of finding content or text that drove overall marketing success. It did this by analyzing successful campaign content versus unsuccessful content. I believe 2017 will see that work get personalized by combining content analysis at the campaign level with content analysis at the individual level. The interconnected data makes it possible.

Between January and February 2017, we've ranked nearly 2,000 Machine Learning articles to pick the Top 10 stories (0.5% chance) that can help advance your career. Topics included in this Machine Learning list are: NLP, Voice Recognition, Video Game AI, Tensorflow, Scikit-Learn, Translation, Neural Networks, Deep Learning, Reinforcement Learning and Artificial Intelligence. Mybridge AI ranks articles based on the quality of content measured by our machine and a variety of human factors including engagement and popularity. This is a competitive list and you'll find the experience and techniques shared by the leading data scientists particularly useful.

Artificial intelligence has become as meaningless a description of technology as "all natural" is when it refers to fresh eggs. At least, that's the conclusion reached by Devin Coldewey, a Tech Crunch contributor. AI is also often mentioned as a potential cybersecurity technology. At the recent RSA conference in San Francisco, RSA CTO Zulfikar Ramzan advised potential users to consider AI-based solutions carefully, in particular machine learning-based solutions, according to an article on CIO. AI-based tools are not as new or productive as some vendors claim, he cautioned, explaining that machine learning-based cybersecurity has been available for over a decade via spam filters, antivirus software and online fraud detection systems.

"Let us consider an augmented architect at work. He sits at a working station that has a visual display screen some three feet on a side, this is his working surface, controlled by a computer with which he can communicate by means of small keyboards and various other devices." This vision of the future architect was imagined by engineer and inventor Douglas Engelbart during his research into emerging computer systems at Stanford in 1962. At the dawn of personal computing he imagined the creative mind overlapping symbiotically with the intelligent machine to co-create designs. This dual mode of production, he envisaged, would hold the potential to generate new realities which could not be realized by either entity operating alone.