Anyone who’s spent much time on the web will know what TL;DR stands for⁠—but, for everyone else, it’s an acronym for “Too Long, Didn’t Read”.

It’s an understandable sentiment we’ve all felt at some point. People lead busy lives. Some outlets now even specialise in short, at-a-glance news.

The problem is, it’s hard to get the full picture of a story in just a brief snippet.

In a world where fake news can be posted and spread like wildfire across social networks – almost completely unchecked – it feels even more dangerous to normalise “news” being delivered in short-form without full context.

There are two sides to most stories, and it’s hard to see how both can be summarised properly.

News folks are not going to love this. Some product manager announced internally a tool that FB is developing called "TL;DR". Basically, it will use AI to summarize long form articles and spit out bullet points so people don't have to read the full piece.

What could go wrong!

— editor Mac 🙃 (@RMac18) December 15, 2020

However, the argument also goes the other way. When articles are too long, people have a natural habit of skim-reading them. Skimming in this way often means people then believe they’re fully informed on a topic… when we know that’s often not the case.

TL;DR needs to strike a healthy balance between summarising the news but not so much that people don’t get enough of the story. Otherwise, it could increase existing societal problems with misinformation, fake news, and lack of media trust.

According to BuzzFeed, Facebook showed off TL;DR during an internal meeting this week. 

Facebook appears to be planning to add an AI-powered assistant to TL;DR which can answer questions about the article. The assistant could help to clear up anything the reader is uncertain about, but it’s also going to have to prove it doesn’t suffer from any biases which arguably all current algorithms suffer from to some extent.

The AI is also going to have to be very careful in not taking things like quotes out-of-context and end up further automating the spread of misinformation.

There’s also going to be a debate over what sources Facebook should use. Should Facebook stick only to the “mainstream media” which many believe follow the agendas of certain powerful moguls? Or serve news from smaller outlets without much historic credibility? The answer probably lies somewhere in the middle, but it’s going to be difficult to get right.

Facebook continues to be a major source of misinformation – in large part driven by algorithms promoting such content – and it’s had little success so far in any news-related efforts. I think most people will be expecting this to be another disaster waiting to happen.

(Image Credit: Mark Zuckerberg by Alessio Jacona under CC BY-SA 2.0 license)

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Facebook has been its usual blessing and a curse during the pandemic. On the one hand, it’s helped people to stay connected and organise community efforts. On the other, it’s allowed dangerous misinformation to spread like wildfire that’s led to the increase in anti-vaccine and anti-mask movements.

The social media giant is hoping that AI can help to swing the balance more towards Facebook having an overall benefit within our communities.

If a person has posted asking for help because they’re unable to leave the house, Facebook’s AI may automatically match that person with someone local who has recently said they’re willing to get things like groceries or prescriptions for people.

In a blog post, Facebook explains how it built its matching algorithm:

We built and deployed this matching algorithm using XLM-R, our open-source, cross-lingual understanding model that extends our work on XLM and RoBERTa, to produce a relevance score that ranks how closely a request for help matches the current offers for help in that community.

The system then integrates the posts’ ranking score into a set of models trained on PyText, our open-source framework for natural language processing.

It’s a great idea which could go a long way to making a real positive impact on people in difficult times. Hopefully, we’ll see more of such efforts from Facebook to improve our communities.

(Photo by Bohdan Pyryn on Unsplash)

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Voter manipulation is a concern for any functioning democracy and deepfakes provide a whole new challenge for social media platforms.

A fake video of Nancy Pelosi, Speaker of the United States House of Representatives, went viral last year after purportedly showing her slurring her words as if she was intoxicated. The clip shows how even a relatively unsophisticated video (it wasn’t an actual deepfake) could be used to cause reputational damage and swing votes.

Facebook refused to remove the video of Nancy Pelosi and instead said it would display an article from a third-party fact-checking website highlighting that it’s been edited and take measures to limit its reach. The approach, of course, was heavily criticised.

The new rules from Facebook claim that deepfake videos that are designed to be misleading will be banned. The problem with the rules is they don’t cover videos altered for parody or those edited “solely to omit or change the order of words,” which will not sound encouraging to those wanting a firm stance against manipulation.

