Microsoft, GitHub, and OpenAI are pleading with the court to dismiss a proposed class action lawsuit that accuses these companies of scraping licensed code to create GitHub’s AI-powered Copilot tool, reported Reuters.
According to a pair of filings submitted to a San Francisco court, the Microsoft-owned GitHub and OpenAI say that the claims outlined in the lawsuit do not hold up. Launched in 2021, Copilot uses OpenAI’s technology to generate and suggest codes directly within the code editor of a programmer.
The tool, which has been trained using publicly available code from GitHub, caused concerns over whether it violates copyright laws right after its release. Things escalated when programmer and lawyer Matthew Butterick and the legal team at Joseph Saveri Law Firm filed a proposed class action lawsuit last November, claiming that the tool relies on “software piracy on an unprecedented scale.”
Butterick and his team later filed a second lawsuit on similar grounds on behalf of two anonymous software developers. This is the lawsuit that Microsoft, OpenAI, and GitHub want to be dismissed.
As noted in the filing, GitHub and Microsoft say the complaint fails on two intrinsic defects, lack of an otherwise viable claim and lack of injury. OpenAI similarly said the plaintiffs “allege a grab bag of claims that fail to plead violations of cognizable legal rights.”
According to the companies, the plaintiffs are relying on “hypothetical events” to back their claim and say they do not describe how they were personally harmed by the tool. The court hearing to dismiss the lawsuit will take place in May.
The promise of reducing traffic congestion and human-made accidents has caught our interest in autonomous vehicles for a decade. While we are still far from having fully autonomous vehicles hit the road, the idea of having self-driving cars steer us towards a new futuristic normal is undeniable. However, according to a new study from MIT, it might come at a massive cost.
In a startling revelation, MIT researchers found that the energy needed in the future to power just the computers on a worldwide fleet of autonomous vehicles could produce as much greenhouse gas emissions as all of the data centers in the world right now. The researchers discovered this while investigating the potential energy usage and associated carbon emissions in the likelihood that autonomous vehicles will receive wide adoption in the future.
It is well known that data centers that contain physical computing infrastructure that power online applications have a huge carbon footprint. The International Energy Agency estimates that data centers used about 200 terawatt-hours (TWh), or close to 1% of the world’s electricity demand, and contributed 0.3% of global CO2 emissions in 2018.
Noting insufficient research devoted to studying the possible carbon footprint of autonomous vehicles, MIT researchers developed a statistical model to investigate the issue. According to their findings, 1 billion autonomous vehicles, each powered by a computer using 840 watts, would be using enough energy to produce almost the same amount of global data center emissions from 2018. As a point of reference, there are presently about 1.5 billion automobiles on the planet’s roadways.
The researchers also discovered that in over 90% of the modeled scenarios, EV computers would need to consume less than 1.2 kilowatts (kW) of computing power just to stay within the existing range (below 2018 levels) of data center emissions, which is something we just cannot achieve with present hardware efficiencies. For instance, a different statistical model that examines a scenario in which 95% of all vehicles are autonomous by 2050 and computing workloads double every 3 years reveals that for emissions to remain at the current levels, hardware efficiencies in automobiles would need to double every 1.1 years. Meanwhile, the Moore’s Law rate, which has been widely recognized in the industry for decades, indicates that computer power doubles approximately every two or more years. To make things worse, this rate is anticipated to slow down rather than accelerate eventually.
Soumya Sudhakar, lead MIT researcher on the study, said that though the findings are only projections, they should urge those working on self-driving cars to understand that doing things “as usual” is insufficient and that computer efficiency should be a top priority. This is crucial to minimize the emissions from computers onboard autonomous vehicles. Soumya adds, “This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start.”
