Artificial Intelligence is moving from the backend to the frontend, in part thanks to emerging solutions designed to optimize how AI runs. The market is still young, but Deci is one of the emerging challengers in this space. The Tel Aviv-based company aims to bring AI to "the real world." The New Stack asked co-founder Yonatan Geifman to explain what that meant. "A lot of AI is currently in the lab, for example on Kaggle on some experimentation phase, and we are trying to help people to get from the lab to the real world," Geifman said.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Deci, a deep-learning software maker that uses AI templates designed to create AI-based applications, today launched v2.0 of its development platform, which it claims speeds the way for developers to build, optimize and deploy computer vision models. The term "speed" and AI application development are rarely used in the same sentence, but by using this platform, resulting AI models can be more swiftly prepared to run on any hardware and environment, including cloud, edge and mobile – with accuracy and high runtime performance, Deci CEO and co-founder Yonatan Geifman said in a media advisory. This is because much of the grunge work has been eliminated by the Deci series of DeciNet templates made available in the v2.0 platform. Using Deci, the company says, AI developers can achieve improved inference performance and efficiency to enable effective deployments on resource-constrained edge devices, maximize hardware use and reduce training and inference cost, Geifman said.

Developing efficient and reliable applications involving complicated components such as artificial intelligence and machine learning is difficult even for experienced software engineers, so let's face it: Any previously built in model or template shortcut that helps smooth the way to a useful solution in such a development project is a good shortcut. Deci is a deep-learning software provider that uses AI to build AI-powered apps. The Tel Aviv-based company this week unveiled a new set of image classification models, dubbed DeciNets, specifically for servers using using Intel Cascade Lake central processing units (CPUs). While graphics processing units have conventionally been the hardware of choice for running power-intensive convolutional neural networks, CPUs -- far more commonly utilized for general computing -- serve as a much less-expensive alternative for many enterprises who already have them on hand. Although it is possible to run deep-learning inference on CPUs, they are significantly less powerful than GPUs; thus, deep learning models typically perform three to 10 times slower on a CPU than on a GPU.

"We have an AI-driven algorithm that automatically designs AI algorithms to be more accurate and run faster in a production environment," said Yonatan Geifman, Deci AI co-founder and CEO. "Our technology automatically designs new structures of neural networks, optimizing them for the data and machine learning problems we are trying to solve and to run faster on the production hardware." "Deci is a company that was founded a little more than two years ago with a goal of making AI more accessible and scalable, with a technology that improves the way people develop, build, optimize and deploy AI," Geifman asserted. "So basically, we help data scientists to solve their problems faster with automated tools." Geifman founded the company in 2019 along with its chief scientist, Prof. Ran El-Yaniv, who was Geifman's professor at the Technion, and COO Jonathan Eliel, who served with Geifman in a top air force intelligence unit.

Building usable models to run AI algorithms requires not just adequate data to train systems, but also the right hardware subsequently to run them. But because the theoretical and practical are often not the same thing, there is often a gap between what data scientists may hope to do and what they practically do. Today, a startup called Deci that has built a deep learning platform to help bridge that gap -- by building models that can work with the data and hardware that are available to use -- is announcing some funding after finding strong traction for its products with Fortune 500 tech companies running mass-market, AI-based products based on video and other computer vision-based services. The Tel Aviv-based startup has picked up a Series A of $21 million, money that it will be using to continue expanding its product and customer base. Insight Partners is leading the round, with previous backers Square Peg, Emerge and Jibe Ventures, alongside some new backers: Samsung Next, Vintage Investment Partners, and Fort Ross Ventures.

Intel today announced a strategic business and technology collaboration with Deci to optimize machine learning on the former's processors. Deci says that in the coming weeks, it will work with Intel to deploy "innovative AI technologies" to the companies' mutual customers. Machine learning deployments have historically been constrained by the size and speed of algorithms and the need for costly hardware. In fact, a report from MIT found that machine learning might be approaching computational limits. A separate Synced study estimated that the University of Washington's Grover fake news detection model cost $25,000 to train in about two weeks.

Deep learning startup Deci today announced that it raised $9.1 million in a seed funding round led by Israel-based Emerge. According to a spokesperson, the company plans to devote the proceeds to customer acquisition efforts as it expands its Tel Aviv workforce. Machine learning deployments have historically been constrained by the size and speed of algorithms and the need for costly hardware. In fact, a report from MIT found that machine learning might be approaching computational limits. A separate Synced study estimated that the University of Washington's Grover fake news detection model cost $25,000 to train in about two weeks.