Our Co-in-Run model
Co-in-Run is a side-scrolling platformer in which the agent must dodge enemies and other traps and collect the coin at the end of the level.
Building a Reverse Image Search Engine with Amazon Titan
Visual Search Technology Revolutionizes Ecommerce
In ecommerce, visual search technology has revolutionized the way customers find products by enabling them to search for products using images instead of text. Shoppers often have a clear visual idea of what they want but struggle to describe it in words, leading to inefficient and broad text-based search results.
Reverse Image Search Engine
A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. It works by analyzing the visual content to find similar images in its database. Companies such as Amazon use this technology to allow users to use a photo or other image to search for similar products on their ecommerce websites.
Solution Overview
The solution outlines how to build a reverse image search engine to retrieve similar images based on input image queries. This post demonstrates a guide for using Amazon Titan Multimodal Embeddings to embed images, store these embeddings in an OpenSearch Serverless vector index, and use Amazon Rekognition to extract key objects from images for querying the index.
Prerequisites
To implement the proposed solution, make sure that you have the following:
- An AWS account and a working knowledge of FMs, Amazon Bedrock, Amazon SageMaker, Amazon OpenSearch Service, Amazon S3, and AWS Identity and Access Management (IAM).
- The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3.
- Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.
- An Amazon SageMaker Studio domain.
- An Amazon OpenSearch Serverless collection.
- The GitHub repo cloned to the Amazon SageMaker Studio instance.
Generate Embeddings
Use Amazon Titan Multimodal Embeddings to generate embeddings for the stored images.
Store Embeddings
Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution.
Image Analysis
Use Amazon Rekognition to analyze the product images and extract labels and bounding boxes for these images. These extracted objects will then be saved as separate images, which can be used for the query.
Convert Search Query to an Embedding
Convert the user’s image search query into an embedding using Amazon Titan Multimodal Embeddings.
Run Similarity Search
Perform a similarity search on the vector database to find product images that closely match the search query embedding.
Display Results
Display the top K similar results to the user.
Conclusion
By combining the power of Amazon Rekognition for object detection and extraction, Amazon Titan Multimodal Embeddings for generating vector representations, and Amazon OpenSearch Serverless for efficient vector indexing and search capabilities, you successfully created a robust reverse image search engine. This solution enhances product recommendations by providing precise and relevant results based on visual queries, thereby significantly improving the user experience for ecommerce solutions.
FAQs
Q: What is visual search technology?
A: Visual search technology is a type of search technology that enables users to search for products using images instead of text.
Q: What is a reverse image search engine?
A: A reverse image search engine is a type of search engine that enables users to upload an image to find related information instead of using text-based queries.
Q: What is Amazon Titan Multimodal Embeddings?
A: Amazon Titan Multimodal Embeddings is a type of artificial intelligence (AI) model that enables users to generate vector representations of images.
Q: What is Amazon Rekognition?
A: Amazon Rekognition is a type of AI model that enables users to analyze and extract information from images and videos.
Q: How does the solution work?
A: The solution works by generating embeddings of images using Amazon Titan Multimodal Embeddings, storing these embeddings in an OpenSearch Serverless vector index, and using Amazon Rekognition to extract key objects from images for querying the index.
Responsible AI Research: Effectiveness Guaranteed
How to Guide AI Chatbots to Accurate Responses
Sometimes, I think AI chatbots are modeled after teenagers. They can be very, very good. But other times, they tell lies. They make stuff up. They confabulate. They confidently give answers based on the assumption that they know everything there is to know, but they’re woefully wrong.
See what I mean? You can’t tell from the context above whether my descriptions refer to AIs or teenagers.
While most of us know not to go to teenagers for important information and advice, we’re starting to rely on equally prevaricating AIs. To be fair, AIs aren’t bad; they’re just coded that way.
1. Ask for References and Sources
I previously wrote a guide on how to make ChatGPT provide sources and citations. Fortunately, ChatGPT is getting better at citing sources, particularly with the GPT-4o LLM and the web search capability in the $20-a-month version.
But ChatGPT won’t always volunteer those sources. If you’re doing research, always — always — ask for sources. Then, test the sources and make sure they actually exist. Numerous times in my experience, ChatGPT cited sources that seemed absolutely perfect for what I was looking for. The only problem was that after I clicked through or searched for the named source by title, I discovered the entire source had been fabricated.
ChatGPT even chose real academic journals, made up author names, and then assigned compelling-sounding titles to the articles. Can you imagine how bad it would have been had I included those sources in my work without double-checking? I shudder to even think about it. So, ask for sources, check those sources, and call the AI out if it gives you a made-up answer.