The MIT team built a framework to study the operational emissions from the computers onboard a global fleet of autonomous electric vehicles. This model is dependent on several factors, including the total number of cars in the global fleet, the computing capacity of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity that powers each computer. Because the MIT team is considering a future application that is not yet available, Soumya stated that each variable in the function equation involves a great deal of uncertainty. This was important because, according to some prior research, people might spend more time driving in autonomous vehicles since the hands-off steering wheel means multitasking! Autonomous vehicles could also encourage more younger and older drivers to get behind the wheel. At the same time, other research suggests that the amount of time spent driving can reduce as a result of algorithms discovering the fastest routes to destinations. Another problem is attempting to model for cutting-edge hardware and software technology that doesn’t yet exist.
As a result, researchers used a multitask deep neural network, a well-known method for autonomous cars, to mimic the workload of the algorithm. According to MIT, semi-autonomous vehicles currently use multitask-deep neural networks to navigate their surroundings by continuously receiving real-time data from several high-resolution cameras. Next, the research team explored several situations using the probabilistic model. They were surprised to learn how rapidly the workload of the algorithms increased.
According to one estimate, if a self-driving car employed ten deep neural networks to analyze video from 10 cameras for one hour of driving, it would produce 21.6 million inferences daily. Now consider how many inferences would be generated if one billion cars were used. 21.6 quadrillion conclusions! To put it into context, every Facebook data center in the world generates a few trillion inferences per day, reveals MIT. Imagine how energy-hungry autonomous vehicles are when put together!
Researchers suggested that to improve efficiency, engineers should create specialized hardware that would power navigation and perception tasks as well as run specific driving algorithms. However, Soumya noted that there is a problem with that, as cars frequently have lifespans of 10 to 20 years, and creating specific hardware now would create an additional challenge of making it “future-proof” so that it can support new algorithms.
The authors of the study suggested that researchers could also attempt to develop algorithms that are more effective and use less processing resources. However, that would mean compromising accuracy for effectiveness and possibly endangering vehicle safety.
After establishing this framework, the MIT teams intend to continue to investigate hardware efficiency and algorithm improvements in autonomous vehicles. Furthermore, they believe that characterizing embodied carbon from autonomous cars (i.e., the carbon emissions produced during the production of a car) and emissions from a vehicle’s sensors can improve their model.
The research, which has been published in IEEE Micro‘s January-February edition, was funded by the National Science Foundation and the MIT-Accenture Fellowship. The paper was co-authored by Soumya and her co-advisors, Sertac Karaman, an associate professor of aeronautics and astronautics and the director of the Laboratory for Information and Decision Systems (LIDS), and Vivienne Sze, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE).
Vivienne hopes that this research will motivate automakers to integrate emissions and carbon efficiency metrics into their designs.
Every business is always looking for ways to embrace technology and improve its operations. Technology helps companies work quicker, make fewer mistakes, and generally boosts the business as a whole. One of the most popular types of technology for businesses of all shapes and sizes is RPA (robotic process automation).
It has been catching on with businesses of all shapes and sizes and helps automate various tasks that would have had to be handled manually in the past. But what sorts of specific benefits does this technology provide? Well, that is precisely what this blog post will take a closer look at.
Take Your Customer Service to New Heights
Image Credit: Pixabay
One of the primary uses of RPA among businesses is to improve digital customer service. There are many adverse outcomes of bad customer service, so you want to ensure you are offering the best experience possible to customers who come to you with questions or concerns.
RPA takes customer service to the next level by automating much of the mundane, repetitive, and time-consuming work done by agents before, during, and after customer interactions. By taking these off their plate, agents have more time to focus on customers and help them through their issues.
RPA can help speed up response times, reduce disputes, and customers will be more satisfied with how smoothly their interactions with you have become. Customer demands are constantly changing, and they are always expecting more, and using RPA can ensure your service can meet those high demands.
Boost Overall Productivity and Efficiency
In many cases, the most significant benefit of RPA is that it will work wonders in helping your entire operation become more productive and efficient. RPA can dramatically improve productivity by letting employees take on the detailed and demanding tasks they are needed for and use robots to handle all of the mundane tasks.
