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The release of Julia 1.0 has been eagerly anticipated - but it's finally here. At JuliaCon2018 in London the team got together to mark the project's landmark step. Take a look at the video below: It's taken more than six years for Julia to hit this milestone. The language was first launched in February 2012, and since then it has grown slowly to become a popular high-level dynamic programming language. The projects aims for Julia have been hugely ambitious since the start. As the team said in this post back in 2012: "We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled." However, despite the level of ambition it hasn't quite yet managed to expand beyond its core strength: numerical computing. However, that could change with version 1.0. What new features are in Julia 1.0? The team behind Julia are keen to stress that Julia 1.0 offers greater stability than the language ever has. They explain in a blog post announcing the new release: "The single most significant new feature in Julia 1.0, of course, is a commitment to language API stability: code you write for Julia 1.0 will continue to work in Julia 1.1, 1.2, etc. The language is “fully baked.” The core language devs and community alike can focus on packages, tools, and new features built upon this solid foundation." There are also many other new features, including: A new built in package manager Simplified scope rules Improved consistency in all of Julia's APIs A new canonical representation for missing values You can find out more about the new features here. Hang on... wasn't Julia 0.7 just released? Yes, Julia 0.7 has been released alongside version 1.0. This was done "to provide an upgrade path for packages and code that predates the 1.0 release." Version 0.7 simply includes deprecation warnings that aren't included in version 1.0. How to get started with Julia 1.0 If you're ready to get started on Julia 1.0 you can download it here. It's advised that if you're currently using Julia 0.6 or earlier, you should start with the 0.7 release - the deprecation warnings in Julia 0.7 act as a guide through the upgrade process.

After the consecutive release of Dagger 2.15 and 2.16 in May earlier this year, Dagger 2.17 was released with enhanced performance and bug fixes. This dependency injection framework for Java and Android allows developers to focus on the interesting classes (the classes that actually do stuff!). You just need to declare the dependencies, specify how to satisfy them, and ship your app. What’s new in Dagger 2.17? Bug fixes and error improvements: Previously, when a @Binds method in a parent was used only from a child, whose dependency was missing in the parent but present in the child, it used to result in a valid graph. Dagger now reports an error in such cases. An error is reported for binding methods that have more than one scope annotation, instead of throwing an exception. If two entry point methods with different keys are inherited from different supertypes of a component type, Dagger reports an error. Dagger reports an error for scope annotations on @BindsOptionalOf methods. Apply scope to the non-optional binding that satisfies the optional binding, instead of the @BindsOptionalOf method. You should install AndroidInjectionModule or AndroidSupportInjectionModule when using dagger.android, otherwise Dagger 2.17 will throw a missing binding error. Bug fixed to report cycles if some components have no entry points that depend on the cycle. Bug fixed where scope annotations in error messages were missing annotation attributes. Additions and deprecations: An option is added to use string keys for dagger.android and allow the keys to be obfuscated. You can enable this mode with the -Adagger.android.experimentalUseStringKeys flag. experimentalAndroidMode is renamed to fastInit. dagger.android.DaggerFragment is deprecated, use dagger.android.support.DaggerFragment instead. This is done to match Android Pie’s deprecation of framework fragments. Checkout Dagger’s Github page for more on the 2.17 release. Introducing Android 9 Pie, filled with machine learning and baked-in UI features All new Android apps on Google Play must target API Level 26 (Android Oreo) or higher, to publish Android Studio 3.2 Beta 5 out, with updated Protobuf Gradle plugin

Facebook has recently been granted a patent titled “Selection and Presentation of News Stories Identifying External Content to Social Networking System Users” on July 31st, 2018. It aims to analyze the user data to curate a personalized news feed for the users. This will also include providing users with control over the kind of news they want to see. Facebook wants to add a Filter option in its news feed. This will make it easier for the users to find relevant news items. As per the patent application, “the news stories may be filtered based on filter criteria allowing a viewing user to more easily identify new stories of interest”. For instance, the filter can be added to view stories associated with either some other user or some news source. You can also add a keyword filter to get all the stories related to that specific keyword.  Facebook news feed filter tool   There are a lot of groups and pages on Facebook which helps reflect the user’s interests. The kind of content that the user posts also says a lot about his/her preferences. As there is a lot of user data present, Facebook automatically analyzes the user’s profile to optimize the news feed as per the choice of the user. There is also a ranking criterion involved when it comes to filtering news feed. The patent reads “news stories are scored and ranked based on their scores. News stories may be ranked based on the popularity of the news story among users of the social networking system. Popularity may be based on the number of views, likes, comments, shares or individual posts of the news story in the social networking system.” News stories can also be ranked based on the chronological order.   