AI Articles and Analysis about Data management

Episode Summary: If you're going to apply machine learning (ML) in a business context, you need a lot of data, and algorithms across the board perform better with more recent, rich, and relevant data. Today, there are companies whose entire business models are predicated on helping others make sense of and use of this type of information, as more entities look for the first place to apply ML in their organization.  In this episode, we speak with the CTO and Co-Founder of one such company—Palo Alto-based Cloudera. CTO Amr Awadallah, PhD, speaks with us this week about where he sees "data lakes" (or "data hubs", Cloudera's preferred term) and warehouses play an important role in ML applications in business. Based on his experiences helping a variety of companies in many countries set up data lakes, Amwadallah is able to distill and communicate these uses in three broad categories that apply across industries as companies look to apply ML applications to solve tough problems and ask more complex questions using unstructured data.

Episode Summary: What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning machine learning algorithms, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.

Episode Summary: What are executives missing the boat on and what do they need to think about when it comes to AI and machine learning? This week, we speak with John Straw, who has had a number of businesses in the UK and US and is currently a senior advisor to McKinsey & Co.
John works with a lot of executive teams in finding new applications for AI and machine learning and pinpointing ROI for those technologies in industry. This week, Straw shares his insights on how to solve business problems with machine learning. Straw also touches on aspects he believes are most commonly overlooked when executives learn machine learning, specifically in finding applications that can keep them up to speed with their competitors in the field.

It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 - but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.

1 - Found in Translation: More Accurate, Fluent Sentences in Google Translate

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.

Episode Summary: This episode's guest is Uri Sarid, PhD, CTO for MuleSoft, Inc. Sarid speaks about where he believes the future of machine learning (ML) applications in industry might go - he thinks applications might stay small and niche-based, and will develop based on how well each serves its individual purposes. He also gives his perspective on how companies may adapt to deal with these disparate ML technologies, and expands on his belief that finding ways to connect technologies will be an important path in the development of machine learning applications and platforms across industries.