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

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

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

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

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

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