Facebook news feed filter tool patent Once Facebook is done analyzing the user profile, filtering the feed based on filter criteria, and ranking the stories based on the ranking criteria, a newly customized news feed will be generated and presented to the user. Facebook has been taking measures to curb fake news from its feed. The news filter tool is expected to help further. It will prevent irrelevant and fake news from occurring on users’ news feed as the users can choose to see news only from trusted resources. In fact, Facebook recently acquired Bloomsbury AI to fight fake news. Additionally, the latest news sources, accounts, groups, and pages will also be recommended to users based on data analyzed. With so much data floating around on Facebook feeds, this patent idea seems like a much-needed one. There are no details currently on when or if this feature will hit the Facebook feed. What do you think about Facebook’s news feed filter tool patent? Let us know in the comments below. Facebook launched new multiplayer AR games in Messenger Facebook launches a 6-part Machine Learning video series Facebook open sources Fizz, the new generation TLS 1.3 Library  

Microsoft Azure has just released a Blockchain-as-a-Service product that uses Ethereum to support blockchain with a set of templates to deploy and configure your choice of blockchain network. This can be done with minimal Azure and blockchain knowledge. The conventional blockchain in the open is based on Proof-of-Work (PoW) and requires mining as the parties do not trust each other. An enterprise blockchain does not require PoW but is based on Proof-of-Authority (PoA) where approved identities or validators on a blockchain, validate the transactions on the blockchain. The PoA product features a decentralized application (DApp) called the Governance DApp. Blockchains in this new model can be deployed in 5-45 minutes depending on the size and complexity of the network. The PoA network comes with security features such as identity leasing system to ensure no two nodes carry the same identity. There are also other features to achieve good performance. Web assembly smart contracts: Solidity is cited as one of the pain areas when developing smart contracts on Ethereum. This feature allows developers to use familiar languages such as C, C++, and Rust. Azure Monitor: Used to track node and network statistics. Developers can view the underlying blockchain to track statistics while the network admins can detect and prevent network outages. Extensible governance: With this feature, customers can participate in a consortium without managing the network infrastructure. It can be optionally delegated to an operator of their choosing. Governance DApp: Provides a decentralized governance in which network authority changes are administered via on-chain voting done by select administrators. It also contains validator delegation for authorities to manage their validator nodes that are set up in each PoA deployment. Users can audit change history, each change is recorded, providing transparency and auditability. Source: Microsoft Blog Along with these features, the Governance DApp will also ensure each consortium member has control over their own keys. This enables secure signing on a wallet chosen by the user. The blog mentions “In the case of a VM or regional outage, new nodes can quickly spin up and resume the previous nodes’ identities.” To know more visit the official Microsoft Blog. Read next Automate tasks using Azure PowerShell and Azure CLI [Tutorial] Microsoft announces general availability of Azure SQL Data Sync Microsoft supercharges its Azure AI platform with new features

On Monday, Oracle Corp filed a protest with the Government Accountability Office(GAO) against Pentagon’s $10 billion JEDI(Joint Enterprise Defense Infrastructure) cloud contract. Oracle believes should not be awarded only to a single company but instead, allow for multiple winners. https://twitter.com/92newschannel/status/1027090662162944000 The U.S Defence Department unveiled the competition in July and stated that only a single winner, the one with the most rapid adoption of the cloud technology would be awarded. Deborah Hellinger, Oracle’s spokeswoman, said in a statement on Tuesday, “The technology industry is innovating around next-generation cloud at an unprecedented pace and JEDI virtually assures DoD will be locked into a legacy cloud for a decade or more. The single-award approach is contrary to the industry's multi-cloud strategy, which promotes constant competition, fosters innovation and lowers prices.” A bid protest is a challenge to the terms of a solicitation or the award of a federal contract. The GAO, which adjudicates and decides these challenges, will issue a ruling on the protest by November 14. This has been the first bid protest ever since the competition started a decade ago. Amazon.com is being seen as a top contender throughout the deal. Amazon Web Services or AWS is the only company approved by the U.S. government to handle secret and top secret data. Thus, this competition has attracted criticism from companies that fear Amazon Web Services, Amazon’s cloud unit, will win the contract. This would choke out hopes for others (Microsoft Corp (MSFT.O), Oracle (ORCL.N), IBM (IBM.N) and Alphabet Inc’s (GOOGL.O) Google) to win the government cloud computing contract. Read more about this news on The Register. Oracle makes its Blockchain cloud service generally available Google employees quit over company’s continued Artificial Intelligence ties with the Pentagon Oracle reveals issues in Object Serialization. Plans to drop it from core Java.  

