Dead Brothel Owner Not Ruling Out Presidential Run in 2020

After winning the election for assemblyman in Nevada’s 36th district, an ebullient Dennis Hof greeted supporters to declare victory and drop a major hint about his presidential ambitions. “Donald…


独家优惠奖金 100% 高达 1 BTC + 180 免费旋转

The AI Gold Rush

We are now living in what can be called the AI Gold Rush. There are thousands of innovative AI startups popping up every day and investors are quick to claim stake. Venture capital funding of AI companies soared 72% last year, hitting a record of $9.3 billion.

The increase in AI investment partly reflects the frothy funding environment overall. But there is an elevated excitement with AI technology as it has matured in recent years. College students last year enrolled in introductory AI and machine learning classes in record numbers and US officials mentioned the technology in more than 70 meetings of the Congress.

The biggest harm that comes out of all of this is that consumers will not able to tell the difference between companies that make legitimate claims and companies that make false claims. With companies both big and small riding the so-called AI wave it becomes increasingly difficult for consumers to know what companies they can trust to deliver the very best product/service using AI technology. The plausible solution to this issue is for investors to filter out the startups that misuse the AI name right from the get-go by denying them funding. That way genuine AI startups will be able to flourish and enrich this emerging tech ecosystem.

While AI startups provide plenty of vertical industry solutions, deep-pocketed technology giants like Google, Microsoft, and Amazon dominate the rest of the AI value chain. They are the picks and shovels of this gold rush as they offer the chips, cloud services, and algorithms needed to propel AI technology forward.

It takes at least $100M for a startup to design, build and distribute hardware chips optimized for AI processes like machine learning. In addition, they are facing competition from the likes of Google, Facebook, and Microsoft who are introducing their own AI optimized chips.

Tech giants are also in heavy contention to see which of their cloud service can run the millions of AI applications that will be everywhere. The primary contenders are AWS Startups, Google Could, Microsoft Azure or Chinese Alibaba. The battle is heating up as the overall cloud market is estimated to be worth a whopping $400 billion in 2020. And the increasingly the cloud market competition will be over the AI enable cloud.

Lastly, tech giants are battling to provide the best underlying AI algorithms and cognitive services to power millions of AI applications that will be built. If we look at it today, computer programmers can write a few lines of code and insert it into really powerful AI services through application program interfaces (APIs). Aforementioned companies like Google, Amazon, Microsoft, IBM are all offering machine learning and cognitive services in the cloud. This new category of AI-as-a-Service (AIaaS) will power a wealth of conversational agents and chat-bots, speech, natural language processing (NLP) and semantics, vision, and enhanced core algorithms programs.

Knowing all of this begs the question: What makes a successful AI startup?

Well, there are a few key principles that have allowed certain companies to raise such large sums of money.

The secret sauce of these companies is that they provide valuable point solutions to enterprises and are succeeding as they have access to (1) large and proprietary data training sets, (2) domain knowledge that gives them deep insights into the opportunities within a sector, and (3) a deep pool of talent around applied AI.

Even with all the right elements, sometimes it can be difficult for AI startups (or any startup for that matter) to gain momentum and raise significant capital. As an AI startup ourselves, we know the human element of operating a startup holds just as much importance as the service that we provide.

Corl strives to make access to growth capital fast, competitive, flexible, and human. For more information about Revenue Sharing model, Corl put together a breakdown on “Revenue Sharing? Cool! What the heck is that?”

Add a comment

Related posts:

When Agile means chaos

If I had a dollar for every time I heard someone say that in a project, I would be rich by now… well, perhaps not rich but I would have a little extra money in my pocket, that’s for sure. So many…

Agile Case Study

Finding your way through the sea of information about what differentiates project management and collaboration tools can be daunting. Which tool is really going to meet the needs of your team and…

Ho letto American Pastoral di Roth..

Alcune riflessioni. Innanzi tutto, qual è la cosa più incredibile in questa narrazione a multi-livello? Poniamoci dal punto di vista dello scrittore. Il suo intento è quello di descrivere il degrado…