Marcos López de Prado has been at the forefront of machine learning innovation in finance. The New-York based Spaniard was the first-ever head of machine learning at AQR, one of the world’s largest investment management firms, before he left earlier this year to start his own firm, which sells machine learning expertise and algorithms to Wall Street.
Science, not speculation, is the right way to invest, a top expert tells TIME.
The Autonomous Travel Suites will have built-in beds, private bathrooms with a toilet and sitting shower, a small kitchen, and entertaining space.
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Ultraprecise 3D printing technology is a key enabler for manufacturing precision biomedical and photonic devices. However, the existing printing technology is limited by its low efficiency and high cost. Professor Shih-Chi Chen and his team from the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), collaborated with the Lawrence Livermore National Laboratory to develop the Femtosecond Projection Two-photon Lithography (FP-TPL) printing technology.
By controlling the laser spectrum via temporal focusing, the laser 3D printing process is performed in a parallel layer-by-layer fashion instead of point-by-point writing. This new technique substantially increases the printing speed by 1,000—10,000 times, and reduces the cost by 98 percent. The achievement has recently been published in Science, affirming its technological breakthrough that leads nanoscale 3D printing into a new era.
The conventional nanoscale 3D printing technology, i.e., two-photon polymerization (TPP), operates in a point-by-point scanning fashion. As such, even a centimeter-sized object can take several days to weeks to fabricate (build rate ~ 0.1 mm3/hour). The process is time-consuming and expensive, which prevents practical and industrial applications. To increase speed, the resolution of the finished product is often sacrificed. Professor Chen and his team have overcome the challenging problem by exploiting the concept of temporal focusing, where a programmable femtosecond light sheet is formed at the focal plane for parallel nanowriting; this is equivalent to simultaneously projecting millions of laser foci at the focal plane, replacing the traditional method of focusing and scanning laser at one point only. In other words, the FP-TPL technology can fabricate a whole plane within the time that the point-scanning system fabricates a point.
The California DMV set the rules for driverless light-duty vehicle testing on public roads on Tuesday. Among other things, the autonomous trucks and vans must have a link to a remote operator, or a human safety driver on board.
Can A.I. make music? Can it feel excitement and fear? Is it alive? Will.i.am and Mark Sagar push the limits of what a machine can do. How far is too far, and how much further can we go?
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A team in Switzerland has created a soft robotic insect that can withstand a multitude of hits from a flyswatter.
A new soft robotic insect could one day form part of a swarm designed to perform a number of different tasks. A team from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland developed the insect and showed it is incredibly durable, even when being battered by a flyswatter.
Publishing its findings to Science Robotics, the team said the insect – called DEAnsect – is propelled 3cm per second by artificial muscles. Two versions were produced: one tethered with ultra-thin wires, the other being untethered and autonomous weighing less than 1g, including its battery and components.
John Giannandrea, Vice President of Engineering with responsibility for Google’s Computer Science Research and Machine Intelligence groups; leading teams in Machine Learning, Machine Intelligence, Computer Perception, Natural Language Understanding, and Quantum Computing, “I’m definitely not worried about the AI apocalypse, I just object to the hype and soundbites that some people are making” said at the TechCrunch Disrupt conference in San Francisco.
Google’s John Giannandrea sits down with Frederic Lardinois to discuss the AI hype/worry cycle and the importance, limitations, and acceleration of machine learning.
