Artificial Intelligence In Industry With Dan Faggella

In this episode of the AI in Industry podcast, we interview Grant Ingersoll at Lucidworks, about enterprise search. Ingersoll talks about how companies have massive amounts of siloed data, making it difficult to find within enterprise systems. We hope businesses might take away from this interview what is required and what is involved in building search applications to make corporate data more accessible and structured. Ingersoll will also discuss how data strategies are going to evolve and how scientists and data experts might come together to build an enterprise search application.

We receive a lot of interest from business leaders in the domain of data enrichment, and we've executed on a few campaigns for these businesses. At the same time, our audience seems particularly interested in the collection of data to train a bespoke machine learning algorithm for business, asking questions related to how to get started on data collection and from where that data could come. This week on AI in Industry, we seek to answer those questions. We are joined by Daniela Braga, CEO and founder of DefinedCrowd, a data enrichment and crowdsourcing firm, who discusses with us how a business might determine what kind of data it might need for its AI initiative. We hope the insights garnered from this interview will help business leaders get a better idea of how they could go about starting an AI initiative and seeing it through from data collection or enhancement to solving its business problem.

There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data. For this episode, we spoke with Mark Brayan, CEO of Appen, a firm that offers crowdsourced training data for machine learning applications. We discuss how developing a sound data strategy is essential for using AI to solve business problems. Brayan also helped us detail how and when a business can make use of certain data collection and enrichment methods depending on their business goals.

Over the last year, we've covered a lot of marketing applications. Many people know of our deep marketing research we've done on the landscape of machine learning in marketing applications and which industries will be affected first. But marketing doesn't tell the whole story when it comes to B2B sales. At some point, we need to take these clicks and turn them into appointments, for example. In this episode of AI in Industry, we are joined by Vitaly Gordon, VP of Data Science and Engineering at Einstein, Salesforce’s customer relationship management application driven by artificial intelligence. We speak with Vitaly about where AI is serving a role in sales enablement today and how the CRM and sales tool ecosystem might be different in the near-term future; how will salespeople be able to leverage AI to make themselves more productive? Vitaly paints an interesting picture of where he sees the low hanging fruit and the unique challenges with sales data and B2B data that are quite different from the challenges

In this episode of the AI in Industry podcast, we interview Sumit Borar, Senior Director of Data Sciences and Engineering at Myntra, an eCommerce site for fashion, about the current and future state of eCommerce personalization and how the way customers in India purchase products online affect that personalization. Myntra talks about the challenges of bringing dialed-in personalized recommendations to the physical world and the challenges of bringing eCommerce into the developing world. In addition, he discusses with us the different ways that eCommerce is being experienced in rural parts of India and some of the unique hurdles that they’ve had to overcome. Business leaders looking to apply machine learning and data science to the eCommerce world in developing markets and business leaders aiming to bring data science to the physical retail world should tune into this episode. Read the full interview article here: www.techemergence.com/ai-retail-ecommerce-india-challenges-opportunities

This week on AI in Industry, we speak with Amir Saffari, Senior Vice President of AI at BenevolentAI, a London-based pharmaceutical company that uses machine learning to find new uses for existing drugs and new treatments for diseases. In speaking with him, we aim to learn two things: How will machine learning play a role in the phases of drug discovery, from generating hypotheses to clinical trials? In the future, what are the roles of man and machine in drug discovery? What processes will machines automate and potentially do better than humans in this field? We hope the insights in this episode provide business leaders in the pharma industry with an understanding of the current state of AI in their space and where it might play a role in their industry in the next two to three years. See the full interview article here: www.techemergence.com/future-drug-discovery-ai-role-man-machine

We usually discuss the impact of artificial intelligence on a business's bottom line, but governments and NGOs are also considering AI as a mechanism for improving society. This week on the AI in Industry podcast, Anandan Padmanabhan, CEO of the Wadhwani Institute for Artificial Intelligence in India, speaks to us about where and how the public sector should consider leveraging AI. Padmanabhan discusses the challenges that the Indian government faces in providing education and healthcare to its citizens. Although AI might help overcome these challenges, those who need these services most may not have access to the technologies necessary to work with it. See the full interview article here: www.techemergence.com/ai-government-ngo-social-good-initiatives-interview-wadhwani-institute

