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Artificial Intelligence and BigData. We are far to slow in Government policy in providing incentives and organizing the capital markets to raise, distribute and commit to investment levels that are necessary to make this transition. Behind-the-meter batteries. Electric-vehicle smart charging. Internet of Things. Blockchain.
Dan Akerson, retired CEO of General Motors, recently commented on Apple's alleged entrance into the automotive market with the following remarks: "We take steel, raw steel, and turn it into a car. While car sales in other markets, such as China, are still growing, a look at cities, here too, signals that this trend won't hold for long.
In my book The BigData Opportunity In Our Driverless Future I identify two distinct value chains that have been established because of the car ownership-centric model that has been dominant for the past 70+ years: the vehicle manufacturing and sale value chain , and the vehicle use value chain.
The forecast is that by the middle of 2030, we’ll have a trillion devices connected. Like Tony Stark, data scientists and organizations need to adapt to the opportunities and data volume of IoT. Understand the relationship between BigData and Iron Man armor. Is too much data a problem?
In the age of data and AI, industry players are looking to maximize operational efficiency and enhance customer experience with game-changing IoT logistics and fleet management solutions. It is not surprising that the global IoT market in logistics is expected to reach $100,984.5 million by 2030. .
It would not be surprising, at the current pace of acceleration, for the Fourth Industrial Revolution to last only 20 years, from say 2010 to 2030, and for the Fifth Industrial Revolution that comes after that – whatever it will look like – to last for only 10 years⃜ say 2030 to 2040.
A significant step in this journey has been taken at a recently concluded COP26 summit, where various governments have pledged to reduce emissions by 2030. One can say that AI has spearheaded the technological revolution in transportation, with the global AI in transportation market expected to reach $3.87 GHG emissions in 2022.
It finds that AI could (in aggregate and netting out competition effects and transition costs) deliver an additional $13 trillion to global GDP by 2030, averaging about 1.2% At the industry level they include (a) the extent of AI diffusion in economies; (b) the build-up of corporate profit; and (c) labor market dynamics.
So barring some unforeseen problem, an agreement to reduce carbon emissions between now and 2030 will come to fruition. The myth that creating a clean economy will damage the economy is crumbling , and all countries — but especially China and India – are coming in with serious offerings. For example, the U.S.
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