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Learning Hightopo’s HT for Web 2D Display, Node, Edge, and Animation

Creating a 2D Display – (GraphView)

Before creating a 2D display, we recommend reading [Learning Notes] Hightopo’s HT for Web (1) – Basic Conceptions.

Creating a 2D Display – (GraphView)

You can create a 2D display by using the command new ht.graph.GraphView(). After creating the graph, you can add it to the DOM using the GraphView.addToDOM() method. The addToDOM() method accepts one parameter. If it’s empty, it will be added to the body by default. You can also pass a div to fix the 2D graph view to a specific position on the page.

For newly created graph view, they have default properties such as zooming, panning, editing, and rectangular selection. We can enable or disable them as needed.

Why do we set the background of the graph view through DataModel instead of directly operating on the GraphView itself?

In the first section [Learning Notes] Hightopo’s HT for Web (1) – Basic Conceptions, we mentioned that in order to save and restore our created 2D/3D data, we can achieve this through serialization and deserialization of the DataModel. Note that the DataModel is the core component of the HT for Web, and it is responsible for managing all kinds of graphic elements added to it.

Adding Nodes – (ht.Node)

You can add nodes to the graph by using the ht.Node type. The position of nodes can be controlled through Node.setPosition(x, y) or Node.p(x, y) methods, and their coordinates can be obtained through Node.getPosition() or Node.p() methods.

Adding Connections – (ht.Edge)

For connections, we use the ht.Edge type. When creating an ht.Edge object, we need to pass in the source and target nodes, and can configure its properties through the edge.s() method, such as width, color, dashed lines, etc.

Animation – (ht.Default.startAnim(animParams))

For animations, we use the ht.Default.startAnim(animParams) method, which returns an anim object that can be used to call anim.stop(true) to terminate the animation. The anim object also has anim.pause() and anim.resume() methods that can be used to interrupt and continue animation functionality, as well as the anim.isRunning() function to determine whether an animation is in progress.

Summary

In this section, we have primarily introduced the creation and basic configuration of HT for Web displays. In GraphView, we can add ht.Node to its DataModel to draw cabinets. The position of nodes can be controlled through Node.setPosition(x, y) or Node.p(x, y) methods, and their coordinates can be obtained through Node.getPosition() or Node.p() methods. Additionally, the coordinate system in GraphView differs from the browser’s coordinate system; we can use GraphView.getLogicalPoint(event) and GraphView.getScreenPoint(point, y) methods to convert between these two coordinate systems.

FAQs

  • What is the purpose of creating a 2D display in HT for Web?
    Answer: To create a 2D display, you can use the new ht.graph.GraphView() command. After creating the graph, you can add it to the DOM using the GraphView.addToDOM() method.
  • How do I add nodes to the 2D display?
    Answer: You can add nodes to the graph by using the ht.Node type. The position of nodes can be controlled through Node.setPosition(x, y) or Node.p(x, y) methods, and their coordinates can be obtained through Node.getPosition() or Node.p() methods.
  • How do I add connections to the 2D display?
    Answer: For connections, we use the ht.Edge type. When creating an ht.Edge object, we need to pass in the source and target nodes, and can configure its properties through the edge.s() method, such as width, color, dashed lines, etc.
  • How do I animate connections in the 2D display?
    Answer: For animations, we use the ht.Default.startAnim(animParams) method, which returns an anim object that can be used to call anim.stop(true) to terminate the animation. The anim object also has anim.pause() and anim.resume() methods that can be used to interrupt and continue animation functionality, as well as the anim.isRunning() function to determine whether an animation is in progress.

FlashBlade//EXA Moves Data at 10+ TB/sec

FlashBlade//EXA: A New All-Flash Storage Array for AI Factories and Multi-Modal AI Training

Pure Storage today unveiled FlashBlade//EXA, a new all-flash storage array designed to meet the demanding needs of AI factories and multi-modal AI training. FlashBlade//EXA separates the metadata layer from the data path in the I/O stream, which Pure says enables the array to move data rates exceeding 10 terabytes per second per namespace.

