Artificial Intelligence to the Rescue? by Peter Voss

Imagine

Imagine a hundred thousand PhD-level researchers focusing their total efforts on life-extension and anti-aging research. Imagine them working 24/7, with no distraction from grant proposals, office politics, or attractive co-workers. Imagine the fantastic progress we would see in finding solutions for eliminating debilitating disease and reversing the deathly effects of aging.

This optimistic vision is rapidly moving closer to reality: The foundational knowledge and technology to build computers with human-level learning and thinking ability are now finally emerging. Recent advances in computer technology combined with insights from fields as varied as psychology, philosophy, evolution, brain physiology, and information theory allow us to finally solve the previously intractable problems of creating real AI. The long-promised power of truly intelligent machines will soon be available to help us solve the many problems holding back human flourishing and longevity.

Now, I expect skepticism. Haven’t we been promised real artificial intelligence for 30 years or more? Yet all we see around us are ‘stupid’ computer programs that don’t understand what we actually want to do, and respond with cryptic error messages when things go wrong. What is more, they cannot adapt to changing circumstances or requirements, and they don’t learn from their mistakes.

A new approach to AI, called ‘artificial general intelligence’, or AGI, has emerged which promises to finally overcome the limitations of traditional AI, and usher in a new era of vastly superior computer systems and tools. Given adequate development effort, this nascent technology could provide systems that learn, think, and innovate like humans before the end of this decade. In fact, as we will see below, these computers will in many ways be more capable than human researchers.

What exactly is AGI, and how does it differ from conventional AI?

Computer systems based on AGI technology (AGIs) are specifically engineered to be able to learn. They are able to acquire a wide range of knowledge and skills via learning – similar to the way we do. Unlike current computer systems, AGIs do not need to be programmed to do new tasks. Instead, they are simply instructed and taught by humans. Additionally, these systems also learn by themselves – both implicitly ‘on-the-job’, and explicitly by reading and practicing. Furthermore, just like humans, they resiliently adapt to changing circumstances.

This general ability to learn through natural interaction with the environment as well as from teachers, allows them to autonomously expand and adapt their abilities over time – they become ever more knowledgeable, smarter, more useful.

In addition to their intrinsic learning ability, AGIs are also designed to function in a goal-directed manner. This means that they automatically focus their attention on features and activities that are likely to help solve problems they have been given. For example, an AGI trained and instructed to look for inconsistencies in arthritis medication studies will spend its time perusing relevant articles, news, and background information, and request pertinent additional information or clarification from other researchers. On the other hand, an AGI assigned to be a personal assistant will seek out knowledge and skills necessary for that job, such as learning how to deal with various types of business associates, schedules, priorities, and travel arrangements, as well as its boss’s personal preferences.

AGIs learn both conceptually and contextually. Conceptual learning implies that knowledge is assimilated in a suitably generalized and abstract form: Skills acquired for one task are available for similar, but non-identical tasks, while at the same time making the system much more useful and robust when coping with environmental changes. Context, on the other hand, allows the system to utilize relevant background information to appropriately tailor its responses to each specific situation. It can take into account such crucial factors as recent actions and events, current goals and priorities, who it is communicating with, and anything else that affects its current actions.

Other central AGI features include an ability to anticipate events and outcomes, and the ability to introspect – to be aware of its own cognitive states (such as novelty, confusion, certainty, its level of ability, etc). These design features, combined with the fact that AGIs directly perceive their environments via built-in senses, endow them with human-like understanding of facts and situations.

In contrast, systems based on conventional AI technology provide little or no learning capability beyond their initial one-time training phase (if any). Traditional computer programs are designed for narrow applications, and are incapable of being used for any other purpose. In fact, even within their given domain any new requirements or changes to their operating environment require costly program changes.

To use a human analogy to highlight the difference, imagine an entirely unschooled person: If we wanted to put them to work on an assembly line, we could instruct them with a very detailed script for a specific set of actions, in other words: rote learning, with no real understanding (like programming a traditional AI or ‘expert system’). Or, we could take on the much more difficult task of teaching them to read and write, to think logically and to learn. This would enable them to learn and re-learn any number of jobs in the factory and elsewhere; and to perform them much more intelligently – with understanding. This is the AGI approach. Furthermore, an educated person (or AGI) can also manage entities with low-level skills, or those that possess highly specialized knowledge, thereby greatly increasing their productivity.

