Many-minds interpretation (of Quantum Mechanics)

From Wikipedia, the free encyclopedia

The many-minds interpretation of quantum mechanics extends the many-worlds interpretation by proposing that the distinction between worlds should be made at the level of the mind of an individual observer. The concept was first introduced in 1970 by H. Dieter Zeh as a variant of the Hugh Everett interpretation in connection with quantum decoherence,^[1] and later (in 1981) explicitly called a many or multi-consciousness interpretation. The name many-minds interpretation was first used by David Albert and Barry Loewer in 1988.^[2]

History

Interpretations of quantum mechanics

The various interpretations of quantum mechanics typically involve explaining the mathematical formalism of quantum mechanics, or to create a physical picture of the theory. While the mathematical structure has a strong foundation, there is still much debate about the physical and philosophical interpretation of the theory. These interpretations aim to tackle various concepts such as:

1. Evolution of the state of a quantum system (given by the wavefunction), typically through the use of the Schrödinger equation. This concept is almost universally accepted, and is rarely put up to debate.
2. The measurement problem, which relates to what we call wavefunction collapse – the collapse of a quantum state into a definite measurement (i.e. a specific eigenstate of the wavefunction). The debate on whether this collapse actually occurs is a central problem in interpreting quantum mechanics.

The standard solution to the measurement problem is the "Orthodox" or "Cophenhagen" interpretation, which claims that the wave function collapses as the result of a measurement by an observer or apparatus external to the quantum system. An alternative interpretation, the Many-worlds Interpretation, was first described by Hugh Everett in 1957^[3][4] (where it was called the relative state interpretation, the name Many-worlds was coined by Bryce Seligman DeWitt starting in the 1960s and finalized in the 70s^[5]). His formalism of quantum mechanics denied that a measurement requires a wave collapse, instead suggesting that all that is truly necessary of a measurement is that a quantum connection is formed between the particle, the measuring device, and the observer.^[4]

The many-worlds interpretation

In the original relative state formulation, Everett proposed that there is one universal wavefunction that describes the objective reality of the whole universe. He stated that when subsystems interact, the total system becomes a superposition of these subsystems. This includes observers and measurement systems, which become part of one universal state (the wavefunction) that is always described via the Schrödinger Equation (or its relativistic alternative). That is, the states of the subsystems that interacted become "entangled" in such a way that any definition of one must necessarily involve the other. Thus, each subsystem's state can only be described relative to each subsystem with which it interacts (hence the name relative state).
This has some interesting implications. For starters, Everett suggested that the universe is actually indeterminate as a whole. To see this, consider an observer measuring some particle that starts in an undetermined state, as both spin-up and spin-down, for example - a superposition of both possibilites. When an observer measures that particle's spin, however, it always registers as either up or down. The problem of how to understand this sudden shift from "both up and down" to "either up or down" is called the Measurement problem. According to the many-worlds interpretation, the act of measurement forced a “splitting” of the universe into two states, one spin-up and the other spin-down, and the two branches that extend from those two subsequently independent states. One branch measures up. The other measures down. Looking at the instrument informs the observer which branch she's on, but the system itself is indeterminate at this and, by logical extension, presumably any higher level.

The “worlds” in the many worlds theory is then just the complete measurement history up until and during the measurement in question, where splitting happens. These “worlds” each describe a different state of the universal wave function and cannot communicate. There is no collapse of the wavefunction into one state or another, but rather you just find yourself in the world leading up to what measurement you have made and are unaware of the other possibilities that are equally real.