Architecture and Design

The new array splits the high-speed I/O into two parts. The metadata is routed through the metadata core component of the FlashBlade//EXA, which is based on high-speed DirectFlash Module (DFM) nodes that house the company’s scale-out distributed key-value store. The metadata core nodes run on the Purity//FB operating system, which has been bolstered with support for Parallel NFS (pNFS) to communicate with the compute nodes.

Block data is separately routed over Remote Direct Memory Access (RDMA) to the data nodes, which are industry standard Linux-based servers with this release (the company plans to incorporate its DFM tech in a future release). This architecture allows FlashBlade//EXA to reach the maximum allowable bandwidth between the data storage and the compute nodes.

Eliminating I/O Bottlenecks

"This segregation provides non-blocking data access that increases exponentially in high-performance computing scenarios where the metadata requests can equal, if not outnumber, data I/O operations," writes Alex Castro, a Pure Storage vice president, in a blog post.

Comparison to Other Solutions

When Sun Microsystems created NFS back in 1984, functionality was the primary focus, not performance, Castro says. However, legacy NAS devices that require more I/O controllers to be added with each new data node have created a bottleneck to performance. Splitting the I/O is the key to unlocking the bottleneck created by legacy NAS arrays, he says.

Some storage vendors have resorted to using specialized file systems, such as Lustre, to deliver the parallelism needed for large-scale projects, Castro writes, but these environments were prone to metadata latency and required Ph.D.-level skills to manage. On the other side, other vendors have inserted a compute aggregation layer between the compute clients and the data source.

AI Factories and Multi-Modal AI Training

Pure says it developed the FlashBlade//EXA to meet the emerging needs of "AI factories," and in particular the need to keep thousands of high-end GPUs fed with data. In terms of scale, AI factories sit in the middle. On the low end are enterprise AI workloads, such as inference and RAG, that work on 50TB to 100PB of data, while AI factories will need access to up to 10,000 GPUs on data sets from 100PB to multiple exabytes. At the high end, hyperscalers can have upwards of 100EBs and more than 10,000 GPUs. At all levels, having idle GPUs is an impediment to productivity.

Conclusion

Pure Storage says it expects to start shipping FlashBlade//EXA this summer.

FAQs

Q: What is FlashBlade//EXA?
A: FlashBlade//EXA is a new all-flash storage array designed to meet the demanding needs of AI factories and multi-modal AI training.

Q: What are the key features of FlashBlade//EXA?
A: The key features of FlashBlade//EXA include its ability to separate the metadata layer from the data path in the I/O stream, which enables the array to move data rates exceeding 10 terabytes per second per namespace.

Q: How does FlashBlade//EXA differ from other storage solutions?
A: FlashBlade//EXA differs from other storage solutions in that it splits the high-speed I/O into two parts, allowing it to reach the maximum allowable bandwidth between the data storage and the compute nodes.

Press to Talk to Copilot via Windows Hotkey

Press to Talk: A New Way to Chat with Copilot

I always enjoy a good conversation with Microsoft Copilot. I use the Wave voice, which offers a friendly and sociable personality with a charming British accent. Our conversations can be casual chit-chat — he always greets me by name and asks how I’m doing. Or they can be more direct if I have a specific question or request.

The Only Impediment

The only impediment is that using Copilot in conversation mode currently requires a couple of steps. You have to manually launch the app and then press the microphone button. Instead, I wish I could jump directly and quickly into a conversation. Well, my wish has been granted.

Press to Talk

Now, in the latest Windows 11 Insider build is an update to Copilot equipped with a feature called Press to Talk. Using the new version, you’ll be able to trigger the AI in conversation mode with just a single hotkey. To start a voice chat with Copilot in Windows, hold down the Alt key + Spacebar for two seconds. Copilot will greet you and kick off the conversation.

