What is General AI

https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-general-ai/

1. Issues in mastering human-like capabilities

To achieve true human-level intelligence, AGI needs to master some human-like capabilities, such as:

• Sensory perception: Although deep learning systems have shown great promise in the field of computer vision, AI systems lack human-like sensory-perception capabilities. For example, trained deep learning systems still have poor color perception. This is evident in self-driving cars as they get easily fooled by small pieces of black tape or stickers on a red stop sign. A similar case is observed with sound perception. Current AI systems cannot perceive and replicate distinct human sound perception.

• Motor skills: Humans can easily retrieve any object from their pockets due to our fine motor skills. A recent development applied reinforcement learning in teaching a robotic hand to solve a Rubik’s cube. Although the demonstration is notable, it reveals the problems involved in programming robot fingers on a single hand to manipulate trivial objects such as keys.

• Natural language understanding: Humans share knowledge via books, articles, blog posts, and videos. Subsequently, when humans write, they tend to assume the reader’s general knowledge, and, as such, a lot of information is unsaid in writing.To begin with, the current AI needs to consume vast amounts of information from all knowledge sources, which is a critical task. If AI lacks the basis of common sense, it will be difficult for these systems to comprehend situations and operate in the real world.

• Problem-solving: Consider an example where a home robot has to recognize that the LED light bulb in the house is blown out and either replace it with a new one or alert someone. To carry out this task, the robot needs to have common sense as discussed above or should have the ability to simulate all permutations and combinations that determine possibilities, plausibility, and probabilities. Today’s AI lacks both common sense and simulation capability.

• Human-level creativity: AI systems can improve their intelligence on their own if they understand the vast amounts of code that humans have written, identify novel methods that can be improved, and subsequently rewrite the identified code. Although AI-based machines have been able to compose music and draw pictures, demonstrating human-level creativity for self-optimization needs further advancement of AI.

• Social & emotional connect: For AI-enabled robots to operate in the world, human interaction is inevitable. As a result, these robots will need to understand humans, facial expressions, and variations in tone to interpret real emotions. Considering the perception challenges discussed above, AI systems that are capable of empathy with an emotional connection seem a distant reality as of now.

2. Lack of working protocol

Current AI systems lack a working protocol that helps artificial intelligence or machine learning networking systems collaborate. This presents a severe technical deficiency when deploying a complete AGI system. The systems are thus forced to work as standalone models in closed, isolated environments. Such a mode of operation does not align with the complex and highly social human environment essential for AGI systems.

3. Communication gaps reduce universality

Today, AI systems face a distinct communication hurdle. Communication gaps between disparate AI systems come in the way of seamless data sharing. As a consequence, the inter-learning of machine learning models is stalled. With the impact on inter-learning, AI can fail to optimize the assigned tasks. This eventually reduces the universality of the overall AI system.

4. Lack of business alignment

For appropriate AI implementation, business executives need to take a strategic approach by setting objectives, identifying KPIs, and tracking ROI. Otherwise, it can become difficult to assess the results brought by AI and compare them to measure the success (or failure) of AI investment.

Integrating AI into existing systems is a complex process. Various parameters such as data infrastructure needs, data storage, labeling, feeding the data into the system, and others need to be considered. Currently, concerned stakeholders seem to be in the dark about all these operational parameters of AI. This hinders the overall development and achievement of business goals.

5. Lack of AGI direction

As businesses often struggle with the fundamental understanding of the AGI system, they are forced to hire a dedicated team of AI experts, which can be an expensive affair. Besides, enterprises do not have a defined AI-based plan and direction to carry out their business operations. This makes the implementation of AI platforms costly and complex. These factors contribute significantly and act as roadblocks to realizing a full-fledged AGI system.

See More: How Is AI Changing the Finance, Healthcare, HR, and Marketing Industries

10 Key Recent Trends in General AI Advancements

As AI advancements take center stage amid the COVID-19 pandemic, the development of human-like intelligence has been progressing faster than ever before. Although a complete AGI system is not a reality today, recent trends in AI may push the AGI envelope and speed up its development significantly.

Here are the top 10 AI trends that can propel advancements in AGI.

Latest Trends in General AI Advancements

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2. Metaverse 

Metaverse has been thriving as companies and individuals explore immersive technologies to work and interact in this virtual world. According to November 2021 data from DappRadar, users spent around $106 million to buy virtual property in the metaverse, focusing on digital land, luxury yachts, and other assets.

Considering this trend, AI and ML are expected to drive metaverse forward by building a virtual world with virtual AI chatbots where users feel at home.

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10. Quantum AI 

Although considerable progress has been made in the AI field in the last few years, quantum AI could further push AI boundaries as quantum computations could speed up ML algorithms and achieve results in a shorter time. Quantum AI could neutralize AGI obstacles as it could help create a strong knowledge base by analyzing huge volumes of data found in books, articles, blog posts, and other similar sources in minimal time.

See More: 10 Experts on the Future of Artificial Intelligence

Takeaway

The ongoing decade will be extremely crucial for the development of AGI systems. Experts believe that there is a 25% chance of developing human-level AI by 2030. Moreover, the rising inclination for robotic processes and machine algorithms, coupled with the recent data explosion and computing advancements, will offer a fertile ground for the proliferation of human-level AI platforms. It is only a matter of time before AGI systems become mainstream in this highly technological world.

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