The many-minds interpretation

The many-minds interpretation of quantum theory is many-worlds with the distinction between worlds constructed at the level of the individual observer. Rather than the worlds that branch, it is the observer’s mind.^[6]

The purpose of this interpretation is to overcome the fundamentally strange concept of observers being in a superposition with themselves. In their 1988 paper, Albert and Loewer argue that it simply makes no sense for one to think of the mind of an observer to be in an indefinite state. Rather, when someone answers the question about which state of a system they have observed, they must answer with complete certainty. If they are in a superposition of states, then this certainty is not possible and we arrive at a contradiction.^[2] To overcome this, they then suggest that it is merely the “bodies” of the minds that are in a superposition, and that the minds must have definite states that are never in superposition^[2]

When an observer measures a quantum system and becomes entangled with it, it now constitutes a larger quantum system. In regards to each possibility within the wave function, a mental state of the brain corresponds. And ultimately, only one mind is experienced, leading the others to branch off and become inaccessible, albeit real.^[7] In this way, every sentient being is attributed with an infinity of minds, whose prevalence correspond to the amplitude of the wavefunction. As an observer checks a measurement, the probability of realizing a specific measurement directly correlates to the number of minds they have where they see that measurement. It is in this way that the probabilistic nature of quantum measurements are obtained by the Many-minds Interpretation.

Quantum non-locality in the many-minds interpretation

The body remains in an indeterminate state while the minds picks a stochastic result.

Now, consider an experiment where we are measuring the polarization of two photons. When the photon is created it has an indeterminate polarization. If a stream of these photons is passed through a polarization filter, 50% of the light is passed through. This corresponds to each photon having a 50% chance of aligning perfectly with the filter and thus passing, or being misaligned (by 90 degrees relative to the polarization filter) and being absorbed. Quantum mechanically, this means the photon is in a superposition of states where it is either passed or observed. Now, consider the inclusion of another photon and polarization detector. Now, the photons are created in such a way that they are entangled. That is, when one photon takes on a polarization state, the other photon will always behave if it has the same polarization. For simplicity, take the second filter to either be perfectly aligned with the first, or to be perfectly misaligned ( 90 degree difference in angle, such that it is absorbed). If the detectors are aligned, both photons are passed (i.e. we say they agree). If they are misaligned, only the first passes and the second is absorbed (now they disagree). Thus, the entanglement causes perfect correlations between the two measurements - regardless of separation distance, making the interaction non-local. This sort of experiment is further explained in Tim Maudlin's Quantum Non-Locality and Relativity,^[8] and can be related to Bell test experiments. Now, consider the analysis of this experiment from the many minds point of view:

No sentient observer

Consider the case where there is no sentient observer, i.e. no mind around to observe the experiment. In this case, the detector will be in an indefinite state. The photon is both passed and absorbed, and will remain in this state. The correlations are withheld in that none of the possible "minds", or wave function states, correspond to non correlated results.^[8]

One sentient observer

Now expand the situation to have one sentient being observing the device. Now, he too enters the indefinite state. His eyes, body, and brain are seeing both spins at the same time. The mind however, stochastically chooses one of the directions, and that is what the mind sees. When this observer goes over to the second detector, his body will see both results. His mind will choose the result that agrees with the first detector, and the observer will see the expected results. However, the observer's mind seeing one result does not directly affect the distant state - there is just no wave function in which the expected correlations do not exist. The true correlation only happens when he actually goes over to the second detector.^[8]

Two sentient observers

When two people look at two different detectors that scan entangled particles, both observers will enter an indefinite state, as with one observer. These results need not agree – the second observer's mind does not have to have results that correlate with the first's. When one observer tells the results to the second observer, their two minds cannot communicate and thus will only interact with the other's body, which is still indefinite. When the second observer responds, his body will respond with whatever result agrees with the first observer's mind. This means that both observer's minds will be in a state of the wavefunction that always get the expected results, but individually their results could be different.^[8]

Locality of the many-minds interpretation

As we have thus seen, any correlations seen in the wavefunction of each observer's minds are only concrete after interaction between the different polarizers. Even though the correlations on the level of individual minds correspond to the appearance of non-locality (or equivalently, violation of Bell's inequality). However, since the interactions only take place in individual minds they are local, since there is no real interaction between space-like separated events that could influence the minds of observers at two distant points. This, like the many worlds theory, makes the many-minds theory completely local.^[8]

Support

There is currently no empirical evidence for the many-minds interpretation. However, there are theories that do not discredit the many-minds interpretation. In light of Bell’s analysis of the consequences of quantum non-locality, empirical evidence is needed to avoid inventing novel fundamental concepts (hidden variables).^[9] Two different solutions of the measurement problem then appear conceivable: von Neumann’s collapse or Everett’s relative state interpretation.^[10] In both cases a (suitably modified) psycho-physical parallelism can be re-established.

