AI symphony. AI for Education Global HackWeek. Autonomous cargo ship. http://cognitionx.com/news-briefing/.
Canadian researchers (in partnership with IBM) have greatly increased their ability to diagnose schizophrenia with the help of machine learning, according to recently published research.
This is just one example of the impact of AI on diagnosing diseases. With the help of AI, the diagnosing of Huntington’s disease, heart disease, cancer, and more has become easier and more accurate. Academic researchers (and Facebook) have even found ways in which they can use machine learning to predict people who are likely to commit suicide and to improve suicide prevention.
Our research team has been hard at work mapping the AI healthcare market. If you work at a biotech startup and want to chat about how your company is making an impact, we’d love to hear from you.
Two Norwegian companies are teaming together to construct a short-range, all-electric coastal container ship that will eventually operate autonomously—eliminating up to 40,000 diesel truck trips per year. The ship, the Yara Birkeland, will begin operations in 2018 with a crew, but it’s expected to operate largely autonomously (and crewless) by 2020 (regulatory clearance permitting, of course).
The $25 million Birkeland—described by some shipping executives as the “Tesla of the Seas”— is being jointly developed by the fertilizer company Yara and the maritime and defense technology firm Kongsberg Gruppen. The ship will initially be crewed from an on-board control center within a cargo container. Eventually, the container will be moved ashore, and the ship will be remotely operated. It will navigate autonomously by utilizing GPS and avoid collisions using a combination of sensors.
Article to Share With Your Less Data Savvy Friends
This is a short and sweet article from Steve Levine (Axios) on recent developments in the world of AI. He discusses the ‘artificial intelligence bubble’, China’s AI race with the US, the demise of commercial real estate, and some other great reading material.
Check out Genius Loci, which is part of a series of projects which are creatively using AI, such as AI lyric writing, AI text rewriting, and more.
Genius Loci will consist of a limited series of concerts in European capitals (London, Paris, Berlin, Lisbon, Moscow) where classical musicians perform the five parts symphony composed by Pianola neural network. Every part is devoted to a particular city. Every part consists of two themes. Every theme is created in accordance with the style of famous composers from Bach to Fernando Lopes-Gracia. The symphony is accompanied by a real-time light show, also created by a machine.
OpenEd.ai is awarding up to $17K in cash prizes and computing resources for open-source AI projects solving problems in Education during the global AI for Education HackWeek (July 28 — Aug 4). Anyone can participate remotely from anywhere in the world for free.
Judges include Computer Science faculty from Harvard
University and NLP experts from around the world, and the event is generously supported by Omidyar Network (our grand prize sponsor), IBM Watson, Google Developer Groups, Amazon Web Services, Digital Ocean, and a number of other leading tech firms.Sign up now.
A team of researchers from led by Mihaela van der Schaar have done very interesting work on applying machine learning to risk prediction tools. Their novel “predictive pursuit” algorithm out-performs currently available clinical risk prediction scores as well as the best machine learning tools for prediction of wait-list and post-cardiac transplant mortality. The predictive pursuit algorithm has potential to personalise and greatly improve accuracy of risk prediction.
Be sure to check out their web-based tool based on the research here.
Recent rapid advances in AI and machine learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, Iyad Rahwan proposes a conceptual framework for the regulation of AI and algorithmic systems.
To achieve this, he argues that we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, he proposes an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.’
The same technology dynamics that drive other industries are impacting how we grow, distribute and consume food as well. That was one of the key messages to emerge from this year’s FOOD IT: From Fork to Farm event in Mountain View, California.
With a labor shortage looming in the restaurant sector, there was a great deal of discussion at FOOD IT over how the industry was going to use technology, specifically robotics, to bridge the gap. “As you hit the button to load that coke, you can hit the button to load that salad while you run off to put the rest of the entrée meals together,” said Jeff Frick, co-host of theCUBE, SiliconANGLE’s mobile live-streaming studio.
The Open Philanthropy Project awarded a grant of $2.4 million over four years to the Montreal Institute for Learning Algorithms (MILA) to support technical research on potential risks from AI.
$1.6 million of this grant will support Professor Yoshua Bengio and his co-investigators at the Université de Montréal, and $800,000 will support Professors Joelle Pineau and Doina Precup at McGill University. They see Professor Bengio’s research group as one of the world’s preeminent deep learning labs and are excited to provide support for it to undertake AI safety research.
Mazdak Rezvani (Chatkit) argues that if a modern conversation engine hopes to go beyond answering simple, one-level questions, it must blend the most prominent techniques emerging from the field of deep learning with solid statistics, linguistics, other machine learning techniques, and more structured classical techniques, such as semantic parsing and program induction.
He says that we are still in the early stages of the AI-powered conversational revolution, and it is fair to assume some problems that seem insurmountable today will likely be solved in the coming years. We are quickly moving toward a world in which you will be able to have long and complex interactions with your AI assistants, which will not only understand what you want to say but will know your preferences and tailor your experience accordingly.