Getty Images, a company that provides photographs and illustrations to news organizations and businesses, has prohibited the sale of AI-generated artwork on its website out of concern about copyright infringement. The upload and sale of illustrations created with AI art tools like DALL-E, Midjourney, Craiyon, and Stable Diffusion are therefore prohibited for users.
Since last year, DALL-E and other AI art generators have grown in popularity because they make it simple for nonprofessional artists to produce stunning digital art. In addition to being displayed in galleries all around the world, AI art has been auctioned at Christie’s auction house. Unfortunately, the repercussions of selling it to customers are causing several image distribution websites to struggle.
Getty Images CEO Craig Peters expressed reservations about the legitimacy of AI images in an interview with The Verge. With systems like Stable Diffusion blatantly ripping off artists’ styles, generative art sits in a legal gray area. According to Peters, the individuals who license the content are also in trouble because of the use of these photographs. In light of this, it is safer to solely license user-created photos in the near future.
Whether Getty Images has faced legal problems for selling AI-generated content is a question Peters has declined to answer. He reiterated that the firm was merely implementing this policy to prevent danger to customers’ reputation, brand, and bottom line and that such content was very rare on the site.
Although Shutterstock, Getty’s main rival, appears to be limiting the photos in search results, it has not yet prohibited AI-generated material. Other photo distribution websites, such as Newgrounds, PurplePort, and FurAffinity, have also banned AI-generated content.
Peters acknowledged that it could be impractical to outright prohibit all AI content. The company will rely on users to report any content that appears to have been produced using AI. In other words, there won’t be an automated filter for the time being.
The Verge notes that scraping is permitted in the US, and it appears that the “fair use” theory also applies to the software’s output. However, fair use affords less protection to commercial activities such as selling photographs, and several artists whose work has been scraped and reproduced by AI image generators, thus, original artists are calling for new regulations to govern this realm.
James Earl Jones has bid farewell to Darth Vader after 45 years of portraying one of the most iconic figures in cinematic history. The 91-year-old actor, who has been providing the voice of Darth Vader since “Star Wars: A New Hope” premiered in 1977, handed over the rights to his archived voice recordings earlier this year, allowing Vader’s dialogues to now to be produced by artificial intelligence. After being shown the work done by Ukrainian start-up Respeecher on the most recent Disney+ series, Obi-Wan Kenobi, Skywalker Sound presented Jones with the choice, and Jones approved it.
Respeecher employs “archival recordings and a proprietary AI algorithm to construct fresh conversation using the voices of artists from a bygone era. According to Lucasfilm supervising sound editor Matthew Wood, the company also ensures that the results sound at par with the human voice. James Earl Jones acknowledged that the startup’s technological prowess in restoring his voice for Obi-Wan Kenobi left him amazed.
In the newly released miniseries “Star Wars: Obi-Wan Kenobi,” actors Ewan McGregor and Hayden Christensen have reprised their roles as Obi-Wan and Anakin Skywalker, respectively. Although Christensen donned Vader’s famed costume in the series, Respeecher delivered his voice during his battles with his former mentor and the Sith Inquisitor Reva Sevander.
Wood claimed that James Earl Jones acted as a mentor for Respeecher, which mainly relied on recordings of his voice to train their AI program to mimic the terror in his voice that he had in 1977 when he originally played Darth Vader. They also used recordings from Jones’ more recent performances in the “Star Wars” movies “Rogue One” and “The Rise of Skywalker” to train their AI software to reproduce his voice but not as much as the first movie. This explains why Vader sounds a lot like he did in the previous films in the Obi-Wan Kenobi series. The same algorithm was used to reproduce Mark Hamill’s youthful voice as Luke Skywalker in another Disney+ series, “The Book of Boba Fett.”
India’s finance ministry is reportedly working on a comprehensive goods and services tax (GST) regime regarding crypto assets.
A source said that the authorities are still discussing the applicability of GST for crypto assets. Currently, it is levied on services, so they need to see if crypto assets are declared as a service or good. Previously, the government was considering imposing either an 18% or a 28% GST on crypto assets.
Another source said that a better understanding of how cryptocurrencies fit into our legal system is the prerequisite for deciding the GST rate. The GST will only be applied on the service fees or margin and not on the entire asset value, the publication conveyed, adding that the government is also examining the treatment of specific transactions, like mining or airdropped crypto tokens.
An Indian ministerial panel allegedly met at the end of June to discuss the GST taxation on crypto transactions. However, the officials did not come up with any decision from the meeting.
