The Customer Service Journal

Volume 5 August 2019

Knowledge for the World of Customer Service & Support

ISSN: 2374-9296

AI Robotic Automation News & Commentary




Microsoft Invests In and Partners with  OpenAI to Support Us Building Beneficial AGI

Microsoft is investing $1 billion in OpenAI to support us building artificial general intelligence (AGI) with widely distributed economic benefits. We’re partnering to develop a hardware and software platform within Microsoft Azure which will scale to AGI. We’ll jointly develop new Azure AI supercomputing technologies, and Microsoft will become our exclusive cloud provider—so we’ll be working hard together to further extend Microsoft Azure’s capabilities in large-scale AI systems.


Each year since 2012, the world has seen a new step function advance in AI capabilities. Though these advances are across very different fields like vision (2012), simple video games (2013), machine translation (2014), complex board games (2015), speech synthesis (2016), image generation (2017), robotic control (2018), and writing text (2019), they are all powered by the same approach: innovative applications of deep neural networks coupled with increasing computational power. But still, AI system building today involves a lot of manual engineering for each well-defined task.


In contrast, an AGI will be a system capable of mastering a field of study to the world-expert level, and mastering more fields than any one human — like a tool which combines the skills of Curie, Turing, and Bach. An AGI working on a problem would be able to see connections across disciplines that no human could. We want AGI to work with people to solve currently intractable multi-disciplinary problems, including global challenges such as climate change, affordable and high-quality healthcare, and personalized education. We think its impact should be to give everyone economic freedom to pursue what they find most fulfilling, creating new opportunities for all of our lives that are unimaginable today.



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Hacker Noon,


Ten Trending Applications of Artificial Intelligence

In 2018, we all experienced a dramatic emergence of the tools, platforms and applications based on Artificial Intelligence and Machine Learning. These technology tools not only transformed the internet and software industry, but it also had a massive impact on a wide range of verticals, including manufacturing, health, agriculture and automobile.

AI-related and ML technologies will continue to grow in 2019 and the coming years. Organizations such as IBM, Facebook and Google are investing a lot of money and time in development and research of AI techniques to offer benefits to the users.



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Editor's Notes:

The ten applications referenced in the article are:

1. Intoduction of Ai-enabled chips

2. Facial Recognition

3. The convergence of Ai and IoT

4. Socio-economic models

5. Interoperability among neural networks

6. Automating DevOps via AiOps

7. Automated Machine Learning Models

8. Deep Learning

9. The convergence of Ai and Blockchain.

10. Policy and Privacy


Peter Sweeney, Toward Data Science


Sharpening The AI Problem

Artificial general intelligence will be humanity’s greatest achievement.

But researchers must first agree on the problem they’re solving.


In 2017, the cognitive scientist and entrepreneur, Gary Marcus, argued that AGI needs a moonshot. In an interview with Alice Lloyd George, he said, “Let’s have an international consortium kind of like we had for CERN, the large hadron collider. That’s seven billion dollars. What if you had $7 billion dollars that was carefully orchestrated towards a common goal.”

Marcus felt that the political climate of the time made such a collective effort unlikely. But the moonshot analogy for AGI has taken hold in the private sector and captured the public imagination. In a 2017 talk, the CEO and co-founder of DeepMind, Demis Hassabis, evoked the moonshot analogy to describe his company as “a kind of Apollo program effort for artificial intelligence.” Hassabis unpacks his vision with pitch deck efficiency: First they’ll understand human intelligence, then they’ll recreate it artificially. AGI will thereafter solve everything else.

A similar moonshot vision was expressed in the recent $1-billion partnership between OpenAI and Microsoft, a competitive response to Google and Amazon. As reported by Cade Metz, “Eventually, [Sam] Altman and his colleagues believe, they can build A.G.I. in a similar way [through reinforcement learning and enormous amounts of raw computing power]. If they can gather enough data to describe everything humans deal with on a daily basis — and if they have enough computing power to analyze all that data — they believe they can rebuild human intelligence.”



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Editor's Notes:

This is a fascinating article that asks do we know the problem we are trying to solve with Ai.  It gives you a glimpse into the struggle that is taking place in the Ai development community.  This is a well researched and thought provoking article that is well worth the read.


Ron Miller, TechCrunch


Opsani helps optimize cloud applications with AI


Opsani, a Redwood City, Calif. startup, wants to go beyond performance monitoring to continually optimizing cloud applications, using artificial intelligence to help the software learn what is the optimal state.


“We have come up with a machine learning technique centered around reinforcement learning to tune the performance of applications in the cloud,” company co-founder and CEO Ross Schibler told TechCrunch.


Schibler says each company has its own unique metrics and that’s what they try to optimize around. “We’re modifying these parameters around the resource, and we’re looking at the performance of the application. So in real time, what is the key business metric that the application is producing as a service? So it might be the number of transactions or it might be latency, but if it’s important to the business, then we use that,” he explained.


He claims that what separates Opsani  from a monitoring tool like New Relic or AppDynamics is that they watch performance and then provide feedback for admins, but Opsani actually changes the parameters to improve the application performance in real time, based on what it knows about the application and what the developers want to optimize for.




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Bruno Aziza, CIO Network: Forbes


Want To Know The Latest On AI? Read These Books!


Readers of this column know that Artificial intelligence (AI) is hot.  Managing Director at Redpoint Capital’s Tomasz Tunguz recently pointed out that the AI startup ecosystem blossomed in just a few years: there are 400 AI startups today and there were none 8 years ago.   You might also think that the hype is getting out of hand when you read this week’s Gartner report on Artificial Intelligence: the research group claims that AI Augmentation Will Create $2.9 Trillion of Business Value in 2021.


If you’re feeling “behind the AI ball”, you might find solace the latest AI report from the UK's Defence Science and Technology Laboratory; they found that, of the start-ups claiming to use AI, only 40% actually did …


And, despite its bullish views on the future of AI, Gartner did find major impediments to “AI Success”.  The research firm’s latest survey shows that the #1 issue is skilled help and difficulty in understanding AI use cases.


So, wherever you might be on the “AI Maturity Curve”, I hope that my suggested "Top 3 AI books" can help.  I’d love to hear your feedback: please suggest books in the comment section!




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Editor's Notes:

This article has some interesting statistics that reveals the acceleration of Ai in the marketplace.


Jennifer Huergo:

The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST)


Plan Outlines Priorities for Federal Agency Engagement in AI Standards Development


GAITHERSBURG, Md. — The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) has released a plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI). The plan recommends that the federal government “commit to deeper, consistent, long-term engagement” in activities to help the United States speed the pace of reliable, robust and trustworthy AI technology development.


“The federal government can help the U.S. maintain its leadership in AI by working closely with our experts in industry and academia, investing in research, and engaging with the international standards community,” said Under Secretary of Commerce for Standards and Technology and NIST Director Walter G. Copan. “This plan provides a path to ensure the federal government supports AI standards that are flexible and inclusive—and suited for a world of rapidly changing technologies and applications.”


A February 2019 Executive Order directed NIST to develop a plan that would, among other objectives, “ensure that technical standards minimize vulnerability to attacks from malicious actors and reflect Federal priorities for innovation, public trust, and public confidence in systems that use AI technologies; and develop international standards to promote and protect those priorities.”


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Editor's Notes:

You get the impression that Ai is getting real when the government want to develop standards for it.

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