In summary, an AGI’s ability to learn implies a number of advantages over conventional AI technology: It can be taught, instead of having to be programmed; it learns from experience and can learn by itself; it can deal with ambiguity and unknown situations, know when to ask for help, and recover from errors resiliently and autonomously.

Note that all these advantages are in addition to computer systems’ natural strengths: photographic memory, high-speed accuracy, upgradeability, seamless interfacing with other systems, etc. Another key feature of such trainable/ trained systems is that, unlike skilled humans, they can be duplicated, and efficiently pool knowledge and experience. These capabilities allow for rapid up-scaling of research projects. For example, various AGIs, after having been trained in particular research specialties, could pool their knowledge and then be duplicated hundreds of times – imbuing each one of them with their combined knowledge. From there on these AGIs can pursue coordinated, yet individual, research paths, while regularly updating each other. Naturally, this strategy can also be applied across multiple research organizations.

How AGI will improve our lives

A computer’s natural habitat comprises the Internet and local networks, email, and software. On the other hand high degrees of physical mobility and dexterity are difficult and expensive to achieve. Early applications of AGI will leverage their inherent strengths, while minimizing weaknesses. Because of this, computer-bound applications will be first to arrive; robotics, and especially systems with human-level sense acuity and dexterity, will come later.

One obvious early application is that of Personal Assistant. This system observes your computer activity and automatically learns your details, preferences, and usage patterns. It is always ready to check, sort, and respond to your email, make phone calls on your behalf, place and track orders for you, and asks you to explain or clarify anything it doesn’t know how to handle – it learns ‘on the job’. The AGI takes standing (verbal) instructions, such as general reminders or paying bills, alerting you when a hard-to-get item becomes available, or interrupting you when some specific person calls. It knows about your business associates and friends, how to get hold of them, and their respective preferences and priorities. Best of all, the more you use it, the better it becomes at anticipating your requirements.

A very powerful and lucrative commercial use of AGI technology is as Call Center Operators. Applications include sales and technical support, market research, and other service hotline functions such as banking. Apart from lowering cost of operation, these AGI operators are always available, have infinite patience, and the same cheery disposition. Furthermore, a customer calling will always be speaking to the ‘same person’, meaning that the operator remembers you, your previous calls, as well as knowing all about past and current transactions, inventory status, technical product details, and any ad hoc personal preferences you may have. The service is further personalized by the AGI knowing, for example, that you live in a cold climate, but are currently experiencing a heat wave – thus suggesting that your supplement order should perhaps be shipped overnight.

An interesting consequence of AGI call centers is that America will again in-source many of these functions from overseas – completing the circle!

As helpful as the above-mentioned applications are, the really exciting uses for many of us are in the area of research. Once we employ AGIs to help us solve the many problems threatening our lives and flourishing, we will see accelerating progress in many crucial domains – including biotech, nanotech, as well as energy, materials, and environmental science.

Successful research requires both low- to medium-level intelligence (to do the ‘grunt’ work) as well as the most brilliant minds one can muster. Research papers and data, technical books and articles, and conference proceedings are now being published at such a furious pace that even experts in highly specialized fields are unable to keep up.

Research Assistant AGIs will allow us finally handle to the enormous information overhang we’re accumulating. Working diligently 24/7, they will be able to peruse research publications of all kinds, consolidate findings while identifying consensus and contradictions, and annotate summaries with links to relevant authors, sources, and references. These computerized helpers will have the time and skill to assess the studies’ quality by reputation referencing, and by analyzing methodology and protocols. This will help to identify bias and statistical problems such as inadequate sample size. They can also contact authors to request additional details or data, or to clarify points. All of this will make it much easier for higher-level researchers to evaluate the state-of-the-art, and to design highly effective follow-up studies and experiments. Naturally, disparate AGI research assistants will be able to exchange and share information with each other almost instantaneously. Moreover, an AGI trained to any given level of expertise and experience can easily be copied or multiplied an unlimited number of times.

Another huge bonus from using AGIs for research is that they can assimilate, compare, and integrate inter-disciplinary work – something that is extremely difficult for humans to do.