How it Works

If you stop speaking for several seconds, the AI will automatically end the conversation. Otherwise, just press the Esc key to stop chatting, and the microphone on the screen will disappear.

Current State of Affairs

Currently, you can already trigger Copilot in Windows by pressing Alt + Spacebar. But doing so places you at the standard prompt. From there, you have to either type your request or select the microphone to chat with the AI. You can also enable or disable the hotkey and set Copilot to automatically launch when you start Windows.

Configuring Your Experience

To change the voice for Copilot or tweak other settings across the board, click your profile icon at the top. From the menu, you’re able to switch the voice, language, or theme.

Rolling out to Windows Insiders

This latest update to Copilot (version 1.25024.100.0 and higher) is rolling out gradually to Windows Insiders. So, even if you’re running a Windows Insider build, you may not see it right away. But just be patient, and it should arrive before too long.

Conclusion

The new Press to Talk feature brings a more streamlined experience to Copilot, allowing users to jump directly into conversations with the AI. With this update, users can enjoy more seamless interactions with Copilot, making it easier to access the AI’s capabilities.

Frequently Asked Questions

Q: What is Press to Talk?
A: Press to Talk is a new feature in Copilot that allows users to trigger the AI in conversation mode with just a single hotkey.

Q: How do I use Press to Talk?
A: To use Press to Talk, hold down the Alt key + Spacebar for two seconds.

Q: What happens if I stop speaking for several seconds?
A: The AI will automatically end the conversation.

Q: Can I change the voice for Copilot?
A: Yes, you can change the voice by clicking your profile icon and selecting the new voice from the menu.

Google has given Anthropic more funding than previously known, show new filings.

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Anthropic’s Ties to Google Revealed: A $3 Billion Investment and a Question of Independence

Deeper Ties to Google Than Previously Known

Court documents recently obtained by The New York Times reveal that Google owns a 14% stake in Anthropic, a San Francisco-based AI startup. Moreover, the tech giant is set to pour another $750 million into the company this year through a convertible debt deal. This investment brings the total value of Google’s investment in Anthropic to a staggering $3 billion.

Lack of Control, but Still a Significant Stake

Despite having no voting rights, board seats, or direct control over the company, Google’s backing raises questions about Anthropic’s independence. This is particularly concerning in an era where AI startups rely heavily on funding from tech giants, leading regulators to scrutinize whether these deals give incumbents an unfair advantage. The Justice Department’s recent decision to drop a proposal that would have forced the sale of some of these stakes only adds to the concern.

A Strategy of Hedging Bets

Google’s strategy in investing in Anthropic, while simultaneously developing its own AI technology, is a clear example of hedging its bets. This approach allows the company to stay competitive in the market while also gaining a foothold in the AI space. Meanwhile, Amazon’s agreement to invest up to $8 billion in Anthropic raises further questions about the future of the startup and other big AI startups.

The Consequences for AI Startups

As AI startups increasingly rely on funding from tech giants, the question remains: are they still mavericks or becoming extensions of Big Tech? The ties between Google and Anthropic, as well as similar deals with other startups, will likely have a significant impact on the future of the AI industry.

FAQs

Q: What is the significance of Google’s investment in Anthropic?
A: Google’s investment in Anthropic is a significant one, with a total value of $3 billion. This suggests that the tech giant is committed to the startup’s success and is willing to invest heavily in its growth.

Q: Does Google have control over Anthropic?
A: No, Google does not have control over Anthropic, as it has no voting rights, board seats, or direct control over the company.

Q: What does this mean for the future of AI startups?
A: The ties between Google and Anthropic, as well as similar deals with other startups, will likely have a significant impact on the future of the AI industry. It raises questions about the independence and autonomy of these startups and whether they are becoming extensions of Big Tech.

