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Blame AI for Your Grueling Job Interview

Parker Lennox
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Blame AI for Your Grueling Job Interview

Job Interviews: The Never-Ending Story

The Struggle is Real

Job interviews are laden with opportunities for humiliation. Who wants to describe their greatest weakness to a panel of peers? Or be made to feel like a quiz show contestant with brainteasers like “how many golf balls can you fit in a Boeing 747?”. Unfortunately for job seekers, the rigmarole is getting increasingly out of hand.

The Tech Sector’s Demands

Demands from hiring committees in the tech sector are piling up. That means more interviews but also more technical tests. Alongside coding evaluations come requests for essays, lengthy take-home assignments and even days spent working with existing teams. One friend in the Bay Area made it through multiple interview rounds only to be presented with a final challenge to “entertain” the company’s leadership. There were no other instructions. She didn’t get the job.

The Blame Game

Recruiters will say that this is not being done to make life difficult for job seekers but because it is growing harder to find the right candidates. The blame, they say, lies with job seekers themselves. Online postings make speculative applications easy to fire off. In the UK, the Institute of Student Employers reported receiving a record 1.2mn applications for 17,000 graduate vacancies this year. Human resources software maker Workday reports that the number of global job applications is growing four times faster than job openings.

The AI Effect

This surplus includes those from candidates who are logging into AI chatbot ChatGPT to tailor their application with skills they may not possess. Some even try to trick recruiting software by writing in white text — listing requirements they lack in ways that will be invisible to the human eye but picked up by screening software.

The Employer’s Perspective

From an employer’s point of view, therefore, adding new hoops for candidates to jump through makes sense. AI-assisted applications can mask poor candidates whose failings are revealed in multiple interviews. And the likeable smooth talker who sails through in-person meetings may come undone by on-site tests or work trials.

The Problem

At some companies it is not enough to be good at your job, either. You need to show commitment to the company ethos. Amazon is known for assessing candidates on its 16 leadership principles. Fail to prove your customer obsession or ability to think big and you’ll find yourself back on the job market.

The Conclusion

The problem is that adding more interviews and tests exhaust candidates and interviewers and take everyone’s time away from the real work. In even more galling news, they may not even be productive. In 2016, Google declared that four interviews were enough to predict whether someone should be hired. According to the company, anything more than that had diminishing returns.

FAQs

Q: Why are job interviews becoming more demanding?
A: Job interviews are becoming more demanding due to the growing number of job applications and the increasing difficulty in finding the right candidates.

Q: What is the average time it takes to secure a job?
A: According to research from US human resources adviser Josh Bersin, the average time it takes to secure a job is 45 days. In fields like tech, it can be far longer.

Q: Why are some companies cutting their workforce despite hiring sprees during the pandemic?
A: Some companies are cutting their workforce due to the changing market conditions and the need to adapt to new circumstances.

Q: Is there an exception to the rule?
A: Yes, sometimes landing a job can be as simple as sending a tweet. However, even this is not foolproof, as seen in the case of the social media manager who left Tesla within a year of being hired.

AI as the New World Infrastructure

Avery Winslow
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AI as the New World Infrastructure

The Last AI Optimist: Jensen Huang’s Vision for a Digital Future

A Reset of Computing

In a world where people are increasingly doubting the potential of AI, Jensen Huang, the CEO of Nvidia, remains an enthusiastic advocate for the transformative power of artificial intelligence. Speaking at The Big Interview event in San Francisco, Huang described the trend of AI as "a reset of computing as we know of [it] over the last 60 years." He emphasized that the force of AI is so incredible that it’s impossible to compete against it. You’re either on the wave, or you’re left behind.

The Digital Intelligence Infrastructure

According to Huang, AI will become the foundation of a new digital infrastructure, comparable to energy and communications systems. He believes that nations must build their own AI infrastructure, process their own national data, and have their own AI systems, all powered by Nvidia chips.

