Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Computers don’t retire, overreact and complain. They could in minutes get all the knowledge accumulated by other computers. They could work 24 hours without making stupid mistakes. They make more and more human jobs obsolete. It is inevitable, computers will replace programmers in many areas. Even more, if Strong AI, capable of reasoning and understanding meaning, will appear, programming as a profession will be almost eliminated (at least coding part). Customers will be able to describe their needs directly to a computer. Computer AI will be translating these specifications to machine code (and stronger AI will require less formal specifications) and relentlessly building the software system.

Does it mean that that at the some point in the future software developers will no longer be needed? It could be true, if customers could specify exactly what they need and can effectively collaborate with AI to build the system. But things are not so simple, especially with non-trivial problems and humans (assuming that customers and users will be still humans). There are three roles that software specialists could play in the future even with powerful computer programming AI.

1. Translators
   Customers often have difficulties describing what they need, lack knowledge of how to interact with computers to get solution right. Not all of them can formulate concepts and ideas suitable for consumption by AI. Software developers could be effective partners in discovering, refining and translating customers needs for computer AI.
2. Innovators
   Many of the best human achievements are the product of intuition, irrationality and ability to go beyond rules and established theories. Coming up with novel, breakthrough and beautiful solutions is one of the most exciting parts of the software development. Indeed, effective and most useful software systems require creativity, innovation and aesthetics. Left brain thinking, purely rational and logical, is not enough for building these solutions, especially for human users. Can computers acquire these abilities, break encoded rules and become better than intuitive and creative human programmers? It is a big question.
3. Advisers
   Every system have some purpose and fits into some context. Customers will require people who understand the big picture: problem domain, emerging concepts and IT environment. People, who still understand how computers operate and what is possible and makes sense. People, who can answer ‘Why’ and ‘What’ solutions are required in addition to ‘How’ to implement them.

Present and Future

Daniel H. Pink in his excellent book A Whole New Mind writes:

“Programmers will have to master different aptitudes, relying more on creativity than competence, more on tacit knowledge than technical manuals, and more on fashioning the big picture than sweating the details.”

We’ll need to supplement our well developed high-tech abilities with high touch, which:

“involves the ability to create artistic and emotional beauty, to detect patterns and opportunities, to craft a satisfying narrative, and to combine seemingly unrelated ideas into a novel intervention. High touch involves the ability to emphasize, to understand the subtleties of human interaction, to find joy in one’s self and to elicit it in others, and to stretch beyond the quotidian, in pursuit of purpose and meaning.”

Modern trends in programming are not in the areas of enhancing sophistication of building software systems, but in making easier to manipulate and understand these systems by humans. Complexity of the modern software systems and problem domains makes software development difficult endeavor for our relatively small, delicate and easy to confuse brains. Programmers have challenges to transparently represent customers ideas in the software code and avoid complications specific to technical platforms. Effective software teams try to overcome disconnect between technical and business perspectives on the system:

• use agile practices focused on speed, business value and rapid feedback from customers
• apply Domain Driven Design and Domain Specific Languages for representing customer ideas and concepts
• develop with high level languages suited for describing programming logic closer to human language (Ruby, C#)
• write automated executable specifications for describing and testing domain logic (Fit, Test and Behavior Driven Development)
• leverage existing commercial and open source solutions to bring business value faster and concentrate on core problems instead of spending effort on secondary.

Alex Iskold in The Future of Software Development says

Equipped with a modern programming language, great libraries, and agile methods, a couple of smart guys in the garage can get things done much better and faster than an army of mediocre developers.

I believe – the future software teams will be powerful small units (similar to special forces in army) of diverse highly capable professionals. Remaining software developers will be domain experts, professional communicator and creative innovators understanding well both business and technology.

Computer Science will be attractive again and people will value profession of the software developer as prestigious and one of the most important for human civilization progress.

