Search Results for: military

Our cities, streets, homes, and businesses are built for beings that walk on two legs (biped). From stairs to the shape of hallways to the placement of kitchen cabinets, all have been designed for bipeds. The fact that a society of two-legged creatures designed everything around them for bipeds is so obvious most people don’t even think about it, but it becomes a serious issue when talking about the future of robotics.

While in previous decades military unmanned aerial vehicles (UAV) were very simple pieces of equipment, the technology has advanced rapidly. They are now used all over the world and are a multi-billion dollar industry. According to the Teal Group, current worldwide military UAV production stands at around $2.8 billion, and they project it will grow to $9.4 billion in 2025.

Historically, space has been an industry only governments and heavyweight airspace corporations (such as Boeing) could handle. Uses of AI in space technology might be expected to be even more expensive, but the last decade of innovation has made space more accessible - and thanks to AI - data from space is becoming much more useful for businesses and governments.

Episode summary: Unlike the field of self-driving cars, the fields of construction, mining, agriculture, and other classes of “heavy industry” involve a huge variety of equipment and use-cases that go beyond traveling from A to B. The heavy industry leaders of today are no farther behind automakers in their understanding that AI and automation will be essential for the future of their companies. In this episode, guest Dr. Sam Kherat discusses the applications of AI in heavy industry, including: What type of capabilities and functions are automate-able, and at what level. He also shines a light on how AI might affect the future of the industry within the next 2-3 years, and in what ways we can expect large equipment to become more autonomous.

High costs and technical limitations kept the uses of drones relatively limited until recently. After significant excitement starting around 2012, the FAA's 2016 adoption of regulations - combined with the drop in price - has made drones an economically viable option for a broad range of commercial functions.

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle—from assembly to post-sale customer service interactions, but retail players need answers to important questions:

The rise of AI industrial robotics experienced record double-digit expansion in various countries in 2014 and 2015, but such large scale segments i.e. 'industrial' versus 'medical' or 'military', were more or less one amalgam of parts a couple of decades ago. Examples of medical and military applications can be found in our updated machine learning in robotics guide. There was a time before the early 1980s when it was possible for AI researchers to keep up with all that was going on in the AI and the robotics industry as a whole, but it seems the tides had changed by 1982.

Episode Summary: Most of our recent investor interviews have been Bay area investors, like Accenture and Canvas, and we don't usually get to speak with investors overseas, particularly in Asia. This week, however, we interviewed Tak Lo, a partner with Zeroth.ai, an accelerator program and cohort investing firm based in Hong Kong and focused on startup artificial intelligence (AI) and machine learning (ML) companies. Lo speaks about when he saw AI take off in Hong Kong and the differences in that rise compared to the U.S. He also gives valuable insight on consumer differences in how the two populations interact with technology (a topic echoed in an earlier Emerj interview with Baidu's Adam Coates), and how these differences in the Asian market drive different business opportunities in Hong Kong than in the U.S.

Episode Summary: Market research and trends is important when discussing AI and business, but it's also worthwhile to contemplate the ethical and social implications further down the line. How will countries deal with potential unemployment problems? How might countries collaborate to hedge against the risks that AI poses to the future of work and other economic facets? A relatively small group is helping people do just that i.e. getting organizations and countries to think through how they could hedge against the grander risks inherent in a world powered by AI.
In this episode, we speak with Jerome Glenn, head of the Millennium Project, a global participatory think tank with 60 Nodes around the world that that focuses on research implementing the organizational means, operational priorities, and financing structures necessary to address 15 Global Challenges. Glenn talks about how he gets principalities of the world to bring their big industrial players and the public to talk through possible scenarios that are 30, 40, even 50 years in the future, and about ways we might potentially hedge against risks and make the most of the upsides of AI in a global economy. If you enjoyed listening to our recent podcast with OpenAI's Ilya Sutskever on preparing for the future of AI, then you may find Glenn's ideas and the mission of The Millennium Project to be an interesting and useful perspective on this issue as well.

1 - Artificial-Intelligence System Surfs Web to Improve Its Performance

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics’ Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings - if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.