Facebook announced a new “multiplayer AR games” feature for its popular messaging platform,”Messenger”, today. This feature makes your chatting experience even more fun as it lets you challenge your friends to games while video chatting.      Facebook Messenger AR games Facebook seems inspired by Snapchat, as the new feature is quite similar to Snapchat’s multiplayer AR video chat games, called Snappables, launched in April 2018. The only difference is that Snapchat transforms your whole screen using AR, taking you into space or a disco dance hall. On the other hand, Facebook AR games only overlay a few graphics on the world around you. Snapchat Snappables The new feature powered by AR Studio, a platform that was released last year at Facebook F8 which allows developers to build AR experiences for Facebook Camera. It lets you challenge up to six people at a time. There are currently two games rolled out namely, “Don’t Smile” and “Asteroids Attack”. “Don’t smile” is a game where the person making the serious face for the longest time wins. In “Asteroids attack”, the person who is able to navigate their spaceship better, wins”. Facebook is planning to release more games in the future, such as Beach Bump and Kitten Craze. You need to have the latest version of Messenger updated on your phones to be able to play these games. You can either open an existing conversation or find the person to chat with, then tap the video icon on the upper right corner of your phone screen. After this, you just need to tap the star button and select one of the AR games. This will notify the person you are chatting with to join you in the game. Video chats in Messenger have been on the rise with over 17 billion video chats last year and twice as many as the year before. Also, Facebook seems to be quite invested in incorporating Augmented Reality into different aspects of its business. Last month, Facebook announced that it’s planning to launch AR ads on its news feed to let you try on products virtually. Messenger is doing a good job of connecting people in real-time, and now, AR games are like the cherry on top which will help people create memories and have fun. Facebook launches a 6-part Machine Learning video series Facebook plans to use Bloomsbury AI to fight fake news Is Facebook planning to spy on you through your mobile’s microphones?  

Google has announced the availability of Python 3.7 in beta on the App Engine standard environment. Developers can now easily run their web apps using up-to-date versions of popular languages, frameworks, and libraries, with Python being one of them. The Second Generation runtimes remove previous App Engine restrictions, giving developers the ability to write portable web apps and microservices. Now web apps can take full advantage of App Engine features such as auto-scaling, built-in security, and pay-per-use billing model. Python 3.7 was introduced as one of the new Second Generation runtimes at Cloud Next. Python 3.7 runtime brings developers up-to-date with the language community's progress. As a Second Generation runtime, it enables a faster path to continued runtime updates. It also supports arbitrary third-party libraries, including those that rely on C code and native extensions. The new Python 3.7 runtime also supports the Google Cloud client libraries. Developers can integrate GCP services into their app, and run it on App Engine, Compute Engine or any other platform. LumApps, a Paris-based provider of enterprise Intranet software, has chosen App Engine to optimize for scale and developer productivity. Elie Mélois, CTO & Co-founder, LumApps says, "With the new Python 3.7 runtime on App Engine standard, we were able to deploy our apps very quickly, using libraries that we wanted such as scikit. App Engine helped us scale our platform from zero to over 2.5M users, from three developers to 40—all this with only one DevOps person! " Check out the documentation to start using Python 3.7 today on the App Engine standard environment. Deploying Node.js apps on Google App Engine is now easy Hosting on Google App Engine Should you move to Python 3? 7 Python experts’ opinions

Source code for what is believed to be a small part of Snapchat's iOS application was posted on GitHub after being leaked back in May. After being notified, Snap Inc., Snapchat's parent company, immediately filed a DMCA request to GitHub to get the code removed. A copy of the request was found by a 'security researcher' tweeting from the handle @x0rz, who shared a link to a copy of the request on GitHub: https://twitter.com/x0rz/status/1026735377955086337 You can read the DMCA request in full here. [caption id="attachment_21477" align="aligncenter" width="916"] Part of the Snap Inc. DMCA request to GitHub[/caption] The initial leak back in May was caused by an update to the Snapchat iOS application. A spokesperson for Snap Inc. explained to CNET: "An iOS update in May exposed a small amount of our source code and we were able to identify the mistake and rectify it immediately... We discovered that some of this code had been