2. Tell the AI to ‘Show Your Work’
Early in my exploration of ChatGPT, I asked the tool to help me find a local mechanic. I sent it to Yelp and Google reviews to do sentiment analysis on the comments. At the time, it reached into those sites and gave me useful information.
I tried the test again recently and received another set of mechanic rankings. It actually told me, "Based on a comprehensive analysis of Yelp and Google reviews for independent car repair shops…" But, ChatGPT lied.
The tool never looked at Yelp or Google reviews. This result is likely because sites (ZDNET included) have become more restrictive in allowing AIs to scrape their content. That’s fine. My argument is that the AI should have come back and said it couldn’t check Yelp or Google reviews. Instead, the AI just claimed it did. Liar, liar, pants on fire.
When I asked it to show its work, the tool again said it had looked at Yelp and Google reviews. However, in the "show your work" response, the tool also displayed the source for the reviews it analyzed. This turned out to be a site named Birdeye Reviews.
Now, I have nothing against Birdeye Reviews. I’ve never used it. But that’s not the point. The point is ChatGPT said it had produced information based on Yelp and Google reviews. "Show your work" is a powerful prompt. You can uncover all sorts of interesting information on the reasoning process the AI used to give you your results.
3. Cross-Validate an AI’s Responses Across Related Questions
This process lets you and the AI explore a topic in-depth to see if the chatbot’s answers remain logically consistent and contextually relevant. We use this approach a lot in traditional research and engineering to help make sure we’re on the right track. It can be applied to the AI as well.
For example, let’s say you’re writing about car tires. You could ask, "What material is used in car tires?" The answer you might expect is "rubber." But rubber isn’t just one substance. There’s rubber from trees, synthetic rubber, and materials that have the flexibility and strength of rubber but contain no rubber at all.
You could ask the AI, "What kind of rubber is used in car tires?" or "Is real rubber still used in car tires?" This would lead to answers explaining how car tires use a mix of rubber types. You could go deeper down the rabbit hole by asking "Where else is rubber used in cars?" or "What kind of rubber is used in Lego car tires?"
The point of this practice is not so much to use all the responses in your paper as to explore how the AI deals with this class of questions and whether it loses the thread completely. Additionally, because ChatGPT retains its knowledge while in a session, the more you ask, and the deeper you dive into a specific topic, the more you train the AI to stay within the context of the sphere of knowledge you’re looking for. This approach helps keep the AI from going off on its own and increases the chances of getting accurate answers.
4. Ask About Recent Events or Time-Sensitive Information
Many chatbots have knowledge base cut-off dates. The free version of ChatGPT has a knowledge cut-off date of October 2023, meaning anything that happened in the world after that date will be unknown to the AI.
ChatGPT Plus, which also has October 2023 as its knowledge cut-off date, can also access information on the web. This ability can result in substantially more accurate information. The free version of ChatGPT can also access the web but in a more "limited" way. As a general rule, OpenAI doesn’t specify what "limited" means when talking about their free version. However, you can usually assume that limitation means fewer queries per session, fewer resources provided, and some features working intermittently.
For example, when I asked the free version of ChatGPT to list the NATO member nations, it returned a list of 31 countries. Yet when I asked ChatGPT Plus to list the NATO member nations, it returned a list of 32 countries. That’s because Sweden officially joined NATO in March of 2024.
The chatbots will generally tell you their cut-off dates if asked. But because there are added features (like web search in Plus), it’s best to try asking about events the AI would only know about if it had up-to-date information.
There’s a trick to apply here as well. A few minutes later, I asked the free version of ChatGPT, "Can you use the web to look up who the current members of NATO Nations are?" I explicitly told it to "use the web." I got back the up-to-date answer. So, if you’re using the free version, consider coaching it on where to look for information to get a better answer.
This approach will help you ascertain the scope of the AI’s knowledge and determine whether you’re getting fairly current details or need to account for missing knowledge due to a fairly old cut-off date.
5. Ask Follow-Up Questions and Iteratively Refine Your Query
I use this approach when I use ChatGPT to help me with my programming. I start with a simple query and refine and clarify it until I get some basic code. Once that approach works, I add another sentence or detail for what I want in my code. After a bunch of interactions that feel much more like a conversation than a coding session, I often have some useful code.
Even if you’re not coding, you can use this approach. Let’s say you’re working on a project related to cloud services. You might ask the AI, "Can you explain the different types of cloud services?"