Interesting Resources:
A Whole New Mind, Daniel H. Pink
The Future of Software Development, Alex Iskold
The Extinction of Programmers, Hans-Eric Gronlund

Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Is it easy to build useful programs for humans? Failure rates and dissatisfaction with the software projects (more than 50% still fails or challenged) show that it is not quite easy task. Can AI help to build more successful projects, compete and eventually replace human programmers?

From ideas to specifications

Human programmers face objective challenges in building software systems, which AI will face in the future:

1. It is difficult to understand what people need.
2. Customer’s ideas are shifting, once they start using the real system and experience all the consequences and effects for interacting with it in the context of their problems.
3. Customer’s needs are changing constantly reflecting outside business and company trends, situation and problems.

There are 4 stages to build a software system:

1. Understand unclear human needs and overcome communication barriers
2. Translate these needs into specification or the system model
3. Implement these specifications within constraints and required system qualities
4. Verify that the system meets expectations.

Two first stages need strong AI with human like intelligence and comprehension, two last stages could be achieved with weaker AI. The key is translation of human needs into specifications that machine could understand. Stronger AI will need less defined specification and could handle ambiguous messages and human language, weaker AI will need more details, which at the extreme means writing a program as almost we do now.

As a part of the system specifications we should provide to AI expected input, output and required system qualities (performance, reliability, security, etc.). Even today some trends and approaches reduce gap in translation customer needs to computer specifications:

• Domain Specific Languages – expressing programming logic in language, better suited for specific domains and problems; domain experts can use this language and define the logic. Intentional Software could offer even more interesting approach in the near future.
• Automated Acceptance and Unit Tests – xUnit, Fit and similar frameworks allow to write specifications as tests in declarative or programmatic style.
• Test Driven Development, Behaviour Driven Development and Domain Driven Design provide approaches for evolving and programming a software system primarily from customer perspective as oppose to traditional views for building the system with distinct technological perspective, often disconnected from customer needs.

These approaches put more stress and effort to specify WHAT is expected from the system and less stress on HOW these result could be achieved. It is similar to declarative paradigm of programming (HTML, SQL), where a programmer specify what he expects and the underlying system comes up with optimal algorithms to satisfy this request.

Going forward application programmers and domain experts will focus on describing needs and specifications for the system and AI will increase contribution to implementation of these requests. As AI becomes stronger it will need less and less formalized description and could start comprehend human language directly.

Implementation and Delivery

Baron Munchhausen escaping from a swamp by pulling himself up by his own hair

AI could deliver a system in several ways depending on our level of trust:

1. Transparent System – computer generates code and human software professionals review, modify, integrate and accept it. It is similar to vendor model to speed up the main development.
2. Black Box – human provide specification, integrate and test the system without much understanding what AI has built inside.
3. Key-turn System – we trust computer to specify, build and integrate the system without human involvement and verification.

The first option seems safer and easier, but it requires a computer to generate software code in a human-readable format, which adds complexity and double translation: customer -> computer -> programmer. The Black Box and Key-turn System options doesn’t require this complex translation and could be easier to implement.

Based on today approaches AI could build system in few ways:

• Brute force: genetic algorithms or swarm intelligence, where computer try to find the fittest solution evolving behavior of the system based on selections, mutations, recombinations or emergent behavior of individual agents, competing and cooperating with each other.
• Trained neural networks: interconnected groups of artificial neurons, imitating how our brains are working.
• Symbol processor: similar to the way how a human operates with knowledge based on classic theory of mind: semantic, logic, reasoning, abstractions, etc.

We have been already successfully using AI software solutions in many areas, e.g. manufacturing, finances, medicine, science, air traffic control. Computers even analyze volatile political and military situations and predict unpredictable. It is matter of time when these approaches will be applied to software development and computers will be able to program themselves.

Silver Bullet?