posted online and it has been subsequently removed. This did not compromise our application and had no impact on our community." This code was then published by a someone using the name Khaled Alshehri, believed to be based in Pakistan, on GitHub. The repository created - called Source-SnapChat - has now been taken down. A number of posts linked to the GitHub account suggests that the leaker had tried to contact Snapchat but had been ignored. "I will post it again until I get a reply" they said. https://twitter.com/i5aaaald/status/1025639490696691712 Leaked Snapchat code is still being traded privately Although GitHub has taken the repo down, it's not hard to find people claiming they have a copy of the code that they're willing to trade: https://twitter.com/iSn0we/status/1026738393353465858 Now the code is out in the wild it will take more than a DMCA request to get things under control. Although it would appear the leaked code isn't substantial enough to give much away to potential cybercriminals, it's likely that Snapchat is now working hard to make the changes required to tighten its security.  Read next Snapchat is losing users – but revenue is up 15 year old uncovers Snapchat’s secret visual search function

Let's Encrypt is a Certification Authority that enables HTTPS on your website. Initially, major browsers and root certificate programs were, however, apprehensive of trusting this CA. The page has now turned for Let's Encrypt, who, in their announcement yesterday stated that they are now directly trusted by major root programs like Microsoft, Google, Apple, Mozilla, Oracle and Blackberry. Knowing that these big names are now associated with Let's Encrypt’s SSL Certificate, end users are in for a host of advantages. They can obtain a trusted certificate from Let's Encrypt for zero cost. Not only can software running on web server obtain a certificate, but also be securely configured for use and automatically renew the certificate as and when needed. This certification authority also ensures that TLS security is taken seriously. They aim to benefit the community by maintaining transparency in issuing and revoking certificates- which will be publicly recorded for inspection. This will be published as an open standard for others to adopt. Initially, they started off with the trust base of many browsers excluding the major root programs. The main reason for this being that it was a very new certificate authority launched in early April 2016. To overcome this roadblock, their intermediate “Let's Encrypt Authority X3” is signed by ISRG Root X1. The intermediate now stands cross-signed by another certificate authority- ‘IdenTrust’. IdenTrust has always been a major name whose root is already trusted in all major browsers. Thus, this indirect circle of trust has been a game changer for Let's Encrypt. There are still many older versions operating systems, browsers, and devices that do not directly trust Let's Encrypt. Some of these will eventually be updated to trust them directly. Some will not. And until they move out of the trust and security scene, they plan to use a cross signature. By currently providing certificates for more than 115 million websites,  Let's Encrypt is definitely making its presence felt! Head over to the official site of Let's Encrypt for more insights on this new announcement. You can also check out Black Hill’s post for information on why Let's Encrypt is making rounds on the internet these days. A new WPA/WPA2 security attack in town: Wi-fi routers watch out! Top 5 cybersecurity trends you should be aware of in 2018 Mozilla’s new Firefox DNS security updates spark privacy hue and cry  

SingularityNET to collaborate with Mindfire to team up their best talents and work on something similar to Mindfire Mission 1. The mission was dedicated to “cracking the brain code”, to understand more about how the human brain works. They hope to combine their talents and work on AI services, education and also towards combining their blockchain tokens. SingularityNET is an AI solution platform powered by a decentralized protocol that lets anyone create, share, and monetize on its AI services. Mindfire is focused on understanding the building blocks of AI which forms a human level intelligence. Mindfire believes “The partnership between SingularityNET and the Mindfire Foundation will grow the talent pool of both entities and increase the productivity not only of AI services but also of the number of relevant insights in human-level artificial intelligence.