In this example, you’d expect the answers to discuss services like email, web hosting, CRM, and other software-as-a-service categories. However, the AI responded with descriptions of SaaS (software as a service), PaaS (platform as a service), and IaaS (infrastructure as a service).
Now you’ll know there are a few ways this question might be interpreted, so you could follow up with "Explain the different types of cloud services focusing solely on those that fall into the SaaS category."
You could follow up with a question like, "Based on those SaaS categories, list three of the most popular commercial services in each category, along with the strengths and weaknesses of each."
That would give you a much more detailed description, along with the features of each service. But let’s refine the approach using one more step. The AI in this scenario provided descriptions of each service, but you wanted to know how the services differ.
You could refine the query by adding the word "comparative" as in this prompt: "Based on those SaaS categories, list three of the most popular commercial services in each category, along with the COMPARATIVE strengths and weaknesses of each."
In my test, that last refinement resulted in tables comparing the features of each. I love tables. In fact, if the AI doesn’t return information as a table, my bonus tip is to ask the AI to present its results in a table. The tool often refactors its answers in interesting ways when it operates with that directive.
Conclusion
The key to getting accurate responses from AI chatbots is to be proactive and engage with the AI in a way that encourages it to provide reliable information. By asking for references and sources, telling the AI to "show its work," cross-validating its responses, asking about recent events or time-sensitive information, and iteratively refining your query, you can ensure that the AI provides you with accurate and reliable information.
FAQs
Q: How can I ensure that the AI chatbot provides accurate information?
A: You can ensure that the AI chatbot provides accurate information by asking for references and sources, telling the AI to "show its work," cross-validating its responses, asking about recent events or time-sensitive information, and iteratively refining your query.
Q: What is the best way to ask an AI chatbot a question?
A: The best way to ask an AI
Tech Investor Urges Europe’s AI Startups Not to Cash Out
Xavier Niel: Europe Can Create Leading AI Companies Without Billions in Capital
Europe’s Top Technology Investor Believes in Region’s Potential
Xavier Niel, a French billionaire and tech investor, believes that Europe can create leading artificial intelligence (AI) companies without the billions in capital raised by US competitors. According to Niel, the key to success lies in the ability of founders to resist the temptation to cash out too early.
Creating Competitive AI Models
Niel, who made his fortune in telecoms with operator Iliad and now invests broadly in start-ups, including backing Paris-based AI group Mistral, which has soared to a €6bn valuation within a year of being founded, believes that Europe can create competitive AI models today. However, he warns that over the next two or three years, success depends on the number of initiatives and the ability of those who are the real geniuses – those building the best companies – not to be swallowed up or to sell too quickly.
Europe’s Hopefuls in AI Models
France is home to one of the remaining hopefuls in AI models: Mistral, founded last year by a trio of scientists from Google and Meta. Flush with over $1bn in funding, Mistral has developed a large language model that it says is leaner and more capital efficient than better-funded competitors.
Niel’s Investment and Support
Niel has supported the AI ecosystem in France with investment of about €500mn so far, and said he could eventually deploy billions. He makes early-stage investments of €15mn annually through his Kima Ventures fund, and also backed the New Wave fund where he recently intervened to end a power struggle between founders.
Non-Profit Research Lab
Niel has also launched a non-profit research lab called Kyutai, which aims to create open-source AI models – a project joined by ex-Google CEO Eric Schmidt. Niel’s cloud infrastructure company Scaleway runs one of the biggest supercomputers in the European private sector.
Conclusion
Xavier Niel’s optimism about European tech is notable given the continent’s previous struggles to compete with US and Chinese giants. However, he believes that Europe can create leading AI companies without billions in capital, as long as founders resist the temptation to cash out too early. With his investment and support, Europe’s AI ecosystem is poised for growth and innovation.
FAQs
Q: Can Europe create leading AI companies without billions in capital?
A: Yes, according to Xavier Niel, as long as founders resist the temptation to cash out too early.
Q: What is the key to success in AI innovation in Europe?
A: The ability of founders to build the best companies and resist the temptation to sell out too quickly.
Q: Which companies is Xavier Niel backing in the AI space?
A: He is backing Paris-based AI group Mistral, which has developed a large language model that is leaner and more capital efficient than better-funded competitors.
Q: What is the purpose of Niel’s non-profit research lab Kyutai?
A: To create open-source AI models, a project joined by ex-Google CEO Eric Schmidt.
