Daily Artificial Intelligence News Roundup #121

1. AI bot gets human expressions

Read More

2. Is Your Company Ready to Protect Its Reputation from Deep Fakes?

What looms ahead are deep fakes, realistic forgeries of people appearing to say or do things that never actually happened. Imagine, for example, an authentic-seeming video that shows your CEO promising to donate $100 million to a charitable cause — or saying something racist or sexist.

After a public outcry over privacy and their inability — or unwillingness — to address misleading content, Facebook, Twitter, and other social media platforms finally appear to be making a real effort to take on fake news. Read More

3. AI to Neigh About: Magic AI Trots Out Horse Intelligence | NVIDIA Blog

Magic AI is galloping into the internet of horses arena. Horses are often times in a barn in remote places without any security that they are ok when you are sleeping,” she said.

Magic AI’s StableGuard, a system of cameras that works with a mobile app to keep tabs on horses, provides GPU-driven video monitoring and emergency alerts. Read More

4. Want to Become a Data Engineer? Here’s a Comprehensive List of Resources to get Started


Before a model is built, before the data is cleaned and made ready for exploration, even before the role of a data scientist begins – this is where data engineers come into the picture.

Data Warehousing/Big Data Tools

Distributed file systems like Hadoop (HDFS) can be found in any data engineer job description these days. Apart from that, you need to gain an understanding of platforms and frameworks like Apache Spark, Hive, PIG, Kafka, etc. Read More

5. World’s first AI presenter unveiled in China – video

n/a Read More

6. AI is not “magic dust” for your company, says Google’s Cloud AI boss

Andrew Moore is the new head of Google’s Cloud AI business, a unit that is striving to make machine-learning tools and techniques more accessible and useful for ordinary businesses.
It’s healthy for the world to have people who are thinking about 25 years into the future—and people who are saying “What can we do right now?”

There’s one project at Carnegie Mellon that involves a 70-foot-tall robot designed to pick up huge slabs of concrete and rapidly create levees against major flooding. How can I build something to counter that?”

I can’t think of anything more exciting than being at a place that is not just doing AI for its own sake anymore, but is determined to bring it out to all these other stakeholders who need it. Read More

7. [1811.03532] Scalable Robust Kidney Exchange

n/a Read More

8. MIT trained an AI to spot fake and biased news stories –

Any fact-checker who works in the media has a straightforward but challenging job – make sure all the claims in an article are true. It’s an important task, and in an era of outright fake news, especially considering the 2016 US election and the recent midterms, it’s becoming even more crucial. Then, the AI analysed information about news websites, considering sources like articles on the sites themselves, their Wikipedia pages, Twitter accounts, and even URLs. Read More

9. Evolving Speech and AI as the Window into Mental Health

Mental health and neurological disorders are a growing epidemic.

Yet there is a growing shortage of mental health professionals to adequately treat this need.

In January 2017, IBM made the bold statement that within five years, health professionals could apply AI to better understand how words and speech paint a clear window into our mental health. Read More

10. The Math Behind Machine Learning

Machine learning is a wildly popular field of technology that is being used by data scientists around the globe. Mastering machine learning can be achieved via many avenues of study, but one arguably necessary ingredient to success is a fundamental understanding of the mathematics behind the algorithms.

What math subjects are used in machine learning, and how are they used?  In this research paper, we look at the mathematics behind the machine learning techniques linear regression, linear discriminant analysis, logistic regression, artificial neural networks, and support vector machines. Read More

11. Embedded Curiosities

Previous posts:

Embedded Agents  —  Decision Theory  —  Embedded World-ModelsRobust Delegation  —  Subsystem Alignment

A final word on curiosity, and intellectual puzzles:

I described an embedded agent, Emmy, and said that I don’t understand how she evaluates her options, models the world, models herself, or decomposes and solves problems.

In the past, when researchers have talked about motivations for working on problems like these, they’ve generally focused on the motivation from AI risk. When people figure out how to build general AI systems, we want those researchers to be in a better position to understand their systems, analyze their internal properties, and be confident in their future behavior. Read More

12. Spinning Up in Deep RL

We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning.

At OpenAI, we believe that deep learning generally—and deep reinforcement learning specifically—will play central roles in the development of powerful AI technology. While there are numerous resources available to let people quickly ramp up in deep learning, deep reinforcement learning is more challenging to break into. Read More

13. Human Made | What’s Next: Top Trends

I was having a coffee with editor of Wired magazine a few days ago and we got onto the subject of ‘human’ being a trend in the future. He said that the founder of Net-a-Porter said that in the future we will buy clothes with labels saying “Human-Made”. We might even get to a situation where people buy human-made vs AI made in the same way we currently buy organic vs non-organic food. Read More