Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.

At Data By The Bay in May, we saw a great talk by Netflix's Justin Basilico: Recommendations for Building Machine Learning Software. Justin describes some principles for effectively developing machine learning algorithms and integrating them into software products. We found ourselves nodding violently in agreement, and we wanted to recapitulate a few of his points that resonated most strongly with us, based on our experience working with data science teams in other organizations. Justin emphasizes that "developing models is iterative" and experimentation is important. He also suggests "avoiding dual implementations" so it's easy to use a model in production once it's been built, without a re-implementation step.

Plexure unlocks ML models for event driven decision making, or for live queries directly from customers that want immediate answers. Choose from an existing library or build your own custom model, and plug it into decision making live. Traditional CRM makes it hard to operationalize ML models, generally they can only be used for batch jobs to carry out items like segmentation.

In this video, NYU researchers describe their plans to advance deep learning with their new Nvidia DGX-1 AI supercomputer. New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a DGX-1 to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech.

Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype--it's a production-grade enterprise technology. Francisco explores the theoretical underpinnings of semantic folding, which solves the representational problem and the semantic grounding problem--both well known by AI-researchers since the 1980s, and offers an introduction to the Retina Engine, an Apache Spark library for semantic processing of text. Along the way, Francisco demonstrates functional prototypes of semantic classification, semantic filtering, and semantic searching and explains the applications of semantic folding for the finance, media, automotive, legal, medical, and safety and security industries.

Artificial intelligence, intelligence as exhibited by machines, is not something that is new to this world. Nearly twenty years ago IBM's supercomputer Deep Blue beat world chess champion Gary Kasparov. The win was symbolically significant and a sign that artificial intelligence was catching up with human intelligence. Fast forward 20 years and the application of AI technologies is something we encounter on a regular basis. For example, manufacturing and the use of robots in assembly and packaging has revolutionised how our favourite products are made.

Two decades ago, Google was an Internet search engine company. Today, the Mountain View-based firm is one of the tech industry's biggest leaders with initiatives ranging from operating systems, apps, cloud services, and communications solutions to self-driving vehicles, artificial intelligence, virtual reality, wearables, and smartphones. To say that Google has diversified its portfolio would be a huge understatement. Furthermore, that strategy is seemingly paying off. The Alphabet-owned company currently has the second most valuable brand in the world and is a leading innovator in most branches of the tech industry.

This means that programmers must account for every type of road situation a car may encounter. MIT's Technology Review spoke with Amnon Shashua, CTO and cofounder of the technology firm to learn more about the initiative. Mobileye has been in the news of late for another reason--its system was the one being used by the Tesla vehicle that was involved in a car crash in Florida recently--the incident is still under investigation by the NHTSA. Tesla publicly blamed Mobileye, and because of that, a rift developed between the companies, which are now no longer partners. Shashua does not believe that will harm the company's new initiative, though--building a system based on neural networking, which, if all goes according to plan, will allow a car or truck to learn how to drive in much the same way that humans do. First, by observing someone else doing it, and then by practicing (which the company calls reinforcement learning).

This is the shortest path I see towards machine intelligence: first, we develop ways to allow specialized AIs to manipulate formal concepts, write programs, run experiments, and at the same time develop mathematical intuition (even creativity) about the concepts they are manipulating. Then, we use our findings to develop an AI scientist that would assist us in AI research, as well as other fields. It would be a specialized superhuman artificial intelligence to be applied to scientific research. This would tremendously speed up the development of AI. At first we would apply it to solve well-scoped problems: for instance, developing agents to solve increasingly complex and open-ended games.

Zero Zero Robotics' Hover Camera is now called the Hover Camera Passport. I got a first look at the prototype of the paperback-sized "flying camera" (the company's still not calling it a drone) earlier this year. After securing over 25 million in investor funding, Zero Zero Robotics is finally launching the foldable quadcopter camera -- the first in a lineup of planned flying cameras from the company. Zero Zero Robotics CEO Meng Qiu "MQ" Wang told Mashable the company was shooting for under 600 for the consumer release. A buck under 600 technically still qualifies, technically.