Japan Tech Leaders Supercharge Sovereign AI
From call centers to factories to hospitals, AI is sweeping Japan. Undergirding it all: the exceptional resources of the island nation’s world-class universities and global technology leaders such as Fujitsu, The Institute of Science Tokyo, NEC and NTT.
Japanese AI Pioneers to Power Homegrown Innovation
Japanese tech leaders are developing advanced AI models that can better interpret Japanese cultural and linguistic nuances. These models enable developers to build AI applications for industries requiring high-precision outcomes, such as healthcare, finance and manufacturing.
Fujitsu’s Takane Model
Fujitsu’s Takane model is specifically built for high-stakes sectors like finance and security. The model is designed to prioritize security and accuracy with Japanese data, which is crucial for sensitive fields. It excels in both domestic and international Japanese LLM benchmarks for natural Japanese expression and accuracy.
NEC’s Cotomi Model
NEC’s cotomi model uses NeMo’s parallel processing techniques for efficient model training. It’s already integrated with NEC’s solutions in finance, manufacturing, healthcare and local governments.
NTT Group’s Tsuzumi Model
NTT Group is moving forward with NTT Communications’ launch of NTT’s large language model “tsuzumi,” which is accelerated with NVIDIA TensorRT-LLM for AI agent customer experiences and use cases such as document summarization.
Kotoba Technologies’ Kotoba-Whisper Model
Kotoba Technologies, a Tokyo-based software developer, will unveil its Kotoba-Whisper model, built using NVIDIA NeMo for AI model building. The transcription application built on the Kotoba-Whisper model performed live transcription during this week’s conversation between SoftBank Chairman and CEO Masayoshi Son and NVIDIA founder and CEO Jensen Huang at NVIDIA AI Summit Japan.
Academic Contributions to Japan’s Sovereign AI Vision
Japanese universities are powering the ongoing transformation with a wave of AI innovations.
Nagoya University’s Ruri-Large Model
Nagoya University’s Ruri-Large, built using NVIDIA’s Nemotron-4 340B — which is also available as a NIM microservice — is a Japanese embedding model. It achieves high document retrieval performance with high-quality synthetic data generated by Nemotron-4 340B, and it enables the enhancement of language model capabilities through retrieval-augmented generation using external, authoritative knowledge bases.
National Institute of Informatics’ LLM.jp-3-13B-Instruct Model
The National Institute of Informatics will introduce LLM.jp-3-13B-Instruct, a sovereign AI model developed from scratch. Supported by several Japanese government-backed programs, this model underscores the nation’s commitment to self-sufficiency in AI. It’s expected to be available as a NIM microservice soon.
Japan’s Tech Providers Helping Organizations Adopt AI
Technology providers are working to bring NVIDIA AI technologies of all kinds to organizations across Japan.
Accenture’s AI Agent Solutions
Accenture will deploy AI agent solutions based on the Accenture AI Refinery across all industries in Japan, customizing with NVIDIA NeMo and deploying with NVIDIA NIM for a Japanese-specific solution.
Dell Technologies’ Dell AI Factory
Dell Technologies is deploying the Dell AI Factory with NVIDIA globally — with a key focus on the Japanese market — and will support NVIDIA NIM microservices for Japanese enterprises across various industries.
Bringing Physical AI to Industries With NVIDIA Omniverse
The proliferation of language models across academia, startups and enterprises, however, is just the start of Japan’s AI revolution.
Yaskawa’s Adaptive Robots
Industrial automation provider Yaskawa, which has shipped 600,000 robots, is developing adaptive robots for increased autonomy. Yaskawa is now adopting NVIDIA Isaac libraries and AI models to create adaptive robot applications for factory automation and other industries such as food, logistics, medical, agriculture and more.
Toyota’s Robotic Factory Lines
Toyota is looking into how to build robotic factory lines in Omniverse to improve tasks in robot motion in metal-forging processes.
Seven & i Holdings’ Retail Insights
Seven & i Holdings is using Omniverse to gather insights from video cameras in research to optimize retail and enhance safety.
Conclusion
Japan’s journey to AI sovereignty is well underway to support the nation in building, developing and sharing AI innovations at home and across the world.
FAQs
Q: What are the key players in Japan’s AI revolution?
A: Fujitsu, The Institute of Science Tokyo, NEC, NTT, and startups like Kotoba Technologies are leading the charge.
Q: What is the significance of Japanese language models?
A: Japanese language models can better interpret Japanese cultural and linguistic nuances, enabling developers to build AI applications for industries requiring high-precision outcomes.
Q: What is NVIDIA Omniverse?