Since conscious awareness has to be coupled with local physical systems, the observer’s physical environment has to interact and can influence the brain. The brain itself must have some physico-chemical processes that affect the states of awareness. If these neural processes can be described and analyzed then some experiments could potentially be created to test whether affecting neural processes can have an effect on a quantum system. Speculation about the details of this awareness-local physical system coupling on a purely theoretical basis could occur, however experimentally searching for them through neurological and psychological studies would be ideal.^[11]

When considering psycho-physical parallelism, superpositions appear rich enough to represent primitive conscious awareness. It seems that quantum superpositions have never been considered, for example, in neuronal models, since only classical states of definite neuronal excitation are usually taken into account. These quasi-classical states are also measured by external neurobiologists. Quantum theory would admit their superpositions, too, thus giving rise to a far greater variety of physical states which may be experienced by the subjective observer. When used for information processing, such superpositions would now be called “quantumbits” or qubits. As demonstrated by M. Tegmark, they can not be relevant for neuronal and similar processes in the brain.^[12]

Objections

Objections that apply to the Many-worlds Interpretation also apply to the Many-minds Interpretation. On the surface both of these theories arguably violate Occam's Razor; proponents counter that in fact these solutions minimize entities by simplifying the rules that would be required to describe the universe.

Nothing within quantum theory itself requires each possibility within a wave function to complement a mental state. As all physical states (i.e. brain states) are quantum states, their associated mental states should be also. Nonetheless, it is not what we experience within physical reality. Albert and Loewer argue that the mind must be intrinsically different than the physical reality as described by quantum theory.^[6] Thereby, they reject type-identity physicalism in favour of a non-reductive stance. However, Lockwood saves materialism through the notion of supervenience of the mental on the physical.^[7]

Nonetheless, the Many-minds Interpretation does not solve the mindless hulks problem as a problem of supervenience. Mental states do not supervene on brain states as a given brain state is compatible with different configurations of mental states.^[13]

Another serious objection is that workers in No Collapse interpretations have produced no more than elementary models based on the definite existence of specific measuring devices. They have assumed, for example, that the Hilbert space of the universe splits naturally into a tensor product structure compatible with the measurement under consideration. They have also assumed, even when describing the behaviour of macroscopic objects, that it is appropriate to employ models in which only a few dimensions of Hilbert space are used to describe all the relevant behaviour.

Furthermore, as the Many-minds Interpretation is corroborated by our experience of physical reality, a notion of many unseen worlds and its compatibility with other physical theories (i.e. the principle of the conservation of mass) is difficult to reconcile.^[6] According to Schrödinger's equation, the mass-energy of the combined observed system and measurement apparatus is the same before and after. However, with every measurement process (i.e. splitting), the total mass-energy would seemingly increase^[14]

Peter J. Lewis argues that the Many-minds Interpretation of quantum mechanics has absurd implications for agents facing life-or-death decisions.^[15]

In general, the Many-minds theory holds that a conscious being who observes the outcome of a random zero-sum experiment will evolve into two successors in different observer states, each of whom observes one of the possible outcomes. Moreover, the theory advises you to favour choices in such situations in proportion to the probability that they will bring good results to your various successors. But in a life-or-death case like getting into the box with Schrödinger's cat, you will only have one successor, since one of the outcomes will ensure your death. So it seems that the Many-minds Interpretation advises you to get in the box with the cat, since it is certain that your only successor will emerge unharmed. See also quantum suicide and immortality.