At the next level, AGIs can be employed as Lab Assistants to control and monitor connected lab equipment, and help collate, file and report results.

The highest payoff, however, comes from utilizing the accuracy, persistence, knowledge and intelligence of high-level AGIs to pursue and direct research in their own right – discovering solutions that equal or exceed mankind’s best achievements. These AGI Researchers engaged in primary research will suggest and justify decisive experiments in situations where empirical methods are called for.

The longer term

Projecting the capabilities of AGIs a little further into the future – as they accumulate substantial knowledge and skills in a large number of fields, and as costs plummet and performance skyrockets – we see many far-reaching benefits that promise to save and improve human lives.

Imagine applying the brainpower of millions of highly trained specialist AGIs to solving mankind’s most challenging problems; problems that are currently beyond human ingenuity. The implications are truly staggering.

Think about how this will accelerate progress in conquering disease and ageing. Consider how this can diminish a host of existential risks – both natural and man-made: floods and earthquakes, climate change and pollution, many types of accidents and terrorism.

AGI will also greatly assist in the rapid development of safe nanotechnology. This in turn, will help bring about radical life-extension.

Rational, wise thinking machines may even foster more objective legal systems, productive governments, and a reduction of political conflict.

Imagine the difference this would make to the quality and quantity of life. It will provide the opportunity for us to more fully realize our human potential.

Making it happen

Fully operational AGIs do not currently exist – however, the required knowledge to build them does. Surprisingly, very few companies are actively pursuing this goal (see next section for an analysis).

Adaptive A.I. Inc. is a small but innovative company that I formed in 2001 with the express purpose of developing and commercializing AGI technology. After an initial three-year research phase, the company is now engaged in an ambitious multi-year development project to actually build a fully functioning AGI with human-level cognitive ability.

While we expect the initial system’s cognitive ability to roughly match that of a 10-year old, in many respects it will be much more capable. As indicated above, it will have encyclopedic knowledge, the patience and self-discipline of a saint, and enjoy the accuracy, memory and speed of a… well, a computer.

Because this is not a copy of a human mind, but something completely new – an artificial or synthetic mind - one must expect it to have some strange ‘quirks’ and mannerisms relative to humans; like someone from a foreign culture and background, only more so. Additionally, its strengths will not be human-like dexterity, sense acuity, or the shared context of everyday human experience. The initial model will not be robotic, nor directly interface with the real world. Its natural environment is that of computer data, software tools, network resources, and the Internet. It will interact with people via voice and text (using computer and phone interfaces).

By avoiding the expense and complexity of robotics, and through various other fundamental strategic decisions, we expect to be able to deliver commercial AGIs well before the end of the decade.

Our company’s approach calls for the extensive leveraging of existing technology – instead of re-inventing, we aim to capitalize on existing (and soon-to-be-available) hardware and software components, as well as published theoretical research. We believe that to a large extent, the ‘pieces of the puzzle’ for achieving AGI already exist. Our ingenuity is applied primarily to finding, selecting, and intelligently integrating existing know-how, while inventing and developing the crucial missing pieces.

Another crucial business decision is to dedicate our company entirely to the task of developing competitive, commercial AGI technology. We do nothing else. Furthermore, being privately owned and funded by a limited number of individual investors who share our futurist vision, we do not need to satisfy a large number of investors with conflicting priorities, nor are we distracted by pressure for ‘next quarter earnings’, or marketing pressures and arbitrary release dates. We are singularly focused on our goal. This significantly contributes to our cost effectiveness and success.

Having said this, once our current development goals have been achieved, we do plan to transform the company into a product and services organization that supplies complete AGI solutions to a wide range of customers. We plan to be leaders in this new multi-billion dollar industry. There will be a massive demand for such systems, and the numbers are substantial. A million human-level AGIs at $100,000 each comes to $100 billion.

Given its enormous commercial potential, one may wonder why AGI isn’t a well-known, well-funded area of research and development. This is an interesting question.

Why such a dearth of AGI projects?

Several contributing factors seem to be accidents of history.

