Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
Data science is the big draw in business schools
640 days ago
7 Effective Methods for Fitting a Liner
650 days ago
3 Thoughts on Why Deep Learning Works So Well
650 days ago
3 million at risk from the rise of robots
650 days ago
Top 10 Hot Artificial Intelligence (AI) Technologies
Element AI, a platform for companies to build AI solutions, raises $102M
The race for artificial intelligence technology is on, and while tech giants like Google and Facebook snap up top talent to build out their own AI-powered products, a new startup has just raised a huge round of funding to help the rest of us.
Element AI - a Montreal-based platform and incubator that wants to be the go-to place for any and all companies (big or small) that are building or want to include AI solutions in their businesses, but lack the talent and other resources to get started - is announcing a mammoth Series A round of $102 million. It plans to use the funding for hiring talent, for business development, and also, to put some money where its mouth is, by selectively investing in some of the solutions that will be built within its doors.
"Our goal remains to lower the barrier for entry for commercial applications in AI," said Jean-François Gagné, the CEO of Element AI, in an interview. "Everyone wants to have these capabilities, it's hard for most companies to pull it off because of the lack of talent or access to AI technology. That is the opportunity." The company currently has 105 employees and the plan is to ramp that up to 250 in the next couple of months, he said.
The round was led by the prolific investor Data Collective, with participation from a wide range of key financial and strategic backers. They include Fidelity Investments Canada, Korea's Hanwha, Intel Capital, Microsoft Ventures, National Bank of Canada, NVIDIA, Real Ventures, and "several of the world's largest sovereign wealth funds."
This large Series A has been swift: it comes only six months after Element AI announced a seed round from Microsoft Ventures (of an undisclosed amount), and only eight months after the company launched.
We've asked Gagné and Element AI's investors, but no one is disclosing the valuation. However, what we do know is that the startup already has several companies signed up as customers and working on paid projects; and it has "hundreds" of potential companies on its list for more work.
"As we've been engaging with corporates and startups [to be in our incubator] we have realized that being engaged in both at the same time is not easy," Gagné said. "We've started to put together a business network, including taking positions in startups to help them by investing capital, resources, providing them with technology and bringing them all the tools they need to accelerate the development of their apps and help them connect with large corporates who are their customers." The aim is to back up to 50 startups in the field, he said.
The strategic investors also fit into different parts of Element AI's business funnel. Some like Nvidia are working as partners for business - in its case, using its deep learning platform, according to Jeff Herbst, VP of business development for NVIDIA. "Element AI will benefit by continuing to leverage NVIDIA's high performance GPUs and software at large scale to solve some of the world's most challenging issues," he said in a statement. Others, like Hanwha, are coming in as customer-investors, there to take advantage of some of the smarts.
AI in its early days may have been the domain of tech companies like Google, Apple and IBM when it came to needing and commercializing it, but these days, the wide range of solutions that can be thought of as AI-based, and applications for it, can touch any and all aspects of a business, from back-office functions and customer-facing systems, through to cybersecurity and financial transactions, to manufacturing, logistics and transportation, and robotics.
But the big issue has been that up to now, the most innovative startups in these areas are getting snapped up by the large tech giants (sometimes directly from the universities where they form, sometimes a bit later).
Then consider those that are independent and aren't getting acquired (yet). There still remains a gap for most companies between what skills are out on the market to be used, and what would be the most useful takeaway for their own businesses.
In other words, many considering how to use AI in their businesses are effectively starting from scratch. Longer term, that disparity between the AI haves and have-nots could prove to be disastrous for the idea of democratising intellectual power and all the spoils that come with it.
"There is not a lot left in the middle," Data Collective's Matt Ocko said in an interview. "The issue with corporations, governments and others trapped in that no mans' land of AI 'have-nots' is that their rivals with superior AI-powered decision making and signal processing will dominate global markets."
The idea of building an "AI incubator" or "safe space" where companies that might even sometimes compete against each other, are now sitting alongside each other talking to the same engineers to build their new products, may be an industry first.
But the basic model is not: Element AI is tackling this problem essentially by leaning on trends in outsourcing: systems integrators, business process outsourcers, and others have built multi-billion dollar businesses by providing consultancy or even fully taking the reins on projects that businesses do not consider their core competency.
The same is happening here. Element AI says that initial products that can be picked up there include predictive modeling, forecasting models for small data sets, conversational AI and natural language processing, image recognition and automatic tagging of attributes based on images, 'aggregation techniques' based on machine learning, reinforcement learning for physics-based motion control, compression of time-series data, statistical machine learning algorithms, voice recognition, recommendation systems, fluid simulation, consumer engagement optimization and computational advertising.
I asked, and I was told multiple times, that essentially colocating their R&D next to other first, for now, is not posing a problem for the companies who are getting involved. If anything, for those who understand the big-data aspect of AI intelligence, they can see that the benefit for one will indirectly benefit the rest, and speed everything up.
"That model is what made Yoshua Bengio" - "the godfather of machine learning" - "so excited about co-founding this company," Ocko said. "That massive research advantage leads Element AI to be able to deliver technically advantaged, increasingly cost effective solutions. It means they don't have to treat AI decision making capability as a scare resource, wielded like a club on everyone else." Continue Reading>>