Finally, it supposes that there is some physical distinction between a conscious observer and a non-conscious measuring device, so it seems to require eliminating the strong Church–Turing hypothesis or postulating a physical model for consciousness.

Machine learning

From Wikipedia, the free encyclopedia

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.^[1]

The name machine learning was coined in 1959 by Arthur Samuel.^[2] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,^[3] machine learning explores the study and construction of algorithms that can learn from and make predictions on data^[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,^[5]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,^[6] optical character recognition (OCR),^[7] learning to rank, and computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,^[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.^[5]:vii[9] Machine learning can also be unsupervised^[10] and be used to learn and establish baseline behavioral profiles for various entities^[11] and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.^[12]

Effective machine learning is difficult because finding patterns is hard and often not enough training data are available; as a result, machine-learning programs often fail to deliver.^[13][14]

Overview

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."^[15] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".^[16] In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

Machine learning tasks

Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system:

• Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback:
  • Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing.
  • Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
  • Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.^[5]:3
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

Machine learning applications

A support vector machine is a classifier that divides its input space into two regions, separated by a linear boundary. Here, it has learned to distinguish black and white circles.

Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:^[5]:3

• In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam".
• In regression, also a supervised problem, the outputs are continuous rather than discrete.
• In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
• Density estimation finds the distribution of inputs in some space.
• Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

History and relationships to other fields

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM^[17]. As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.^[18] Probabilistic reasoning was also employed, especially in automated medical diagnosis.^[19]:488

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.^[19]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.^[20] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.^{[19]:708–710; 755} Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.^[19]:25

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.^[20] It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.^[21]

Relation to statistics

Machine learning and statistics are closely related fields. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.^[22] He also suggested the term data science as a placeholder to call the overall field.^[22]
Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model,^[23] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.^[24]

Theory

A core objective of a learner is to generalize from its experience.^[25][26] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.^[27]

In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

Approaches

Decision tree learning

Decision tree learning uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item's target value.

Association rule learning

Association rule learning is a method for discovering interesting relations between variables in large databases.

Artificial neural networks

An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is vaguely inspired by biological neural networks. Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Deep learning

Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.^[28]

Inductive logic programming

Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.

Support vector machines

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Clustering

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters. Other methods are based on estimated density and graph connectivity. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.

Bayesian networks

A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning.

Reinforcement learning

Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.

Representation learning

Several learning algorithms, mostly unsupervised learning algorithms, aim at discovering better representations of the inputs provided during training. Classical examples include principal components analysis and cluster analysis. Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse (has many zeros). Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors.^[29] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.^[30]

Similarity and metric learning

In this problem, the learning machine is given pairs of examples that are considered similar and pairs of less similar objects. It then needs to learn a similarity function (or a distance metric function) that can predict if new objects are similar. It is sometimes used in Recommendation systems.

Sparse dictionary learning

In this method, a datum is represented as a linear combination of basis functions, and the coefficients are assumed to be sparse. Let x be a d-dimensional datum, D be a d by n matrix, where each column of D represents a basis function. r is the coefficient to represent x using D. Mathematically, sparse dictionary learning means solving ${\displaystyle x\approx Dr}$ where r is sparse. Generally speaking, n is assumed to be larger than d to allow the freedom for a sparse representation.

Learning a dictionary along with sparse representations is strongly NP-hard and also difficult to solve approximately.^[31] A popular heuristic method for sparse dictionary learning is K-SVD.

Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine which classes a previously unseen datum belongs to. Suppose a dictionary for each class has already been built. Then a new datum is associated with the class such that it's best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.^[32]

Genetic algorithms

A genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms found some uses in the 1980s and 1990s.^[33][34] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.^[35]

Rule-based machine learning

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.^[36] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Learning classifier systems

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.^[37]

Applications

Applications for machine learning include:

• Agriculture
• Automated theorem proving^[38][39]
• Adaptive websites^{[citation needed]}
• Affective computing
• Bioinformatics
• Brain–machine interfaces
• Cheminformatics
• Classifying DNA sequences
• Computational anatomy
• Computer Networks
• Telecommunication
• Computer vision, including object recognition
• Detecting credit-card fraud
• General game playing^[40]
• Information retrieval
• Internet fraud detection^[27]
• Linguistics
• Marketing
• Machine learning control
• Machine perception
• Medical diagnosis
• Economics
• Insurance
• Natural language processing
• Natural language understanding^[41]
• Optimization and metaheuristic
• Online advertising
• Recommender systems
• Robot locomotion
• Search engines
• Sentiment analysis (or opinion mining)
• Sequence mining
• Software engineering
• Speech and handwriting recognition
• Financial market analysis
• Structural health monitoring
• Syntactic pattern recognition
• Time series forecasting
• User behavior analytics
• Translation^[42]

In 2006, the online movie company Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.^[43] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.^[44]

In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of Machine Learning to predict the financial crisis. ^[45]

In 2012, co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.^[46]

In 2014, it has been reported that a machine learning algorithm has been applied in Art History to study fine art paintings, and that it may have revealed previously unrecognized influences between artists.^[47]

Model assessments

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the N-fold-cross-validation method randomly splits the data in k subsets where the k-1 instances of the data are used to train the model while the kth instance is used to test the predictive ability of the training model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.^[48]

In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model’s diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver operating characteristic (ROC) and ROC’s associated Area Under the Curve (AUC).

Ethics

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.^[49] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.^[50][51] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Because language contains biases, machines trained on language corpora will necessarily also learn bias.^[52]

Designer baby

From Wikipedia, the free encyclopedia

A designer baby is a human embryo which has been genetically modified, usually following guidelines set by the parent or scientist, to produce desirable traits. This is done using various methods, such as germline engineering or preimplantation genetic diagnosis (PGD). This technology is the subject of ethical debate, bringing up the concept of genetically modified "superhumans" to replace modern humans.

Preimplantation genetic diagnosis

In medicine and (clinical) genetics pre-implantation genetic diagnosis (PGD or PIGD) (also known as embryo screening) is a procedure performed on embryos prior to implantation, sometimes even on oocytes prior to fertilization. The methods of PGD help to identify and locate genetic defects in early embryos that were conceived through in vitro fertilization (IVF).^[1] The IVF procedure is carried out by the removal of one or two cells when the embryo is at a specific stage in development. The PGD uses the IVF technique to obtain oocytes or embryos for evaluation of the organism's genome.

The PGD procedures allow scientists to identify damaged or mutated genes associated with diseases in the oocytes or embryos by using in-situ hybridization (ISH).^[2] The ISH technique labels specific nucleic acid sequences on a gene that can help detect genetic abnormalities.^[3]

Conversely, this technique can also help select for desirable traits by avoiding implanting embryos with genes that have serious diseases or disabilities. Examples of desirable traits that could be selected would be increased muscle mass, voice pitch, or high intelligence. Overall, the procedure of PGD to select for a positive trait is referred to the creation of a "designer baby".^[2]

This is not a new technology - the first PGD babies, and thus also the first designer babies were created in 1989 and born in 1990.^[4]

A 2012 article by Carolyn Abraham in The Globe and Mail stated that "Recent breakthroughs have made it possible to scan every chromosome in a single embryonic cell, to test for genes involved in hundreds of 'conditions,' some of which are clearly life-threatening while others are less dramatic and less certain". There is already a "microchip that can test a remarkable 1,500 genetic traits at once, including heart disease, seasonal affective disorder, obesity, athletic ability, hair and eye color, height, susceptibility to alcohol and nicotine addictions, lactose intolerance and one of several genes linked to intelligence. It is still difficult to get enough DNA for such extensive testing but the chip designer thinks this technical problem will be solved soon.^[5]

Regulation of Preimplantation Genetic Diagnosis

PGD has been used primarily for medical purposes, but as the possibilities of the procedure increase the idea of non-medical uses has become a popular topic of debate. Non-medical motivations could lead to potential problems when trying to make the distinction of when the procedure is needed or desired.