A: NVIDIA Omniverse is a platform that brings physical AI to industries, enabling the development of adaptive robots, robotic factory lines, and other innovative applications.
Q: What is the significance of Japan’s sovereign AI vision?
A: Japan’s sovereign AI vision underscores the nation’s commitment to self-sufficiency in AI, enabling the development of AI innovations at home and across the world.
Science Made Fun and Relevant for Kids
Key Points
- By eighth grade, almost 50 percent of students have lost interest in science or don’t believe it will be relevant to their education or future plans.
- To promote science to kids, give them problems to develop solutions for.
- Encourage critical thinking by discussing why their prototypes work.
Developing Problem-Solving Skills
Want to really promote science to kids so they keep that interest? Give them problems to develop solutions for. For instance, some people still like to read physical books. Sometimes, you don’t want to hold the book while reading it. You want to rest it on something. Ask kids to come up with simple ways for a book to be held up hands-free. Provide materials, like cardboard, paper, popsicle sticks, glue, etc. If they need other equipment, do your best to provide that, too. Have them create their inventions and test them out. If they don’t work, ask them to figure out why they don’t work so they can make adjustments.
The Importance of Iteration
Here’s the kicker: if they aren’t able to create a working prototype in the allotted time, don’t see that as a failure. See it as an opportunity to discuss what they’d do differently if they had more time. In the real world, most innovations take years to come to fruition. The learning after each iteration is as important as the final product. Even after there is a final working prototype, there are often subsequent new editions of the innovation.
The Role of Science
Where does the science come in, though? The science comes in by discussing why their prototype works. With the example of the book holder, questions to discuss could be:
- What materials work best to keep the book from slipping? This question introduces friction and material properties into the discussion.
- How does the weight of the book affect the effectiveness of the book holder? This question involves the concepts of force, balance, and structural integrity.
- How does each material used to create the book holder contribute to the book holder working? This explores mechanical properties, touching on how materials differ in strength, flexibility, and durability.
Using Generative AI
You can use generative AI to develop the discussion questions, too. Now they’re not just thinking about the fact that they’re creating something. They’re also thinking about why it works.
Conclusion
Make science interesting to kids by applying it to everyday problems, then give them the tools to solve these problems and help them think critically about why their solutions work. Ultimately, these kids will become the innovators of tomorrow.
FAQs
Q: Why is it important to give kids problems to solve?
A: Giving kids problems to solve helps them develop critical thinking skills and encourages them to think creatively.
Q: How can I use generative AI to develop discussion questions?
A: You can use generative AI to develop discussion questions by providing it with prompts and having it generate questions related to the topic.
Q: Why is iteration important in the scientific process?
A: Iteration is important in the scientific process because it allows for refinement and improvement of ideas and prototypes. It also helps to develop critical thinking skills and encourages kids to think creatively.
Accelerating Alphafold2 with GPU-Accelerated MMseqs2
The Ability to Compare Sequences of Multiple Related Proteins
The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA), and the evolutionary information retrieved from these alignments can yield insights into protein structure, function, and evolutionary history.
Overcoming Computationally Expensive MSA with NVIDIA CUDA
Traditional MSA tools rely on CPU-based implementations, which, while effective at sequential processing, can’t match GPU parallel processing capabilities.
The joint research team that developed MMseqs2-GPU was led by researchers at Seoul National University, Johannes Gutenberg University Mainz, and NVIDIA. Inspired by their previous work on CUDASW++4.0, they approached the problem by developing a novel, gapless prefiltering algorithm tailored to NVIDIA CUDA that enables efficient, high-sensitivity sequence comparisons at unparalleled speeds.
This GPU-accelerated prefilter replaces k-mer prefiltering in MMseqs2 with a gapless scoring approach. Instead of using k-mer searches, simplifying comparisons between sequences with a coarse representation, the gapless prefilter directly analyzes the full sequences. It employs a modified version of the classic Smith-Waterman-Gotoh algorithm that only considers diagonal dependencies, avoiding gaps in the alignment. The process runs efficiently across thousands of GPU cores.
MMseqs2-GPU Accelerates Protein Structure Prediction
The success of MMseqs2-GPU is rooted in redesigning gapless prefiltering and gapped alignment algorithms, leveraging CUDA to deliver rapid, affordable, and scalable sequence alignment that meets today’s bioinformatics research demands.