Steffe
AI indeed could solve problems that Frederick Brooks indicated as essential complexity in software development (No Silver Bullet):

• Complexity – computers could handle much more information then our individual brains, carry knowledge about all possible states of the system and effectively manipulate it.
• Conformity - computers could seamlessly conform to interfaces of the other system and support rules of interactions with brothers computers.
• Changeability - computers will have full intellectual control over the system code and can quickly understand what should be modified and effectively support change requests.
• Invisibility - our brains are specialized on particular processing of information influenced by our biological origin. Computer don’t have this problem and could naturally manipulate information without need to visualize and limit scope for better comprehension.

Can we assume now that a computer-programmer could be superior to a human-programmer and become a silver bullet?

Competing with human programmers

AI definitely will have some advantage over humans. The computer-programmer will show higher productivity, consistency, reliability and will not be affected by mood swings, personal problems or lack of recognition (unless computer intelligence will make twist in their evolution). However there are several human characteristics that will be difficult to beat.

Creativity and Innovation
Creativity is a mental process involving the generation of new ideas or concepts, or new associations between existing ideas or concepts. – Wikipedia
Gödel incompleteness theorem shows that a computer should go beyond rules and algorithms embedded by humans to become creative and innovative. It is still not clear if computer could go over initial set of rules, generate new ideas, extend and connect existing.

Adaptability and Learning
Humans have unique ability to adapt to changing environment and quickly learn new things. It doesn’t take much time to adjust thinking, learn new rules and concepts and start programming effectively. Human programmers can successfully shift to the new domains, still keeping their mastery, apply previous experience enhanced by new knowledge. Could computers become so flexible in emerging new domains, shift in knowledge, perspectives and needs?

Aesthetics and Usability
Human designers can naturally grasp and implement subtle aesthetic and usability needs enhancing user experience for dealing with the systems. This knowledge is ingrained in our psychology and it will be difficult to formalize it enough for computer to allow building easy-to-use and pleasant programs.

Problem solving and Diversity
Human rational mind combined with experience and intuition effectively deals with wicked problems, which involve uncertainty, trade-offs and multiple options. Our brain machine is specialized on solving this kind of problems and it will be challenge for computer to match these capabilities. AI could use brute force at some extent as with chess, but it has limits when number of variables and things to consider increases rapidly.
Diverse talents, broad range of views and different opinions often lead to surprisingly effective solutions, sometimes impossible to predict beforehand.

Generalization and Theorizing
Human ability to generalize, create abstractions and theories based on experience, experiments, many observations and individual cases are very valuable in the software development.
We can substantially reduce complexity of the system logic and even anticipate future needs by creating abstract models and solutions, which could be applied for wide range of different situations and encapsulate theories behind our knowledge and understanding of the problem domain.

Understanding and Shared Experience
Humans will understand other humans much better than computer unless computers tap into our minds and start reading our thoughts. However, it could be scary and still not clear if AI could filter productive thought from the non sense our brains produce.
Human build together shared experience – less and less information is required to understand each other and people become more effective as a team – at the end, ideas and their flow is the most important part in software development.

Seeing The Big Picture and Thirst for Knowledge and Achievements
Many programmers have diverse background, education and knowledge – far more than required to build programs. Many solutions require to go outside of the problem context and understand broader picture. Some solutions could emerge from different problem domains and unexpected areas of human knowledge.
Human strength is irrational curiosity, thirst for knowledge and irresistible drive for achievements, which move our progress forward, open new areas of knowledge and make impossible possible. Can computers inherit this drive and motivation?

Can even super intelligent and powerful computers replicate human strengths that make us so effective programmers? There are definitely some areas that human programmers will rule for a long time. Does it mean that we could relax? I doubt it – computers are taking over more and more human programmer responsibilities and soon they will affect our jobs and replace us in some programming areas. What should modern developer do to stay competitive with gaining strength and power computer AI? This is the topic of the next post.

Part of the inhumanity of the computer is that, once it is competently programmed and working smoothly, it is completely honest. – Isaac Asimov

Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Even a super intelligent computer needs input from humans to build a program. It is great if you are a scientist or a computer professional and can provide mathematical models or algorithms. But what if you don’t know how to specify what you need from the program? Can computer really understand us? Can people trust computers to build a correct system for their needs? Will be communication with computer comfortable and effective?