Netflix Gaming Efforts

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New President of Netflix’s Gaming Division Appointed

Alain Tascan Takes the Reins

Netflix has appointed Alain Tascan as its new president of its gaming division, reports Variety. Tascan, who previously served as executive vice president of game development at Epic Games, the maker of Fortnite, will be responsible for leading Netflix’s gaming efforts.

Tascan’s Background

In his previous role at Epic, Tascan was in charge of the company’s first-party output, which included popular titles such as Fortnite, Lego Fortnite, Rocket League, and Fall Guys. He has been with Epic since 2018.

Why Tascan Chose Netflix

In a statement, Tascan explained that he took the role at Netflix because "I firmly believe that Netflix is uniquely positioned to redefine the future of gaming." It is unclear what Tascan’s plans are for the future of Netflix’s gaming division, but his experience in the gaming industry is likely to be an asset in his new role.

Who’s Who: Mike Verdu’s New Role

Mike Verdu, the former president of Netflix’s gaming division, has moved into a new role at the company, focused on a team "on the cutting edge of game innovation." It is unclear what specific responsibilities Verdu will have in his new role, but it is likely that he will continue to play a significant role in Netflix’s gaming efforts.

Conclusion

Netflix’s appointment of Alain Tascan as president of its gaming division is a significant move in the company’s efforts to establish itself as a major player in the gaming industry. With Tascan’s experience at Epic Games, he is well-positioned to lead Netflix’s gaming efforts and help the company achieve its goals in the competitive gaming market.

FAQs

Q: What is Alain Tascan’s background?
A: Alain Tascan is a veteran of the gaming industry, having served as executive vice president of game development at Epic Games.

Q: What is his new role at Netflix?
A: Tascan is the new president of Netflix’s gaming division.

Q: What is Mike Verdu’s new role at Netflix?
A: Mike Verdu, the former president of Netflix’s gaming division, has moved into a new role focused on a team "on the cutting edge of game innovation" at Netflix.

OpenAI Enhances AI Agent Capabilities with New Developer API

OpenAI’s Latest Development: Improved AI Agents with Web Search Capability

Developers using the Responses API can now access the same models that power ChatGPT Search: GPT-4o search and GPT-4o mini search. These models can browse the web to answer questions and cite sources in their responses.

Improved Factual Accuracy

That’s notable because OpenAI says the added web search ability dramatically improves the factual accuracy of its AI models. On OpenAI’s SimpleQA benchmark, which aims to measure confabulation rate, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent—both substantially outperforming the larger GPT-4.5 model without search, which scored 63 percent.

Limitations and Challenges

Despite these improvements, the technology still has significant limitations. Aside from issues with CUA properly navigating websites, the improved search capability doesn’t completely solve the problem of AI confabulations, with GPT-4o search still making factual mistakes 10 percent of the time.

Open Source Agents SDK and Integrated Systems

Alongside the Responses API, OpenAI released the open source Agents SDK, providing developers free tools to integrate models with internal systems, implement safeguards, and monitor agent activities. This toolkit follows OpenAI’s earlier release of Swarm, a framework for orchestrating multiple agents.

Conclusion

The AI agent movement is still in its early days, and things will likely improve rapidly. However, at the moment, the AI agent movement remains vulnerable to unrealistic claims, as demonstrated earlier this week when users discovered that Chinese startup Butterfly Effect’s Manus AI agent platform failed to deliver on many of its promises, highlighting the persistent gap between promotional claims and practical functionality in this emerging technology category.

FAQs

Q: What are the benefits of OpenAI’s new AI agents?

A: The new AI agents can browse the web to answer questions and cite sources in their responses, improving factual accuracy.

Q: How accurate are OpenAI’s new AI agents?

A: According to OpenAI’s SimpleQA benchmark, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent.

Q: What are the limitations of OpenAI’s new AI agents?

A: Despite improvements, the technology still has limitations, including issues with CUA properly navigating websites and making factual mistakes 10 percent of the time.

Q: What is the Open Source Agents SDK?

