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On Monday, the UN Global Working Group (GWG) on Big Data published UN Handbook on Privacy-Preserving Computation Techniques. This book talks about the emerging privacy-preserving computation techniques and also outlines the key challenges in making these techniques more mainstream. https://twitter.com/UNBigData/status/1112739047066255360 Motivation behind writing this handbook In recent years, we have come across several data breaches. Companies collect users’ personal data without their consent to show them targeted content. The aggregated personal data can be misused to identify individuals and localize their whereabouts. Individuals can be singled out with the help of just a small set of attributes. This large collections of data are very often an easy target for cybercriminals. Previously, when cyber threats were not that advanced, people used to focus mostly on protecting the privacy of data at rest. This led to development of technologies like symmetric key encryption. Later, when sharing data on unprotected networks became common, technologies like Transport Layer Security (TLS) came into the picture. Today, when attackers are capable of penetrating servers worldwide, it is important to be aware of the technologies that help in ensuring data privacy during computation. This handbook focuses on technologies that protect the privacy of data during and after computation, which are called privacy-preserving computation techniques. Privacy Enhancing Technologies (PET) for statistics This book lists five Privacy Enhancing Technologies for statistics that will help reduce the risk of data leakage. I say “reduce” because there is, in fact, no known technique that can give a complete solution to the privacy question. #1 Secure multi-party computation Secure multi-party computation is also known as secure computation, multi-party computation (MPC), or privacy-preserving computation. A subfield of cryptography, this technology deals with scenarios where multiple parties are jointly working on a function. It aims to prevent any participant from learning anything about the input provided by other parties. MPC is based on secret sharing, in which data is divided into shares that are random themselves, but when combined it gives the original data. Each data input is shared into two or more shares and distributed among the parties involved. These when combined produce the correct output of the computed function. #2 Homomorphic encryption Homomorphic encryption is an encryption technique using which you can perform computations on encrypted data without the need for a decryption key. The advantage of this encryption scheme is that it enables computation on encrypted data without revealing the input data or result to the computing party. The result can only be decrypted by a specific party that has access to the secret key, typically it is the owner of the input data. #3 Differential Privacy (DP) DP is a statistical technique that makes it possible to collect and share aggregate information about users, while also ensuring that the privacy of individual users is maintained. This technique was designed to address the pitfalls that previous attempts to define privacy suffered, especially in the context of multiple releases and when adversaries have access to side knowledge. #4 Zero-knowledge proofs Zero-knowledge proofs involve two parties: prover and verifier. The prover has to prove statements to the verifier based on secret information known only to the prover. ZKP allows you to prove that you know a secret or secrets to the other party without actually revealing it. This is why this technology is called “zero knowledge”, as in, “zero” information about the secret is revealed. But, the verifier is convinced that the prover knows the secret in question. #5 Trusted Execution Environments (TEEs) This last technique on the list is different from the above four as it uses both hardware and software to protect data and code. It provides users secure computation capability by combining special-purpose hardware and software built to use those hardware features. In this technique, a process is run on a processor without its memory or execution state being exposed to any other process on the processor. This free 50-pager handbook is targeted towards statisticians and data scientists, data curators and architects, IT specialists, and security and information assurance specialists. So, go ahead and have a read: UN Handbook for Privacy-Preserving Techniques! Google employees filed petition to remove anti-trans, anti-LGBTQ and anti-immigrant Kay Coles James from the AI council Ahead of Indian elections, Facebook removes hundreds of assets spreading fake news and hate speech, but are they too late? Researchers successfully trick Tesla autopilot into driving into opposing traffic via “small stickers as interference patches on the ground”

The AI Now Institute, New York University, released its third annual report on the current state of AI, yesterday.  2018 AI Now Report focused on themes such as industry AI scandals, and rising inequality. It also assesses the gaps between AI ethics and meaningful accountability, as well as looks at the role of organizing and regulation in AI. Let’s have a look at key recommendations from the AI Now 2018 report. Key Takeaways Need for a sector-specific approach to AI governance and regulation This year’s report reflects on the need for stronger AI regulations by expanding the powers of sector-specific agencies (such as United States Federal Aviation Administration and the National Highway Traffic Safety Administration) to audit and monitor these technologies based on domains. Development of AI systems is rising and there aren’t adequate governance, oversight, or accountability regimes to make sure that these systems abide by the ethics of AI. The report states how general AI standards and certification models can’t meet the expertise requirements for different sectors such as health, education, welfare, etc, which is a key requirement for enhanced regulation. “We need a sector-specific approach that does not prioritize the technology but focuses on its application within a given domain”, reads the report. Need for tighter regulation of Facial recognition AI systems Concerns are growing over facial recognition technology as they’re causing privacy infringement, mass surveillance, racial discrimination, and other issues. As per the report, stringent regulation laws are needed that demands stronger oversight, public transparency, and clear limitations. Moreover, only providing public notice shouldn’t be the only criteria for companies to apply these technologies. There needs to be a “high threshold” for consent, keeping in mind the risks and dangers of mass surveillance technologies. The report highlights how “affect recognition”, a subclass of facial recognition that claims to be capable of detecting personality, inner feelings, mental health, etc, depending on images or video of faces, needs to get special attention, as it is unregulated. It states how these claims do not have sufficient evidence behind them and are being abused in unethical and irresponsible ways.“Linking affect recognition to hiring, access to insurance, education, and policing creates deeply concerning risks, at both an individual and societal level”, reads the report. It seems like progress is being made on this front, as it was just yesterday when Microsoft recommended that tech companies need to publish documents explaining the technology’s capabilities, limitations, and consequences in case their facial recognition systems get used in public. New approaches needed for governance in AI The report points out that internal governance structures at technology companies are not able to implement accountability effectively for AI systems. “Government regulation is an important component, but leading companies in the AI industry also need internal accountability structures that go beyond ethics guidelines”, reads the report.  This includes rank-and-file employee representation on the board of directors, external ethics advisory boards, along with independent monitoring and transparency efforts. Need to waive trade secrecy and other legal claims The report states that Vendors and developers creating AI and automated decision systems for use in government should agree to waive any trade secrecy or other legal claims that would restrict the public from full auditing and understanding of their software. As per the report, Corporate secrecy laws are a barrier as they make it hard to analyze bias, contest decisions, or remedy errors. Companies wanting to use these technologies in the public sector should demand the vendors to waive these claims before coming to an agreement. Companies should protect workers from raising ethical concerns It has become common for employees to organize and resist technology to promote accountability and ethical decision making. It is the responsibility of these tech companies to protect their workers’ ability to organize, whistleblow, and promote ethical choices regarding their projects. “This should include clear policies accommodating and protecting conscientious objectors, ensuring workers the right to know what they are working on, and the ability to abstain from such work without retaliation or retribution”, reads the report. Need for more in truth in advertising of AI products The report highlights that the hype around AI has led to a gap between marketing promises and actual product performance, causing risks to both individuals and commercial customers. As per the report, AI vendors should be held to high standards when it comes to them making promises, especially when there isn’t enough information on the consequences and the scientific evidence behind these promises. Need to address exclusion and discrimination within the workplace The report states that the Technology companies and the AI field focus on the “pipeline model,” that aims to train and hire more employees. However, it is important for tech companies to assess the deeper issues such as harassment on the basis of gender, race, etc, within workplaces. They should also examine the relationship between exclusionary cultures and the products they build, so to build tools that do not perpetuate bias and discrimination. Detailed account of the “full stack supply chain” As per the report, there is a need to better understand the parts of an AI system and the full supply chain on which it relies for better accountability. “This means it is important to account for the origins and use of training data, test data, models, the application program interfaces (APIs), and other components over a product lifecycle”, reads the paper. This process is called accounting for the ‘full stack supply chain’ of AI systems, which is necessary for a more responsible form of auditing. The full stack supply chain takes into consideration the true environmental and labor costs of AI systems. This includes energy use, labor use for content moderation and training data creation, and reliance on workers for maintenance of AI systems. More funding and support for litigation, and labor organizing on AI issues The report states that there is a need for increased support for legal redress and civic participation. This includes offering support to public advocates representing people who have been exempted from social services because of algorithmic decision making, civil society organizations and labor organizers who support the groups facing dangers of job loss and exploitation. Need for University AI programs to expand beyond computer science discipline The report states that there is a need for university programs and syllabus to expand its disciplinary orientation. This means the inclusion of social and humanistic disciplines within the universities AI programs. For AI efforts to truly make social impacts, it is necessary to train the faculty and students within the computer science departments, to research the social world. A lot of people have already started to implement this, for instance, Mitchell Baker, chairwoman, and co-founder of Mozilla talked about the need for the tech industry to expand beyond the technical skills by bringing in humanities. “Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations”, reads the paper. For more coverage, check out the official AI Now 2018 report. Unity introduces guiding Principles for ethical AI to promote responsible use of AI Teaching AI ethics – Trick or Treat? Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms

The Google Walkout for Real Change group tweeted out their protest against the news of ‘Google confirming that it paid $135 million as exit packages to the two top execs accused of sexual assault, on Twitter, earlier this week. The group castigated the ‘multi-million dollar payouts’ and asked people to use the hashtag #GooglePayoutsForAll to demonstrate different and better ways this obscenely large amount of ‘hush money’ could have been used. https://twitter.com/GoogleWalkout/status/1105556617662214145 The news of Google paying its senior execs, namely, Amit Singhal (former Senior VP of Google search) and Andy Rubin (creator of Android) high exit packages was first highlighted in a report by the New York Times, last October. As per the report, Google paid $90 million to Rubin and $15 million to Singhal. A lawsuit filed by James Martin, an Alphabet shareholder, on Monday this week, further confirmed this news. The lawsuit states that this decision taken by directors of Alphabet caused significant financial harm to the company apart from deteriorating its reputation, goodwill, and market capitalization. Meredith Whittaker, one of the early organizers of the Google Walkout in November last month tweeted, “$135 million could fix Flint's water crisis and still have $80 million left.” Vicki Tardif, another Googler summed up the sentiments in her tweet, “$135M is 1.35 times what Google.org  gave out in grants in 2016.” An ACLU researcher pointed out that $135M could have in addition to feeding the hungry, housing the homeless and pay off some student loans, It could also support local journalism killed by online ads. The public support to the call for protest using the hashtag #GooglePayoutsForAll has been awe-inspiring. Some shared their stories of injustice in cases of sexual assault, some condemned Google for its handling of sexual misconduct, while others put the amount of money Google wasted on these execs into a larger perspective. Better ways Google could have used $135 million it wasted on execs payouts, according to Twitter Invest in people to reduce structural inequities in the company $135M could have been paid to the actual victims who faced harassment and sexual assault. https://twitter.com/xzzzxxzx/status/1105681517584572416 Google could have used the money to fix the wage and level gap for women of color within the company. https://twitter.com/sparker2/status/1105511306465992705 $135 million could be used to adjust the 16% median pay gap of the 1240 women working in Google’s UK offices https://twitter.com/crschmidt/status/1105645484104998913 $135M could have been used by Google for TVC benefits. It could also be used to provide rigorous training to the Google employees on what impact misinformation within the company can have on women and other marginalized groups.   