In secondary school, if you read that big textbook WAEC recommended for Biology, titled Modern Biology, you would have noticed that life is built up in phases. Yes, cells coming together will give tissues, and when tissues are working together you get organs, and groups of organs will deliver systems which then give the organism. My Biology teacher, Mr Bobo, used the central nervous system, connecting how the brain and spinal cord serve as organs to make that system function.

The Microbiology graduate of University of Ibadan would explain how cells are the functional unit of life, consisting of molecules, and comprising different parts, including DNA, cytoplasm, and ribosomes. That was natural philosophy in the biological sense of it, but we can see five core things: cells, tissues, organs, systems and organisms.

Now, there is an AI-evolution and OpenAI is giving equivalents in the workplace. Yes, ChatGPT’s OpenAI has identified 5 levels of artificial intelligence evolution:

• Chatbots, Al with conversational language
• Reasoners, human-level problem solving
• Agents, systems that can take actions
• Innovators, Al that can aid in invention
• Organizations, Al that can do the work of an organization

Good People, they think they have the “cells” and are working on the “tissues”. If they succeed, we can have a modern organism (yes, the organization) where AI is the firm. Scary?

OpenAI has introduced a five-tier system to track its progress towards achieving Artificial General Intelligence (AGI). The levels range from Level 1, representing current conversational AI, to Level 5, envisioning AI capable of managing and performing the work of an entire organization. OpenAI believes it is approaching Level 2, which involves problem-solving akin to a PhD without tools. The framework aims to provide a structured approach to understanding and developing AI systems that could eventually surpass human intelligence.

In an ambitious stride to maintain its leading position in the rapidly evolving field of artificial intelligence, OpenAI is secretly working on a novel approach to its AI models under the code name “Strawberry.”

This revelation comes from internal documentation reviewed by Reuters and a person familiar with the matter. The Microsoft-backed startup, renowned for its ChatGPT product, is racing to demonstrate that its models are capable of advanced reasoning capabilities, which could mark a significant leap forward in AI technology.

A Glimpse Inside the ‘Strawberry’ Project

According to a recent internal document seen by Reuters in May, OpenAI teams are deeply engrossed in the Strawberry project. While the exact timeline of the document remains unclear, it outlines OpenAI’s plan to leverage Strawberry for advanced AI research.

Described as a work in progress, the project remains shrouded in secrecy even within the company. The goal of Strawberry is to enable AI to not only generate answers but also to autonomously and reliably navigate the internet to perform what OpenAI terms “deep research.”

“This is something that has eluded AI models to date,” said the source, noting the project’s ambitious nature.

Asked about Strawberry and the details reported in this story, an OpenAI spokesperson said in a statement: “We want our AI models to see and understand the world more like we do. Continuous research into new AI capabilities is a common practice in the industry, with a shared belief that these systems will improve in reasoning over time.”

The spokesperson did not directly address questions about Strawberry.

From Q to Strawberry: A New Era of Reasoning

Strawberry is the successor to a previous project known as Q. According to two sources, Q was already seen within OpenAI as a breakthrough in its ability to answer complex science and math questions beyond the reach of current commercially available models.

This year, during an internal all-hands meeting, OpenAI demonstrated a research project showcasing new human-like reasoning skills, according to Bloomberg. Although Reuters could not confirm if the project demonstrated was Strawberry, it aligns with the company’s ongoing efforts to enhance AI reasoning.

OpenAI CEO Sam Altman has emphasized the importance of reasoning in AI, stating earlier this year that “the most important areas of progress will be around reasoning ability.”

The Challenge of AI Reasoning

Improving reasoning in AI models is seen by researchers as the key to achieving human or super-human-level intelligence. While large language models can summarize texts and compose prose efficiently, they often falter on common-sense problems and logical tasks, leading to “hallucinations” or the generation of incorrect information.

Reasoning, as described by AI researchers, involves the AI’s ability to plan, understand the physical world, and work through multi-step problems.

“Reasoning is key to AI achieving human or super-human-level intelligence,” said an AI researcher interviewed by Reuters.

OpenAI’s Strawberry project aims to overcome these challenges by employing a specialized post-training process. This involves fine-tuning the AI models after they have been pre-trained on extensive datasets.

According to a source, Strawberry’s method bears similarities to Stanford’s “Self-Taught Reasoner” (STaR), which allows AI models to iteratively create their own training data, potentially enabling them to achieve higher intelligence levels.

“I think that is both exciting and terrifying… if things keep going in that direction, we have some serious things to think about as humans,” Stanford professor Noah Goodman, one of STaR’s creators, commented.

Long-Horizon Tasks and Autonomous Research

Among the ambitious goals for Strawberry is the ability to perform long-horizon tasks (LHT), which require the AI to plan and execute a series of actions over an extended period.

