The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary Many essential consumer goods, from life-saving chemotherapy drugs to the fragrances and flavors in everyday products, come from compounds found in plants. These are cornerstones of multi-billion dollar markets where consumer demand for sustainable, natural products is growing at an unprecedented rate. However, the methods used to acquire them are often anything but sustainable. The development of more sustainable approaches, such as Microbial Cell Factories, presents complex engineering challenges, leading to costly and slow R D. Active Learning can revolutionize the traditional research cycle by delivering a faster, cheaper and more intelligent R D process, paving the way for a sustainable future.
This blog post is the second part of our on-going blog series about 3D computer vision. If you haven’t read the first blog post, you can check it out here. This second article (Part 2) provides an overview of 3D optical acquisition methods. We cover the differences between various types of sensors and how they can benefit specific use-cases. We also cover different 3D data formats and storage options.
Think about the last time you spoke to a digital assistant. Now, imagine that this assistant truly understands you and responds naturally in your language. What if it even acts on your behalf? But before you picture a sudden robot takeover, let's clarify: this is not about replacing your human touch. This is the rapidly advancing reality of AI voice agents. Continue reading to find out if your business can leverage voice agents.
Transform Enterprise Operations with Google’s Latest AI Stack: Smarter Agents, Multimodal Interfaces, Real Business Impact The annual Google Cloud Next event took place in Las Vegas in April, where Google presented its latest innovations for Google Cloud Platform (GCP).
Every week, 30 of Europe’s leading experts in Leukemia and Lymphoma gather online for the International Leukemia/Lymphoma Tumor Board (iLTB). Their mission is crucial: to tackle some of the most complex and treatment-resistant pediatric leukemia and lymphoma cases across Europe. This multidisciplinary team is composed of specialists in immunotherapy, genetics, molecular biology, clinical trials, and cellular therapies, from all around Europe, who work together to identify life-saving treatment plans for these children.
The hidden dangers of AI: key risks every business should know
What does the day in the life of a medical specialist who encounters a patient with an unclear diagnosis look like? Its combing through tens or maybe hundreds of scientific papers to find a gene, cell therapy or something else that may be the key to saving their patient’s life. As you can imagine, this can be a lengthy and time-consuming process. But what if there was a tool this specialist could use to get this information through simple queries, cutting down the amount of time it takes to find the needed information?
Generative AI is ubiquitous these days, and organizations are rapidly integrating GenAI into their business processes. However, building GenAI applications comes with its own set of specific challenges. The models are often large, meaning inference costs for running these models can quickly get out of hand, and model selection often requires balancing performance against costs and latency. Other common challenges include the misuse of generative models or data leakage. While implementing measures such as rate limiting, monitoring, and guardrailing in your GenAI applications can help overcome these problems, doing so for every individual project brings significant overhead for your engineering teams. It also becomes easy to lose track of global usage of generative AI within your organization and leads to many cases of reinventing the wheel as teams solve the same problems over and over again.
Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.