https://twitter.com/EricaAmerica/status/1105546835526107136 For $135M, Google could have paid the 114 creators featured in its annual "YouTube Rewind" who are otherwise unpaid for their time and participation. https://twitter.com/crschmidt/status/1105641872033230848 Improve communities by supporting social causes Google could have paid $135M to RAINN, a largest American nonprofit anti-sexual assault organization, covering its expenses for the next 18 years. https://twitter.com/GoogleWalkout/status/1105450565193121792 For funding 1800 school psychologists for 1 year in public schools https://twitter.com/markfickett/status/1105640930936324097 To build real, affordable housing solutions in collaboration with London Breed, SFGOV, and other Bay Area officials https://twitter.com/jillianpuente/status/1105922474930245636 $135M could provide insulin for nearly 10,000 people with Type 1 diabetes in the US https://twitter.com/GoogleWalkout/status/1105585078590210051 To pay for the first year for 1,000 people with stage IV breast cancer https://twitter.com/GoogleWalkout/status/1105845951938347008 Be a responsible corporate citizen To fund approximately 5300 low-cost electric vehicles for Google staff, and saving around 25300 metric tons of carbon dioxide from vehicle emissions per year. https://twitter.com/crschmidt/status/1105698893361233926 Providing free Google Fiber internet to 225,000 homes for a year https://twitter.com/markfickett/status/1105641215389773825 To give $5/hr raise to 12,980 service workers at Silicon Valley tech campuses https://twitter.com/LAuerhahn/status/1105487572069801985 $135M could have been used for the construction of affordable homes, protecting 1,100 low-income families in San Jose from coming rent hikes of Google’s planned mega-campus. https://twitter.com/JRBinSV/status/1105478979543154688 #GooglePayoutsForAll: Another initiative to promote awareness of structural inequities in tech   The core idea behind launching #GooglePayoutsForAll on Twitter by the Google walkout group was to promote awareness among people regarding the real issues within the company. It urged people to discuss how Google is failing at maintaining the ‘open culture’ that it promises to the outside world. It also highlights how mottos such as “Don’t be Evil” and “Do the right thing” that Google stood by only make for pretty wall decor and there’s still a long way to go to see those ideals in action. The group gained its name when more than 20,000 Google employees along with vendors, and contractors, temps, organized Google “walkout for real change” and walked out of their offices in November 2018. The walkout was a protest against the hushed and unfair handling of sexual misconduct within Google. Ever since then, Googlers have been consistently taking initiatives to bring more transparency, accountability, and fairness within the company. For instance, the team launched an industry-wide awareness campaign to fight against forced arbitration in January, where they shared information about arbitration on their Twitter and Instagram accounts throughout the day. The campaign was a success as Google finally ended its forced arbitration policy which goes into effect this month for all the employees (including contractors, temps, vendors) and for all kinds of discrimination. Also, House and Senate members in the US have proposed a bipartisan bill to prohibit companies from using forced arbitration clauses, last month.    Although many found the #GooglePayoutsForAll idea praiseworthy, some believe this initiative doesn’t put any real pressure on Google to bring about a real change within the company. https://twitter.com/Jeffanie16/status/1105541489722081290 https://twitter.com/Jeffanie16/status/1105546783063752709 https://twitter.com/Jeffanie16/status/1105547341862457344 Now, we don’t necessarily disagree with this opinion, however, the initiative can't be completely disregarded as it managed to make people who’d otherwise hesitate to open up talk extensively regarding the real issues within the company. As Liz Fong-Jones puts it, “Strikes and walkouts are more sustainable long-term than letting Google drive each organizer out one by one. But yes, people *are* taking action in addition to speaking up. And speaking up is a bold step in companies where workers haven't spoken up before”. The Google Walkout group have not yet announced what they intend to do next following this digital protest. However, the group has been organizing meetups such as the one earlier this month on March 6th where it invited the tech contract workers for discussion about building solidarity to make work better for everyone. We are only seeing the beginning of a powerful worker movement take shape in Silicon Valley. Recode Decode #GoogleWalkout interview shows why data and evidence don’t always lead to right decisions in even the world’s most data-driven company Liz Fong Jones, prominent ex-Googler shares her experience at Google and ‘grave concerns’ for the company Google’s pay equity analysis finds men, not women, are underpaid; critics call out design flaws in the analysis

Julian Assange, the Wikileaks founder, was arrested yesterday in London, in accordance with the U.S./UK Extradition Treaty. He was charged with assisting Chelsea Manning, a former intelligence analyst in the U.S. Army, to crack a password on a classified U.S. government computer. The indictment states that in March 2010, Assange assisted Manning by cracking password stored on U.S. Department of Defense computers connected to the Secret Internet Protocol Network (SIPRNet), a U.S. government network used for classified documents and communications. Being an intelligence analyst, Manning had access to certain computers and used these to download classified records to transmit to WikiLeaks. “Cracking the password would have allowed Manning to log on to the computers under a username that did not belong to her. Such a deceptive measure would have made it more difficult for investigators to determine the source of the illegal disclosures”, the indictment report states. “Manning confessed to leaking more than 725,000 classified documents to WikiLeaks following her deployment to Iraq in 2009—including battlefield reports and five Guantanamo Bay detainee profiles”, Gizmodo reports. In 2013, Manning was convicted of leaking the classified U.S. government documents to WikiLeaks. She was jailed in early March this year as a recalcitrant witness after she refused to answer the grand jury’s questions. According to court filings, after Manning’s arrest, she was held in solitary confinement in a Virginia jail for nearly a month. Following Assange’s arrest, a Swedish software developer and digital privacy activist, Ola Bini, who is allegedly close to Wikileaks founder Julian Assange has also been detained. “The official said they are looking into whether he was part of a possible effort by Assange and Wikileaks to blackmail Ecuador’s President, Lenin Moreno”, the Washington Post reports. Bini was detained at Quito’s airport as he was preparing to board a flight for Japan. Martin Fowler, a British software developer and renowned author and speaker, tweeted on Bini’s arrest. He said that Bini is a strong advocate and developer supporting privacy, and has not been able to speak to any lawyers. https://twitter.com/martinfowler/status/1116520916383621121 Following Assange’s arrest, Hillary Clinton, who was the nominee for the 2016 Presidential elections, said, “The bottom line is that he has to answer for what he has done”. “WikiLeaks’ publication of Democratic emails stolen by Russian intelligence officers during the 2016 election season hurt Clinton’s presidential campaign”, the Washington Post reports. Assange, who is an Australian citizen, was dragged out of Ecuador’s embassy in London after his seven-year asylum was revoked. He was granted Asylum by former Ecuadorian President Rafael Correa in 2012 for publishing sensitive information about U.S. national security interests. Australian PM, Scott Morrison told Australian Broadcasting Corp. the charge is a “matter for the United States” and has nothing to do with Australia. He was granted asylum just after “he was released on bail while facing extradition to Sweden on sexual assault allegations. The accusations have since been dropped but he was still wanted for jumping bail”, the Washington Post states. A Swedish woman alleged that she was raped by Julian Assange during a visit to Stockholm in 2010. Post Assange’s arrest on Thursday, Elisabeth Massi Fritz, the lawyer for the unnamed woman, said in a text message sent to The Associated Press that “we are going to do everything” to have the Swedish case reopened “so Assange can be extradited to Sweden and prosecuted for rape.” She further added, “no rape victim should have to wait nine years to see justice be served.” “In 2017, Sweden’s top prosecutor dropped a long-running inquiry into a rape claim against Assange, saying there was no way to have Assange detained or charged within a foreseeable future because of his protected status inside the embassy”, the Washington Post reports. In a tweet, Wikileaks posted a photo of Assange with the words: “This man is a son, a father, a brother. He has won dozens of journalism awards. He’s been nominated for the Nobel Peace Prize every year since 2010. Powerful actors, including CIA, are engaged in a sophisticated effort to dehumanize, delegitimize and imprison him. #ProtectJulian.” https://twitter.com/wikileaks/status/1116283186860953600 Duncan Ross, a data philanthropist, tweeted, “Random thoughts on Assange: 1) journalists don’t have to be nice people but 2) being a journalist (if he is) doesn’t put you above the law.” https://twitter.com/duncan3ross/status/1116610139023237121 Edward Snowden, a former security contractor who leaked classified information about U.S. surveillance programs, says the arrest of WikiLeaks founder Julian Assange is a blow to media freedom. “Assange’s critics may cheer, but this is a dark moment for press freedom”, he tweets. According to the Washington Post, in an interview with The Associated Press, Rafael Correa, Ecuador’s former president, was harshly critical of his successor’s decision to expel the Wikileaks founder from Ecuador’s embassy in London. He said that “although Julian Assange denounced war crimes, he’s only the person supplying the information.” Correa said “It’s the New York Times, the Guardian and El Pais publishing it. Why aren’t those journalists and media owners thrown in jail?” Yanis Varoufakis, Economics professor and former Greek finance minister, tweeted, “It was never about Sweden, Putin, Trump or Hillary. Assange was persecuted for exposing war crimes. Will those duped so far now stand with us in opposing his disappearance after a fake trial where his lawyers will not even now the charges?” https://twitter.com/yanisvaroufakis/status/1116308671645061120 The Democracy in Europe Movement 2025 (@DiEM_25) tweeted that Assange’s arrest is “a chilling demonstration of the current disregard for human rights and freedom of speech by establishment powers and the rising far-right.” The movement has also put a petition against Assange’s extradition. https://twitter.com/DiEM_25/status/1116379013461815296 Google employees filed petition to remove anti-trans, anti-LGBTQ and anti-immigrant Kay Coles James from the AI council A security researcher reveals his discovery on 800+ Million leaked Emails available online Leaked memo reveals that Facebook has threatened to pull investment projects from Canada and Europe if their data demands are not met

The ongoing 31st annual Conference on Neural Information Processing Systems (NIPS 2017) in Long Beach, California is scheduled from December 4-9, 2017. The 6-day conference is hosting a number of invited talks, demonstrations, tutorials, and paper presentations pertaining to the latest in machine learning, deep learning and AI research. This year the conference has grown larger than life with a record-high 3,240 papers, 678 selected ones, and a completely sold-out event. Top tech members from Google, Microsoft, IBM, DeepMind, Facebook, Amazon, are among other prominent players who enthusiastically participated this year. Here is a quick roundup of some of the top research papers till date. Generative Adversarial Networks Generative Adversarial Networks are a hot topic of research at the ongoing NIPS conference. GANs cast out an easy way to train the DL algorithms by slashing out the amount of data required in training with unlabelled data. Here are a few research papers on GANs. Regularization can stabilize training of GANs Microsoft researchers have proposed a new regularization approach to yield a stable GAN training procedure at low computational costs. Their new model overcomes the fundamental limitation of GANs occurring due to a dimensional mismatch between the model distribution and the true distribution. This results in their density ratio and the associated f-divergence to be undefined. Their paper “Stabilizing Training of Generative Adversarial Networks through Regularization” turns GAN models into reliable building blocks for deep learning. They have also used this for several datasets including image generation tasks. AdaGAN: Boosting GAN Performance Training GANs can at times be a hard task. They can also suffer from the problem of missing modes where the model is not able to produce examples in certain regions of the space. Google researchers have developed an iterative procedure called AdaGAN in their paper “AdaGAN: Boosting Generative Models”, an approach inspired by boosting algorithms, where many potentially weak individual predictors are greedily aggregated to form a strong composite predictor. It adds a new component into a mixture model at every step by running a GAN algorithm on a re-weighted sample. The paper also addresses the problem of missing modes. Houdini: Generating Adversarial Examples The generation of adversarial examples is considered as a critical milestone for evaluating and upgrading the robustness of learning in machines. Also, current methods are confined to classification tasks and are unable to alter the performance measure of the problem at hand. In order to tackle such an issue, Facebook researchers have come up with a research paper titled “Houdini: Fooling Deep Structured Prediction Models”, a novel and a flexible approach for generating adversarial examples distinctly tailormade for the final performance measure of the task taken into account (combinational and non-decomposable tasks). Stochastic hard-attention for Memory Addressing in GANs DeepMind researchers showcased a new method which uses stochastic hard-attention to retrieve memory content in generative models. Their paper titled “Variational memory addressing in generative models” was