For example, PGD has the ability to select an embryo based on gender preferences (Stankovic). Since changing a gender is not needed, but desired this could cause much controversy. Additionally, the procedure is able to create a donor offspring or a “savior sibling”, which can assist a pre-existing offspring for medical purposes.^[2] The “savior sibling” is a brother or sister that is created to donate life-saving tissue to an existing child.^[6] There has been arguments against the procedures of “savior siblings” because many believe that this will lead humans closer to the creation of designer babies. For example, one critique said, “the new technique is a dangerous first step towards allowing parents to use embryo testing to choose other characteristics of the baby, such as eye colour and sex”.^[7]

The artificial selection of traits through the use of PGD has become a widely debated topic and governments have started to regulate this procedure.

Many countries completely prohibit PGD, including Austria, Germany, Ireland, and Switzerland. Other countries restrict PGD to medical use only, including Belgium, France, Greece, Netherlands, Italy, Norway, and the United Kingdom.

In contrast, the United States federal law does not have any regulation of PGD. Those who are in favor of PGD believe the government should not be involved in the procedure and parents should have a reproductive choice. The opposing side has argued that PGD will allow embryo selection based on trivial traits. While other critics believe that this procedure could lead to a new form of Eugenics.^[1]

The regulation of PGD has become an important topic, however much of the artificial trait selection remains only prospective until technology advances. For example, scientist do not know which specific gene is associated with specific traits, like voice pitch or intelligence. Nevertheless, with the current rate of technological advancements, it is believed that in the next twenty years the artificial selection of desirable traits will exist.^[2]

Genetic engineering of human gametes, zygotes, or embryos (a.k.a. germline modification)

The other use for designer babies concerns possible uses of gene therapy techniques to create desired traits of a child, such as disease resistance, sex, hair color and other cosmetic traits, athletic ability, and intelligence.^[8]

Understanding of genetics for human traits

Genetics explains the process of parents passing down certain genes to their children. Genes are inherited from both biological parents, and each gene expresses a specific trait. The traits expressed by genes can be something physically seen—such as hair color, eye color, or height—or can be things such as diseases and disorders.^[9]

Human genes are found within chromosomes. Humans have 23 pairs of chromosomes, 46 individual chromosomes. 23 chromosomes are inherited from the father, and 23 from the mother. Each chromosome can carry about 20,000 genes.^[9]

Researchers have already connected the genes in the striped zebra fish which control the colour of the fish to genes in humans that determine skin colour.^[10] Many other things could be discovered in further years especially with the new possibilities of cloning animals.

Scientists have been able to better understand the genetic traits of humans through projects such as The Human Genome Project. This project was launched around 1990 and was an international research project that had an end goal of mapping and understanding every gene in the human body.^[11] As a part of the Human Genome Project, we have been able to pin point specific locations for about 12,800 specific genes within different chromosomes.^[9]

Germline modification

Germline modification has been around since the 1980s, as there have been successful animal trials dating back to that time.^[12] In order for germline modification to be successful, medical professionals must know how to introduce a gene into the patient's’ cell and the germline so that it will be transferred subsequent generations and still maintain the proper functionality.^[13] The way in which genes are integrated into the DNA is what determines that difference between germline modification and somatic cell modification.^[14] In order to be transferred to subsequent generations, these changes need to be carried out through the development of germ cells.^[15] Changes in the germline result in permanent and heritable changes to the DNA.^[14] While amplification of positive effects would occur, there is also the risk that amplification of possible negative effects would also occur.^[15] Since the results are generational, it is more complicated to study the long-term effects and therefore it is not a simple task to figure out if the benefits of germline modification outweigh the harm.^[15] Allowing families to have the ability to design their children and select for desirable traits is another major concern that germline modification presents.^[15]

