University of Kwazulu-Natal

Ai Ibm

Bonisiwe Shabane
-
ai ibm

Enjoy transformative outcomes with AI that integrates and unifies your data across applications​ IBM helps you build a competitive edge by enabling you to use AI to automate workflows and tasks in critical areas such as customer service, supply chain management, HR tasks, IT and more.​ Partnering with leading innovators like AWS, Microsoft and SAP, we can deliver AI solutions tailored to your needs. No matter where you are on your AI journey, IBM is here to help you move forward. Discover our portfolio of AI products that accelerate generative AI into core workflows, driving automation and productivity. Find data and AI tools to build, scale and govern your custom solutions across the AI lifecycle.

Create personalized AI assistants and AI agents to automate repetitive tasks, simplify complex processes and accelerate your work. Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts.

They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). But in 2024, most AI researchers, practitioners and most AI-related headlines are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning. Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement. Your subscription will be delivered in English.

You will find an unsubscribe link in every newsletter. You can manage your subscriptions or unsubscribe here. Refer to our IBM Privacy Statement for more information. This article was featured in the Think newsletter. Get it in your inbox. In 2026, the smartest AI models may not be the biggest ones.

That is the bet now being placed by labs, investors and researchers who spent the past year watching their assumptions collapse. The coming 12 months will be defined not by the race to build larger systems, but by the scramble to develop wiser ones, models that think before they speak, that do more with less. “You can get a small language model performing at the same level, or even better, than much larger models,” Kush Varshney, an IBM Fellow, told IBM Think in an interview. A year ago, that would have sounded like heresy. For a decade, AI had operated according to a brutally simple catechism: more data, more parameters, more computing power, more intelligence. Labs competed to announce parameter counts like bodybuilders flexing in a mirror.

Training runs consumed the electrical output of small cities. The whole enterprise had the feeling of a land rush, except the territory being claimed was measured in teraflops.

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They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). But in 2024, most AI researchers, practitioners and most AI-related headlines are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to firs...

You Will Find An Unsubscribe Link In Every Newsletter. You

You will find an unsubscribe link in every newsletter. You can manage your subscriptions or unsubscribe here. Refer to our IBM Privacy Statement for more information. This article was featured in the Think newsletter. Get it in your inbox. In 2026, the smartest AI models may not be the biggest ones.

That Is The Bet Now Being Placed By Labs, Investors

That is the bet now being placed by labs, investors and researchers who spent the past year watching their assumptions collapse. The coming 12 months will be defined not by the race to build larger systems, but by the scramble to develop wiser ones, models that think before they speak, that do more with less. “You can get a small language model performing at the same level, or even better, than mu...