Artificial Intelligence: The Technology that Changes Life ... · call “unconscious competents.” In other words, they knew how to make the right decisions very well, but they were

Artificial Intelligence: The Technology that Changes Life as We Know It, Page 1

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Table of Contents

Where Do We Stand with Artificial Intelligence Today? ............................... 3

Artificial Intelligence and Human Intelligence Will Remain Quite Different for a Long, Long Time .......... 4

The Shift from Knowledge Based Systems to Self-Learning Systems ....... 5

Machine Learning Templates Are Designed for the Task at Hand ............ 6

ML Will Become Pervasive in Every Aspect of Our Lives ............................... 8

The Moral, Privacy, and Legal Issues are Still Unresolved ............................. 10

References ............................................ 12


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A r t i f i c i a l Intelligence: Techno logy that Changes L i f e a s W e K n o w I t

Where Do We Stand with Artificial Intelligence Today? Artificial Intelligence (AI) deals with the creation of software that can learn. This learning proceeds by reviewing extremely large amounts of raw data about the situation at hand and developing classifications and “insights” about the data. Once the software has “learned” about its domain or area of interest, it can tackle new problems in the same area. The software discovers patterns in the data – patterns that people experienced in the field may never pick up on. What is remarkable – and so very, very valuable – is that these artificial intelligence applications now usually outperform the human experts in their areas of expertise: One law firm has “recruited” an artificial intelligence system that focuses on one aspect of the law. The system has “read” all the law and “understands” the context in which the law is applicable. It has proven to be an outstanding contributor to the success of the firm.


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Companies that write computer games are now using AI to accelerate the detailed design and construction of those games. This is particularly valuable because games have become so complex that it takes programmers working manually years to complete a game for commercial release. AI systems can handily beat the experts in chess and Go (Fig 1). They can diagnose cancers that might go undetected otherwise. A team at the University of Toronto was successful in developing new drugs even though none of the team members had any background in drugs, chemistry, or the life sciences! They did it simply by applying a deep

learning template to masses of drug related data.

Artificial Intelligence and Human Intelligence Will Remain Quite Different for a Long, Long Time AI applications today are all application specific In other words, scientists have developed one application to play Go (Fig 1) and another to drive cars and a third to translate languages. However, there is no AI system under development that will integrate all of these application specific systems into a unified application that takes a holistic view of the world as people do.

Figure1https://www.youtube.com/watch?v=g-dKXOlsf98


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In spite of the increasingly relative poor performance of people in those domains where AI applications excel, people still take a holistic view of the world that AI cannot. People understand the implications that their decisions in one part of their lives will have on other parts of their lives. For example, a manager may see that his company is about to make a mistake and he wants to argue against the decision. But he also recognizes that this argument could jeopardize his career and the welfare of his family because top management is already committed. No AI system is able to look at this bigger picture. And it is unlikely that AI will be able to see these “big picture” issues for quite a while.

The Shift from Knowledge Based Systems to Self-Learning Systems In the early days of Artificial Intelligence, the field was geared to collecting the knowledge of recognized experts and codifying that knowledge into algorithms that replicated the way that those experts thought. This meant that “knowledge engineers” would need to work closely with experts to understand every decision she made in each specific

case. As the cases became more complex, the explanations would also become more complex. In fact, the experts occasionally had some difficulty providing rational arguments that justified some of their decisions. They were what some psychologists call “unconscious competents.” In other words, they knew how to make the right decisions very well, but they were at a loss to explain how they made their decisions. Further, this process was very time consuming and expensive. Knowledge engineers had to spend months working with experts to learn how they made their decisions and then codify those decision-making processes in ways that could be translated into algorithms and, eventually, a piece of computer code that would replicate what the expert would do. When new problems came up that the system had not been programmed to address, it was at a total loss. These early AI systems did not have the ability to learn. Knowledge was hardcoded into computer code. Eventually, AI practitioners developed a novel approach they called “Machine Learning.” (Fig 2) The fundamental idea behind Machine Learning was that the AI scientists would develop general purpose algorithms that had no domain expertise at all. Instead, these algorithms would accept huge


