Category: Tech

  • Flask: A Comprehensive Guide with Examples

    Flask: A Comprehensive Guide with Examples

    Introduction

    Flask is a micro web framework for Python, designed to be lightweight and modular while still offering the flexibility needed to build robust web applications. It is widely used for its simplicity, scalability, and extensive community support. This guide will take you from the very basics of Flask to advanced features, ensuring a solid understanding of the framework.


    1. What is Flask?

    Flask is a web framework for Python that provides tools, libraries, and technologies for building web applications. Unlike Django, which is a full-fledged web framework with built-in features, Flask follows a minimalistic approach, allowing developers to choose their tools as needed.

    Features of Flask:

    • Lightweight & Simple: Does not come with built-in ORM, authentication, or admin panel.
    • Modular: Allows integration of extensions as per project needs.
    • Flexible: Supports RESTful API development.
    • Jinja2 Templating: Provides powerful templating for rendering dynamic HTML pages.
    • WSGI-based: Uses Werkzeug, a WSGI toolkit for request handling.

    2. Setting Up Flask

    Installation

    To get started, install Flask using pip:

    pip install flask
    

    Creating a Simple Flask Application

    Create a Python file, e.g., app.py, and write the following code:

    from flask import Flask
    
    app = Flask(__name__)
    
    @app.route('/')
    def home():
        return "Hello, Flask!"
    
    if __name__ == '__main__':
        app.run(debug=True)
    

    Running the Flask App

    python app.py
    

    Navigate to http://127.0.0.1:5000/ in your browser to see the output.


    3. Routing in Flask

    Flask provides routing functionality to map URLs to functions.

    @app.route('/about')
    def about():
        return "This is the about page."
    

    Dynamic Routing

    @app.route('/user/<string:name>')
    def greet(name):
        return f"Hello, {name}!"
    

    URL Converters in Flask

    Flask allows type-specific URL converters:

    @app.route('/post/<int:post_id>')
    def show_post(post_id):
        return f"Post ID: {post_id}"
    

    Using Multiple Routes

    @app.route('/contact')
    @app.route('/support')
    def contact():
        return "Contact us at support@example.com"
    

    Handling 404 Errors

    @app.errorhandler(404)
    def page_not_found(e):
        return "Page not found", 404
    

    4. Flask Templates with Jinja2

    Flask uses Jinja2 for rendering dynamic content in HTML.

    Creating an HTML Template

    Create a templates directory and add index.html inside:

    <!DOCTYPE html>
    <html>
    <head>
        <title>Home</title>
    </head>
    <body>
        <h1>Welcome, {{ name }}!</h1>
    </body>
    </html>
    

    Rendering the Template

    from flask import render_template
    
    @app.route('/welcome/<string:name>')
    def welcome(name):
        return render_template('index.html', name=name)
    

    Using Control Structures in Jinja2

    <ul>
    {% for item in items %}
        <li>{{ item }}</li>
    {% endfor %}
    </ul>
    

    Extending Templates

    Create base.html:

    <!DOCTYPE html>
    <html>
    <head>
        <title>{% block title %}My Site{% endblock %}</title>
    </head>
    <body>
        <nav>My Navigation Bar</nav>
        {% block content %}{% endblock %}
    </body>
    </html>
    

    Extend in another template:

    {% extends "base.html" %}
    {% block title %}Home{% endblock %}
    {% block content %}
        <h1>Welcome to my site!</h1>
    {% endblock %}
    

    5. Handling Forms and User Authentication

    To handle user input, Flask provides the request object.

    from flask import request
    
    @app.route('/login', methods=['GET', 'POST'])
    def login():
        if request.method == 'POST':
            username = request.form['username']
            return f"Welcome, {username}"
        return '''<form method="post">Username: <input type="text" name="username"><input type="submit"></form>'''
    

    User Authentication with Flask-Login

    from flask_login import LoginManager, UserMixin, login_user, logout_user
    
    login_manager = LoginManager()
    login_manager.init_app(app)
    
    class User(UserMixin):
        pass
    

    6. Flask with Databases (SQLAlchemy)

    Creating and Connecting a Database

    from flask_sqlalchemy import SQLAlchemy
    
    app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db'
    db = SQLAlchemy(app)
    

    Creating Models

    class User(db.Model):
        id = db.Column(db.Integer, primary_key=True)
        name = db.Column(db.String(100))
    

    Fetching Data from Database

    @app.route('/users')
    def get_users():
        users = User.query.all()
        return {"users": [user.name for user in users]}
    

    7. Advanced Backend Concepts in Flask

    Session Management

    from flask import session
    
    @app.route('/set_session')
    def set_session():
        session['username'] = 'JohnDoe'
        return "Session set!"
    