Traditionally, workers spend a ton of time on work that can be automated, and RPA takes all of it off of their plates. This includes filing, copying, and pasting information, sending invoices, scheduling meetings, sorting emails, and many others. This gives them more time and energy to use on other work that may be more important.
When it comes to efficiency, robots, and automation, in general, can often perform tasks much quicker than would ever be possible manually. Humans can only work for a certain amount of time and need things like breaks and sleep, whereas robots can work around the clock.
Reduce Your Mistakes
Another major benefit of automation within your business is that it has the potential to reduce errors and mistakes. The high costs of business errors can be awful for your company, and you want to ensure you are accurate in all that you do.
No matter how well-trained someone is, they are still human and will thus make mistakes from time to time. However, robots are programmed to do the same thing each and every time, with a much lower chance of a problem or mistake taking place.
Of course, your RPA will need to be tested thoroughly to ensure it doesn’t make errors, but with the right testing and training, it can be a wonderful way to drastically reduce costly mistakes within your company.
In conclusion, these are just some of the many benefits that RPA can provide for your business.
The CEO of New York startup DoNotPay, Joshua Browder, recently announced that his company’s robot will represent a defendant to fight a traffic ticket in a courtroom on February 22.
“DoNotPay AI will whisper in somebody’s ear exactly what to say and what not to. We will release the results and share more with you after it happens,” he said.
However, we may never know how the “robot lawyer” will fare in court as after a few days, Browder announced that DoNotPay is putting on hold its court case after receiving jail threats from state bar prosecutors going through with his plan.
The CEO told NPR that several state bar associations had threatened their company, and one even said he could get imprisoned for six months. He told a media organization, “Even if it would not happen, the threat of criminal charges was enough to give it up.
Browder created DoNoPay as a free AI-powered chatbot that can help one draft letters and fill out forms for several legal matters, including the appeals for parking tickets.
The company’s “robot lawyer” uses several AI text generators, including DaVinci and ChatGPT, that have been trained to focus on law.
Uber Freight has laid off 150 employees, which is about 3% of the segment’s total head count. The unit marked $1.8 billion in revenue for 2022’s third quarter, up 336% year over year.
Uber Freight CEO Lior Ron said Monday that the layoffs affect the digital brokerage team of the division. These are the first layoffs since 2020, which were in the early weeks of the Covid lockdowns.
Uber had launched its freight unit in 2017 with the notion that trucking companies and laden goods can be matched using the same concept that underpinned the company’s ride-hailing technology.
“As you know, the logistics market is currently facing a number of headwinds which has impacted our customer base as well as the overall industry,” a spokesperson told the employees at Uber Freight.
“We accelerated hiring last year within certain areas of our Brokerage business, planning for a different economic reality, but the volumes did not materialize as expected,” he added.
Dara Khosrowshahi, Uber CEO, said last week at the World Economic Forum (WEF) in Davos that he is not planning company-wide layoffs.
Udacity and Swift, the world’s leading provider of secure financial messaging services, are partnering to provide job-seekers with job-ready digital skills and an opportunity to land a role at Swift through Swift Tech Scholarship.
The two organizations will offer scholarships for Udacity’s DevOps Engineer with Java Nanodegree program. This is a train-to-hire program, which means that after the successful completion of the course, learners will get a chance to apply for a full-time position with Swift, where they will be able to put their newly-learned skills into practice.
Candidates for this program are required to have existing knowledge of command line, Python web development, SQL, and HTML. The goal is for learners to develop intermediate DevOps skills related to Java, like cloud-native fundamentals and application development. The duration of the program is five months.
Individuals interested in learning digital skills for free and landing a new career are encouraged to apply. The program starts on March 27. Applications for the same have already begun. The last date to fill out the application is March 6. Recipients will be notified of the acceptance on March 20.
United Kingdom applicants interested in our Product Manager Nanodegree program are encouraged to apply for that scholarship as well. This is a beginner program, so no experience is required.