Germline modification can be accomplished through different techniques that focus on modification of the germinal epithelium, germ cells, or the fertilized egg.^[14] Most of the techniques include transporting transgenes and then the transgenes are integrated with the DNA of the zygote.^[12] After integration, the transgene becomes a stable and functioning portion of the host’s genome.^[12] One technique involves a specific sequence of cloned DNA being inserted into the fertilized egg using the microinjection technique.^[14] The sequence is inserted directly into the pronucleus. The second technique uses the transfection process. Stem cells obtained from the embryo during the blastocysts stage are modified, combined with naked DNA, and the resulting cell is reinserted into the embryo that is developing.^[14] The third technique focuses on carrying DNA into the embryo by using retroviruses.^[14]

Feasibility of gene therapy

Gene therapy is the use of DNA as a pharmaceutical agent to treat disease. Gene therapy was first conceptualized in 1972, with the authors urging caution before commencing gene therapy studies in humans.^[16] The first FDA-approved gene therapy experiment in the United States occurred in 1990, on a four-year-old girl named, Ashanti DeSilva, she was treated for ADA-SCID.^[17][18] This is a disease that had left her defenseless against infections spreading throughout her body. Dr. W French Anderson was a major lead on this clinical trial, he worked for the National Heart, Lung, and Blood Institute^[19] Since then, over 1,700 clinical trials have been conducted using a number of techniques for gene therapy.^[20]

Techniques in gene therapy

The techniques used in gene therapy, which are also referred to as vectors, have a method of using a healthy gene to attack and replace an infected gene. The number of techniques or vectors that have been used to conduct these clinical trials vary. A few of the techniques are basic processing, gene doping, and viral vectors. Viral infections can be life-threatening in patients who are immune-compromised because they cannot mount an effective immune response. Approaches to protection from infection using gene therapy include T cell-based immunotherapy, stem-cell based therapy, genetic vaccines, and other approaches to genetic blockade of infection.^[21] There are also other approaches known as T cell-based approaches, cell therapy, stem-cell-based approaches, and genetic vaccines.

Basic processing can be achieved through replacement of a mutated gene, inactivation of a mutated gene, or introduction of a new gene to help fight a disease caused by mutation. Secondly, gene doping is a procedure of gene therapy that modulates gene expression of a particular gene. This procedure is mainly used to improve athletic ability for sporting events. This is a genetic form of human enhancement that is able to treat muscle-wasting disorders. It is a highly controversial procedure because the results do nothing unusual to the bloodstream, so athletic officials would be unable to detect chemicals in a blood or urine test. An example of gene doping would be proving athlete with erythropoietin (EPO), a hormone that increases the red blood cell count. Lastly, viral vectors are able to mimic the methods of a normal virus in the human body to introduce favorable genes into a human cell. For instance, scientists are able to positively change the host's genome by removing the genes that cause disease from a virus and replacing it with genes of the desired trait (“Types of Gene Therapy”).

The aforementioned techniques have been used by scientists, but the most popularized techniques are naked DNA and DNA complexes. The injection of the naked DNA is the simplest form of the vector delivery method. The naked DNA is a histone-free, modified DNA sequence that removes proteins that would normally surround these structures. This form of delivery is sometimes used as a natural compound, but the United States has been making large waves of synthetic compounds for gene delivery. The other form, which is DNA complexes, has been used when a compound is crossed with a chemical mix in order to produce the desired compound. There are other studies that are currently underway that have been referred to as the hybrid method because there is a combination of two or more gene therapy techniques used. This can instill the idea that the desired gene will stick during the delivery, transfer, and implant.

The manipulation of an organism’s genome for a desirable trait is related to the medical procedure of cloning. The process of cloning results in making genetically identical organisms. Moreover, scientists can use gene therapy vectors to modify the DNA to be identical to a particular organism. Moreover, the techniques established by the field of gene therapy can potentially be used to create “designer babies”. This can be achieved through the use of IVF to assist in creating a genetically designed baby.