    JWT Authentication

    from flask_jwt_extended import JWTManager, create_access_token
    
    app.config['JWT_SECRET_KEY'] = 'secret'
    jwt = JWTManager(app)
    
    @app.route('/token')
    def get_token():
        return {"token": create_access_token(identity='user')}
    

    Conclusion

    Flask is a powerful framework that provides the flexibility to develop everything from simple web pages to complex APIs. This guide covered everything from setup to deployment, authentication, databases, error handling, middleware, caching, WebSockets, and background tasks, providing a strong foundation for working with Flask.

    Flask: A Comprehensive Guide with examples
    Flask: A Comprehensive Guide with examples
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  • Programmers are increasingly adopting tools powered by artificial intelligence, and this shift is reshaping their approach to coding.

    Programmers are increasingly adopting tools powered by artificial intelligence, and this shift is reshaping their approach to coding.

    Since the spotlight embraced generative AI tools like OpenAI LP’s ChatGPT and Google LLC’s Bard, the world has been captivated by their conversational abilities and impressive utility. One of their early and prominent applications is assisting software developers in writing code.

    Following ChatGPT’s surge in popularity and its remarkable conversational and writing capabilities, numerous code-generating chatbots and developer tools have flooded the market. These include GPT-4, the same model behind the premium ChatGPT Plus service, Microsoft Corp.’s GitHub Copilot coding assistant, Amazon Web Services Inc.’s Code Whisperer, and others.

    According to a June survey by Stack Overflow involving over 90,000 developers, almost 44% reported using AI tools in their work, with an additional 26% expressing openness to using them soon. The tools were primarily employed for code production (82%), debugging and assistance (48%), and learning about specific codebases (30%).

    To understand how developers are integrating AI tools into their daily work, SiliconANGLE interviewed several developers. Two major coding tool modes have emerged: chatbots like ChatGPT that generate code snippets or check code, and coding assistants like GitHub Copilot that provide suggestions as developers write.

    For instance, Oliver Keane, an associate web developer at Adaptavist Group Ltd., uses ChatGPT as a coding companion to swiftly establish code foundations for projects. Keane provided an example of creating a content management system, where GPT was asked to generate a method for admins to update FAQs. He highlighted that, although he could code it himself, using GPT results in a solution 80% of the time on the first attempt.

    However, Keane emphasized the need for upfront work, as chatbots like ChatGPT require a conversational history or context to function effectively. Developers must build a rapport by prompting the tool with relevant code until it understands the project, after which it provides better responses.

    Once trained, developers can use these models for code reviews, bug discovery, and revisiting old work, essentially turning them into “pair programmers” for discussions on improving old code. Keane noted that, while the AI tool meets his needs 80% of the time, refining prompts can enhance its performance.

    Chatbots, besides aiding in coding tasks, also prove valuable for learning new frameworks and coding languages. Well-trained language models can serve as effective tutors, rapidly bringing developers up to speed, often surpassing traditional learning resources due to their conversational nature and interactive code discussions.

    AI code completion tools like GitHub Copilot have been a revelation for me and many other developers. They’re not chatbots – they’re like having an extra pair of hands at the keyboard. Copilot helps me write better code, even though I’m not writing most of it myself.

    The way this works is that Copilot suggests code snippets as I type. These snippets are often exactly what I need, and they force me to write my own code in a more descriptive and precise way. This is because if my code is too vague, Copilot will either go haywire or suggest irrelevant code.