Accenture has invested in Forma Vision, a provider of live-streamed, 3D volumetric video technology that allows 3D holographic images of people, environments, and objects to be beamed into metaverse from any location such as office, home, or other.
Until now, the metaverse opportunity has been restricted to enterprise use cases suitable for avatar-to-avatar interactions. Now, using Forma Vision’s cost-effective, live-streaming volumetric video technology, enterprises of any scale can teleport people, things, and places into their metaverse experience.
Senior managing director of Accenture’s Metaverse Continuum business group, David Treat, said, “We believe Forma Vision’s volumetric video technology will enable more immersive, engaging interactions and help further bridge real and virtual worlds by allowing people, places, and things to be more authentically represented in the metaverse.”
Forma Vision is now the latest company to be a part of Accenture Ventures’ Project Spotlight, which is an engagement and investment program focusing on investing in the companies that create or apply disruptive enterprise technologies.
Tom Lounibos, managing director of Accenture Ventures, said, “For enterprise teams, Forma Vision’s holographic meeting platform can enable remote meetings and other experiences in a highly engaging, 3D virtual format.”
Representative Ted Lieu introduced a nonbinding measure on Thursday that would make the House of Representatives to take a look at a bill on artificial intelligence written entirely by ChatGPT, the online AI chatbot.
Using a simple prompt, Lieu generated a standard congressional resolution. The prompt said: “You are Congressman Ted Lieu. Write a comprehensive congressional resolution generally expressing support for Congress to focus on AI.” It is not specified in the resolution that it was written using AI technology.
Acknowledging the potential positive impacts of AI, Lieu’s resolution specifically highlights Congress’ responsibility to oversee that the development and deployment of AI are handled in a manner that is considered safe and ethical, and upholds the rights and privacy of all Americans.”
“The rapid advancements in artificial intelligence technology have made it clear that the time to act is now, to ensure that AI is used in ways that are safe, ethical, and beneficial for society. Failure to do so may lead to a future where the risks of AI far outweigh its benefits,” wrote ChatGPT in an op-ed that Lieu published in The New York Times recently.
The rise of AI and tools like ChatGPT is already raising concerns about its potential misuses, but Lieu, who is one of the few members of Congress having technology backgrounds, wrote in his Times op-ed that the harm caused by AI could even be “deadly.”
Python is one of the most used programming languages today. It contains a wide range of libraries and frameworks for every technical domain. Python frameworks are generally collections of modules or packages that assist developers with the prebuilt implementation of redundant tasks. This process helps developers spend less development time and focus more on application logic. Every framework has advantages and disadvantages depending on application requirements and developers’ preferences. This article overviews such widely used Python frameworks for web development.
Ruby on Rails
Ruby on rails is the serverside web application framework written in Ruby programming language. Developed by David Heinemeier Hansson, Ruby in Rails is licensed under MIT. As per StackOverflow’s record, 55.34 % of people have chosen Ruby on Rails as the most liked web development framework.
Ruby on Rails supports model-view-controller architecture to provide a default structure for databases, web pages, and web services. It uses HTML, CSS, and JavaScript languages for the user interface and data formats like JSON and XML to transfer data.
Developed by FriendFeed, Tornado is a Python framework and asynchronous network library. Tornado uses a non-blocking network I/O to handle thousands of active server connections. It can be used in applications that require long polling, WebSockets, and long-time connection with users. Unlike the other Python web development frameworks, Tornado is not based on Web Server Graphical Interface (WSGI). However, it supports some features of the WSGI.
Aiohttp is an asynchronous Python web development framework that depends on Python 3.5 features like async and awaits. It uses Python’s asyncio library and serves as a client and server web framework. Aiohttp uses request objects and routers for the redirection of queries.
Growler is an asynchronous Python web development framework built on the Python asyncio library. It is based on Node.js’s express framework and is used for implementing complex applications. Growler handles requests by passing them through middleware technology. With Growler, businesses can use the asyncio library at its lowest levels.