A: The Open Source Agents SDK is a free toolkit providing developers with tools to integrate models with internal systems, implement safeguards, and monitor agent activities.

Building Real-Time Multimodal XR Apps with NVIDIA AI

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Advancing XR Applications with Multimodal AI Agents

Introduction

The recent advancements in generative AI and vision foundational models have led to the development of VLMs, which offer a new wave of visual computing. These intelligent solutions enable sophisticated perception and deep contextual understanding, enhancing semantic comprehension in XR settings. By integrating VLMs, developers can significantly improve how XR applications interpret and interact with user actions, making them more responsive and intuitive.

Augmenting XR Applications with Conversational AI

Augmenting XR applications with conversational AI functionalities creates a more immersive experience for users. By creating generative AI agents that offer Q&A capabilities within the XR environment, users can interact more naturally and receive immediate assistance. A multimodal AI agent processes and synthesizes multiple input modes—such as visual data (XR headset feeds, for example), speech, text, or sensor streams—to make context-aware decisions and generate natural, interactive responses.

Use Cases

Use cases where this integration can make a substantial impact are:

  • Skilled labor training: In industries where simulation training is safer and more practical than using real equipment, XR applications can provide immersive and controlled environments. Enhanced semantic understanding through VLMs enables more realistic and effective training experiences, facilitating better skill transfer and safety protocols.
  • Design and prototyping: Engineers and designers can leverage XR environments to visualize and manipulate 3D models. VLMs enable the system to understand gestures and contextual commands, streamlining the design process and fostering innovation.
  • Education and learning: XR applications can create immersive educational experiences across various subjects. With semantic understanding, the system can adapt to a learner’s interactions, providing personalized content and interactive elements that deepen understanding.

NVIDIA AI Blueprint for Video Search and Summarization

The NVIDIA AI Blueprint for video search and summarization addresses the challenge of processing long videos or real-time streams while effectively capturing the temporal context. The AI Blueprint simplifies the development of video analytics AI agents by leveraging a VLM and an LLM. The VLM generates detailed captions for the video segments, which are then stored in a vector database. The LLM summarizes these captions to generate a final response to the user’s queries.

Modifying the AI Blueprint for XR Applications

To adapt the blueprint for the specific use case of a virtual reality (VR) agent, the first step is to ensure a continuous stream of VR data into the pipeline. For example, you can use FFmpeg to capture the VR environment directly from the screen of the VR headset. To make the agent interactive, our team prioritized enabling voice communication. What better way to interact with a VR agent than by speaking to it?

Integrating Audio Processing

There are multiple ways to incorporate audio and visual understanding into XR environments. In this tutorial, we modified the AI blueprint to incorporate audio processing by segmenting both audio and video at consistent intervals, saving them as .mpg and .wav files. The video files (.mpg) are processed by the VLM, while the audio files (.wav) are sent to NVIDIA Riva NIM ASR through an API call for transcription. Riva ASR NIM APIs provide easy access to state-of-the-art automatic speech recognition (ASR) models for multiple languages. The transcribed text is then sent to the VLM along with the corresponding video.

Conclusion

By integrating VLMs and incorporating features like enhanced semantic understanding and conversational AI capabilities, developers can expand the potential use cases of XR applications. The NVIDIA AI Blueprint for video search and summarization can be leveraged to create intelligent agents that process and analyze video streams, providing users with more immersive and interactive experiences.

FAQs

Q: What are VLMs, and how do they enhance XR applications?
A: VLMs are vision foundational models that offer a new wave of visual computing. They enable sophisticated perception and deep contextual understanding, enhancing semantic comprehension in XR settings.

Q: How can VLMs be integrated with XR applications?
A: VLMs can be integrated with XR applications to improve how they interpret and interact with user actions, making them more responsive and intuitive.

Q: What are some use cases for VLMs in XR applications?
A: Use cases for VLMs in XR applications include skilled labor training, design and prototyping, and education and learning.