    In the beginning, I found that Copilot would only suggest short stints of code, like a few lines at a time. But as I got used to using it, Copilot started suggesting longer and longer snippets, sometimes even 20 lines or more. This has saved me a lot of time and effort, and it’s given me more time to focus on other parts of my job, such as marketing and business development.

    In a recent survey of Stack Overflow users, 37% of professional coders said that the main benefit of using AI code completion tools is improved productivity. Another 27% said that greater efficiency is the main benefit, and 27% said that speed of learning is the main benefit.

    I can definitely see why productivity is the primary benefit for all types of developers. AI code completion tools can save you a lot of time and effort, which can free you up to focus on other things. And if you’re just learning to code, these tools can help you learn faster and more effectively.

    Overall, I’m a big fan of AI code completion tools. They’re not perfect, but they’re a valuable tool for any developer.

    While these AI tools can be incredibly helpful for developers, they’re not without their challenges. For instance, large language models may sometimes produce misinformation or “hallucinate,” generating problematic code. When using tools like chatbots, bugs might be caught by the compiler or a seasoned coder during a code review. However, code-suggesting AIs can introduce similar issues, requiring additional time to understand what went wrong after the fact.

    Reeve, for example, shared his experience with anxiety when GitHub Copilot generated a substantial amount of code at once. While it initially seemed like a time-saving boon, a bug emerged hours later due to a minor mistake in the AI-generated code. This highlights a certain level of uncertainty when the tool anticipates too far into the future.

    According to a Stack Overflow survey, only about 42% of developers trust AI models, with 31% expressing doubts and the rest having more serious concerns about the outputs. Some AI models may also be more prone to hallucinations than others.

    On the flip side, these tools enable rapid code production in ways not seen before, potentially sparing developers from tedious tasks. However, there’s a concern that overreliance on AI may hinder newer developers from fully grasping the fundamentals of programming languages.

    Jodie Burchell, a developer advocate at JetBrains, emphasizes that AI coding tools should be viewed as tools and assistants. Developers remain responsible for ensuring that the code aligns with their intentions, even if the AI provides imperfect guidance. Burchell underscores the importance of critical thinking, stating that there’s no shortcut to simply letting models develop code without scrutiny.

    The 2023 Accelerate State of DevOps Report from Google’s cloud division suggests that while AI tools slightly improve individual well-being, their impact on group-level outcomes, such as overall team performance, is neutral or even negative. This mixed evidence is attributed to the early stages of AI tool adoption among enterprises.

    Despite potential challenges, more developers are expressing interest in incorporating AI tools into their workflows, as indicated by the Stack Overflow survey. This trend is particularly notable among developers learning to code, with 54% showing interest compared to 44% of professionals. Veteran coders, on the other hand, tend to be less enthusiastic about adopting these new AI tools.

    In the realm of generative AI developer tools, it’s still early days, but adoption is progressing rapidly. Companies like OpenAI and Meta continuously enhance their models, such as GPT-4, Codex, and Code Llama, for integration into more tools. As these models evolve and become more ingrained in the development process, developers may find themselves spending a significant portion of their coding time collaborating with AI tools. Learning to provide effective prompts, maintaining precision coding for guiding predictive algorithms, and understanding the models’ limitations will likely be crucial for navigating this AI-centric future.

  • Nvidia and iPhone manufacturer Foxconn are teaming up to construct factories dedicated to artificial intelligence (AI).

    Nvidia and iPhone manufacturer Foxconn are teaming up to construct factories dedicated to artificial intelligence (AI).

    The world’s most valuable chip company Nvidia and iPhone maker Foxconn are joining forces to build so-called “AI factories”.

    Nvidia and Foxconn are teaming up to create a novel kind of data center powered by Nvidia chips, designed to support a broad range of applications. These applications include the training of autonomous vehicles, robotics platforms, and the operation of large language models. This collaboration comes amid the U.S. government’s recent announcement of plans to restrict advanced chip exports to China, posing a challenge for Nvidia.

    According to Nvidia, the new export restrictions will prohibit the sale of two high-end artificial intelligence chips, A800 and H800, specifically developed for the Chinese market. Nvidia’s CEO, Jensen Huang, and Foxconn’s chairman, Young Liu, made this joint announcement at Foxconn’s annual tech showcase in Taipei. Huang referred to the emerging trend of manufacturing intelligence, highlighting that the data centers powering it are essentially AI factories. He emphasized Foxconn’s capability and scale to establish these factories on a global scale.