The Indian government has already begun taxing crypto income and transactions. A 30% tax on income from cryptocurrency assets came into effect on April 1. Moreover, a 1% tax deducted at source (TDS) on payment of crypto assets begun applying on July 1.
Meanwhile, the Indian government is also discussing the country’s crypto policy. The government plans to decide its stance on the legality of cryptocurrencies by early next year to qualify as Financial Action Task Force (FATF) compliant.
In August, Prime Minister Narendra Modi voiced his desire that on August 15, 2022, when we celebrate our 75th Independence Day, ‘Azadi ka Amrit Mahotsav,’ every Indian should hoist a flag from the rooftops of their homes. In addition, he encouraged people to take selfies with the national flag at their residences during the exercise from August 13 to August 15 and upload them on the “Har Ghar Tiranga” official website.
Thanks to aggressive campaigning government’s Har Ghar Tiranga campaign received unprecedented participation nationwide, according to the BJP party’s official statement on August 12, 2022. The Ministry of Culture reported that by August 15, approximately 60 million Indian citizens had posted images of themselves holding the national flag on the website, describing it as a “stupendous achievement.” About 50 million people have geotagged their homes with images and provided their phone numbers to register on the platform.
Digital rights advocates questioned the initiative at the same time, claiming that what appeared to be a simple voter outreach exercise could actually be an elaborate plan to gather personal information from residents. Earlier, the Har Ghar Tiranga website claimed data would be deleted after the campaign. However, the geotagged selfies have been online for over a month now. The images are still available on the internet, they can be zoomed and could be saved too. Further, according to the website, it doesn’t intentionally gather any personally identifying data from users under the age of 18. However, the publicly available selfies on the website prove otherwise.
Srinivas Kodali, a researcher with the Free Software Movement of India, expressed reservations about the Indian government’s extensive use of geotagging of its own populace. The magnitude of response for Har Ghar Tiranga was unmatched, despite several earlier attempts to geotag citizens to cash in data. This information can be used to fabricate votes, thereby influencing the outcome of elections.
Adding fuel to the fire, last week, a viral news story published by the international non-profit journalism group, Rest of the World, penned by Srishti Jaswal, highlighted digital rights advocates could be right in stating the Bharatiya Janata Party’s voter outreach project, was a veiled scheme to gather data from citizens, which might now be exploited by commercial corporations looking to sell personal data.
She claimed that their initial plan was to compile information on nearly 200 million Indians and that Independence Day celebrations offered the perfect opportunity to carry out this notorious plan because the majority of the public is susceptible to manipulation due to nationalist sentiment, patriotism, and zeal. The alleged pro-BJP propaganda gained momentum when well-known celebrities and athletes, including Amitabh Bachchan, Rajnikanth, Anupam Kher, Sachin Tendulkar, and Rohit Sharma, increased traffic to the website by taking part in the initiative. Even the caller tones have been replaced with a voice requesting telecom customers to sign up for the Har Ghar Tiranga initiative. The Ministry of Culture awarded “Har Ghar Tiranga Certificates” to anyone who registered on the website, revealed their location, and uploaded a selfie. This certificate played a huge role in catalysizing participation from the citizens.
The union government had hired around 200 manufacturers, including small and medium-sized firms and self-help organizations, to manufacture the flags. According to the ministry, the program was designed to encourage both a physical and emotional connection to the flag within a personal setting. Before, Indian people could only hoist the National Flag on specific occasions.
This changed when Naveen Jindal, an industrialist, won a decade-long legal battle that resulted in the historic Supreme Court ruling of January 23, 2004, which stated that the freedom to fly the national flag with respect and dignity is a fundamental right of an Indian citizen under the terms of Article 19(1)(a) of the Indian Constitution.
The Har Ghar Tiranga portal is hosted on Amazon web servers, in contrast to the majority of Indian government websites, which are housed on nic.in’s official servers. Tagbin, a private firm located in India, Singapore, and Dubai, is behind the website, according to a press release released by Asian News International (ANI). The location of where the website stores the data it collects is unclear. Additionally, the website shares your IP address with over 15 other websites, some of which have country code extensions from other regions of the world, leaving Indian people’ sensitive information prone to exploitation.
Ayushman Kaul, a senior threat intelligence analyst at Logically, a technology company that specializes in monitoring and combating disinformation, told Rest of World that the website also employs cookies to track users’ surfing patterns. Kaul thinks this is a baldfaced sign whoever is behind the website wants to collect more user data. Furthermore, linking a Google sign-in with the website might possibly allow the website creators to gather extra personally identifiable information from Indian citizens who used the website. This will allow user privacy abusers to build a complete demographic and psychological profile of the entire population by combining a variety of such information and personal markers.