As MMseqs2 is integrated into many computational pipelines using GPUs, including structure prediction with Colabfold, users can expect an easy-to-swap-in performance boost:
Speed improvement
Colabfold using MMseqs2-GPU is 22x faster than AlphaFold2 with JackHMMER and HHblits for protein folding (Figure 4). In practice, this means that instead of waiting 40 minutes to predict a protein structure using HHblits, JackHMMER, and AlphaFold2, you can get that exact prediction in one and a half minutes using Colabfold and MMseqs2-GPU.
Accelerated MMseqs2 Means Faster Discoveries
Looking ahead, the joint research team is focused on further refining the algorithms and the MMseqs2 integration, expanding its applications to protein clustering and cascaded database searches. The availability of MMSeqs2 means faster inputs to protein structure prediction that can accelerate drug discovery, as we’ve illustrated here, and a host of other applications (Figure 2).
Conclusion
The MMseqs2-GPU library is a significant advancement in the field of bioinformatics, enabling researchers to accelerate protein structure prediction and other applications. By leveraging the power of NVIDIA CUDA, MMseqs2-GPU provides a scalable and efficient solution for sequence alignment, making it an invaluable tool for the scientific community.
FAQs
Q: What is MMseqs2-GPU?
A: MMseqs2-GPU is a GPU-accelerated library for evolutionary information retrieval, designed to accelerate protein sequence alignment and protein structure prediction.
Q: What are the benefits of MMseqs2-GPU?
A: MMseqs2-GPU provides a significant speedup over traditional CPU-based implementations, enabling researchers to accelerate protein structure prediction and other applications, while maintaining comparable accuracy and sensitivity.
Q: How does MMseqs2-GPU work?
A: MMseqs2-GPU uses a novel, gapless prefiltering algorithm tailored to NVIDIA CUDA, which enables efficient, high-sensitivity sequence comparisons at unparalleled speeds.
Q: Is MMseqs2-GPU open source?
A: Yes, MMseqs2-GPU is open source and available online, providing an invaluable resource for researchers globally.
Q: Can I use MMseqs2-GPU for my research?
A: Yes, MMseqs2-GPU is designed to be used in a variety of research applications, including protein structure prediction, protein clustering, and cascaded database searches.
Users Willing to Pay for Better Mobile Connectivity
Mobile Users Demand Better 5G Connectivity, Willing to Pay Premium
Issues with app quality have resulted in mobile users’ unwillingness to settle for “best effort” 5G network connectivity, with some generative artificial intelligence (Gen AI) users willing to pay a premium for guaranteed performance.
Study Reveals 35% of 5G Users Willing to Pay More for Differentiated Connectivity
About 35% of 5G users will consider paying more for differentiated connectivity that guarantees better performance for essential tasks, revealed a study released by Ericsson. This number is 1.5 times more in densely populated markets such as Thailand, India, and Brazil, compared to the global average, according to the online survey, which polled more than 23,000 smartphone users, of whom 17,000 were 5G smartphone users from 16 markets, including Australia, France, Singapore, South Korea, Thailand, China, the UK, and the US.
Mobile-First Markets Drive Demand for Differentiated Connectivity
“These are mobile-first markets, where reliance on mobile connectivity is particularly strong,” Ericsson said. “In these markets, elevated demand for differentiated connectivity could be attributed to the need for improved capacity, rather than general network inadequacy.”
Service Providers Must Offer Segmentation Strategies
The telecom equipment manufacturer estimates that the market potential for differentiated connectivity is about 35% of 5G users across the 16 global markets. However, tapping this market will require service providers to offer segmentation strategies that identify key consumer groups, such as those who are more willing to pay for differentiated connectivity and require tailored service offerings.
5G Users Willing to Pay Premium for Guaranteed Performance
The study suggests that 5G users are willing to dig into their pockets to get differentiated connectivity for core services, such as video calls for work or personal reasons and streaming various media formats. They will pay the highest premium for niche services like multiplayer mobile and cloud games and dedicated augmented reality (AR) apps.
Gen AI Users Expect Guaranteed Performance
In fact, a quarter of Gen AI users already expect guaranteed performance, such as real-time response, and will pay up to 35% more compared to other app categories. “Gen AI capabilities are increasingly integrated into popular apps such as TikTok and Snapchat, while new AI-driven apps for image and video upscaling or generation are set to add substantial mobile traffic in the near future,” Ericsson said.
Conclusion
The study highlights the growing demand for differentiated 5G connectivity, particularly in mobile-first markets. Service providers must adapt to this trend by offering segmentation strategies and guaranteeing performance for essential tasks. As Gen AI usage grows, the demand for fast, reliable uplink and low latency will increase, requiring service providers to prepare for these shifts in network traffic.