“ Together they plan to target three key areas: Talent: Choose the best talents, leading minds from the pool in different AI disciplines AI services: This focuses on building a decentralized hub for AI services where the talents from Mindfire can work on and use SingularityNET’s platform AI education: Implementing practical courses and lectures in AI and its business applications and opportunities Ben Goertzel, founder of SingularityNET says, “The SingularityNET decentralized AI platform is open to any possible approach to AI or any complex systems”. Even though Ben’s approach is less “brain-focused” unlike Mindfire, he believes there is great potential for collaboration. Mindfire with SingularityNET has launched a call for applications for their successive missions, Mission-2 and Mission-3. These missions focus on prototype development including drones, robots, and other carrier systems for AI. Mission-2 is planned for November 11-16, 2018. There are 10 planned missions to come. Head on to their website to learn more and apply! Mindfire also announced the publication of a completely revised white paper, that is set for release on August 15, 2018. The revised white paper will detail the key functionality of the ERC20 protocol based token, MFT. It will also elaborate on Mindfire’s business model and outline the Mindfire token sale including a reward campaign. SingularityNET tweeted on July 31: https://twitter.com/mindfire_global/status/1024275219391959040 Mindfire’s talents can collaborate to create applications or products on the SingularityNET platform and leverage it. There’s also potential for SingularityNET to connect Mindfire with people building applications or doing research. SingularityNET can direct various AI problem areas at Mindfires talent. Ben believes they may be able to build mechanisms where the AGI token of SingularityNET can convert automatically to Mindfire’s MFT Token. The focus here is on the exchangeability of these tokens to promote the development of SingularityNET’s decentralized protocol. This is a future area of collaboration that can lead to a two way incentivization benefiting both companies. You can view Ben, CEO and Chief Scientist of SingularityNET express his collaborative visions in this YouTube video. To know more visit the official Mindfire blog. Attention designers, Artificial Intelligence can now create realistic virtual textures Top languages for Artificial Intelligence development 7 Popular Applications of Artificial Intelligence in Healthcare

Yesterday, Uber Inc.,  open-sourced its robust and scalable metrics infrastructure, M3 for Prometheus, a popular monitoring and alerting solution. Uber has been using M3 for a long time to access metrics on their backend systems. However, by open sourcing M3 as a remote storage backend for Prometheus, Uber wants others in the broader community to benefit from their metrics platform. Prior to releasing M3, Uber released M3DB, the scalable storage backend for M3. M3DB is a distributed time series database that can be used for storing real-time metrics at long retention Along with M3, Uber also open sourced M3 Coordinator, a bridge that users can deploy to access the benefits of M3DB and Prometheus. The M3 Coordinator performs downsampling, ad hoc retention, and aggregation of metrics using retention and rollup rules. This helps in applying specific retention and aggregations to subsets of metrics on the go. The rules of the process are stored in etcd, which runs embedded in the binary of an M3DB seed node. M3 for Prometheus Although Prometheus is a popular monitoring and alerting solution, its scalability and durability is limited by single nodes. The M3 metric platform provides a turnkey, scalable, and configurable multi-tenant store for Prometheus metrics. Source: Uber Engineering Uber, before using M3, emitted metrics to a Graphite stack, which stored them using the Whisper file format in a sharded Carbon cluster. Uber then made use of Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. However, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. Thus, this solution was not worth continuing as Uber kept expanding. This led them to build M3, a system which provides fault-tolerant metrics ingestion, storage, and querying as a managed platform. Released in the year 2015, M3 now houses over 6.6 billion time series. Features of M3 include: It optimizes every part of the metrics pipeline. This gives engineers an improved storage and results in lesser hardware usage. M3 ensures that the data is as highly compressed to reduce hardware footprint. This further optimizes Gorilla’s TSZ compression to compress float64 values, known as M3TSZ compression. Maintains a lean memory footprint for storage to avoid memory becoming a bottleneck since a significant portion of each data point can be “write once, read never.” To speed up access time, a Bloom filter and index summary per shard time window block in mmap’d memory is available. This allows ad-hoc queries of up to 100,000 unique time series in a single query over long retention periods (in some cases, spanning years of retention). With M3, one can avoid compactions where possible, including the downsampling path. This will further increase the utilization of host resources for more concurrent writes and provide steady write/read latency. One can also use a native design for time series storage that does not require vigilant operational attention to run with a high write volume. The M3 architecture The M3 architecture M3 architecture includes a single global view of all metrics With such a global view, upstream consumers need not navigate