Colin Sumter, Jennifer Shin, and Craig Brown sit down with Dave Vellante and John Walls at the IBM Machine Learning Everywhere, build your ladder to AI event in New York City, Feb 2018
#IBMML #theCUBE
https://siliconangle.com/2018/03/02/beyond-ai-hype-experts-weigh-ai-growing-pains-modern-use-cases-ibmml/
Beyond the AI hype: Experts weigh in on AI growing pains, modern use cases
Artificial intelligence is creeping inevitably into daily life, and there is no shortage of opinions about what that may mean for the future.
Russian President Vladimir Putin issued a prediction last fall that whoever emerges as the leader in AI will rule the world. Tesla Inc. co-founder and Chief Executive Elon Musk has suggested in recent months that the threat from North Korea is a mere stroll in the park compared to the havoc that AI could bring upon civilization. And Masayoshi Son, chief executive officer of Japan’s SoftBank Group Corp., is on record as saying that the time when computers and AI will surpass mankind is nearly upon us.
Despite all of the noise surrounding AI, there are still a few important details to be worked out, a process that remains firmly in the hands of information technology architects, researchers and data scientists who are actually building AI solutions for deployment in the enterprise. They are grappling with the challenges of big data sets, flawed training methodologies, or, in some cases, what to do when there is little data at all.
Without the right processing or the correct data source, AI still becomes a technology with great promise but disappointing results. “Without those two things, you’ll either have a lot of great data that you can’t process in time, or you’ll have a great process or a great algorithm that has no real information, so your output is useless,” said Jennifer Shin (pictured, center), founder and chief data scientist for 8 Path Solutions LLC and instructor at Columbia University. “Those are the fundamental things you really do need to have any sort of AI solution built.”
Shin spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Signature Moment — Machine Learning Everywhere event in New York. She was joined in a panel discussion by Colin Sumter (pictured, left), IoT architect at CrowdMole, and Craig Brown (pictured, right), senior big data architect and data science consultant. They discussed limitations and challenges confronting AI today, the role of robotic process automation, customer use cases and future development. (* Disclosure below.)
Growth and growing pains
Recent forecasts show an upward curve for AI and machine learning in the technology world. Data reported last month by Forbes showed that machine learning patents are now the third-fastest growing category of all patents issued and spending on AI and machine learning is expected to increase from $12 billion in 2017 to $57 billion by 2021.
Yet, the intelligence space is still encountering growing pains that encompass both technology issues and customer understanding of how to effectively use the new tools. Rob High, IBM Watson’s chief technology officer, recently expressed concern that the biggest challenge for machine learning today was how to train the all-important models using less data. And Google’s top AI executive, John Giannandrea, echoed his colleague’s point when he described the issues surrounding AI’s ability to recognize human intent in conversational speaking.
“There aren’t that many very sophisticated documents you can find about how to implement it in real-world conditions,” Shin explained. “They all tend to use the same core data set, a lot of these machine learning tutorials you’ll find, which is hilarious because the data set is actually very small.”
In addition to the technology challenges, there are also questions about customer readiness or adoption. Panel participants described disconnects among customers between AI’s capabilities and how they can be effectively applied for use within organizations.
“I’m in these meetings with senior executives, and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology,” Brown described. “And then we get in a meeting with the data stewards and we hear, ‘What are these guys talking about? They don’t understand what’s going on at the data level and what data we have.’”
...
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Signature Moment — Machine Learning Everywhere event. (* Disclosure: TheCUBE is a paid media partner for the IBM Signature Moment — Machine Learning Everywhere event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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Colin Sumter, Jennifer Shin, and Craig Brown sit down with Dave Vellante and John Walls at the IBM Machine Learning Everywhere, build your ladder to AI event in New York City, Feb 2018
#IBMML #theCUBE
https://siliconangle.com/2018/03/02/beyond-ai-hype-experts-weigh-ai-growing-pains-modern-use-cases-ibmml/
Beyond the AI hype: Experts weigh in on AI growing pains, modern use cases
Artificial intelligence is creeping inevitably into daily life, and there is no shortage of opinions about what that may mean for the future.
Russian President Vladimir Putin issued a prediction last fall that whoever emerges as the leader in AI will rule the world. Tesla Inc. co-founder and Chief Executive Elon Musk has suggested in recent months that the threat from North Korea is a mere stroll in the park compared to the havoc that AI could bring upon civilization. And Masayoshi Son, chief executive officer of Japan’s SoftBank Group Corp., is on record as saying that the time when computers and AI will surpass mankind is nearly upon us.
Despite all of the noise surrounding AI, there are still a few important details to be worked out, a process that remains firmly in the hands of information technology architects, researchers and data scientists who are actually building AI solutions for deployment in the enterprise. They are grappling with the challenges of big data sets, flawed training methodologies, or, in some cases, what to do when there is little data at all.
Without the right processing or the correct data source, AI still becomes a technology with great promise but disappointing results. “Without those two things, you’ll either have a lot of great data that you can’t process in time, or you’ll have a great process or a great algorithm that has no real information, so your output is useless,” said Jennifer Shin (pictured, center), founder and chief data scientist for 8 Path Solutions LLC and instructor at Columbia University. “Those are the fundamental things you really do need to have any sort of AI solution built.”
Shin spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Signature Moment — Machine Learning Everywhere event in New York. She was joined in a panel discussion by Colin Sumter (pictured, left), IoT architect at CrowdMole, and Craig Brown (pictured, right), senior big data architect and data science consultant. They discussed limitations and challenges confronting AI today, the role of robotic process automation, customer use cases and future development. (* Disclosure below.)
Growth and growing pains
Recent forecasts show an upward curve for AI and machine learning in the technology world. Data reported last month by Forbes showed that machine learning patents are now the third-fastest growing category of all patents issued and spending on AI and machine learning is expected to increase from $12 billion in 2017 to $57 billion by 2021.
Yet, the intelligence space is still encountering growing pains that encompass both technology issues and customer understanding of how to effectively use the new tools. Rob High, IBM Watson’s chief technology officer, recently expressed concern that the biggest challenge for machine learning today was how to train the all-important models using less data. And Google’s top AI executive, John Giannandrea, echoed his colleague’s point when he described the issues surrounding AI’s ability to recognize human intent in conversational speaking.
“There aren’t that many very sophisticated documents you can find about how to implement it in real-world conditions,” Shin explained. “They all tend to use the same core data set, a lot of these machine learning tutorials you’ll find, which is hilarious because the data set is actually very small.”
In addition to the technology challenges, there are also questions about customer readiness or adoption. Panel participants described disconnects among customers between AI’s capabilities and how they can be effectively applied for use within organizations.
“I’m in these meetings with senior executives, and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology,” Brown described. “And then we get in a meeting with the data stewards and we hear, ‘What are these guys talking about? They don’t understand what’s going on at the data level and what data we have.’”
...
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Signature Moment — Machine Learning Everywhere event. (* Disclosure: TheCUBE is a paid media partner for the IBM Signature Moment — Machine Learning Everywhere event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