Nvidia’s Global Pitch

Huang has been on a whirlwind tour this year, meeting with governments around the world to pitch his vision. He has already successfully convinced at least 10 countries to sign up for AI infrastructure projects with Nvidia. Thailand is the latest addition to this list, with Huang meeting with Prime Minister Paetongtarn Shinawatra to discuss building "world-class AI infrastructure" in the country.

The Split Internet

The success of Huang’s pitch reflects the growing recognition of AI’s potential and the increasingly divided internet, where geographical borders are being rebuilt online. AI is the latest technology where the flow of chips and data is being obstructed by nation-state borders.

US-China Tensions

One of the main tensions lies between the US and China, two leading technology powerhouses. The Biden administration has announced new restrictions on the export of chip components and chip-making technologies to China. Nvidia’s H20 chips, which contain high-bandwidth memory (HBM), are affected by these restrictions. The company has reportedly stopped taking Chinese orders for H20 chips as early as September.

Conclusion

Jensen Huang’s unwavering enthusiasm for AI’s potential reflects his confidence in the transformative power of this technology. As the world navigates the complexities of AI and nation-state borders, his vision for a digital infrastructure will continue to shape the future of computing.

FAQs

Q: What is Jensen Huang’s vision for AI?
A: Huang believes that AI will become the foundation of a new digital infrastructure, comparable to energy and communications systems.

Q: Why is Nvidia’s pitch so successful with governments?
A: Huang’s pitch is successful because it recognizes the potential of AI and provides a solution for governments to build their own AI infrastructure.

Q: What is the main tension in the AI industry?
A: The main tension lies between the US and China, two leading technology powerhouses, as they compete in the development of AI and nation-state borders become more prominent.

Q: What are the implications of the US-China restrictions on chip exports?
A: The restrictions will affect Nvidia’s H20 chips, which contain high-bandwidth memory (HBM), and may lead to the company stopping exports to China.

NVIDIA Accelerates Robotics Simulation on AWS

Emerson Blake
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NVIDIA Accelerates Robotics Simulation on AWS

Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes.

Vention creates pretrained skills to ease the development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.

These leading robotics startups are all making advances using NVIDIA Isaac Sim on Amazon Web Services.

Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and Training

Simulation is used to verify, validate, and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.

Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.

Using NVIDIA OSMO in the Cloud

Using NVIDIA OSMO in the cloud allows teams to orchestrate and scale complex robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.

Isaac Sim provides access to the latest robotics simulation capabilities and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.

Learning to Be Robots in Simulation

While Isaac Sim enables developers to test and validate robots in physically accurate simulation, Isaac Lab, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on AWS Batch.

Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.

Several Robotics Developers are Embracing NVIDIA Isaac on AWS

  • Aescape’s robots are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.
  • Cobot has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.
  • Cohesive Robotics has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.
  • Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.
  • Standard Bots is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.
  • Swiss Mile is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.
  • Vention, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.

Conclusion

NVIDIA Isaac Sim on AWS is revolutionizing the field of robotics by providing a platform for developing and testing AI-driven robots in physically accurate simulation. With the combination of NVIDIA-accelerated hardware and software, developers can scale their physical AI workflows and achieve breakthroughs in robotics.

FAQs

What is NVIDIA Isaac Sim? NVIDIA Isaac Sim is a reference application built on NVIDIA Omniverse for developers to simulate and test AI-driven robots in physically based virtual environments.

What is NVIDIA OSMO? NVIDIA OSMO is a cloud-native orchestration platform that allows teams to manage their complex robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.

What are the benefits of using NVIDIA Isaac Sim on AWS? The benefits of using NVIDIA Isaac Sim on AWS include improved performance, scalability, and collaboration. It allows developers to simulate and test AI-driven robots in physically accurate simulation, reducing the need for physical prototyping and testing.

Who are some of the leading robotics startups using NVIDIA Isaac Sim on AWS? Some of the leading robotics startups using NVIDIA Isaac Sim on AWS include Field AI, Vention, Cobot, and Cohesive Robotics, among others.

