A year has passed since OpenAI quietly introduced ChatGPT as a “research preview,” a chatbot powered by a sophisticated large language model (LLM). These LLMs are a specific application of transformer neural networks, a technology initially presented in a 2017 Google paper.

ChatGPT offered a user-friendly interface to the underlying LLM, GPT-3.5, and became the fastest-growing consumer technology in history, attracting over a million users within just five days of its launch. Today, there are hundreds of millions of ChatGPT users, and various other similar bots built on different LLMs from multiple companies have emerged. One of the latest additions is Amazon Q, a chatbot tailored for business purposes.

These technological advancements have the potential to reshape creative and knowledge work profoundly. For instance, a recent MIT study focused on tasks such as crafting cover letters, composing sensitive emails, and conducting cost-benefit analyses. The study demonstrated that using ChatGPT led to a 40% reduction in the time required to complete these tasks and an 18% improvement in output quality, as evaluated by independent assessors.

Comparisons to foundational discoveries like electricity and fire are apt because AI, like these innovations, has the power to revolutionize nearly every facet of our lives. It can alter how we work, communicate, and address complex challenges, much like electricity transformed industries, and fire changed early human societies.

Racing toward the future, consulting firm McKinsey has estimated that generative AI will contribute over $4 trillion annually to the global economy. Consequently, tech giants like Microsoft and Google are aggressively pursuing opportunities in this market.

Debates about the impact and safety of AI technology have been ongoing since the advent of ChatGPT. These debates, spanning from the U.S. Congress to the historic Bletchley Park (formerly a hub for British code-breaking during World War II), essentially fall into two perspectives: AI “accelerationists” and “doomers.”

Accelerationists advocate for rapid AI development, highlighting its immense potential benefits, while “doomers” advocate for a cautious approach that emphasizes the potential risks associated with unchecked AI development. These debates have prompted significant actions in AI regulation. While the EU AI Act has been in development for several years, the U.S. has taken a proactive stance with a comprehensive Executive Order on “Safe, Secure, and Trustworthy Artificial Intelligence,” aiming for a balanced approach between unbridled development and rigorous oversight.

Countries worldwide are actively pursuing AI strategies in response to the LLM revolution. Russian President Vladimir Putin has recently announced plans for a new Russian AI development strategy to counter Western dominance in the field, albeit belatedly, as the U.S., China, the U.K., and others have already made substantial progress. Interestingly, Putin had famously stated in 2017 that the nation leading in AI “will be the ruler of the world.”

Reflecting on this whirlwind year in AI, one might have thought it reached its peak when OpenAI’s board of directors fired Sam Altman, the CEO. However, Altman returned within a week following an investor and employee revolt, and the board underwent changes.

Now, a new enigma surrounds OpenAI in the form of Project Q* (pronounced “Q-star”). Researchers assigned the name “Q” to represent the “Quartermaster,” a top-secret figure known for creating gadgets for the fictional James Bond character.

According to Reuters, the OpenAI board received a letter from researchers just days before Altman’s dismissal, warning that Q* could pose a threat to humanity. Speculation abounds regarding what Q* might entail, ranging from a groundbreaking neuro-symbolic architecture (a significant development) to a more modest yet impressive fusion of LLMs and existing techniques to outperform current state-of-the-art models.

An effective neuro-symbolic architecture of this scale does not currently exist but could enable AI to learn from minimal data while offering clearer explanations for its behavior and reasoning. Several organizations, including IBM, view this architecture as a pathway to achieving Artificial General Intelligence (AGI), the capacity for AI to process information at or beyond human capabilities, at machine speed.

Although Q* may not represent such a breakthrough, if it enters the market, it would mark another step toward AGI. NVIDIA CEO Jensen Huang has even suggested that AGI could be attainable within five years. Microsoft President Brad Smith, on the other hand, has a more conservative view, stating that achieving AGI, where computers surpass human capabilities, will likely take many years, if not decades.