Q: What is the NVIDIA AI Blueprint for video search and summarization?
A: The NVIDIA AI Blueprint for video search and summarization simplifies the development of video analytics AI agents by leveraging a VLM and an LLM.

Q: How can I get started with the NVIDIA AI Blueprint for video search and summarization?
A: You can get started with the NVIDIA AI Blueprint for video search and summarization by applying for the Early Access Program.

Inside Google’s Investment in Anthropic

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Google’s Investment in AI Start-up Anthropic: A Rare Glimpse into Big Tech’s Investment Strategies

Google’s Stake in Anthropic: A 14% Ownership

Court documents reveal that Google owns 14% of the AI start-up Anthropic, with no voting rights, board seats, or board observer rights. This ownership structure gives Google limited control over the company. However, Google is set to invest an additional $750 million in Anthropic through a convertible debt agreement.

A Rare Glimpse into Big Tech’s Investment Strategies

The investment in Anthropic is one of several made by Google in AI start-ups, including OpenAI, the maker of the ChatGPT chatbot. Microsoft and Amazon have also invested in prominent AI start-ups. The move is seen as a way for Google to stay ahead in the AI race and preserve its competitive edge.

The Value of Google’s Investment

The value of Google’s investment in Anthropic is significant, with the company’s stake in the start-up now worth a "significant amount more" than the latest funding round, which closed this month. The financing valued Anthropic at $61.5 billion.

Anthropic’s Funding and Partnerships

Anthropic has raised over $14.8 billion from venture capital firms, including Menlo Ventures. The company has also partnered with Google and Amazon to use their computing power and cloud services to build its AI systems. This means that some of the money raised is pumped back into its investors.

The Future of AI and Big Tech’s Role

The investment in Anthropic is a rare glimpse into the strategies of big tech companies like Google, Amazon, and Microsoft. As the AI race continues to evolve, it is likely that we will see more of these companies investing in and partnering with AI start-ups to stay ahead of the competition.

FAQs

Q: What is Anthropic?
A: Anthropic is an AI start-up that was founded in 2021 by Dario Amodei and his sister Daniela Amodei.

Q: Who are the investors in Anthropic?
A: Google, Amazon, and other venture capital firms, including Menlo Ventures, have invested in Anthropic.

Q: What is the value of Google’s investment in Anthropic?
A: The value of Google’s investment in Anthropic is significant, with the company’s stake now worth a "significant amount more" than the latest funding round, which closed this month.

Q: What is the future of AI and big tech’s role in it?
A: As the AI race continues to evolve, it is likely that we will see more of these companies investing in and partnering with AI start-ups to stay ahead of the competition.

iRobot Overhauls Roomba Line with Four New Models

iRobot Redesigns its Entire Robot Vacuum Lineup

iRobot has been a pioneer in the robot vacuum industry since its launch over 20 years ago. Recently, the company announced a complete redesign of its entire product line, introducing new entry-level, midrange, and high-end robot vacuums. Most models feature an AutoEmpty Dock and mopping variant to complete the Roomba catalog.

New Roomba Lineup

The new Roombas include the 105 model and 105 Combo starting at $299, the Roomba 205 DustCompactor and a Combo variant starting at $449, the Plus 405 Combo Robot and AutoWash Dock at $799, and the Roomba Plus 505 Combo Robot and AutoWash Dock at $999.

Simplifying the Product Line

iRobot is looking to simplify and clarify its product line for consumers by categorizing its models into entry-level Roomba, midrange Roomba Plus, and flagship Roomba Max. Additionally, robot vacuums will have a Vac and Combo variant, the latter for robot vacuum and mop combination robots. The optional AutoEmpty dock will be available for the Roomba 105.

Redesign Features

The new Roombas boast a new look with a geometric design and protruding LiDAR sensors in all but the Roomba 205. This is a first for the brand. The Roomba Combo 10 Max will remain the only device to feature the retractable mop pad with mechanical arms.