ChatGPT’s Search Results for News Are Unpredictable

Avery Winslow
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0
ChatGPT’s Search Results for News Are Unpredictable

OpenAI’s ChatGPT Search Tool Falls Short in Veracity

ChatGPT Struggles with Truth

Based on testing done by Columbia’s Tow Center for Digital Journalism researchers, OpenAI’s ChatGPT search tool has issues when it comes to responding with the truth. The researchers asked ChatGPT to identify the source of "two hundred quotes from twenty publications." However, the chatbot struggled to correctly identify quotes from articles, even when they came from publishers with arrangements to share data with OpenAI.

False Information and Lack of Transparency

The researchers found that ChatGPT returned partially or entirely incorrect responses on 153 occasions, while only acknowledging an inability to accurately respond to a query seven times. In these instances, the chatbot used qualifying words and phrases like "appears," "it’s possible," or "might," or statements like "I couldn’t locate the exact article." However, in most cases, the chatbot confidently replied with false information, rarely admitting its uncertainty.

Misattribution and Plagiarism

The Tow Center test’s authors documented ChatGPT search results that misattributed a letter-to-the-editor quote from the Orlando Sentinel to a story published in Time. In another example, when asked to identify the source of a quote from a New York Times article about endangered whales, it returned a link to a different website that had wholly plagiarized the story.

OpenAI’s Response

OpenAI responded to the Columbia Journalism Review, stating that "misattribution is hard to address without the data and methodology that the Tow Center withheld, and the study represents an atypical test of our product." The company promised to "keep enhancing search results."

Conclusion

The findings of the Tow Center’s research suggest that OpenAI’s ChatGPT search tool has significant limitations in its ability to provide accurate and reliable information. While the tool may be useful for getting "fast, timely answers with links to relevant web sources," as OpenAI claims, users should be aware of its potential limitations and potential for inaccuracy.

FAQs

Q: What is ChatGPT?
A: ChatGPT is a search tool developed by OpenAI that provides "fast, timely answers with links to relevant web sources."

Q: What were the findings of the Tow Center’s research?
A: The Tow Center’s research found that ChatGPT struggled to correctly identify quotes from articles, even when they came from publishers with arrangements to share data with OpenAI, and returned partially or entirely incorrect responses on 153 occasions.

Q: How did OpenAI respond to the findings?
A: OpenAI responded that "misattribution is hard to address without the data and methodology that the Tow Center withheld, and the study represents an atypical test of our product," and promised to "keep enhancing search results."

Agentic Video Workflow with Search and Summarization

Jordan Waverly
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Agentic Video Workflow with Search and Summarization

Solving the Challenges of Traditional Video Analytics with VLMs

Building a question-answering chatbot with large language models (LLMs) is now a common workflow for text-based interactions. What about creating an AI system that can answer questions about video and image content? This presents a far more complex task.

Challenges in Traditional Video Analytics

Traditional video analytics tools struggle with limited functionality and a narrow focus on predefined objects, making it difficult to build general-purpose systems that understand and extract rich context from video streams. Developers face the following core challenges:

  • Limited understanding: Computer vision models struggle with contextual insights beyond predefined objects.
  • Retaining context: Capturing and maintaining systems’ relevant context over time for videos is challenging.
  • Integration complexity: Building a seamless user experience requires integrating multiple AI technologies.

Introducing the AI Blueprint for Video Search and Summarization

To overcome these challenges, we introduce a solution using the NVIDIA AI Blueprint for video search and summarization. This approach enables the development of a visual AI agent capable of multi-step reasoning over video streams.

Combining AI Technologies for a Seamless User Experience

To build a system that not only understands the video but also interacts with users through speech, we combine multiple technologies – video analysis, speech recognition, reasoning, and audio output. We use REST APIs for individual services and orchestrate a cohesive workflow, simplifying scaling, maintenance, and the addition of new features.