presented at the 2nd day of the conference and is an advancement over the popular differentiable soft-attention mechanism. Their new technique allows developers to apply variational inference to memory addressing. This leads to more precise memory lookups using target information, especially in models with large memory buffers and with many confounding entries in the memory. Image and Video Processing A lot of hype was also around developing sophisticated models and techniques for image and video processing. Here is a quick glance at the top presentations. Fader Networks: Image manipulation through disentanglement Facebook researchers have introduced Fader Networks, in their paper titled “Fader Networks: Manipulating Images by Sliding Attributes”. These fader networks use an encoder-decoder architecture to reconstruct images by disentangling their salient information and the values of particular attributes directly in a latent space. Disentanglement helps in manipulating these attributes to generate variations of pictures of faces while preserving their naturalness. This innovative approach results in much simpler training schemes and scales for manipulating multiple attributes jointly. Visual interaction networks for Video simulation Another paper titled “Visual interaction networks: Learning a physics simulator from video Tuesday” proposes a new neural-network model to learn physical objects without prior knowledge. Deepmind’s Visual Interaction Network is used for video analysis and is able to infer the states of multiple physical objects from just a few frames of video. It then uses these to predict object positions many steps into the future. It can also deduce the locations of invisible objects. Transfer, Reinforcement, and Continual Learning A lot of research is going on in the field of Transfer, Reinforcement, and Continual learning to make stable and powerful deep learning models. Here are a few research papers presented in this domain. Two new techniques for Transfer Learning Currently, a large set of input/output (I/O) examples are required for learning any underlying input-output mapping. By leveraging information based on the related tasks, the researchers at Microsoft have addressed the problem of data and computation efficiency of program induction. Their paper “Neural Program Meta-Induction” uses two approaches for cross-task knowledge transfer. First is Portfolio adaption, where a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. The second one is Meta program induction, a k-shot learning approach which makes a model generalize itself to new tasks without requiring any additional training. Hybrid Reward Architecture to solve the problem of generalization in Reinforcement Learning A new paper from Microsoft “Hybrid Reward Architecture for Reinforcement Learning” highlights a new method to address the generalization problem faced by a typical deep RL method. Hybrid Reward Architecture (HRA) takes a decomposed reward function as the input and learns a separate value function for each component reward function. This is especially useful in domains where the optimal value function cannot easily be reduced to a low-dimensional representation. In the new approach, the overall value function is much smoother and can be easier approximated by a low-dimensional representation, enabling more effective learning. Gradient Episodic Memory to counter catastrophic forgetting in continual learning models Continual learning is all about improving the ability of models to solve sequential tasks without forgetting previously acquired knowledge. In the paper “Gradient Episodic Memory for Continual Learning”, Facebook researchers have proposed a set of metrics to evaluate models over a continuous series of data. These metrics characterize models by their test accuracy and the ability to transfer knowledge across tasks. They have also proposed a model for continual learning, called Gradient Episodic Memory (GEM) that reduces the problem of catastrophic forgetting. They have also experimented with variants of the MNIST and CIFAR-100 datasets to demonstrate the performance of GEM when compared to other methods. In our next post, we will cover a selection of papers presented so far at NIPS 2017 in the areas of Predictive Modelling, Machine Translation, and more. For live content coverage, you can visit NIPS’ Facebook page.

Last week, the Facebook AI research team published a progress report on dialogue research that is fundamentally building more engageable and personalized AI systems. According to the team, “Dialogue research is a crucial component of building the next generation of intelligent agents. While there’s been progress with chatbots in single-domain dialogue, agents today are far from capable of carrying an open-domain conversation across a multitude of topics. Agents that can chat with humans in the way that people talk to each other will be easier and more enjoyable to use in our day-to-day lives — going beyond simple tasks like playing a song or booking an appointment.” In their blog post, they have described new open source data sets, algorithms, and models that improve five common weaknesses of open-domain chatbots today. The weaknesses identified are maintaining consistency, specificity, empathy, knowledgeability, and multimodal understanding. Let us look at each one in detail: Dataset called Dialogue NLI introduced for maintaining consistency Inconsistencies are a common issue for chatbots partly because most models lack explicit long-term memory and semantic understanding. Facebook team in collaboration with their colleagues at NYU, developed a new way of framing consistency of dialogue agents as natural language inference (NLI) and created a new NLI data set called Dialogue NLI, used to improve and evaluate the consistency of dialogue models. The team showcased an example in the Dialogue NLI model, where in they considered two utterances in a dialogue as the premise and hypothesis, respectively. Each pair was labeled to indicate whether the premise entails, contradicts, or is neutral with respect to the hypothesis. Training an NLI model on this data set and using it to rerank the model’s responses to entail previous dialogues — or maintain consistency with them — improved the overall consistency of the dialogue agent. Across these tests they say they saw 3x lesser contradictions in the sentences. Several conversational attributes were studied to balance specificity As per the team, generative dialogue models frequently default to generic, safe responses, like “I don’t know” to some query which needs specific responses. Hence, the Facebook team in collaboration with Stanford’s AI researcher Abigail See, studied how to fix this by controlling several conversational attributes, like the level of specificity. In one experiment, they conditioned a bot on character information and asked “What do you do for a living?” A typical chatbot responds with the generic statement “I’m a construction worker.” With control methods, the chatbots proposed more specific and engaging responses, like “I build antique homes and refurbish houses." In addition to specificity, the team mentioned, “that balancing question-asking and answering and controlling how repetitive our models are make significant differences. The better the overall conversation flow, the more engaging and personable the chatbots and dialogue agents of the future will be.” Chatbot’s ability to display empathy while responding was measured The team worked with researchers from the University of Washington to introduce the first benchmark task of human-written empathetic dialogues centered on specific emotional labels to measure a chatbot’s ability to display empathy. In addition to improving on automatic metrics, the team showed that using this data for both fine-tuning and as retrieval candidates leads to responses that are evaluated by humans as more empathetic, with an average improvement of 0.95 points (on a 1-to-5 scale) across three different retrieval and generative models. The next challenge for the team is that empathy-focused models should perform well in complex dialogue situations, where agents may require balancing empathy with staying on topic or providing information. Wikipedia dataset used to make dialogue models more knowledgeable The research team has improved dialogue models’ capability of demonstrating knowledge by collecting a data set with conversations from Wikipedia, and creating new model architectures that retrieve knowledge, read it, and condition responses on it. This generative model has yielded the most pronounced improvement and it is rated by humans as 26% more engaging than their knowledgeless counterparts. To engage with images, personality based captions were used To engage with humans, agents should not only comprehend dialogue but also understand images. In this research, the team focused on image captioning that is engaging for humans by incorporating personality. They collected a data set of human comments grounded in images, and trained models capable of discussing images with given personalities, which makes the system interesting for humans to talk to. 64% humans preferred these personality-based captions over traditional captions. To build strong models, the team considered both retrieval and generative variants, and leveraged modules from both the vision and language domains. They defined a powerful retrieval architecture, named TransResNet, that works by projecting the image, personality, and caption in the same space using image, personality, and text encoders. The team showed that their system was able to produce captions that are close to matching human performance in terms of engagement and relevance. And annotators preferred their retrieval model’s captions over captions written by people 49.5% of the time. Apart from this, Facebook team has released a new data collection and model evaluation tool, a Messenger-based Chatbot game called Beat the Bot, that allows people to interact directly with bots and other humans in real time, creating rich examples to help train models. To conclude, the Facebook AI team mentions, “Our research has shown that it is possible to train models to improve on some of the most common weaknesses of chatbots today. Over time, we’ll work toward bringing these subtasks together into one unified intelligent agent by narrowing and eventually closing the gap with human performance. In the future, intelligent chatbots will be capable of open-domain dialogue in a way that’s personable, consistent, empathetic, and engaging.” On Hacker News, this research has gained positive and negative reviews. Some of them discuss that if AI will converse like humans, it will do a lot of bad. While other users say that this is an impressive improvement in the field of conversational AI. A user comment reads, “I gotta say, when AI is able to converse like humans, a lot of bad stuff will happen. People are so used to the other conversation partner having self-interest, empathy, being reasonable. When enough bots all have a “swarm” program to move conversations in a particular direction, they will overwhelm any public conversation. Moreover, in individual conversations, you won’t be able to trust anything anyone says or negotiates. Just like playing chess or poker online now. And with deepfakes, you won’t be able to trust audio or video either. The ultimate shock will come when software can render deepfakes in realtime to carry on a conversation, as your friend but not. As a politician who “said crazy stuff” but really didn’t, but it’s in the realm of believability. I would give it about 20 years until it all goes to shit. If you thought fake news was bad, realtime deepfakes and AI conversations with “friends” will be worse.  Scroll Snapping and other cool CSS features come to Firefox 68 Google Chrome to simplify URLs by hiding special-case subdomains Lyft releases an autonomous driving dataset “Level 5” and sponsors research competition

At day one of the ongoing Amazon re:Invent 2019, there was a flurry of announcements made for AWS. Most importantly, AWS announced the preview launch of Braket, its own quantum computing service following the likes of IBM, Microsoft, and Google. Amazon also released Amazon SageMaker Operators for Kubernetes to help data scientists using Kubernetes to train, tune, and deploy machine learning models in Amazon SageMaker. re:Invent is Amazon’s flagship conference hosted by Amazon Web Services for the global cloud computing community. This year re: Invent is taking place in Las Vegas, December 2-6, 2019. re:Invent 2019 Day One announcements Braket: AWS’ new quantum service in preview now Amazon Braket (named after the common notation for quantum states) is a fully managed service that helps you get started with quantum computing. Braket consists of a full development environment that helps data scientists to: design quantum algorithms from scratch or choose from a set of pre-built algorithms, test these algorithms on simulated quantum computers (including gate based and quantum annealing superconductors, and ion trap hardware) run them on your choice of different quantum hardware technologies ( including D-Wave, IonQ, and Rigetti) Once your tests are complete, you will be automatically notified and your results will be stored in Amazon S3. Amazon Braket publishes event logs and performance metrics such as completion status and execution time to Amazon CloudWatch. To make it easier to develop hybrid algorithms that combine classical and quantum tasks, Amazon Braket helps manage classical compute resources and establish low-latency connections to the quantum hardware. At re:Invent 2019, AWS also launched the Amazon Quantum Solutions Lab, a collaborative research program that connects you with quantum computing experts from Amazon and its technology and consulting partners. They can help you identify potential uses of quantum computing, build internal expertise, and collaborate on programs to design and test quantum algorithms. Braket is available in preview now. Amazon SageMaker Operators for Kubernetes Now developers and data scientists can use Kubernetes to train, tune, and deploy machine learning models in Amazon SageMaker, with the new Amazon SageMaker Operators for Kubernetes. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create Amazon SageMaker jobs natively using the Kubernetes API and command-line Kubernetes tools such as ‘kubectl’. Operators can be used to train machine learning models, optimize hyperparameters for a given model, run batch transform jobs over existing models, and set up inference endpoints. With these operators, users can manage their jobs in Amazon SageMaker from their Kubernetes cluster in Amazon Elastic Kubernetes Service EKS. Amazon SageMaker Operators for Kubernetes are available in select AWS regions. AWS DeepComposer, a creative way to learn Machine Learning Amazon has launched AWS DeepComposer, the world’s first machine learning-enabled musical keyboard at re:Invent 2019. AWS DeepComposer is an educational tool to teach people Machine Learning. AWS DeepComposer gives developers of all skill levels a creative way to experience machine learning – music. https://youtu.be/XH2EbK9dQlg You can input a melody by connecting the AWS DeepComposer keyboard to your computer, or play the virtual keyboard in the AWS DeepComposer console. You can generate an original music composition using the pre-trained genre models in the console. You can then publish your tracks to SoundCloud. It is designed specifically to educate developers by means of tutorials, sample code, and training data. These can be used to get started with building generative AI models, all without having to write a single line of code. With AWS DeepComposer, you can train and optimize GAN models to create original music. GAN models pit two different neural networks against each other to produce new and original digital works based on sample inputs. AWS DeepComposer is available in preview now. Amazon Transcribe now extended to healthcare patients Amazon’s automatic speech recognition service Amazon Transcribe is now available for medical speech as announced in re:Invent 2019. Amazon Transcribe Medical allows physicians to easily and quickly dictate their clinical notes and see their speech converted to accurate text in real-time, without any human intervention. Clinicians can use natural speech and do not have to explicitly call out punctuation like “comma” or “full stop”. This text can then be automatically fed to downstream applications such as EHR systems, or to AWS language services such as Amazon Comprehend Medical for entity extraction. To make it work, you need to capture audio using your device’s microphone and send PCM (Pulse-code modulation) audio to a streaming API based on the popular Websocket protocol. This API will respond with a series of JSON blobs with the transcribed text, as well as word-level time stamps, punctuation, etc. Optionally, you can save this data to an Amazon Simple Storage Service (S3) bucket. Amazon Transcribe Medical is available in US East (N. Virginia) and US West (Oregon) regions. Updates to Microsoft Windows Server AWS has released a bring-your-own-license (BYOL) experience for customers as an easier way to bring, and manage, their existing licenses for Microsoft Windows Server and SQL Server to AWS. The new BYOL experience enables customers who want to use their existing Windows Server or SQL Server licenses to seamlessly create virtual machines in EC2, while AWS takes care of managing their licenses to help ensure compliance to licensing rules specified by the customer. Amazon is also providing End-of-Support Migration Program (EMP) for Windows Server. On January 14, 2020, support for Windows Server 2008 and 2008 R2 will end. Having an application that can run only on an unsupported version of Windows Server is problematic as you will no longer get free security patch updates, leaving you vulnerable to security and compliance risks. This new program combines technology with expert guidance, to migrate your legacy applications running on outdated versions of Windows Server to newer, supported versions on AWS. Other updates announced at Amazon re:Invent 2019 Amazon EventBridge Schema Registry is now in preview.  The schema registry stores the structure (schema) of Amazon EventBridge events and maps them to Java, Python, and Typescript bindings so that you can use the events as typed objects. The existing AWS IoT SiteWise preview adds new features such as creating a virtual representation of your facility, monitor production performance metrics and use AWS IoT SiteWise Monitor to visualize the data in real-time. AWS IoT SiteWise Monitor is a new SaaS application that lets you monitor and interact with the data collected and organized by AWS IoT SiteWise. The upcoming AWS DeepRacer Evo car will include a stereo camera and a Light Detection and Ranging (LIDAR) sensor.  The DeepRacer League in 2020 will have 8 additional races in 5 countries. The preview of EC2 Image Builder, a service that makes it easier and faster to build and maintain secure OS images for Windows Server and Amazon Linux 2, using automated build pipelines. Amazon re:Invent will continue throughout this week (the last day is the 6th of December). You can access the Livestream here. Keep checking this space for news on other updates and launches. Amazon EKS Windows Container Support is now generally available Amazon’s hardware event 2019 highlights: a high-end Echo Studio, the new Echo Show 8, and more 10 key announcements from Microsoft Ignite 2019 you should know about

The recent horrifying terrorist attack in New Zealand has cast new blame on how technology platforms police content. There are now questions about whether global internet services are designed to work this way? And if online viral hate is uncontainable? Fifty one people so far have been reported to be dead and 50 more injured after the terrorist attacks on two New Zealand mosques on Friday. The victims included children as young as 3 and 4 years old, and elderly men and women. The alleged shooter is identified as a 28 year old Australian man named Brenton Tarrant. Brenton announced the attack on the anonymous-troll message board 8chan. There, he posted images of the weapons days before the attack, and made an announcement an hour before the shooting. On 8chan, Facebook and Twitter, he also posted links to a 74-page manifesto, titled “The Great Replacement,” blaming immigration for the displacement of whites in Oceania and elsewhere. The manifesto cites “white genocide” as a motive for the attack, and calls for “a future for white children” as its goal. Further he live-streamed the attacks on Facebook, YouTube; and posted a link to the stream on 8chan. It’s terrifying and disgusting, especially when 8chan is one of the sites where disaffected internet misfits create memes and other messages to provoke dismay and sow chaos among people. “8chan became the new digital home for some of the most offensive people on the internet, people who really believe in white supremacy and the inferiority of women,” Ethan Chiel wrote. “It’s time to stop shitposting,” the alleged shooter’s 8chan post reads, “and time to make a real-life effort post.” Many of the responses, anonymous by 8chan’s nature, celebrate the attack, with some posting congratulatory Nazi memes. A few seem to decry it, just for logistical quibbles. And others lament that the whole affair might destroy the site, a concern that betrays its users’ priorities. Social media encourages performance crime The use of social media technology and livestreaming marks the attack as different from many other terrorist incidents. It is a form of violent “performance crime”. That is, the video streaming is a central component of the violence itself, it’s not somehow incidental to the crime, or a trophy for the perpetrator to re-watch later. In the past, terrorism functioned according to what has been called the “theatre of terror”, which required the media to report on the spectacle of violence created by the group. Nowadays with social media in our hands it's much easier for someone to both create the spectacle of horrific violence and distribute it widely by themselves. There is a tragic and recent history of performance crime videos that use live streaming and social media video services as part of their tactics. In 2017, for example, the sickening murder video of an elderly man in Ohio was uploaded to Facebook, and the torture of a man with disabilities in Chicago was live streamed. In 2015, the murder of two journalists was simultaneously broadcast on-air, and live streamed. Tech companies on the radar Social-media companies scrambled to take action as the news—and the video—of the attack spread. Facebook finally managed to pull down Tarrant’s profiles and the video, but only after New Zealand police brought the live-stream to the company’s attention. It has been working "around the clock" to remove videos of the incident shared on its platform. In a statement posted to Twitter on Sunday, the tech company said that within 24 hours of Friday’s shooting it had removed 1.5 million videos of the attack from its platform globally. YouTube said it had also removed an “unprecedented volume” of videos of the shooting. Twitter also suspended Tarrant’s account, where he had posted links to the manifesto from several file-sharing sites. The chaotic aftermath mostly took place while many North Americans slept unaware, waking up to the news and its associated confusion. By morning on the East Coast, news outlets had already weighed in on whether technology companies might be partly to blame for catastrophes such as the New Zealand massacre because they have failed to catch offensive content before it spreads. One of the tweets say Google, Twitter and Facebook made a choice to not use tools available to them to stop white supremacist terrorism. https://twitter.com/samswey/status/1107055372949286912 Countries like Germany and France already have a law in place that demands social media sites move quickly to remove hate speech, fake news and illegal material. Sites that do not remove "obviously illegal" posts could face fines of up to 50m euro (£44.3m). In the wake of the attack, a consortium of New Zealand’s major companies has pledged to pull their advertising from Facebook. In a joint statement, the Association of New Zealand Advertisers (ANZA) and the Commercial Communications Council asked domestic companies to think about where “their advertising dollars are spent, and carefully consider, with their agency partners, where their ads appear.” They added, “We challenge Facebook and other platform owners to immediately take steps to effectively moderate hate content before another tragedy can be streamed online.” Additionally internet service providers like Vodafone, Spark and Vocus in New Zealand are blocking access to websites that do not respond or refuse to comply to requests to remove reuploads of the shooter’s original live stream. The free speech vs safety debate puts social media platforms in the crosshairs Tech Companies are facing new questions on content moderation following the New Zealand attack. The shooter posted a link to the live stream, and soon after he was apprehended, reuploads were found on other platforms like YouTube and Twitter. “Tech companies basically don’t see this as a priority,” the counter-extremism policy adviser Lucinda Creighton commented. “They say this is terrible, but what they’re not doing is preventing this from reappearing.” Others affirmed the importance of quelling the spread of the manifesto, video, and related materials, for fear of producing copycats, or of at least furthering radicalization among those who would be receptive to the message. The circulation of ideas might have motivated the shooter as much as, or even more than, ethnic violence. As Charlie Warzel wrote at The New York Times, the New Zealand massacre seems to have been made to go viral. Tarrant teased his intentions and preparations on 8chan. When the time came to carry out the act, he provided a trove of resources for his anonymous members, scattered to the winds of mirror sites and repositories. Once the live-stream started, one of the 8chan user posted “capped for posterity” on Tarrant’s thread, meaning that he had downloaded the stream’s video for archival and, presumably, future upload to other services, such as Reddit or 4chan, where other like-minded trolls or radicals would ensure the images spread even further. As Warzel put it, “Platforms like Facebook, Twitter, and YouTube … were no match for the speed of their users.” The internet is a Pandora’s box that never had a lid. Camouflaging stories is easy but companies trying hard in building AI to catch it Last year, Mark Zuckerberg defended himself and Facebook before Congress against myriad failures, which included Russian operatives disrupting American elections and permitting illegal housing ads that discriminate by race. Mark Zuckerberg repeatedly invoked artificial intelligence as a solution for the problems his and other global internet companies have created. There’s just too much content for human moderators to process, even when pressed hard to do so under poor working conditions. The answer, Zuckerberg has argued, is to train AI to do the work for them. But that technique has proved insufficient. That’s because detecting and scrubbing undesirable content automatically is extremely difficult. False positives enrage earnest users or foment conspiracy theories among paranoid ones, thanks to the black-box nature of computer systems. Worse, given a pool of billions of users, the clever ones will always find ways to trick any computer system, for example, by slightly modifying images or videos in order to make them appear different to the computer but identical to human eyes. 8chan, as it happens, is largely populated by computer-savvy people who have self-organized to perpetrate exactly those kinds of tricks. The primary sources of content are only part of the problem. Long after the deed, YouTube users have bolstered conspiracy theories about murders, successfully replacing truth with lies among broad populations of users who might not even know they are being deceived. Even stock-photo providers are licensing stills from the New Zealand shooter’s video; a Reuters image that shows the perpetrator wielding his rifle as he enters the mosque is simply credited, “Social media.” Interpreting real motives is difficult on social The video is just the tip of the iceberg. Many smaller and less obviously inflamed messages have no hope of being found, isolated, and removed by technology services. The shooter praised Donald Trump as a “symbol of renewed white identity” and incited the conservative commentator Candace Owens, who took the bait on Twitter in a post that got retweeted thousands of times by the morning after the attack. The shooter’s forum posts and video are littered with memes and inside references that bear special meaning within certain communities on 8chan, 4chan, Reddit, and other corners of the internet, offering tempting receptors for consumption and further spread. Perhaps worst of all, the forum posts, the manifesto, and even the shooting itself might not have been carried out with the purpose that a literal read of their contents suggests. At the first glance, it seems impossible to deny that this terrorist act was motivated by white-extremist hatred, an animosity that authorities like the FBI expert and the Facebook officials would want to snuff out before it spreads. But 8chan is notorious for users with an ironic and rude behaviour under the shades of anonymity.They use humor, memes and urban slang to promote chaos and divisive rhetoric. As the internet separates images from context and action from intention, and then spreads those messages quickly among billions of people scattered all around the globe. That structure makes it impossible to even know what individuals like Tarrant “really mean” by their words and actions. As it spreads, social-media content neuters earnest purpose entirely, putting it on the same level as anarchic randomness. What a message means collapses into how it gets used and interpreted. For 8chan trolls, any ideology might be as good as any other, so long as it produces chaos. We all have a role to play It’s easy to say that technology companies can do better. They can, and they should. But ultimately, content moderation is not the solution by itself. The problem is the media ecosystem they have created. The only surprise is that anyone would still be surprised that social media produce this tragic abyss, for this is what social media are supposed to do, what they were designed to do: spread the images and messages that accelerate interest and invoke raw emotions, without check, and absent concern for their consequences. We hope that social media companies get better at filtering out violent content and explore alternative business models, and governments think critically about cyber laws that protect both people and speech. But until they do we should reflect on our own behavior too. As news outlets, we shape the narrative through our informed perspectives which makes it imperative to publish legitimate & authentic content. Let’s as users too make a choice of liking and sharing content on social platforms. Let’s consider how our activities could contribute to an overall spectacle society that might inspire future perpetrator-produced videos of such gruesome crime – and act accordingly. In this era of social spectacle, we all have a role to play in ensuring that terrorists aren’t rewarded for their crimes with our clicks and shares. The Indian government proposes to censor social media content and monitor WhatsApp messages Virality of fake news on social media: Are weaponized AI bots to blame, questions Destin Sandlin Mastodon 2.7, a decentralized alternative to social media silos, is now out!