Disease control in gene therapy

Gene therapy is being studied for the treatment of a wide variety of acquired and inherited disorders. Retroviruses, adenoviruses, poxviruses, herpesviruses, and others are being engineered to serve as gene therapy vectors and are being administered to patients in a clinical setting.^[22] Some of the other genetic disorders that could potentially be implemented in clinical trials are ADA-SCID, which as stated earlier is severe combined immune deficiency CGD which is a chronic granulomatous disorder, and haemophilia. These examples of disorders are only a few among numerous others that are being discovered. Some of the acquired diseases that can be potentially controlled in a clinical trial with gene therapy are cancer and neurodegenerative diseases such as Parkinson's disease or Huntington's disease.^[23]

Ethics and risks

Lee Silver has projected a dystopia in which a race of superior humans look down on those without genetic enhancements, though others have counseled against accepting this vision of the future.^[24] It has also been suggested that if designer babies were created through genetic engineering, that this could have deleterious effects on the human gene pool.^[25] Some futurists claim that it would put the human species on a path to participant evolution.^[24][26] It has also been argued that designer babies may have an important role as counter-acting an argued dysgenic trend.^[27]
There are risks associated with genetic modifications to any organism. When focusing on the ethics behind this treatment, medical professionals and clinical ethicists take many factors into consideration. They look at whether or not the goal and outcome of the treatment are supposed to impact an individual and their family lineage or a group of people.^[14] The main ethical issue with pure germline modification is that these types of treatments will produce a change that can be passed down to future generations and therefore any error, known or unknown, will also be passed down and will affect the offspring.^[13] New diseases may be introduced accidentally.^[10][28]

The use of germline modification is justified when it is used to correct genetic problems that cannot be treated with somatic cell therapy, stabilize DNA in a mating that has the potential to be high risk, provide an alternative to the abortion of embryos that genetically problematic for a family, and intensify the incidence of genes that are favorable and desirable.^[14] This can ultimately lead to perfected lineages on a genotypic level and possibly a phenotypic level. Ultimately, these issues raise potential questions about the welfare and identity of individuals that have been genetically modified through the germline.^[12]

Safety is a major concern when it comes to the gene editing and mitochondrial transfer. Since the effects of germline modification can be passed down to multiple generations, experimentation of this treatment brings forth many questions and concerns about the ethics of completing this research.^[14] If a patient has undergone germline modification treatment, the coming generations, one or two after the initial treatment, will be used as trials to see if the changes in the germline have been successful.^[14] This extended waiting time could possess harmful implications since the effect of the treatment is not known until it has been passed down to a few generations. Problems with the gene editing may not appear until after the child with edited genes is born.^[29] If the patient assumes the risk alone, consent may be given for the treatment, but it is less justified when it comes to giving consent for future generations.^[14] On a larger scale, germline modification has the potential to impact the gene pool of the entire human race in a negative or positive way.^[12] Germline modification is considered a more ethically and morally acceptable treatment when a patient is a carrier for a harmful trait and is treated to improve the genotype and safety of the future generations.^[14] When the treatment is used for this purpose, it can fill the gaps that other technologies may not be able to accomplish.^[12]

Since experimentation of the germline occurs directly on embryos, there is a major ethical deliberation on experimenting with fertilized eggs and embryos and killing the flawed ones.^[14] The embryo cannot give consent and some of the treatments have long-lasting and harmful implications.^[14] In many countries, editing embryos and germline modification for reproductive use is illegal.^[30] As of 2017, the United States of America restricts the use of germline modification and the procedure is under heavy regulation by the FDA and NIH.^[30] The American National Academy of Sciences and National Academy of Medicine gave qualified support to human genome editing in 2017 once answers have been found to safety and efficiency problems "but only for serious conditions under stringent oversight."^[31] Germline modification would be more practical if sampling methods were less destructive and used the polar bodies rather than embryos.^[14]