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quantities of unstructured data, analyze it, and develop its own set of rules for handling the work at hand. This relieved the AI practitioners from being obliged to have any domain expertise whatsoever. Their algorithms literally extracted the knowledge they needed from the huge quantities of data fed to those algorithms. This transition from developing algorithms that capture the expertise of experts to self-learning led to a field called “Machine Learning.” (Fig 2) In Machine Learning, algorithms with no domain expertise review and process In fact, there was a standing joke in one project team focused on language translation. The team consisted of both computer scientists and linguists. The team leader quipped that “the quality of our language translation program improves every time a linguist leaves the team!” The message was clear. The team working on a domain specific project did not need to have domain specific knowledge. The team needed the skill to tune their algorithms to allow the machine to learn the linguistic rules based on the huge quantities on information fed to it. In fact, when you come to think of it, this is how babies learn language: Babies listen to others speak language all the time for a few years and then learn how to express their own ideas in that language.

Machine Learning Templates Are Designed for the Task at Hand Machine Learning (ML) is a specialized area in Artificial Intelligence that has grown to dominate the AI field.

Machine Learning has led to the development of dozens of models or templates. Each template is useful and appropriate for dealing with a particular problem. Choosing the right template – or developing a new template if need be – is key to developing an application that is useful. We will list a few of the most common types of templates. The classification template – This template is useful for categorizing each piece of data. For example, the purpose of a credit scoring application is to sort loan applicants into two categories – low-risk applicants and high-risk applicants. Lending

Figure2WhatisMachineLearning?(https://www.youtube.com/watch?v=WXHM_i-fgGo)


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institutions can use this ML application to decide who is credit worthy and who is not. Pattern recognition – The easiest pattern recognition problem is reading bar codes. Reading characters written for optical character recognition is somewhat more complex, but not much. Recognizing hand written characters is more difficult. Non-linear models – These models are clearly more complex than their linear cousins. Non-linear models are useful, for example, in determining the price of used cars. Interestingly, the selling prices of used cars do not drop by a fixed amount every year; rather they drop about 15% a year. Projecting the selling price of a used car after several years, then, requires a non-linear model. Generative model – This model is based on the idea that there are hidden factors that cause the phenomenon we observe. The generative model works with very large quantities of data to determine what those previously hidden factors are and how they lead to the observable phenomenon. For example, we could observe a patient with a runny nose. Our generative model could suggest that the patient has the flu and the flu causes the runny nose (with a certain probability). Diagnostics operates in the opposite direction. Having a runny

nose helps a clinician diagnose that the patient has the flu (with a certain probability). Face recognition – Faces are almost two dimensional. Faces are more complex that characters (discussed above). And pictures of faces are taken from various angles and with varying levels of lighting. In other words, face recognition is a far more complex problem and requires its own template. Speech recognition – Recognizing speech is probably an even more complex problem. The template needs to recognize the phonemes that make up the sounds of the words and then assemble those phonemes to comprise an entire word. This process is complicated by the fact that phonemes change with people of various ages, different sexes, varying education, and varying regional backgrounds. Natural language translation – This is one of the most complex templates. Experience shows that the best way for an ML app to learn a language is to process large quantities of samples of language. The best apps are able to determine the deep meaning or abstract meaning of a sentence. If we do this for two languages and we can correlate the sentences in the two languages, a natural language translation app can move from the words in one language to the deep


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meaning which is language independent to the words in another language. Legal and legislative documents written in both English and French provide ample material for this type of learning between English and French. The European Union maintains many pieces of legislation and regulations in all the official EU languages. Again, this provides natural language translation practitioners with the large volumes of material for their apps to learn the languages.

Decision trees – These templates are based on simple if-then logic. For example, a model that determines the credit worthiness of a client may require approved applicants to have earnings and savings above some given threshold. In this case, the first if-then test would examine earnings. If the earnings were above the threshold, the model would continue its assessment; otherwise it would reject

the applicant. Continuing the assessment would likely mean applying an if-then test to savings. If the applicant’s savings were too low, the application would be rejected; otherwise it would be accepted. This is a particularly simple template and should be one of the first templates ML practitioners should consider when tackling a new domain. There are dozens of other machine learning templates. Each template is designed to handle a particular type of problem and reasoning algorithm. A few other templates are active learning, rank ordering, Bayesian methods, neural networks, deep learning, connectionist models, auto-encoder neural networks, and temporal difference learning. There is no doubt that Machine Learning practitioners will develop more specialized templates during the next few years.

