What is generative AI, what are foundation models, and why do they matter?

In the near term, generative AI models will move beyond responding to natural language queries and begin suggesting things you didn’t ask for. For example, your request for a data-driven bar chart might be answered with alternative graphics the model suspects you could use. In theory at least, this will increase worker productivity, but it also challenges conventional thinking about the need for humans to take the lead on developing strategy. Generative AI will significantly alter their jobs, whether it be by creating text, images, hardware designs, music, video or something else.

Essentially, it’s about setting boundaries, limits that an AI can’t cross. And ensuring that those boundaries create provable safety all the way from the actual code to the way it interacts with other AIs—or with humans—to the motivations and incentives of the companies creating the technology. And we should figure out how independent institutions or even governments get direct access to ensure that those boundaries aren’t crossed. On the how—I mean, like, I’m not going to go into too many details because it’s sensitive.

How generative AI—like ChatGPT—is already transforming businesses

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers.

Another solution, sometimes called Transformers, avoids the adversarial approach. Microsoft has one, known as GPT-n for General, Pre-trained Network, that’s been trained over the years using large blocks of text gathered from Wikipedia and the general internet. The latest version, GPT-3, is closed source and licensed directly for many tasks including generative AI. Several other similar models include Google’s  LaMDA (Language Model for Dialogue Applications) and China’s Wu Dao 2.0. Such a specialized generative AI model can respond by synthesizing information from the entire corporate knowledge base with astonishing speed.

Applications of Predictive AI

To that end, the company also recently announced the incorporation of generative AI capabilities into its human resources software, Oracle Fusion Cloud Human Capital Management (HCM). There are many earlier instances of conversational chatbots, starting with the Massachusetts Institute of Technology’s ELIZA in the mid-1960s. But most previous chatbots, including ELIZA, were entirely or largely rule-based, so they lacked contextual understanding. Their responses were limited to a set of predefined rules and templates.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Was interesting to discover that Google allowed employees to allocate 20% of their time to fun projects to promote innovation. While each technology has its own application and function, they are not mutually exclusive. Consider an application such as ChatGPT — this application is conversational Yakov Livshits AI because it is a chatbot and is generative AI due to its content creation. While conversational AI is a specific application of generative AI, generative AI encompasses a broader set of tasks beyond conversations such as writing code, drafting articles or creating images.

Traditionally, they would need to consolidate that data as a first step, which requires a fair bit of custom software engineering to give common structure to disparate data sources, such as social media, news, and customer feedback. Individual roles will change, sometimes significantly, so workers will need to learn new skills. Historically, Yakov Livshits however, big technology changes, such as generative AI, have always added more (and higher-value) jobs to the economy than they eliminate. It’s going to have the potential freedom, if you give it, to take actions. It’s truly a step change in the history of our species that we’re creating tools that have this kind of, you know, agency.

Nvidia’s chips are driving many areas of the economy that consumers might take for granted, like product recommendations, customer service chatbots, and faster product development cycles. The company is also involved in other markets that could provide solid growth over the long term, including making chips for gaming, autonomous driving, and graphics development for metaverse applications. Generative AI is an exciting new technology with potentially endless possibilities that will transform the way we live and work. Traditionally, AI has been the realm of data scientists, engineers, and experts, but now, the ability to prompt software in plain language and generate new content in a matter of seconds has opened up AI to a much broader user base. Based on answers to these questions, you can use respective tools from any subfields of AI.

Other examples include Midjourney and Dall-E, which create images, and a multitude of other tools that can generate text, images, video, and sound. Their combined work demonstrated the viability of large, multilayer neural networks and showed how such networks could learn from their right and wrong answers through credit assignment via a backpropagation algorithm. Oracle has partnered with AI developer Cohere to help businesses build internal models fine-tuned with private corporate data, in a move that aims to spread the use of specialized company-specific generative AI tools.