    Liu expressed Foxconn’s ambition to transform from a manufacturing service company into a platform solution company, envisioning applications beyond AI factories, such as smart cities and smart manufacturing. The strategic use of Nvidia’s advanced chips in AI applications has significantly boosted Nvidia’s market value, surpassing $1 trillion and making it the fifth U.S. company to join the “Trillion dollar club,” alongside Apple, Microsoft, Alphabet, and Amazon.

    Simultaneously, Foxconn, known for producing over half of the world’s Apple products, is diversifying its business. In a June interview with the BBC, Liu highlighted electric vehicles (EVs) as a key growth driver for the company in the coming decades. The partnership between Foxconn and Nvidia, announced in January, focuses on developing autonomous vehicle platforms, with Foxconn handling the manufacturing of electronic control units based on Nvidia’s chips.

  • Nvidia is facing a lawsuit following a video call error that exposed ‘allegedly stolen’ data.

    Nvidia is facing a lawsuit following a video call error that exposed ‘allegedly stolen’ data.

    In the fast-paced world of technology, where data is often considered the new currency, a recent incident involving Nvidia has sent shockwaves through the industry. The renowned tech giant found itself entangled in a legal battle after a video call mistake inadvertently revealed what appeared to be ‘stolen’ data. This article delves into the intricacies of the incident, the subsequent lawsuit against Nvidia, and the broader implications for data security.

    Introduction:-

    In a landscape where technology reigns supreme, Nvidia stands as a behemoth, known for its groundbreaking innovations in graphics processing units (GPUs) and artificial intelligence. However, even giants can stumble, and Nvidia recently faced a stumble of colossal proportions.

    The Video Call Mishap:-

    The incident in question unfolded during a routine video call, where a technical glitch led to the inadvertent display of sensitive data that seemed to have been pilfered. The nature of this ‘stolen’ data raised eyebrows and prompted immediate scrutiny.

    Lawsuit Against Nvidia:-

    In the wake of the video call mishap, legal action was swift. A lawsuit was filed against Nvidia, alleging negligence and breach of data protection laws. This section explores the details of the lawsuit, examining the legal grounds and specific claims made against the tech giant.

    Implications for Data Security:-

    The incident serves as a stark reminder of the critical importance of secure video communication in today’s interconnected world. This section discusses the potential consequences of data exposure and the broader implications for data security.

    Nvidia’s Response:-

    Facing a public relations crisis, Nvidia promptly issued an official statement addressing the incident. This section delves into the company’s response, detailing the actions taken to rectify the situation and prevent future occurrences.

    Lessons Learned:-

    The debacle serves as a valuable lesson for the entire tech industry. This section explores the broader implications, emphasizing the importance of robust cybersecurity measures and learning from such incidents.

    Rebuilding Trust:-

    Trust, once lost, is challenging to regain. Nvidia undertakes steps to rebuild trust, and this section analyzes the effectiveness of these measures. It also delves into public perception and reactions to Nvidia’s efforts.

    Future of Video Conferencing Security:-

    The incident raises questions about the overall security of video conferencing tools. This section discusses the industry-wide implications and the pressing need for enhanced security measures in the future.

    The Intersection of Technology and Privacy:-

    As technology advances, the delicate balance between innovation and privacy becomes more pronounced. This section explores the challenges in maintaining this balance and the ongoing public discourse regarding data security in the tech realm.

    Nvidia’s Role in the Tech Landscape:-

    Nvidia’s significance in the tech world cannot be overstated. This section provides an overview of Nvidia’s role and assesses the impact of the incident on the company’s reputation and standing in the industry.

    The Broader Legal Landscape:-

    The lawsuit against Nvidia places it in a broader context within the tech industry. This section examines similar cases, legal precedents, and the potential implications for the entire sector.

    Media Coverage and Public Reaction:-

    Media plays a pivotal role in shaping public perception. This section analyzes the media coverage of the incident and the varied reactions on social media platforms, providing a comprehensive view of public sentiment.

