We’re seeing AI make a major leap from simply mimicking patterns (“thinking fast”) to true reasoning at inference time (“thinking slow”). This shift is pushing the boundaries of possibilities with AI, and it’s exciting to see models like OpenAI’s Strawberry leading the charge.
For startups like ours, the opportunity is clear: the application layer. As reasoning-based applications become more sophisticated, AI can transform services, opening up massive new markets.
In the AI game, it’s time to explore how agentic applications can shape the future.
At Cubyts we’re already harnessing AI-driven insights to help teams discover and resolve tech debt in real time—bringing the power of reasoning AI into engineering workflows.
Here👇is a very interesting read from Sequoia Capitalhttps://lnkd.in/gPFSJH_U#AI#GenerativeAI#Innovation#Startups#Tech
🚀 Just In: EdgeRunner AI surges forward with a staggering $5.5M seed funding to revolutionize Generative AI for the edge! 🌐
Picture this: Cutting-edge AI solutions right where you need them the most, on the edge, with lightning-fast responses and groundbreaking capabilities.
Want to know how this can transform your business? Click to read more about their incredible journey and how they're set to reshape the AI landscape➡ https://lnkd.in/gJiCc24k
Innovation is here. Are you ready to be part of the future? Drop your thoughts below! ⬇️🔥
🚀 Breaking News: EdgeRunner AI Emerges from Stealth with $5.5M Seed Funding! 💰
Feeling overwhelmed by generative AI solutions that don't quite hit the mark for your business? You're not alone.
Here's a game-changer you need to know about. EdgeRunner AI, a trailblazer focusing on generative AI for the Edge, has just secured a whopping $5.5 million in seed funding! According to Tyler Xuan Saltsman, Co-Founder, "The challenge with generative AI today lies in its broad applicability without addressing specific business needs."
So why should you care? Imagine AI solutions that are tailor-made for your unique business challenges. That's what EdgeRunner is promising, and they now have the capital to deliver.
👏 A massive shoutout to the innovators who are paving the way for more focused and effective AI solutions. Don't get left behind. Stay ahead of the curve!
Curious about how this breakthrough could benefit you? Check out the full story here: [Read More](https://lnkd.in/gV53ErWX)
Ready to revolutionize your business with AI? Let's connect 🤝 #AI#Innovation#GenerativeAI#BusinessTransformations
In the rapidly evolving world of artificial intelligence (AI), the state of AI as of April 2024 presents both opportunities and challenges for companies looking to build and grow. One of the hottest topics in the AI space is the intersection of AI and company building, with many entrepreneurs and businesses looking to leverage the power of AI to gain a competitive edge. However, with the dominance of major AI companies like Google and Microsoft, it can be difficult for smaller startups to compete.
One key question for startups is how to focus their efforts in anticipation of upcoming AI capabilities. Some experts suggest that startups should assume that the big foundation models from major AI companies will get significantly better and consider whether this improvement will be beneficial or detrimental to their business. For example, if a startup's business model relies on a particular AI capability that is likely to be surpassed by a major AI company, it may need to pivot or risk being left behind.
Another challenge for startups is competing with established players that have massive compute and data scale advantages. These companies have access to vast amounts of data and computing power, which can be difficult for smaller startups to match. However, there are still opportunities for startups to differentiate themselves and succeed. One approach is to focus on architectural differences, such as building more efficient or specialized models that can outperform general-purpose models from major AI companies. Another approach is to focus on domain-specific models that are tailored to a particular industry or use case. By building expertise in a specific area, startups can gain an advantage over more generalized AI models.
Distillation techniques can also provide opportunities for startups to compete with established players. Distillation involves training a smaller, more efficient model to mimic the behavior of a larger, more complex model. This can enable startups to build AI capabilities that are similar to those of major AI companies, but with lower computational requirements and costs. However, distillation can also present challenges, such as ensuring that the smaller model accurately captures the behavior of the larger model and avoiding the risk of overfitting.
As AI capabilities continue to advance, ethical considerations and potential risks must also be taken into account. For example, there are concerns about the potential misuse of AI for malicious purposes, such as deepfakes or autonomous weapons. There are also concerns about the impact of AI on employment, privacy, and societal norms. Startups and companies building AI systems must be mindful of these risks and take steps to mitigate them, such as implementing robust security measures and engaging in transparent and ethical AI development practices.
#ai#artificialintelligence#computingpower#distillation#startup
In the last two years, we’ve seen an explosion of AI startups, not because more people suddenly became AI/ML experts, but because the ecosystem changed dramatically. Today, we have an abundance of pre-trained models, user-friendly APIs, and endless tutorials that make it easy to build with AI. This shift opened the door for a wave of companies, but many solutions are “me too” products, essentially wrappers around LLMs that don’t offer much differentiation.
One trend I’ve noticed is that AI is often equated with LLMs. While large language models are impressive, they’re not always the best choice for every task. Many solutions would be better served by other machine learning methods or even simpler techniques that target the problem more directly.
It’s also become common to see the AI being promoted over the actual problem it’s meant to solve, a pattern we see whenever a new tech trend gains hype. Building successful products should be about using the best tools for the job, not just the trendiest.
There’s nothing wrong with using accessible APIs or pre-trained models for rapid prototyping. But having a clear vision for how your solution stands out and truly improves on what’s out there is essential. The tech should support the vision, not define it.
#ai#startup#newideas
Amnon Shashua's new AI venture aims to push the limits of machine learning.
Founded by Shashua and several other leading minds in the field, new startup AAI believes its Artificial Expert Intelligence (AEI) can deliver more effective solutions than current AI platforms.
When we started seeing teams swiftly move away from machine learning (ML), which is actually machine intelligence, to artificial intelligence (AI), which is again machine intelligence, we noticed that the quality of deliverables went down rather quickly. Is anyone noticing that if costs are going down, as well as the quality of tangible deliverables, then something isn't right? Welcome to Brilliant Products.
#AI#MachineLearning#innovation#startups#digitaltransformation