AI has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. Searching for content within videos is more challenging because video is not just voice and sound, it is also a collection of moving and still images on screen. How could AI work to overcome that challenge? In this episode of the AI in Industry podcast, we interview Manish Gupta, CEO and co-founder of VideoKen, about the future of video search as machine learning is increasingly integrated into the process. Dr. Gupta talks about how video is becoming more searchable and discusses his own forecasts about what that will look like in the future. He also predicts what machine learning will allow Youtube to do as people continue to search for more specific video content. Our Content Lead, Raghav Bharadwaj, joins us for this interview. See the full video article here: www.techemergence.com/machine-learning-video-search-video-education-how-it-works/

This week on AI in Industry, we are talking about the ethical consequences of AI in business. If a system were to train itself to act in unethical or legally reprehensible ways, it could take actions such as filtering or making decisions about people in regards to race or gender. When machine learning is integrated into technology products, could a misbehaving system put the company at financial and legal risk? Our guest this week, Otto Berkes, Chief Technology Officer of New York-based CA Technologies, speaks to us about realistic changes in the technology planning and testing process that leaders need to consider. We discussed how businesses could integrate machine learning into the products and services, while still protecting themselves from potential legal downsides. See the full interview article featuring Otto Berkes live at: https://www.techemergence.com/?p=13752&preview=true

When we think of recommendation engines, we might think of Amazon or Netflix, but while consumer goods and entertainment might be the most prominent domains for recommendation engines, there are others. This week, we speak with Madhu Gopinathan of MakeMyTrip.com, one of the few Indian unicorn companies, about recommendation engines for travel companies. According to Madhu, MakeMyTrip’s recommendation engine has to figure out the best hotels for customer given their destination, but recommending hotels to first-time users and those who don’t frequent the site can prove challenging. How does a travel company’s AI-based recommendation engine start the process of making well-informed recommendations? Madhu talks to us about how a recommendation engine might match people immediately with their preferred product or service when the on-site data does not exist to inform the AI-driven recommendations. See the full interview article here: www.techemergence.com/recommendation-engines-actually-work-strategies-principle

When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar. With AI and machine learning news increasing in tech media, a business leader may find it challenging to cut through the hype and identify valid, useful case studies. We talked to Ben Lorica, the Chief Data Scientist at O’Reilly Media, to get his insights on what key details executives should be looking for within a case study. To see the our interview article, visit https://www.techemergence.com/what-executives-should-be-asking-about-ai-use-cases-in-business

Episode Summary: In this episode of the podcast, we interview AIG’s Chief Data Science Officer, Dr. Nishant Chandra, about natural language processing (NLP) for internal and team communication. Dr. Chandra talks about how NLP can help with sharing documents with specific team members whose roles warrant viewing those documents. Instead of a broad memo that would go out across the company, a document could be transformed to a tailored message depending on the individual receiving it. For instance, a document could be presented in a digestible way to the executive team, but be distilled to contain fewer details for the technology team to make it relevant to them. How might NLP serve this summarization role for internal communications in the next 5 years? See the full interview article here: www.techemergence.com/nlp-text-summarization-team-communication

This week’s episode of the AI in Industry podcast focuses on two main questions. First, how should business leaders determine the most fruitful, potential applications of AI in their business? Second, how do they choose the right one into which to invest resources? This week, we interview someone who has spoken with a number of CTOs and CIOs about early adoption strategies for machine learning for customer service, marketing, manufacturing and other applications. He is Madhusudan Shekar, Principal Evangelist at Amazon Internet Services. See the full interview article here: www.techemergence.com/how-to-determine-the-best-artificial-intelligence-application-areas-in-your-business

At TechEmergence, we often talk about the software capabilities of AI and the tangible return on investment (ROI) of recommendation engines, fraud detection, and different kinds of AI applications. We rarely talk about the hardware side of the equation, and that will be our focus today. For hardware companies like Nvidia, stock prices have soared thanks to the popularity of new kinds of AI hardware being needed not only in academia but also among the technology giants. Increasingly, AI hardware is about more than just graphics processing units (GPUs). Today we interview Mike Henry, CEO of Mythic AI. Mike speaks about the different kinds of AI-specific hardware, where they are used, and how they differ depending on their function. More specifically, Mike talks about the business value of AI hardware. Can specific hardware save money on energy, time, and resources? Where can it drive value? Where is AI hardware necessary to open new capabilities for AI systems that may not have been possible with older hardwar