Facebook is constantly under the spotlight these days when it comes to controversies regarding user’s data and privacy. A new research paper published by the Princeton University researchers states that Facebook shares the contact information you handed over for security purposes, with their advertisers. This study was first brought to light by a Gizmodo writer, Kashmir Hill. “Facebook is not content to use the contact information you willingly put into your Facebook profile for advertising. It is also using contact information you handed over for security purposes and contact information you didn’t hand over at all, but that was collected from other people’s contact books, a hidden layer of details Facebook has about you that I’ve come to call “shadow contact information”, writes Hill. Recently, Facebook introduced a new feature called custom audiences. Unlike traditional audiences, the advertiser is allowed to target specific users. To do so, the advertiser uploads user’s PII (personally identifiable information) to Facebook. After the uploading is done, Facebook then matches the given PII against platform users. Facebook then develops an audience that comprises the matched users and allows the advertiser to further track the specific audience. Essentially with Facebook, the holy grail of marketing, which is targeting an audience of one, is practically possible; nevermind whether that audience wanted it or not. In today’s world, different social media platforms frequently collect various kinds of personally identifying information (PII), including phone numbers, email addresses, names and dates of birth. Majority of this PII often represent extremely accurate, unique, and verified user data. Because of this, these services have the incentive to exploit and use this personal information for other purposes. One such scenario includes providing advertisers with more accurate audience targeting. The paper titled ‘Investigating sources of PII used in Facebook’s targeted advertising’ is written by Giridhari Venkatadri, Elena Lucherini, Piotr Sapiezynski, and Alan Mislove. “In this paper, we focus on Facebook and investigate the sources of PII used for its PII-based targeted advertising feature. We develop a novel technique that uses Facebook’s advertiser interface to check whether a given piece of PII can be used to target some Facebook user and use this technique to study how Facebook’s advertising service obtains users’ PII,” reads the paper. The researchers developed a novel methodology, which involved studying how Facebook obtains the PII to provide custom audiences to advertisers. “We test whether PII that Facebook obtains through a variety of methods (e.g., directly from the user, from two-factor authentication services, etc.) is used for targeted advertising, whether any such use is clearly disclosed to users, and whether controls are provided to users to help them limit such use,” reads the paper. The paper uses size estimates to study what sources of PII are used for PII-based targeted advertising. Researchers used this methodology to investigate which range of sources of PII was actually used by Facebook for its PII-based targeted advertising platform. They also examined what information gets disclosed to users and what control users have over PII. What sources of PII are actually being used by Facebook? Researchers found out that Facebook allows its users to add contact information (email addresses and phone numbers) on their profiles. While any arbitrary email address or phone number can be added, it is not displayed to other users unless verified (through a confirmation email or confirmation SMS message, respectively). This is the most direct and explicit way of providing PII to advertisers. Researchers then further moved on to examine whether PII provided by users for security purposes such as two-factor authentication (2FA) or login alerts are being used for targeted advertising. They added and verified a phone number for 2FA to one of the authors’ accounts. The added phone number became targetable after 22 days. This proved that a phone number provided for 2FA was indeed used for PII-based advertising, despite having set the privacy controls to the choice. What control do users have over PII? Facebook allows users the liberty of choosing who can see each PII listed on their profiles, the current list of possible general settings being: Public, Friends, Only Me.   Users can also restrict the set of users who can search for them using their email address or their phone number. Users are provided with the following options: Everyone, Friends of Friends, and Friends. Facebook provides users a list of advertisers who have included them in a custom audience using their contact information. Users can opt out of receiving ads from individual advertisers listed here. But, information about what PII is used by advertisers is not disclosed. What information about how Facebook uses PII gets disclosed to the users? On adding mobile phone numbers directly to one’s Facebook profile, no information about the uses of that number is directly disclosed to them. This Information is only disclosed to users when adding a number from the Facebook website. As per the research results, there’s very little disclosure to users, often in the form of generic statements that do not refer to the uses of the particular PII being collected or that it may be used to allow advertisers to target users. “Our paper highlights the need to further study the sources of PII used for advertising, and shows that more disclosure and transparency needs to be provided to the user,” says the researchers in the paper. For more information, check out the official research paper. Ex-employee on contract sues Facebook for not protecting content moderators from mental trauma How far will Facebook go to fix what it broke: Democracy, Trust, Reality Mark Zuckerberg publishes Facebook manifesto for safeguarding against political interference

A new study by researchers from Princeton University and the University of Chicago suggests that shopping websites are abundant with dark patterns that rely on consumer deception. The researchers conducted a large-scale study, analyzing almost 53K product pages from 11K shopping websites to characterize and quantify the prevalence of dark patterns. They discovered 1,841 instances of dark patterns on shopping websites, which together represent 15 types of dark patterns. Note: All images in the article are taken from the research paper. What are dark patterns Dark patterns are generally used by shopping websites as a part of their user interface design choices. These dark patterns coerce, steer, or deceive users into making unintended and potentially harmful decisions, benefiting an online service. Shopping websites trick users into signing up for recurring subscriptions and making unwanted purchases, resulting in concrete financial loss. These patterns are not just limited to shopping websites, and find common applications on digital platforms including social media, mobile apps, and video games as well. At extreme levels, dark patterns can lead to financial loss, tricking users into giving up vast amounts of personal data, or inducing compulsive and addictive behavior in adults and children. Researchers used a web crawler to identify text-based dark patterns The paper uses an automated approach that enables researchers to identify dark patterns at scale on the web. The researchers crawled 11K shopping websites using a web crawler, built on top of OpenWPM, which is a web privacy measurement platform. The web crawler was used to simulate a user browsing experience and identify user interface elements. The researchers used text clustering to extract recurring user interface designs from the resulting data and then inspected the resulting clusters for instances of dark patterns. The researchers also developed a novel taxonomy of dark pattern characteristics to understand how dark patterns influence user decision-making. Based on the taxonomy, the dark patterns were classified basis whether they lead to an asymmetry of choice, are covert in their effect, are deceptive in nature, hide information from users, and restrict choice. The researchers also mapped the dark patterns in their data set to the cognitive biases they exploit. These biases collectively described the consumer psychology underpinnings of the dark patterns identified. They also determine that many instances of dark patterns are enabled by third-party entities, which provide shopping websites with scripts and plugins to easily implement these patterns on their websites. Key stats from the research There are 1,841 instances of dark patterns on shopping websites, which together represent 15 types of dark patterns and 7 broad categories. These 1,841 dark patterns were present on 1,267 of the 11K shopping websites (∼11.2%) in their data set. Shopping websites that were more popular, according to Alexa rankings, were more likely to feature dark patterns. 234 instances of deceptive dark patterns were uncovered across 183 websites 22 third-party entities were identified that provide shopping websites with the ability to create dark patterns on their sites. Dark pattern categories Sneaking Attempting to misrepresent user actions. Delaying information that users would most likely object to once made available. Sneak into Basket: The “Sneak into Basket” dark pattern adds additional products to users’ shopping carts without their consent Hidden Subscription:  Dark pattern charges users a recurring fee under the pretense of a one-time fee or a free trial Hidden Costs: Reveals new, additional, and often unusually high charges to users just before they are about to complete a purchase. Urgency Imposing a deadline on a sale or deal, thereby accelerating user decision-making and purchases. Countdown Timers: Dynamic indicator of a deadline counting down until the deadline expires. Limited-time Messages: Static urgency message without an accompanying deadline Misdirection Using visuals, language, or emotion to direct users toward or away from making a particular choice. Confirmshaming:  It uses language and emotion to steer users away from making a certain choice. Trick Questions: It uses confusing language to steer users into making certain choices. Visual Interference: It uses style and visual presentation to steer users into making certain choices over others. Pressured Selling: It refers to defaults or often high-pressure tactics that steer users into purchasing a more expensive version of a product (upselling) or into purchasing related products (cross-selling). Social proof Influencing users' behavior by describing the experiences and behavior of other users. Activity Notification:  Recurring attention grabbing message that appears on product pages indicating the activity of other users. Testimonials of Uncertain Origin: The use of customer testimonials whose origin or how they were sourced and created is not clearly specified. Scarcity Signalling that a product is likely to become unavailable, thereby increasing its desirability to users. Examples such as Low-stock Messages and High-demand Messages come under this category. Low-stock Messages: It signals to users about limited quantities of a product High-demand Messages: It signals to users that a product is in high demand, implying that it is likely to sell out soon. Obstruction Making it easy for the user to get into one situation but hard to get out of it. The researchers observed one type of the Obstruction dark pattern: “Hard to Cancel”. The Hard to Cancel dark pattern is restrictive (it limits the choices users can exercise to cancel their services). In cases where websites do not disclose their cancellation policies upfront, Hard to Cancel also becomes information hiding (it fails to inform users about how cancellation is harder than signing up). Forced Action Forcing the user to do something tangential in order to complete their task. The researchers observed one type of the Forced Action dark pattern: “Forced Enrollment” on 6 websites. Limitations of the research The researchers have acknowledged that their study has certain limitations. Only text-based dark patterns are taken into account for this study. There is still work needed to be done for inherently visual patterns (e.g., a change of font size or color to emphasize one part of the text more than another from an otherwise seemingly harmless pattern). The web crawling lead to a fraction of Selenium crashes, which did not allow researchers to either retrieve product pages or complete data collection on certain websites. The crawler failed to completely simulate the product purchase flow on some websites. They only crawled product pages and checkout pages, missing out on dark patterns present in other common pages such as the homepage of websites, product search pages, and account creation pages. The list of dark patterns can be downloaded as a CSV file. For more details, we recommend you to read the research paper. U.S. senators introduce a bipartisan bill that bans social media platforms from using ‘dark patterns’ to trick its users. How social media enabled and amplified the Christchurch terrorist attack Can an Open Web Index break Google’s stranglehold over the search engine market?