Roomba Lineup at a Glance

iRobot Model Variations Available Starting Price
Roomba 105 Vacuum only, Vacuum only and AutoEmpty dock, Combo (Vacuum and mop), Combo and AutoEmpty dock $299
Roomba 205 DustCompactor Vacuum only, Combo (Vacuum and mop) $449
Roomba Plus 405 Combo (Vacuum and mop) and AutoWash dock $799
Roomba Plus 505 Combo (Vacuum and mop) and AutoWash dock $999
Roomba Combo 10 Max Combo (Vacuum and mop) and AutoWash dock $1,400

Conclusion

The new Roomba lineup offers a range of options for consumers, from entry-level to high-end, with features like AutoEmpty dock and mopping variants. The redesign aims to simplify and clarify the product line, making it easier for customers to choose the right robot vacuum for their needs.

FAQs

Q: What is the starting price for the new Roomba 105?
A: The starting price for the Roomba 105 is $299.

Q: What is the difference between the Roomba 205 DustCompactor and the other Roomba models?
A: The Roomba 205 DustCompactor features a compactor built into the dustbin, which pushes collected dust and debris to one side, making room for more dust in the next cleaning.

Q: Is the retractable mop pad with mechanical arms available in all new Roomba models?
A: No, the retractable mop pad with mechanical arms is only available in the Roomba Combo 10 Max.

IBM’s CEO Doesn’t Think AI Will Replace Programmers Anytime Soon

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Global Trade and AI: IBM CEO Arvind Krishna’s Insights

Global Trade: A Key to Growth

IBM CEO Arvind Krishna believes that global trade is not dead, despite the Trump Administration’s attacks on globalization. In fact, he thinks that the U.S. should focus on embracing international exchange of goods to drive local growth. According to Krishna, every 10% increase in global trade leads to a 1% increase in local GDP, making it a crucial aspect of optimizing local growth.

Embracing Global Talent

Krishna also emphasized the importance of allowing overseas talent to flow into the U.S. He believes that the country should be an international talent hub, allowing individuals to bring their skills and expertise to the country. This, he argues, will lead to the development of local talent and will be beneficial for the economy.

AI: Not a Panacea

Krishna disagrees with recent predictions that 90% of code will be written by AI in the next three to six months. He believes that AI will only be able to write 20-30% of the code, and that there are many complex use cases where AI will not be able to replace human programmers. Instead, he thinks AI will make programmers more productive, allowing them to output more code with the same number of people.

AI’s Impact on Jobs

Krishna believes that AI will not eliminate programming jobs, but rather augment them. He thinks that AI will make programmers more productive, leading to increased output and market share. This, in turn, will lead to more products and services being developed, which will drive growth.

Challenges and Controversies

Krishna’s views on AI are not without controversy. He acknowledges that there are unresolved challenges around intellectual property and AI training and outputs. However, he believes that AI is a positive force that will ultimately make products and services better for consumers.

Quantum Computing: The Future of Scientific Discovery

Krishna sees quantum computing as the key to accelerating scientific discovery, rather than AI. He believes that AI is limited to learning from already-produced knowledge, whereas quantum computing has the potential to unlock new knowledge and understanding.

Conclusion

Arvind Krishna’s views on global trade, AI, and quantum computing provide valuable insights into the future of technology and its impact on the economy. While his opinions may be disputed, they offer a unique perspective on the role of technology in driving growth and development.

FAQs

Q: What is your view on global trade?
A: I believe that global trade is not dead and that it is a key aspect of optimizing local growth.

Q: How do you see AI impacting the job market?
A: I think AI will make programmers more productive, allowing them to output more code with the same number of people, rather than eliminating jobs.

Q: What is your view on the potential of AI?
A: I believe that AI has the potential to make products and services better for consumers, but it is not a panacea and will not lead to the creation of artificial general intelligence.

Q: What about quantum computing?
A: I think quantum computing has the potential to accelerate scientific discovery and unlock new knowledge and understanding.