Toronto-based Canvass Analytics has raised $6.5 million CAD from Google's AI-focused Gradient Ventures, the firm's first lead round in a Canadian company. Participating companies include Bedrock Industries, Viaduct Ventures, and existing investors Real Ventures and Barney Pell. The Canvass Analytics funding is Gradient Ventures' first lead round in a Canadian company. Canvass Analytics' analytics platform targets the industrial IoT industry--including agriculture, energy, and mining--to help companies identify operational improvements. The platform has helped customers reduce greenhouse gas emissions by tens of millions of pounds, and generate "millions of dollars" in incremental revenue through quality improvements, production optimization and asset utilization, according to a press statement.
Artificial intelligence (AI) is poised to massively disrupt traditional financial services. The World Economic Forum identifies nine ways AI is creating new threats, new opportunities, and new competitive forces for financial institutions. The result may be a marketplace where only the largest and most specialized niche players survive. Subscribe to The Financial Brand via email for FREE!Mid-size and small banks and credit unions could soon find themselves at a serious competitive disadvantage. Ongoing developments in artificial intelligence have the potential to significantly change the way back offices operate and the experiences consumers receive from financial institutions.
The potential for data-driven medicine and predictive analytics is clear. AI and machine learning are assisting healthcare providers with more informed clinical decision-making and enabling patients to take an active role in their own health, with access to real-time data via wearables and mobile apps. However, as we've seen with many previous social, economic and business challenges that hailed big data as the solution, the key to success will be creating enduring systems for collecting, cleaning and analyzing this wealth of information in thoughtful, efficient, secure and actionable ways. 'To achieve these benefits, whether access to information or applying the latest AI algorithms, implies a data infrastructure that connects all the myriad devices.' – Florian Leibert, Co-Founder and Chief Executive Officer, Mesosphere What's more, systems will need to seamlessly interconnect from doctor to patient to hospital, pharmacy and lab, allowing for information to flow where and when needed. The proliferation of medical data, from electronic health records to real-time data from connected medical devices, is the foundation for this new era of personalized, precision medicine.
NumPy is a container of generic data. It contains N-dimensional array object, tools for integrating C/C code, Fourier transform, random number capabilities, and other functions, NumPy is one of the most useful packages for scientific computing pandas is an open source library that provides users with easy-to-use data structures and analytic tools for Python. Matplotlib is a service that provides 2D plotting library that creates publication quality figures . Scikit-learn is an efficient tool for data analysis. NumPy is a container of generic data.
Is artificial intelligence (AI) a panacea to make work and life nearly effortless – or the first step to a nightmare scenario of self-aware machines? There's been both ballyhoo and angst over the prospects of AI, including some that think AI poses a risk to the "existence of human civilization." On the other hand, senior military and national security leaders have asserted that to not pursue AI is the real existential threat, as it can put the country at risk from other nations that don't share the need for caution. With apologies to those who fear the worst, federal and SLED governments have come down squarely on the side of vigorously pursuing artificial intelligence. Companies that sell storage solutions, automation, big data, security and data mining tools should be encouraged by all the buzz going on in government about AI.
It might seem like automation in the workplace is a relatively new phenomenon. The reality is that it's been on a steady march through business for more than 200 years. Nevertheless, the past three years or so have seen a huge step-change in the variety of technologies being promoted to help businesses automate aspects of their work. Automation tools are becoming less expensive, they can be deployed more quickly and are easier to use. Moreover, data to train and improve "smart" systems powered by artificial intelligence (AI) is more readily available.
Why didn't they make the leap? Did they think the assembly line was too complex, too expensive or a fad? It was probably a mix of these reasons coupled with a lack of grit. Change is hard...but failure sucks. The biggest companies in the world are today at the same inflection point as manufacturers in the early 20th century.
In recent times, these words have created a hype which is undoubtedly due to the fact that human history and interaction are ultimately going to be altered in no mean terms by these concepts. The end of this article is to simply bring light to this seemingly technical world which would affect us positively if we understand and make use of the advantages it comes with. Big data basically refers to data sets that are so large in multiple varieties (example videos and images) and comes at such a speed that it is virtually impossible to use the traditional data processing applications to handle them. Every day, data is being generated in various ways and forms through devices, sensors and new technology platforms. Due to the nature of collection, it is often unstructured, however big data encompasses all data whether structured, semi structured or unstructured.
I've spent the last 8 months going out and pitching big ideas for artificial intelligence solutions. I'm very frequently faced with business people who have been schooled for the last decade on the importance of data. However, this means my services often get conflated with data analytics and big-data consulting. From the business person's perspective, their ask is simple: "We have all this big-data. Can you come in and make us more money from it?"
There has been a surge in applications of machine learning over the last few years as companies look for ways to leverage big data in their products and services. That has corresponded with a big increase in another type of machine learning application – i.e. those sent to the United States Patent and Trademark Office for protection. But the ramifications of the machine learning-patent uptick are not yet clear. Statistical and anecdotal evidence suggests we're in the midst of major upswing in patent protection requests for machine learning inventions. While hard numbers can be tough to come by due to intricacies of the USPTO process (and the fact that it will conceal applications upon request), several researchers have identified what they see as a surge in interest in protecting machine learning products over the past several years.