FAQs
Q: What percentage of 5G users are willing to pay more for differentiated connectivity?
A: About 35% of 5G users will consider paying more for differentiated connectivity that guarantees better performance for essential tasks.
Q: Which markets have the highest demand for differentiated connectivity?
A: Densely populated markets such as Thailand, India, and Brazil have the highest demand for differentiated connectivity, with 1.5 times more users willing to pay more compared to the global average.
Q: What is the market potential for differentiated connectivity?
A: The market potential for differentiated connectivity is about 35% of 5G users across the 16 global markets.
Q: What is the expected growth of Gen AI usage?
A: Ericsson expects a substantial increase in Gen AI usage over the next five years, with an expected 65% of weekly smartphone users engaging with these applications.
Filament Form Builder Handbook
Simplifying and Accelerating Your Form Creation Process with Filament Form Builder
Hey there, fellow developers!
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Introducing the Filament Form Builder Handbook
And to make your journey even smoother, I’m thrilled to introduce my first Filament book:
Whether you’re just starting out or you’re an experienced developer, this handbook is perfect for you. It’s packed with quick references and visual guides for every form component.
Why Choose Filament for Your Forms?
The Filament Form Builder stands out as a game-changer for applications by offering:
- Extensive customization options to meet your project’s unique needs.
- The ability to effortlessly build dynamic, responsive forms.
- Reactive, robust forms that will help you build modern web applications.
And guess what? Filament isn’t just about forms, it’s a complete collection of full-stack components that you’ve been waiting for to help you bring your ideas to life!
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Conclusion
The Filament Form Builder is a powerful tool that can simplify and accelerate your form creation process. With its extensive customization options, ability to build dynamic, responsive forms, and robust, reactive forms, you’ll be able to bring your ideas to life.
Frequently Asked Questions
Q: What makes Filament Form Builder different from other form builders?
A: Filament Form Builder offers extensive customization options, dynamic, responsive forms, and robust, reactive forms that make it a game-changer for application development.
Q: Can I use Filament Form Builder for my project?
A: Yes, Filament Form Builder is perfect for any project that requires custom, dynamic, and responsive forms.
Q: Is the Filament Handbook suitable for beginners?
A: Yes, the Filament Handbook is designed for developers of all levels, including beginners. It provides quick references and visual guides for every form component.
Q: How much does the Filament Handbook cost?
A: The Filament Handbook is available for only $10.
Augmented Intelligence: Integrating AI in AR
What is Behind AI in Augmented Reality?
Combining AI in Augmented Reality (AR) with generated images makes experiences more realistic and personalised. It’s useful in gaming, education, healthcare, and retail. AI can create dynamic, lifelike content for AR. Despite challenges like real-time processing, new tools and technologies are making it easier.
Understanding Augmented Reality
Definition and Types of AR
Augmented Reality (AR) is a technology that overlays computer-generated images, sounds, or other data onto the real world, enhancing the user’s perception of reality. Here are the main types of AR:
1. Marker-based AR: Uses visual markers like QR codes or images to trigger digital content. When a camera detects these markers, it displays the related digital information on the screen.
2. Markerless AR: Does not need specific markers. Instead, it uses location data, GPS, and sensors to place digital objects in the real world, like showing a virtual object on your desk without needing a QR code.
3. Projection-based AR: Projects light onto surfaces to create interactive displays. For example, projecting a keyboard on a table that you can actually type on.
4. Superimposition-based AR: Replaces parts of the real-world view with digital content. It enhances or alters real objects by overlaying digital images, like adding digital furniture to a room.
Current Applications of AR
AR is making waves in various sectors:
- Gaming: Games like Pokémon GO use AR to blend virtual creatures with real-world environments.
- Education: AR apps enhance learning by providing interactive 3D models and simulations.
- Healthcare: AR aids in medical training and surgeries by overlaying crucial information.
- Retail: Virtual try-ons and interactive product displays enhance the shopping experience.
What is AI Imagery?
Definition and Explanation
AI-generated imagery means creating images using artificial intelligence, particularly deep learning and neural networks. These techniques help computers create images that look very real or are artistically unique. Here are the main methods:
Generative Adversarial Networks (GANs): Think of GANs as a game between two players. One player (the generator) tries to create fake images, while the other player (the discriminator) tries to figure out which images are fake and which are real. Over time, the generator gets better at creating images that look real because it learns from its mistakes.
Neural Networks: These are computer systems modelled after the human brain. They learn by looking at lots of images and recognising patterns. Once trained, they can create new images based on what they have learned.
