Artificial Intelligence in Industry with Dan Faggella (machine learning)

You might be aware that some of the articles online about sports or financial performance of companies are article written by machines; this machine learning-based technology is the burgeoning field of natural language generation (NLG), which aims to create written content as humans would—in context— but at greater speed and scale. Yseop is one such enterprise software company, whose product suite turns data into written insight, explanations, and narrative. In this episode we interview Yseop's Vice President Matthieu Rauscher, who talks about the fundamentals of natural language generation in business, and what conditions need to be in place in order to drive key objectives. Rauscher also addresses the difference between discover-oriented machine learning (ML) and production-level ML, and why different industries might be drawn to one over the other.

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

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. 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 solve tougher problems and ask more complex questions using unstructured data.

Direct download: TEP-Amr_Awadallah-Mixdown.mp3
Category:machine learning -- posted at: 7:55am PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

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 PDT

Sumo Logic CTO - How Machine Learning Shines Light on Business Blind Spots

CTO and Co-founder of Sumo Logic Christian Beedgen gives his take on how to glean return on investment from applying machine learning to companies. There are o easy answers, but Beedgen boils down simple concepts for thinking about humans thinking through causation, machines working out correlations, and how the combination of the two can glean us better ideas and get to answers faster than humans could do alone.

Direct download: Christian_Beedgen_Mixed_1.mp3
Category:machine learning -- posted at: 5:00pm PDT

How Machine Learning Shapes Your eBay Experience

At Facebook headquarters, I learned there are 1 billion active users every month. In a more recent interview at eBay headquarters in San Jose, l learned that the well-known digital store has over 1 billion products for sale. eBay is, without a doubt, the world’s largest marketplace, and there’s enough incoming data to keep a large team of data scientists busy for years. I speak with Zoher Karu, eBay’s chief data officer, about how eBay leverages data and machine learning to create a better experience for its customers and also their sellers, shedding light on important lessons for anyone looking to sell a product online.

Direct download: Zoher_Karu_Mixed.mp3
Category:machine learning -- posted at: 5:00pm PDT

Machine Learning Cyber Security May Help Speed Response to Hack Attacks

In this week’s episode, I speak with Igor Baikalov, chief scientist at cybersecurity company Securonix, about the trends in data security and where security itself has had to take a step up in the last five years. Igor touches on major meta-trends that have forced data security to advance, as well as what has made AI and machine learning a ‘requirement’ of modern data security strategy, something that has changed significantly in the last decade. Igor sheds light on these issues and likely future trends in cybersecurity over the next five to 10 years.

Direct download: Igor_Baikalov_Mixed.mp3
Category:machine learning -- posted at: 5:08pm PDT

Facebook Artificial Intelligence and the Challenge of Personalization

In this week's episode, we feature an in-person interview from Facebook's headquarters with Hussein Mehanna, director of engineering of the Core Machine Learning group. Mehanna and I talk in-depth about the topic of personalization, touching on the pros and cons, how it works at Facebook, and how his team is working to overcome technological barriers to implement personalization in a way that improves the customer experience.

Direct download: Hussein_Mehanna.mp3
Category:machine learning -- posted at: 9:08pm PDT

What Can Machines Do That Lawyers Can't? A.I. Applications for Law

When one thinks through important industry apps of AI, law or legal apps are not usually the first to jump to mind, but there’s certainly a need. Richard Downe PhD is vice president of Data Science at Casetext, a startup working on improving search and natural language processing and democratizing legal information. In this episode, he speaks about the current bottlenecks for people trying to get more out of of legal case documents, as well as some of the apps on which the Casetext team is working, to make these processes easier and to gain strategic advantage in this industry.

