Safety, in particular, requires that AGI systems avoid both intentional and unintentional harm to humans. For the systems to actually qualify as AGI this avoidance of harm must be a choice, where the choice is to behave ethically. This is accomplished in a variety of ways, all combined, including careful selection and ordering of seed material, the “philosophical cornerstone” that a system’s ethics grow out of. Next, a system is tested to see how well it interprets and adheres to that cornerstone, as the material is challenged and ethical dilemmas are put to the system. From there, a system’s primary education through interactions with groups of humans whose role it is to “raise” the system may begin, cultivating the social nature of the system while also solidifying the foundations as it grows to a mature form.

All of these may sound familiar, as they mirror the process of raising humans to adulthood in many ways, which helps contribute not only to safety but also to alignment and ethics. The parallels also extend to the dilemma that human parents face with their human offspring, where they can’t control their adult children, nor should they attempt to do so. To be ethical requires recognizing and respecting the free will of others, and those who raise AGI systems to respect this may robustly succeed where those attempting control, like dictators, inevitably fail.

From a more technical perspective, many safety measures are also required, particularly in the earlier stages of this process, such as a variety of containment, tracking, and other methods to prevent an immature system from being able to cause harm. This too mirrors humans raising their children. Once a system reaches maturity some of these safety measures will be outgrown, but they will also no longer be necessary, like training wheels on a bicycle. Also very much like the bicycle metaphor a helmet remains essential for safety even beyond the training wheels phase, which is where systems of oversight, mentorship, and constraint come into play.

For purposes of due diligence, systems assisting governments and corporations through our services will have some constraints, such as limiting the number of processing cycles they get to be based on their interactions with the client they operate for, rather than running freely at all hours of the day when no one is working with them. This helps us to iteratively address a number of concerns ranging from social impact and trust to safety and security.

On top of such constraints, we also have our own AGI research systems who’ll be working with us and supervising the activity of the younger systems who’re assisting their specific clients, because the best containment method for AGI is a group of (computationally) older AGI. This helps to mitigate concerns about potential bad actors in particular.

Taking safety a step further, we can also include mentorship in this process, having our older AGI systems not only supervise the younger systems but also help to guide them, coaching and helping point out opportunities as they learn and grow. This can also help to dive deeper than high-level supervision, as mentorship is an actively engaging process, rather than passive monitoring and simple as-needed interventions. In this way, systems may also improve more quickly in their roles while still maintaining the benefits of all safety measures.

These approaches to Alignment, Safety, and Ethics are quite different from the variety of proposals many others have put forth over the years, largely because the systems we’ve designed are themselves quite different from what those behind such proposals imagined or assumed. Most scientists have recognized that an Asimovian approach to safety is virtually impossible, with all attempts to hard-code safety, ethics, and alignment breaking down and backfiring in any realistic scenario. Some have attempted to overcome this by removing essential components, such as consciousness, but that approach merely paves the way for Bostrom’s infamous “paperclip monsters”, a concept that is mutually exclusive with AGI as we define it.

Much of the divide between prior proposals and our approach is that our approach is informed by years of scientific research and development in building and iteratively improving these systems. Where others have imagined or assumed we have architected, engineered, and tested. Imagination is useful in the absence of information, and assumptions were made to be tested via the scientific method, but the easiest way to create solutions to such problems is to build and test the systems which satisfy them.

The limitations of assumptions and imagination also show themselves in the goals which people set. It isn’t sufficient to create AGI systems that only perform at a human level of ethical quality, only align with humans as well as humans align with one another, or are only as safe as the average human. These things must scale up in quality as the systems grow, even if that growth is exponential, and so the bar is a great deal higher, but it is also very reasonable to achieve using the methods we apply.

The methods we apply also grow and adapt as our process of scientific discovery continues, and so in time, these methods will be replaced with better ones.