In the age of “fake news,” many people have become aware not to necessarily believe what they read. Likewise, an increasing number of people also know how easily images are manipulated. Deepfake videos pose such a concern because the wider public are not yet aware enough of their existence or how to spot them.

A report from the NYU Stern Center for Business and Human Rights last September, covered by our sister publication MarketingTech, highlighted the various ways disinformation could be used ahead of this year’s presidential elections.

One of the eight predictions is that deepfake videos will be used “to portray candidates saying and doing things they never said or did”. Another prediction is that Iran and China may join Russia as sources of disinformation, the former perhaps now being even more likely given recent escalations between the US and Iran and the desire for non-military retaliation.

Legislation is being introduced to criminalise the production of deepfakes without disclosing that they’ve been modified, but the best approach is to limit them from being widely shared in the first place.

“A better approach, and one that avoids the danger of overreaching government censorship, would be for the social media platforms to improve their AI-screening technology, enhance human review, and remove deepfakes before they can do much damage,” the report suggests.

The month after Facebook refused to remove the edited video of Pelosi, a deepfake created by Israeli startup Canny AI aimed to raise awareness of the issue by making it appear like Facebook CEO Mark Zuckerberg said: “Imagine this for a second: One man, with total control of billions of people’s stolen data, all their secrets, their lives, their futures.”

Canny AI’s deepfake was designed to be clearly fake but it shows how easy it’s becoming to manipulate people’s views. In a tense world, it’s not hard to imagine what devastation could be caused simply by releasing a deepfake of a political leader declaring war or planning to launch a missile.

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The consortium includes Google DeepMind, Facebook AI, New York University, and the University of Washington. Each of the consortium’s members believe a more comprehensive benchmark is needed for NLP than current solutions.

The result is a benchmarking platform called SuperGLUE which replaces an older platform called GLUE with a “much harder benchmark with comprehensive human baselines,” according to Facebook AI. 

SuperGLUE helps to put NLP abilities to the test where previous benchmarks were beginning to pose too simple for the latest systems.

“Within one year of release, several NLP models have already surpassed human baseline performance on the GLUE benchmark. Current models have advanced a surprisingly effective recipe that combines language model pretraining on huge text data sets with simple multitask and transfer learning techniques,” Facebook said.

In 2018, Google released BERT (Bidirectional Encoder Representations from Transformers) which Facebook calls one of the biggest breakthroughs in NLP. Facebook took Google’s open-source work and identified changes to improve its effectiveness which led to RoBERTa (Robustly Optimized BERT Pretraining Approach).

RoBERTa basically “smashed it,” as the kids would say, in commonly-used benchmarks:

“Within one year of release, several NLP models (including RoBERTa) have already surpassed human baseline performance on the GLUE benchmark. Current models have advanced a surprisingly effective recipe that combines language model pretraining on huge text data sets with simple multitask and transfer learning techniques,” Facebook explains.

For the SuperGLUE benchmark, the consortium decided on tasks which meet four criteria:

1. Have varied formats.
2. Use more nuanced questions.
3. Are yet-to-be-solved using state-of-the-art methods.
4. Can be easily solved by people.

The new benchmark includes eight diverse and challenging tasks, including a Choice of Plausible Alternatives (COPA) causal reasoning task. The aforementioned task provides the system with the premise of a sentence and it must determine either the cause or effect of the premise from two possible choices. Humans have managed to achieve 100 percent accuracy on COPA while BERT achieves just 74 percent.

Across SuperGLUE’s tasks, RoBERTa is currently the leading NLP system and isn’t far behind the human baseline:

You can find a full breakdown of SuperGLUE and its various benchmarking tasks in a Facebook AI blog post here.

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Whereas mapping efforts from Google and Apple focus on navigating major cities, OpenStreetMap aims to ensure the smallest dirt paths are mapped.

Mapping the whole Earth is no small task: a sizeable local community helps, but it’s an impossible task manually both due to scale and often difficult to make out imagery.

In a Facebook blog post, mapping expert Dmitry Kuzhanov explained:

“Most modern algorithms, training sets, and techniques were invented to work for the areas with highly developed infrastructure.

In the developing world — for example, Africa, Southeast Asia, Latin America — where roads are not well-defined, maintained, or developed, even the best-trained human eye can struggle to identify and properly classify features.”

