Artificial Intelligence (AI) has been all the rage lately, especially with the rise of generative AI technologies like ChatGPT. But let’s be real, companies jumping on the AI bandwagon have some serious challenges to face. From technological hurdles to training and procedural issues, it’s no walk in the park.
But hey, the good news is that AI adoption is on the rise. According to the IBM Global AI Adoption Index 2022 report, the global AI adoption rate grew by a solid four points from the previous year, bringing it to a cool 35 percent. And get this, an additional 42 percent of respondents are just dipping their toes into the AI waters. It’s a whole new world, my friends.
Now, let’s talk short-term decisions. When it comes to AI, companies have to figure out their path. Do they wanna build in-house capabilities? Do they wanna play around with open-source models? Or do they wanna take the easy route and work through an API like Open AI? Decisions, decisions. There’s pros and cons to each, but it all boils down to control over model performance and data privacy.
But wait, there’s more! Long-term thinking comes into play too. Let’s say a company wants to create a kickass generative AI service that can improve efficiency, workflows, and scale like nobody’s business. Well, they have to consider the implications of scaling the model that powers their solution. We’re talkin’ massive generative models with billions of parameters that require serious compute power. How do you keep up with the cost? It’s a real head-scratcher.
But fear not, my AI-loving friends. The combination of generative AI and powerful server hardware is paving the way for companies to design, build, and deliver new AI applications and models faster than ever before. According to IBM, AI is a game-changer for businesses. It’s automating repetitive tasks, saving time and costs, improving efficiency, and making customers happier. Efficiency is the name of the game, and AI is the MVP.
Take customer care, for example. One company out there is planning to use generative AI to handle the majority of their customer support calls. No more basic non-AI chatbots, my friends. And here’s another mind-blowing example: a company using generative AI to write 90 percent of their product descriptions. Talk about efficiency!
So, what’s the secret sauce behind all this AI greatness? It’s called Automated Neural Architecture Construction, or AutoNAC for short. This bad boy helps build efficient neural network architectures optimized for specific use cases, hardware, and performance goals. It’s like finding the perfect balance between accuracy, low latency, and high throughput. And it’s all tailor-made for different tasks and data characteristics.
But that’s not all, folks. Deci’s got another trick up their sleeve called Infery. This tool optimizes the runtime performance of the neural network to ensure it runs smoothly on specific hardware. Let’s face it, generative AI models are a whole different ball game compared to your run-of-the-mill static models. They require specialized tools to unleash their full potential.
The Deci platform, paired with Lenovo ThinkSystem servers, is making waves in industries like manufacturing, retail, and even agriculture. We’re talking visual inspections, animal health monitoring, automatic checkout, and so much more. It’s like AI is taking over the world, one industry at a time.
And speaking of partnerships, Lenovo and Deci are joining forces to make AI adoption a breeze. With Deci’s deep learning platform and models configured to run like a well-oiled machine on Lenovo servers, it’s a match made in AI heaven. They’re even part of the Lenovo AI Innovators Program, which gives them access to Lenovo’s AI expertise and pre-configured hardware. It’s a win-win for everyone.
So, if you’re ready to dive headfirst into the AI revolution, consider Deci and Lenovo your trusted allies. They’ve got the tools, the know-how, and the passion to make your AI dreams a reality. It’s time to level up your business with the power of AI. Let’s do this.