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.)

About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unified-data-analytics-platform Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner

Data Science: Present and Future | IBM Data Science For All

Wikibon Presents: Software is Eating the Edge | The Entangling of Big Data and IIoT

About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unified-data-analytics-platform Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner

Panelists reveal how to clear the murky waters of the data science world | #BigDataNYC by Nelson Williams | Sep 27, 2016 The data science world is a strange place. No one is entirely sure what data science means or what data scientists should do. People come to the field with different skills and a wide variety of experiences. In fact, the field requires such a broad array of skills that it’s better suited to teams than individuals. While the insights delivered by data science are vital to modern business, the field itself has yet to settle into a defined shape. To learn more about the data science field and the people inside it, Dave Vellante (@dvellante) and James Kobielus, host and guest host of theCUBE, from the SiliconANGLE Media team, visited the BigDataNYC 2016 conference in New York. There, they met with a panel of experts from the data science world to look behind the data science curtain. An evolution in data science The roundtable discussion opened up with a look at what the panelists felt were major changes in data science. Gregory Piatetsky-Shapiro, president and editor of KDnuggets, described data science as the second-oldest profession. People have a built-in need to find patterns and organize data. People do this well on the small scale, but not the large scale. He felt that was the challenge of data science. Craig Brown, author of Untapped Potential — The Supreme Partnership of Self, mentioned that data science isn’t just about understanding and dissecting data, but now it’s concerned with helping people see more in the data than just raw information. Joe Caserta, president of Caserta Concepts, saw a big shift in how they were preparing data for machines now, instead of other people. A growing skill set among data scientists The panelists discussed the transition of business from being driven by conventional wisdom to the new data-driven style. Along with that change, the needs of data science have expanded. A broad skillset is required for what is becoming an interdisciplinary position. Brown explained that talking about data science means talking about a collection of experiences. There is a wide combination of skills, enough so that one needs that combination among a team. The group briefly explored their general skillsets, being very adept in mathematics. Jennifer Shin, founder and chief data scientist at 8 Path Solutions, LLC, related that as data science became a real career, she took a different approach. She learned to write applications. This helps her because a person can’t do the same kind of data science while developing a product; the wide range of models and flexibility isn’t there. By doing the development herself, she saves the team from that part of the process. Miriam Friedel, research scientist at Elder Research, backed up this concept. She stated that a company needs someone who can deploy the applications built around their data models. A business needs a team of people to take advantage of different skillsets. Friedel also pointed out that once a model application goes into production, there has to be a model-management process to adjust the model for external factors and changes over time. Delivering data insights Data science is more than just numbers, it’s also about communication. The data science team must be able to present their findings to the company leadership. Caserta felt there is a science behind data visualization. The team has to consider their audience. Who is the consumer of that data? They have to tailor the information to that person. On the other side of the communication coin is a notion of the “citizen data scientist,” a hobbyist researcher who engages in data science. The panel roundly dismissed the idea. They felt data was important, too important to have someone without proper training interpreting it. “We don’t ask citizen-dentists to clean our teeth or citizen-pilots to fly our planes,” Piatetsky-Shapiro said. Friedel agreed, saying it was too easy to make a spurious correlation and ascribe significance to it. Finally, the panelists considered their final thoughts on the discussion. Friedel mentioned that it was interesting to hear her fellows talk about the same themes. Although everyone came from different places, they were experiencing the same challenges.