You may already use some form of next-generation AI systems at an elementary level. As we have seen with almost every technology, there is often a \u201ceureka\u201d moment when something truly unique is discovered. From then on, development is usually about finding applications for that discovery and refining their functions. Artificial intelligence is perhaps the clearest example of this concept as it becomes increasingly more capable.\u00a0<\/p>\n\n\n\n
AI can be described as a technology that decides an outcome. For example, a calculator cannot give you different answers when you add one plus one because it has hard-wired circuitry. However, artificial intelligence produces output that is not programmed in advance. Instead, AI is designed to:<\/p>\n\n\n\n
Within this very simple explanation lies a roadmap for how AI will develop, namely: <\/p>\n\n\n\n
Within a short time, communication with AI systems has gone from programming language to natural language<\/a>. Whereas chatbots once accepted only defined text queries, we can now use AI interactive avatars<\/a> that understand what you say, and will soon interpret how you feel. This ability to combine input types is called multimodal AI and is meant to simulate how people communicate. Other applications are also being modified to accept new forms of input, such as LiDAR for self-driving cars (instead of the types of cameras being used by Tesla). <\/p>\n\n\n\n As AI technologies mature, they will require even less input from people, while AI programming will be able to interpret the implied meaning of a query. Known as agentic AI<\/a>, future applications that use AI will fill in the blanks of a natural language query and then produce a range of outcomes. In case the system makes a mistake, the reinforcement learning component of agentic AI will compensate for it next time. <\/p>\n\n\n\n Also called \u201cdemocratization\u201d, model downsizing involves the development of smaller, less expensive models used by AI to search for data related to queries. Model downsizing creates more accessible data sources that might be custom-built or of somewhat reduced functionality compared to the large language models currently in use. One of the vital areas where smaller models are needed is mobile phone applications. The trend towards democratization is illustrated by the creation of models like Llama<\/a>, Mistral<\/a>, and GPT-4o-mini.<\/p>\n\n\n\n Behind advanced artificial intelligence capabilities are a variety of new technological areas. They cover software and hardware developments, and even delve into theoretical concepts that are just now being brought into reality. <\/p>\n\n\n\n The electrical requirements to power microchips and issues related to heat have always meant limitations for how compact computing devices can be. The energy needs for AI applications make this an even bigger challenge. Concepts to reduce microchip power consumption include electrical flows that are activated only when needed, as opposed to a constant flow, and chips that combine memory and computing functions. <\/p>\n\n\n\n As part of the effort to move AI closer to human abilities, artificial neural networks (ANN) will need to be replaced by HDC. For example, to understand a blue square, ANN has a place in its memory that recognizes \u201cblue\u201d and \u201csquare\u201d. But humans perceive objects in thousands of dimensions at once. It is easy for us to picture a blue square in a crowded office, yet an ANN’s memory and energy requirements to produce such a picture are relatively intensive. HDC seeks to simplify this process by mimicking how humans perceive their surroundings. <\/p>\n\n\n\n AGI is to artificial intelligence as AI is to calculators because it represents the next step of knowledge capability. Whereas regular AI technology depends on designers providing models for the AI to learn, AGI finds its learning solutions. This will allow it to apply its knowledge to a wider range of areas. For example, a system for self-driving cars equipped with AGI would be able to pilot an aircraft. AGI is also known as strong AI. <\/p>\n\n\n\n Many see AI as an unstoppable force that will eventually affect everyone. However, early adopters are already starting with technologies that incorporate the above innovations. There are a wide range of areas where artificial intelligence plays a critical role. Let\u2019s have a look at an application that represents many of the developments that characterize next-generation AI. Interactive avatars are currently used in areas like customer service, sales, recruiting<\/a>, and education. They have evolved from being interactive avatars for chat in text form to capabilities that include dynamic verbal communication. Similarly, interactive avatar creators use increasingly sophisticated generative AI that allows the application to give expansive and accurate responses in real-time. Similarly, high-quality avatar platforms enable creators to upload their own model as a database containing their customized responses to user queries.<\/p>\n\n\n\n Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.<\/a><\/p>\n\n\nAutonomy<\/h3>\n\n\n\n
Model Downsizing<\/h3>\n\n\n\n
Technologies Driving Next-Generation AI Systems <\/h2>\n\n\n\n
Low Power Chips<\/h3>\n\n\n\n
Hyperdimensional Computing (HDC)<\/h3>\n\n\n\n
Artificial General Intelligence (AGI)<\/h3>\n\n\n\n
How to Get Started with Next-Generation AI Systems<\/h2>\n\n\n\n
Getting Started<\/h2>\n\n\n\n