Episode Summary: Facebook and Google’s advertising complex is founded on machine learning, allowing people to self-serve their data needs across a broad audience. India-based InMobi is a company in the advertising technology space that delivers 10 billion ad requests daily. Today, we speak with Avi Patchava, Vice-President of Data Sciences and Machine Learning at InMobi, which operates in China, Europe, India, and the US. Patchava explains how machine learning plays a role in appropriately matching advertising requests to the right audience at scale,  whether on mobile, desktop or different devices and media. Patchava paints a robust picture of what this technology will look like moving forward and how it will change the game for marketers and advertisers, especially with the emphasis on data and machine learning. See the full interview article here: www.techemergence.com/future-advertising-machine-learning-audience-targeting-reach

Companies with wells of data at their disposal may find themselves asking how they can use them in meaningful ways. Generally speaking, a clean set of data is the foundation for AI applications, but business owners may not know how exactly to organize their data in a way that allows them to best leverage AI. How exactly does a business transition from having data with the potential for usefulness to having data that’s going to allow for an accurate, helpful machine learning tool—one that can actually help solve business problems? In this episode of the podcast, we speak with Bryon Jacob, Co-founder and Chief Technology Officer at data.world, a company that offers products and services that help enterprises manage their data. In our conversation, Bryon walks us through the common errors companies make when creating and organizing data sets, and how these companies can transition to a more organized and meaningful data management system. The details in this interview should provide business leaders with a bette

Episode summary: In this episode of Ai in industry, we speak with Manoj Saxena, the Executive Chairman of CognitiveScale, about how AI and automation are being applied to white-collar processes in the healthcare sector. In simple business language, Manoj summarizes key healthcare applications such as invoicing handling, bad debt reduction, claims combat, and the patient experience, and explains how AI and automation can make these processes more efficient to improve the patient experience in healthcare organizations. Interested readers can listen to the full interview with Manoj here:  https://www.techemergence.com/white-collar-automation-in-healthcare/  

Episode Summary: Natural language processing (NLP) has become popular in the past two years as more businesses processes implement this technology in different niches. In inviting our guest today, we want to know specifically which industries, businesses or processes NLP could be leveraged to learn from activity logs. For instance, we aim to understand how car companies can extract insights from the incident reports they receive from individual users or dealerships, whether it is a report related to manufacturing, service or weather. In the same manner, how can insights be gleaned from the banking or insurance industries based on activity logs? We speak with the University of Texas’s Dr. Bruce Porter to discover the current and future use-cases of NLP in customer feedback.  Interested readers can listen to the full interview with Bruce here: https://www.techemergence.com/using-nlp-customer-feedback-automotive-banking  

Episode summary: This week on AI in Industry, we speak to Rana el Kaliouby, Co-founder and CEO of Affectiva about how machine vision can be applied to detecting human emotion - and the business value of emotionally aware machines. Enterprises leveraging cameras today to gain an understanding of customer engagement and emotions will find Rana’s thoughts quite engaging, particularly her predictions about the future of marketing and automotive. We’ve had guests on our podcast say that the cameras of the future will most likely be set up for their outputs to be interpreted by AI, rather than by humans. Increasingly machine vision technology is being used in sectors like automotive, security, marketing, and heavy industry - machines making sense of data and relaying information to people. Emotional intelligence is an inevitable next step in our symbiotic relationship with machines, an in this interview we explore the trend in depth. Interested readers can listen to the full interview with Rana here: https://www.

A myriad of customer service channels exist today, such as social media, email, chat services, call centers, and voice mail. There are so many ways that a customer can interact with a business and it is important to take them all into account. Customers or prospects who interact via chat may represent just one segment of the audience, while the people that engage via the call center represent another segment of the audience. The same might be said of social media channels like Twitter and Facebook. Each channel may offer a unique perspective from customers – and may provide unique value for business leaders eager to improve their customer experience. Understanding and addressing all channels of unstructured text feedback is a major focus for natural language processing applications in business – and it’s a major focus for Luminoso. Luminoso founder Catherine Havasi received her Master’s degree in natural language processing from MIT in 2004, and went on to graduate with a PhD in computer science from Brandei