The AI, called Project Debater, appeared on stage in a packed conference room at IBM’s San Francisco office embodied in a 6ft tall black panel with a blue, animated “mouth”. It was a looming presence alongside the human debaters Noa Ovadia and Dan Zafrir, who stood behind a podium nearby.

Although the machine stumbled at many points, the unprecedented event offered a glimpse into how computers are learning to grapple with the messy, unstructured world of human decision-making.

For each of the two short debates, participants had to prepare a four-minute opening statement, followed by a four-minute rebuttal and a two-minute summary. The opening debate topic was “we should subsidize space exploration”, followed by “we should increase the use of telemedicine”.

In both debates, the audience voted Project Debater to be worse at delivery but better in terms of the amount of information it conveyed. And in spite of several robotic slip-ups, the audience voted the AI to be more persuasive (in terms of changing the audience’s position) than its human opponent, Zafrir, in the second debate.

It’s worth noting, however, that there were many members of IBM staff in the room and they may have been rooting for their creation.

IBM hopes the research will eventually enable a more sophisticated virtual assistant that can absorb massive and diverse sets of information to help build persuasive arguments and make well-informed decisions – as opposed to merely responding to simple questions and commands.

Project Debater was a showcase of IBM’s ability to process very large data sets, including millions of news articles across dozens of subjects, and then turn snippets of arguments into full flowing prose – a challenging task for a computer.

[…]

Once an AI is capable of persuasive arguments, it can be applied as a tool to aid human decision-making.

“We believe there’s massive potential for good in artificial intelligence that can understand us humans,” said Arvind Krishna, director of IBM Research.

One example of this might be corporate boardroom decisions, where there are lots of conflicting points of view. The AI system could, without emotion, listen to the conversation, take all of the evidence and arguments into account and challenge the reasoning of humans where necessary.

“This can increase the level of evidence-based decision-making,” said Reed, adding that the same system could be used for intelligence analysis in counter-terrorism, for example identifying if a particular individual represents a threat.

In both cases, the machine wouldn’t make the decision but would contribute to the discussion and act as another voice at the table.

Source: Man 1, machine 1: landmark debate between AI and humans ends in draw | Technology | The Guardian

Essentially, Project Debater assigns a confidence score to every piece of information it understands. As in: how confident is the system that it actually understands the content of what’s being discussed? “If it’s confident that it got that point right, if it really believes it understands what that opponent was saying, it’s going to try to make a very strong argument against that point specifically,” Welser explains.

”If it’s less confident,” he says, “it’ll do it’s best to make an argument that’ll be convincing as an argument even if it doesn’t exactly answer that point. Which is exactly what a human does too, sometimes.”

So: the human says that government should have specific criteria surrounding basic human needs to justify subsidization. Project Debater responds that space is awesome and good for the economy. A human might choose that tactic as a sneaky way to avoid debating on the wrong terms. Project Debater had different motivations in its algorithms, but not that different.

The point of this experiment wasn’t to make me think that I couldn’t trust that a computer is arguing in good faith — though it very much did that. No, the point is that IBM showing off that it can train AI in new areas of research that could eventually be useful in real, practical contexts.

The first is parsing a lot of information in a decision-making context. The same technology that can read a corpus of data and come up with a bunch of pros and cons for a debate could be (and has been) used to decide whether or not a stock might be worth investing in. IBM’s system didn’t make the value judgement, but it did provide a bunch of information to the bank showing both sides of a debate about the stock.

As for the debating part, Welser says that it “helps us understand how language is used,” by teaching a system to work in a rhetorical context that’s more nuanced than the usual Hey Google give me this piece of information and turn off my lights. Perhaps it might someday help a lawyer structure their arguments, “not that Project Debater would make a very good lawyer,” he joked. Another IBM researcher suggested that this technology could help judge fake news.

How close is this to being something IBM turns into a product? “This is still a research level project,” Welser says, though “the technologies underneath it right now” are already beginning to be used in IBM projects.

https://www.theverge.com/2018/6/18/17477686/ibm-project-debater-ai

The system listened to four minutes of its human opponent’s opening remarks, then parsed that data and created an argument that highlighted and attempted to debunk information shared by the opposing side. That’s incredibly impressive because it has to understand not only the words but the context of those words. Parroting back Wikipedia entries is easy, taking data and creating a narrative that’s based not only on raw data but also takes into account what it’s just heard? That’s tough.

In a world where emotion and bias colors all our decisions, Project Debater could help companies and governments see through the noise of our life experiences and produce mostly impartial conclusions. Of course, the data set it pulls from is based on what humans have written and those will have their own biases and emotion.

While the goal is an unbiased machine, during the discourse Project Debate wasn’t completely sterile. Amid its rebuttal against debater Dan Zafrir, while they argued about telemedicine expansion, the system stated that Zafrir had not told the truth during his opening statement about the increase in the use of telemedicine. In other words, it called him a liar.

When asked about the statement, Slonim said that the system has a confidence threshold during rebuttals. If it’s feeling very confident it creates a more complex statement. If it’s feeling less confident, the statement is less impressive.

https://www.engadget.com/2018/06/18/ibm-s-project-debater-is-an-ai-thats-ready-to-argue/?guccounter=1

IBM site

https://www.research.ibm.com/artificial-intelligence/project-debater/

An artificially intelligent system has been demonstrated generating URLs for phishing websites that appear to evade detection by security tools.