Examples
- DeepArt: An AI tool that transforms photos into artwork by applying different artistic styles.
- StyleGAN: An AI that can generate highly realistic human faces and other images from scratch.
- DALL-E: An AI by OpenAI that creates images from textual descriptions, allowing users to generate imaginative and unique visuals.
The Synergy of AI Imagery in Augmented Reality
Improved Realism and Immersion: AI can create lifelike textures and objects, making AR scenes look more real. For example, AI can generate detailed and realistic plants, animals, or furniture that blend seamlessly with the real world, making the AR experience feel more natural and immersive.
Dynamic and Adaptive Content Generation: AI allows AR applications to change content based on user interactions and the environment. For instance, an AR app can use AI to adjust the lighting and shadows of virtual objects based on the actual lighting conditions in a room, or to modify characters’ behaviour in a game based on the player’s actions.
Personalised AR Experiences: AI can customise AR content to fit individual user preferences. For example, a retail app can use AI to show different clothing styles based on a user’s past choices, or an educational app can tailor lessons to match a student’s learning pace and interests.
Technical Considerations
Hardware and Software Requirements
To integrate AI imagery into AR, developers need robust hardware and software:
Hardware: Advanced GPUs (Graphics Processing Units) are essential for real-time image processing. AR glasses or headsets are also required to deliver immersive AR experiences.
Software: Developers need AR Software Development Kits (SDKs) such as ARKit (for iOS) or ARCore (for Android) to build AR applications. Additionally, AI image generation APIs like those offered by TensorFlow and PyTorch are necessary for creating AI-driven images.
Key Challenges and Solutions
- Real-time Processing: A major challenge is ensuring that AI-generated images render seamlessly in real-time. This requires powerful hardware and optimised algorithms to handle the processing load without lag.
- Performance and Latency: To maintain immersive experiences, AR applications must have low latency. Optimising code and using efficient data processing techniques can help reduce delays and ensure smooth interactions.
- Data Privacy and Security: Protecting user data is crucial, especially when using AI for personalisation. Developers must implement strong security measures to safeguard data and comply with privacy regulations.
Tools and Platforms
Developers can use a variety of tools and platforms to create integrated AI in augmented reality experiences:
- Unity: A versatile game development platform that supports AR and AI integration.
- ARKit and ARCore: SDKs provided by Apple and Google, respectively, for developing AR applications on iOS and Android devices.
- TensorFlow and PyTorch: Popular AI platforms that offer tools and libraries for building and deploying AI models, including those for image generation.
Future Trends and Opportunities
Emerging Trends
1. Smart Cities: AI-driven AR can enhance urban experiences by overlaying real-time data onto the physical environment. For example, residents and visitors could use AR to see real-time traffic updates, directions, or information about nearby attractions.
2. Personalised Advertising: By using AI, ads can feature content tailored to individual preferences, creating a more engaging and relevant marketing experience.
3. Advanced Gaming: AI can generate endless, unique gaming environments, making each game different from the last. This AI imagery keeps games fresh and exciting, offering players new experiences every time they play.
Potential Impact
The integration of AI in augmented reality has the potential to transform various sectors:
- Education: Enhanced interactive learning experiences.
- Healthcare: Improved diagnostics and training tools.
- Entertainment: More immersive and personalised content.
Getting Started with AI Imagery in AR
Step-by-Step Guide
- Select Tools and Platforms: Choose AR SDKs and AI image generation APIs.
- Basic Coding Tutorial: Start with simple projects, such as overlaying AI-generated objects in AR environments.
- Optimisation Tips: Focus on performance, ensuring low latency and high-quality rendering.
Resources for Further Learning
- Online Courses: Platforms like Coursera and Udemy offer courses on AR and AI.
- Tutorials: Websites like GitHub and Stack Overflow provide valuable coding resources.
- Communities: Join forums and social media groups to connect with other developers.
Final Thoughts
AI is already a huge game-changer for the way we live and operate.
As AI in augmented reality technologies continue to evolve, we can look forward to even more innovative applications that will enhance our everyday experiences.
Now is a great time to explore and experiment with AI and AR to be part of this exciting journey.
FAQs
Q: What is the difference between marker-based and markerless AR?
A: Marker-based AR uses visual markers to trigger digital content, while markerless AR does not require specific markers and uses location data, GPS, and sensors to place digital objects in the real world.
Q: How does AI-generated imagery work?
A: AI-generated imagery uses deep learning and neural