Amazon’s inaugural re:MARS event kicked off on Tuesday, June 4 at the Aria in Las Vegas. This 4-day event is inspired by MARS, a yearly invite-only event hosted by Jeff Bezos that brings together innovative minds in Machine learning, Automation, Robotics, and Space to share new ideas across these rapidly advancing domains. re:MARS featured a lot of announcements revealing a range of robots each engineered for a different purpose. Some of them include helicopter drones for delivery, two robot dogs by Boston Dynamics, Autonomous human-like acrobats by Walt Disney Imagineering, and much more. Amazon also revealed Alexa’s new Dialog Modeling for Natural, Cross-Skill Conversations. Let us have a brief look at each of the announcements. Robert Downey Jr. announces ‘The Footprint Coalition’ project to clean up the environment using Robotics Popularly known as the “Iron Man”, Robert Downey Jr.’s visit was one of the exciting moments where he announced a new project called The Footprint Coalition to clean up the planet using advanced technologies at re:MARS. “Between robotics and nanotechnology we could probably clean up the planet significantly, if not entirely, within a decade,” he said. According to The Forbes, “Amazon did not immediately respond to questions about whether it was investing financially or technologically in Downey Jr.’s project.” “At this point, the effort is severely light on details, with only a bare-bones website to accompany Downey’s public statement, but the actor said he plans to officially launch the project by April 2020,” Forbes reports. A recent United Nations report found that humans are having an unprecedented and devastating effect on global biodiversity, and researchers have found microplastics polluting the air, ocean, and soil. The announcement of this project has been opened to the public because the “company itself is under fire for its policies around the environment and climate change”. Additionally, Morgan Pope and Tony Dohi of Walt Disney Imagineering, also demonstrated their work to create autonomous acrobats. https://twitter.com/jillianiles/status/1136082571081555968 https://twitter.com/thesullivan/status/1136080570549563393 Amazon will soon deliver orders using drones On Wednesday, Amazon unveiled a revolutionary new drone that will test deliver toothpaste and other household goods starting within months. This drone is “part helicopter and part science-fiction aircraft” with built-in AI features and sensors that will help it fly robotically without threatening traditional aircraft or people on the ground. Gur Kimchi, vice president of Amazon Prime Air, said in an interview to Bloomberg, “We have a design that is amazing. It has performance that we think is just incredible. We think the autonomy system makes the aircraft independently safe.” However, he refused to provide details on where the delivery tests will be conducted. Also, the drones have received a year’s approval from the FAA to test the devices in limited ways that still won't allow deliveries. According to a Bloomberg report, “It can take years for traditional aircraft manufacturers to get U.S. Federal Aviation Administration approval for new designs and the agency is still developing regulations to allow drone flights over populated areas and to address national security concerns. The new drone presents even more challenges for regulators because there aren’t standards yet for its robotic features”. Competitors to Amazon’s unnamed drone include Alphabet Inc.’s Wing, which became the first drone to win an FAA approval to operate as a small airline, in April. Also, United Parcel Service Inc. and drone startup Matternet Inc. began using drones to move medical samples between hospitals in Raleigh, North Carolina, in March. Amazon’s drone is about six feet across with six propellers that lift it vertically off the ground. It is surrounded by a six-sided shroud that will protect people from the propellers, and also serves as a high-efficiency wing such that it can fly more horizontally like a plane. Once it gets off the ground, the craft tilts and flies sideways -- the helicopter blades becoming more like airplane propellers. Kimchi said, “Amazon’s business model for the device is to make deliveries within 7.5 miles (12 kilometers) from a company warehouse and to reach customers within 30 minutes. It can carry packages weighing as much as five pounds. More than 80% of packages sold by the retail behemoth are within that weight limit.” According to the company, one of the things the drone has mastered is detecting utility wires and clotheslines. They have been notoriously difficult to identify reliably and pose a hazard for a device attempting to make deliveries in urban and suburban areas. To know more about these high-tech drones in detail, head over to Amazon’s official blogpost. Boston Dynamics’ first commercial robot, Spot Boston Dynamics revealed its first commercial product, a quadrupedal robot named Spot.  Boston Dynamics’ CEO Marc Raibert told The Verge, “Spot is currently being tested in a number of “proof-of-concept” environments, including package delivery and surveying work.” He also said that although there’s no firm launch date for the commercial version of Spot, it should be available within months, certainly before the end of the year. “We’re just doing some final tweaks to the design. We’ve been testing them relentlessly”, Raibert said. These Spot robots are capable of navigating environments autonomously, but only when their surroundings have been mapped in advance. They can withstand kicks and shoves and keep their balance on tricky terrain, but they don’t decide for themselves where to walk. These robots are simple to control; using a D-pad, users can steer the robot as just like an RC car or mechanical toy. A quick tap on the video feed streamed live from the robot’s front-facing camera allows to select a destination for it to walk to, and another tap lets the user assume control of a robot arm mounted on top of the chassis. With 3D cameras mounted atop, a Spot robot can map environments like construction sites, identifying hazards and work progress. It also has a robot arm which gives it greater flexibility and helps it open doors and manipulate objects. https://twitter.com/jjvincent/status/1136096290016595968 The commercial version will be “much less expensive than prototypes [and] we think they’ll be less expensive than other peoples’ quadrupeds”, Raibert said. Here’s a demo video of the Spot robot at the re:MARS event. https://youtu.be/xy_XrAxS3ro Alexa gets new dialog modeling for improved natural, cross-skill conversations Amazon unveiled new features in Alexa that would help the conversational agent to answer more complex questions and carry out more complex tasks. Rohit Prasad, Alexa vice president and head scientist, said, “We envision a world where customers will converse more naturally with Alexa: seamlessly transitioning between skills, asking questions, making choices, and speaking the same way they would with a friend, family member, or co-worker. Our objective is to shift the cognitive burden from the customer to Alexa.” This new update to Alexa is a set of AI modules that work together to generate responses to customers’ questions and requests. With every round of dialog, the system produces a vector — a fixed-length string of numbers — that represents the context and the semantic content of the conversation. “With this new approach, Alexa will predict a customer’s latent goal from the direction of the dialog and proactively enable the conversation flow across topics and skills,” Prasad says. “This is a big leap for conversational AI.” At re:MARS, Prasad also announced the developer preview of Alexa Conversations, a new deep learning-based approach for skill developers to create more-natural voice experiences with less effort, fewer lines of code, and less training data than before. The preview allows skill developers to create natural, flexible dialogs within a single skill; upcoming releases will allow developers to incorporate multiple skills into a single conversation. With Alexa Conversations, developers provide: (1) application programming interfaces, or APIs, that provide access to their skills’ functionality; (2) a list of entities that the APIs can take as inputs, such as restaurant names or movie times;  (3) a handful of sample dialogs annotated to identify entities and actions and mapped to API calls. Alexa Conversations’ AI technology handles the rest. “It’s way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,” Prasad said. To know more about this announcement in detail, head over to Alexa’s official blogpost. Amazon Robotics unveiled two new robots at its fulfillment centers Brad Porter, vice president of robotics at Amazon, announced two new robots, one is, code-named Pegasus and the other one, Xanthus. Pegasus, which is built to sort packages, is a 3-foot-wide robot equipped with a conveyor belt on top to drop the right box in the right location. “We sort billions of packages a year. The challenge in package sortation is, how do you do it quickly and accurately? In a world of Prime one-day [delivery], accuracy is super-important. If you drop a package off a conveyor, lose track of it for a few hours  — or worse, you mis-sort it to the wrong destination, or even worse, if you drop it and damage the package and the inventory inside — we can’t make that customer promise anymore”, Porter said. Porter said Pegasus robots have already driven a total of 2 million miles, and have reduced the number of wrongly sorted packages by 50 percent. Porter said the Xanthus, represents the latest incarnation of Amazon’s drive robot. Amazon uses tens of thousands of the current-generation robot, known as Hercules, in its fulfillment centers. Amazon unveiled Xanthus Sort Bot and Xanthus Tote Mover. “The Xanthus family of drives brings innovative design, enabling engineers to develop a portfolio of operational solutions, all of the same hardware base through the addition of new functional attachments. We believe that adding robotics and new technologies to our operations network will continue to improve the associate and customer experience,” Porter says. To know more about these new robots watch the video below: https://youtu.be/4MH7LSLK8Dk StyleSnap: An AI-powered shopping Amazon announced StyleSnap, a recent move to promote AI-powered shopping. StyleSnap helps users pick out clothes and accessories. All they need to do is upload a photo or screenshot of what they are looking for, when they are unable to describe what they want. https://twitter.com/amazonnews/status/1136340356964999168 Amazon said, "You are not a poet. You struggle to find the right words to explain the shape of a neckline, or the spacing of a polka dot pattern, and when you attempt your text-based search, the results are far from the trend you were after." To use StyleSnap, just open the Amazon app, click the camera icon in the upper right-hand corner, select the StyleSnap option, and then upload an image of the outfit. Post this, StyleSnap provides recommendations of similar outfits on Amazon to purchase, with users able to filter across brand, pricing, and reviews. Amazon's AI system can identify colors and edges, and then patterns like floral and denim. Using this information, its algorithm can then accurately pick a matching style. To know more about StyleSnap in detail, head over to Amazon’s official blog post. Amazon Go trains cashierless store algorithms using synthetic data Amazon at the re:MARS shared more details about Amazon Go, the company’s brand for its cashierless stores. They said Amazon Go uses synthetic data to intentionally introduce errors to its computer vision system. Challenges that had to be addressed before opening stores to avoid queues include the need to make vision systems that account for sunlight streaming into a store, little time for latency delays, and small amounts of data for certain tasks. Synthetic data is being used in a number of ways to power few-shot learning, improve AI systems that control robots, train AI agents to walk, or beat humans in games of Quake III. Dilip Kumar, VP of Amazon Go, said, “As our application improved in accuracy — and we have a very highly accurate application today — we had this interesting problem that there were very few negative examples, or errors, which we could use to train our machine learning models.” He further added, “So we created synthetic datasets for one of our challenging conditions, which allowed us to be able to boost the diversity of the data that we needed. But at the same time, we have to be careful that we weren’t introducing artifacts that were only visible in the synthetic data sets, [and] that the data translates well to real-world situations — a tricky balance.” To know more about this news in detail, check out this video: https://youtu.be/jthXoS51hHA The Amazon re:MARS event is still ongoing and will have many more updates. To catch live updates from Vegas visit Amazon’s blog. World’s first touch-transmitting telerobotic hand debuts at Amazon re:MARS tech showcase Amazon introduces S3 batch operations to process millions of S3 objects Amazon Managed Streaming for Apache Kafka (Amazon MSK) is now generally available