A BJP member told Rest of World that individuals often post their photos on Facebook or Instagram without worrying about privacy issues in response to a question regarding the possible exploitation of these images. They dismissed the privacy concerns surrounding the data present on the website. The representative also denied the presence of sensitive information on the website and pointed out that users themselves consented to use the selfies while taking part in the Har Ghar Tiranga initiative.
The BJP-led Indian government has attempted to gather data unfairly before. The Aadhar identity database allegedly released all registered citizens’ personal information in 2018 including names, bank information, and biometric information. Last year, WhatsApp sued the Indian government to stop the implementation of new regulations that called for platforms for instant messaging to reveal the “originator” of communications upon request from law enforcement.
At least 1500 persons who took the COVID-19 test in 2021 had their names, birth dates, testing centers’ locations exposed on official websites. Interestingly, the data was freely accessible via Google indexing but was not sold on the dark web.
The government had also introduced the contact tracing app Aarogya Setu as part of its frantic attempts to tackle COVID-19. The goal was to locate users using geolocation and alert them if they had contact with an infected individual. The Indian government was already tracking potential illnesses and locating hand-stamped individuals who had vowed not to travel using airline and train reservation data. The Aarogya Setu app used a range of technology strategies to make contact tracking and location monitoring possible. These include real-time geotagging-enabled selfie-based hourly check-in(s), and face recognition software systems for those who are being isolated at home. However, it was often noticed that the app lacked accuracy!
The government assured that all contact and location tracking information was removed from the phone on a rolling 30-day period. Unless you test positive, in which case all contact tracing and location information was erased 60 days after being certified healed, the identical data on the server was deleted 45 days after the upload.
However, the Aarogya Setu Data Access and Knowledge Sharing Protocol specifies that de-identified data could be shared with any government institution, provided that it is done so in order to combat Covid-19. The protocol specifies that any received data shall be permanently erased after 180 days. However, privacy advocates claim there is no way to determine if that has actually occurred.
While it is undeniable that these technological interventions were essential for locating hotspots, coordinating efforts between relief organizations, allocating resources, and facilitating quick decision-making, they also pose the risk of systematic mass surveillance, so it is important to be aware of these risks. This suggests that the information from Har Ghar Trianga might be used for widespread monitoring and geo-targeted political advertisements.
Although the location data is not made publicly accessible, the Har Ghar Tiranga website retains it, according to Kodali, which could result in theft, hacking, and stalking. People may become more susceptible to geo-propaganda when siloed data, such as phone numbers, photos, and location, is combined with other data sets, such as constituency demographics and voting preferences. This Orwellian possibility draws parallel to the mass abuse of user data privacy using US Presidential elections. While Cambridge Analytica’s activities are widely known, during the 2020 US Presidential elections, Catholics who regularly attended church services were identified via geotagging. The Donald Trump campaign subsequently targeted these individuals with personalized advertisements.
India presently lacks data protection legislation that may protect its citizens from cyber risks. Just days after the Har Ghar Tiranga initiative was introduced, the Indian government abruptly withdrew a draft of Personal Data Protection Bill that had been making its way through parliament for over three years on August 3. The 2019 bill suggested strict controls on international data transfers and allowed the Indian government to request user data from businesses.
With the lack of data privacy laws, considering past attempts to misuse user data, it is probable that the geo-tagged data from the Har Ghar Tiranga website can see similar risks. Not only that, the use of such data can enable the government to nip any public dissent with measures ranging from lawsuit, unlawful monitoring, and social media account suspension!
Ecommerce giant Flipkart is planning to announce its interactive and Metaverse-themed virtual shopping platform, Flipverse, during the ongoing ‘Big Billion Days Sale.’
Flipkart has partnered with Ethereum Layer-2 and social media giant Meta, scaling startup Polygon for the project. They also said the Metaverse project could be launched as soon as this year’s Diwali (around October).
Flipverse will deploy a host of Web3.0 technologies to elevate the everyday user experience to include a virtual reality experience. The new offering will enable users to walk through the virtual shopping center and interact with digital storefronts. According to the report, Flipverse will host games, NFTs, product launches, and other contests.
This comes after weeks of overt and covert teasing of the Metaverse platform by a clutch of media reports and digital bloggers. Earlier this week, Polygon co-founder Sandeep Nailwal also tweeted about ‘Flipverse by Flipkart’, saying that Polygon and eDAO powered it.