    Recommendations for Companies:-

    In light of this incident, it becomes imperative for companies to enhance their cybersecurity protocols. This section offers practical recommendations for companies to fortify their defenses against potential data breaches.

    Future Challenges in Data Security:-

    As technology evolves, so do the threats to data security. This section explores emerging challenges in the tech landscape and emphasizes the need for proactive measures to address these challenges.

    Conclusion:-

    In conclusion, the Nvidia video call mistake and its aftermath serve as a cautionary tale for the entire tech industry. The incident underscores the fragility of data security and the ever-present need for vigilance. As we navigate the intricate dance between technology and privacy, lessons learned from such incidents will shape a more secure digital future.

  • Elon Musk’s company, X, is taking legal action against Media Matters for their analysis of antisemitism.

    Elon Musk’s social media platform X has sued a left-leaning pressure group that accused the site of allowing antisemitic posts next to advertising.

    The litigation filed by X accuses Mediak for America of “rigging” figures with the intention of killing the old Twitter.

    However, firms such as Apple, Disney, IBM, and Comcast have suspended ads on X since the watchdog made its report public.

    Thereafter, after he was threatened with the lawsuit, Media Matters labeled him a bully.

    Last week an advocacy group stated that among the posts supporting this ideology from Nazism, there were Hitler quotes that appeared next to X. In the lawsuit, x complained that he was the only viewer who noted that Comcast, Oracle, and IBM ads appeared in association with media matters’ hateful content.

    Linda Yaccarino, chief executive of X, posted on Monday: The truth is that none of the real users of X came across IBM’s, Comcast’s, and Oracle’s ads alongside the content in Media Matters’ post.

    In addition, Mr Musk was charged in his person, for having amplified an allegedly anti-Semetic trope a week earlier. The lawsuit, filed in Texas on Monday, argues: Media Matters purposely made a series of side-by-side photos with the following message, “Here is how most x users see posts from the advertising community alongside the Neo Nazi extremists propaganda”.

    Media Matters came up with these visuals, as well as its overall advertising campaign; this was done in order to push away corporate advertisements and ruin XX Corp.

    In his suit, X accuses Media Matters for America of manipulating numbers so they could destroy Twitter/formerly Twitter.

    Since the watchdog issued its analysis, firms, such as Apple, Disney, IBM, and Comcast, have ceased advertising in X.

    Following Mr Musk’s threat of the lawsuit, Media Matters referred to him as ‘a bully’.

    Last week’s statement by an advocacy group indicated that words like Hitler quotes and “Holocaust Denier” were being found against the backdrop of X. In the lawsuit, X stated that the only type of Comcast, Oracle, or IBM ads were shown along with hate speech but nothing was played before the viewer.

    Linda Yaccarino, chief executive of X, posted on Monday: “It’s true. None of the genuine users of X saw ads linked with MM article by their counterparts like IBM, Comcast, and Oracle.”

    On a separate note, Mr Musk was also recently accused of reinforcing an antisemitic trope in the service last week. The lawsuit, filed in Texas on Monday, argues: Media Matters intentionally created and juxtaposed pictures showing adjacent postings of advertisers on x corporation’s website with white supremacists and neo nazi content, and portrayed these images as though it was common for the average user of x to see such content in

    Media Matters created these two images with a strategy that aimed at driving advertisers away from the platform so as to kill X Corp.

    Following the accusations from Media Matters, several big names, including the European Commission, Warner Bros Discovery, Paramount, and Lionsgate, have decided to stop advertising with X.

    On Saturday, Elon Musk promised to file a strong lawsuit against Media Matters and anyone involved in a “fraudulent attack” on his company. In response, Media Matters’ president, Angelo Carusone, confidently said they would prevail in any legal action. Carusone criticized Musk, saying he’s not the free speech advocate he claims to be but rather a bully trying to silence accurate reporting.

    Media Matters, founded in 2004, is known for criticizing conservative commentators and media outlets. It defines itself as a non-profit, progressive research center dedicated to monitoring, analyzing, and correcting conservative misinformation in the US media.