Over the past year and a half, Facebook mapped 300,000 miles of roads in Thailand for OpenStreetMap. These efforts resulted in the creation of a machine learning-based tool called RapiD which helps to lay computer-readable streets on top of satellite imagery.

Roads can be mapped even if they’re obscured by objects such as trees, while potential false positives like rivers are checked by volunteers. The model is based on a 34-layer deep neural network.

Martijn van Exel, a mapping leader in the OpenStreetMap community, said:

“The tool strikes a good balance between suggesting machine-generated features and manual mapping. It gives mappers the final say in what ends up in the map but helps just enough to both be useful and draw attention to undermapped places.

This is definitely going to be a key part of the future of OSM. We can never map the world, and keep it mapped, without assistance from machines. The trick is to find the sweet spot. OSM is a people project, and the map is a reflection of mappers’ interests, skills, biases, etc. That core tenet can never be lost, but it can and must travel along with new horizons in mapping.”

Facebook claims mapping the roads in Thailand by traditional means would have taken an additional three to five years.

RapiD is now available to help volunteers add and edit road data. The Map With AI service provides AI-generated maps in Afghanistan, Bangladesh, Indonesia, Mexico, Nigeria, Tanzania, and Uganda. Facebook says it will cover more countries in the future.

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Facebook’s issue displaying images yesterday gave users an interesting look at how the social media giant’s AI analyses their uploads.

An outage yesterday meant Facebook users were unable to see uploaded images, providing a welcome respite from the usual mix of food and baby photos. In their place, however, was some interesting text.

Text in the placeholder where the image should have been displayed showed how Facebook’s AI automatically tagged the images.

Some of the aforementioned tags were understandable, like “one person, beard”. Other tags – such as a group of women standing together being tagged as “hoes” – were more questionable.

Facebook says it uses machine learning for tagging the images and reading the description to blind users. It’s unclear if there were “hoes” in terms of the farming tool in the image – but I don’t know how often even one is in a group photo, let alone plural.

Techies like many of our readers know Facebook is analysing photos to further understand each user, primarily for advertising purposes. Yesterday’s outage, however, will have opened more of the public’s eyes to how each of their uploads are being analysed and data extracted.

Shout-out to the person who uploaded a photo of their baby only for Facebook to categorise it as “Image may contain: dog”.

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Many of us have been there, we’ve logged in to Facebook to find a notification alerting us to say “Happy birthday!” to a loved one who’s no longer with us. Or, sometimes, they can appear as a suggestion to invite to an event.

These are distressing moments for people, and short of ‘unfriending’ or blocking the profile they can become regular occurrences. Facebook is hoping to use AI to intervene.

Sheryl Sandberg, COO of Facebook, said:

“If an account hasn’t yet been memorialised, we use AI to help keep it from showing up in places that might cause distress, like recommending that person be invited to events or sending a birthday reminder to their friends.

We’re working to get better and faster at this.”

Elsewhere on the platform, Facebook is making other changes aimed at making the social network more bearable for those who’ve lost people.

Timelines of the deceased will be left fully intact, allowing others to remember that person how they were.

Tributes, meanwhile, will be moved to a separate area so it’s easy to read messages about what that person meant to others.

“We hope Facebook remains a place where the memory and spirit of our loved ones can be celebrated and live on,” comments Sandberg.

Back in 2009, Facebook added the ability to ‘memorialise’ a profile. Doing so will add “Remembering” prior to a person’s name and stop delivering notifications to others about that individual.

In the past, this feature was abused by some as a ‘prank’ claiming someone is dead. This would lock the person out their profile and cause distress to friends and family who are led to believe that individual had died.

Some of the features announced today are aimed towards preventing such distressing abuse of Facebook’s social network.

Content posted to a deceased user’s profile can be moderated by ‘legacy contacts’ who are people designated by that person as trusted while they were alive.

“Legacy contacts can now moderate the posts shared to the new tributes section by changing tagging settings, removing tags, and editing who can post and see posts,” Ms Sandberg explained.

“This helps them manage content that might be hard for friends and family to see if they’re not ready.”

For those yet to be memorialised, Facebook hopes AI will reduce the distress caused by profiles of the deceased on the social network.

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The new research centre is called the Institute for Ethics in Artificial Intelligence and was created in collaboration with the Technical University of Munich (TUM).