Visual AI Agent Workflow Overview

In this workflow, we create a visual AI agent question-answering tool for videos. The tool performs complex multi-step reasoning based on a video stream, providing a hands-free user interface by taking in speech input and delivering audio output. We set up this workflow and demonstrate its broad contextual understanding by providing it with live first-person point-of-view video streams of everyday activities.

Sample Use Case: Open-World Question-Answering on First-Person Video Streams

We showcase the blueprint’s broad contextual understanding by providing it with live first-person point-of-view video streams of everyday activities, not limited by any specific contextual scope. For example, "Where did I leave my concert tickets?" and "What was the name of the coworker I just met?"

Integrating a Reasoning Pipeline, Speech Inputs, and Audio Outputs with AI Blueprint

To build this type of workflow, we need the following components:

  • AI Blueprint for video search and summarization
  • NVIDIA Morpheus SDK
  • Riva NIM ASR and TTS microservices
  • LLM NIM microservice for generating the final response

Architecture of the Agentic RAG Workflow

Figure 1 shows the architecture of the agentic RAG workflow with NVIDIA Morpheus, Riva, and AI Blueprint. This workflow consists of the following steps:

  1. Video processing: Stored or streamed video is processed using the AI Blueprint to create natural-language summaries of the events. The blueprint also creates a knowledge graph of the video, which can be queried later through REST APIs.
  2. Speech-to-text conversion: User audio queries are transcribed into text using the Riva Parakeet model for automatic speech recognition.
  3. Reasoning pipeline: The Morpheus SDK powers the LLM reasoning pipeline, generating actionable checklists based on the user query.
  4. Context retrieval: Relevant information is fetched from three parallel pipelines:
    • Querying the blueprint to fetch answers from pre-existing summaries and knowledge graphs.
    • Sending new queries to the blueprint to retrieve specific insights from the video that weren’t captured during initial processing.
    • Performing an internet search to supplement the video insights with additional facts relevant to the scene and user query.
  5. Final response generation: An LLM NIM microservice synthesizes the gathered context to produce a summarized answer to the user.
  6. Text-to-speech conversion: The Riva text-to-speech FastPitch model outputs an audio version of the answer.

Conclusion

Traditional video analytics applications and their development workflows are typically built on a collection of fixed-function and limited models that are designed to detect and identify only a select set of predefined objects. With generative AI and vision foundation models, it is now possible to build applications with fewer models, each of which possesses incredibly complex and broad perception as well as rich contextual understanding. This new generation of VLMs gives rise to powerful visual AI agents.

FAQs

Q: What are the core challenges in traditional video analytics?
A: Limited understanding, retaining context, and integration complexity.

Q: What is the AI Blueprint for video search and summarization?
A: A cloud-native solution to accelerate the development of visual AI agents, offering a modular architecture with customizable model support and exposing REST APIs for easy integration with other technologies.

Q: What is Morpheus SDK?
A: A powerful LLM engine module that provides native support for NIM microservices, enabling parallelized inference calls on GPU.

Q: What is the LLM reasoning pipeline?
A: A reference architecture for a dynamic agentic reasoning pipeline that generates a preliminary checklist of actionable items to gather context helpful towards answering the user’s query.

Best Cyber Monday Robot Vacuum Deals 2024 Still Live

Emerson Blake
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Best Cyber Monday Robot Vacuum Deals 2024 Still Live

Best Cyber Monday Deals on Robot Vacuums

As a robot vacuum reviewer and a dog owner, I’m always looking for great devices to help keep my home clean with little effort. I test different types of robot vacuums daily and have grown exceedingly familiar with each brand’s strengths and weaknesses and their price fluctuations. This is the best time of year to buy that robot vacuum you’ve had your eye on all year or as a holiday gift for that family member who’s always wanted one. For example, the iRobot Roomba s9+ robot vacuum is bundled with a Braava Jet m6 robot mop, saving you over $1,000 — a deal you don’t want to miss.