Flipkart has been experimenting with Web3.0 technologies for some time now. In April this year, the eCommerce major launched Flipkart Labs to mark its foray into the world of Web3.0 technologies. The project plans to leverage Flipkart’s in-house capabilities to create technology-based solutions to redefine eCommerce.
The company also unveiled FireDrops earlier this year to explore NFT-related use cases and to bring Web3.0 to a broader audience.
Artificial intelligence is an increasingly important technology that enables computers to simulate human intelligence-based tasks. With a robust foundation of software and hardware, AI can accomplish numerous human tasks with much more efficiency. It works by building and training algorithms using programming languages like Python, R, and more. Once you have an algorithm, the next step is to ingest training data in the model, analyze it, and find patterns/abnormalities to forecast future states. There are many learning materials on artificial intelligence and related technologies. However, not all of those materials credibly explain AI concepts and applications.
Additionally, going through an entire book is time-consuming. Learning from a PPT on artificial intelligence is much easier than reading an entire book or research paper. This blog highlights some popular presentations on artificial intelligence and machine learning.
Some popular presentations topics on artificial intelligence
Introduction to Artificial Intelligence
Going through this AI PowerPoint presentation will be an excellent place to start learning about artificial intelligence. It is a brief presentation introducing you to artificial intelligence and its subsets like machine learning and deep learning without diving into more essential details. It describes what AI, machine learning, and deep learning are. Proceeding with AI, the presentation specifies types of artificial intelligence and its use cases. You will see that AI has applicability in various applications like supply chain management, human resource, knowledge management, customer insights, predictive analytics, and many more. Some of these use cases have been explored and explained via flowcharts to make them more accessible for the reader to understand. Finally, the AI PPT explains the rising trends observed in 2020.
Introduction to ML/AI is yet another artificial intelligence PPT that will introduce you to the subject. The presentation will explain machine learning and discuss several commercial applications. The PowerPoint presentation will also differentiate between machine learning and artificial intelligence. You will be acquainted with supervised machine learning methods using regression and classification techniques and unsupervised learning via clustering, association, and data reduction. The AI PPT will also explain the problems faced while working with the algorithms and the consequent failures. You will have a broad overview of how to begin working with AI/ML, the entire procedure, and the result.
This artificial intelligence presentation is yet another PPT that briefs you about what AI is. It begins by explaining the term “intelligence” and providing several alternative definitions leading to a holistic one. The description is done in tandem with the need for incorporating such technologies. The AI presentation discusses advancements in AI technology, starting with the Turing Test developed by Alan Turing to test for intelligence in the 1950s. Using the test results, one can build a representative model with real-world knowledge and reasoning. There is a detailed procedure for understanding and using the test to solve problems.
You can download PPT on artificial intelligence if you wish to download it here.
Applications of Artificial Intelligence in the Real World
AI is becoming highly applicable in many real-world applications, and this PowerPoint presentation discusses common areas where AI is applied. This presentation talks about AI developments in creating fundamental technologies that can be used in daily life. It begins with recent developments in artificial intelligence and explores some use cases in everyday life. It talks about AI in self-driving cars, chatbots, social media platforms, healthcare, etc.
Sub-ML Framing: Economics of Data Science use cases
In this AI applications PPT, you will learn about experimenting with use cases via artificial intelligence. Before jumping directly into creating fully functional use cases, you can always start by developing basic versions and adding incremental updates. This approach is referred to as the sub-ML approach. The presentation discusses the feature store requirements to implement several use cases in machine learning. You can easily understand the framework by only going through the architectures given in this paper presentation on machine learning without delving into more details.
This PowerPoint presentation on artificial intelligence will focus on its usefulness in healthcare. AI in healthcare is a trending artificial intelligence topic for presentation due to its increasing popularity and usefulness. New technologies and algorithms have enabled doctors and healthcare professionals to diagnose abnormalities and predict prognosis. The presentation will highlight the different sub-fields where AI-based tools support decision-making and provide more comprehensive access to information regarding health.
Artificial intelligence in Finance
AI in Finance is another application of artificial intelligence PPT you will frequently encounter. This presentation will show how AI can help automate financial service provision, enhance efficiency, and reduce costs. It begins by briefing you about machine learning and its application in four primary segments: capital markets, consumer banking, the insurance industry, and the stock market. The AI PPT will explain financial institutions in the context of big data and discuss several case studies for each primary segment, explaining how artificial intelligence and machine learning are used in the industry.