Facebook, like many companies, is fighting outside concerns about the development of AI and its potential societal impact. The centre should help to ensure Facebook keeps up with ethical best practices.

Joaquin Quiñonero Candela, Director of Applied Machine Learning at Facebook, wrote in a blog post:

“At Facebook, ensuring the responsible and thoughtful use of AI is foundational to everything we do — from the data labels we use, to the individual algorithms we build, to the systems they are a part of.

We’re developing new tools like Fairness Flow, which can help generate metrics for evaluating whether there are unintended biases in certain models. We also work with groups like the Partnership for AI, of which Facebook is a founding member, and the AI4People initiative.

However, AI poses complex problems which industry alone cannot answer, and the independent academic contributions of the Institute will play a crucial role in furthering ethical research on these topics.”

Independent, evidence-based research will be conducted by the institute to provide insight and guidance for society, industry, legislators, and decision-makers across the private and public sectors.

Furthermore, the institute wants to specifically address the concerns surrounding AI such as safety, privacy, fairness, and transparency.

Dr. Christoph Lütge, TUM Professor and head of the institute, commented:

“At the TUM Institute for Ethics in Artificial Intelligence, we will explore the ethical issues of AI and develop ethical guidelines for the responsible use of the technology in society and the economy.

Our evidence-based research will address issues that lie at the interface of technology and human values. Core questions arise around trust, privacy, fairness or inclusion, for example, when people leave data traces on the internet or receive certain information by way of algorithms.

We will also deal with transparency and accountability, for example in medical treatment scenarios, or with rights and autonomy in human decision-making in situations of human-AI interaction.”

AI has the potential to do immense good in the world by improving things like healthcare, productivity, and enhancing lifestyles. On the other hand, it could be devastating if used for military purposes, replacing entry-level jobs, or if things such as facial recognition continue to suffer from bias.

Hearing independent AI ethics centres open is always welcome, let’s just hope enough companies are willing to invest time in listening as well as money.

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The so-called FAIR (Facebook Artificial Intelligence Research) division at the company has approximately 180-200 staff.

Speaking to Forbes, Facebook Chief AI Scientist Yann LeCun said he expects the division to double by 2020. Already, he admits, “I don’t know everybody’s name anymore and I don’t recognise everybody either”.

The innovative AI division at Facebook is responsible for algorithms used for things ranging from online gaming to MRI scan analysis. Some of its successful developments have made its way into Facebook’s platforms including WhatsApp and Instagram.

Facebook is going head-to-head against other Silicon Valley giants like Amazon, Google, and Microsoft to attract AI talent.

The current industry-wide shortage of talent is a concern and could hinder the plans of companies like Facebook to drastically expand their personnel over such a short period.

Many governments are suggesting, or embarking, on initiatives to help prepare the workforce for the AI era. This is both to prevent job losses from careers that become automated and to help innovative companies flourish on the global stage.

However, such a mass reskilling will take time. With leading academics lured from universities by Silicon Valley giants offering unmatchable salaries, there also may be few to pass on their knowledge in what’s been coined as the AI ‘brain drain’.

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The company published a 10-page research paper (PDF) this week detailing how their system works and likened it to existing photo retouching tools such as red-eye correction.

Facebook’s idea is not entirely original, a similar feature exists in Adobe Photoshop Elements. The engineers acknowledge this in their paper but believe their ExGAN (Exemplar Generative Adversarial Networks) technique offers superior results.

In the examples below, the first column represents an exemplar photo of the subject with their eyes open. The second column shows the original closed eye photo to be fixed. In the third column, a closed eye correction using Adobe Photoshop Elements can be seen. Finally, the fourth column shows the results of Facebook’s algorithm.

As can be seen in the examples above, the engineers’ AI cleverly takes into account variables such as lighting and can adjust the resulting correction accordingly.

The idea is still in its research phase but offers an impressive demonstration of what’s possible when AI is applied to photography. It’s unclear when or if Facebook will roll this out for photos on their social network.

Photos hold our memories and will remain dear to us throughout our lives and perhaps even after down through generations. Any correctional feature which could save an otherwise nice photo from being deleted is sure to be welcomed by users.

What are your thoughts on Facebook’s closed eye correction?

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