The Journalism AI global survey: what we’ve learned so far

Over the last few weeks, newsrooms from all over the world have been completing our Journalism AI survey. Contributions came in from Europe, South and North America, Africa, and Asia. We’re immensely grateful to each and every one of the respondents. When we launched Journalism AI earlier this year, we had just a vague picture of how artificial intelligence technologies were being applied to journalism. And that is why we started this ambitious investigation, with the support of the Google News Initiative.

Now, thanks to the invaluable expertise and insights that news organisations are sharing with us through meetings, one-on-one in-depth interviews, and answers to the survey, we can start painting a more detailed picture of what AI actually means for journalism — and what it is likely to mean in the immediate future.  Read More

#nlp

How AI companies can avoid ethics washing

One of the essential phrases necessary to understand AI in 2019 has to be “ethics washing.” Put simply, ethics washing — also called “ethics theater” — is the practice of fabricating or exaggerating a company’s interest in equitable AI systems that work for everyone. A textbook example for tech giants is when a company promotes “AI for good” initiatives with one hand while selling surveillance capitalism tech to governments and corporate customers with the other. Read More

#ethics

This AI magically removes moving objects from videos

We’ve previously seen developers harness the power of artificial intelligence (AI) to turn pitch black pics into bright colorful photos, flat images into complex 3D scenes, and selfies into moving avatars. Now, there’s an AI-powered software that effortlessly removes moving objects from videos.

All you need to do to wipe an object from footage is draw a box around it, and the software takes care of the rest for you. Read More

#fake, #image-recognition

Deep Flow-Guided Video Inpainting (CVPR 2019)

Read More

#image-recognition, #videos

Artificial Intelligence in Industry with Dan Faggella

Podcast for those interested in learning applying machine learning and AI technologies within their company or organization. The host, Dan Faggella, interviews top AI and machine learning researchers and executives on how they are using AI and machine learning. Read More

#artificial-intelligence, #podcasts

TWiML Presents AI Platforms, Vol 2

Over the next few weeks on the podcast, we’re bringing you volume 2 of our AI Platforms series. You’ll recall that last fall we brought you AI Platforms Volume 1, featuring conversations with platform builders from Facebook, Airbnb, LinkedIn, Open AI, Shell and Comcast. This series turned out to be our most popular series of shows ever, and over 1,000 of you downloaded our first eBook on ML platforms, “Kubernetes for Machine Learning, Deep Learning & AI.” Well now it’s back, and we’re sharing more experiences of teams working to scale and industrialize data science and machine learning at their companies. Read More

#devops, #podcasts

TWiML Presents AI Platforms, Vol 1

As many of you know, part of my work involves understanding the way large companies are adopting machine learning, deep learning and AI. While it’s still fairly early in the game, we’re at a really interesting time for many companies. With the first wave of ML projects at early adopter enterprises starting to mature, many of them are asking themselves how can they scale up their ML efforts to support more projects and teams

Part of the answer to successfully scaling ML is supporting data scientists and machine learning engineers with modern processes, tooling and platforms. Now, if you’ve been following me or the podcast for a while, you know that this is one of the topics I really like to geek out on.

Well, I’m excited to announce that we’ll be exploring this topic in depth here on the podcast over the next several weeks. Read More

#devops, #podcasts

How can attackers abuse artificial intelligence?

Artificial intelligence (AI) is rapidly finding applications in nearly every walk of life. Self-driving cars, social media networks, cybersecurity companies, and everything in between uses it.

But a new report published by the SHERPA consortium – an EU project studying the impact of AI on ethics and human rights – finds that while human attackers have access to machine learning techniques, they currently focus most of their efforts on manipulating existing AI systems for malicious purposes instead of creating new attacks that would use machine learning. Read More

#assurance, #cyber

You Can’t Fix What You Can’t See: The Realities of AI and Satellite Data

Earth observation (EO), the monitoring of the Earth from space using satellites, has undergone fundamental changes in the last decade. We have seen the convergence of two exciting trends in remote sensing and processing algorithms that now herald a new era of space renaissance.

The implementation of ambitious government initiatives such as the European Union’s Copernicus Programme, and an explosion in commercial satellite sensing constellations like Planet’s, has been matched by incredible breakthroughs in algorithm performance. This is due to advancements in accelerated computing, open source software, and broadly accessible training data. Read More

#image-recognition

The Overplayed, Turbohyped, and Underwhelming World of Artificial Intelligence

Move over, smart beta and risk parity: Today’s buzzword is AI. It’s difficult to find a manager that does not claim to be using artificial intelligence to improve its investment process. However, significant obstacles can impede a manager’s adoption of AI — obstacles that threaten to transform not only a manager’s investment processes, but its entire business. Thus many managers’ claims of adoption tend to be more aspirational than genuine. The evidence for these claims typically comes down to one of three things:

The inclusion of a new, nontraditional data set (typically some type of web scraping) into existing traditional, non-AI investment processes; the hiring of a “computer scientist” or “data scientist”; or the simple misappropriation of the term “machine learning” to include traditional quantitative processes

Such hand waving presents asset allocators with a challenge: separating the counterfeit from the authentic.  Read More

#ai-first, #investing