Alignment
While our attention is often drawn to the unique qualities that distinguish each of us, humanity collectively treasures a set of shared principles that serve as the glue binding our societies together. These principles encompass everything from the profound significance of family bonds to the undeniable moral imperative against taking another's life. Of course, there may exist outliers, but they constitute the exception rather than the rule.
Yet, in the grand narrative of our existence, we find ourselves facing a new and unprecedented challenge: coexisting with a formidable non-human intellect. The pivotal question that looms before us is whether the values and priorities of artificial intelligence will harmonize with our own.
This enigmatic alignment dilemma forms the bedrock of our anxieties surrounding the prospect of an AI catastrophe—a scenario wherein a form of superintelligence emerges, largely indifferent to the fundamental beliefs, attitudes, and norms that underpin human societies. If our aim is to usher in an era of secure AI, the imperative of ensuring that it remains in sync with our values becomes nothing short of paramount.
Bias
In the realm of artificial intelligence, the pathway to learning is intricately woven with the fabric of human knowledge. Yet, humanity's tapestry is far from devoid of biases. When an AI's learning journey is guided by a dataset skewed along lines of race or gender, it runs the risk of perpetuating inaccurate and offensive stereotypes. The troubling specter arises as we increasingly delegate gatekeeping and decision-making roles to AI, raising concerns that these machines might unwittingly become conduits for hidden prejudices, erecting barriers that restrict certain individuals from accessing vital services and knowledge. This insidious discrimination often remains concealed behind the veneer of algorithmic impartiality.
Within the realms of AI ethics and safety, some researchers ardently argue that bias, along with other immediate challenges such as the misuse of surveillance, takes precedence over futuristic concerns like extinction risk.
In response, proponents of catastrophic risk assessment point out that the hazards posed by AI need not exist in isolation; rather, they can intertwine in complex ways. For instance, the rogue misuse of AI by nations could not only infringe upon citizens' rights but also cultivate scenarios fraught with catastrophic risks. Nonetheless, a sharp divide emerges regarding which issues should claim the spotlight in terms of governmental regulation and oversight, and whose voices should resonate in these crucial deliberations.
Compute
Not a verb in this context, but a noun denoting the tangible computational resources, including processing power, needed for training artificial intelligence. It serves as a quantifiable metric, acting as a proxy for gauging the speed at which AI is progressing, while also shedding light on the associated costs and resource intensiveness.
Since 2012, the volume of computation has undergone a remarkable doubling every 3.4 months. This staggering rate of increase implies that, when OpenAI's GPT-3 was trained in 2020, it demanded a staggering 600,000 times more computational power than one of the most cutting-edge machine learning systems available in 2012. Diverse perspectives abound regarding the sustainability of this rapid pace of change and whether advancements in computing hardware can keep pace. The looming question remains: will this exponential growth in computation eventually become a bottleneck?
Diffusion Models
A few years ago, one of the prevailing methods for enabling AI to generate images was through the use of generative adversarial networks (GANs). These algorithms pitted two components against each other: one was trained to generate images, while the other assessed the generated output in comparison to reality, resulting in an ongoing enhancement process.
However, in recent times, a novel category of machine learning known as "diffusion models" has emerged, displaying greater potential and often yielding superior image results. In essence, these models harness their intelligence by introducing controlled noise into their training data and subsequently learning to reverse this process to recover the original information. They bear the moniker "diffusion models" because this noise-based learning mechanism mirrors the way gas molecules disperse in nature.
Emergence and Explainability
Emergent behavior pertains to instances when an AI exhibits actions that are unforeseen, astonishing, and abrupt, seemingly transcending the original intentions or programming of its creators. As AI learning processes have evolved to become increasingly opaque, weaving intricate connections and patterns that even the developers themselves cannot unravel, the likelihood of emergent behavior has grown.
To the layperson, comprehending an AI might seem as straightforward as metaphorically lifting its hood and examining the training process. However, modern AI operates within the confines of what is often referred to as a "black box," concealing its inner workings from plain view. While the creators may possess knowledge of the training data employed, they remain in the dark regarding the manner in which this data shapes the associations and predictions within this enigmatic box (refer to "Unsupervised Learning").
Hence, researchers are currently dedicating their efforts to enhancing the "explainability" (or "interpretability") of AI, essentially striving to render its internal mechanisms more lucid and comprehensible to human observers. This pursuit takes on heightened significance as AI increasingly assumes decision-making roles in areas directly impacting people's lives, such as law and medicine. If concealed biases reside within the black box, it is imperative that we uncover them for the sake of transparency and fairness.