    The controversy started last Wednesday when Musk responded to a post sharing a conspiracy theory about Jewish communities. Musk later clarified that his comments were not directed at all Jewish people but specifically at groups like the Anti-Defamation League, a Jewish anti-hate monitor. Despite denying antisemitism, Musk faced criticism.

    Texas Republican Attorney General Ken Paxton announced on Monday that he had initiated an investigation into Media Matters for potential fraudulent activity regarding its allegations against X. Paxton labeled the liberal group a “radical anti-free speech organization” and vowed to prevent deception by left-wing organizations aiming to limit freedom of expression.

    On the same day, the White House announced that President Joe Biden would be joining Threads, a Meta-owned rival to X. Accounts for the president, first lady, vice president, and second gentleman have been created on Threads.

  • “My Journey: Closing Down the Dangerous Chat Site, Omegle”

    “My Journey: Closing Down the Dangerous Chat Site, Omegle”

    Warning: this story contains disturbing details of abuse

    “I feel personal pride that no more children will be added to Omegle’s body count,” says the woman who successfully forced the infamous chat site to shut down.

    In her first public statement since the platform’s shutdown, “Alice,” also known as “A.M.” in court documents, reveals to the BBC that she played a pivotal role in demanding the closure of the controversial chat site, Omegle, as part of an out-of-court settlement.

    Alice, who initiated a groundbreaking lawsuit in 2021, shares her sense of “validation” amid an “outpouring of gratitude” from people sharing distressing stories about the platform. Her fight for justice began after being randomly paired with a predator on Omegle, leading to years of digital exploitation.

    The lawsuit gained traction in 2021, coinciding with the sentencing of her abuser, Ryan Fordyce, to eight years in prison in Canada. Fordyce had victimized Alice and five other girls, using Omegle for grooming and exploitation.

    While Alice initially aimed for a jury trial seeking $22 million in compensation, she ultimately opted for an out-of-court settlement earlier this month. She believes this decision allows for a more tailored outcome, including the shutdown of the site.

    Reflecting on the complex structure of Omegle, Alice explains that the settlement provided a result she couldn’t have achieved in court. The acknowledgment of the human cost of Omegle by its creator, Leif Brooks, adds a significant note to the resolution.

    Omegle, launched in 2009, gained popularity with its “talk to strangers” concept, attracting around 73 million monthly visitors. The platform, lacking age verification and robust moderation, became known for its wild and sometimes explicit encounters.

    Despite warnings added to the homepage and increased notoriety during the pandemic, Omegle faced numerous disturbing cases. The site’s closure, acknowledged by Brooks as an attack on internet freedom, comes after Alice’s legal team employed a Product Liability lawsuit, arguing the site’s defective design.

    Alice’s case sets a legal precedent by holding a social platform liable for an incident of child trafficking, challenging the protective Section 230 law. Her attorneys utilized a Product Liability angle, marking a growing trend in similar cases against platforms like Instagram and Snapchat.

    While Omegle’s closure is viewed positively by child protection organizations, questions remain about the responsibility of social media companies in ensuring user safety. Despite the victory, Alice acknowledges that returning to normal life may be impossible, but she is relieved that Omegle is no longer a constant concern.

    The case underscores the broader challenges faced by social media platforms in balancing freedom and responsibility, and Alice’s resilience serves as a testament to the potential impact of legal actions against online platforms facilitating harm.

  • Amazon is letting go of hundreds of employees from its Alexa unit as part of a shift in focus towards generative AI.

    Amazon.com Inc. is letting go hundreds of employees from the business unit that develops its Alexa voice assistant.

    Daniel Rausch, the Vice President of Amazon’s Alexa and Fire TV unit, shared the recent developments in an internal memo, as reported by GeekWire.

    Rausch explained that Amazon is making a workforce reduction, affecting “several hundred” roles across the U.S., Canada, India, and other countries. The impacted employees will receive a separation payment, transitional health insurance benefits, external job placement support, and paid time to secure a new position.