The internal documentation indicates that OpenAI is training and evaluating models on a “deep-research” dataset to enable these capabilities. Although the specifics of the dataset and the duration of the extended period remain undisclosed, the aim is clear: to allow AI to conduct research autonomously with the aid of a computer-using agent (CUA) that can act on its findings.

The Competitive AI Industry

OpenAI is not alone in its quest to enhance AI reasoning. Major tech companies like Google, Meta, and Microsoft, along with numerous academic labs, are also exploring different techniques to improve AI reasoning capabilities. However, opinions differ on whether large language models can incorporate long-term planning and advanced reasoning into their predictions.

Yann LeCun, a pioneer in modern AI working at Meta, has frequently expressed skepticism about the ability of large language models (LLMs) to achieve human-like reasoning.

Strawberry represents a crucial component of OpenAI’s strategy to address the limitations of current AI models. By developing more advanced reasoning capabilities, OpenAI aims to unlock new possibilities for AI, from making scientific discoveries to creating new software applications. The company has been signaling to developers and partners that it is on the verge of releasing technology with significantly enhanced reasoning skills.

Strawberry’s development includes post-training methods such as fine-tuning, which involves human feedback and iterative learning processes. These techniques are designed to refine AI models and improve their performance on specific tasks.

The advancements made through Strawberry could redefine the capabilities of AI and set new standards for what these models can achieve. While the path forward is fraught with challenges, the potential rewards are immense, heralding a new era of intelligent, autonomous AI systems.

In the words of OpenAI’s spokesperson, “We want our AI models to see and understand the world more like we do.” If Strawberry succeeds, it could bring us one step closer to realizing that vision.

In the dynamic world of cryptocurrency, liquidity is a vital aspect that reflects the health and efficiency of the market. Liquidity refers to the ease with which assets can be bought or sold in the market without causing a significant movement in the price. A liquid market is characterized by a stable environment where transactions can occur swiftly and at consistent prices. The role of retail trading in enhancing the liquidity of the crypto market is a topic of increasing relevance and interest.

The cryptocurrency market has witnessed a significant transformation with the influx of retail traders. These individual investors have played a pivotal role in enhancing market liquidity, which is crucial for the health and efficiency of financial markets. High liquidity levels indicate a robust market with a plethora of buyers and sellers, ensuring smooth transactions and stable prices.

Retail traders, often individuals who buy and sell cryptocurrencies through exchanges, contribute significantly to the market’s liquidity. Their collective trading activities ensure a continuous flow of transactions, which helps to stabilize prices and reduce volatility. Retail traders bring diversity to the market, as they have varied trading strategies, goals, and levels of risk tolerance. This diversity is beneficial because it creates a more resilient and robust market that can better absorb large trades without significant price fluctuations.

One of the keyways retail trading boosts market liquidities is through the sheer volume of transactions. As more individuals participate in trading, the number of buy and sell orders increases, creating a more active market. This activity attracts more participants, including institutional investors, who are often looking for a liquid market to execute large trades efficiently.

Retail traders’ active participation brings more orders to the market, which helps to fill the gap between bid and ask prices, thus reducing the spread. This reduction in spread not only benefits the retail traders by providing them with better pricing but also attracts institutional investors who seek efficient markets for their large-volume trades.

Moreover, retail traders often utilize online platforms and exchanges that offer incentives for liquidity provision, such as reduced trading fees or rewards for market-making activities. These incentives encourage continuous trading, further bolstering liquidity. Additionally, the diverse strategies and trading patterns of retail traders add to the market’s dynamism, making it more resilient to large trades that could otherwise cause price slippage.

Moreover, retail traders often act as contrarian forces in the market. When institutional investors or whales make large trades that could potentially move the market, retail traders can provide the necessary counterbalance. By taking the opposite side of these trades, they help to maintain equilibrium in the market and prevent extreme price swings.

Cryptocurrency exchanges have recognized the importance of retail traders and have implemented various strategies to attract and retain them. These strategies include user-friendly trading platforms, educational resources, and incentives such as lower transaction fees or rewards programs. By fostering a welcoming environment for retail traders, exchanges enhance the overall liquidity of the market.

Furthermore, the advent of decentralized finance (DeFi) platforms has provided retail traders with additional avenues to contribute to market liquidity. DeFi platforms often rely on liquidity pools, where users can deposit their assets to facilitate trading. Retail traders who participate in these pools earn transaction fees or other rewards, incentivizing them to provide liquidity to the market.

Retail trading plays a crucial role in bolstering the liquidity of the cryptocurrency market. The collective actions of individual traders create a more active and stable market, attracting further participation from various market players. As the crypto market continues to evolve, the contribution of retail traders to its liquidity will remain an essential factor in its growth and maturity.