Firstly, we now find ourselves in the depth of the ‘AI winter’ – a period of deep pessimism and lethargy towards AGI ambitions following the spectacular failure of early AI promises. In backlash to unfulfilled expectations of 30 and 40 years ago, ‘Artificial Intelligence’ is still a swearword to many. Without delving into detailed analysis of these early failures, suffice it to say that hardware and software technologies and cognitive theories had simply not advanced sufficiently to enable the creation of human-level artificial intelligence.

However, while limitations of early technology were a definite handicap, several other theoretical and practical limitations, errors, and blind spots were – and are – even bigger impediments. These include the following:

• ‘Human-level AI is impossible’ – At the most basic level, this is usually caused by remnants of an ancient philosophical position called ‘Dualism’. This long-since-discredited idea that there is an inherent dichotomy between mind and body leads many AI researchers to reject even the theoretical possibility of AGI. Thus they don’t even try to solve the problem.

• ‘Not in my lifetime’ – Of those who do not in principle object to the possibility of AGI, many do not believe that it can happen in their lifetime, if ever. Some hold this position because they themselves tried and failed ‘in their youth’. Others believe that AGI is not the best or fastest approach to achieving ‘AI’, or are at a total loss on how to go about it. One popular idea is that we need to reverse-engineer the human brain – one function at a time – in order to create intelligent machines.

• ‘There is no such thing as general intelligence’ - A great percentage of researchers reject the validity or importance of ‘general intelligence’. For many, controversies in psychology (such as those stoked by The Bell Curve) make this an unpopular, if not taboo subject. Others, conditioned by decades of domain-specific work, simply do not see the benefits of AGI – of having intelligent systems with general learning ability.

• ‘We should not try to create AGI’ – Several groups oppose AGI development on moral grounds, or because they fear it.

• ‘We don’t know how to do it’ – Many potential AGI entrepreneurs and researchers simply don’t enter our field, because they lack crucial insights on how to achieve real artificial intelligence. There are many ways to be misdirected, and academia, if anything, hinders in that regard. To name just one of the most common errors entrenched in conventional AI thinking: the mistaken belief that intelligence is primarily about having knowledge. We see the ability to acquire knowledge – i.e. to learn – as far more fundamental.

• Poor AI theory – There are a many theories of artificial intelligence. Most of them will not lead to practical systems possessing general intelligence. Several theoretical errors and blind spots have already been mentioned. While this is not the forum to explore this subject in any detail, here are some additional errors worth listing: The belief that AI can be solved by language alone (as in chat-bots), or conversely, that they require full embodiment (robotics); approaches that focus unduly on vision (or any other single aspect, for that matter); overly abstract mathematical or philosophical theories that lack real-world grounding (universal Turing Machines, quantum consciousness and qualia); rigid rule-based designs, and statistical models that require infinite processing power.

• Short-term academic and commercial pressure - Today, the bulk of AI research and development focuses on narrow applications that are quite domain specific. From a competitive point-of-view it doesn’t really matter whether this results from a theoretical rejection of ‘general intelligence’, or simply from practical, short-term commercial or academic pressures; it is a lot quicker and cheaper to solve specific problems one at a time than to develop general learning. Of course, many are so focused on particular, narrow aspects of intelligence that they simply don’t get around to looking at the big picture – they leave it to others to make it happen. It is also important to note that there are often strong financial and institutional pressures to pursue specialized AI.

• Loss of project focus – The few projects that do pursue AGI based on relatively sound models run yet another risk: they can easily lose focus. Sometimes commercial considerations hijack a project’s direction, while others get sidetracked by (relatively) irrelevant technical issues, such as trying to match an unrealistically high level of performance, fixating on biological feasibility of design, or attempting to implement high-level functions before their time.

AGI to the rescue

Artificial General Intelligence is the emerging technology of intelligent computer systems that are able to learn and reason; systems that dynamically interact with us; computers that understand. Employing such AGIs with human-level cognitive ability in research promises to enormously accelerate progress in many fields.

Many of us look forward to using these intelligent systems to help solve the many hard problems that currently prevent or limit human well-being and longevity. We expect to have hundreds of thousands, if not millions, of highly intelligent and trained AGIs working away at finally conquering the killer diseases, and to figure out how to stop and reverse the devastating effects of aging. Beyond that, we look forward to technology to further improve the quality of human life – to make us truly flourish.