routing. This increases the overall simplicity of metrics discoverability. For workloads that failover applications between regions or workloads sharded across regions, the single global view makes it much easier to sum and query metrics across all regions in a single query. This lets users see all operations of a specific type globally, and look at a longer retention to view historical trends in a single place. How can one achieve the single global view? To achieve this single pane view, metrics are written in M3 to local regional M3DB instances. In this setup, replication is local to a region and can be configured to be isolated by availability zone or rack. Queries fan out to both the local region’s M3DB instances and coordinators in remote regions where metrics are stored, returning compressed M3TSZ blocks for matched time series wherever possible. Uber engineers plan to further upgrade M3 to push query aggregations to remote regions to execute before returning results, as well as to the local M3DB storage node wherever possible. Read more about M3 in detail in Uber Engineering official blog post. China’s Baidu launches Duer OS Prometheus Project to accelerate conversational AI Log monitoring tools for continuous security monitoring policy [Tutorial] Monitoring, Logging, and Troubleshooting

The latest addition to the list of companies acquired by Google is a startup called GraphicsFuzz. The team at GraphicsFuzz consisting of Alastair Donaldson, Hugues Evrard and Paul Thomson, will be joining Google’s Android Graphics Team to integrate their graphics driver testing technology within the Android ecosystem. What does GraphicsFuzz do? GraphicsFuzz is an automated testing technology, which uses fuzzy logic and metamorphic testing to find the reliability and security-related issues in the graphics driver. The following is an overview of the logic it uses: Reference shader: It starts with a reference shader program, which basically is a program defined by the user to be run on some stage of a graphics processor. Source: GraphicsFuzz Variant shaders: To obtain variant shaders with significantly different source code that still has the same effect, GraphicsFuzz applies semantics-preserving source code transformations. These variant shaders will have the same image as the reference. Source: GraphicsFuzz Bug finding: If a variant shader leads to different image then this indicates that we have detected a graphics driver bug. Source: GraphicsFuzz Intelligent Reducer: The variant shader is of thousands of lines coming from the semantics-preserving transformations. Intelligent Reducer removes irrelevant codes to the bug and then we obtain a small difference which is enough to expose the driver issue. Source: GraphicsFuzz What does GraphicsFuzz bring to the Android testing ecosystem? They have proven their capabilities by exposing bugs in large number of graphics drivers across mobile and desktop platforms. For instance, they were awarded a bug bounty by Google for discovering a security issue in the ARM driver for the Samsung Galaxy S6. While testing of the Snapdragon Samsung Galaxy S9, GraphicsFuzz found an error in the Adreno 630’s graphics driver. This error could cause a whole-phone reboot via a valid WebGL Internet page when using the stock Samsung Internet browser. This acquisition will surely prove helpful to Google in developing secure and reliable graphics drivers for Android systems to provide a better user experience. Head over to GraphicsFuzz’s official website to know more about this announcement. Google’s exploring cloud partnerships with Tencent and others All new Android apps on Google Play must target API Level 26 (Android Oreo) or higher Google to launch a censored search engine in China, codenamed Dragonfly

This year, in the FIFA World Cup researchers and scientists, tried to use Artificial Intelligence (AI) to predict the outcomes of all 64 matches. That did not work out well, accounting to probability and human nature that cannot be predicted. Now we see another implementation of AI in a major global sports event. This time it’s not to predict outcomes, but to identify players with facial recognition. In 2020, facial recognition will be used for the first time widely in an Olympic event to identify athletes. The Japanese IT firm NEC Corp will provide the facial recognition system. The facial recognition system will also be used in the 2020 Paralympics;  it was tested in the Rio 2016 Olympics. The system is built around an AI engine called NeoFace. In addition to athletes, the system will be used to identify volunteers, media, and other staff. It will be used to identify around 300,000 people across more than 40 venues. The Olympics will begin on July 24, 2020. People attending the event are expected to submit their data in advance before the Olympics start. It will be approved and stored in a database and used to identify players before entry. The system will link the person’s facial data with an IC card that will be carried by them. So entry would be permitted only if the facial data stored in the database