Toloka’s Post

AI Infrastructure is an extremely interesting place to be: whatever happens in the development of the industry, passes through you :) And the evolution Toloka undergone since the start of GenAI revolution is truly remarkable. Today, we have moved beyond simply connecting Tolokers with requesters, we're designing sophisticated pipelines that integrate human insight with the power of automation. Our approach leverages expert knowledge, optimizes data production on a large scale, and enhances model performance. Still, as previously, our development is grounded on a deep understanding: AI's true power lies in its ability to mirror real-world experiences, which is only possible through a seamless blend of human expertise and cutting-edge technology. This synergy has guided us for over a decade, from pioneering large-scale crowdsourcing to now curating specialized datasets crafted by highly skilled domain experts. We will be posting more details about our journey along with the evolution of AI industry in our blog: https://lnkd.in/eUHnMGJc

At Toloka, we’re not just connecting data contributors with AI projects—we’re shaping the future of AI architecture. By designing pipelines that merge human expertise with synthetic data, we aim to enhance and solidify AI models. Read this article from our CEO, Olga Megorskaya, on this pioneering approach. #AI #DataArchitecture #SyntheticData #LLMs #Toloka #genAI #LLMs

Unlocking the full value of generative AI requires companies to rewire their operations and build a scalable technology foundation. It's essential to avoid getting stuck in endless analysis over minor decisions, like the choice of language models, as these are becoming commodities. A major issue is that many companies treat generative AI as a technology program rather than a broad business priority. While solving individual pieces is straightforward, integrating them is challenging and hampers scalability. Companies with well-organized data products are ahead of the game. To succeed, it's crucial to assign a platform owner and establish a cross-functional team to develop reusable assets, including approved tools, code, and frameworks, for product teams. https://lnkd.in/eKEJ6b-D

🆕 We know novel situations call for novel #solutions, especially when the "situation" is trying to navigate the complexities of emerging #GenAI. Our latest blog post "Why Developers Need Model-Agnostic AI Tools" explores the “why” and the "what" behind the benefits model-agnostic AI tools can provide. 📣 Here's just a hint at the topics we cover: 👉 Flexibility 👉 Interoperability 👉 Scalability 👉 Efficiency 👉 Adaptability 👉 Collaboration 👉 Innovation Click below to read all about it. #DigitalTransformation #SecurityforAI https://lnkd.in/eJQvTxMR

As we dive deeper into the world of generative AI (gen AI), it's clear that the initial excitement has evolved into a quest for real, scalable value. With only 11% of companies successfully adopting gen AI at scale, the path ahead requires a strategic pivot. Here are some key takeaways for all the CIOs and tech leaders out there, most of them we also experience at Bayer: Prioritize Impact 🎯: Cut through the noise and focus on gen AI applications that tackle significant business challenges. It's time to move beyond pilots and harness gen AI's true potential. Integration is Key 🔗: The magic happens not in the individual pieces of tech but in how they seamlessly integrate to create secure, efficient solutions. Master Your Costs 💸: Dive deep into the cost structure of gen AI. From change management to ongoing expenses, understanding and managing costs is crucial for sustainable scaling. Simplify Your Tech Stack 🛠️: Overcome the clutter of tools and technologies. Choose wisely to support your business needs while keeping an eye on flexibility for future growth. Build for Value 🌟: Shift from tech-centric to value-centric teams. Embed gen AI into the business fabric with cross-functional collaboration to unlock its full potential. Data Quality Matters 📊: Invest in your data infrastructure. Quality and well-managed data are the bedrock of effective gen AI solutions. Embrace Reusability ♻️: Speed up development and foster innovation by building reusable gen AI assets. Think strategically about common functionalities across use cases

This is game changing. The first time I saw the power of what this new ReGenAI technology from Dataminr could do, I was blown away. ReGenAI is the first Generative AI that automatically regenerates in real time as events unfold, completely eliminating the need for prompt-based engineering and bypassing the limitations of static reports. Built from the ground up by Dataminr, this is an incredible AI innovation that has strong implications for tracking real-time events across millions of data sources. It's already been used to track critical events in real time, like the bridge collapse in Baltimore, the recent attack by Iran on Israel, and more (and ahead of any other news sources). Congratulations to Ted Bailey and the entire team on this launch. The future of AI is here! https://lnkd.in/gtwZUUu5

Real-time, real-world, react and regenerate: Data convergence and insight to watch #regenai #generativeai #ai #futureofwork #iot #technology #data

Integrating generative AI into business processes is complex enough, and scaling this technology adds another level of complexity. Companies can't just throw AI into pre-existing structures; they have to rewire the way these structures actually work. In this McKinsey article, Aamer Baig, Douglas Merrill, and Megha Sinha, with Danesha Mead and Stephen Xu share 7 helpful steps for CIOs that want to take a "Shaper" approach to AI adoption—an approach in which companies connect large language models to internal applications and data sources: 💠 Eliminate the noise, and focus on the signal. Cut down on experiments. Direct your efforts toward solving important business problems. 💠It’s about how the pieces fit together, not the pieces themselves. Too much time is spent assessing individual components of a GenAI engine. It's better to figure out how they work together securely. 💠 Get a handle on costs before they sink you. Understand where the costs lurk, and apply the right tools and capabilities to rein them in. 💠 Tame the proliferation of tools and tech. Narrow down to the capabilities that best serve the business, and take advantage of available cloud services while still preserving your flexibility. 💠 Create teams that can build value, not just models. Getting to scale requires a team with a broad cross-section of skills to not only build GenAI applications but also make sure they generate the value they’re supposed to. 💠 Go for the right data, not the perfect data. Targeting which data matters most and investing in its management over time has a big impact on how quickly you can scale. 💠 Reuse it or lose it. Reusable code can increase the development speed of generative AI use cases by 30 to 50 percent. #GenerativeAI #CIO #ScalingAI

Insightful article from VentureBeat on why organizations are opting for integrated #AI solutions.

Insightful article from VentureBeat on why organizations are opting for integrated #AI solutions.