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 those in the B2C world might deal with.

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-principles

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