    In the memo, Rausch emphasized that despite the layoffs, Alexa remains widely used by consumers, with users interacting tens of millions of times per hour. The decision to reduce the workforce is part of Amazon’s strategic reallocation of resources towards enhancing Alexa’s generative artificial intelligence (AI) capabilities.

    “We’re shifting some of our efforts to better align with our business priorities, and what we know matters most to customers — which includes maximizing our resources and efforts focused on generative AI,” wrote Rausch.

    Amazon introduced the latest generative AI feature for Alexa, called Let’s Chat, in September. This feature eliminates the need for users to say a wake word before each request, allowing Alexa to consider information from past interactions when processing new user requests.

    In a previous update, Amazon added a more advanced generative AI feature to its Alexa-powered Echo Show smart screens, enabling users to create short animated stories through natural language prompts, including illustrations, background music, and sound effects.

    Looking ahead, Amazon might prioritize allowing Alexa to interact with other applications in its generative AI roadmap. Google has already ventured into this territory with its Assistant, introducing a generative AI version capable of fetching information from Gmail and Google Docs and performing tasks like writing travel itineraries.

    Google’s new Assistant is powered by the Bard chatbot, driven by an internally developed large language model called PaLM 2, which understands over 100 languages, processes more data at once, and has access to an extensive knowledge repository.

  • YouTube is giving Shorts creators the ability to craft music using AI-powered tools that mimic the styles of well-known artists.

    YouTube Shorts creators are in for a treat as they’ll soon have access to some cool tools powered by fancy AI tech. These tools, backed by Google’s DeepMind, will let them whip up music for their super short videos in the style of famous artists.

    In this musical collaboration between YouTube and DeepMind, they’ve rolled out an advanced AI model called Lyria. This nifty tool is designed to get creative with music, handling everything from instrumentals to vocals. It’s not just a one-trick pony either; Lyria can pick up on an existing piece of music and seamlessly continue it.

    Explaining the complexity of the task, Google shared, “Music contains huge amounts of information — consider every beat, note, and vocal harmony in every second.” Lyria, however, is up for the challenge, excelling in maintaining musical flow across different sections and passages.

    The initial test run for Lyria will be in the Shorts realm, those snappy videos maxing out at 60 seconds. It’s like a playground for experimenting with AI-generated music, even though the trial will yield a 30-second soundtrack. A select group of users will get their hands on this generative AI model, allowing them to input a text prompt describing the kind of music they’re after. Lyria then works its magic, producing a complete soundtrack with harmony, melody, and vocals for their video.

    But here’s the cool part: Some big-name artists like John Legend, Sia, Charli XCX, Louis Bell, and T-Pain have pitched in, lending their unique styles and voices to DeepMind. So, users can pick their favorite artist, describe the vibe they’re going for, and let the AI generate a soundtrack that fits the genre and style. It’s like having a virtual collaboration with your musical idols for that perfect background tune to your Shorts video.

    “The first time YouTube presented me with a proposal, I was quite skeptical and remain so today. “AI is going to change our world, including that of the music culture,” said Chari XCX who had her track speed drive adopted in this summer’s Barbie movie. This experiment, though, provides only a minor glimpse at the potential creativity involved. It will be exciting to see what results emerge from this.

    In addition, Google showed its AI music tools that would be rolled out within few months to the creators that would help them during the imaginative limits of their creativity.

    During a demonstration, Google showed how one can convert melody into the brass line using Lyria by singing or humming a part taken from music. For instance, the other part of the song created by the MIDI keyboard is changeable and can take the form of authentic choir or imitating musical accompaniments.

    According to the company, lyria music tools could be used for creating fresh music genres and even a whole instrumental track using only music from one genre or one instrument. In this, artists could decide to go from folky music to metal in order to understand its impact on their music at once.

    Adding a touch of detective work to the mix, the Lyria model team spilled the beans about a little something called SynthID. This system, crafted by the brilliant minds at DeepMind, acts like an invisible signature for content generated by Google’s Imagen image-generating AI model in Vertex AI, the company’s go-to platform for AI building. Essentially, it’s a nifty watermarking tool that leaves an indelible trace on the images, making it possible for users to track down artwork created by this AI wizardry. And guess what? This same SynthID comes in handy for Lyria’s musical creations too, letting users verify if a track is indeed a brainchild of Lyria.