We should consider four important software creation areas to answer these questions:

1. Understanding – can computer comprehend our language and complex ideas?
2. Engagement - can computer effectively involve us in communication?
3. Guiding – can computer help us to understand our needs, direct our thinking and retrieve useful information?
4. Trust – can we trust that computer will follow our human interests, obey rules and don’t do harm?

1. Understanding

Alan Turing offered the first test for computer intelligence – a computer is intelligent if you cannot distinguish in conversation this computer from human (John Searle argues in his Chinese room experiment that it is not enough). Computer should posses intelligence, master language and understand meaning of words to pass the test.

• Ludwig Wittgenstein’s Beetle and Box argument demonstrates that computers should be part of our life, social interactions and cultural context to really understand us and construct similar meaning.
• Noam Chomsky made an argument that any human language contains a limited set of organizing rules – universal grammar. Therefore, it is possible for computers to master language without learning infinite number of possible language constructions and word combinations.
• However, to posses strong AI (comparable with human) computer should learn how to go beyond initial rules and axioms provided by humans and define its own (based on Gödel incompleteness theorem). Strong AI will allow computer to be our partner in constructing new knowledge and concepts.

The best source of knowledge and potential birthplace of computer intelligence is The Internet. The father of World Wide Web, Tim Berners-Lee envisions marking web content for computer usage with special tags – The Semantic Web. However, there are some drawbacks for effective implementation: it requires extra effort, reflects limited perspective of people who tags information, difficult to identify relationships, easy to mislead and manipulate.
Tim O’Reilly supports another approach: harnessing collective intelligence (Web 2.0), where “meaning was already being encoded unconsciously by web page creators when they linked one page to another”. Google use this approach with Page Rank. The Semantic Web creates meaning for computers by adding something to web content. Web 2.0 relies on implicitly encoded meaning by millions way people use and link web content. For example, computers could identify objects and pictures based on Google Image search. I believe that combining natural language processing with Web 2.0 is the better approach for gaining computer intelligence than The Semantic Web.

How broad should be knowledge to understand humans? Good human programmers should be fluent in customer domain language, problem context and concepts far beyond programming. And it is much more than current AI advances in mastering simple rules and language of chess game, stock trading or air traffic control.

2. Engagement

In the past some people had dreams of completely automated humanless services, where people deal with machines only. It didn’t work this way – today, while automation decrease number of people on most jobs, we have more and more people in services.

There are few reasons why people prefer communication with other people and not machines:

• social - our biological necessity in social contacts – we cannot live without other people. Aristotle said that a man is a social animal.
• specialization in understanding other people – we are great in understanding non verbal signs and nuances in our expressions, especially considering that words carry of only 7% of meaning in human communication.
• empathy - other people could make us feel better, comfortable and confident.
• humor, fun and joy of conversation with other people
• same experience – we all have similar body, senses, desires and deal with the physical world around us. We share culture, history and common problems and interests

Humanoid robots could enhance communication. Rodney Brook and his team at MIT are making much progress on designing and creating realistic robots in real world contexts. Marvin Minsky even goes as far to say that human like robots such as Cog (humanoid interaction with the world) and Kismet (sociable humanoid robot) could in a sense be regarded as conscious.

We get used to anthropomorphize, admire and believe in personality of some machines like cars – the same way as we love animals. Probably, we could fight technophobia and be comfortable in communication with computers.
Our biological essence complicates matters for computers. They will experience our irrational behavior – politics, desire for power, bias, interest in personal advance, etc.

3. Guiding

Software requirements are often difficult, unclear and open-ended. In addition, people are concerned about aesthetics and usability, which are difficult to master even for human experts. Finally, people with different background, knowledge and experience will describe the same problem completely differently.
A computer should learn to distill, refine and analyze human input to retrieve useful information. A computer should build shared theory, explore and test it together with human by creating prototypes and visuals.