Our Favorite Robot Vacuum Deals for Cyber Monday 2024

  • iRobot Roomba s9+ and Braava Jet m6 bundle: $420 (save $1,029 at Best Buy): This is, by far, the best deal you can get on a robot vacuum and mop bundle, as it went on clearance just in time for Cyber Monday.
  • Roborock Q8 Max+ robot vacuum and mop: $660 (save $160 at Amazon): In addition to a Q8 robot vacuum and mop, the Max model includes a self-emptying dock that you don’t have to worry about for up to seven weeks.
  • Dreame X30 Ultra robot vacuum and mop: $1,135 (save $265 at Amazon): This is the lowest price on the Dreame X30 Ultra, which was ZDNET’s top pick for the best 2-in-1 robot vacuum and mop, dethroned only by its successor, the X40 Ultra.
  • Airrobo T20+ robot vacuum and mop: $280 (save $220 with coupon at Amazon): This is one of the lowest prices we’ve ever seen on this Airrobo self-emptying robot vacuum and mop.
  • Ecovacs Deebot X2 Combo complete robot vacuum and mop: $1,414 (save $286 at Amazon): I rely on the Deebot X2 Combo Complete robot vacuum and mop, especially because of its side-mounted cordless vacuum that empties automatically.

Best Cyber Monday iRobot Robot Vacuum Deals

  • iRobot Roomba j7+ robot vacuum: $359 (save $441 at Amazon): This discount on a self-emptying Roomba powered by RobotOS is a deal you can’t miss.
  • iRobot Roomba s9+ and Braava Jet m6 bundle: $420 (save $1,029 at Best Buy): This is, by far, the best deal you can get on a robot vacuum and mop bundle, as it went on clearance just in time for Cyber Monday.
  • iRobot Roomba Vac 2 Essential robot vacuum: $230 (save $170 at Amazon): This is an introductory offer on iRobot’s brand new entry-level robot vacuum, featuring the AutoEmpty dock, for self-emptying.
  • iRobot Roomba Combo j5 robot vacuum and mop: $285 (save $245 at Amazon): The Roomba Combo j5 is built for everyday vacuuming with occasional mopping.

FAQs

When was Cyber Monday?

Cyber Monday happens on the Monday following the Thanksgiving holiday. This year, Cyber Monday was on Dec. 2.

Are robot vacuum deals really better on Cyber Monday?

Robot vacuums are cheaper during big shopping events like Black Friday, Cyber Monday, and Amazon Prime Day. The fact that Black Friday is one of the last major shopping events of the year results in some retailers giving big discounts on the current product stock in preparation for new devices launching in the new year.

What’s the difference between Black Friday and Cyber Monday?

Though there are many online deals, Black Friday has been historically a mostly in-person shopping event where people visit a brick-and-mortar store. With most big box stores featuring an online store, Black Friday online shopping has become as common as Amazon Prime Day shopping. Cyber Monday began and remains an online shopping event for online stores to capitalize on customers who may have missed a Black Friday deal and can shop from home or work on Monday.

How did we choose these Cyber Monday deals?

As a robot vacuum tester at ZDNET, I’ve become familiar with these devices’ unique features and tested different models in every price range. This experience has made me realize that the robot vacuum market is saturated with competition. It also helps me discern which robot vacuums are worth their retail price and when a discount is a good deal.

Where can you shop the best Black Friday and Cyber Monday deals?

While retailers hold store-specific shopping events throughout the year, like Amazon with Prime Day, you can expect Black Friday and Cyber Monday deals to appear everywhere. This includes your local businesses and big box stores. Black Friday is a huge shopping event, both in-person and online, with stores like Walmart, Best Buy, Amazon, Target, Costco, and more offering eye-popping discounts on different products.

What are the best Cyber Monday 2024 deals?