The year ahead promises a wide range of emotions and developments. Breakthroughs like ChatGPT and projects like Q* have sparked optimism, concerns, regulatory discussions, competition, and speculative thoughts. The rapid advancements in AI over the past year are not just technological milestones but also a reflection of our unwavering pursuit of knowledge and mastery over our creations.

As we look ahead, the coming year is shaping up to be as exciting and unsettling as the last, depending on how effectively we channel our energy and guidance in this transformative field.

In the midst of the ongoing power struggle and mass resignations at OpenAI, Microsoft, a long-standing supporter of AI research, continues to advance its AI initiatives without slowing down. Today, the research division of Microsoft, led by Satya Nadella, unveiled Orca 2, a pair of compact language models that demonstrate remarkable performance, surpassing much larger language models, including Meta’s Llama-2 Chat-70B, in complex reasoning tasks conducted under zero-shot conditions.

These models come in two sizes, with 7 billion and 13 billion parameters, building upon the groundwork laid by the original 13B Orca model, which had already showcased strong reasoning capabilities by emulating the step-by-step reasoning processes of more powerful models a few months ago.

In a joint blog post, Microsoft researchers stated, “With Orca 2, we continue to demonstrate that improved training techniques and signals can empower smaller language models to achieve enhanced reasoning capabilities, typically associated with much larger models.”

Microsoft has made both of these new models open-source to encourage further research into the development and evaluation of smaller models that can match the performance of their larger counterparts. This initiative provides enterprises, especially those with limited resources, a more cost-effective option to address their specific use cases without the need for extensive computing resources.

Teaching Small Models to Reason:

While large language models like GPT-4 have long impressed both enterprises and individuals with their reasoning abilities and complex question answering, smaller models have often fallen short in this regard. Microsoft Research aimed to bridge this gap by fine-tuning Llama 2 base models using a highly customized synthetic dataset.

Instead of simply replicating the behavior of more capable models through imitation learning, as is commonly done, the researchers trained these models to employ various solution strategies tailored to specific tasks. The rationale behind this approach was that strategies designed for larger models might not always work optimally for smaller ones. For instance, while GPT-4 can directly answer complex questions, a smaller model may benefit from breaking down the same task into multiple steps.

“In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task,” the researchers explained in a recently published paper. The training data for this project was obtained from a more capable teacher model in a way that guided the student model on when and how to use reasoning strategies for specific tasks.

Orca 2 Outperforms Larger Models:

When evaluated on 15 diverse benchmarks under zero-shot conditions, covering aspects like language comprehension, common-sense reasoning, multi-step reasoning, math problem solving, reading comprehension, summarization, and truthfulness, Orca 2 models delivered impressive results, often matching or surpassing models five to ten times their size.

The overall average of benchmark results indicated that Orca 2 7B and 13B outperformed Llama-2-Chat-13B and 70B, as well as WizardLM-13B and 70B. Only in the GSM8K benchmark, which included 8.5K high-quality grade school math problems, did WizardLM-70B perform notably better than the Orca and Llama models.

It’s worth noting that, despite their outstanding performance, these models may still inherit certain limitations common to other language models and the base model upon which they were fine-tuned.

Microsoft also highlighted that the techniques used to create the Orca models can potentially be applied to other base models in the field.

Future Prospects: Despite some limitations, Microsoft sees great potential for future advancements in areas such as improved reasoning, specialization, control, and safety of smaller language models. Leveraging carefully filtered synthetic data for post-training emerges as a key strategy for these improvements. As larger models continue to excel, the work on Orca 2 represents a significant step in diversifying the applications and deployment options of language models, according to the research team.

Expect More High-Performing Small Models:

With the release of the open-source Orca 2 models and ongoing research in this space, it is likely that we will see more high-performing, compact language models emerge in the near future. Recent developments in the AI community, such as the release of a 34-billion parameter model by China’s 01.AI and Mistral AI’s 7 billion parameter model, demonstrate the growing interest in smaller, yet highly capable language models that can rival their larger counterparts.