Essentially, the software can come up with URLs for webpages that masquerade as legit login pages for real websites, when in actual fact, the webpages simply collect the entered username and passwords to later hijack accounts.

Blacklists and algorithms – intelligent or otherwise – can be used to automatically identify and block links to phishing pages. Humans should be able to spot that the web links are dodgy, but not everyone is so savvy.

Using the Phishtank database, a group of computer scientists from Cyxtera Technologies, a cybersecurity biz based in Florida, USA, have built <a target=”_blank” rel=”nofollow” href=”“>DeepPhish, which is machine-learning software that, allegedly, generates phishing URLs that beat these defense mechanisms.

[…]

The team inspected more than a million URLs on Phishtank to identify three different phishing miscreants who had generated webpages to steal people’s credentials. The team fed these web addresses into AI-based phishing detection algorithm to measure how effective the URLs were at bypassing the system.

The first scumbag of the trio used 1,007 attack URLs, and only 7 were effective at avoiding setting off alarms, across 106 domains, making it successful only 0.69 per cent of the time. The second one had 102 malicious web addresses, across 19 domains. Only five of them bypassed the threat detection algorithm and it was effective 4.91 per cent of the time.

Next, they fed this information into a Long-Short Term Memory network (LSTM) to learn the general structure and extract features from the malicious URLs – for example the second threat actor commonly used “tdcanadatrustindex.html” in its address.

All the text from effective URLs were taken to create sentences and encoded into a vector and fed into the LSTM, where it is trained to predict the next character given the previous one.

Over time it learns to generate a stream of text to simulate a list of pseudo URLs that are similar to the ones used as input. When DeepPhish was trained on data from the first threat actor, it also managed to create 1,007 URLs, and 210 of them were effective at evading detection, bumping up the score from 0.69 per cent to 20.90 per cent.

When it was following the structure from the second threat actor, it also produced 102 fake URLs and 37 of them were successful, increasing the likelihood of tricking the existent defense mechanism from 4.91 per cent to 36.28 per cent.

The effectiveness rate isn’t very high as a lot of what comes out the LSTM is effective gibberish, containing strings of forbidden characters.

“It is important to automate the process of retraining the AI phishing detection system by incorporating the new synthetic URLs that each threat actor may create,” the researchers warned. ®

Source: Here’s some phish-AI research: Machine-learning code crafts phishing URLs that dodge auto-detection • The Register

Following an open selection process, the Commission has appointed 52 experts to a new High-Level Expert Group on Artificial Intelligence, comprising representatives from academia, civil society, as well as industry.

The High-Level Expert Group on Artificial Intelligence (AI HLG) will have as a general objective to support the implementation of the European strategy on AI. This will include the elaboration of recommendations on future AI-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges.

Moreover, the AI HLG will serve as the steering group for the European AI Alliance’s work, interact with other initiatives, help stimulate a multi-stakeholder dialogue, gather participants’ views and reflect them in its analysis and reports.

In particular, the group will be tasked to:

1. Advise the Commission on next steps addressing AI-related mid to long-term challenges and opportunities through recommendations which will feed into the policy development process, the legislative evaluation process and the development of a next-generation digital strategy.
2. Propose to the Commission draft AI ethics guidelines, covering issues such as fairness, safety, transparency, the future of work, democracy and more broadly the impact on the application of the Charter of Fundamental Rights, including privacy and personal data protection, dignity, consumer protection and non-discrimination
3. Support the Commission on further engagement and outreach mechanisms to interact with a broader set of stakeholders in the context of the AI Alliance, share information and gather their input on the group’s and the Commission’s work.

Source: High-Level Group on Artificial Intelligence | Digital Single Market

Researchers from NVIDIA developed a deep learning-based system that can produce high-quality slow-motion videos from a 30-frame-per-second video, outperforming various state-of-the-art methods that aim to do the same. The researchers will present their work at the annual Computer Vision and Pattern Recognition (CVPR) conference in Salt Lake City, Utah this week. 

“There are many memorable moments in your life that you might want to record with a camera in slow-motion because they are hard to see clearly with your eyes: the first time a baby walks, a difficult skateboard trick, a dog catching a ball,” the researchers wrote in the research paper.  “While it is possible to take 240-frame-per-second videos with a cell phone, recording everything at high frame rates is impractical, as it requires large memories and is power-intensive for mobile devices,” the team explained.

With this new research, users can slow down their recordings after taking them.

Using NVIDIA Tesla V100 GPUs and cuDNN-accelerated PyTorch deep learning framework the team trained their system on over 11,000 videos of everyday and sports activities shot at 240 frames-per-second. Once trained, the convolutional neural network predicted the extra frames.

The team used a separate dataset to validate the accuracy of their system.

The result can make videos shot at a lower frame rate look more fluid and less blurry.

“Our method can generate multiple intermediate frames that are spatially and temporally coherent,” the researchers said. “Our multi-frame approach consistently outperforms state-of-the-art single frame methods.”

To help demonstrate the research, the team took a series of clips from The Slow Mo Guys, a popular slow-motion based science and technology entertainment YouTube series created by Gavin Free, starring himself and his friend Daniel Gruchy, and made their videos even slower.

The method can take everyday videos of life’s most precious moments and slow them down to look like your favorite cinematic slow-motion scenes, adding suspense, emphasis, and anticipation.

Source: Transforming Standard Video Into Slow Motion with AI – NVIDIA Developer News CenterNVIDIA Developer News Center