Artificial Intelligence in Industry with Dan Faggella (machine learning)

One facet of business that nearly any industry has in common is the need to stay on top of news in their respective market, including competitor strategies or understanding changes in news related to the field. Media monitoring is a domain that machine learning (ML) is well suited for, with it's ability to coax out headlines, contextual information, and financial data from the seemingly endless stream of social, blog, and other information on the web today. Signal is a company that uses ML specifically for these purposes. In this episode, we speak with Signal Media's Chief Data Scientist and Co-founder Dr. Miguel Martinez, who dives into real business use cases illustrating the use of machine learning for media monitoring across industries.

 

Direct download: TEP-Miguel_Alvarez-Mixdown_v2.mp3
Category:machine learning -- posted at: 6:30pm PST

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, 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.

Direct download: TEP-Scott_Clark-Mixdown2.mp3
Category:machine learning -- posted at: 5:00pm PST

Uday Veeramachaneni is taking a new approach to machine learning in infosecurity, AKA infosec. Traditionally, infosec has approached predicting attacks in two ways: through a system of hand-designed rules, and through anomaly detection, a technique that detects statistical outliers in the data. The problem with these approaches, Veermachaneni says, is that the signal-to-noise ratio is too low. In this episode, Veermachaneni discusses how his company, PatternEx, is using machine learning to provide more accurate attack prediction. He also discusses the cooperative role of man and machine in building robust AI applications in data security and walks us through a common security attack scenario.

Direct download: TEP-Uday_Veeram-Mixdown.mp3
Category:machine learning -- posted at: 8:00pm PST

When it comes to finding an expert on interviewing and finding machine learning (ML) talent, Parshu Kulkarni may just be the guy to ask. Not only is Kulkarni one of a small subsegment of the global population with an advanced degree in data science who has also been hired to work in tech companies like eBay, but he's been on the unique side hiring of ML and AI talent. Today, Kulkarni works full-time as Head of Data Science at Hired, Inc., a giant platform for hiring top talent in tech and other areas. In this episode, he provide an interesting distinction between what individuals with experience in data science look for in potential hires versus those who do not have the tech background tend to look for, and also dives into the supply-and-demand landscape for data scientists now and in the future—an interesting interview for anyone looking to hire or be hired in the ML and AI space.

Direct download: TEP-Parshu_Kulkarni-Mixdown.mp3
Category:machine learning -- posted at: 5:00pm PST

What are executives missing the boat on and what do they need to think about when it comes to AI and ML? This week, we speak with John Straw, who has had a number of businesses in the UK and US, currently a senior advisor to McKinsey & Co., and who works with a lot of executive teams in terms of finding new applications for AI and finding ROI for those technologies in industry. We speak this week about how executives can get up to speed, what degree of knowledge and in what way they should learn it so they can find opportunities in their own companies. Straw also touches on what he sees as the biggest areas of oversight, in terms of preventing companies from finding those applications that can keep them up to speed with competitors and the big technology players.

Direct download: TEP-John_Straw-Mixdown.mp3
Category:machine learning -- posted at: 5:41pm PST

A medium-size business with a $20m marketing budget can run into issues when aiming to track an attribute, what marketing dollars brought in customers, etc. But when you're managing $90B for customers all over the world and working in every conceivable channel, things get all the more complicated. Josh Sutton, global head of Data and AI at Publicis.Sapient, speaks in this episode about the future of advertising attribution with machine learning. Specifically, Sutton discusses how his team of publicists is working on managing, tracking, and determining cohorts and attribution across more channels and numerous clients, and touches on ways that the company is applying ML to make sense of marketing data and spend marketing dollars more effectively.

Direct download: TEP-Josh_Sutton-Mixdown.mp3
Category:machine learning -- posted at: 6:12pm PST

Human Resource Management Meets Predictive Analytics

How do you know if you’ve made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on best hiring decisions. In this episode, Pasha Roberts, chief scientist at Talent Analytics, tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.

Direct download: TEP-Pasha_Roberts-Mixdown.mp3
Category:machine learning -- posted at: 5:00pm PST

Zillow: Data-Driven Real Estate Appraisals at Your Fingertips

Big data is often a buzz word, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information  - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, chief analytics officer and economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

Direct download: TEP-Stan_Humphries-Mixdown.mp3
Category:machine learning -- posted at: 5:00pm PST

MuleSoft's CTO Envisions Connected Machine Learning Network

This episode's guest is Uri Sarid, CTO at Mulesoft. 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 they each serve their individual purposes. He also speaks on his belief that companies will get used to dealing with disparate ML technologies and that finding ways to connect these technologies will be an important path for future trends in technology development.

Direct download: Uri_Sarid.rtf
Category:machine learning -- posted at: 5:00pm PST

How Companies Can Get Started Using Machine Learning for Business

Predictive analytics and machine learning are all the rage in Silicon Valley, but how do companies actually derive value by leveraging these technologies? We asked this question to Dr. Ronen Meiri, CTO and Founder of DMWay, a predictive analytics and machine learning platform company based in Israel. In this episode, Ronen speaks about what his company does and how smart executives are starting to make decisions how to choose and decide on the a smart, user-friendly platform that fits their business' needs.

Direct download: TEP-Ronen-Meiri-Mixdown_1.mp3
Category:machine learning -- posted at: 7:27pm PST