Notify me of new posts via email. Clean coding Great code reads like a great prose. Architecture A software architecture is important to the successful development of a software system. Enterprise Application Integration Interesting applications rarely live in isolation. Object oriented paradigm Object oriented analysis, design and programming help to see the software elements as real world objects.
Latest Posts. Share this: Twitter Facebook. Thank you Like Like. Netherlands Antilles Like Like. Leave a Reply Cancel reply Enter your comment here With software, what would traditionally take months or years of battery material development and testing condenses into mere weeks or days.
Moreover, modern-day software will enable chemical architectures to be built in the cloud before being incorporated into physical lab bench testing.
All these progressions will enable manufacturers to have more time to iterate and simulate thousands or millions of battery material combinations, resulting in significantly fewer costs and shorter development timelines down the line. A major driving force of battery technology adoption lies in cost; as we saw decades prior in the solar industry, huge strides are being made in the battery sector to reduce hard costs.
A recent influx of cost-competitive materials and advanced manufacturing techniques has reduced waste, improved quality and decreased hardware costs. But how can we further drive down costs in order to drive cost-parity for EV or grid-scale batteries? In order to unlock substantial market share, maximize operational efficiency and decrease these soft costs, we must look to breakthrough technologies that can streamline critical and innovative processes while remaining economical.
As we saw with the solar industry, a battery industry at this scale can and will support standalone, venture-backable software businesses to elevate the battery ecosystem even further.
In reality, the battery market is continuing to experience rapid growth as companies are spending 5 to10 percent of installed costs on software. Right now is the peak of the battery era, and we are fast-approaching full electrification. Tyler Lancaster is a principal at Energize Ventures, a Chicago-based global alternative investment manager focused on digital innovation for energy and industry, where he drives investment activity and portfolio management.
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Save my name, email, and website in this browser for the next time I comment. Notify me of follow-up comments by email. With more codes being written by data, in , deep learning empowered almost all large-scale internet services including search, social media, and video recommendations. During the next decade, it is expected that the most important software will be created by deep learning, enabling self-driving cars, accelerated drug discovery, and more.
The emergence of conversational AI, self-driving cars, and consumer applications are all powered by AI and expected to drive high potential market growth in the future.
Deep learning empowers conversational AI and helps design more cost-efficient digital experiences. Conversational AI allows artificial intelligence technologies like chatbots to interact with people in a humanlike way. By bridging the gap between human and computer language, it makes communication between the two easy and natural. Conversational AI bots have had a positive impact on businesses in a number of ways, prominently in improving the customer experience.
The bots can enhance user engagement by resolving the basic queries of the customers instantly and enhance employee productivity by passing on complex problems to the human agents. A bot can engage in multiple conversations at a time solving multiple queries at an instance; hence, the need to hire employees at a large scale is eliminated.
This results in huge cost savings for businesses. Additionally, deep learning empowers self-driving cars and helps reduce the costs of human workload and labor costs. With the advancement of deep learning and automotive technologies, the U. Furthermore, AI consumer applications such as Tiktok have already demonstrated the power of deep learning and suggested the high potential revenue growth that deep learning may bring.
Powered by AI, TikTok can engage its average new users up to 10 minutes that is three times the capability of Instagram.
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