Direct download: Richard_Downe_Mixed.mp3
Category:machine learning -- posted at: 6:40pm PDT

Start with a Problem: How Fast-Growing Startups Can Leverage Machine Learning

Learning about the research behind machine learning is always fun, but so is learning about the real-world applications. In today’s episode, we’re joined by the CEO and founder of Wrike, Andrew Filev. Filev speak about where Wrike is currently applying machine learning and AI in their fast-growing, data-driven company. He shares his insights as to why he thinks marketing might be the most ripe for disruption by AI, and also discusses how most companies can prepare to take advantage of machine learning in any industry.

Direct download: Andrew_Filev_Mixed.mp3
Category:machine learning -- posted at: 12:00pm PDT

Building to Scale: How Yahoo! Turns Machine Learning into Company-Wide Systems

Many employers (and employees) are familiar with the ‘painful’ learning curves of using multiple software products or platforms at once, but these may not be gripes you want to share with Amotz Maimon. This week, we feature an interview recorded at Yahoo headquarters with its Chief Architect, Amotz Maimon. He speaks about technology governance and how companies small and large can make faster and better decisions around what technologies to use, how to integrate and streamline the processes, and how to integrate machine learning into the mix (which Yahoo has been using for the past decade). This episode provides important insights for those looking to scale such technologies within their own businesses.

Direct download: Amotz_Maimon.mp3
Category:machine learning -- posted at: 5:01am PDT

Pulling Back the Curtain on Machine Learning Apps in Business

If you’re in the San Francisco Bay area, it’s not all that novel to be trained in or working on some form of AI; however, to be doing so in the 1980s and 1990s was a more rare occurrence. Dr. Lorien Pratt has been working with neural nets and AI applications for many decades, and she does lots of consulting work in implementing these technologies with companies in the Bay area. In this episode, Lorien provides her unique perspective on decades of development and adoption in AI as we ask, where is the traction today in places where it wasn’t 5 or 10 years ago? We also discuss where Lorien thinks machine learning applications in business and government seem to be headed in the near term.

Direct download: Lorien_Pratt.mp3
Category:machine learning -- posted at: 10:40pm PDT

How Cognitive Computing Can Change the Nature of Business Operations

When you go to Harvard Business School and then to McKinsey company to work in private equity, there’s really only one thing left to do - go to Silicon Valley and start an AI startup. At least, this is exactly what CEO Praful Krishna did when he moved to San Francisco to start Coseer, an AI company focused on understanding natural language and unstructured data. In this week’s episode, we speak about where unstructured data lives in a business, and how a business can be changed if the right data is unlocked. Krishna also discusses his experience in how executives are making decisions around how or how not to leverage AI in their companies.

Direct download: Praful_Krishna.mp3
Category:machine learning -- posted at: 6:25pm PDT

Machine Learning Still Getting Sea Legs in the World of Midsize Business

While we’ve featured quite a few companies that use and implement AI systems, we’ve more rarely gone behind the scenes with companies or consultants providing AI-related services to companies. In this week’s episode, we talk with Machine Learning Consultant Charles Martin, a data scientist and machine learning expert who has done freelance consulting on machine learning systems at companies including eBay, GoDaddy, and Aardvark. In this interview, Charles talks about the areas in AI that he believes are ripe for implementation in a business context, and where he sees businesses getting AI ‘wrong’ before getting to the hard work of implementing systems that work for them.

Direct download: Charles_Martin_.mp3
Category:machine learning -- posted at: 7:17pm PDT

How Machine Learning Builds Meaning from Our Chats, Tweets, and Likes

There’s a small lab in Pennsylvania that may know your gender, age, and understands facets about your personality, whether you’re introverted or extroverted, for example…and it's using machine learning to help make conclusions from social media information. For those who are raising an eyebrow, know that they’re not tapping into people’s accounts without permission. The described study is happening at University of Pennsylvania and is led in part by Dr. Lyle Ungar. In this episode, we talk about the focus of his work - on finding patterns between users and their language on social media content, and building an understanding for how this information might help individuals and communities in the future.

Direct download: Lyle-Ungar_1.mp3
Category:machine learning -- posted at: 6:00am PDT

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