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Ford targets fully autonomous vehicle by 2021

#artificialintelligence

RELATED ARTICLES: Ford wins best'small engine' for 5th year running New Ford Trucks series launched in Qatar Ford recreates march to Aqaba with truck line-up Ford has announced its intention to produce a fully autonomous, or "SAE level 4-capable" (operable without a wheel of pedals), vehicle for commercial operations by 2021 as part of a ride-hailing or ride-sharing service. To achieve this, the company is investing in or collaborating with four start-ups and doubling both its Silicon Valley team and Palo Alto technology campus in San Antonio, Texas. "The next decade will be defined by automation of the automobile, and we see autonomous vehicles as having as significant an impact on society as Ford's moving assembly line did 100 years ago," said Mark Fields, Ford president and CEO. "We're dedicated to putting an autonomous vehicle on the road that can improve safety and solve social and environmental challenges for millions – not just those who can afford luxury vehicles." Raj Nair, Ford executive VP for global product development and chief technical officer, noted: "Ford has been developing and testing autonomous vehicles for more than 10 years.


On Human Robot Interaction using Multiple Modes

arXiv.org Artificial Intelligence

Today robotics is a vibrant field of research and it has tremendous application potentials not only in the area of industrial environment, battle field, construction industry and deep sea exploration but also in the household domain as a humanoid social robot. To be accepted in the household, the robots must have a higher level of intelligence and they must be capable of interacting people socially around it who is not supposed to be robot specialist. All these come under the field of human robot interaction (HRI). Our hypothesis is- "It is possible to design a multimodal human robot interaction framework, to effectively communicate with Humanoid Robots". In order to establish the above hypothesis speech and gesture have been used as a mode of interaction and throughout the thesis we validate our hypothesis by theoretical design and experimental verifications.


IBM's Watson Health wing left looking poorly after 'massive' layoffs

#artificialintelligence

IBM has laid off approximately 50 and 70 per cent of staff this week in its Watson Health division, according to inside sources. The axe, we're told, is largely falling on IBMers within companies the IT goliath has taken over in the past few years to augment Watson's credentials in the health industry. These include medical data biz Truven, which was acquired in 2016 for $2.6bn, medical imaging firm Merge, bought in 2015 for $1bn, and healthcare management business Phytel, also snapped up in 2015. Yesterday and today, staff were let go at IBM's offices in Dallas, Texas, as well as in Ann Arbor, Michigan, Cleveland, Ohio, and Denver, Colorado, in the US, and elsewhere, it is claimed. A spokesperson for Big Blue was not available for comment.


FS92-01-018.pdf

AAAI Conferences

Functional Knowledge Representation in AI Applications for Scientific Computing Michael Lucks Space Telescope Science Institute* 3700 San Martin Drive Baltimore, MD 21218 Ian Gladwell Department of Mathematics Southern Methodist University Dallas, TX 75275 Abstract We describe a knowledge representation scheme in which expertise is encoded via expert-supplied mappings, or knowledge functions. This functional representation technique was originally developed for the Selection Advisor for Initial Value Software (SAIVS), a prototype system for recommending ordinary differential equation software from numerical subroutine libraries. We discuss the deficiencies in previous knowledge representation schemes that motivated the development of functional scheme, and then present the method. We propose other classes of mathematical software to which the existing SAIVS shell may be applied. Recently, the representation has been adapted for use in the Parallel Object Matching System (POMS), operational system for scheduling parallel scientific observations on the Hubble Space Telescope.


Classification of pulsars with Dirichlet process Gaussian mixture model

arXiv.org Machine Learning

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e.\ magnetically powered neutron stars with decaying super-strong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in period $P$ and period derivative $\dot{P}$ parameter space. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and proper motion of all discovered components. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.