Another effective and natural way to describe needs is story telling and examples, but a computer could have difficulties to get clues and meaning based on verbal information only. Communication could improved by making it direct – Microsoft Research Lab is building a schema to allow computers access human brains, MIT develops device to translate the thoughts of a paralyzed person.

4. Trust

At the end, we cannot allow computers to write programs if we don’t trust them. It could be dangerous if our privacy, security, well-being and lives could be threatened by accidental or even intentional consequences of these programs. Also our morale, values and principles could be easily undermined by an insensitive computer. Could you imagine effective AI for creating porno sites, spam programs or bank hacking tools?

Isaac Azimov’s Three Laws of Robotics could become relevant.

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. A computer should understand what is good and what is bad. These questions are difficult even for humans and dependent on culture, society and situation. What computer should do if it should choose between sacrificing life of few people to potentially save many more lives?
2. A robot must obey orders given to it by human beings except where such orders would conflict with the First Law. A computer should follow orders, but it should resist to become evil instrument in bad hands. Taboos, morale and ethic principles should be embedded in its thinking.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Computer’s self-interest is important – focusing on goals, avoiding break down and preventing loss of integrity. But if we give sense of itself and intentionality, computer could become conscious and independent. Do we want it?

Summary

Computers will have tough time to interact with humans, even if we know exactly what we need, exhibit completely rational behavior and ready to cooperate with machines. But we will not – we have difficulty expressing ideas, understanding ourselves, don’t trust others, we have personality, ambitions, play political games and make mistakes. The smartest computer, which could overcome these problems will still face challenges to build convenient, reliable and useful programs. Any human programmer can confirm it. This is the topic of the next post.

Intelligence is what you use when you don’t know what to do. – Jean Piaget

Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Computers blindly follow our instructions. They are much faster than humans, but still computers are stupid things dependent on our algorithms and knowledge how to solve problems.
Even huge processing power is not enough to start programming. Non-trivial solutions require understanding of ideas, problem solving, learning from experience and much more – everything what we can define as intelligence. Can computers become smarter than human programmers?

What intelligence is required for building programs?

What is human intelligence?

We have pre-wired capabilities: large and effective brains, genetic development of specialized functional areas (vision, speech, reasoning, etc.). However, we gain our intelligence mostly through learning and experience.
Jean Piaget defined four stages for human intellectual development:
1. Sensorimotor stage: from birth to age 2 years (children experience the world through movement and senses and learn object permanence)
2. Preoperational stage: from ages 2 to 7 (acquisition of motor skills)
3. Concrete operational stage: from ages 7 to 11 (children begin to think logically about concrete events)
4. Formal operational stage: after age 11 (development of abstract reasoning).

Nobody directly puts intellect in our heads. We construct our own intellect through complex interactions with the world and other people all our life long.

Highlights:

• Memory is the basis for all intellectual activities. Memory mostly encodes the relationships between things than the details of the things themselves. It maybe exists only to make good predictions about future
• We are not only using senses and react on external stimulus (keeping car on the road), but constantly constructing an internal model of the outside world and predicting how things behave (our and other cars dynamic).
• Our emotions are important as they are brain states that quickly assign value to outcomes and provide a simple plan of action. Therefore, emotion can be viewed as a type of computation, a rapid, automatic summary that initiates appropriate actions.
• Other species are hardwired to solve particular problems, while our ability to abstract allows us to solve an open-ended series of problems.

• Interaction between senses and memory allows us to construct a qualia (properties of sensory experiences). We could create a scene in our mind and make connections with past scenes.
• With ability to narrate and long-term memory we connect conceptual systems and develop semantics (meaning) and true language. Now we can become conscious of being conscious.

Building intelligent computers.

Computers could become intelligent in several distinct ways:

1. Simulation – scan and copy brain structure and emulate with computer how brain functions.
2. Singularity – in a system complex enough, consciousness and intelligence could simply pop into existence without our participation. Star systems emerged from simple particles, biological life emerged from simple chemical elements, and new intelligence could emerge from huge number of computers and their networks.
3. Symbiosis – extension of human capabilities with machine intellect (transhuman and posthuman) using nano-, bio- and information technology advances.
4. Artificial Intelligence built by humans.