ML Will Become Pervasive in Every Aspect of Our Lives Health monitoring applications that run on wearable devices such as an Apple Watch will connect with machine learning applications and databases that operate in the cloud. This gives

Figure3Google’sMultilingualNeuralMachineTranslationProgram(https://www.youtube.com/watch?v=nR74lBO5M3s)


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our local, limited devices access to the most powerful computers and machine learning applications available. This leads to a qualitative change in the health monitoring applications we will use on a daily basis. Within a few years we will use machine learning based search engines that carry out searches using more sophisticated techniques than key words. Further, those searches are going to search many data repositories – not just the repository offered by one company like Google. Human Resources staff will increasingly use machine learning applications to

select candidates to interview and who to offer positions to. Health care professionals will monitor the signals patient devices send in the course of everyday living and make appointments if they see reason for concern. AI is rapidly developing language translation systems that will read one language, determine its “deep meaning” which is language independent, and then translate that idea into another language in a form that that is not only grammatically correct, but idiomatic as well. These translation programs have trouble with satire and sarcasm, but that could

Figure4GetReadyforHybridThinking(https://www.ted.com/talks/ray_kurzweil_get_ready_for_hybrid_thinking)


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change. ML applications regularly monitor credit card transactions looking for unusual transactions that could well be fraudulent. Since these applications run in real time, they can deny a transaction before it is completed and call a human to take a closer look. Large stock trading companies use ML to monitor stock prices along with a variety of other parameters and make buy/sell decisions for stocks in its portfolios. ML drives the decisions without human intervention. Chat Bots – or applications that can speak – will be used in customer service centers to answer customer calls, understand their issues, and take appropriate action. Customers will never have to wait “on hold” because a Chat Bot will always be available. These Chat Bots will have direct access to relevant databases and can trigger corrective action. Some scientists see an eventual hybrid between biological and non-biological thinking. In this scenario, our human brains will be able to instantly access networks of ML computers in the Cloud to assist in some intensive, short term thinking exercise. But the non-biological portion will grow exponentially over time! The implications of hybrid thinking are staggering. (Fig 4)

Clearly, this list could fill volumes.

The Moral, Privacy, and Legal Issues are Still Unresolved Data privacy and security is becoming increasingly important in the use on machine learning applications. People provide their information for very specific purposes – such as applying for a mortgage. They provide that information with the specific understanding that it will not be used for any other purpose. However, we’ve seen that organizations can make huge leaps in understanding their fields of interest by applying machine learning to data that was collected for other purposes. Of course, they try to do so in a way that protects each individual’s privacy. Unfortunately, experience shows that clever analysts who have access to different databases can uncover the identity of each person in the sample. Machine Learning applications are developing so fast and are being integrated into routine business operations so rapidly that society has not had time to develop a body of law dealing with the recommendations or predictions and decisions that this technology makes.


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We are now using Machine Learning systems to advise on who a company should hire, which convict is more likely to reoffend if released, and which news items should we show a viewer. But these decisions have no determinate answers; they are subjective and require judgment. We have no benchmarks to guide us in messy human affairs. To make things worse, Machine Learning apps are opaque – no one understands how they operate. This means that these systems can build in biases and none of the users would be aware of those biases. We cannot afford to outsource decision

making in murky areas or on moral issues to computers.

Figure5MachineIntelligenceMakesHumanMoralMoreImportant(https://www.ted.com/talks/zeynep_tufekci_machine_intelligence_makes_human_morals_more_important)


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References Machine Learning for Beginners: An Introduction to Artificial Intelligence and Machine Learning, John Slavio Artificial Intelligence: Understanding A.I. and the Implications of Machine Learning Machine Learning: The Ultimate Guide for Beginners and Starters (Artificial Intelligence, Algorithms, Data Science, Machine Learning for Beginners), Andy Grey Machine Learning: The New A.I., Ethem Alpayden Get Ready for Hybrid Thinking, Ray Kurzweil, TED Talk, https://www.ted.com/playlists/310/talks_on_artificial_intelligen Machine Intelligence Makes Human Morals More Important, Zeynep Tufekci, TED Talk https://www.ted.com/playlists/310/talks_on_artificial_intelligen , Jeremy Howard, TED Talk,

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Artificial Intelligence: The Technology that Changes Life ... · call “unconscious competents.” In other words, they knew how to make the right decisions very well, but they were