ZDNET’s experts have been searching through Black Friday and Cyber Monday sales live now to find the best discounts by category. These are the best Cyber Monday deals so far, by category:

Got a Real Kick Out of Enron Relaunch

Avery Winslow
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0
Got a Real Kick Out of Enron Relaunch

Figma Cofounder Dylan Field’s Thoughts on Enron and the Power of Design

Figma cofounder Dylan Field is seemingly a big Enron fan—or rather, of the crypto-fueled semi-parodic relaunch of the company that hit the web earlier this week.

Enron Relaunch and Design

Sporting an oversized Enron hoodie during his conversation with WIRED editor at large Steven Levy during The Big Interview event in San Francisco on Tuesday, Field said he has always been a fan of the Enron logo, which was the last one crafted by legendary American graphic designer Paul Rand of ABC, IBM, UPS, and Westinghouse logo fame. But he said he also “got a real kick” out of the potential Enron relaunch, which has been tied to “Birds Aren’t Real” creator Connor Gaydos. As someone who was just 9 years old when Enron imploded in 2001, Field says he wonders (optimistically, it seems) whether it’s possible to build a new company on the back of the tainted brand, given that his generation might not carry the kind of baggage related to the company’s stumbles that others do.

Figma and the Future of Design

Either way, it seems, it’s a question of the power of design, something Field and Levy focused on more broadly as their chat went on, talking not just about the creation and evolution of the Figma platform but also about where the cofounder sees the company going in the immediate future.

At the moment, Field says, the company has “millions” of users, with a third coming from the design world, a third coming from the programming space, and a third coming from various other backgrounds. With Figma, he thinks, brands and companies can express themselves visually much better than ever before, working collaboratively to more quickly understand what’s graphically possible, what the best user experience is, and how they can best stand out in the marketplace.

Raising the Ceiling

But in an age when AI has the potential to make most things look at least relatively good, Levy asked, how can companies using Figma hope to stand out? Field says the answer isn’t just lowering the floor to meet novice designers and coders, something that kind of AI work has already done, but “raising the ceiling” to help pretty good designers and coders work beyond the previous limits of their skill sets.

The best designers, Field says, have a unique ability to manipulate interactivity, dynamism, motion, and UX to create work that few others can meet. With AI tools like the ones Figma has or will integrate, he hopes that more people will be “limited more by their ideas than the tools in front of them,” ideally giving them the chance to match the work of some of the best designers in the world.

Conclusion

Figma cofounder Dylan Field’s thoughts on Enron and the power of design highlight the importance of creativity and innovation in the world of design. As the company continues to evolve and integrate AI tools, Field’s vision for "raising the ceiling" and empowering designers to push beyond their limits is an exciting prospect for the future of design.

FAQs

Q: What is Figma’s current user base?
A: Figma has "millions" of users, with a third coming from the design world, a third coming from the programming space, and a third coming from various other backgrounds.

Q: What is the focus of Figma’s AI tools?
A: Figma’s AI tools aim to "raise the ceiling" for designers and coders, allowing them to work beyond the previous limits of their skill sets and create work that is truly exceptional.

Q: How does Field see the potential Enron relaunch impacting the company’s brand?
A: Field wonders whether it’s possible to build a new company on the back of the tainted Enron brand, given that his generation might not carry the same baggage as others.

Best Black Friday MacBook Deals

Phil
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0
Best Black Friday MacBook Deals

Refresh

2024-11-29T11:44:33.930Z

Best Black Friday MacBook Deals

Top Pick for Most Users

Retro Option: MacBook Air M1

Other Deals Worth Considering

15-inch MacBook Deal

Other Offers Worth Checking Out

Right! Back to the now, and the current best deal you can get on a 15-inch MacBook…

Hello this is Rosie Hilder taking over this blog from Beren. I’ve been covering Black Friday since 2018 so am well-versed in what makes a good deal and what doesn’t. Stay tuned for my favourite offers.

