AI Articles and Analysis about Business intelligence and analytics

Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks.
We wanted to know - what work functions have the most potential for near-term automation?
In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks - on which business functions have the greatest potential for automation today, and in the coming five years ahead.

Episode Summary: In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with, so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. Artificial intelligence in stock trading certainly isn't a new phenomena, but access to it's capabilities has historically been rather limited to large firms.
This week, we’re joined by CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services.

In July of 2016, we sent out a series of survey questions to past guests who have been featured on the Emerj podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. In this article, we focus on responses to the following question:

Episode Summary: I remember reading an article in Scientific American years ago about a poster of a person looking in the direction people sitting in a school dining room, and that this poster would make people sitting in the dining room less likely to litter. This seems like an absurd example of holding people accountable for their actions, but as it turns out, there are a lot more serious consequences to ensuring behavior change through observation, and one area where this matters is medicine.
Today, there’s a major issue with people who don't adhere to their medical regimens, only to relapse or experience more serious symptoms later on. This week's guest, Cory Kidd, CEO of Catalia Health and known for his work at MIT on human-robotic interaction, is working to help solve this problem by developing a robot that adds some of that physical presence and accountability. This is likely one of many novel medical AI applications that we're likely to see roll out in healthcare over the next decade.

Episode Summary: Right now, you can take a picture of a flower in your garden and post it on social media to see if anyone knows its proper name. Wouldn’t it be nice, though, if a machine could identify the correct name and species in the picture you just took? Solving this problem in applications of machine vision is something that CEO Igal Raichelgauz and his team are working on at Cortica, a machine learning company that is not focused on deep learning, but is instead taking a more "shallow" approach. In this episode, Raichelgauz articulates Cortica's approach, which is based on neurology and goes against some of the current approaches in getting machines to learn. We discuss some of these primary differences and dive into Cortica's goals for applying machine vision in consumer products.

Episode Summary: Accenture is a leading global professional services company in the tech space, providing services to many of the Fortune 500 and their global equivalents. The company recently conducted a study, combined with expertise from economists and AI researchers, about the longer-term economic impact of artificial intelligence around the world. In this episode, I spoke with Chief Technology Officer Paul Daugherty, who has been with Accenture since 1986, and who was joined by Global Technology R&D Lead Marc Carrel-Billiard. We met up at a coffee shop after an AI Summit in San Francisco, and I asked Paul and Marc about what they had learned from this newly-published study and what they consider to be the significant impacts of *AI and automation on the future job market.

Episode Summary: Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off is in finance and hedge funds. In this episode, we speak with Numerai Founder Richard Craib, whose company is crowdsourcing a machine learning hedge fund. Their model is based on pooling data science talent from all over the world and using "anonymous" models to train financial data. These models compete against one another, and the winning models' creators are rewarded in bitcoin - a process based entirely on encryption and anonymity. Craib speaks about his overarching vision for the company, and also delves into his thoughts on the past, present, and future of AI applications in finance.
 

While there have been and continue to be innovative and significant machine learning applications in healthcare, the industry has been slower to come to and embrace the big data movement than other industries. But a snail's pace hasn't kept the data from mounting, and the underlying value in the data now available to health care providers and related service providers is a veritable goldmine. In this editorial, we provide an overview of where healthcare's big data actually comes from, and why providing robust data analytics services in this sector matters.