Artificial general intelligence (AGI) describes AI that is capable of learning an intellectual task in the same way humans do.
Understanding artificial general intelligence
Artificial general intelligence is the representation of general human cognitive abilities in software that enables AI to solve problems in the same way a person does.
The precise definition of AGI varies from one expert to the next since each approaches the subject of human intelligence from a different perspective.
Psychologists, for example, may define it as the ability to adapt and survive while computer scientists may consider intelligence to be more related to goal achievement.
In any case, AGI is believed to be a form of strong artificial intelligence. This form contrasts with weak or narrow AI that is used to perform specific tasks or solve specific problems.
Autonomous vehicle technology and IBM’s Watson supercomputer are two such examples.
At present, AGI is a theoretical construct and remains the stuff of science fiction. When Gato was released by Alphabet subsidiary Deepmind in May 2022, it was touted as a “generalist agent” that could perform over 600 tasks from captioning an image to driving a robot.
Gato is probably the most advanced AI system in the world, but it can only make inferences from the information stored in its giant database.
For AGI to come to fruition, many argue it will require innovators to do more than simply force algorithms to parse more data.
On the subject of when artificial general intelligence may materialize there is much debate. Some academics believe AGI is decades away, while others predict the technology will not be developed this century.
Characteristics of artificial general intelligence
While artificial general intelligence remains theoretical, there is scope that the performance of an AGI system will not only be indistinguishable from a human but far exceed it.
This is because these systems will likely possess comprehensive cognitive computing capabilities and the ability to process vast data sets at incredible speeds.
Nevertheless, some of the human characteristics artificial general intelligence must be able to replicate include:
Common sense.
The ability to understand cause and effect.
Transfer learning – the application of knowledge learned from completing one task to solving a different but related problem.
Sensory perception – this includes subjective color perception and depth perception in static images.
Fine motor skills.
Superior navigation skills – while existing GPS can pinpoint a specific location, it is envisioned that AGI will be able to better project movement through physical spaces.
Natural language understanding (NLU) – this would require AGI to possess a level of intuition enabling it to understand human language which is heavily context-dependent.
Other capabilities such as the comprehension of belief systems, symbols, and metacognition which includes self-awareness and critical thinking.
Key takeaways:
Artificial general intelligence (AGI) describes AI that is capable of learning an intellectual task in the same way as humans do.
While recent advancements in AI technology such as Gato have been commendable, AGI is at present a theoretical construct. Some experts believe it will take centuries for artificial general intelligence to be developed.
With access to large datasets and superior processing power, there is scope that AGI may be able to outperform humans in the future. Before that happens, however, scientists must be able to replicate difficult human characteristics such as abstract thinking, fine motor skills, and sensory perception, among many others.
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results.
Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual.
Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.
OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemiummodel. Microsoft also commercializes opener products through its commercial partnership.
OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