Mark Zuckerberg yesterday (April 10 2018) testified in front of congress. That's a pretty big deal. Congress has been waiting some time for the chance to grill the Facebook chief, with "Zuck" resisting. So the fact that he finally had his day in D.C. indicates the level of pressure currently on him. Some have lamented the fact that senators were given so little time to respond to Zuckerberg - there was no time to really get deep into the issues at hand. However, although it's true that there was a lot that was superficial about the event, if you looked closely, there was plenty to take away from it. Here are the 5 of the most important things we learned from Mark Zuckerberg's testimony in front of Congress. Policy makers don't really understand that much about tech The most shocking thing to come out of Zuckerberg's testimony was unsurprising; the fact that some of the most powerful people in the U.S. don't really understand the technology that's being discussed. More importantly this is technology they're going to have to be making decisions on. One Senator brought printouts of Facebook pages and asked Zuckerberg if these were examples of Russian propaganda groups. Another was confused about Facebook's business model - how could it run a free service and still make money? Those are just two pretty funny examples, but the senators' lack of understanding could be forgiven due to their age. However, there surely isn't any excuse for 45 year old Senator Brian Schatz to misunderstand the relationship between Whatsapp and Facebook. https://twitter.com/pdmcleod/status/983809717116993537 Chris Cillizza argued on CNN that "the senate's tech illiteracy saved Zuckerberg". He explained: The problem was that once Zuckerberg responded - and he largely stuck to a very strict script in doing so - the lack of tech knowledge among those asking him questions was exposed. The result? Zuckerberg was rarely pressed, rarely forced off his talking points, almost never made to answer for the very real questions his platform faces. This lack of knowledge led to proceedings being less than satisfactory for onlookers. Until this knowledge gap is tackled, it's always going to be a challenge for political institutions to keep up with technological innovators. Ultimately, that's what makes regulation hard. Zuckerberg is still held up as the gatekeeper of tech in 2018 Zuckerberg is still held up as a gatekeeper or oracle of modern technology. That is probably a consequence of the point above. Because there's such a knowledge gap within the institutions that govern and regulate, it's more manageable for them to look to a figurehead. That, of course, goes both ways - on the one hand Zuckerberg is a fountain of knowledge, someone who can solve these problems. On the other hand is part of a Silicon Valley axis of evil, nefariously plotting the downfall of democracy and how to read your WhatsApp messages. Most people know that neither is true. The key point, though, is that however you feel about Zuckerberg, he is not the man you need to ask about regulation. This is something that Zephy Teachout argues on the Guardian. "We shouldn’t be begging for Facebook’s endorsement of laws, or for Mark Zuckerberg’s promises of self-regulation" she writes. In fact, one of the interesting subplots of the hearing was the fact that Zuckerberg didn't actually know that much. For example, a lot has been made of how extensive his notes were. And yes, you certainly would expect someone facing a panel of Senators in Washington to be well-briefed. But it nevertheless underlines an important point - the fact that Facebook is a complex and multi-faceted organization that far exceeds the knowledge of its founder and CEO. In turn, this tells you something about technology that's often lost within the discourse: the fact that its hard to consider what's happening at a superficial or abstract level without completely missing the point. There's a lot you could say about Zuckerberg's notes. One of the most interesting was the point around GDPR. The note is very prescriptive: it says "Don't say we already do what GDPR requires." Many have noted that this throws up a lot of issues, not least how Facebook plan to tackle GDPR in just over a month if they haven't moved on it already. But it's the suggestion that Zuckerberg was completely unaware of the situation that is most remarkable here. He doesn't even know where his company is on one of the most important pieces of data legislation for decades. Facebook is incredibly naive If senators were often naive - or plain ignorant - on matters of technology - during the hearing, there was plenty of evidence to indicate that Zuckerberg is just as naive. The GDPR issue mentioned above is just one example. But there are other problems too. You can't, for example, get much more naive than thinking that Cambridge Analytica had deleted the data that Facebook had passed to it. Zuckerberg's initial explanation was that he didn't realize that Cambridge Analytica was "not an app developer or advertiser", but he corrected this saying that his team told him they were an advertiser back in 2015, which meant they did have reason to act on it but chose not to. Zuckerberg apologized for this mistake, but it's really difficult to see how this would happen. There almost appears to be a culture of naivety within Facebook, whereby the organization generally, and Zuckerberg specifically, don't fully understand the nature of the platform it has built and what it could be used for. It's only now, with Zuckerberg talking about an "arms race" with Russia that this naivety is disappearing. But its clear there was an organizational blindspot that has got us to where we are today. Facebook still thinks AI can solve all of its problems The fact that Facebook believes AI is the solution to so many of its problems is indicative of this ingrained naivety. When talking to Congress about the 'arms race' with Russian intelligence, and the wider problem of hate speech, Zuckerberg signaled that the solution lies in the continued development of better AI systems. However, he conceded that building systems actually capable of detecting such speech could be 5 to 10 years away. This is a problem. It's proving a real challenge for Facebook to keep up with the 'misuse' of its platform. Foreign Policy reports that: "...just last week, the company took down another 70 Facebook accounts, 138 Facebook pages, and 65 Instagram accounts controlled by Russia’s Internet Research Agency, a baker’s dozen of whose executives and operatives have been indicted by Special Counsel Robert Mueller for their role in Russia’s campaign to propel Trump into the White House." However, the more AI comes to be deployed on Facebook, the more that the company is going to have to rethink how it describes itself. By using algorithms to regulate the way the platform is used, there comes to be an implicit editorializing of content. That's not necessarily a bad thing, but it does mean we again return to this final problem... There's still confusion about the difference between a platform and a publisher Central to every issue that was raised in Zuckerberg's testimony was the fact that Facebook remains confused about whether it is a platform or a publisher. Or, more specifically, the extent to which it is responsible for the content on the platform. It's hard to single out Zuckerberg here because everyone seems to be confused on this point. But it's interesting that he seems to have never really thought about the problem. That does seem to be changing, however. In his testimony, Zuckerberg said that "Facebook was responsible" for the content on its platforms. This statement marks a big change from the typical line used by every social media platform - that platforms are just platforms, they bear no responsibility for what is published on them. However, just when you think Zuckerberg is making a definitive statement, he steps back. He went on to say that "I agree that we are responsible for the content, but we don't produce the content." This statement hints that he still wants to keep the distinction between platform and publisher. Unfortunately for Zuckerberg, that might be too late. Read Next OpenAI charter puts safety, standards, and transparency first ‘If tech is building the future, let’s make that future inclusive and representative of all of society’ – An interview with Charlotte Jee What your organization needs to know about GDPR 20 lessons on bias in machine learning systems by Kate Crawford at NIPS 2017

On Monday, at the opening ceremony of Web Summit 2018, Antonio Guterres, the secretary general of the United Nations (UN) spoke about the benefits and challenges that come with cutting edge technologies. Guterres highlighted that the pace of change is happening so quickly that trends such as blockchain, IoT, and artificial intelligence can move from the cutting edge to the mainstream in no time. Guterres was quick to pay tribute to technological innovation, detailing some of the ways this is helping UN organizations improve the lives of people all over the world. For example, UNICEF is now able to map a connection between school in remote areas, and the World Food Programme is using blockchain to make transactions more secure, efficient and transparent. But these innovations nevertheless pose risks and create new challenges that we need to overcome. Three key technological challenges the UN wants to tackle Guterres identified three key challenges for the planet. Together they help inform a broader plan of what needs to be done. The social impact of the third and fourth industrial revolution With the introduction of new technologies, in the next few decades we will see the creation of thousands of new jobs. These will be very different from what we are used to today, and will likely require retraining and upskilling. This will be critical as many traditional jobs will be automated. Guterres believes that consequences of unemployment caused by automation could be incredibly disruptive - maybe even destructive - for societies. He further added that we are not preparing fast enough to match the speed of these growing technologies. As a solution to this, Guterres said: “We will need to make massive investments in education but a different sort of education. What matters now is not to learn things but learn how to learn things.” While many professionals will be able to acquire the skills to become employable in the future, some will inevitably be left behind. To minimize the impact of these changes, safety nets will be essential to help millions of citizens transition into this new world, and bring new meaning and purpose into their lives. Misuse of the internet The internet has connected the world in ways people wouldn’t have thought possible a generation ago. But it has also opened up a whole new channel for hate speech, fake news, censorship and control. The internet certainly isn’t creating many of the challenges facing civic society on its own - but it won’t be able to solve them on its own either. On this, Guterres said: “We need to mobilise the government, civil society, academia, scientists in order to be able to avoid the digital manipulation of elections, for instance, and create some filters that are able to block hate speech to move and to be a factor of the instability of societies.” The problem of control Automation and AI poses risks that exceed the challenges of the third and fourth industrial revolutions. They also create urgent ethical dilemmas, forcing us to ask exactly what artificial intelligence should be used for. Smarter weapons might be a good idea if you’re an arms manufacturer, but there needs to be a wider debate that takes in wider concerns and issues. “The weaponization of artificial intelligence is a serious danger and the prospects of machines that have the capacity by themselves to select and destroy targets is creating enormous difficulties or will create enormous difficulties,” Guterres remarked. His solution might seem radical but it’s also simple: ban them. He went on to explain: “To avoid the escalation in conflict and guarantee that international military laws and human rights are respected in the battlefields, machines that have the power and the discretion to take human lives are politically unacceptable, are morally repugnant and should be banned by international law.” How we can address these problems Typical forms of regulations can help to a certain extent, as in the case of weaponization. But these cases are limited. In the majority of circumstances technologies move so fast that legislation simply cannot keep up in any meaningful way. This is why we need to create platforms where governments, companies, academia, and civil society can come together, to discuss and find ways that allow digital technologies to be “a force for good”. You can watch Antonio Guterres’ full talk on YouTube. Tim Berners-Lee is on a mission to save the web he invented MEPs pass a resolution to ban “Killer robots” In 5 years, machines will do half of our job tasks of today; 1 in 2 employees need reskilling/upskilling now – World Economic Forum survey