matches the data stored in the IC carried by the person. Tokyo 2020 has security challenges since venues are not that large. This will result in long wait times before the players can get into the venues. With the summer heat in Tokyo, this presents a problem for the players. The events will be spread out across the metropolitan area, and people will have to authenticate themselves at every entry. The NeoFace system is introduced to address these problems in the Tokyo Olympics venues. The facial recognition system is aimed at strengthening security and minimizing waiting times for athletes. NeoFace will also help with identifying forged ID cards and help athletes avoid the stress of waiting in long lines for identification. NEC has substantial experience in the facial recognition field and their technology has been used at airports for several years. The Tokyo Olympics 2020 may be the Olympics event with most security implemented yet. For more information, you can check out the coverage by Reuters. Read Next: Microsoft’s Brad Smith calls for facial recognition technology to be regulated Amazon is selling facial recognition technology to police Admiring the many faces of Facial Recognition with Deep Learning

The U.S. Defense Advanced Research Projects Agency ( DARPA) has come out with AI-based forensic tools to catch deepfakes, first reported by MIT technology review yesterday. According to MIT Technology Review, the development of more tools is currently under progress to expose fake images and revenge porn videos on the web. DARPA’s deepfake mission project was announced earlier this year. Alec Baldwin on Saturday Night Live face swapped with Donald Trump As mentioned in the MediFor blog post, “While many manipulations are benign, performed for fun or for artistic value, others are for adversarial purposes, such as propaganda or misinformation campaigns”. This is one of the major reasons why DARPA Forensics experts are keen on finding methods to detect deepfakes videos and images How did deepfakes originate? Back in December 2017, a Reddit user named “DeepFakes” posted extremely real-looking explicit videos of celebrities. He used deep learning techniques to insert celebrities’ faces into adult movies. Using Deep learning, one can combine and superimpose existing images and videos onto original images or videos to create realistic-seeming fake videos. As per the MIT technology review,“Video forgeries are done using a machine-learning technique -- generative modeling -- lets a computer learn from real data before producing fake examples that are statistically similar”. Video tampering is done using two neural networks -- generative adversarial networks which work in conjunction “to produce ever more convincing fakes”. Why are deepfakes toxic? An app named FakeApp was released earlier this year which helped create deepfakes quite easily. FakeApp uses neural networking tools developed by Google's AI division. The app trains itself to perform image-recognition tasks using trial and error. Ever since its release, the app has been downloaded more than 120,000 times. In fact, there are tutorials online on how to create deepfakes. Apart from this, there are regular requests on deepfake forums, asking users for help in creating face-swap porn videos of ex-girlfriends, classmates, politicians, celebrities, and teachers. Deepfakes is even be used to create fake news such as world leaders declaring war on a country. The toxic potential of this technology has led to a growing concern as deepfakes have become a powerful tool for harassing people. Once deepfakes found their way on the world wide web, many websites such as Twitter and PornHub, banned them from being posted on their platforms. Reddit also announced a ban on deepfakes, earlier this year, killing The “deepfakes” subreddit which had more than 90,000 subscribers, entirely. MediFor: DARPA’s AI weapon to counter deepfakes DARPA’s Media Forensics group, also known as MediFor, works in a group along with other researchers is set on developing AI tools for deepfakes. It is currently focusing on four techniques to catch the audiovisual discrepancies present in a forged video. This includes analyzing lip sync, detecting speaker inconsistency, scene inconsistency and content insertions. One technique comes from a team led by Professor Siwei Lyu of SUNY Albany. Lyu mentioned that they “generated about 50 fake videos and tried a bunch of traditional forensics methods. They worked on and off, but not very well”. As the deepfakes are created using static images, Lyu noticed that that the faces in deepfakes videos rarely blink and that eye-movement, if present, is quite unnatural. An academic paper titled "In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking," by Yuezun Li, Ming-Ching Chang and Siwei Lyu explains a method to detect forged videos. It makes use of Long-term Recurrent Convolutional Networks (LRCN). According to the research paper, people, on an average, blink about 17 times a minute or 0.283 times per second. This rate increases with conversation and decreases