TurboGears is a web application framework written in Python programming language. In 2005, Kevin Dangoor developed TurboGears. However, the latest version of Turbogears is managed by a group of developers led by Florent Aide and Mark Ramm.
Like many of the modern Python frameworks, TurboGears also follows the Model-View-Controller (MVC) paradigm to develop web applications. It is made of three sections:
Model: It is the lowest level of pattern responsible for maintaining data.
View: It is responsible for displaying all or a portion of the data to the user.
Controller: It is a software code that controls the interaction between Model and View.
TurboGears is designed for solving complex industrial-strength problems and supports MongoDB as one of its primary storage backends. It consists of a transaction manager to help with multi-database deployments and supports many template engines.
Flask is the most used Python web application framework for developing complex web applications. It was developed by Armin Ronacher, who led a team of international Python enthusiasts ‘Pocoo.’ Flask depends on the Werkzeg WSGI toolkit and the Jinja2 template.
The WSGI (Web Server Gateway Interface) is used as a standard for Python web application development. It specifies a common interface between web servers and web applications. On the other hand, Werkzeg is a WSGI toolkit to implement requests, response objects, and utility functions. The Flask framework uses Werkzeg as one of its bases.
Flask is mainly referred to as a micro-framework designed to keep the application scalable and simple. Since Flask is a microframework, it is used to implement tech projects like web apps.
Flask also supports modular programming, which is where Flask’s functionality can be split into many interchangeable modules. Each module in Flask acts as an independent building block that can execute one part of the functionality.
Web2Py is an open-sourced web framework for agile development that consists of database-driven web applications. It is written in Python programming language. Web2Py is a full-stack framework containing all the components a developer requires to build a fully functional web application.
The Web2Py framework follows the Model-View-Controller pattern of running applications. It has an in-built feature to manage cookies and sessions. After committing a transaction in SQL, the session is also stored simultaneously. Web2Py can run tasks in scheduled intervals after the completion of specific actions.
It has the potential to address various issues that can lead to security vulnerabilities by following well-established practices. The Web2Py framework consists of Database Abstraction Layer (DAL) that can write SQL dynamically.
CherryPy is mostly used object-oriented web frameworks in Python. It enables developers to build a web application effortlessly, which means developing less code in a short time. Remi Delon developed the first version of CherryPy in June 2002. Now, CherryPy is more than 10 years old, and it has proven its reliability.
Since CherryPy is designed on the multithreading concept, it can handle multiple tasks simultaneously. CherryPy follows the Model-View-Controller approach to developing web services. Therefore, CherryPy is a fast and developer-friendly Python framework.
CherryPy framework has the potential to build web services such as RESTful Web Service (RWS), SOAP, WSDL, and more. You can use it to build e-commerce websites or authentication services by integrating various other Python modules.
CherryPy can be easily deployed cost-effectively, as it has its HTTP server to host applications on multiple gateways.
Django was first introduced by teams between the year of 2003 and 2005 who were responsible for creating and maintaining newspaper websites. After creating many sites, this team began to figure out and reuse many common codes and design patterns. These common codes evolved into a generic Python web development framework known as Django in July 2005.
Django encourages rapid development and clean design in web applications. It is an open-source and active community with excellent documentation and many free and paid support options. Django is used to build almost any type of website, from content management systems and wikis to news sites and social networks. Django can work with any client-side framework and deliver content in almost any format, such as HTML, JSON, XML, and more.
Django enables developers to avoid many common security mistakes by providing a framework engineered to “do the right things” to protect your website automatically. For example, Django offers a secure way to manage user accounts and passwords, avoiding using session information in cookies or storing passwords rather than password hash directly.
Developed in July 2008, Pyramid is an open-sourced, lightweight web application framework built in Python programming language. This framework offers only the core tools required for developing web applications.
The Pyramid framework is based on core concepts like Zope, Pylons, and Django. It is based on Zope regarding extensibility, traversal, and declarative security. Zope is a family of open-source web application servers in Python. It is a robust framework that allows many people to collaborate while developing websites.