The Future of AR, VR, and Self-Driving Cars
In this future of artificial intelligence PPT, you will get numerical insights on how artificial intelligence is utilized in virtual reality, wearable technology, and self-driving vehicles. It explains the efficiency and applicability of AI in the mentioned areas and enables you to decide whether to go for a self-driving car based on its relevance in daily life. The presentation talks about VR headsets and their usefulness. Finally, the concluding slides show current trends and future predictions of rising demands and advancements in these areas.
Introduction to Machine Learning
This presentation is an AI applications PPT that will teach you about one of the most utilized sub-field of AI, machine learning. It is an introductory presentation that will brief you about the concept without getting into detail. The first few slides explain machine learning and the difference between ML and standard computer software. You will also read about different types of machine learning: supervised, unsupervised, and reinforcement learning. The PPT also mentions some advantages and disadvantages of machine learning.
Deep learning is another sub-field of artificial intelligence that deals with applications that function like human brains. The word “deep” is related to the multiple layers used in the learning process. This poster presentation on AI, specifically on a sub-field of AI, combines 2-3 presentations. The first few slides introduce several deep learning models, specifically logistic regression and gradient boosting. These two models have been explained in detail, covering the types and their basic architectures. In the second half, the presentation discusses convolutional neural networks (CNN) in detail. It also explains how you can create one by pooling multiple layers. The third section discusses GANs (generative adversarial networks) and RNNs (recurrent neural networks). Going through this presentation will give you a great insight into deep learning. You can download it from here.
The above presentation topics on artificial intelligence allow you to gain a brief understanding of AI.
Metaform and XP&DLand have announced a MetaPujo today, which will foray to bring Durga puja festivities of Kolkata to the metaverse. Deshapriya Park, Ballygunge Cultural, Ahiritola Sarbojanin, and Tala Prattay pandals will be accessible via three-dimensional (3D) twins on the metaverse.
MetaPujo will introduce four NFTs of Durga idols from the most visited Durga pandals of Kolkata. The platform has planned centers around 3D recreations of the pandals, where users in a metaverse platform, ‘Spatial,’ can enter a shared social space where people from worldwide can come together, interact, and even take photographs.
Users have to create a meta-realistic avatar of themselves, with the platform accessible to all through simple smartphones, tablets, and wearables. As stated by Sukrit Singh, co-founder of Metaform and XP&DLand, the platform aims to bridge the gap between devotion and technology.
“This initiative aims to give Durga an address on Web3.0, where the devout from across the world can enter a three-dimensional recreation of the pandals and participate in darshans from the popular pandals of Kolkata. While doing so, attendees could claim four NFTs of her idols that have been created especially for the current occassion, each correlating to one of the pandals,” he added.
“We aim to democratize the metaverse. One doesn’t have to be in Kolkata to celebrate the pujas now. Meta pujos will allow people from across the world to enter pandals as family and friends, separated by physical distance but united in celebration.”
“In addition to presenting these experiences, introducing the concept of digital ownership, we intend to drop digital collectibles or NFTs, which can be purchased. These digital tokens will generate a value over time given their real-life utility. In the future, the plan is to monetize the collectibles and drive community with utility (whether for community, experiences, or collectibles) at a later stage with the intent to build a business,” Suveer Bajaj, co-founder of Metaform and XP&Dland, stated.
Meta and Google are reducing staff to cut costs amid the economic downturn. The companies have put some employees on traditional 30 to 60 days lists to find a new role within the company or leave.
Wall Street Journal reported that Meta is planning to cut costs by at least 10% in the coming months and has put more and more workers whose jobs are being eliminated on its traditional 30-day list.
Moreover, Google’s parent Alphabet has reportedly employed a similar approach, typically allowing workers 60 days to apply for a new role if fired.
Meta has a long history of eliminating employees whose roles are subject to termination if they cannot find a new job internally at the company within a month. Meta had a total of 83,553 employees at the end of this year’s second quarter.
Last month, Google fired about 50 workers at its incubator Area 120 and gave them more 30 days to find another job. A Google spokesman said that nearly 95% of employees found new roles within the notice period.
Alphabet CEO Sundar Pichai aims to make the company 20% more efficient, hinting at job cuts. The tech giant recently canceled the projects at its in-house research and development (R&D) division called Area 120.
Today, people use different coding languages for better results or just the zeal to learn something new. The history of coding languages is quite interesting and dates back to the early 1800s. Many modern coding languages have roots in Ada Lovelace’s first machine algorithm developed in 1843. Until now, nearly 9000 coding languages have been created, among which only the most popular ones that count to 150 are vastly used. Coders prefer some coding languages for a specific field like Python for developing websites and software, R programming for statistics, etc. With the development in technology, the coding languages are evolving too, which enhances the way of working, outputs, and the user experience. The translation of a code script or to translate coding languages is required when companies switch from one language to another, as Commonwealth bank of Australia did from COBOL to Java which cost them $750 million in five years. With AI’s application, transpilers are developed as coding languages translators to save money and time.
