The Next Generation of AI

Mastering the Tools for the Next Generation of AI

January 5, 2024

Christina Cardoza

Next Generation of AI

AI is changing the world—evolving in, and into, different industries every day. That is in part because developers are building new solutions and working hard to stay on top of each new generation of AI possibilities. And Intel helps to make it all possible—giving those developers the technologies and tools they need, such as OpenVINO^™ and Intel^® Geti^™. What’s more, OpenVINO and Geti are designed to connect developers and business domain users together, further facilitating the next generation of AI solutions and use cases.

To learn more about the next generation of AI development, we talked with Paula Ramos, AI Evangelist at Intel. She discusses real-world problems that AI solves, Intel capabilities involved, and the democratization and spread of AI (Video 1). Because it’s not only about making tools and technologies available to developers; it’s also crucial to provide education and resources to bring more people into the field in the first place.

Video 1. Paula Ramos, AI Evangelist at Intel, looks at the trends and technologies making the next generation AI possible. (Source: insight.tech)

How do you see AI advancing and solving real-world problems right now?

AI is advancing fast; new things are coming every single day. Right now there is even more awareness for it than ever, as those real-world problems turn into amazing solutions. Startups are using silicon power together with the capability of AI to solve these problems.

For example, AI helps people to communicate using translations—translating text between a hundred languages. Another great example is a self-driving-car system that some vehicle brands are using to control vehicle steering, acceleration, and braking, with the potential to reduce traffic fatalities and accidents. AI can help doctors to diagnose cancer, to develop personalized treatment plans, or to accelerate the deployment of new drugs and treatments. It is helping farmers reduce the use of pesticides and herbicides. AI is helping humanity to solve their problems faster.

What is the importance of making AI more accessible for developers?

I have three thoughts on this. First, creating more accessible information for developers can accelerate the speed of AI innovation. The more developers who have access to AI tools and the easier it is, the faster the technology will advance to accelerate the speed of innovation. Access to the latest hardware improvements is also important. It is really another tool, because it enables developers to build and deploy applications more efficiently and effectively.

The second point is democratizing AI. We need to be sure that AI is for everybody, and that every single developer has the opportunity to benefit from the technology. By making it more accessible, we can help to bridge the AI adoption gap.

And the third one is solving the AI talent shortage. Right now there is high demand for AI developers and not enough developers in the world. By making AI easier to learn and use, we can help train more developers and close the AI talent gap.

What tools and hardware have come out to address the democratization of AI?

I’m so excited about the Intel announcement of the new AI PC. It is built with the Intel^® Core^™ Ultra processor that incorporates GPU and also a new element called an NPU—Neural Processing Unit. I showed it at the generative-AI booth at the Intel^® Innovation conference in September, and it had a great performance. Pat Gelsinger, the CEO of Intel, demonstrated running a Llama 2 chatbot—an LLM model—on an AI PC, locally on a Windows machine. It is a lot more secure to utilize AI without an internet connection, without sending that data out to the cloud, for sure.

“The more #developers who have access to #AI tools and the easier it is, the faster the #technology will advance to accelerate the speed of innovation.” – Paula Ramos, @intel via @insightdottech

You know, a lot of people think that Intel is just a hardware company, but we are doing a great job of showing developers how they can easily improve their solutions using frameworks or systems such as OpenVINO, the inference and deployment framework that Intel has. The AI PC also has the capability to run OpenVINO. And OpenVINO is showing us the potential we have at Intel to leverage AI. OpenVINO is everywhere now: in smart cities, manufacturing, retail, healthcare, and also agriculture. Downloads of it have increased 90% in the past year.

Can you tell us more about that relationship between OpenVINO^™ and AI?

OpenVINO is a toolkit that Intel provides to developers on client and edge platforms; it is powering AI at the edge and making AI—generative AI—more accessible, optimizing neural network inference across multiple hardware platforms. However, the main goal of OpenVINO is to run the optimization and quantization of the models so we can reduce their size. We can reduce the memory footprint and also run the models faster in a wide range of hardware—Intel and non-Intel hardware. What we are doing with OpenVINO is solving the real pain points for developers.

Another important thing is that once you have a model in the intermediate representation format—that is, the OpenVINO format—you can deploy it everywhere. This is one of the differences between Intel and some of its competitors.

How are developers leveraging Intel^® Geti^™?

Intel Geti is a platform that helps organizations to rapidly develop computer vision models. In short, it brings all the necessary things together—annotation, training, optimization, testing. You can create projects, upload your annotation, upload new production data or previous data. You can modify trainable features, test the deployment running in the server, and also download your deployment for running it locally.

So there are a lot of benefits to using it. Data scientists, machine learning professionals, systems integrators, and domain experts can work together using the same platform. This is because it is easy to use and has the potential to control multiple aspects of the training and optimization process. It can also provide modeling in different formats, and one of these formats is that intermediate representation format—OpenVINO, which is behind the scenes of the Geti platform for optimization and quantization.

The Intel Geti platform also offers an SDK that helps users to take advantage of easy-to-use functionalities. It utilizes OpenVINO to build deployment pipelines and to accelerate inference on various Intel hardware platforms that include CPUs and GPUs, without the need for computer vision expertise. That is the beauty of this platform.

Deployment with the Intel Geti SDK makes it super easy for developers since the SDK is agnostic to the computer vision task and also agnostic to the model architecture. Developers don’t need to prepare the data for model input and don’t need to prepare the model output to show the results.

What are some of the challenges women face entering the AI space?

I’m so passionate about this topic. Personally, I represent two minorities in the global tech workforce. The first is women in tech, and the second is Latin women in tech—and you can interpret “tech” as artificial intelligence or engineering in general. Fifty percent of the global tech workforce are women but just two percent are Latin women. So this is a huge underrepresentation, and I want to contribute to reducing the lack of access to education and training that we have in Latin countries, particularly.

But I also want to inspire more women in general to work in AI. I want to reduce discrimination and bias. Everyone deserves the opportunity to have success in tech, regardless of gender. Women can also sit at the table and have serious discussions about technology. Women bring a unique perspective about how to solve problems; we have unique skills to create products and services that meet the needs of all users.

And we have made some significant contributions to the field of AI. One example is Dr. Fei-Fei Li, an AI researcher and Co-Founder of the Stanford Institute for Human-Centered AI. She has contributed to the birth of deep learning—she developed the ImageNet initiative, and that initiative has played a major role in the deployment of deep learning. This is a remarkable impact that one woman has made to AI, so I can imagine how many ideas that other women are also capable of contributing to the field.

Is there anything you would like to add about the future of AI development?

There is one important question that I want to share with developers: Where are your dreams? You can achieve those goals, because AI is a powerful tool that has the potential to make the world a better place for everyone.

So try new technologies, new models and algorithms. Try to participate and be an active contributor in an open-source project. Stay tuned to the latest trends that will make AI more practical. We need to build an AI that is inclusive, fair, and beneficial to all. The engine is in your imagination.