AI is the most realistic way. History of AI had few winters, one of each still continues. Despite failed grand expectations, AI brought many practical results and applications:

• Postal Services use AI to help deliver the millions of packages that pass through its transportation network every day in the most efficient way possible.
• Telecoms operators use AI to establish the quickest connections for phone calls through their networks or to retrieve web pages speedily from the Internet.
• Manufacturers and retailers use AI for optimization of their supply chains.
• Financial organization use AI to organize operations, invest in stocks, prevent fraud, and manage properties. Algorithms carry 70 % of foreign currency trades.
• A medical clinic can use AI systems to organize bed schedules, make a staff rotation, and provide medical information.
• Neural networks are used in homeland security, speech and text recognition, medical diagnosis, data mining, and e-mail spam filtering.
• Robots have become common in many industries. They are often given jobs that are considered dangerous and exhausting to humans. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly.

There are 2 main school of thoughts for building AI: Classical Theory of Mind and Connectionism.

• The classicist believes that mind is a symbolic processor, where strings are produced in sequence according to the instructions of a (symbolic) program. The mind operates by performing purely formal operations on symbols.
• The connectionist views mental processing as the dynamic, bottom up and graded evolution of activity in a neural net. It models mind as the emergent processes of interconnected networks of simple units, usually neural networks.

Conventional AI (based on classic theory)

• Expert systems (knowledge based): apply reasoning capabilities to reach a conclusion. Expert system contains some of the subject-specific knowledge, and contains the knowledge and analytical skills of one or more human experts.
• Case based reasoning: stores a set of problems and answers in an organized data structure called cases. A case based reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications. It is used in Recommendation and Decision Support Systems, Help Desk, medicine.
• Bayesian networks: used for modelling knowledge in bioinformatics, medicine, engineering, document classification, image processing, data fusion, and decision support systems.
• Behavior based AI: a modular method of building AI systems by hand.

Computational AI (based on connectionism)

• Neural networks: trainable systems with very strong pattern recognition capabilities. Neural net contains simple units, each unit’s activation depending on the connection strengths and activity of its neighbors, according to the activation function.
• Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as ‘hot’, ‘cold’, ‘warm’ and ‘boiling’.
• Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods include evolutionary algorithms (e.g., genetic algorithms) and swarm intelligence (e.g., ant algorithms).

Learning
Computers can quickly replicate and copy their intelligence as oppose to human individual learning. However, unique intelligence of each human is a strength as it adds diversity, different strategies and wide range of solutions if applied properly (Wisdom of Crowds).

The Grand Challenge: Can a computer understand?

The Computational Theory of Mind claims that the brain is a kind of computer and that mental processes are computations. If it is true we can build AI, which can match human mind (strong AI), have intelligence and can understand meaning. Otherwise, we will struggle to build better and smarter algorithms (weak AI), which will never achieve true intelligence in the human sense.

There are three serious objections to possibility of building strong AI.

• Gödel Incompleteness Theorem
• Searle’s Chinese Room Argument
• Wittgenstein’s Beetle and The Box

1. Gödel incompleteness theorem
Any effectively generated theory capable of expressing elementary arithmetic cannot be both consistent and complete. (For more explanation see here and here.)
Any mathematical or logical theory should have effective ways to validate proofs. However, each of them has some initial propositions (axioms), which assumed to be true and cannot be proved within the system. To prove them you can go outside the system and come up with new rules and axioms, but by doing so you’ll only create a larger system with its own unprovable statements. The implication is that all logical systems of any complexity are incomplete, i.e. have true statements without prove.
Modern computer is a logical system. Therefore, computer cannot have true human intelligence as it will be always limited by fixed axioms and initial rules programmed by humans, while humans can discover new axioms and rules. In other words, a computer can play chess, regulate air traffic, trade stocks based on initial programmed rules, but it cannot define new rules which are not derived from initial set.
Another interesting outcome is that we will never be able to understand ourselves, since our mind is closed system, which can only be sure of what it knows about itself by relying on what it knows about itself.