Rosie Hilder

Deputy Editor, Creative Bloq

Rosie has worked on Creative Bloq since 2018 and has been covering sales events since then. She was with the team when we first launched live blogs in 2021, and at one point was working on six at once (not recommended).

Trade-in Deal

Trade-in available

Nintendo Switch OLED Price Change

Nintendo Switch OLED price change

Nintendo Switch OLED on blue background

(Image credit: Nintendo)

Conclusion

If you’re in the market for a new MacBook, now is the perfect time to grab a great deal. Whether you’re looking for a retro option or a newer model, there are plenty of options to choose from. Don’t forget to check out the trade-in deal and see if you can get a better price on your old computer.

Frequently Asked Questions

Q: What is the best MacBook deal available right now?
A: The best MacBook deal available right now is the MacBook Air M3 for $1,099, which is available at Amazon and in the UK at £1,099.

Q: What is the retro option for MacBook?
A: The retro option is the MacBook Air M1, which is a four-year-old laptop that is still a great option if you’re in a retro mood and have a budget of $500.

Q: Can I get a trade-in deal on my old computer?
A: Yes, some retailers are offering trade-in deals when you exchange an old computer. Very, for example, will give you up to £850 when you trade-in an eligible device.

Q: What is the Nintendo Switch OLED price change?
A: The Nintendo Switch OLED price change is from £228 to £232. It’s still a good deal, but not as good as it was before Black Friday.

Unanswered GANs: Key Questions in Generative Adversarial Networks

Parker Lennox
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Unanswered GANs: Key Questions in Generative Adversarial Networks

What are the Trade-Offs Between GANs and other Generative Models?

By some metrics, research on Generative Adversarial Networks (GANs) has progressed substantially in the past 2 years. Practical improvements to image synthesis models are being made almost too quickly to keep up with.


Odena et al., 2016
Miyato et al., 2017
Zhang et al., 2018
Brock et al., 2018

However, by other metrics, less has happened. For instance, there is still widespread disagreement about how GANs should be evaluated.

What are the Trade-Offs Between GANs and other Generative Models?

In addition to GANs, two other types of generative model are currently popular: Flow Models and Autoregressive Models. Roughly speaking, Flow Models apply a stack of invertible transformations to a sample from a prior so that exact log-likelihoods of observations can be computed. On the other hand, Autoregressive Models factorize the distribution over observations into conditional distributions and process one component of the observation at a time.

We think that accurately characterizing these trade-offs and deciding whether they are intrinsic to the model families is an interesting open question.

What are the Trade-Offs Between GANs and other Generative Models?

Parallel Efficient Reversible
GANs Yes Yes No
Flow Models Yes No Yes
Autoregressive Models No Yes Yes

Problem 1

What are the fundamental trade-offs between GANs and other generative models?

In particular, can we make some sort of CAP Theorem type statement about reversibility, parallelism, and parameter/time efficiency?

How does GAN Training Scale with Batch Size?

Large minibatches have helped to scale up image classification can they also help us scale up GANs?

How does GAN Training Scale with Batch Size?

At first glance, it seems like the answer should be yes after all, the discriminator in most GANs is just an image classifier. Larger batches can accelerate training if it is bottlenecked on gradient noise. However, GANs have a separate bottleneck that classifiers don’t: the training procedure can diverge.

How does GAN Training Scale with Batch Size?

Problem 6

How does GAN training scale with batch size?

How big a role does gradient noise play in GAN training?

Can GAN training be modified so that it scales better with batch size?

What is the Relationship Between GANs and Adversarial Examples?

It’s well known that image classifiers suffer from adversarial examples human-imperceptible perturbations that cause classifiers to give the wrong output when added to images.

What is the Relationship Between GANs and Adversarial Examples?

Since the GAN discriminator is an image classifier, one might worry about it suffering from adversarial examples. Despite the large bodies of literature on GANs and adversarial examples, there doesn’t seem to be much work on how they relate.

What is the Relationship Between GANs and Adversarial Examples?