The Public Voice Coalition, an organization that promotes public participation in decisions regarding the future of the Internet, came out with guidelines for AI, namely, Universal Guidelines on Artificial Intelligence (UGAI), today. The UGAI were announced at the currently ongoing, 40th International Data Protection and Privacy Commissioners Conference (ICDPPC), in Brussels, Belgium, today. The ICDPPC is a worldwide forum where independent regulators from around the world come together to explore high-level recommendations regarding privacy, freedom, and protection of data. These recommendations are addressed to governments and international organizations. The 40th ICDPPC has speakers such as Tim Berners Lee (director of the world wide web), Tim Cook (Apple Inc, CEO), Giovanni Butarelli (European Data Protection Supervisor), and Jagdish Singh Khehar (44th Chief Justice of India) among others attending the conference. The UGAI combines the elements of human rights doctrine, data protection law, as well as ethical guidelines. “We propose these Universal Guidelines to inform and improve the design and use of AI. The Guidelines are intended to maximize the benefits of AI, to minimize the risk, and to ensure the protection of human rights. These guidelines should be incorporated into ethical standards, adopted in national law and international agreements, and built into the design of systems”, reads the announcement page. The UGAI comprises twelve different principles for AI governance that haven’t been previously covered in similar policy frameworks. Let’s have a look at these principles in UGAI. Transparency principle Transparency principle puts emphasis on an individual’s right to interpret the basis of a particular AI decision concerning them. This means all individuals involved in a particular AI project should have access to the factors, the logic, and techniques that produced the outcome. Right to human determination The Right to human determination focuses on the fact that individuals and not machines should be responsible when it comes to automated decision-making. For instance, during the operation of an autonomous vehicle, it is impractical to include a human decision before the machine makes an automated decision. However, if an automated system fails, then this principle should be applied and human assessment of the outcome should be made to ensure accountability. Identification Obligation This principle establishes the foundation of AI accountability and makes the identity of an AI system and the institution responsible quite clear. This is because an AI system usually knows a lot about an individual. But, the individual might now even be aware of the operator of the AI system. Fairness Obligation The Fairness Obligation puts an emphasis on how the assessment of the objective outcomes of the AI system is not sufficient to evaluate an AI system. It is important for the institutions to ensure that AI systems do not reflect unfair bias or make any discriminatory decisions. Assessment and accountability Obligation This principle focuses on assessing an AI system based on factors such as its benefits, purpose, objectives, and the risks involved before and during its deployment. An AI system should be deployed only after this evaluation is complete. In case the assessment reveals substantial risks concerning Public Safety and Cybersecurity, then the AI system should not be deployed. This, in turn, ensures accountability. Accuracy, Reliability, and Validity Obligations This principle focuses on setting out the key responsibilities related to the outcome of automated decisions by an AI system. Institutions must ensure the accuracy, reliability, and validity of decisions made by their AI system. Data Quality Principle This puts an emphasis on the need for institutions to establish data provenance. It also includes assuring the quality and relevance of the data that is fed into the AI algorithms. Public Safety Obligation This principle ensures that institutions assess the public safety risks arising from AI systems that control different devices in the physical world. These institutions must implement the necessary safety controls within such AI systems. Cybersecurity Obligation This principle is a follow up to the Public Safety Obligation and ensures that institutions developing and deploying these AI systems take cybersecurity threats into account. Prohibition on Secret Profiling This principle states that no institution shall establish a secret profiling system. This is to ensure the possibility of independent accountability. Prohibition on Unitary Scoring This principle states that no national government shall maintain a general-purpose score on its citizens or residents. “A unitary score reflects not only a unitary profile but also a predetermined outcome across multiple domains of human activity,” reads the guideline page. Termination Obligation Termination Obligation states that an institution has an affirmative obligation to terminate the AI system built if human control of that system is no longer possible. For more information, check out the official UGAI documentation. The ethical dilemmas developers working on Artificial Intelligence products must consider Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms Introducing Deon, a tool for data scientists to add an ethics checklist

Update, 12th April 2018: Amazon shareholders will now be voting on at the 2019 Annual Meeting of Shareholders of Amazon, on whether the company board should prohibit sales of Facial recognition tech to the government. The meeting will be held at 9:00 a.m., Pacific Time, on Wednesday, May 22, 2019, at Fremont Studios, Seattle, Washington.  Over 30 researchers from top tech firms (Google, Microsoft, et al), academic institutions and civil rights groups signed an open letter, last week, calling on Amazon to stop selling Amazon Rekognition to law enforcement. The letter, published on Medium, has been signed by the likes of this year’s Turing award winner, Yoshua Bengio, and Anima Anandkumar, a Caltech professor, director of Machine Learning research at NVIDIA, and former principal scientist at AWS among others. https://twitter.com/rajiinio/status/1113480353308651520 Amazon Rekognition is a deep-learning based service that is capable of storing and searching tens of millions of faces at a time. It allows detection of objects, scenes, activities and inappropriate content. However, Amazon Rekognition has long been a bone of contention among public eye and rights groups. This is due to the inaccuracies in its face recognition capability and over the concerns that selling Rekognition to law enforcement can hamper public privacy. For instance, an anonymous Amazon employee spoke out against Amazon selling its facial recognition technology to the police, last year, calling it a “Flawed technology”. Also, a group of seven House Democrats sent a letter to Amazon CEO, last November, over Amazon Rekognition, raising concerns and questions about its accuracy and the possible effects. Moreover, a group of over 85 coalition groups sent a letter to Amazon, earlier this year, urging the company to not sell its facial surveillance technology to the government. Researchers argue against unregulated Amazon Rekognition use Researchers state in the letter that a study conducted by Inioluwa Deborah Raji and Joy Buolamwini shows that Rekognition possesses much higher error rates and is imprecise in classifying the gender of darker skinned women than lighter skinned men. However, Dr. Matthew Wood, general manager, AI, AWS and Michael Punke, vice president of global public policy, AWS, were irreverent about the research and disregarded it by labeling it as “misleading”. Dr. Wood also stated that “facial analysis and facial recognition are completely different in terms of the underlying technology and the data used to train them. Trying to use facial analysis to gauge the accuracy of facial recognition is ill-advised”.  Researchers in the letter have called on that statement saying that it is 'problematic on multiple fronts’. The letter also sheds light on the real world implications of the misuse of face recognition tools. It talks about Clare Garvie, Alvaro Bedoya and Jonathan Frankle of the Center on Privacy & Technology at Georgetown Law who studies law enforcement’s use of face recognition. According to them, using face recognition tech can put the wrong people to trial due to cases of mistaken identity. Also, it is quite common that the law enforcement operators are neither aware of the parameters of these tools, nor do they know how to interpret some of their results. Relying on decisions from automated tools can lead to “automation bias”. Another argument Dr. Wood makes to defend the technology is that “To date (over two years after releasing the service), we have had no reported law enforcement misuses of Amazon Rekognition.”However, the letter states that this is unfair as there are currently no laws in place to audit Rekognition’s use. Moreover, Amazon has not disclosed any information about its customers or any details about the error rates of Rekognition across different intersectional demographics. “How can we then ensure that this tool is not improperly being used as Dr. Wood states? What we can rely on are the audits by independent researchers, such as Raji and Buolamwini..that demonstrates the types of biases that exist in these products”, reads the letter. Researchers say that they find Dr. Wood and Mr. Punke’s response to the peer-reviewed research is ‘disappointing’ and hope Amazon will dive deeper into examining all of its products before deciding on making it available for use by the Police. More trouble for Amazon: SEC approves Shareholders’ proposal for need to release more information on Rekognition Just earlier this week, the U.S. Securities and Exchange Commission (SEC) announced a ruling that considers Amazon shareholders’ proposal to demand Amazon to provide more information about the company’s use and sale of biometric facial recognition technology as appropriate. The shareholders said that they are worried about the use of Rekognition and consider it a significant risk to human rights and shareholder value. Shareholders mentioned two new proposals regarding Rekognition and requested their inclusion in the company’s proxy materials: The first proposal called on Board of directors to prohibit the selling of Rekognition to the government unless it has been evaluated that the tech does not violate human and civil rights. The second proposal urges Board Commission to conduct an independent study of Rekognition. This would further help examine the risks of Rekognition on the immigrants, activists, people of color, and the general public of the United States. Also, the study would help analyze how such tech is marketed and sold to foreign governments that may be “repressive”, along with other financial risks associated with human rights issues. Amazon chastised the proposals and claimed that both the proposals should be discarded under the subsections of Rule 14a-8 as they related to the company’s “ordinary business and operations that are not economically significant”. But, SEC’s Division of Corporation Finance countered Amazon’s arguments. It told Amazon that it is unable to conclude that “proposals are not otherwise significantly related to the Company’s business” and approved their inclusion in the company’s proxy materials, reports Compliance Week. “The Board of Directors did not provide an opinion or evidence needed to support the claim that the issues raised by the Proposals are ‘an insignificant public policy issue for the Company”, states the division. “The controversy surrounding the technology threatens the relationship of trust between the Company and its consumers, employees, and the public at large”. SEC Ruling, however, only expresses informal views, and whether Amazon is obligated to accept the proposals can only be decided by the U.S. District Court should the shareholders further legally pursue these proposals.   For more information, check out the detailed coverage at Compliance Week report. AWS updates the face detection, analysis and recognition capabilities in Amazon Rekognition AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers Amazon Rekognition can now ‘recognize’ faces in a crowd at real-time