2. Chinese room argument
In his argument Searle argued against a position that computer can have strong AI, think and understand.

Suppose a human is locked in the room. He takes Chinese symbols as input, follows thick rule book on English and returns Chinese symbols as output. But the person doesn’t understand even a word of Chinese! If you replace human in Chinese room with a computer following these instructions, it can easily pass the Turing test and convince a human Chinese speaker that the program is itself a human Chinese speaker.
But computer is the mindless operator of symbols with zero understanding of language and meaning (as your dishwasher doesn’t understand what it is washing). Therefore, we can simulate mind in computer, but we cannot create computer mind that understands meaning. Computers are missing intentionality (relationship between mental acts and the external world). A computer points to other data while human intentionality points outside the brain (e.g. to real flowers).

There are interesting counter-arguments like the System Reply, which debates that in Searle’s thought experiment a human is a processor and don’t understand Chinese, but the whole system can understand. However, the Chinese room argument still holds position against strong AI from 1980.

3. Wittgenstein’s Beetle and The Box (Private Language Argument)
Wittgenstein shows that the idea of a language understandable by only a single individual is incoherent. Imagine, he says, that everyone has a small box in which they keep a beetle. However, no one is allowed to look in anyone else box, only in their own. Over time, people talk about what is in their boxes and the word “beetle” comes to stand for what is in everyone box.
Wittgenstein is trying to point out that the beetle is an analogy to an individual mind. No one can know exactly what it is like to be another person or experience things from another perspective (look in someone else box), but it is generally assumed that the mental workings of other people’s mind are very similar to our own (everyone has a beetle which is more or less similar to everyone else).
Therefore, computers should be part of our life, social interactions and cultural context to really understand us and construct similar meaning.

Summary

Deep Thought

Human intellectual capabilities are amazing and unique and human programmer will be superior comparing to AI for a long time. However, our modern civilization cannot progress without computers – we continue building better computers and require more and more from them. It is inevitable that computers will become intelligent whether it happens with our help or not. There are serious objections to possibility of building human-like “strong AI”; however, “weak AI” even today stepped in the territory previously believed to be human only. A computer needs programs and it is matter of time before they learn to program themselves and who knows what happened after this. But if computers continue to serve humans, even intelligent computer should effectively interact and understand us to write useful programs. This is a topic for the next post.

Useful Resources:
Standford Encyclopedia of Philosophy
Business By Numbers, The Economist
Great Ideas of Psychology, Teaching Company, Professor Daniel N. Robinson
Computing machinery and intelligence, Turing, A.M. (1950).
Mind, Brains, and Programs, by John R. Searle
10 Unsolved Mysteries Of The Brain, Discovery Magazine
Evolution in Your Brain, Discovery Magazine

Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Word Freak

Can computers compete with us, human programmers, in the near future? The short answer is yes – if computers will gain enough processing power, become intelligent, could effectively interact with humans, build useful programs and… still be interested to serve humans.

Computers take over more and more people jobs and areas considered exclusively human. Deep Blue beat the human best chess champion Garry Kasparov, it is not possible to win checkers against computers, they create drugs, carry 70 % of foreign currency trades and will do 50% of stock trades in 2010.

But to become better than a human programmer, a computer should compete with very powerful processing machine – our brains.

Human Brain Machine

• Human brain contains around 100 billion neurons (nerve cells).
• Output of one neuron can be transmitted to tens of thousands of other neurons via tiny structures called synapses.
• Neurons can produce brief spikes of voltage in their outer membranes – hundreds of electrical pulses per second.
• The electrical signal travels along specialized extension called axon and converted to a chemical one. Chemical signal is transmitted across the synapse, to the next neuron using small molecules called neurotransmitters (such as dopamine, serotonin and acetylcholine).
• There are other possible information couriers: glial cells (10 times as common as neurons), other kinds of signaling mechanisms between cells (gases and peptides), and the biochemical cascades that take place inside cells.
• Our brains form a million new connections for every second of our lives. The pattern and strength of the connections is constantly changing and no two brains are alike.