while reading. There are a lot of other techniques which are used for eye blink detection such as detecting the eye state by computing the vertical distance between eyelids, measuring eye aspect ratio ( EAR ), and using the convolutional neural network (CNN) to detect open and closed eye states. But, Li, Chang, and Lyu use a different approach. They rely on  Long-term Recurrent Convolutional Networks (LRCN) model. They first perform pre-processing to identify facial features and normalize the video frame orientation. Then, they pass cropped eye images into the LRCN for evaluation. This technique is quite effective. It is also better as compared to other approaches, with a reported accuracy of 0.99 (LRCN) compared to 0.98 (CNN) and 0.79 (EAR). However, Lyu says that a skilled video editor can fix the non-blinking deepfakes by using images that shows blinking eyes. But, Lyu’s team has a secret effective technique in the works to fix even that, though he hasn’t divulged any details. Others in DARPA are on the look-out for similar cues such as strange head movements, odd eye color, etc as these little details are leading the team even closer to detection of deepfakes. As mentioned in the MIT Technology review post, “the arrival of these forensics tools may simply signal the beginning of an AI-powered arms race between video forgers and digital sleuths” and how”. Also, MediFor states that “If successful, the MediFor platform will automatically detect manipulations, provide detailed information about how these manipulations were performed, and reason about the overall integrity of visual media to facilitate decisions regarding the use of any questionable image or video”. Deepfakes need to stop and the U.S. Defense Advanced Research Projects Agency ( DARPA) seems all set to fight against them. Twitter allegedly deleted 70 million fake accounts in an attempt to curb fake news A new WPA/WPA2 security attack in town: Wi-fi routers watch out! YouTube has a $25 million plan to counter fake news and misinformation  

Snapchat's redesign was met with considerable anger earlier this year, with 1.2 million users signing a petition opposing the redesign. The impact of that anger was revealed on Tuesday with Snap Inc (Snapchat's parent company) confirming in a prepared Q2 earnings call statement that Snapchat had dropped from 191 million to 188 million daily active users in a quarter. However, CEO Evan Spiegel countered this 2% drop by revealing that Snapchat's monthly active users are actually growing in number. However, despite losing users, Snap Inc. has seen impressive results in terms of revenue. Year on year, revenue has grown 44% - in Q2 2017 it was 182 million up to 262 million in Q2 2018. That's 44% growth.  Snapchat's redesign was a gamble Clearly, Snapchat's decision to redesign its platform was a gamble - whether or not it will pay off isn't immediately clear. The trade-off is one between a design that users had grown to love and making the platform more friendly to advertisers and publishers that want to reach those users. This is something Spiegel recognizes in his statement: We feel that we have now addressed the biggest frustrations [about the redesign] we’ve heard and are eager to make more progress on the tremendous opportunity we now have to show more of the right content to the right people. For example, the number of people that watch Publisher Stories and Shows on iOS every day has grown by more than 15 percent this year, and we are excited to bring the learnings from our iterations on iOS to our Android application. Additionally, more Snaps from Publisher Stories and Shows were viewed in July than any other month in our history. You might say that this is now a critical moment for Snap Inc. This could just be a blip as it moves to grow its revenue streams with a more publisher-friendly platform. But equally, this might be an indication that users are beginning to fall out of love with Snapchat. This is all good news for Instagram, who might now smell blood as it appears to be moving from strength to strength in the battle for audience share. Is Snapchat's case unique in the social media world? To a certain extent, Snap's challenges aren't actually that unique in the context of social media companies. At the end of July, for example, Facebook shares fell 20% after its average daily visitors missed analyst projections. Twitter has also been in a similar situation with its market value struggling. However, one analysis put forward by Bloomberg suggests that Snapchat is having an identity crisis. Having "defined itself in opposition to the internet establishment," the platform is now "borrowing liberally from the internet conventions it has scorned." These 'conventions' are those design tactics deployed by platforms to drive revenue through advertising. We'll just have to wait and see how this identity crisis evolves, and what Snap Inc. decides to do next. Read next 15 year old uncovers Snapchat’s secret visual search function How to stay safe while using Social Media Facebook, Apple, Spotify pull Alex Jones content