    Now, this revelation unfolds against the backdrop of some stormy weather in the realm of AI and entertainment relations. Universal Music Group threw down the gauntlet in October, slapping a lawsuit on AI startup Anthropic for copyright infringement. Their bone of contention? Anthropic’s chatbot Claude allegedly scraped song lyrics from the Universal Music Group’s clients to fuel its AI training, pumping out reproductions and even churning out new song lyrics and poems mimicking the artists’ styles.

    And if that wasn’t enough drama, the Screen Actors Guild – American Federation of Television and Radio Artists recently wrapped up a strike, and guess what played a part? You got it – AI. One of the major wins from the negotiations involved setting up some AI guardrails. Now, film and TV producers have to get the green light from actors before using their likenesses in generative AI replicas. Plus, there’s compensation in the mix for putting those AI-created doppelgängers to work. Looks like the AI entertainment saga is getting more intricate by the day!

  • A report is cautioning that AI has the potential to exacerbate cyber threats.

    According to a report from the UK government, artificial intelligence might heighten the risk of cyber-attacks and undermine trust in online content by 2025.

    The technology could potentially be used in planning biological or chemical attacks by terrorists, according to the report. However, some experts have raised doubts about whether the technology will develop as projected.

    Prime Minister Rishi Sunak is expected to discuss the opportunities and challenges presented by this technology on Thursday.

    The government’s report specifically examines generative AI, the kind of system that currently powers popular chatbots and image generation software. It draws in part from declassified information provided by intelligence agencies.

    The report issues a warning that by 2025, generative AI could potentially be employed to “accumulate information related to physical attacks carried out by non-state violent actors, including those involving chemical, biological, and radiological weapons.”

    The report points out that while companies are striving to prevent this, the effectiveness of these protective measures varies.

    Obtaining the knowledge, raw materials, and equipment required for such attacks poses obstacles, but these barriers are diminishing, potentially due to the influence of AI, as per the report’s findings.

    Furthermore, it predicts that by 2025, AI is likely to contribute to the creation of cyber-attacks that are not only faster but also more efficient and on a larger scale.

    Joseph Jarnecki, a researcher specializing in cyber threats at the Royal United Services Institute, noted that AI could aid hackers, particularly in overcoming their challenges in replicating official language. He explained, “There’s a particular tone used in bureaucratic language that cybercriminals have found challenging to emulate.”

    The report precedes a speech by Mr. Sunak scheduled for Thursday, where he is expected to outline the UK government’s plans to ensure the safety of AI and position the UK as a global leader in AI safety.

    Mr. Sunak is expected to express that AI will usher in new knowledge, economic growth opportunities, advancements in human capabilities, and the potential to address problems once deemed insurmountable. However, he will also acknowledge the new risks and fears associated with AI.

    He will commit to addressing these concerns directly, ensuring that both current and future generations have the opportunity to benefit from AI in creating a better future.

    This speech paves the way for a government summit set for the next week, focusing on the potential threat posed by highly advanced AI systems, often referred to as “Frontier AI.” These systems are believed to have the capacity to perform a wide range of tasks, surpassing the capabilities of today’s most advanced models.

    Debates about whether such systems could pose a threat to humanity are ongoing. According to a recently published report by the Government Office for Science, many experts consider this a risk with low likelihood and few plausible pathways to realization. The report indicates that, to pose a risk to human existence, an AI would need control over crucial systems like weapons or financial systems, the ability to enhance its own programming, the capacity to avoid human oversight, and a sense of autonomy. However, it underscores the absence of a consensus on timelines and the plausibility of specific future capabilities.

    Major AI companies have generally acknowledged the necessity of regulation, and their representatives are expected to participate in the summit. Nevertheless, Rachel Coldicutt, an expert on the societal impact of technology, questioned the summit’s focus. She noted that it places significant emphasis on future risks and suggested that technology companies, concerned about immediate regulatory implications, tend to concentrate on long-term risks. She also pointed out that the government reports are tempering some of the enthusiasm regarding these futuristic threats and highlight a gap between the political stance and the technical reality.