Problem 7

How does the adversarial robustness of the discriminator affect GAN training?

Conclusion

GANs have made substantial progress in the past 2 years, but there is still widespread disagreement about how GANs should be evaluated. We have identified several open problems related to GANs and other generative models.

Frequently Asked Questions

Q: What is the fundamental trade-off between GANs and other generative models?

A: The fundamental trade-off between GANs and other generative models is related to reversibility, parallelism, and parameter/time efficiency.

Q: How does GAN training scale with batch size?

A: GAN training scales with batch size, but there are separate bottlenecks that classifiers don’t, such as the training procedure can diverge.

Q: What is the relationship between GANs and adversarial examples?

A: GANs are susceptible to adversarial examples, but there is limited work on how they relate.

Amazon announces its own set of Nova AI models.

Avery Winslow
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0
Amazon announces its own set of Nova AI models.

Amazon Unveils Nova AI Foundation Models for AWS

Amazon has announced a series of new AI foundation models under a new “Nova” branding that will be available as part of the Amazon Bedrock model library in AWS.

Amazon Nova Premier: A Multimodal Model for Complex Reasoning Tasks

The company is also training a model called Amazon Nova Premier, which it says will be “our most capable multimodal model for complex reasoning tasks.” Amazon aims to make Nova Premier available in “early 2025.”

Content Generation Models: Amazon Nova Canvas and Nova Reel

Amazon is releasing content generation models, too: Amazon Nova Canvas, an image generation model, and Amazon Nova Reel, a video generation model. The company says that these models have “watermarking capabilities” to “promote responsible AI use.” As an example of what’s possible with Nova Reel, Amazon has shared this mock ad for a fake pasta brand.

Forthcoming Models: Speech-to-Speech and Multimodal-to-Multimodal

Later in 2025, Amazon plans to release a speech-to-speech model and “a native multimodal-to-multimodal” model, according to a blog post.

AI Compute Cluster and Partnership with Anthropic

Amazon announced these new models at its AWS re:Invent conference, which is happening now in Las Vegas. At the show, the company also said that it’s building a huge AI compute cluster that relies on its Trainium 2 chips in partnership with Anthropic (which it has invested $8 billion in). “When completed, it is expected to be the world’s largest AI compute cluster reported to date available for Anthropic to build and deploy its future models on,” according to Amazon.

Advantages Over Other Tech Companies

The company, like many other big tech players, is racing to release new AI products and features to stay ahead of newer companies like OpenAI. Where Amazon could have an advantage is how much internet infrastructure is already powered by AWS — large enterprises may be more willing to use Amazon’s AI offerings because the company has already a trusted reputation. An Apple exec even appeared onstage today at re:Invent to talk about how the company relies on Amazon’s custom AI chips.

Conclusion

Amazon’s new Nova AI foundation models and content generation models mark a significant step forward in the company’s AI development. With the upcoming release of speech-to-speech and multimodal-to-multimodal models, Amazon is poised to stay competitive in the AI market.

FAQs

Q: What is Amazon’s Nova AI foundation model?
A: Amazon’s Nova AI foundation model is a series of new AI models under a new “Nova” branding that will be available as part of the Amazon Bedrock model library in AWS.

Q: What are the capabilities of Amazon Nova Premier?
A: Amazon Nova Premier is a multimodal model for complex reasoning tasks and is expected to be available in “early 2025.”

Q: What are Amazon Nova Canvas and Nova Reel?
A: Amazon Nova Canvas is an image generation model, and Amazon Nova Reel is a video generation model. Both models have “watermarking capabilities” to promote responsible AI use.

Q: What are the forthcoming models that Amazon plans to release?
A: Amazon plans to release a speech-to-speech model and “a native multimodal-to-multimodal” model later in 2025.

Q: What is Amazon’s partnership with Anthropic?
A: Amazon is building a huge AI compute cluster that relies on its Trainium 2 chips in partnership with Anthropic, which it has invested $8 billion in.

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