In this special guest feature, Chris Heineken, CEO and Co-founder of Atrium, suggests that as Machine Learning (ML) is growing in the IT and cloud space, understanding how to best utilize its capabilities will change the approach to implementing new IT investments. As CEO of Atrium, Chris leads a world-class team in empowering companies to embrace the next generation of tech through the power of AI. Prior to founding Atrium, Chris was the COO at Appirio where he was responsible for leading the Company's global consulting, sales, and operations teams. Chris started his career with Accenture and later founded Bay Street Solutions, a CRM/Siebel consulting firm, acquired by Perficient. He earned his undergraduate degree from UC Davis and MBA from UC Berkeley.

Please join us at Hardy Coffee in Benson for a workshop on Interpretable Machine Learning by Dr. Aimee Schwab-McCoy. First National Bank is our sponsor for the evening, providing coffee and refreshments, beginning at 5:00pm. Please RSVP so we can plan accordingly. How do we extract meaningful information from black box models? Sure, predictive accuracy is important – but what about domain-specific knowledge?

Throughout higher education, we see a great deal of excitement around the potential of AI, though we've yet to develop a common vocabulary around it to more clearly communicate to institutional leaders the potential value. Nearly all of our stakeholders, from senior administrators to newly-admitted students, want to be consumers of AI-driven insights (mention "AI-driven dashboards" to an administrator, and you get almost immediate buy-in), but few people outside of the computer and data science fields have an accurate understanding of what it takes to create and/or deploy AI software in different contexts.

Machine learning is fundamentally different from all the tech that came before it in one major way: As time passes, it gets smarter. It's the opposite of a static solution because it actually provides more value with age. Unsurprisingly, companies in all industries have been eager to embrace machine learning in recent years, and that shows no signs of slowing. We've already seen machine learning applications enter the mainstream via web search algorithms and product recommendations. Transformative though they may be, they're just the inaugural applications.

In fact, the realization that feature engineering is more important than hyperparameter tuning came to me as a lesson -- an awakening and vital lesson -- that drastically changed how I approached problems and handled data even before building any machine learning models. When I first started my first full time job as a research engineer in machine learning, I was so excited and obsessed with building fancy machine learning models without really paying much attention to the data that I had. As a matter of fact, I was impatient. I wanted results so badly that I only cared about squeezing every single percent of performance out of my model. Needless to say, I failed after so many attempts and wondered why.

Meet DigiDoug, the first digital human to give a TED talk in real time. DigiDoug is the virtual version of Dr. Doug Roble, senior director of Software R&D at Digital Domain, the award-winning visual effects studio behind the characters and visual effects for movies like The Curious Case of Benjamin Button, Maleficent, Disney's The Beauty and the Beast and Avengers: Endgame. Roble and Digital Domain's Digital Human Group have presented DigiDoug at multiple events, showcasing their state-of-the-art digital human technology that's driven by an inertial motion-capture suit and a single camera capture for facial animation. But to capture and recreate emotions and actions in real time, the Los Angeles-based studio turned to more powerful and advanced technology: machine learning and real-time rendering. With NVIDIA RTX technology and Unreal Engine from Epic Games, Digital Domain is bringing photorealistic digital humans to life -- all in real time.

TED Talks are relatively short videos from some of the most engaging speakers around the world. These videos cover an endless range of topics. In this post, we have shortlisted the top 12 TED talks on Artificial Intelligence.

About 3.85 million people studied Japanese at a record 18,604 institutions overseas in fiscal 2018, with the number of institutions soaring in Asia, according to a survey released this week. The number of Japanese language institutions jumped nearly fourfold to 818 in Vietnam from the previous survey in fiscal 2015 and nearly tripled to 400 in Myanmar, said the survey by the Japan Foundation, a government-backed organization conducting international cultural exchange programs. The number of Japanese learners overseas rose 5.2 percent to 3,846,773, led by a 169.0 percent surge to 174,461 in Vietnam, it said. The survey found a record high 142 countries and territories offering Japanese language education, five more than the fiscal 2015 level. The five include East Timor, Zimbabwe and Montenegro.

Education systems face a multitude of challenges in today's fast-moving world. Teacher workload is ever-increasing, while delivering personalised lessons to students and fostering their critical thinking skills are crucial but elusive goals. Many people lack access to high-quality learning materials and qualified professors. Fortunately, technologies such as artificial intelligence (AI) can provide schools with much needed assistance, and companies have developed smart algorithms that refine educational experiences in many different ways. Whether through personalised learning and smart content or through transcribing words and improving cognitive performance, AI-driven tools are transforming the way children learn and develop new skills.

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