Stay ConnectedThe AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups – uncovering trends, use-cases, and best practices for practical AI adoption.
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Copyright: © Daniel Faggella, 2014
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
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 hardware? What is the right business approach to AI hardware?
This interview was brought to us by Kisaco Research, which partnered with TechEmergence to help promote their AI hardware summit on September 18 and 19 at the Computer History Museum in Mountain View California.
See the full interview article here:
www.techemergence.com/financial-roi-ai-hardware-top-line-bottom-line-impact
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 better understanding of some of the processes involved in getting started with AI initiatives, and how to hire data science-related roles into a company.
See the full interview article with Bryon Jacob live at:
https://www.techemergence.com/how-existing-bus…ta-assets-for-ai/
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