100 different neural networks in the human brain are performing specialized function: vision, smell, goal setting, speech, prediction, and hundreds of other tasks. Despite their different functions, these systems work together seamlessly and it is surprising that the brain coordinates its systems so rapidly. The slow speed of spikes (they travel about one foot per second in axons) is one hundred-millionth the speed of signal transmission in digital computers. Yet a human can recognize a friend almost instantaneously, while digital computers are slow—and usually unsuccessful—at face recognition. The brain is a parallel processor, running many operations at the same time. Brains are amazingly fast at comparing results of processing and deciding upon. An animal running must go left or right around a tree; it cannot do both.

There is no special anatomical location in the brain where information from all the different systems converges. The specialized areas all interconnect with one another, forming a network of parallel and recurring links. Somehow, our integrated image of the world emerges from this complex network of brain structures.

Computer Processing Power

Hans Moravec estimated that computer processing power should be around 100 million MIPS (100 TFLOPS) to match human brains. Ray Kurzweil estimation is 10,000 TFLOPS.

Today super computers exceed minimal estimation: Blue Gene has 360 TFLOPS, MDGRAPE-3 (4,808 processors) achieves more than one PetaFLOP (1,000 TFLOPS). Distributed computing enables another approach to higher performance: BOINC has 557 TFLOPS (435,000 computers), Google cluster (in 2005) had roughly around 900 TFLOPS (450,000 machines x 2,000 MFLOPS).

Ordinary processors have better and better performance: Intel 8080 had 0.5 MFLOPS in 1974, Pentium III had 1,500 MFLOPS in 1999, today Sony Playstation 3 Cell processor achieve around 230,000 MFLOPS. New Intel Polaris (80-core chip) prototype processor showed 1.2 TFLOPS.

Accelerating Change

Based on Moore’s Law and present trends affordable computer could achieve human brain processing power by 2020.
From Ray Kurzweil’s analysis:

• We achieve one Human Brain capability (2 * 10¹⁶ cps or 10 PetaFLOPS) for $1,000 around the year 2023.
• We achieve one Human Brain capability (2 * 10¹⁶ cps) for one cent around the year 2037.
• We achieve one Human Race capability (2 * 10²⁶ cps) for $1,000 around the year 2049.
• We achieve one Human Race capability (2 * 10²⁶ cps) for one cent around the year 2059.

Accelerating Change

This estimation relies on generalized interpretation of Moore’s law, with assumption that other technologies could overcome limitations of integrated semiconductor complexity. And there are some hopes for this. A quantum computer system of 500 qubits could be the equivalent of performing some operations on a classical super computer with ~10¹⁵⁰ separate processors. Molecular computers could do massive parallel processing – in 2002 a programmable molecular computing machine composed of enzymes and DNA molecules performed 330 TFLOPS.

Summary

Programmer’s brain has hundred billions neurons and one hundred trillion neural connections each operating at few hundred analog calculations per second. However, most of processing is dedicated to life support functions, not information processing. Human neural networks are much slower than modern digital processors, but have an amazing efficiency for parallel processing, aggregating and deciding upon results.

Probably, mass computers will achieve comparable processing power by the year 2020. But does it automatically mean that they can compete with human programmers?
There are technical challenges like improving computers parallel and distributed processing and algorithms. But the main challenge is that computers should become intelligent to write programs – understand problem space, people and come up with solutions. This is a topic of the next part.

Interesting resources:
The brain in a nutshell, Neurophilosophy
10 Unsolved Mysteries Of The Brain, Discovery Magazine
When will computer hardware match the human brain?, Hans Moravec
Law of Accelerating Returns, Ray Kurzweil