The Pyramid framework uses the (WSGI) Web Server Gateway Interface standard for promoting usability and separating functionality into distinct modules. Lastly, Pyramid is based on the Django web application framework that allows the rapid development of secure and maintainable websites.
Pyramid is the fastest Python web framework supporting small and large projects. It also supports events that are similar to Zope and Pylon frameworks. You can also generate URLs for routes, resources, and static assets in the Pyramid framework. It becomes easy for users to work with APIs that generate URLs. By generating various APIs of the Pyramid framework, you can alter the configuration arbitrarily without worrying about breaking links with any web pages.
Quixote is a popular web application framework for Python programmers. Developed by Andrew Kuchling, Neil Schemenauer, and Greg Ward in August 2000, Quixote consists of the Python Template Language (PTL) to produce HTML with Python code.
The Quixote Python framework makes it easier to develop web applications that are highly flexible and offer robust performance. With Quixote, you can develop web applications in a more organized and structured manner. Therefore, the development can be faster, and you can incorporate many third-party Python modules without confusion.
Quixote is mainly developed for Python developers building dynamic web applications. If you want to build web applications with complex programming algorithms, then Quixote is the perfect framework for you. If your concern is more about functionality in web applications rather than looks, then Quixote is the best option for you.
Bottle is a fast, lightweight, and simple WSGI (Web Server Gateway Interface) microweb framework in Python. It is distributed as a single file module with no dependencies except Python standard libraries. Bottle framework is the best choice for small applications and is mainly used to build APIs.
It is a great Python framework if you want to prototype an idea quickly. Since Bottle is simple to use, new developers can use it. Bottle framework has only one drawback: it has less documentation and support than other frameworks like Flask and Django.
The Model-View-Controller approach of the Django frameworks makes the maintenance of the applications more accessible. But, it becomes difficult on small applications where you play with random ideas. Therefore, the Bottle framework is a good option if you are not worried about your application’s long time structure.
Dash is an open-sourced microframework used to develop analytical web applications. It is the most popular framework among data scientists who are not familiar with web development. Dash uses React.js for front-end rendering.
Dash applications can also run web servers like Flask and communicate with JSON packets through HTTP requests. Dash applications are called cross-platform and mobile-ready applications, as they can be rendered in the browser and deployed on the server.
One of the essential features of the dash framework is that it does not require much coding to develop applications and offers a high level of customization. With dash, you also get plugin support and effective error handling.
CubicWeb is a well-defined semantic web application framework in Python that focuses on applications’ reusability, quality, and efficiency. The CubicWeb framework is different from the other framework as it is semantic and allows developers to build applications by following object-oriented principles and reusing components called cubes.
Cube is the primary and essential unit in the CubicWeb framework. It is a minimal web application that consists of a data model (schema), logic (entities), and user interface (view). You can build a web application from a single cube, but two or more cubes are mainly required to provide broader functionality.
CubicWeb supports multiple databases and provides security and reusable components. It uses RQL, i.e., relational query language, to simplify data-related queries. CubicWeb also supports Web Ontology Language (WOL) and Resource Description Framework (RDF).
In a conversation with Klaus Schwab, the World Economic Forum (WEF) chairman Satya Nadella, CEO of Microsoft, stated that the sense of presence achieved while using metaverse tech is ‘game-changing.’
Nadella believes the pandemic has changed the environment, especially regarding meetings, forcing people to use video calls and implementing more immersive technologies. According to him, this is the real impact that the technologies like metaverse and others will have on today’s society.
Microsoft is one of the partners of the WEF in developing the Global Collaboration Village, a virtual replica of Davos in the metaverse. The main goal of this digital world is to allow organizations to bring together leaders from across the globe to be part of the constant conversations on policies and world issues.
As per Schwab’s statements, there are already 70 to 80 organizations behind the Global Collaboration Village initiative, which is to be built with Mesh, a metaverse platform of Microsoft that allows collaboration and communication.
