Cyber-Physical systems (CPS) have complex lifecycles involving multiple stakeholders, and the transparency of both hardware and software components' supply chain is opaque at best. This raises concerns for stakeholders who may not trust that what they receive is what was requested. There is an opportunity to build a cyberphysical titling process offering universal traceability and the ability to differentiate systems based on provenance. Today, RFID tags and barcodes address some of these needs, though they are easily manipulated due to non-linkage with an object or system's intrinsic characteristics. We propose cyberphysical sequencing as a low-cost, light-weight and pervasive means of adding track-and-trace capabilities to any asset that ties a system's physical identity to a unique and invariant digital identifier. CPS sequencing offers benefits similar Digital Twins' for identifying and managing the provenance and identity of an asset throughout its life with far fewer computational and other resources. Across domains, manufactured and assembled system complexity is increasing. Constituent components require compliance with stringent specifications, must have low defect rates, and increasingly require known provenance relating to origin and interaction histories. At the same time, economic and other constraints affecting production and assembly may necessitate involving diverse and untrusted vendors: a vehicle's parts may be made abroad and assembled domestically, while a medication might be compounded in one country before being shipped to another for packaging and a third for distribution. Power generation plant components might be manufactured globally but require certification in the country of use, while electronics manufacturing for a globally-distributed device may require trust-related integrated circuits to be provided and validated by a single-source vendor.
Artificial intelligence is transforming the world by disrupting every sector. As mentioned above, this article sets out how AI and Data Science may generate economic value and explains how a clear business objective, data strategy is aligned with business strategy and organisational culture (people matter too!) are all key to successful AI programs. Furthermore, this article is written against a background of ongoing turmoil caused by the Covid crisis to our economies and also to our healthcare globally. Source for image above Visualcapitalist Moreover, there is also an increasing political attention towards the consequences of climate change. There is a pressure on our political and business leaders to pursue policies that would promote economic growth and job creation and yet at the same time environmental campaigners are pressing for concrete action on the mitigation of climate change that in turn would require a break from business as usual (BAU). The election results in Germany that will result in the eventual departure of Angela Merkel as Chancellor of Germany will likely also result in the Green Party exerting greater influence in Germany and de facto across the EU in light of Germany's role as the leading economy of the region.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
The early 2000s were not a good time for technology. After entering the new millennium amid the impotent panic of the Y2K bug, it wasn't long before the Dotcom Bubble was bursting all the hopes of a new internet-based era. Fortunately the recovery was swift and within a few years brand new technologies were emerging that would transform culture, politics and the economy. They have brought with them new ways of connecting, consuming and getting around, while also raising fresh Doomsday concerns. As we enter a new decade of the 21st Century, we've rounded up the best and worst of the technologies that have taken us here, while offering some clue of where we might be going. There was nothing much really new about the iPhone: there had been phones before, there had been computers before, there had been phones combined into computers before. There was also a lot that wasn't good about it: it was slow, its internet connection barely functioned, and it would be two years before it could even take a video.
The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.
There are plenty of reasons to consider investing in artificial intelligence (AI). For one, it will likely transform the transportation industry over the next two decades through driverless cars. AI is also being used to help speed along drug research and even improve detection of life-threatening illnesses. Additionally, developing smart AI computer systems that can analyze data faster and apply their insights more efficiently is expected to add $15.7 trillion to the global GDP by 2030. All of this means investors would be wise to take a closer look at some of the leading tech companies in this space, namely Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL), NVIDIA Corporation (NASDAQ: NVDA), and Amazon (NASDAQ: AMZN).
There are plenty of reasons to consider investing in artificial intelligence (AI). For one, it will likely transform the transportation industry over the next two decades through driverless cars. AI is also being used to help speed along drug research and even improve detection of life-threatening illnesses. Additionally, developing smart AI computer systems that can analyze data faster and apply their insights more efficiently is expected to add $15.7 trillion to the global GDP by 2030. All of this means investors would be wise to take a closer look at some of the leading tech companies in this space, namely Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL), NVIDIA Corporation (NASDAQ:NVDA), and Amazon (NASDAQ:AMZN).
A staff member displays a DJI Phantom 3 4K drone during CES (Consumer Electronics Show) in Las Vegas, Nevada. It may come as a surprising fact that there are now 14 Chinese AI companies valued at $1 billion or more. These unicorns are worth a combined $40.5 billion, according to a report China Money Network recently released during the World Economic Forum's Summer Davos gathering in Beijing. Just to put these numbers in perspective. Google bought DeepMind for over $500 million in 2014. Chinese voice recognition giant iFlytek Co. has a market capitalization of 63 billion yuan ($9.2 billion). Chinese AI startups raised $27.7 billion via 369 VC deals in 2017, according to a recent report from Tsinghua University. So naturally, it raises questions on if there is a bubble waiting to pop in the Chinese AI space. How could these companies, with an average age of less than five years, be worth so much money?
Back when smartphones were first introduced, mobile data was less than one-tenth of carrier revenue. Now that smartphones have all but taken over the world, Morgan Stanley is betting that an even bigger disruption will come from the introduction of data-hungry autonomous vehicles. The bank estimates the connected-car revolution could net cell phone carriers more than $1 trillion annually. But getting there won't be easy - it will take significant investment by telecom services companies, tower providers, and other communications infrastructure providers - to shore up the grid in order for data-hungry cars to take the road. "The list is populated irrespective of specific 12-month recommendations and so includes some Underweight-rated names, in addition to M Equal-weight and Overweight-rated names," the bank said.