JDA Software and KPMG LLP recently published a wide-ranging survey regarding supply-chain technology. The main takeaway: end-to-end visibility is the No. 1 priority. But in order to make this a reality, the survey also notes that AI (Artificial Intelligence), machine learning (ML) and cognitive analytics will be critical. Yet pulling this off is far from easy and fraught with risks. Well, I recently had a chance to talk to Dr. Michael Feindt.

While homo sapiens were capable of abstract thought almost 100,000 years ago, it took much longer for the human mind to invent abstract painting. It wasn't until the beginning of the 20th century that artists such as Wassily Kandinsky, Kazimir Malevich, and Hilma af Klint created abstract works with no identifiable references to the physical world. Abstraction quickly became the lodestar driving artistic production, a trend that largely continues to this day. But just how popular is abstract art with collectors and art enthusiasts? To try and answer this question, we assembled a database of 112,600 Instagram posts made last December for which the geolocation and/or hashtags indicated that the user was in Miami during Art Basel in Miami Beach. Eliminating selfies and other extraneous pictures yielded approximately 74,760 images, which represents the collective visual record of all the artworks Instagram users saw in person that they also elected to share with their followers.

The global autonomous taxi market could be worth over $2 trillion on an annual basis by 2030, according to estimates from UBS analysts cited by Bloomberg. In formulating its estimates, the bank utilized its Evidence Lab to run a simulation of an autonomous taxi fleet in New York City using a "complex algorithm that performs dynamic optimal route generation and passenger-vehicle assignment considering vehicle capacity and rider demand." What does this mean: The rise of a new multi-trillion dollar transportation industry will likely reshape how consumers travel and automakers operate. The bigger picture: There are still major hurdles for the auto industry to overcome in order to see 11 million autonomous taxis on the road as part of a $2 trillion market. Autonomous vehicle (AV) providers have struggled to bring autonomous technology to public roads.

Ford is adding legs to its robocars--sort of. The automaker is announcing today that its fleet of autonomous delivery vans will carry more than just packages: Riding along with the boxes in the back there will be a two-legged robot. Digit, Agility Robotics' humanoid unveiled earlier this year on the cover of IEEE Spectrum, is designed to move in a more dynamic fashion than regular robots do, and it's able to walk over uneven terrain, climb stairs, and carry 20-kilogram packages. Ford says in a post on Medium that Digit will bring boxes from the curb all the way to your doorstep, covering those last few meters that self-driving cars are unable to. The company plans to launch a self-driving vehicle service in 2021.

Artificial intelligence (AI) -- defined by John McCarthy, one of the doyens of AI, as "the science and engineering of making intelligent machines" -- is slowly gaining relevance in the military domain. While commercial use of AI is widening, there are only three countries that are reported to be developing serious military AI technologies: the United States, China and Russia. AI promises a significant military advantage to a nation's offensive and defensive military capabilities. AI now has the capacity to be merged with sophisticated but untried, new weaponry, such as offensive cyber capabilities. This is an alarming development, as it has the potential to destabilize the balance of military power among the leading industrial nations.

The Trump administration might be building walls between America and some countries, but it is eager to forge alliances when it comes to shaping the course of artificial intelligence. The Organization for Economic Co-operation and Development (OECD), a coalition of countries dedicated to promoting democracy and economic development, has announced a set of five principles for the development and deployment of artificial intelligence. The announcement came at a meeting of the OECD Forum in Paris. The OECD does not include China, and the principles outlined by the group seem to contrast with the way AI is being deployed there, especially for face recognition and surveillance of ethnic groups associated with political dissent. Speaking at the event, America's recently appointed CTO, Michael Kratsios, said, "We are so pleased that the OECD AI recommendations address so many of the issues which are being tackled by the American AI Initiative."

Artificial Intelligence in Transportation Market reports provides a comprehensive overview of the global market size and share. Artificial Intelligence in Transportation market data reports also provide a 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. The Artificial Intelligence in Transportation market size will grow from USD XX Million in 2018 to USD XX Million by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2017, and the market size is projected from 2018 to 2023. Look insights of Global Artificial Intelligence in Transportation industry market research report at https://www.pioneerreports.com/report/361684

THE HAGUE, Netherlands – Artificial intelligence will be a requirement for securing carrier 5G networks – which is shaping up to be a technology juggernaut that presents unique challenges unlike any ever seen in the world of telecom until now. That was the assessment at the GSMA Mobile 360 Security for 5G conference, taking place here this week. To understand the challenges and the drivers for artificial intelligence (AI), it's important to understand that existing telecom networks, even today's 4G LTE networks, are built from a hardware-centric perspective, using the vertical-stack Open Systems Interconnection (OSI) model. Features include a heavy reliance on hardware big routers and switches with device-specific software to run them. Functions are hard-coded and largely siloed.

Artificial intelligence (AI) and robotics have become increasingly hot topics in the press and in academia. In October 2017, Bloomberg published an article claiming that artificial intelligence is likely to be the "most disruptive force in technology in the coming decade" and warning that firms that are slow to embrace the technology may risk extinction.1 Similarly, the following month, the Financial Times declared that the "robot army" is transforming the global workplace.2 This interest is likely due to the rapid gains that artificial intelligence has been making in some applications, such as image recognition and abstract strategy games, and that advanced robotics has been making in labs, even though widespread commercial applications may be lagging (Felten et al. 2018). Scholars have been increasingly interested in the economic, social, and distributive implications of artificial intelligence, robotics, and other types of automation. For example, over the past 2 years, economists at the University of Toronto have convened conferences around the economics of artificial intelligence, which have been attended by a dazzling array of economics scholars from diverse point of views including Nobel Prize winners Edmund Phelps, Paul Romer, Joseph StiglitSome research has taken a morez, and others.3

Binary determination of the presence of objects is one of the problems where humans perform extraordinarily better than computer vision systems, in terms of both speed and preciseness. One of the possible reasons is that humans can skip most of the clutter and attend only on salient regions. Recurrent attention models (RAM) are the first computational models to imitate the way humans process images via the REINFORCE algorithm. Despite that RAM is originally designed for image recognition, we extend it and present recurrent existence determination, an attention-based mechanism to solve the existence determination. Our algorithm employs a novel $k$-maximum aggregation layer and a new reward mechanism to address the issue of delayed rewards, which would have caused the instability of the training process. The experimental analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on both synthetic and real-world datasets.