What is a coding languages translator?
A coding language translator is a tool that translates a program code from the source code into the destination code. Translating coding languages is like the same process of translating languages from one to another but complicated. In the process, we alter the data but want to preserve the data structure as far as possible. Theoretically, a piece of code is translatable to any other language if the programming languages are Turing complete. Turing completeness is a property in computability theory, where you can perform any computation on anything that any other computational method is capable of. We use compilers to perform translations on coding languages, but compilers are frequently used to change code so that a machine can interpret it. It requires a subset of compilers called transpilers for the code to be human-readable. Transpiler (aka source-to-source compiler) is a translator capable of translating between programming languages that operate on the same level of abstraction. Unfortunately, creating a transpiler in practice is challenging since different languages have different syntaxes and rely on various platform APIs and standard library functions.
Why it is difficult to translate coding languages?
Translation of coding languages is a tedious task that includes preserving the exact execution semantics and general code similarities at the same time. Besides, both the software you are working on and the languages to be translated makes the process challenging. When an application has many external dependencies that are difficult to replace in the target technology stack, the challenges are different than for an app that implements everything internally. You see, there can be several subtitles in either the source or the destination code, which need to be satisfying or may not matter in translation. Look at the ‘+’ operation in Python, C, and C#; the operand is the same operation yet performs differently. That is, mathematically, the addition is correct in Python but can expand more than 32 bits instead, it stays to 32bits only in C, and in C#, it may throw an exception depending on the mode. Another reason is the calling of library functions. When you translate programming languages with different semantics, for example, Python to C#, the Python functions in your code will not work in C# because they aren’t available in C#. Over time, many researchers came up with the idea of translating coding languages. Here is a list of tools for translating coding languages.
TransCoder AI is a programming languages translator system developed by Facebook researchers, which can translate code among C++, Java, and Python languages. It is based on an unsupervised machine learning algorithm to perform translation and is one of the best Python transpilers. It was trained on over 2.8 million open-source projects and outperformed existing code translation systems using rule-based translation methods. TransCoder was first proposed in the paper, ‘Unsupervised Translation of Programming Languages, ‘ published on arXiv. The paper talks about transcompliers and how TransCoder works. The algorithm used in TransCoder is inspired by the neural machine translation (NMT) system to programming languages. The algorithm in TransCoder identifies common elements between input and output languages called tokens. Tokens can be defined as the keywords in programming languages like ‘for,’ ‘if,’ ‘else,’ ‘try,’ etc., and the mathematical digits and operators. Some tokens are the common strings that are present in the code. Then, the translation quality is improved by the algorithm using the back-translation method. The back-translation method induces to build source to destination code and destination to source code simultaneously and are coupled together at the end to give the final output. You can use the TransCoder by following the steps in the repository.
The testing for accuracy was done using 852 parallel functions in C++, Java, and Python from GeeksforGeeks. The computational accuracy was calculated while translating between C++, Java, and Python based on the BEAM seach of N=25, which is listed here.
Computational accuracy of translation between:
C++ to Java – 74.8%
C++ to Python – 67.2%
Java to C++ – 91.6%
Java to Python – 68.7%
Python to Java – 56.1%
Python to C++ – 57.8%
TransCoder works on a transformer-based sequence-to-sequence architecture consisting of an attention-based encoder and decoder. It follows three principles, one cross-lingual masked language model pretraining, denoising auto-encoding, and back-translation. Below is an illustration presented in the paper mentioned above.
Source: arxiv.org
IBM’s CodeNet
CodeNet is a work-in-progress project of IBM that has aimed to teach AI to code. At IBM’s Think 2021 conference, Arvind Krishna, CEO of IBM, revealed the project CodeNet, which is a two-year effort of Dr. Ruchir Puri and an IBM research team. The project CodeNet is a large-scale dataset with approximately 14 million code samples written in over 50 programming languages. The wide variety of languages and coding problems CodeNet has contains over a hundred solutions each for 80% of the problems. The idea behind project CodeNet is to enable researchers and developers to help in code search, code completion, code-to-code translation, and a combination of other use cases. A detailed description of project CodeNet is done in this paper, ‘CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks.‘ CodeNet is similar to ImageNet, a computer vision dataset having 14 million images scattered across 20,000 categories. Though the operation of CodeNet to translate code-to-code is not ready yet, it is a promising project and expects to perform well.
