Gowild Intelligent Technology held the press conference, entitled "When it comes to AI, we are different", for the launch of its new product in Beijing, China on July 8. The event received widespread attention from thousands of people in academia as well as in the technology, gaming, media and entertainment sectors across China and around the world. Gowild released its first generation AI-based 3D holographic product, holoera, that integrates the latest AI engine and VR technology. Transforming the artificial intelligence sector, holoera is the survival carrier on earth of the two dimensional beauty Amber. Amber not only comes equipped with training programs for daily living and learning, but can also receive instructions from its exclusive master, and be instructed based a customized training program. Furthermore, with her superior artificial intelligence, she can engage in barrier-free interaction with humans.

That's what Microsoft's Project Malmo, formerly known as DNA Storage Project, is doing right now. Publicly unveiled today on the official Mojang blog, Project Malmo has just made its open source tools available to everyone today in an effort to help others help strengthen AI in various ways. Mojang notes that Minecraft is the perfect way to educate AI when it comes to offering solutions to problems, sharpening spatial and temporal reasoning, and even collaboration. Project Malmo can offer diverse opportunities at teaching artificial intelligence software and act as a research tool that can test these theories as well. If you're interested in seeing what Project Malmo has to offer in addition to testing it out for yourself, you can download the mod for the PC/Mac edition of Minecraft here.

The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it. This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how machine learning can be applied. It also includes an increasing number of machine learning enthusiasts with atypical backgrounds who are joining the conversation, bringing in diverse experiences and points of view. The increasingly symbiotic relationship between companies that need machine learning expertise, and data science competition platforms like Kaggle, has greatly impacted how rapid advancement is being achieved. This relationship has also changed the hiring landscape.

Artificial Intelligence (AI) is an umbrella terminology of various technologies, statistical models, algorithms, and approaches aiming to emulate human like intelligence with machines or software. It learns from environment and take action accordingly. Widely popular AI technologies include Machine Learning (ML), Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, Natural Language Processing, and many more. Machine Learning (ML) is sub-field of computer science and a special type of AI technology that involves in the development of self-learning algorithms to solve problem intelligently. All ML is AI but all AI is not ML, for example, symbolic logic, Baysian statistics, evolutionary algorithm etc. are AI but not machine learning (ML).

Take 7 Seconds to Support the OdinText Mission: Help Make Data Science Accessible! I'm excited to announce that OdinText will participate in the IIEX2016 Insight Innovation Competition! The competition celebrates innovation in market research and provides a platform for young companies and startups to showcase truly novel products and services with the potential to transform the consumer insights field. Marketing and research are becoming increasingly complex, and the skills needed to thrive in this environment have changed. To that end, OdinText was designed to make advanced data analytics and data science accessible to marketers and researchers.

Video has taken the world by storm with a myriad of intelligent devices continuously capturing vast amounts of data about how people live and what they do. At the end of 2014, IHS Technology estimated over 245 million operational cameras were active globally. London alone has 500,000 cameras dotted throughout the city, which works out at about one camera for every 16 people. Thanks to smart cameras, CCTV devices, and even drones mounted with intelligent cameras, users are able to record videos at an unprecedented scale and pace. This vast store of data-rich content is used for a range of purposes – from gaming and law enforcement to crowd management at large events.

"How to choose the best topic model?" is the #1 question on our community mailing list. At RaRe Technologies I manage the community for the Python open source topic modeling package gensim. As so many people are looking for the answer, we've recently released an updated gensim 0.13.1 incorporating several new exciting features which evaluate if your model is any good, helping you to select the best topic model. Topic modeling is a technique for taking some unstructured text and automatically extracting its common themes, using machine learning. It is a great way to get a bird's eye view on a large text collection.

A team of scientists have developed a model that can predict the likelihood of bat species carrying Ebola and other filoviruses using a machine learning algorithm. Filoviruses are a group of long filament shaped viruses that encode their genome on a single-stranded RNA. Ebola is the most well-known example; other filoviruses include Marburg disease. Both are lethal viruses that are spread by coming into contact with bodily fluids from an infected person. The last Ebola outbreak happened in 2014 and resulted in 11,310 deaths, according to the World Health Organisation.

THE TERM "artificial intelligence" has been associated with hubris and disappointment since its earliest days. It was coined in a research proposal from 1956, which imagined that significant progress could be made in getting machines to "solve kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be rather optimistic, to say the least, and despite occasional bursts of progress and enthusiasm in the decades that followed, AI research became notorious for promising much more than it could deliver. Researchers mostly ended up avoiding the term altogether, preferring to talk instead about "expert systems" or "neural networks". But in the past couple of years there has been a dramatic turnaround.

Google's recent victory against top-ranked Go player Lee Sedol marks another milestone in artificial intelligence development, and though this might be considered "old" news by today's standard, it's still a fresh achievement for the AI world. As much as this gameboard conflict between man and machine has encouraged conversation about competition, it has also sparked increasing conversation around the potential collaborative possibilities. "I think the main reaction from the Go community will be, as indeed happened after IBM computer Deep Blue achieved grandmaster status in chess, is that people want to get hold of the software and use it in their own games to work out where they went wrong." This kind of industry-specific technology has the potential to be used to further thinking, strategy, and goals within the game; however, Google's Go algorithm still can't manage a daycare or compose a nostalgic sonnet--and there are still many tasks best done by human beings. By utilizing and combining the individual strengths of both humans and AI, the collaborative possibilities are limitless, and are quickly becoming applicable in much more than board games.