GWT, Google web toolkit, is a development toolkit for building and optimizing complex browser-based applications. It is an open-source set of tools allowing developers to create and maintain JavaScrip front-end applications. GWT’s objective is to make it possible to construct high-performance web apps productively without the developer having to be an expert in JavaScript, XMLHttpRequest, or browser quirks. GWT is a popular coding languages translators that work to create and debug AJAX apps in Java, and when pushed to production, the compiler converts your Java application to browser-friendly JavaScript and HTML.
GWT has two major components:
GWT SDK: It contains the Java API libraries, compiler, and development server. It allows the user to write client-side applications in Java and deploy them as JavaScript.
Plugin for Eclipse: It provides IDE support for GWT and app engine web projects.
You can start using GWT. The first step is to download the SDK and take in-depth tutorials to understand the fundamentals of GWT development.
GitHub Copilot
GitHub Copilot is an AI pair programmer powered by OpenAI Codex, a new AI system created by OpenAI. It helps developers to write code faster with fewer efforts by offering autocomplete-style suggestions as you code. The suggestions are given either while writing the code directly or by writing a natural language comment about what you want the code to do. It is similar to giving commands to write code. GitHub Copilot provides various use cases with coding, including writing code with a simple command in more than one language, creating dictionaries of lookup data, navigating a new codebase with GitHub Copilot Labs, and more.
The GitHub Copilot Labs is a companion extension to GitHub Copilot, which can be installed from the visual studio marketplace. It is a visual studio code extension that contains experimental applications of Copilot, which are ML-powered editor features for developers. One of the features of Copilot Labs is translating coding languages from one to another, which comprises around 60 coding languages to choose from.
The process of coding translation consists of only three steps:
Install the GitHub Copilot Labs extension.
After installing Copilot Labs, open the folder you want to translate, then click on the extension icon.
Now, you can see the window ‘Translate code into:’, highlight the code, select your desired language and click on ‘Ask Copilot’.
Following the steps mentioned above, your code will be successfully translated.
The task of code language translation is challenging, and many will find it amusing to become a reality. We have just a handful of coding languages translators, but we are hopeful and believe the best is yet to come. For now, Facebook’s TransCoder shows remarkable results, successfully understanding the syntax specific to each language and learns data structures and their methods. At higher levels, maintaining a transpiler is more difficult than developing one as new technologies emerge and new features are introduced when programming languages advance. As a result, the work on transpilers is a consistent process and will need more hands on the deck.
In a new study, researchers from the University of Washington School of Medicine assert that deep learning can be used for protein molecule synthesis much more accurately and quickly than previously possible. The scientists hope this revelation will lead to many new vaccines, treatments, tools for carbon capture, and sustainable biomaterials.
Proteins are composed of lengthy chains of amino acids connected by peptide bonds. They are essential for the chemistry of the body as well as the structure of cells and communication between them. A protein’s function is determined by its shape. When protein creation goes wrong, the resulting malformed proteins cause failure to perform their crucial tasks or neurological diseases, like Alzheimer’s, Parkinson’s, Huntington’s, and Lou Gehrig’s (ALS) disease. A protein must “fold” in order to perform its function in the cell. Protein folding is the procedure by which a molecule is changed into a complicated 3D structure that can interact with its target in the cell.
The protein won’t form the correct alignment nor carry out its function inside the body if the folding is interrupted. To better understand how cells work and how misfolded proteins contribute to disease, researchers have been focusing on understanding protein folding. Better protein prediction methods will also aid in developing drugs that can target a specific topological region of a protein where chemical reactions occur.
According to the study led by biochemist David Baker at the University of Washington in Seattle, the shapes that can be found in nature are only a tiny portion of what is thought to be conceivable.
In December 2020, DeepMind’s protein structure prediction tool AlphaFold made headlines when it won the Critical Assessment of Protein Structure Prediction, or CASP, competition. The competition, which is held every two years, assesses advancement in one of biology’s most challenging problems: figuring out proteins’ three-dimensional (3D) structures strictly from their amino-acid sequence. Entries made using computer software are compared to protein structures identified using experimental methods like X-ray crystallography or cryo-electron microscopy (cryo-EM), which shoot X-ray or electron beams at proteins to produce an image of their shape.
In the 1990s, Baker’s team began creating a software called Rosetta that helps with protein folding. The software first determined an amino acid sequence corresponding to the structure researchers had originally envisioned for a novel protein, typically by fusing pieces of other proteins together.
But when created in the lab, these “first draft” proteins rarely folded into the required form and were instead locked in various ways. Therefore, another step was required to modify the protein sequence such that it would only fold into the particular desirable structure. This stage was computationally intensive because it involves modeling every possible folding scenario for various sequences.
That time-consuming procedure has been made instantaneous by employing AlphaFold. In a method known as “hallucination,” which Baker’s team created, scientists input random amino-acid sequences into a structure-prediction network; this changes the structure so that it becomes ever more protein-like, as evaluated by the network’s predictions. In simple words, this method consists of taking pieces of an existing structure and asking the AI to fill in the gaps. In a 2021 study, Baker’s group reported finding evidence that around one-fifth of the tiny, “hallucinated” proteins they produced in the lab resembled the predicted form.
In the past year, a team under the direction of Minkyung Baek, a postdoctoral scholar in the Baker lab, created software that employs deep learning to swiftly and accurately predict protein structures from sparse data. Dubbed as RoseTTAFold, the team developed this AI software as it wasn’t clear when DeepMind would make the AlphaFold software or its forecasts publicly available.
Researchers describe RoseTTAFold as a “three-track” neural network, which means it constantly considers potential three-dimensional structures of proteins, patterns in protein sequences, and how amino acids interact with one another. This architecture enables the network to collectively reason about the link between a protein’s chemical components and its folded structure by exchanging one-, two-, and three-dimensional information.
Rosetta is a revolutionary toolset for protein structure prediction, however, its success rates are really relatively low, i.e., just a small percentage of its designs successfully fold and function as intended. Deep learning models like AlphaFold and RoseTTAFold are stepping up the game!
Baker’s team divided the problem of protein design into three pieces and employed novel software solutions for each to create proteins that go beyond the proteins found in nature.
To begin, a new protein form must be created. The scientists demonstrated how artificial intelligence could create novel protein forms in two methods in an article released on July 21 in the journal Science. The first was “hallucination,” and the second was “inpainting,” which is similar to the autocomplete function seen in current search bars.
Baker and his colleagues believed they could create self-assembling proteins using hallucination that would form variously sized and shaped nanoparticles. However, none of the 150 designs worked when scientists taught microbes to create them in the laboratory.
To speed up the process, a deep-learning tool was developed simultaneously by Justas Dauparas, a machine-learning expert. This main objective was to handle the so-called inverse folding issue, which is identifying the protein sequence that matches a given protein’s overall structure. According to the September 15 edition of Science, the ProteinMPNN approach for designing protein sequences is based on deep learning and has exceptional performance in both silico and experimental tests. By changing sequences while keeping the overall shape of the molecules, it can serve as a “spellcheck” for designer proteins developed with AlphaFold and other tools.
The researchers evaluated and improved the predicted sequences using protein structure prediction algorithms and laboratory protein synthesis. Next, the scientists used X-ray crystallography to confirm the protein structures and cryo-electron microscopy to determine the proteins’ shapes. The researchers identified the structures of 30 of their novel proteins, and 27 of them matched the AI-led designs.
ProteinMPNN has a sequence recovery rate of 52.4% on native protein backbones, compared to 32.9% for Rosetta. In creating the molecules experimentally, Baker and his team had significantly more success when they used this second network on their hallucinated protein nanoparticles.
Finally, using AlphaFold, the team independently determines if the proposed amino acid sequences will likely fold into the desired shapes. According to Baker, ProteinMPNN is to protein design what AlphaFold was to the prediction of protein structure. The team explains that while protein structure prediction software is a part of the solution, it cannot provide any original ideas by itself. In order to discover any new functional proteins, you would need to comb through billions of sequences, even if you had the ideal technology for predicting how protein sequences fold.
A group from the Baker lab demonstrated in a different study published on September 15 in Science that combining ProteinMPNN and the other new machine learning methods could consistently produce proteins that worked in the lab. According to the author, you need to understand these molecules in the real world rather than just relying on the computer to build proteins correctly. According to project scientist Basile Wicky, the team discovered that proteins generated using ProteinMPNN were significantly more likely to fold up as planned, and we could construct highly complicated protein assemblies using these approaches.
According to Baker and his colleagues, synthesizing a brand-new protein in the laboratory is the true test of deep learning-based protein structure prediction approaches. This is evident from their early failure to create hallucinated protein assemblies. The scientists created a variety of novel proteins, including nanoscale rings that they anticipate could be repurposed as components for innovative nanomachines. The rings, which had widths about a billion times smaller than a poppy seed, were examined using electron microscopes.
Source: arxiv.org